company financial performance
TRANSCRIPT
ANALYSIS OF COMPANY
FINANCIAL PERFORMANCE
"A company financial model is developed using factor
analysis and published accounting data expressing
financial performance as a single statistic
summarising and weighting company financial dimension"
BY
HAS SAN NIKRHAH BABAEI
B.Sc in Cost Accounting, Tehran, Iran, M.B.A, Texas, U.S.A.
A THESIS SUBMITTED TO THE UNIVERSITY OF BRADFORD
POSTGRADUATE SCHOOL OF STUDIES IN INDUSTRIAL TECHNOLOGY, IN
FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF
PHILOSOPHY.
1988
DEDICATION
Shahnaz, Shahrooz, Farhood
I
ACKNOWLEDGMENT
I would like to thank Dr. J. Betts and Dr. D. Belhoul
for their sincere and helpful advice and friendly assistance
throughout my research for which I am really grateful.
I also wish to thank the computer center advisers who were
very helpful to me.
II
ABSTRACT
Auditors and financial managers often need to have a
picture of companies' financial strengths and weaknesses which
is also required by shareholders. The necessary analysis has
been done well in some cases and failed in others because of
lack of evidence or lack of a scientific approach and
consequently it has not been possible to prevent companies
failing financially and ultimately going into receivership.
In Iran in recent years many companies have experienced
financial difficulties and some have gone into receivership,
because the owners were not aware of their company's
weaknesses and how to protect them at the relevant time. The
causes of these companies financial stress have been many and
varied, for instance; inflation, the influence of trade
unions, government regulations, social responsibilities,
pollution and increased competition. To avoid such stress
company owners need to be more careful about their activities.
They need up to date information about the past financial
performance of their companies. They need also to be able to
prepare reliable plans for the future based on the past data
and the current situation. This can be done by using
quantitative tools and financial models.
The aim of this research is to find a model for analysing
company's financial performance by using a quantitative
approach which can be easily computerised and applied. This
model could be used to indicate companies' financial strengths
III
and weaknesses and to anticipate and guide companies future
performance in such a way that ensures their continued
financial health and growth.
This thesis considers the use of financial ratios in the
analysis of company's overall performance. After a brief
introductory chapter, it reviews the historical background of
financial analysis in Chapter Two by looking at financial
ratios analysis in general. It then continues in Chapter
Three by identifying the most important financial ratios as
measurement tools. In Chapter Four these tools are grouped
and analysed using factor analysis and a financial model has
been constructed for measuring company's financial performance
using techniques described in the Chapter Five. Chapter Six
presents a company financial performance classification and
comparison. Chapter Seven describes a method by which
companies financial performance can be improved or stabilised.
IV
Page No.
DEDICATION
ACKNOWLEDGMENT
ABSTRACT
TABLE OF CONTENTS V
LIST OF TABLES VIII
LIST OF FIGURES X
CHAPTER 1: INTRODUCTION
1.1 DEFINITION OF PRIMARY DATA 3
1.2 DEFINITION OF RATIOS 4
1.3 THE MANAGEMENT'S TOOLS 5
1.4 MEASUREMENT OF PERFORMANCE AND CORPORATE
ACCOUNTING 6
1.5 DEFINITION OF TREND 7
1.6 BANKRUPTCY AND LIQUIDATION 8
1.7 OUTLINE OF RESEARCH 9
1.8 CONCLUSION 11
CHAPTER 2: HISTORICAL BACKGROUND OF FINANCIAL ANALYSIS
2.1 CORPORATE FINANCIAL STATEMENTS 13
2.2 FINANCIAL ANALYSIS DEVELOPMENT 14
2.3 ADDED VALUE AS A PERFORMANCE MEASUREMENT 16
2.4 SECURITY ANALYSIS 27
2.5 RATIO CLASSIFICATION 31
2.6 INDUSTRIAL AVERAGE ANALYSIS 34
2.7 DISCRIMINANT ANALYSIS 38
2.8 FINANCIAL RATIOS IDENTIFICATION 44
2.9 SOME LIMITATIONS OF THE RATIO ANALYSIS 48
2.10 CONCLUSION 50
V
CHAPTER 3: BASIC TOOLS OF PERFORMANCE MEASUREMENT
3.1 CAUSES OF FAILURE 54
3.2 DETECTION OF FAILURE BY RATIOS 55
3.3 PROFITABILITY 56
3.4 MEASURING THE PROFITABILITY 60
3.5 BEHAVIOURAL EQUATIONS 63
3.6 PROFIT VS PROFITABILITY 65
3.7 RISK VS PROFITABILITY 66
3.8 RESTRAINTS IN PROFITABILITY ANALYSIS 69
3.9 PROFITABILITY RATIOS 71
3.10 MANAGERIAL PERFORMANCE 73
3.11 MANAGEMENT VS RISKINESS OF LOAN 75
3.12 MANAGERIAL PERFORMANCE RATIOS 76
3.13 OPTIMUM AMOUNT OF CASH 79
3.14 LEVERAGE ANALYSIS 89
3.15 SOLVENCY RATIOS 92
3.16 CONCLUSION 94
CHAPTER 4: METHODOLOGY OF FACTOR ANALYSIS
4.1 EXTAT LIMITATION 97
4.2 FACTOR ANALYSIS 102
4.3 CORRELATION COEFFICIENTS 104
4.4 THE MODEL OF FACTOR ANALYSIS 111
4.5 FACTOR EXTRACTION 117
4.6 FACTOR ROTATION 120
4.7 THE KAISER VARIMAX METHOD 124
4.8 INTERPRETATION OF FACTOR ANALYTIC RESULTS 132
4.9 CONCLUSION 134
VI
CHAPTER 5: DEVELOPING A FINANCIAL MODEL OF COMPANIES'
PERFORMANCE
5.1 FACTOR SCORE ESTIMATION 137
5.2 BUILDING COMPOSITE FACTOR SCORES FROM THE
FACTOR-SCORE COEFFICIENT MATRIX 141
5.3 TESTING THE EFFECTIVENESS OF THE MODEL 145
5.4 CONCLUSION 209
CHAPTER 6: PERFORMANCE CLASSIFICATION AND COMPARISON
6.1 CLASSIFICATION OF THE PERFORMANCES 211
6.2 FAILURE PREDICTION STUDIES 218
6.3 COMPARISON OF THE MODEL WITH SIMILAR
MODELS AND STUDIES 224
6.4 CONCLUSION 230
CHAPTER 7: PERFORMANCE STABILISATION
7.1 PERFORMANCE STABILISATION 232
7.2 PERFORMANCE IMPROVEMENT 238
7.3 A GRAPHICAL ILLUSTRATION OF IDEAL
PERFORMANCE 244
7.4 CONCLUSION 299
CHAPTER 8: CONCLUSIONS AND RECOMMENDATIONS
8.1 SUMMARY OF THE MAIN CONCLUSIONS 302
8.2 RECOMMENDATION FOR FURTHER STUDY
DYNAMIC ASPECT OF RATIOS 305
APPENDICES
1) GEOMETRIC PRESENTATION OF THE FACTOR MODEL 309
2) FACTOR ROTATION 319
3) FACTOR EXTRACTION BY THE CENTROID METHOD 323
COMPUTER PROGRAMS 330
LIST OF REFERENCES 345
VII
LIST OF TABLES
Page No.
CHAPTER 2
2.3.1 Comparison of Return on Capital for four
Imaginary Companies 19
2.3.2 Manpower Productivity and Capital
Productivity 22
2.3.3 Primary Production Data from Payroll 23
2.3.4 Elementary Production Ratios 25
2.5.1 Ratio Classification 32
2.6.1 Ranges of Selected Ratios and Measures
by Industry taken from Dun & Bradstreet 36
CHAPTER 3
3.3.1 The Factor Combined to Yield Return
On Investment (ROI)
58
3.3.2 Submodels of Profitability
59
3.7.1 Typical Profitability Objectives for Companies
having different level of Risk 68
3.7.2 Influence of Profitability and Risk on the
Value of firm's stock
69
3.14.1 Company's Financial Structure
91
3.14.2 Company's Financial Structure
92
CHAPTER 4
4.3.1 Ratios with Volatile Standard Deviations 108
4.3.2 Ratios with the Highest Correlation
Coefficient 110
4.7.1 Varimax rotation of two (x,y) factors 127
VIII
CHAPTER 5
4.7.2 Ratios with the Highest Varimax Rotated
Factors after Rotation with Kaiser
Normalisation
129
4.7.3 Transforming the Table 4.7.2
131
4.8.1 Scale of Ratio-Factor Correlation
134
5.1.1 Factor Score Coefficients 140
5.3.1 Classification of Companies Performance 146
5.3.2 Effectiveness of the Model 205
5.3.3 Overall Effectiveness of the Model 208
CHAPTER 6
6.1.1 Applying the New Classification to the Sample
Companies 215
6.2.1 The Altman's Predictive Accuracy 223
6.3.1 A Comparison of Current Ratios with differing
Levels of Overall Financial Performance 225
6.3.2 A Comparison of Profitability Ratios with
differing Levels of Overall Comapny's
Financial Performance 226
6.3.3 A Comparison of Cash Position Ratios with
differing Levels of Overall Company's
Financial Performance 227
6.3.4 The Classification Accuracy of some
Financial Performance Models 228
CHAPTER 7
7.1.1 Comparison of Ideal Path with its
Actual Path 236
7.2.1 Performance Improvement Recommendations 241
IX
LIST OF FIGURES
CHAPTER 3
Page No.
3.13.1 Determine the Expected Number of Unit
Periods of Cash Stock 82
3.13.2 The Miller Model of Optimal Amount
of Cash 86
CHAPTER 5
5.3.1 Testing the Effectiveness of Model on General
Electric Co. 151
5.3.2
5.3.3 "
5.3.4 "
5.3.5 "
5.3.6 "
5.3.7 "
5.3.8
5.3.9 "
5.3.10
5.3.11
5.3.12
5.3.13
5.3.14
5.3.15
5.3.16
5.3.17
5.3.18 "
5.3.19
5.3.20
5.3.21
nn " Coalite Group
152
nn " Allied Textile Co Plc
153
▪ " British Home Store Plc
154
" Bell(Arthur) & Sons Plc
155
" Wellcome Fundation
156
" Benford Concrete Machinery Plc 157
11
▪
" Beecham Group Plc 158
If II " Marks & Spencer 159
" Pearsons 160
▪ " Racal Electronics 161
N " BPB Industries Plc 162
" Allied Colloids Plc 163
" Ash & Lacy Plc 164
" Boots Co Plc (THE) 165
" British Gas Corporation 166
" Anglia Television Group Plc 167
" Goodyear Tyre & Rubber Co 168
" " " Babcock International Plc 169
" APV Holdings Plc 170
tt ft " Ault & Wiborg Group Plc 171
X
5.3.22 " " " • Albright & Wilson Ltd 172
5.3.23 ' • • " Barrow Hepburn Group Plc 173
5.3.24 " • A • Pleasurama Plc 174
5.3.25 • " " British Railways Board 175
5.3.26 • • " • Anchor Chemical Group Plc 176
5.3.27 ° ° • ° Baker Perkins Holdings Plc 177
5.3.28 " • • ° Ford Motor Co Ltd 178
5.3.29 ' ' " ° Adams & Gibbon Plc 179
5.3.30 ° /I If ° Armitage Shanks Group Ltd 180
5.3.31 ° 11 • N Atkins Brothers Plc 181
5.3.32 ° • • ° Dunlop Holdings Plc 182
5.3.33 • • • " Barno Industries Plc 183
5.3.34 ° X • ° BBA Group Plc 184
5•3.35 ° • II ° Batleys of Yorkshire Plc 185
5.3.36 • • • n Bemrose Corporation Plc 186
5 .3.37 ' • • ° Bestobell Plc 187
5.3.38 • • • • Brocks Group of Co Ltd 188
5.3.39 • II • ° Stone Platt Industries Plc 189
5.3.40 • • • • British Airways 190
5.3.41 • • • " Viners 191
5.3.42 ° • II ' Blackman & Conrad 192
5.3•43 • • • ° Amalgamated Industrials 193
5.3.44 • • . • Blackwood, Morton & Sons 194
5.3.45 • • ° " Pickles (William) & Co 195
5.3.46 • • • ' Burrell & Co. 196
5.3.47 ' • • ° Cawdaw Industrial HLDGS 197
5.3.48 • le U • Airfix Industries 198
5.3.49 " " " ° Oxley Printing Group 199
5.3.50 ° • • ° Lesney Products & Co. 200
XI
7.3.2 "
•
" Coalite Group
7.3.3 "
7.3.4
7.3.5 "
7.3.6
7.3.7 "
7.3.8
7.3.9 "
7.3.10
7.3.11
7.3.12 "
7.3.13
7.3.14
7.3.15
7.3.16
7.3.17
7.3.18 "
7.3.19
7.3.20
248
• " Allied Textile Co Plc
ft ft " British Home Stores
249
250
nn " Bell(Arthur) & Sons Plc 251
nn " Wellcome Fundation 252
ft h " Benford Concrete Machinery Plc 253
ft Beecham Group Plc 254
ft" Marks & Spencer 255
ft" Pearsons 256
ft" Racal Electronics 257
ff • " BPB Industries plc 258
" Allied Colloids Plc 259
" Ash & Lacy Plc 260
ft • " Boots Co Plc (THE) 261
ft" British Gas Corporation 262
ft" Anglia Television Group Plc 263
• " Goodyear Tyre & Rubber Co 264
• " Babcock International Plc 265
• " APV Holdings Plc 266
5.3.51
It
" Richards & Wallington Ind. 201
5.3.52
II
" Norvic Securities 202
5.3.53 " ft
" Austin (F.)(Leyton) 203
CHAPTER 6
6.1.1 Classification of Performing area 214
CHAPTER 7
7.2.1 Trajectories of Failing Company
Performance
238
7.3.1 A Graphical Illustration of Ideal
Performance of General Electric Co
247
XII
7.3.21 " " Ault & Wiborg Group Plc 267
7.3.22 " Albright & Wilson Ltd 268
7.3.23 II IIn Barrow Hepburn Group Plc 269
7.3.24 . . " Pleasurama Plc 270
7.3.25 . . " British Railways Board 271
7.3.26 " " Anchor Chemical Group Plc 272
7.3.27 n n" Baker Perkins Holdings Plc 273
7.3.28 " . . " Ford Motor Co Ltd 274
7.3.29 . . " Adams & Gibbon Plc 275
7.3.30 . . " Armitage Shanks Group Ltd 276
7.3.31 . " Atkins Brothers Plc 277
7.3.32 . . " Dunlop Holdings Plc 278
7.3.33 " if N" Barno Industries Plc 279
7.3.34 " . . " BBA Group Plc 280
7.3.35 " u n " Batleys of Yorkshite Plc 281
7.3.36 . " Bemrose Corporation Plc 282
7.3.37 " . . " Bestobell Plc 283
7.3.38 a n " Brocks Group of Co Ltd 284
7.3.39 ° Stone Platt Industries Plc 285
7.3.40 . . " British Airways 286
7.3.41 . . " Viners 287
7.3.42 'I" Blackman & Conrad 288
7.3.43 " N II" Amalgamated Industrials 289
7.3.44 " n of" Blackwood, Morton & Sons 290
7.3.45 " . . " Burrell & Co 291
7.3.46 . . " Cawdaw Industrial HLDGS 292
7.3.47 " “ . " Airfix Industries 293
7.3.48 . N" Oxley Printing Group 294
7.3.49 " " " " Lesney Products & Co 295
XIII
7.3.50 " “ “ " Richards & Wallington Ind 296
7.3.51 " n “ " Norvic Securities 297
7.3.52 " . II " Austin(F.)(Leyton) 298
XIV
CHAPTER 1
INTRODUCTION
CHAPTER 1: INTRODUCTION
The financial goals of manufacturing enterprises should be
1) The continuance of profit generation consistent
with their financial health.
2) To improve their competitiveness through the
reduction of costs generated internally by using
more cost effective production processes, and by
eliminating or improving costly systems.
3) Guarding against unusual and unnecessary changes.
4) Being alert to the possibility of generating new
profit centres.
5) To maximise the use of their resources.
Profit planning for the longer term must include action for
growth and survival, the maximisation of profits over the
short term is no guarantee of financial health. In fact, when
profits are maximised to the exclusiam of other
considerations, a company can get into serious difficulties.
The drive for high profits has forced many companies to the
brink of bankruptcy because of the strain placed on the
capital structure by supporting those drives. For example,
the company's financial structure may not be able to stand the
cost of new equipment or new expansion for making high
profits, because this will reduce the company's liquidity and
so interfere with their ability to pay their current
obligations promptly or in their due time.
1
Today industrial competitiveness is improved by modifying
company's manufacturing processes and its productivity which
might have been neglected in the past. Increasing profit now
is possible by guarding against changes that do not increase a
company's productivity and from activating new profit centres.
Once a company has established itself, its survival might
not appear to be its primary goal. Its aim should be to
maximise the use of its resources. Companies often see this
as a completely different goal, although efficiency and
survival are closely interrelated, since a company not
utilising its resources efficiently will experience financial
trouble, and its financial viability will be in question
(Anthony, 1960).
Achieving these objectives is the task of management, who
have to be informed, skillful, balanced and able to act
swiftly for optimum good to the company.
To accomplish these tasks the manager must
1) Be in frequent and intimate con act with all
activities in his company.
2) Receive proper data by which he can evaluate
these activities, make decisions and project a
future plan. These data must be up to date and
accurate, and contain useful financial
information.
3) Feel secure with the data and free to spend time
on other activities for the good of the company.
4) Take action at the appropriate time.
2
5) Consider action in any area not separately, but
as part of an organic whole.
Without proper tools, management is not able to accomplish
all these duties correctly and efficiently.
1.1 DEFINITION OF PRIMARY DATA
In general, data refers to factual material used as a basis
for discussion or decision making and in statistics it refers
to the numerical material available for analysis and
interpretation.
Primary data are defined as those data which can not in
themselves be used to predict or appraise performance
objectively. Primary data are used as the basis for making
the necessary predictions and evaluations. Primary financial
data are used as the ingredients for various types of measure
including financial ratios which can give economic meaning to
events and permit objective diagnoses and decision making. A
reportable primary data can be written as
Indirect labour has increased by 6%
Unusable waste is about 10%
Production activity is off 15%
Management must know about the elements causing these
events while they are happening so that he may be able to
influence the character of the reported data, and take the
proper action to correct a negative circumstance after it has
been reported.
3
1.2 DEFINITION OF RATIOS
Primary financial data in themselves need not necessarily
have any economic meaning. They can stand alone, being
unrelated to any thing else that they affect or that affects
them. A piece of isolated, unrelated primary data requires
another piece of primary data to which it can be compared.
Only when primary data are interrelated, can they become
meaningful to management. This allows management to take
action on what they reveal.
A common method of establishing financial relationships is
by constructing ratios between pieces of primary data. Thus
one piece is weighted against another for evaluation of
effect. Each ratio should be developed for a specific
purpose, for a particular area, and the desirable movement of
each ratio should be known in advance. Primary data are
absolute, and these data have no value unless they are related
to something else. In ratios, the primary data lose their
identity and are evaluated by effect, not magnitude.
For example current assets and current liabilities are both
primary data and do not have economic meaning unless they are
related to each other in the current ratio which is obtained
by dividing current assets by current liabilities. This ratio
measures the working capital of a company.
4
1.3 THE MANAGEMENT'S TOOLS
A tool commonly used by financial management is a set of
ratios from all departments or cost centres comprising a
company with which managers can observe positive and negative
movements in the company's performance. Since many activities
and events are related to each other, it is not easy for
management to know which movements are negative and which are
positive. For example the rise of indirect labour employed
may be positive if it is coupled with higher direct labour and
is negative if it is not. Similarly an event in sales will
have an impact in the sales department, but it will also have
an impact in production, and financial areas such as inventory
level, current assets and so on. These unlimited activities
and events which need to be reported, ought to have their
interelationships' analysed.
On the other hand, the manager must be able to carry on his
plans for the company which becomes increasingly difficult each
year. Thus, he will need more and better data with which to
manage his company so as to compete and survive in a highly
competitive environment. However, when there are proper
managerial tools, management can make valid appraisals
quickly. They can make decisions with the greatest confidence
and objectivity. They can also spend time on other vital
areas fruitfully.
The second managerial tool needed is some form of
visualisation of the way the movement of some ratios affect
5
other ratios and other performance aspects of the company.
Each company needs a kind of control chart which shows
interactions of movements. From these charts, management can
draw inferences and make decisions with maximum knowledge of
their effects.
1.4 MEASUREMENT OF PERFORMANCE AND CORPORATE ACCOUNTING
Although the individual items appearing on a balance sheet
are important, the basic objective behind all balance sheets
and income statements is performance measurement. Accounting
provides a historical perspective, which enables matching
against current achievement. In this way managers, owners,
creditors, governments and the general public can determine
how well or how poorly a company is doing. A better picture
of a company's underlying strengths and weaknesses can be
obtained from the balance sheet, where the assets are compared
with liabilities. For example, excessive borrowing has caused
many companies to seriously reduce their net w rth by paying
high interest, which can eventually lead to bankruptcy. Such
problem can be easily identified by correct scrutiny of the
company balance sheet. To do this and assess the performance
of a company it is necessary to understand corporate
accounting.
We must also understand the basic elements that make for
success or failure of various kinds of businesses, and how
fluctuations in the market effects their performance. We must
be able to find the facts, evaluate them critically, and act
6
on our conclusions with good judgement and a fair amount of
imagination.
The soundness of future performance of a company is
determined by future developments and not by past history or
statistics. However the future can not be analysed, we can
seek only to anticipate it intelligently and to prepare for it
prudently. Here past performance can be of help, because long
experience tells us that performance anticipations, like other
business anticipations, can not be sound or dependable unless
they relate themselves to past performance. In measuring past
performance we should give consideration to both trends and
averages.
1.5 DEFINITION OF TREND
A trend represents the relationship of the individual data
in a time series. Thus, like any statistical measure, it is
derived from the period selected, and is, of course, subject
to any fundamental distortions which exist in the data. The
fundamental difference between the use of a trend line to
measure past performance and its use as a means of projecting
future performance should be stressed. To estimate future
performance by projecting the past trend and then accepting
that projection as a basis for valuing the business may be
sound in specific instances, but it must be used with extreme
caution.
Figures and mathematical equation are involved in computing
a trend, and some people believe that for that reason a trend
7
projection is credible. But while a definite trend shown in
the past is a fact, "future trend" is only an assumption. It
is because there are so many events and uncontrollable changes
in future which can not be predicted, such as new acts and
regulations by the government to adjust or readjust the
business activities.
1.6 BANKRUPTCY AND LIQUIDATION
Bankruptcy in the UK refers to the realisation of the
assets of an insolvent individual in order to try to meet the
legitimate claims of his creditors. In the USA, the term
'bankruptcy' also applies to companies, but in the UK the term
'liquidation', or 'winding up' are used. The management of
the insolvent company is removed from the hands of its
directors, its assets are realised by a 'liquidator', and its
debts are paid, with any balance going to shareholders. A
company may be put into liquidation by a court order, by a
voluntary resolution of members of the company in a general
meeting, or by a voluntary resolution supervised by the court.
John Freear(1980) found at the end of 1973, over 600000
companies were registered in Britain of which only around 3
percent were public, listed companies. In Britain in 1973,
about 1.3 percent (8000) were liquidated. In 1978, the number
had fallen to about 5000. In addition some companies merely
fade away without any positive action on the part of owners or
creditors, and are removed from the register. If these are
considered, a total of about 5 percent (30000) of all
8
registered companies in Britain disappear each year.
Freear lists the 'order of priority' for the payment of
money realised from the sale of assets.
List of payments in order of priority
1) The cost, charges and expenses of the liquidation.
2) Creditors secured by a fixed charge on property.
3) Preferential creditors
a) certain taxes, duties, national insurance
contributions and rates due to central and local
government.
b) wages and salaries of employees.
4) Creditors secured by a floating charges.
5) Other unsecured and non-preferential creditors-trade
creditors, the government (for some taxes).
6) Members of the company in proportion to the nominal
value of the shares held, as modified by the Articles
of Association of the company.
1.7 OUTLINE OF RESEARCH
A brief outline of aims of the research is given below
1) To develop a single statistic model for measuring
companies' financial performance.
2) To use this model:
2.1) To indicate companies' financial strength and
weaknesses.
9
2.2) To anticipate and guide companies' future
performance in such a way that ensures their
health and continued growth.
2.3) To detect the causes of failure and success
in identifying individual changes and how these
individual changes affect overall company
performance.
2.4) To advise on financial planning taking
accounts for companies existing strengths and
weaknesses.
2.5) To determine ideal levels for important
financial dimensions such as current assets,
current liabilities, cash and so on.
2.6) To demonstrate the effectiveness of the model
compared to similar models available elsewhere.
3) A review of other models available and how they
compare against each other.
10
1.8 CONCLUSION
The difficulty with monetary units is that they tend to
change in value over a period of time; consequently, when
examining them in relationship with future plans, it is
essential to put them both on the same basis of valuation.
This problem can be overcome to a certain extent by the use of
percentages, that all values have been affected in the same
way and to the same degree. Because of this, in the
evaluation of a company's financial performance, percentages
and ratios are the key methods for measuring financial
performances.
The role played by and the reason for using financial
ratios has been discussed in this chapter. Hopefully it hasthe
been established that it is in, interests of management to
recognise the advantages of ratio analysis and be aware of the
need to understand it. Once ratios have been computed, action
can be taken to influence a company's future performance.
11
CHAPTER 2
HISTORICAL BACKGROUND OF
FINANCIAL ANALYSIS
12
CHAPTER 2: HISTORICAL BACKGROUND OF FINANCIAL ANALYSIS
Over the past fifty years a number of studies have been
undertaken to investigate the usefulness of financial ratios.
Most of these studies have concentrated on the predictive
aspect of ratios, especially with respect to their ability to
predict various types of corporate difficulties. A number of
other writers have advocated that certain ratios should be
used for particular areas of financial statement analysis.
Some of these studies were done by Tucker (1961), Nelson
(1963), Pringle (1973) and Laurent (1979).
2.1 CORPORATE FINANCIAL STATEMENTS
"In 1866 the Treasurer of the Delaware, Lackawanna,
and Western Railroad company, once reported to a
request for information from the New York Stock
Exchange by writing, 'the Delaware, Lackwanna RR CD.
make no reports and publish no statements and have
done nothing of the kind for last five years'."
Since last century corporate reporting has increasingly
improved. The first book to appear on this subject was that
of Graham and Dodd (1934). Entire sections of Graham and
Dodd's work are devoted to the fine points of recasting a
corporation's income statement and balance sheet into a more
meaningful form and explaining other techniques of financial
statement analysis. Security Analysis was first published in
the era of the Great Depression, when investors had good
13
reason to question whether a corporation with a high level of
bonded indebtedness would be able to re-finance its debt or
meet its interest payments as they fell due. Each investor
had to make his own assessment of the probability of a firm's
failure.
By the end of nineteenth century commercial banks started
requesting financial statements to "borrowers of money". Then
around the beginning of the twentieth century the comparison
of the current assets of firms to their current liabilities
became a widespread practice. Foulke (1961) states that a
current ratio of 2.5 was considered to be a reasonable margin
of protection in those times.
The conditions that prevailed when Graham and Dodd wrote
their work are not prevalent today, however. Various
companies acts like the corporate income tax law and the
heightened sophistication of the accounting profession have
gradually forced most businesses to keep better records and to
adopt more adequate accounting practices.
2.2 FINANCIAL ANALYSIS DEVELOPMENT
Since the end of World War 2, the discipline of corporate
finance has developed and made popular a large number of
analytical tools, including cash budgeting, profit planning,
and capital budgeting. The financial manager of a
corporation, who is trained in these techniques is now able to
anticipate cash flows and plan the earnings of a corporation
much more precisely. By obtaining reliable data about the
14
rate of return that the corporation is expected to earn, from
either the management of the corporation, its customers, or
its competitors, a proper valuation can be made.
Today some corporations reveal to their underwriters, the
major brokerage firms, and large shareholders limited amounts
of information about key developments that will influence the
corporation's expected rate of return. These parties, in
turn, frequently report their findings to the public at large.
A second major development that has had a profound impact
on the course of financial analysis is portfolio management as
a separate, distinct field of study. Questions were arising
for instance, How might one obtain the maximum return from the
portfolio as a whole, with a given variability in the return?
What is the meaning of diversification? What kinds of risk
can management guard against? To answer such questions, more
sophisticated techniques, such as factor analysis and
quadratic programming can be useful. The practical
application of these techniques has been made feasible by
computers. In short, to the permanent large investor, the
relationship among the securities within a portfolio is now a
matter of serious concern, for this reason portfolio
management has become an important field of study in its own
right.
The third major development that has influenced financial
analysis is the use of abstract models in the study of the
interrelationship of the firm and the market. To understand
these models a knowledge of calculus, matrix algebra, and
15
statistics is needed. In short, the field of financial
analysis has changed radically over the years. Sophisticated
tools have been developed to attack problem areas that were
previously thought to be impossible to solve. Wall & Dunning
(1928), Tamari (1966) and Shashua (1974) showed that the use
of financial ratios on an univariate basis presents some
shortcomings. This is because the interrelation between the
different ratios is not taken into account and they may
release conflicting signals. But the use of multivariate
analysis offers a solution to these problems in that several
weighted ratios are combined. Thus, at present, mathematical
models are being built, tested, and amended in the search for
interrelationships within the valuation process and the
financial analysis is becoming a professional discipline.
The tremendous changes in financial analysis can perhaps be
more readily appreciated by reviewing briefly its
developments.
2.3 ADDED VALUE AS A PERFORMANCE MEASUREMENT
The difference between the value of sales and the value of
purchases is called added value. Added value can be used to
measure business performance and productivity. Added value
was used by Harris (1968) to develop a new concept of "work
done and resources used", Raven (1971) used it as a profit
improvement and finally Hochman & Razin (1973) analysed
investment in terms of productive capital.
Return on capital ratios are useful for investors, but
16
added value ratios are important for both employees and
investors. Profit ratios can vary widely with accounting
practices, but added value figures are less readily distorted.
For measuring efficiency in the use of resources, the added
value concept has advantages over other techniques. It is
less distorted by inflation. Most important, it emphasises
the fundamental connection between capital investment,
manpower productivity and wages.
Wood (1978) indicated that the users of added value can be
grouped into four categories as follows:
1) For measuring output
a) Basis of national accounting
b) Measuring business performance
c) Measuring the productivity of manpower and
capital
2) For communication
a) Explaining what business is about
b) Presenting accounting information
C) Basis for employee participation
3) For rewarding employees
a) Basis for wages and salary policy
b) Basis for group bonus schemes
4) For business policy
a) Marketing strategy
b) Capital investment policy
c) Business ratios
He compares the profitability, as the traditional measure
of business performance with added value as a new concept for
17
performance measurement which he claims is more stable and
more reliable than the profitability. He says that the
profitability has the following serious defects.
a) As a measure of performance, it can be
misleading.
b) In the modern climate of public opinion, it takes
a somewhat narrow view.
C) It can not be applied to non-profit-seeking
organisations which nevertheless need to measure
and improve their performance.
One problem with profit is the difficulty of definition.
In theory, two companies could be identical in terms of the
types of products, sales revenue, materials used, numbers
employed, capital employed, etc, yet they could have deferent
profit figures that would arise from differences in
depreciation policy, sources of finance and level of wages.
See Table (2.3.1) for the analysis of four imaginary
companies.
18
TABLE 2.3.1: An Example of Comparisons of Return on Capital
for four Imaginary Companies
COMPANY ( 000s) Al D
SALESI 1000 I 1000 I 1000 I 1000
PURCHASES I 400 I 400 I 400 I 400
ADDED VALUE 600 600 600 600WAGES, etc 450 450 450 425
DEPRECIATION 100 75 75 75
INTEREST ON LOANS 25 25
PROFIT 50 75 50 75CAPITAL 500 525 325 325
LOANS 200 200
RETURN ON CAPITAL Z 10 14.3 j 15.4 I 23 I
A further problem in comparing return on capital is that of
asset valuation. Expert opinions on the value of land and
buildings may vary widely. High rates of inflation have made
a mockery of balance sheet values based on historical costs.
Even without inflation it can be argued that the asset value
depends on the profit record and potential rather than on the
historical price or replacement cost. Finally, two companies
with identical total assets could show different figures of
capital employed if one has a higher proportion of external
liabilities in the form of creditors, overdraft and other
19
loans. This is because the capital employed is equal to total
assets minus current liabilities and there may be different
capital figures because of different current liabilities when
the total assets are identical.
Comparing one company with another in terms of return on
capital is difficult because of the above factors. Even
comparisons within one company over periods of time may be
distorted by some of the factors outlined above. Inflation
accounting techniques can help to reduce the distortion of
return on capital ratios, but profitability can be very
misleading as an index of company performance.
Wood claims that if the technical problems of defining
profit and capital could be overcome, there are emotional
problems of using return on capital to measure company
performance. This is because profit is seen by some people as
evil. The world is associated with the exploitation of
workers. The social climate has changed with the declining
power of individual capitalists and the rising power of the
trade unions and government. A wider view of business
performance is needed. It must take account not just of
investors but of employees, customers, suppliers and
governments.
If however profitability is not reliable or acceptable as a
measure of performance, what is the alternative?
Profitability relates a very small part of the output, the
profit, to only one of the factors of production, the capital
employed. What is needed is a broader measure relating the
20
total output to all the factors of production. The
appropriate word is productivity, the ratio of output to
input.
In order to establish a measure of performance, the output
must be divided by the inputs. The main inputs are materials,
manpower and capital. The use of added value for measuring
output discounts the cost of materials. So the main inputs
are manpower and capital. The index of productivity can then
be expressed in terms of the ratio below:
PRODUCTIVITY = OUTPUT/INPUT = ADDED VALUE/(MANPOWER
+ CAPITAL)
Unfortunately, there is no easy way of adding together
manpower and capital. Various ideas have been put forward for
converting the value of capital into manpower equivalent. But
no answer has yet proved to be acceptable.
Wood says that instead of attempting to achieve the
difficult task of adding together manpower and capital,
performance can be compared in terms of the trends of manpower
productivity and capital productivity over periods of time.
What matters is not so much the ratios in one particular
period, but the trends. If added value per employee is rising
and, at the same time, the added value per unit of capital is
also rising, the rewards to both employees and investors can
increase. If both ratios are falling, the reward to one or
both must suffer. One index rising and the other static is a
better situation than both static or one falling whilst the
other is static. There are nine possible combinations of
21
rising, static or falling productivity of manpower and capital
(Table 2.3.2).
Table 2.3.2: Manpower Productivity and Capital Productivity
MANPOWER PRODUCTIVITY(ADDED VALUE PER
CAPITAL PRODUCTIVITY(ADDED VALUE PER UNIT OF CAPITAL)
EMPLOYEE) RISINGI
STATIC 1 FALLING 1
1
1 RISING EXCELLENT I GOOD POORSTATIC GOOD
ISTATIC
IBAD
FALLING POOR BAD 1 VERY BAD 1
1
As Wood has described, for obtaining a better picture of
the business performance we should evaluate the productivity
rather than profitability. For productivity analysis we must
have the proper analytical tools, which can be a set of
ratios. These ratios must focus on all the possible areas
that are directly or indirectly related to the productivity of
the company.
Spencer and Tucker(1961) constructed these needed ratios
between pieces of primary data which have been extracted from
payroll analysis (Table 2.3.3). The data they used come from
a plant engaged in the manufacture of a line of proprietary
consumer products and employed 275 people. This plant is
chosen as an example, because it has got almost the same
departments or cost centres as other plants and it can be
considered as a typical plant for productivity analysis.
22
Table 2.3.3: Primary Production Data from Payroll
Item 1 Pounds 1 Hours 1
1 code 1 code 1
total gross paid payrolltotal clocked payroll
direct labour:
on standard: earned
on standard: clock
off standard: clock
Total direct labour
Indirect labour:
downtime: miscellaneous waits
for equipment repairs
for tool repairs
excess direct labour: Jig and die trouble
Equipment trouble
Material trouble
other: Tool and fixture making
Setup
Maintenance and repairs
Salvage, re-work, re-process
Service
Supervision
Factory engineering
Factory clerical
Total indirect labour
1 242 25
3 26
27
4 28
5 29
6 30
7 31
8 32
9 33
10 34
11 35
12 36
13 37
14 38
15 39
16 40
17 41
18 42
19 43
20 44
23
Direct labour subsidy (lost) 21
Direct incentive premium (gained) 22
indirect incentive premium 23
Table 2.3.4 is a partial list of typical production ratios
that can be developed from the primary data appearing in table
2.3. To the right of the table 2.4 is the letter U or D.
This refers to whether an upward or downward movement of the
ratio value is considered positive.
24
Table 2.3.4: Elementary Production Ratios
1 1
gross productivity 26/27(27-45+46)/27
Unet productivity
I 3 I work standards coverage 27/29 I U
I 4 I performance index (27-45+46)129 I U
I 5 I improvement index (27-45+46)125 I U
I 6 worker standards 45/46 D
II consistency
I 7 I indirect support 441(29+46) I D
I 8 indirect support cost 20/5 I D
I 9 I indirect usage 44/24 D
1 10 indirect usage cost 20/1 D
I 11 I subsidy 45/27 D
1 12 I excess cost (6+7+8+9+10+11+15+21)/5 D
I 13 down-time cost (6+7+8)/5 D
1 14 excess direct labour cost (9+10+11)/5 D
I 15 tool and fixture cost 12/5 D
1 16 I setup index 37/29 D
I 17 setup cost 13/5 D
1 18 I maintenance and repair 38/29 D
I I index
1 19 maintenance and repair 14/5 D
Icost
1 20 I service index 40/29 D
25
t.1 Ratio title
1
Formula 1P.D 11 no
; U
25
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
service cost
supervisory ratio
supervisory production
factory engineering cost
factory clerical cost
support index
support cost
incentive support
incentive cost
setup effectiveness
maintenance effectiveness
productivity planning
direct usage cost
direct usage index
variable indirect
variable indirect-direct
premium avoidable cost
avoidable content
machinery-labour economy
production, indirect
service-payroll index
direct-productive index
productivity yield index
indirect yield index
net payroll productivity
gross payroll productivity
service-wait index
indirect payroll
11/5
41/25
41/29
18/5
19/5
(40+41+42+43)/29
(16+17+18+19)15
(46-45+47)/29
(21+22+23)/5
37/26
38/26
22/6
5/1
29+46/24
6+7+8+9+10+11+13+14+15/20
6+7+8+9+10+11+13+14+15/5
22/(14+15)
(14+15)/5
(7 10+14)/5
(44/24)(29/20)
40/24
(29+46)/(44+45)
(27-45+46)/24
(27-45+46)/44
(29-45+46)/24
(29-45+46)/25
(6+16)/5
(44+45)/24
26
So by evaluating all or a part of the above ratios
depending on the purpose of the analysis it is possible to
identify the strength or weaknesses in productivity or the
whole company performance. By improving and increasing the
manpower productivity and capital productivity we will achieve
the goals of the company and ensure its health and growth.
Although there is a debate on whether to use added value
instead of profit related ratios (Ball, 1968), the use of a
single ratio as a mean of evaluating performance is common
practice among managers, and are generally accompanied by some
measures of growth. However, this practice has been
criticised on the grounds that a single ratio can not reflect
every aspect of a company performance and sets of ratios have
been proposed to allow a better evaluation of the financial
profile of firms.
2.4 SECURITY ANALYSIS
As discussed in (2.1) security analysis was based on new
techniques and a sophisticated concept of financial statement
analysis for the first time in 1934 by Graham & Dood and then
in 1973 by Barnea & Dennis, which is discussed in further
detail in this section. Again in security analysis ratios are
used. By evaluating these ratios investors can have more
confidence and be more accurate investing their money in the
right place and at the right time.
Graham, Dood and Cottle(1962) tried to establish and
construct a new security analysis using ratios which are most
27
demanded by shareholders and creditors. The key ratios in
security analysis which assess the quality of a company are as
follows:
1- profitability ratios
2- growth ratios
3- stability ratios
4- payout ratios
5- credit ratios
6- price ratios
The first five groups measure the performance and financial
strength of a company, considered apart from the valuation
placed upon it in the market (ratio 6).
PROFITABILITY RATIOS
An important indicator of the success of a company is the
percentage earned on invested capital. That is the percentage
earned on the long-term (non-current) debt and preferred stock
plus the book value of the common stock. The fundamental
merit of return-on-invested capital ratio is that it measures
the basic or overall performance of a business in terms of the
total funds provided by all long-term investors rather than a
single class. Four ratios are used to measure this aspect:
Ratio 1- earnings before depreciation per pound of
capital funds
Ratio 2- earnings per pound of capital funds (return
on capital)
28
Ratio 3- sales per pound of capital funds (sales
ratio)
Ratio 4- earnings per pound of sales (earnings
margin)
GROWTH RATIOS
The test of growth is most informative when they are made
between years representing about the same phase of successive
business cycles.
Ratio 5- pound sales=sales of 2nd business
cycle(BC)/sales of 1st BC
Ratio 6- net profit on total capital in pounds=net
profit after tax (NPAT) for 2nd BC/NPAT
for 1st BC
Ratio 7- earnings per share=EPS for 2nd BC/EPS for
1st BC
STABILITY RATIOS
In stability ratios the lowest year is measured against the
average of 3 previous years to indicate the stability of the
company's activity over time. For example the maximum decline
in rate of return or minimum coverage of charges indicate the
fluctuations or stability in activities.
Ratio 8- minimum coverage of senior charges
Ratio 9- maximum decline in earnings rate on total
capital
29
The latter ratio may be supplemented by measuring the
maximum decline in the return earned on common-stock capital
and/or in per-share earnings.
Blum (1974) emphasised on including the stability of
earnings in the variables assessing companies' performance
where in his failing company model, the standard deviation of
the net income over a period of three years was among the
variable he selected and included in a discriminant function
identifying possible failures amongst going companies.
PAYOUT RATIO
The percentage of available earnings paid out in common
dividends often has an important effect upon the market's
attitude toward those issues not in the "growth-stock"
category.
Ratio 10- payout ratio=common dividends/net profit
for common shares
CREDIT RATIOS
The ability of company to meet its short and long term
obligations are measured here as credit ratios which are
listed as follow:
Ratio 11- working-capital ratio=working capital/
current assets
Ratio 12- common-stock ratio=common-stock equity/
total capital
30
Ratio 13- coverage of senior charges = senior
charges/(fixed charges + preferred
dividend)
PRICE RATIOS
Evaluation of company based on its common stock at market
value are presented here as follows:
Ratio 14- sales per pound of common, at market =
sales/common stock at market value
Ratio 15- earnings per pound of common, at market
(earning yield) = net profit for common/
common stock at market value
Ratio 16- dividends per pound of common, at market
(dividend yield) = common dividends/common
stock at market value
Ratio 17- net assets per pound of common at market
(asset ratio) = common stock equity(book
value)/common stock at market value
2.5 RATIO CLASSIFICATION
The above set of ratios can be reduced in number because
many measure the same dimension of a company's performance.
The reduced set can then be classified. By classifying these
ratios it is possible to assess company performance and
therefore identify changes that could be made to improve
company performance.
31
Weston and Brigham(1975) classified the useful ratios into
four fundamental types:
LIQUIDITY RATIOS
measure the firm's ability to meat its maturity short term
obligations.
a) current ratio= current assets/current liabilities
b) quick ratio= current assets - inventory/current
liabilities
Richard (1964) suggested some ranges for ratios to clarify
and indicate the extends of the company's financial strength
or weaknesses, such a classification can be done for any
company as below, R is equal to current assets/current
liabilities.
Table 2.5.1 Ratio Classification
Liquidity Ratio I
R > 2.02.0 > R > 1.5
1.5 > R > 1.0
R - 1.0
1.0 > R 0.5
R < 0.5
Description
overliquidityoptimal liquidity
under liquidity
marginal liquidity
payment difficulties
danger of bankruptcy
Position
excellentvery good
good
sufficient
deficient
insufficient
LEVERAGE RATIOS
measure the extent to which the firm has been financed by
debt.
C) debt ratio= total debts/total assets
d) time interest earned= EBIT/interest charges
e) fixed charge coverage=EBIT + lease/interest
charges + lease
ACTIVITY RATIOS
measure how effectively the firm is using its resources.
f) inventory turnover= sales/inventory
g) average collection period= receivables/sales per
day
h) fixed assets turnover= sales/net fixed assets
i) total assets turnover= sales/total assets
PROFITABILITY RATIOS
measure management's overall effectiveness as shown by the
returns generated on sales and investment.
k) profit margin= NPAT/sales
j) return on total assets= NPAT/total assets
m) return on net worth= NPAT/net worth
33
2.6 INDUSTRIAL AVERAGE ANALYSIS
Ball (1967) and Lev (1969) argued that inter-industry
differences exist among some financial ratios, but it is not
clear whether all the ratios are affected in the same
direction by industrial characteristics and whether this
influence is consistent over all the financial dimensions of
companies. Horrigan (1965) showed that financial ratios are
uncorrelated to size. The same conclusions were later reached
by Beaver (1967) and Singh & Whettington (1968). Edminster
(1972) carried out some studies with the aim of finding pairs
of companies which possessed as many common characteristics as
possible. Pair of companies should be drawn from the same
industrial sector, have the same size and come from the same
financial year. Although finding a significant number of
ideally paired companies is not impossible, the work involved
is enormous and costly. Edminster examined about 110'000
companies before finding 21 pairs.
Probably the most widely known and used of the industrial
average ratios are those compiled by Dun & Bradstreet, Inc. D
&B provides fourteen ratios calculated for a large number of
industries. Comprising 125 lines of business activity based
on their financial statements. The 125 types of business
activity consist of 71 manufacturing and construction
categories, 30 categories of wholesalers, and 24 categories of
retailers. Sample ratios and explanations are shown in Table
2.6.1. The complete data give the fourteen ratios. The
median ratios can be illustrated by an example. The median
34
ratio of current assets to current debt of manufacturers of
airplane parts and accessories were arranged in a graded
series, with the largest ratio at the top and the smallest at
the bottom. The median ratio of 1.81 is the ratio halfway
between the top and the bottom. To simplify the D & B tables,
we can consider:
LB & NR = Line of Business and Number of concerns
Reporting
CA = Current Assets
CD - Current Debt
NP - Net Profits
NS - Net Sales
TNW = Tangible Net Worth
NWC = Net Working Capital
CP = Collection Period
In - Inventory
FD = Funded Debts
FA = Fixed Assets
TD - Total Debt
D - Days
LR - Largest Ratio
MR - Median Ratio
SR = Smallest Ratio
35
Table 2.6.1: Ranges of selected ratios and measures by
industry taken from Dun and Bradstreet.
LB CA NP NP NP NS NS 1--- 1 CP 1
NR CD NS TNW NWC TNW NWC 1 DI
Agricultural LR 3.78 .0715 .2144 .3682 5.27 8.13 1 25 1Implements & MR 2.27 .0412 .1459 .2068 3.21 4.6 1 39 1
Machinery (74) SR 1.52 .0323 .083 .1495 2.34 2.98 1 52 1
Airplane parts LR 2.4 .0812 .2778 .4496 4.46 8.27 1 34 1& Accessories MR 1.81 .0525 .1811 .3221 3.43 5.29 1 46 1
(59) SR 1.42 .031 .119 .1776 2.72 4.2 1 61 1
Automobile LR 3.77 .0675 .1889 .3211 3.89 6.54 I 35 1Parts & (84) MR 2.58 .0459 .146 .2032 2.99 4.63 I 42 1
Accessories SR 2.03 .0332 .0865 .1409 2.19 3.23 I 51 1
Bedsprings LR 3.6 .0269 .1153 .1503 5.85 8.52 30 1& Mattresses MR 2.33 .0206 .0646 .1095 3.48 5.79 I 42 1
(49) SR 1.87 .008 .0271 .0511 2.61 4.34 I 55 1
LR 3.34 .0648 .1515 .6372 3.23 11.34 I 8 1Breweries MR 2.59 .0475 .1038 .3427 2.49 8.51 1 16 1
(27) SR 1.88 .0128 .0255 .0823 1.72 5.13 1 24 1
Chemicals, LR 2.98 .0387 .1178 .4491 5.11 13.41 1 32 1Agricultural MR 1.73 .0202 .0758 .1773 3.46 6.72 1 55 1
(33) SR 1.33 .0095 .0156 .028 1.98 4.15 1 87 1
36
LB
NR
Airplane parts& Accessories
(59)
AutomobileParts & (84)
Accessories
AgriculturalImplements &
Machinery (74)
Bedsprings &Mattresses
(49)
TD In
TNW NWC
.475 .713
.800 1.049
1.496 1.614
CD 1 FD 1
--- 1 --- 1
In 1 NWC I
.446 1 .178 1
.720 1 .370 1
.984 1 .509 1
.473 .605
.778 .862
1.169 1.005
.487 .548
.728 .768
1.339 1.145
.585 1 .146 1
.797 1 .416 1
1.137 1 .599 1
.556 1 .036 1
.936 1 .266 1
1.548 1 .521 1
Chemicals, ILR I 2.77 1. 0815 1.1 6 07 1.5001 I 3.09 7.05 1 39 1Industrial MR I 2.28 p.0553 J.1245 1. 303 2 I 1.95 I 5.03 1 50 1
(60) ISR I 1.51 J.0393 1. 090 3 1.1795 I 1.52 I 3.39 1 59 1
Contractors, LR 2.06 .0314 .1904 .3304 12.51 20.41 1 b 1
Building (188) MR 1.49 .0138 .1239 .1638 8.09 11.52 1 b 1
construction SR 1.27 .0074 .0620 .0914 4.32 5.79 1 b 1
continues of Table 2.6.1
NS FA CD---
In TNW TNW
LR 6.1 .215 .225MR 3.9 .335 .493
SR 3.1 .636 1.153
LR 8.6 .279MR I 5.9 I .484
SR 3.9 .755
1
LR 8.0 .257 .235MR 5.3 .396 .380
SR 4.2 .555 .634
LR 11.7 .156 .229MR 8.2 .281 .459
SR 5.5 .493 .763
1 1.432 1 .580 1 .738 I .879 1 .141 1.615 11.035 11.034 I 1.00 1 .475 1
1 11.125 11.791 1.547 1.419 .558
1 1 1Breweries 1LR I 21.6 I .537 I .131 1 .204 1 .333 11.082 1 .096 1
1MR 16.4 .594 I .213 I .386 1 .465 11.378 11.186 1
37
(27) 1SR
+ II
1 11.4 1I
.819 1 .341I
1 .975I
1 .877I
11.949 1 762 1I
+
Chemicals, LR 10.4 .295 .336 .584 .621 .895 1 .241 1Agricultural MR 6.6 .536 .731 1.11 1.066 1.229 1 .479 1
(33) SR 5.0 .712 1.23 1.659 1.605 2.373 1 .752 1
Chemicals, LR 10.1 .426 .201 .318 .652 .761 1 .440 1Industrial MR 6.9 .688 .300 .589 .847 .985 1 .942 1
(60) SR 5.5 .889 .500 1.06 1.001 1.287 11.524 1
Constructors,Building (188)
LRMR
bb
.095
.222.6171.38
1.1941.884
b 1 .119 1b 1 .274 1
Construction SR b .421 2.398 3.180 b 1 .834 1
b- Building trades constructors have no inventories in the
credit senses of the term. As a general rule, they have no
customary selling terms, such contracts being a special job
for which individual terms are arranged.
2.7 DISCRIMINANT ANALYSIS
The prediction of corporate performance using financial
ratios data and a multivariate statistical approach is a well
researched area in finance and accounting. The discriminant
analysis technique is a multivariate statistical procedure
with application aimed at distinguishing between the members
of two or more distinct populations on the basis of their
characteristics represented by vector variables. A set of
discriminant functions is derived using data from two or more
distinct groups. These functions can then be used to classify
38
further individuals whose data has been used in constructing
the discriminant functions. In the company bankruptcy
situation the two-groups are the samples of failed and
nonfailed firms.
Walter (1959) and Smith (1965) studied common share
analysis in the area of financial analysis. Smith used
discriminant analysis to classify common stocks into five
investment categories, namely, growth, stability, quality,
income and speculative. Walter selected from a sample of five
hundred companies the highest and the lowest fifty E. P.
(inverse of price- earning) ratio firms. He then analysed the
characteristics specific to each of these groups using
discriminant analysis.
Several analysis have attempted to reproduce bond ratings,
using multivariate methods. Although the first studies
employed multiple regression analysis as the base for their
predictive model, Horrigan (1965) and Pinches (1973) found
multiple discriminant analysis more appropria e to tackle the
problem.
The use of discriminant analysis in regard to the analysis
of company failure was done recently by Taffler (1976),
(1982), and Betts & Belhoul (1982) in the UK. Belhoul (1983)
investigated the financial performance of companies using
multiple discriminant analysis together with methods for the
identification of potential high performance companies.
Taffler (1977) and Mulondo (1981) have restricted their
analysis to companies quoted on the stock exchange, but many
39
studies related to comparison of performance have been carried
out on mixtures of quoted and unquoted companies by Roosta
(1979) and Pohlman & Hollinger (1981). Mulondo study was
concerned with industrial enterprises quoted on the London
Stock Exchange. The bankrupt set of firms from which the
discriminant model was derived consisted of all those
companies meeting certain conditions for inclusion which
failed between 1968 and 1973, a period of 6 years. Failure
was defined as liquidation for the benefit of creditors,
winding up by court order, or entry into receivership. All
told a total of 31 firms met the necessary requirement
although of these only 23, the FAILED23 set, provided turnover
figures in their last available income statement.
61 companies were chosen as the nonfailed firms, termed the
ALL61 set. However not all these could be considered
financially healthy and to arrive at the necessary solvent
subset, the investment analysis of the broking firm were
requested to rate each of them on an investment. 45 firms
were considered sound on the grounds of his fundamental
analysis, the G00D45 set. The remaining 16 were termed the
POOR16 set. Different discriminant functions were derived
using the G00D45 and ALL61 groups.
THE VARIABLES
The variables used in this study were selected on the basis
of effectiveness in previous and related studies, popularity
in the literature, theoretical arguments based on the liquid
40
assets flow model of the firm of Beaver and Blum (1974)
adapted from Walter and suggestions by financial analysts
based on their experience. Three classes of variable were
initially developed, conventional ratios computed from income
statement and balance sheet items, four year trend measures
and ratios computed from the funds statement.
However, despite the theoretical arguments in the
literature asserting the utility of the funds flow ratios,
measuring changes in working capital, turned out without
exception to be highly volatile from one year to the next and
as a result not amenable to any form of statistical analysis.
Consequently they were not considered further. The
distributions of the remaining ratios were carefully examined
for the failed and continuing groups separately and
appropriately transformed to improve normality. Outliers
beyond 4 standard deviations from the mean of the remaining
observations in each case were replaced by the mean and those
between 2.5s and 4s by the appropriate 2.5s limit. Because a
number of these ratios were highly non-n rmal, they were
rejected. When they were removed 52 ratios were left for
further analysis together with 26 trend measures.
Initial discriminant runs using the FAILED23 and G00D45
sets were then undertaken to examine whether trend measures
added anything to the power of the discriminant model.
However the trend measure did not contribute to discrimination
and so were not used in further analysis. Of the remaining 52
measures certain were omitted from subsequent discriminant
runs on the advice of knowledgeable financial analysts as
41
being potentially industry dependent, particularly many of the
turnover ratios, others were added and definitions of some
were changed on the basis of experience gained in the earlier
examinations. This reduced the numbers to 50 as follows:
1. FUNDS FLOW(GROSS TRADING PROFIT)/TOTAL ASSETS
2. FUNDS FLOW/NET WORTH
3. FUNDS FLOW/TOTAL LIABILITIES
4. FUNDS FLOW/CURRENT LIABILITIES
5. FUNDS FLOW/NET TRADING CAPITAL(EQUITY+TOTAL
LIABILITIES-CASH)
6. FUNDS FLOW/NET CAPITAL EMPLOYED(EQUITY+LONG TERM
LIABILITIES)
7. EBIT/TOTAL ASSETS
8. EBIT/NET WORTH(EQUITY)
9. EBIT/TOTAL LIABILITIES
10. EBIT/CURRENT LIABILITIES
11. EBIT/NET TRADING CAPITAL
12. EBIT/NET CAPITAL EMPLOYED
13. CASH FLOW(RETAINED PROFITS - EXCEPTIONAL ITEMS
+ DEPRECIATION)/TOTAL ASSETS
14. CASH FLOW/NET WORTH
15. CASH FLOW/TOTAL LIABILITIES
16. CASH FLOW/CURRENT LIABILITIES
17. CASH FLOW/NET TRADING CAPITAL
18. CASH FLOW/NET CAPITAL EMPLOYED
19. TOTAL LIABILITIES/NET WORTH
20. log (FIXED ASSETS/NET WORTH)
21. FIXED ASSETS/NET CAPITAL EMPLOYED
22. TOTAL LIABILITIES/CURRENT ASSETS
42
23. CURRENT LIABILITIES/TOTAL ASSETS
24. TOTAL LIABILITIES/TOTAL ASSETS
25. TOTAL LIABILITIES/NET CAPITAL EMPLOYED
26. DEBT/EQUITY
27. log (QUICK ASSETS/CURRENT LIABILITIES)
28. (CURRENT ASSETS/CURRENT LIABILITIES)
29. (QUICK ASSETS/TOTAL ASSETS)
30. (CURRENT ASSETS/TOTAL ASSETS)
31. (WORKING CAPITAL/TOTAL ASSETS)
32. log (QUICK ASSETS/NET WORTH)
33. log (CURRENT ASSETS/NET WORTH)
34. WORKING CAPITAL/NET WORTH
35. (QUICK ASSETS/TOTAL LIABILITIES)
36. log (QUICK ASSETS/NET CAPITAL EMPLOYED)
37. log (CURRENT ASSETS/NET CAPITAL EMPLOYED)
38. WORKING CAPITAL/NET CAPITAL EMPLOYED
39. log (INVENTORY/WORKING CAPITAL + SHORT TERM
LOANS - CASH)
40. log (SALES/WC+SHORT TERM LOANS-CASH)
41. SALES/AVERAGE INVENTORY
42. DAYS DEBTORS
43. DAYS CREDITORS
44. log (READY ASSETS(CASH+7 DAYS DEBTORS)/CURRENT
LIABILITIES
45. log (READY ASSETS/TOTAL ASSETS)
46. log (READY ASSETS/NET WORTH)
47. log (READY ASSETS/TOTAL LIABILITIES)
48. READY ASSETS/NET CAPITAL EMPLOYED
43
49. log [(ACCOUNTS RECEIVABLE+INVENTORY)/ACCOUNTS
PAYABLE]
50. (WC+SHORT TERM LOANS-CASH)/NET TRADING CAPITAL
2.8 FINANCIAL RATIOS IDENTIFICATION
In selecting financial ratios for investigation we must
ensure that the chosen set covers all the aspects of the
company. If any financial dimension is not considered the
overall conclusions are not reliable because the ratio profile
is not complete. One of the earlier efforts to identify these
ratios was by Courtis(1978). Such identification enables the
analyst to modify his own preferred set of ratios or if he
chooses not to, at least it will place him in a sounder
position to justify to clients (and himself) his reliance upon
specific ratios when giving investment advice. Identification
of financial ratios which have been found to be more
significant are summarised below.
PROFITABILITY RATIOS
(a) Return on Investment
net income to total assets
net income to net worth
net income to working capital
EBIT to total assets
EPS to price per share
NI minus pref. dividends to common OE
earning per share
44
gross profit to total assets
dividends to net income
dividends to cash flow
dividends per share
net income to total debt
(b) Profit Margin
net income to sales
gross profit to sales
(C) Capital Turnover
sales to total assets
sales to net worth
sales to working capital
sales to fixed assets
MANAGERIAL PERFORMANCE
(a) Inventory
sales to inventory
inventory to current assets
current liabilities to inventory
cost of sales to average goods inventory
inventory to total assets
inventory to working capital
days in period to inventory turnover
(b) Credit Policy
accounts receivable to sales per day
45
sales to accounts receivable
accounts payable to average purchases per day
(c) Administration
operating expenses plus cost of sales to sales
operating expenses to gross margin
cost of sales to sales
operating expenses to total assets
(d) Asset Equity Structure
debt to total debts
working capital to net worth
retained earnings to total assets
debt to working capital
current liabilities to working capital
cash current asset to total current assets
net worth to total assets
fixed assets to net worth
fixed assets to debt
fixed assets to total assets
book value per share
total debt plus pref. stock to total assets
debt to total assets
current liabilities to total assets
retained earnings to net income
SOLVENCY
46
(a) Short Term Liquidity
current assets to current liabilities
current liabilities to net worth
working capital to total assets
no credit interval
cash to total assets
cash to sales
quick assets to current liabilities
cash to current liabilities
basic defensive interval
quick assets to total assets
current assets to total assets
quick assets to sales
current assets to sales
cash interval
reduced sales interval
reduced operations interval
(b) Long Term Solvency
total debts to net worth
net worth to fixed assets
EBIT to interest
total debt to total assets
market value of equity to book value of total debt
EBIT to fixed charges
unsubordinated debt to capital funds
(c) Cash Flow
47
cash flow to total debt
annuals funds flow to current liabilities
cash flow per common share
cash flow to current liabilities
working capital to cash flow
cash flow to sales
cash flow to total assets
cash flow to net worth
cash flow to current maturities of long term debt
Courtis (1978) indicated that empirical research into
predictive ability of financial ratios has been concerned only
with preselected phenomena, for example, specifics such as,
default experience over corporate bond issues, loan defaults,
corporate failure, small business failure, corporate
bankruptcy, corporate bond ratings, corporate rate of return
rankings, and corporate take overs. Generalising the
predictive ability of this ratios beyond the context of their
specific studies ought to be tempered with caution.
Nevertheless, the analyst has available a batt ry of financial
ratios with some experience in filtering corporate financial
characteristics.
2.9 SOME LIMITATIONS OF RATIO ANALYSIS
1- Ratios are constructed from accounting data, and
accounting data are subject to different interpretations and
even to manipulation. For example two firms may use different
depreciation methods or inventory evaluation methods.
48
2- Similar differences can be encountered in the treatment
of research and development expenditures, pension plan costs,
mergers, product warranties, and bad-debt reserves.
3- If firms use different fiscal years, and if seasonal
factors are important, this can influence the comparative
ratios.
4- A high inventory turnover ratio could indicate efficient
inventory management, but it could also indicate a serious
shortage of inventories and suggest the likelihood of stock-
outs.
5- Absence of clearly defined accounting standards,
covering all reporting of company data, makes it possible that
two companies report similar economic events in different
ways.
6- While ratios do provide information about the current
status of the firm, they do not contain information about the
alternative strategies and the intervening economic conditions
confronting management and investors, such as mergers and
deferral of payments.
7- The value of human factors and customer loyalty are not
included in financial analysis. The problem with these two
factors is that they are totally intangible and impossible to
measure exactly.
8- Intangible assets are normally omitted because the value
utilised in the business in respect of these assets is
uncertain.
49
Ratios, then are extremely useful tools. But as with other
analytical methods, they must be used with judgment and
caution, not in an unthinking, mechanical manner.
2.10 CONCLUSION
In the first decade of the twentieth century, financial
ratios began to be increasingly used because credit evaluation
became of major importance. Initially the most frequently
used ratio was the current ratio which was used to determine
the firm's solvency position. However, because of the
limitation of this ratio as an indicator, it was realised that
additional ratios were needed to provide a more comprehensive
view of the firm's economic situation. Since then, anal,sis
by means of the calculation of a series of ratios rapidly
became a popular method of analysis of financial statements.
In spite of considerable advantages of using the added
value technique over profitability methods of measuring
companies' financial performances, it is obvious that a single
measure such as added value can not reflect every aspect of
company performance and so sets of ratios have been proposed
to allow a better evaluation of the financial performance of a
company.
An additional advantage of using financial ratios is that
they allow comparisons of company performance for companies of
different sizes and even in different industrial sectors.
More recently multivariate techniques such as discriminant
50
analysis and factor analysis have been gaining in popularity.
This is because of the increased accessibility of the
necessary hardware and software required to carry out such
analysis.
51
CHAPTER 3
BASIC TOOLS OF
PERFORMANCE MEASUREMENT
52
CHAPTER 3: BASIC TOOLS OF PERFORMANCE MEASUREMENT
The business failures can be categorised in four different
types:
1) Economic failures- A business is an enterprise
organised for profit. It may be said therefore, that a
business that does not make a profit and has no reasonable
expectancy of profitable operations is a failure. This is
true even though it has been successful in meeting its
obligations to creditors.
2) Legal failures- The classification of business
failures that follows relates to difficulties of a company
with its creditors. The business has difficulty in meeting,
or can not meet, the legally enforceable obligations due its
creditors.
3) Financial insolvency- When there has been a
decline of current asset values to an extent requiring new
money, or necessary conversion of fixed assets into cash, or
sacrifices by creditors to correct the situation, the company
is sometimes called financially insolvent.
4) Total insolvency- Total assets, tangible and
intangible, are less than obligations due to creditors.
Obviously, the most drastic remedies are necessary here.
Perhaps the only hope of creditors is to bring about a
liquidation and forced sale of assets under a bankruptcy
proceeding.
53
3.1 CAUSES OF FAILURE
The causes of failure in a specific situation are, like the
reasons for success, difficult to isolate. Failures seem
frequently to occur from a 'complex of diseases', rather than
from one outstanding cause. Lack of working capital is often
named as a cause for failure. It is, however, merely a
symptom of some more deep-rooted ailement that has brought
about the resulting condition. Although incompetence on the
part of the management may in general be cited as the cause of
practically all business failures. Lack of capital, so often
cited as the immediate cause, usually indicates lack of skill
in planning. A brief classification of the causes of business
failures were done by Bonneville, Dewey and Kelly (1959) as
follows:
1) uneconomic or defective initial promotion
2) weak production or distribution policies
3) unwise dividend policy, and paying dividends from
capital
4) over expansion
5) cutthroat competition
6) poor financial planning
7) unforeseen and severe economic readjustment,
brought about by a sudden cessation of demand for
the product, revolutionary or unusual
legislation, wars, radical tariff changes and so
on.
8) operation of the business cycle
54
9) disasters, such as, earthquakes, fires, floods.
10) dishonesty and fraud
3.2 DETECTION OF FAILURE BY RATIOS
One of the most important means of checking progress and
detecting tendencies in a business failure is through the
preparation and study of significant ratios, which indicate
relationships between important items reported in the balance
sheets and profit and loss statements of a business. Usually
a number of ratios must be used and to be of value must be
compared with the same ratios which have been prepared from
financial statements for several periods in the past. In this
way, changes may be observed, their causes analysed, and
trends detected. Johnson (1970) was concerned with failure
predictive aspects of financial ratios analysis.
As we have seen in previous discussions the main causes of
company's failure are classified into three basic categories:
1) profitability deficiency
2) management deficiency
3) solvency deficiency
On the other hand if we accept the premise that
shareholders are interested in increasing the value of their
capital investment, and that the long-run survival of the
company is an essential goal, then interested parties might
ask three vital questions.
1) Is the company making any money? (profitability)
55
2) Is the management any good? (managerial
performance)
3) Is the company going to stay in business?
(solvency)
In so far as these questions can be answered at all from
financial analysis, ratios measuring "profitability",
"managerial performance" and "solvency" were selected.
For a complete and overall performance analysis and
detecting the causes of failures and successes we need to look
at all possible ratios as measurement tools. In this way
individual changes can be identified and quantified and the
way in which these individual changes affect overall company
performance analysed. Finally it would seen desirable to
develop a statistic that summarised those different effects.
This single statistic could then be used to assess company's
financial health.
3.3 PROFITABILITY
As has been stated one of the main types of business
failure is called 'economic failure', which means that the
company can not make adequate profit. Making a profit is what
business is all about. Profits are adequate when they return
to business owners the cost of their personally contributed
resources, a reward for their enterpreneurship, and
compensation for the risks involved. The adequacy of profits
is generally measured in terms of profitability as a return on
invested capital (ROI) which is demonstrated by the popular
56
due Pont system in Table 3.3.1 in which certain ratios are
interrelated meaningfully. The changes and trends in each of
the components affect the family of ratios as a whole. There
has been some other studies such as Haugen (1970),
Litzenberger & Joy (1971), Whittington (1972) and Vickers
(1966) which were analysing the rate of return as a
measurement of profitability.
57
Table 3.3.1 The factors combined to yield Return on
Investment (ROI)
WORKINGI I FIXED I I FIXED I 'LABOR' 'MATERIALS' 'VARIABLECAPITAL' 'INVESTMENT' 'CHARGE RATEI
I I II (OVERHEAD
I PLUSI I
TIMES I I PLUS I PLUS I
I FIXED I (VARIABLE I
I COSTS I I COSTS I
PLUS
I
I TOTAL I I NET I I TOTAL I
I I II
I I
'INVESTMENT I I SALES I I COST I
I DIVIDED INTO I MINUS
IPRETAX I I INCOME
'PROFIT I I TAXES
MINUS
I NET I
(PROFIT '
DIVIDED INTO
I CAPITAL I
I TURNOVER RATE I
'PROFIT I
'MARGIN I
TIMES
I RETURN ON I
IINVESTMENT I
58
1) MARKET OR
INCOME
2) MANUFACTUR-
-ING COST
3) INVESTMENT
0
A
Submodels provide another means of analysis. A
profitability model can be broken down into a group of
separate and distinct submodels, which are shown in the
following Table(3.3.2).
Table 3.3.2 Submodels of Profitability
(UNITS SOLD)(NET PRICE PER UNIT)= NET SALES
NET SALES - SELLING COSTS = NET INCOME FROM SALES
UNIT COST(MATERIAL+LABOR+VARIABLE OVERHEAD)(UNITS
PRODUCED) = DIRECT MANUFACTURING COST (DMC)
DMC + FIXED OVERHEAD + CAPITAL CHARGES
= TOTAL MANUFACTURING COSTS
DEPRECIABLE INVESTMENT(BUILDINGS AND EQUIPMENT)+
EXPENSES & AMORTISED INVESTMENT(ENGINEERING, R & D,I
STARTUP)+LAND+WORKING CAPITAL
(RECEIVABLE & INVENTORY)=TOTAL CAPITAL REQUIREMENT
(TOTAL DEPRECIABLE INVESTMENT)(DEPRECIATION RATE)
4)DEPRECIATION
= ANNUAL DEPRECIATION CHARGE
NET INCOME FROM SALES - TOTAL MANUFACTURING COST
59
5) PROFIT
6) CASH FLOW
— NET OPERATING INCOME (NOI)
NOI - DEPRECIATION - EXCEPTIONAL ITEMS
= NET PROFIT BEFORE INCOME TAXES - INCOME TAXES
= NET PROFIT AFTER TAXES
NET PROFIT AFTER TAXES+DEPRECIATION+EXCEPTIONAL
ITEMS = NET CASH INFLOW
NET CASH INFLOW - NET CASH OUTFLOW(TOTAL CAPITAL
REQUIREMENT) = NET CASH FLOW
3.4 MEASURING THE PROFITABILITY
The return on asset ratio is regarded by many financial
analysts as an adequate measure of overall efficiency. This
view has been shared in many studies such as Harrington (1977)
and Fadel (1977). However, other measures of overall
efficiency have been opposed to EBIT/total assets ratio on the
ground that it only evaluate the contribution of capital
resources.
The rate of return on invested assets (r) sometimes
referred to as the return on investment or ROI, can be defined
as follow
r (profits/sales)(sales/assets)
The first item on the right hand side of the identity is
termed the profit margin and the second item the turnover
ratio. The profit margin is intended to serve as a measure of
the relative efficiency with which the firm produces its
60
output, whereas the turnover figure is designed to measure the
efficiency with which the firm utilises its plant and
equipment. Lerner and Carlton (1966) extracted profits from
the above equation which is
P (1-T)(rA-iL)
where
P Profits
T ... corporate tax rate
3 - rate of return on assets (EBIT/assets)
A - level of assets
i = interest rate paid on debts (I/L)
L = liabilities (borrowed funds)
To modify this expression somewhat further if we let the
symbol E equity. Since A = L+E, the equation can also read
P (1-T)[rL+rE-iL]
or P = (1-T)[r+(r-i)L/E]E
or P I E = (1-T)[r+(r-i)L/E]
Assume that the goal of the corporation is to maximise the
rate of return on shareholder's equity,P/E. Management would
then have to determine what capital structure (LIE) will lead
to this result. Taking the partial derivative of the rate of
return on equity (P/E) with respect to the L/E ratio, and
setting it equal to zero.
d(P/E)/d(L/E) = (1-T)(r-i) = 0
Since (1-T) > 0
Therefore (r-i) = 0
and r = i
61
This is that the corporation should expand its ratio of
debt to equity (LIE) until the rate of return on assets (r) is
equal to the rate of interest. This has also been used as a
"rule" for determining the cut-off point for the use of debt.
The theory is that the use of funds by a company and, in
particular, funds obtained by borrowing, should be no more
than the amount that yields a rate of return, r, equal to the
rate of interest, i.
Unfortunately, there are two serious difficulties with this
conclusion
1) In taking the derivative of P/E with respect to
LIE we treated the values of 'r' and 'i' as constants rather
than as function of other economic variables. But if they are
constants, there is no mechanism that permits the values of
'r' and 'i' to be identical!
2) If the firm should find that its rate of return
on assets equal the interest rate it pays on debt, it still
would have no idea whether the rate of return on equity was a
maximum or a minimum value, for equation (P/E) is linear and
therefore has second and higher derivatives identically equal
to zero.
How then can the optimal relationship between the rate of
return on equity and the capital structure be determined? One
widely used method is to treat i as a function of some other
variable. For example assuming that the interest rate is a
rising function of the ratio of debt to equity, and the rate
of return is independent of changes in the firm's size and
capital structure.
62
P/E = (1-T)[r+(r-i)L/E]
i f(L/E) dL/B ===> d > 0
r r0
by substituting
P/E (1-T)[r + Cr -dL/B)L/E]0 0
2P/E (1-T)[r + r LIE - d(L/E) ]
0 0
d(P/E)/d(L/E)= (1-T)(r -2dL/E) = 00
r 2dL/E = 2i0
L/E r /2d0
Since the second derivative of this expression with respect
to LIE is -2d(1-T) the rate of return on equity with respect
to changes in capital structure (LIE) will be a maximum when
the first derivative equals zero.
3.5 BEHAVIOURAL EQUATIONS
The information available to the financial analyst consists
primarily of the corporation's historical accounting records,
the task of financial analysis is to go behind such data to
reveal the economic structure of the corporation's
performance. The task of going behind corporate financial
records to uncover the underlying economic processes is
complicated by the presence of two closely related problems:
63
First the complex interdependence of the relevant
variables.
Second, the presence of a high degree of uncertainty
in the corporation's decision making environment.
To attack the problem of interdependence, accounting
statement equations (such as A = L + E) must be combined with
statements specifying the economic relationships between
variables (such as the rate of return (r) is a declining
function of the rate of growth (g) of corporate assets). The
economic statements will be called "behavioural statements",
"behavioural equations", or "behavioural constraints". The
behavioural aspects of accounting data was investigated using
multivariate procedures and regression analysis by Chambers
(1966), Benston (1966) and Burns (1970). When the accounting
and behavioural statements are combined, they result in a
model or a system of equations that can be used both as a
frame of reference and as a tool for analyzing observed data.
accounting equation= P/E= (1-T)[r+ (r-i)L/E]
behavioural equation= i= dL/E
exogenous variable= r= r0
It is impossible to derive behavioural relationships that
describe completely all details of reality, the analytic power
of reasonable approximations brought together in a systematic
way can be immense.
Including the relevant variables in appropriate functional
form can never be complete, there will always remain
64
unexplained influences on the behaviour of corporate profits.
The effects of such residual influences, which together can
often be important, are usually taken into account by the
inclusion of a random error term in the behavioural
relationship. Thus
i= dL/E
is rewritten as i= dL/E + u
where u is a random variable. The statistical properties
of u are very important.
As we have seen so far it is almost impossible to evaluate
corporate profitability just by a single criterion. It might
be better to evaluate and analyse all the possible financial
ratios which are directly or indirectly related to
profitability.
3.6 PROFIT VS PROFITABILITY
Profitability is a concern of high-level management but
requires conscious attention at all levels of an organisation
if it is to be attained. Profit and profitability are
different concepts. Accounting principles determine the
measure of profit. Profit is fundamentally a short-term
evaluation that is an income statement for 1 year- and is
therefore amenable to distortion. It is possible for a
company to show a profit for 1 year when in truth it is losing
money over the long run. The recognition of time as a
fundamental constituent of profitability is paramount, and
profitability is a long-term concept.
65
Profitability can be evaluated only after time has elapsed
and future profitability can only be estimated. For example
the automobile industry showed a profit for the late 1970s,
but it failed to tool up for the oncoming demand for small,
economical automobiles. The year 1980 was a disaster for
profits and revealed in retrospect that the profits of the
late 1970s were false because the investment necessary for
change and modernisation had been disregarded. Again nothing
should be given an ultimate judgement on a short-term basis,
and even a short-term venture should be evaluated on the basis
of its long-term effect since it will influence the future
beyond its termination.
3.7 RISK VS PROFITABILITY
Risks may be of either a technical or economic nature.
Some risks must be taken, because they are too inviting to
forego. While some risks are too great to be taken. In any
event, the primary concern should not be with eliminating
risks, but with selecting the right risks to be taken. The
only way to avoid risk is to do nothing, which is actually the
greatest risk of all in business. Actually, risks in business
can never be avoided. Even a course of action that minimises
risk is not always desirable. According to Friend & Blume
(1970) and Wagner & Lau (1971) studies, there are several ways
that risk and uncertainty can be anticipated and handled so
that their possible harmful effects can be minimised.
66
The calculation of desired profitability or ROI of a
company should consider three major elements:
1) pure interest
2) compensation for management
3) compensation for risk
The pure interest represents the return that could be
realised by placing the available funds in some alternative
secure, interest-paying investment. This alternative
investment might be certificates of deposit, treasury bills,
high-grade bonds, or other investment media. In general, the
rate of pure interest applicable to invested funds fluctuates
between 8 and 10 percent, depending on the condition of the
money market. Another 1 or 2 percent should be included in
the ROI as reward for management's seeking out, evaluating,
and reaching a decision on where the funds could best be
placed. Finally, the risk portion of the business return must
be added. This is strictly a judgment factor and can range
from 1 to 40 percent or more, depending on the particular
business. Typically, a business having average risk should
earn from 6 to 10 percent just on the basis of its risk, plus
another 8 to 10 percent for pure interest a d 1 to 2 percent
allowance for investment management. The average business,
then, should earn from 15 to 20 percent, after taxes,
averaging about 18% ROI.
67
Risk level
1) very low2) below average
3) average
4) above average
5) high
Allowance for
Interest !Management
(2) (2) 1
8-10 0-18-10 1-2
8-10 1-2
8-10 1-2
8-10 1-2
Table 3.7.1 Typical profitability objectives for companies
having different levels of risk.
RiskTotalreturn
Typical 1return 1
(2) (2) (2) 1
1-3 9-14 11 1
2-6 11-18 15 1
6-10 15-22 18 I
12-20 21-32 25 1
20-40 29-52 41 1
+
and profitabilityAnother consideration about risk
relationship is that financial decisions affect the value of a
firm's stock by influencing both profitability and riskiness
of the firm. This relationships are illustrated in Figure
3.7.2.
68
- - -
POLICY DECISIONSLine of business
size of firm
type of equipment
use of debt
Liquidity position
and so on
Table 3.7.2 Influence of profitability and risk on the
value of firm's stock.
CONSTRAINTSAntitrust
Product safety
Hiring
Pollution control
and so on
-Value of Firm
Risk
An increase in the cash position, for instance, reduces
risk, however, since cash is not an earning asset, converting
other assets to cash also reduce profitability. Similarly,
the use of additional debt raises the rate of return, or the
profitability, on the stockholders' net worth, at the same
time, more debt means more risk. The financial manager seeks
to strike the particular balance between risk and
profitability that will maximise the wealth of the firm's
stock holders. Wippern (1966) and Elliott (1972) are
recommended for further study.
3.8 RESTRAINTS IN PROFITABILITY ANALYSIS
1) A single criterion is inadequate for a full
evaluation of profitability.
2) The most important estimates in the evaluation of
profitability are the predictions of cash flows, with sales
69
volume and sales price being the most critical factors. The
possibility of enormous changes in raw materials costs and
availability are becoming commonplace in a world that seems to
be running short of everything.
3) Operating costs are more critical than investment
since the former are repetitive and the latter is made only
once.
4) Some restraint should be used when comparing cash
flows. Although it is not a definition, cash flow is
principally the sum of profit plus depreciation. A high cash
flow may merely signify a high depreciation expense, that is,
a high cost for wear and tear of equipment caused by frequent
replacement.
5) Future improvement in quality control must be
anticipated since a competitor may offer a superior product.
Reliability of production may be a factor, involving attention
to inventories from raw materials.
6) Allocation of overhead costs can have an
important influence on apparent costs and profitability of
individual products and services.
7) The influence of government is important. Plants
have closed because they were unable to meet for example
antipollution requirements.
8) During periods of inflation, there is a
temptation to capitalise expenses. A fixed amount, written
off in part of or wholly as a future expenses, will appear to
be less at a future time, when the value of the money is less.
9) There are different ideas among researchers
regarding which value to use as denominator of ratio related
70
to profitability and the possible mathematical pitfalls
accompanying the use of ratios. Recently, some financial
researchers, such as Taffler (1976) and Mao (1976) among
others, have advocated the use of average or beginning of year
figures rather than the usual year-end figures for items
placed in the denominator of profitability and turnover
ratios.
The essential ingredients for profitability for a company
are as follows:
1) profitability should be judged on a long-term
basis.
2) operations must be as efficient as possible,
recognising that technology is always in flux.
3) the diffusion effect of peripheral activities
should be held to a minimum.
3.9 PROFITABILITY RATIOS
Profitability ratios are intended to indicate whether there
has been a satisfactory rate of return for being in business.
To achieving this goal, all the profitability ratios which
have been studied in chapter 2 are summarised here as a
primary ratios for financial analysis.
R NPAT/SALES1
R NPAT/TOTAL ASSETS2
R NPAT/NET WORTH(SHAREHOLDERS' FUND)3
R NPAT/WORKING CAPITAL4
71
R = NPAT/TOTAL DEBT(TA-SF)5
R = NPAT/CURRENT ASSETS6
R = NPAT/FIXED ASSETS7
R = NPAT/(PREF.DIVIDENDS+COMMON DIVIDENDS)8
R = NPAT/(TOTAL ASSETS - CURRENT LIABILITIES)9
R = (NPAT-PREF. DIVIDENDS)/COMMON STOCK10
R = EARNING BEFORE INTEREST AND TAX(EBIT)/TOTAL ASSETS11R = EBIT/SALES12
R = EBIT/NET WORTH(SF)13
R = EBIT/(TOTAL LIABILITIES - CURRENT LIABILITIES)14
R = (EBIT + DEPRECIATION)/NET WORTH(SF)15
R = (EBIT + DEPRE.)/(TOTAL LIABILITIES - CURRENT LIABILITIES)16
R = NET PROFIT FOR COMMON/COMMON STOCK AT MARKET VALUE17
R = EARNING PER SHARE(EPS)/PRICE PER SHARE18R = EPS19
R = SALES/(LONG TERM DEBTS+PREF. STK+COMMON STK)20
R = SALES/TOTAL ASSETS21
R = SALES/NET WORTH22
R = SALES/WORKING CAPITAL23
R = SALES/FIXED ASSETS24
R = SALES/CURRENT ASSETS25
R = SALES/TOTAL DEBT(TA-SF)26
R = (SALES-VARIABLE COSTS)/EBIT27
R = DIVIDENDS/NPAT28
R = DIVIDENDS/NET CASH FLOW(NPAT+DEPRE. +EI)29
R = DIVIDENDS PER SHARE30
R = NET PROFIT PER SHARE OF COMMON FOR 2nd B.C/31 NPPS OF COMMON FOR 1ST BC
R = LOWEST NPAD-AVERAGE 3 PREVIOUS YEARS OF NPAD/32 AVERAGE 3 PREVIOUS YEARS OF NPAD
72
R (DEPRECIATION+TOTAL INTEREST+TOTAL TAX)/(TOTAL CAPITAL)33
3.10 MANAGERIAL PERFORMANCE
Sharpe (1963), Radnor, Rubenstein & Ben (1968) and Thornton
& Byham (1982) were concerned in their studies with the
managerial performance assessment. A study in the USA by Dun
and Bradstreet (1973) came to the conclusion that 93 percent
of the causes of failure stemmed from, managerial inexperience
and incompetence, the rest being 'neglect' 2 percent, 'fraud'
2 percent, 'disaster' 1 percent, and 'unknown' 2 percent, the
evidence for 'bad management' was
a) Inadequate sales 44 percent
b) Competitive weakness 24 percent
C) Heavy operating expenditure 9 percent
d) Inadequate control of debtors 8 percent
e) Excessive fixed assets 4 percent
f) Inadequate inventory control 4 percent
g) Poor geographical location 2 percent
h) Others 4 percent
Argenti (1976) identified five characteristics of bad
management:
1) One-man rule (not by any means necessarily a one-
man business)
2) A non-participating board of directors
3) An unbalanced team of managers (in the functional
and personality senses)
4) A weak finance function
73
5) A company in which the chairman and chief
executive are the same person
The consequences of the poor management are:
1) Deficient accountancy information
2) Not responding to changes
3) Overtrading
4) Launching a big project
5) Rising company's gearing
The deficiencies in the accountancy information system
relate particularly to inadequate budgetary control, cash flow
forecasts and costing system. Because there is no established
way of planning for changes, change- or failure- will be
forced on the company. In the last few months before failure,
the symptoms of failure become more frequently observable and
more severe. The stock market will already have reduced the
price of the company's securities, but even now, Argenti
claimed, 'top managers are protesting that all is well, that
the embarrassment is temporary or non-existent'. Argenti
quoted Sir Denning Pearson, chairman of Rolls Royce Ltd as
saying, seven months before the company's insolvency in 1971,
'the company is in good shape'. Normal dividends are often
paid, and the accounts continue to show that things are not as
bad as other indicators suggest. By this time, the owners of
the company have almost certainly lost their money, and the
creditors are well on the way to losing theirs.
74
3.11 MANAGEMENT VS RISKINESS OF LOAN
The supply of funds that a lender will advance to a
corporation is not unlimited. Rather, the supply is a
function of both the interest rate the lender receives and the
riskiness of the loan. The higher the interest rate, the
greater the quantity of funds that a lender will advance, on
the other hand, the higher the risk exposure, the lower the
quantity offered. Both of these variables, the gross interest
rate and the riskiness of the loan, are functions of other
variables:
Gross interest rate is a function of:
1) competitive conditions
2) growth of the markets serviced by the lending
institutions
3) monetary and fiscal policy of the notion
Riskiness of a loan is the function of:
1) ability of the corporation's management
2) debt/equity
In general, the better the management, the less risky the
loan, for the likelihood that the loan will be repaid is
greater. As the ratio of debt to equity rises, however, the
loan becomes more risky. A company with a low debt-equity
ratio may still fall into bankruptcy if its liabilities fall
due at a time when its assets are unsalable. Nevertheless, we
may safely assume that in most cases the smaller the debt-
equity ratio of any one firm, the less likely it is that the
firm will encounter financial difficulty. To summarise this,
75
the risk exposure of a loan (e) can be expressed as a function
of both Firm's management ability and its debt-equity ratio.
e = f(M, LIE)
where
M - an index of management ability
the partial derivatives are
de/dM = f <0 and de/d(L/E) = f >01 2
Since f is negative, the risk exposure of a loan will fall as1
management improves, since f is positive, the risk exposure2
will rise as debt-equity ratio increases.
3.12 MANAGERIAL PERFORMANCE RATIOS
From 1966 to 1978, some studies were undertaken to
investigate the usefulness of financial ratios in measuring
the managerial performance by Page & Canaway (1966), Prasad
(1966), Stokes (1968), Berkwitt (1971), Simons (1974), Jones
(1976) and Beer, Dawson & Kauanagh (1978).
Although it may be argued that all ratios in some way help
to asses the efficiency of management's actions, there are
specific management functions which can be investigated
directly by ratios. Frequently referred to as efficiency or
activity ratios, they embrace such issues as the time it takes
to receive payment from customers, the time taken to pay
suppliers, the length of the cash conversion cycle, the
76
turnover of inventory, the cost efficiency of operations, and
relative "balance" of debt-equity -working capital-assets
components within the overall structure of financial position.
All these ratios are summarised as:
R SALES/INVENTORY34
R SALES/DEBTORS35
R = SALES/ACCOUNT RECEIVABLES36
R = SALES/COMMON STOCK AT MARKET PRICE37
R SALES/AVERAGE ACCOUNT RECEIVABLES38
R = SALES OF 2nd BUSINESS CYCLE/SALES OF 1st BUSINESS CYCLE39
R (SALES + CHANG IN INVENTORY)/INVENTORY40
R INVENTORY/TOTAL ASSETS41
R INVENTORY/WORKING CAPITAL42
R INVENTORY/SALES43
R INVENTORY/CURRENT LIABILITIES44
R INVENTORY/(TOTAL ASSETS - CURRENT LIABILITIES)45
R INVENTORY/CURRENT ASSETS46
R DEBT/WORKING CAPITAL47
R - CURRENT LIABILITIES/INVENTORY48
R = CURRENT LIABILITIES/WORKING CAPITAL49
R - CURRENT LIABILITIES/TOTAL ASSETS50
R - (TOTAL LIABILITIES + PREF. STOCK)/TOTAL ASSETS51
R - COMMON DIVIDENDS/COMMON STOCK AT MARKET VALUE52
R COMMON DIVIDENDS/NET PROFIT FOR COMMON53
R COMMON DIVIDENDS/NPAT54
R - COMMON STOCK (BOOK VALUE)/TOTAL CAPITAL55
R COMMON STOCK (BOOK VALUE) /COMMON STOCK (MARKET VALUE)56
77
R ... COMMON STOCK/NET WORTH57
R = FIXED ASSETS/NET WORTH58
R = FIXED ASSETS/DEBT59
R ... FIXED ASSETS/TOTAL ASSETS60
R - FIXED ASSETS/(TOTAL ASSETS - CURRENT LIABILITIES)61
R =. RECEIVABLES/SALES PER DAY62
R = RETAINED EARNINGS/TOTAL ASSETS63
R ... RETAINED EARNINGS/NPAT64
R ... RETAINED EARNINGS/NET WORTH65
R = ACCOUNT PAYABLE/PURCHASE PER DAY66
R ... OPERATING EXPENSES/GROWTH MARGIN67
R = OPERATING EXPENSES/TOTAL ASSETS68
R ... (OPERATING EXPENSES+COST OF SALES)/SALES69
R = COST OF SALES/AVERAGE GOODS INVENTORY70
R = COST OF SALES/SALES71
R = DAYS IN PERIOD/INVENTORY TURNOVER72
R = CASH/CURRENT ASSETS73
R = NET WORTH/TOTAL ASSETS74
R - TOTAL INTEREST/TOTAL ASSETS75
R = TOTAL INTEREST/EBIT76
R - TOTAL TAX/NPAT77
R = (PBT+INTEREST CHARGES+LEASE CHARGES)I(INTEREST CHARGES78 + LEASE)
R ... BOOK VALUE PER SHARE79
78
3.13 OPTIMUM AMOUNT OF CASH
The manager's objective is to maintain sufficient cash on
hand or at short call to meet any normally predictable expense
without resorting to expensive overdrafts or other costly
emergency measures. Ideally, however, he will gauge matters
so finely that he never actually has more cash on hand than
will be needed, because surplus cash is an idle asset, and as
such it incurs an opportunity cost: the cost to the company of
what it could earn if invested elsewhere in securities or in
longer-term deposits. The extent to which cash is put to
effective use within the business will reflect agreeably in
the profit level: the more the better, to put it simply. But
there are limits. The loss of liquidity due to maintaining
very low cash balances could lead the company into
difficulties. The key to the management of cash and of all
working capital is therefore a matter of striking a balance
between liquidity and profitability. The success of working
capital management or cash management depends upon knowledge
of the cash flow position of the company.
Bierman (1960), Archer (1966), Wright (1973) and Samuels &
Wilkes (1975) developed some models and quantitative
techniques for determination of company cash balance. Samuels
and Wilkes(1975) suggested that a decision on the optimum
amount of cash a company should hold is a similar question to
the decision on the optimum amount of inventory.
Let
Q- Optimum amount of cash-like assets to be obtained
from outside sources.79
D= The amount of cash to be used in the next time
period
IC= Fixed cost of financial transactions involved in
obtaining new funds.
k- The interest cost of holding cash
D is amount of cash to be used in each of a number of
succeeding time periods and Q is the total amount to be raised
to provide for this, therefore
T = Q/D (no of periods involved)
So The average fixed cost per period will be
KIT KD/Q
The second cost, representing the interest lost through
holding cash-like assets, has an average cost that increases
as the amount of money raised at each attempt increases. The
cash on hand at the beginning of the period is the amount
raised Q, at the time the next amount of cash is raised, the
stock of cash will have fallen to zero and so the average
level of cash is
1/2(Q+0) = 1/2(Q)
the average cost of carrying cash = (kQ)/2
The average total cost incurred per period in maintaining a
certain average level of cash is therefore:
C =kQ/2 + KD/Q
As shown in the section on inventories, using differential
calculus the optimum value of Q is:
80
= \/(2KD/k)
This analysis has assumed that the amount of cash required
during a period is known with certainty. It assumes that, it
is possible to forecast the amount that will be required over
the period. In reality it may not be possible to predict with
certainty the amounts that will be required. There may well
be a cost attached to running out of cash. There are also the
normal cost of holding cash. In a situation of uncertainty,
formulation of an optimum policy involves weighting the costs
of carrying funds against the costs of running out of cash.
More precisely, where uncertainty exist the usual objective is
to minimise 'expected' costs per period of time.
Expected Costs (EC) . Expected transactions cost per
period + expected holding cost per period + expected shortage
cost per period
Where the transactions cost is a known constant K, as
are k(the interest cost of holding 1 pound for one period)
and c(the cost of being short of 1 pound for one period)
we should expect c>k.
The calculation of expected costs implies that the
probability distribution of costs are known. It is also
assumed to be the same for each time period. Often there is
a lag between deciding new funds are required and them
materialising. Here the lag is given the symbol L
81
A
Time
If the expected demand rate is D so the expected cyclelength is
EC - Kii + kP + cP /i1 2
where
P the expected number of unit periods of cash stock1
P the expected number of unit periods of cash shortage2
Figure 3.13.1 can help to determine the expected number of
unit periods of cash stock.
Figure 3.13.1
R - the 're-order point'n
P 1/2TQ + i(R-h)
h - expected leadtime demand - LD
D - distribution
EH (expected holding cost per period) = i(iT/2+R-LE)K/i
S =, expected number of shortage
EC - KE/ii + k( l if2+R-L17)) + cl(-151/&)
82
Which should be minimised witb respect to R. Usually this
would involve an enumerative procedure selecting various
values of R and drawings from the distribution of D rather
than mathematical minimisation.
83
THE MILLER MODEL
The Miller (1975) model has four sets of assumptions.
1.1) The firm has two types of asset-cash and a separately
managed portfolio of liquid assets whose marginal and average
yield is u per pound per day.
1.2) transfers between the two asset accounts can take
place, at a marginal cost of y per transfer.
1.3) such a transfer takes place instantaneously, there is
no leadtime
2) there is a minimum level below which a firm's bank
balance is not permitted to fall.
3) Let 1/t = some small fraction of working day- thus 1(8=
one hour. During this time the cash balance will either
increase by 'm' pounds with probability p, or decrease by 'm'
pounds with probability q = 1-p.
4) It is assumed the firm wishes to minimise its long-run
average cost of managing its cash balance. The cash balance
will be allowed to wander freely between an upper and a lower
limit. As long as the balance is within these limits no
action will be taken, but when the balance reaches the upper
limit (h) above the safety level or the lower limit, a
transfer will take place between the two asset accounts, to
restore the balance to a required level (z) above the safety
level.
84
Let
E(M) = average daily cash balance
E(N) = expected number of portfolio transfers
(in either direction)
y = cost per transfer
u = daily rate of interest earned on a portfolio
2sd = variance of the daily demand for cash
Then cost per day of managing the firm's cash balance over
a finite planning horizon of T is
E(C) = y[E(N)/T] + u[E(M)]
The objective is to minimise this function - this is, the
cost per day. The result is that (starred variables represent
optimum values)
* 3,/ 2Z = \ (3ysd t/4u)
* *h = 3Z
The model obtains a relationship between the average cash
holding of the firm and the three explanatory variables of the
form
2M = 413\ 3ysd /4u
Where M is the average cash balance the firm wishes to
maintain for transaction purposes. The control actions are:
(a) When the balance held for transaction purposes falls to
85
CRI1•.....
a)C.)
gMH0
..0
.4masC.)
PIH• rl0
1::)
x_ . 1_
h2z
-
z
*
the safety level, sell securities of amount Z pounds.
(b) When the balance held for transaction purposes rises to
*
h above the safety level, buy securities of amount 2Z pounds.
Figure 3.13.2 The Miller Model of Optimum Amount of Cash
Safety leve
A
B (1
Time
At the time A the transaction balance reached zero, so
securities to the value of Z pounds were sold. At time B
transaction balance went below the safety level so the
securities to the amount of (z+a) pounds were sold. At the
time C the balance exceeded the safety level by L pound, so
cash was used to buy securities to the value of 2Z pounds,
thereby reducing the transaction balance to Z pounds above the
safety level.
MATHEMATICAL PROGRAMMING APPROACH
The mathematical programming technique was developed by
Haley (1967), Calman (1968), Rao (1973), and Charnes, Cooper &
Miller (1975). Charnes, Cooper & Miller stated that the
amount of cash available might be limited by the sales and
purchases.
86
purchases.
The amount of cash available at any time opening
balance(inventory) + sales - purchases
LetTP - total profits up to the planning horizon
n - the number of periods in the planning horizon
P estimated selling price(per ton) in period j
C - estimated purchase price(per ton) in period j
Y - the quantity to be sold in period j (tons)
X - the quantity to be purchased in period j (tons)
B - warehouse capacity (tons)
A - initial stock at warehouse (tons)
The objective is to maximise:
TP =t13 Y -ItCXj j j=1 j j
subject to
i-1- <B- A
j =1 j j-1 j
i-1Y -X <A
j=1 j 3=0 j
X ,Y > 0j j
(i = 1,2,3,....,n)
(i = 1,2,3,....,n)
(j - 1,2,3 ..... ,n)
At any time (i), purchases in the period (X ), shall not
exceed the warehouse capacity initially available (B - A) plus
i-1
sales up to i ( k__ Y ) minus previous purchases ( X ).j=1 j j=1 j
87
for example if i = 2
X <B-A+Y +Y - X (buying constraints)2 1 2 1
On the other hand the amount available for sale is the
initial stock A plus total purchases up to and including the
i-1previous period (C: X ) minus total sales up to and including
j=1 j
i-1the previous j = 1 period ( Y )
j=1 j
for example if i = 3
Y <A+X+X-Y- Y (selling constraints)3 1 2 1 2
M - the initial cash balance0
M - the minimum cash balance permissible
and write
i-1CX- 7-- PY<M- M
(i = 1,2,3,....,n)j=1 j j j=1 j j 0
Financial constraints require that the value of purchases
in a period shall not exceed the excess of the initial cash
balance over the requisite minimum cash balance (M -M) plus0
the total value of sales up to and including the previous
i-1period (=IP Y ) minus the total value of purchases up to
j=1 j j
i-1and including the previous period (ZC X ).
j=1 j j
For example in the third period it is required that
C X <M-M+PY+PY -CX -CX33 0 11 22 11 22
88
In the above it is being assumed that collection of debts
takes one period but allowance can be made for lags in both
the collection of debts and the payment of creditors. If it
is assumed that payments are made g periods after purchase,
and cash is collected r periods after the sale, then the
financial constraints can be written as
C X -P Y <M -Mj=1 j-g j-g j=1 j-r j-r 0
3.14 LEVERAGE ANALYSIS
The major financial markets available to corporations
include corporate bonds, corporate equities, commercial bank
loans, and commercial paper. Corporate bonds, equities, and
bank loans constitute the most important sources of long-term
capital used in financing companies. Bank loans and
commercial paper are employed extensively as sources of
relatively short-term working capital.
Park and Jackson (1984) determined the annual interest cost
of a bond as:
1) Current market price
2) Redemption or maturity value
3) Coupon rate or annual interest payment
4) Years to maturity
Given price, coupon rate, and maturity date, the present
value can be determined to establish the attractiveness of the
bond as an investment.89
P = M/(1+R) + C[((l+R) -1)/R(1+R) ]
Where
P = present value
M = redemption or maturity value
C = annual interest payment determined by the coupon
rate
R = an appropriate discount rate
N = number of years until the bond matures
A 1000 pound bond maturing in 16 years and bearing a 5
percent coupon (yielding 50 pound annually) is, at a 7 percent
discount rate, worth
16P = 1000/(1.07) + 50[(1.07)-1]/0.07(1.07) = 339 + 472 = 811
The present value of this bond's redemption value is 339
pound, and the annual interest payments are worth 472 pound at
the 7 percent discount rate.
Under these conditions, if the bond is sold for less than
811 pound it will yield(or cost its issuer) more than 72
annually, similarly, if the bond is priced above 811 pound it
will yield or cost less than 7% annually over its remaining
life.
90
Optimal financial and capital structure was studied by
Lintner (1963) and Krouse (1972). Consider a company having
the following financial structure.
Table 3.14.1 Company's Financial Structure
+
I
1
I
I
1 PERCENT OF AFTER TAX(a) I WEIGHTED'TOTAL CAPITAL I COST (Z) I COST (Z)
1
+
SHORT TERM DEBT 5.0 6.0 0.30LONG TERM DEBT 15.0 8.0 1.20
SHAREHOLDERS' EQUITY 80.0 10.0 8.01
+ +
1
1TOTAL 100.0 9.50 I
+ +
a) Interest paid is deductible from gross income, dividends
are not.
1) Shareholders' equity = Capital stock(common+pref)
+ retained earnings + other long term reserves
2) Cost of debt=average market rate for short term
debt= 6%
3) Cost of debt=average market rate for long term
debt - 8%
4) Overall cost of capital - 9.5%
If we change the above capital structure to:
Table 3.14.2 Company's Financial Structure
SHORT TERM DEBT 10 6.0 .60 1
LONG TERM DEBT 30 8.0 2.4 1
SHAREHOLDERS' EQUITY 60 10.0 6.01
TOTAL 100.0 9.0 1
The overall cost of capital would drop to 9%. This company
obviously would prefer debt to equity financing.
3.15 SOLVENCY RATIOS
In the third category, ratios are used in an attempt to
assess the question of whether current debts will be paid on
their due dates, and the capability of meeting both the
principal and interest payment on long-term obligations. In
addition to liquidity aspects, analysts calculate
capitalisation ratios to determine the extent to which a firm
is trading on its equity and the resultant financial leverage.
Accordingly there is an attempt to assess the financial risk
associated with common owners' equity.
R - CURRENT ASSETS/CURRENT LIABILITIES80
R CURRENT ASSETS/TOTAL ASSETS81
R - CURRENT ASSETS/SALES82
92
R = CURRENT ASSETS/NET WORTH83
R = (CURRENT ASSETS - INVENTORY)/TOTAL ASSETS84
R = (CURRENT ASSETS - INVENTORY)/SALES85
R = (CURRENT ASSETS - INVENTORY)/CURRENT LIABILITIES86
R = CASH/(TOTAL ASSETS - CURRENT LIABILITIES)87R = CASH/SALES88
R = CASH/CURRENT LIABILITIES89R = CASH INTERVAL90
R - CASH FLOW/SALES91
R = CASH FLOW/TOTAL ASSETS92
R = CASH FLOW/NET WORTH93
R = CASH FLOW/CURRENT MATURITIES OF LONG TERM DEBT94
R = CASH FLOW/CURRENT LIABILITIES95
R = CASH FLOW PER COMMON SHARE96
R - CASH FLOW/TOTAL LIABILITIES97
R = WORKING CAPITAL/INVENTORY98
R = WORKING CAPITAL/FIXED ASSETS99
R = WORKING CAPITAL/TOTAL ASSETS100
R - WORKING CAPITAL/CASH FLOW101
R = WORKING CAPITAL/SALES102
R = WORKING CAPITAL/NET WORTH103
R = CURRENT LIABILITIES/TOTAL LIABILITIES(TA-SF)104
R = CURRENT LIABILITIES/(CURRENT ASSETS - INVENTORY)105
R = CURRENT LIABILITIES/NET WORTH(SF)106
R = CURRENT LIABILITIES/CURRENT ASSETS107
R = TOTAL LIABILITIES/TOTAL ASSETS108
R = TOTAL LIABILITIES/NET WORTH109
93
R = TOTAL LIABILITIES/CURRENT ASSETS110
R = NET WORTH/FIXED ASSETS
111R = NET WORTH/TOTAL LIABILITIES112
R = EBIT/INTEREST113
R = EBIT/FIXED CHARGES114
R NO CREDIT INTERVAL115
R = ANNUAL FUNDS FLOW/CURRENT LIABILITIES116
R = REDUCED SALES INTERVAL117
R - REDUCED OPERATIONS INTERVAL118
R DEBITORS/CAPITAL FUNDS119
R = LONG TERM LIABILITIES/(STOCK+SURPLUS-INTANGIBLE ASSETS)120
R = DEPRECIATION/TOTAL ASSETS121
R = CREDITS/NET WORTH122
R = BASIC DEFENSIVE INTERVAL123
R = MARKET VALUE OF EQUITY/TOTAL LIABILITIES124
R - MARKET VALUE OF EQUITY/LONG TERM LIABILITIES125
R = (CASH+MARKET SECURITIES-CURRENT LIABILITIES)/PROJECTED126 DAILY OPERATING EXPENDITURE
3.16 CONCLUSION
An analysis of the literature discussed in Chapter Two
reveals that 33 different profitability ratios, 46 different
managerial performance ratios and 47 different liquidity
ratios have been used as the main variables for financial
performance analysis.
The mechanics of these ratios were discussed and
demonstrated in the preceding chapter. An examination of the
94
literature reveals that the techniques available in the past
were wholly inadequate for proper analysis. Also an almost
complete lack of theory pointed to the need to develop further
both the theory and practice of financial analysis. These
aspects of financial analysis and the problems of their
application have been discussed in this chapter; particularly,
the desirability of a shift from univariate to multivariate
financial analysis.
The three dimensions represented by profitability,
managerial and solvency ratios which were discussed in this
chapter jointly measure nearly every aspect of a company's
performance. This indicates that companies' financial affairs
can be effectively controlled by concentrating on these three
dimensions only. Considering just one aspect does not mean
that a company is necessarily doing very well as a whole. For
example if a company is profitable it may not necessarily be
performing well as a whole.
At their best, these three categories of financial ratios
provide a meaningful and quantitative representation of the
results of decisions and the effects of external conditions.
They can and do serve as tools for detecting irregularities in
managerial behaviour and company performance.
95
CHAPTER 4
METHODOLOGY OF FACTOR ANALYSIS
96
CHAPTER 4: METHODOLOGY OF FACTOR ANALYSIS
Considering all the ratios from three different categories
which have been described in Chapter Three, there are 126
different ratios in total. These ratios will comprise the
main source of initial variables which are going to be
analysed and investigated throughout this thesis.
In analysing all these ratios in an attempt to arrive at
some underlying conclusions there is a need to select the most
important and most reliable ratios. In other words, not all
of the ratios identified in the three categories are essential
for initial analysis, because of correlation between ratios.
We should select those ratios, which we use in forming a
profile of corporate financial characteristics. Correlation
of the various ratios with each other, can be expected to
exist simply because ratios use common components as their
numerators and their denominators. Because of this
statistical property only a small number of ratios can provide
a lots of information. Two or three ratios selected from each
category should be sufficient for at east the initial
analysis of a firm's financial statements. Undue
concentration of ratios from one category could bias an
overall appraisal of a firm's position.
4.1 EXSTAT LIMITATION
EXSTAT is a service- provided by Extel Statistical Service
97
Plc- of company data in a computer readable form. It covers
over 3000 British, other European, Australian and Japanese
quoted and unquoted concerns. Information included in EXSTAT
for all companies is as reported in the individual company's
accounts.
The main problem in using the EXSTAT data in the computer
centre at University of Bradford is that not all the financial
data have been made available, such as, market value of
equity, credit interval, cash interval,operating expenditures,
common stock at market value, EPS, price per share, dividend
per share, net profit per share, purchase per day, cost of
sales and so on.
By eliminating uncomputable ratios then we have the
following 86 ratios left which are the whole battery of ratios
for further analysis.
R = NI/SALES1R = NI/TA2
R = NI/SF3
R = NI/(CA-CL)4
R = NI/(TA-SF)5
R = NI/CA6R = NI/FA7
R = NI/(PD+CD)8
R = NI/(TA-CL)9
R = (NI-PD)/0C
10R (PBT+TI)/TA
11
98
R = (PBT+TI)/SALES
12R = (PBT+TI)/SF13
R = (PBT+TI)/(TL-CL)
14R = (PBT+TI+DEPRE)/SF15
R = (PBT+TI+DEPRE)/(TL-CL)16
R = SALES/(TL-CL)20R = SALES/TA21R = SALES/SF22
R - SALES/(CA-CL)23R = SALES/FA24
R = SALES/CA25
R = SALES/(TA-SF)26
R - (PD+CD)/NI28
R - (PD+CD)/(NI+DEPRE+EI)29R - CD/SF30
R = (DEPRE+TI+TT)/(P5+0C+DC)33
R = SALES/INVENT34
R = SALES/DEBTS35
R = INVENT/TA41
R = INVENT/(CA-CL)
42R = INVENT/SALES43
R = INVENT/CL44
R = INVENT/ (TA-CL)45
R = INVENT/CA46
R = (TA-SF)/(CA-CL)47
R = CL/INVENT48R - CL/TA50
99
R = (TA+PS)/TA51R = CD/NI54R = OC/SF57R = FA/SF58
R = FA/(TA-SF)59R = FA/TA60R = RE/TA63R = RE/NI64R = RE/SF65
R = CASH/CA73R = SF/TA74R = TI/TA75
R = TI/(PBT+TI)76R = TT/NI77R = CA/CL80R = CA/TA81R = CA/SALES82R = CA/SF83R = (CA-INVENT)/TA84
R = (CA-INVENT)/SALES85
R = (CA-INVENT)/CL86
R = CASH/(TA-CL)87
R = CASH/SALES88R = CASH/CL89
R = (NI+DEPRE+EI)/SALES91
R = (NI+DEPRE+EI)/TA92R = (NI+DEPRE+EI)/SF93
100
R = (NI+DEPRE+EI)/(TA-SF)94
R = (NI+DEPRE+EI)/CL95
R = (CA-CL)/INVENT98
R = (CA-CL)/FA99
R = (CA-CL)/TA100
R (CA-CL)/(NI+DEPRE+EI)101
R = (CA-CL)/SALES102
R = (CA-CL)/SF103
R = CL/(TA-SF)104
R = CL/(CA-INVENT)105
R = CL/SF106
R = CL/CA107
R = (TA-SF)/TA108
R (TA-SF)/SF109
R = (TA-SF)/CA110
R = SF/FA111
R = SF/(TA-SF)112
R = (PBT+TI)/TI113
R DEBITS/SF119
R = DEPRE/TA121
R = CREDITS/SF122
One of the best techniques for summarising these ratios is
Factor Analysis which extracts a relatively small number of
factor constructs that serve as satisfactory substitutes for a
much larger number of variables. These factor constructs are
themselves variables that may prove to be more useful than the
original variables from which they were derived.
101
4.2 FACTOR ANALYSIS
Factor analysis is a technique for analysing the
interrelationships of a set of variables using different
multivariate procedures. To recognise the interrelationships
between the ratios is particularly important in our type of
study since multivariate methods have the ability of
exploiting the information content of seemingly insignificant
ratios on an univariate basis (Cooly & Lohnes, 1962, Altman,
1969).
The earliest studies in factor analysis was in Psychology
by Burts & Baks (1947), Thomson (1951), Harley & Cattel
(1962), Hendrickson & White (1964) and Turcker, Koopman & Linn
(1969). These studies were based upon a theory of general
intelligence whereby, in a battery of intellectual activity
tests there exists a factor that is measured by all the tests.
Then it was developed rapidly to investigate the
interrelationships among multivariate data.
In some scientific fields the variables are less precisely
defined, there is not so much agreement among scientists
concerning the interrelationship between variables.
Factor analysis is increasingly being used in these less
developed sciences. Factor analytic methods can help
scientists to define their variables more precisely, and
decide which variables they should study and relate to each
other in the attempt to develop the knowledge of their science
to a higher level. Factor analytic methods can also help
102
these scientists to gain a better understanding of the complex
and poorly defined interrelationships among a large number of
imprecisely measured variables.
MAJOR STEPS IN FACTOR ANALYSIS
Comrey (1973) classified factor analysis into five major
steps as follows:
1) Selecting the ratios.
2) Computing the matrix of correlations among the
ratios.
3) Extracting the unrotated factors.
4) Rotating the factors.
5) Interpreting the rotated factor matrix.
When the correlation matrix has substantial correlation
coefficients in it, this indicates that the ratios involved
are related to each other, or overlap in what they measure,
just as weight, for example, is related to height. On
average, tall people are heavier and short people are lighter,
giving a correlation between height and weight in the
neighbourhood of .60. With a large number of ratios, of which
many can be highly correlated it is difficult to identify
their interrelationships. Factor analysis provides a way of
thinking about these interrelationships by positing the
existence of underlying "factors" or "factor constructs" that
account for the value appearing in the matrix of
intercorrelations among these ratios. For example, a "factor"
of "Bigness" could be used to account for the correlation
103
between height and weight. Both height and weight would be
substantially correlated with the factor of Bigness. The
correlation between height and weight would be accounted for
by the fact that they both share a relationship to the
hypothetical factor of bigness. For further information see
Maxwell (1961), Joreskog (1963), Horst (1965), Mattsson,
Olsson & Rosen (1966), Guertin & Bailey (1970), Lawley &
Maxwell (1971) and Comrey (1973).
4.3 CORRELATION COEFFICIENTS
Factor analysis is based on the assumption that there are a
number of general factors which cause the different relations
between the ratios to arise. Such interdependence can be
regarded as a kind of basic pattern of interrelations between
the ratios in question. As Schilderinck (1977) defined the
aim of factor analysis is to group by means of a kind of
transformation the unarranged empirical data of the ratios
under examination in such a way that:
a) A smaller whole is obtained from the original
ratios, whereby all the information given is
reproduced in summarised form.
b) Factors are obtained which each produce a
separate pattern of motion or relation between
the ratios.
C) The pattern of motion can be interpreted
logically.
In general, factor analysis does not begin with the
104
original observations of the ratios. It sets about
normalising them in a certain way in order to make a mutual
comparison possible. Normalization is done by expressing the
deviations from the original observations with regard to their
arithmetical mean and their standard deviations. Some
researchers such as Afifi (1973) and Bartlett (1937) have
developed several tests for multivariate normality, but most
of them are difficult to implement.
If the number of observations ranges from 1 to T and the
number of ratios from 1 to n, and Zi represents a ratio for
which the observations have been normalised, then the
following formula is obtained:
Z x /Sxit it i
Where
x = X - X (i=1,2,3,...,n, t=1,2,3 .... . ,T)it it i
-Tx ITi t=1 it
2 /T 2Sx = = \//k11(X - X ) /T = \/ x /T
t=1 it i t=1 it
The ratios, normalised satisfy therefore the conditions:
_ TZ =Z IT ===x /TSx =(Z=X -TX )/TSx=(TR -TX )/TSx =0i t=1 it t=1 it i t=1 it i i i i
2 T 2 T 2 2 2 2S z Z /T =Z:x /TS x = S x IS x = 1 (i=1,2,3,..,n)
i t=1 it t=1 it i
Herewith all the ratios are expressed in the same, uniform
105
way and made mutually comparable. The actual normalization
occurs not for each ratio individually but by calculating the
correlation matrix of all ratios together.
The simple correlation coefficient between two ratios
equals the sum of the products of their corresponding
normalised observations, divided by the number of
observations.
T 2 T 2Sz z =C--2 Z /T= VZ:x x /TSx Sx ===x x / /C:x xi k t=1 it kt t=1 it kt i k t=1 it kt t=1 it t=1 kt
Sz z =ri k ik
Which equals the simple correlation coefficient between
the ratios X and X . If i=k, then the variance of Z is
obtained, which equals one, thus
2 T T 2 2 2 2S z ===Z Z /T =Z: x /TS x = S x /S x = 1 = r
i t=1 it it t=1 it i i i
If now, the product of the matrices of the normalised
observations of the ratios under examination is determined, we
get:
Z,....,Z11 n1
Z,....,Z1T nT
Z=Z Z Z
t=1 it it t=1 it nt
:=Z Z ,...., --Z Zt=i nt it ti nt nt
106
ITr , ,Tr I 1r , ,r 1
1 11 1n1 1 11 1n1. 1
1= T 1 I= TR1Tr „Tr 1 1r , ,r 1
I nl nnl ml nn1
The matrix of simple correlation coefficients is equal to:
R ZZ//T
As a consequence of (SZ Z =r ) the matrix R is to be regardedi k ik
as a normalised matrix of variance and covariances. As a
2consequence of (S z = 1 = r ) the element of the main diagonal
i ii
equal one.
Ratios should not be based only on measures of central
tendency and it is necessary to consider not only the extent
and direction of the deviation from the measure of central
tendency but also the dispersion and shape of the distribution
from which the measure of central tendency was calculated
(volatile).
The following ratios are eliminated from the whole battery
because of their volatile standard deviation as shown in Table
4.3.1. These standard deviations have been calculated from
for about 600 different companies throughout UR. For example
the minimum standard deviation for R4 in 1985 is .86 and its
maximum value for 1973 is 148.
107
TABLE 4.3.1: RATIOS WITH VOLATILE STANDARD DEVIATIONS
+
II'RATIOS 11971+
1
1721
1731
1741
1751
1761
1771178
1179
1180
1181
1182
1183 184 185 1
+
R4 3.7 4.3 148 9.4 2.8 53 5.8 3.1 6.3 1.9 10 2.1 5.413.81.861R8 293 3.8 3.4 134 66 12 96 5.3 6.1 23 62 88 65 142 126 1
R10 3 2.5 2.8 3.8 3.1 6.6 4.7 5 12 283 13 22 13 115 1.581
R20 4.1 3.5 11 3.2 3 3.9 3.3 2.7 2.9 3.1 4.2 26 5.512.612.11
R22 5.3 4.7 79 4.5 4.7 5.6 5 4.1 3.9 3.9 5.1 26 5.515.712.71
R23 67 139 64 249 44 50 378 46 48 63 159 65 116157 193 1
R24 23 14 18 13 10 9 10 9.1 8.5 8.4 8.7 12 25 112 15•71
R28 404 .9 .67 1.7 .45 11 .61 .29 1.3 100 3.3 2 .761.8911.51
R33 3.7 3.1 3.9 4.7 3.8 4.2 4 5.2 12 7.9 8.1 11 12 110 1.751
R34 49 24 31 27 30 51 34 17 16 15 16 14 16 154 16.31
R35 12 55 28 22 19 26 25 34 33 56 25 18 24 123 121 1
R42 8 20 170 47 6.1 120 39 5.3 20 5.3 17 8.8 21 124 111 1
R47 29 58 43 109 25 27 81 27 17 23 51 22 48 124 129 1
R48 13 13 17 13 15 14 12 9.3 3.2 3.6 4.4 5.5 6.5133 11.61
R54 356 .68 .66 .98 .43 10 .55 .28 1.2 100 3.2 1.9 .711.8411.21
R64 .41 .91 .7 3.6 .46 33 .83 .3 1.3 1.5 3.8 2.1 .781.8911.61
R76 .7 .58 .51 .71 .43 12 1.1 1.4 3.3 1.6 6.2 8.2 1.415.41.551
R99 1.7 1.2 2 1.2 1.9 1.8 1.5 1.7 1.6 1.8 1.8 8.3 48 158 11.31
R101 3.5 18 5.6 16 33 7.5 5.9 34 9.8 67 58 18 15 125 18.51
R105 17 37 37 84 14 215 73 22 11 15 29 19 61 119 127 1
R106 1.3 .97 22 1.9 1.4 1.9 1.5 .94 3.9 1.3 1.6 10 1.711.41.731
R109 1.6 1.3 23 2.5 1.6 2.2 1.8 1.3 10 1.6 1.9 10 1.912.11 1 1
R113 385 166 404 106 267 173 185 217 183 184 145 116 81 168 145 1
+ +
108
When all the interdependence correlation between ratios
have been calculated and the correlation coefficient matrix
has been obtained, then the next step is to identify those
ratios with high correlations. These ratios can be used as
surrogate for each other and therefore many of them can be
eliminated. For example as shown on table 4.3.2, R1 has the
highest correlation with R2,R6, R11,R12,R63,R91,R94 and R95,
it means that all these ratios are nearly identical with R1
and they all contain almost the same information. Therefore
they can be replaced by each other and we can keep R1 and
eliminate the others from the whole battery and from the
model. By the same way we can eliminate the other identical
ratios and come up finally with 27 ratios which are almost
independent to each other and have the lowest correlation.
109
TABLE 4.3.2: RATIOS WITH THE HIGHEST CORRELATION COEFFICIENT
+ +1 1 1 11 RATIOS !YEAR 1N0 COI R2 R6 R11 I R12 I R91 R63 IR95I R94 1+ +
1 11 R1 11971 339 .79 I .62 I .78 .98 .92 I .67 .76 I .67 11 R1 11972 j 607 II .71 I .14 I .26 I .94 I .33 I .54 I .47 1
1 R1 11973 I 562 III .49 I .96 I .93 I .46 I .62 I .52 1
1 R1 11974 I 561 I .68 .79 I .56 I .90 I .91 I .62 I .72 I .58 1
1 R1 11975 I544I .75 I .73 .65 .92 .88 J .69 .75 I .64 1
1 R1 11976 I 541 I .77 I .71 I .69 .94 I .85 I .71 I .72 I .65 1
1 R1 11977 I 574 .66 I .65 I .56 I .94 J .87 .59 I .72 I .70 1
1 R1 11978 I 548 I .67 I .66 I •57 I .91 .89 I .60 I .63 .62 1
1 R1 11979 517 I .72 I .28 I .65 I .92 I .91 I .64 I .72 I .69 1
1 R1 11980 490 .82 I .79 I .77 I .93 .87 I I .74 .71 1
1 R1 11981 I I .79 I .76 I .74 I .93 I .90 I .74 I .70 I .68 1
1 R1 11982 I 496 .81 .81 I .76 I .92 .88 I .66 I .66 .64 1
1 R1 11983 I 509 I .72 .75 I .67 I .93 I .89 I .69 I .67 .65 1
1 R1 11984 I493I .77 I .80 I .70 I .91 .89 I .71 I .65 I .60 1
1 R1 11985 I 142 I .65 .93 .54 I .91 I .89 J .62 I .80 .68 11 1+ +
The remaining ratios are as follows:
R = NI/SALES1R = NI/SF2
R = NI/(TA-SF)5R = NI/FA7
R = SALES/(TA-SF)26
110
R - (PD+CD)/(NI+DEPRE+EI)
29R - CD/ SF30
R - (DEPRE+TI+TT)/(PS+0C+DC)
33R = INVENT/CA
46R - CL/TA50
R = (TL+PS)/TA
51R - OC/SF57R - FA/SF58
R = FA/(TA-SF)
59R -. CASH/CA
73R = TI/TA75R = TT/NI77R = CA/CL80R - CA/SALES82R - CA/SF83
R - (CA-INVENT)/TA84
R - CASH! (TA-CL)87
R = (CA-CL)/INVENT98
R = (CA-CL)/(NI+DEPRE+EI)101
R - (CA-CL)/SF103
R - CL/(CA-INVENT)105
R = DEPRE/TA121
4.4 THE MODEL OF FACTOR ANALYSIS
Factor analysis is based specifically on inter-
correlations. It examines the effect of the general factors
which are present in more than one ratio at the same time.
111
According to Schilderinck (1977), the factors which the
ratios can influence will be classified into three categories.
a) Common factors. F (j=1,2,3, ,n) factors which
iinfluence several ratios Z (i=1,2 .... . ,n) simultaneously.
ib) Specific factors. S (i=1,2,3, ,n) factors
iwhich influence only one ratio at a time.
C) Error factors. e (1=1,2,3, ,n) factors toi
which errors in the observation material are related.
There are two differences between the common factor and the
other two categories of factors.
1) a common factor affects several ratios Z (i=1,2 n) ati
the same time - thereby producing one special pattern of
relations among the ratios- a specific and an error factor
affect only one ratio at the same time.
2) a ratio Z can at the same time be dependent on more than onei
common factor, but only on one specific and one error factor.
Taking account of the three categories of factors the model
of factor analysis- expressed in normalised observations Z ofit
ratio Z - may be written as follows:i
Z = aF +aF +....+aF +bS+C e (5)it il it i2 2t im mt i it i it
112
(i = 1,2,3, ,n), (t = 1,2,3, ,T)
where a (j=1,2,3 ,....,m), b and c are the coefficientsij
corresponding to the three separate categories of factors. The
factor f , S and e can be regarded as the new, theoreticali i
mutually
satisfy the
ratios. There are assumed to be normalised and
independent of each other so that they must
conditions:
a
Tj
2s f
j
sf f
it
f
/T = 0
2/T = 1
jt
(f f )/T = 0
t=1
=
f
T
t=1
j j' t=1 jt j't
i=s
t=1 it/T = 0
2 T 2b= Ss =s /T = 1
i t=1 it
Ss s = (s s )/T = 0i t=1 it i't
E(e ) = e IT = 0i t=1 it
T 2Var(e ) = e IT = 1
i t=1 it
Cov(e e ) = ( e e )/T = 0i t=1 it i't
113
Sf s = E(f s )/T = 0j i t=1 jt it
Sf e (f e )/T = 0i it=1 jt it
Ss e :•(s e )/T = 0ii t=1 it it
2From (SZ = 1 =r ), considering (Z =af +af+....+
i ii it lilt i2 2t
af +bs +ce) and (a), (b), (c), (d), it follows thatim mt i it i it
for finite sums
2S Z =Z:(Z Z )/T =1/T(Z:(a f +a f +...+ a f + b s +
i t=1 it it t=1 ii it i2 2t im mt i it
2 m2 T2 2 T 2 2 T 2C e ) ) =a (==f /T) + b ( --S /T) + c (==e /T) +i it j=1 ij t=1 jt i t=1 it i t=1 it
m m2Z: a a (Z:f f /T) + 2b Z:a (==f s /T)j-1 j 1 =1 ij ij' t=1 it j't i j=1 ij t=1 jt it
+ 2c Z:a (Z:f e /T) + 2b c (17.e e /T) =i j=1 ij t=1 jt it i i t=1 it it
m2 2 2+b +c
j=1 ij i i(i=1,2,3 ,....,n)
so that
2 2 2 2Sz = h +b +c
i i
where
2a) h represents that part of the total variance which
114
associates with the variance of other ratios. This part of the
variance belonging to the common factors is known as the
common variance or communality.
2b) b is the part of the total variance, which shows no
association with the variance of other ratios, this part
belonging to the specific factor is the specific variance or
uniqueness.
2c) c is the part of the total variance which is due to
errors in the observation material or to the ratios relevant
to the examination which have not been taken into
consideration, this is called disturbance term or error factor.
In factor analysis, little attention is paid to specific
and error factors so that the applied factor analysis is
concerned exclusively with common factors and the corresponding
coefficients, which indicate the degree to which Z is related
to the factor f .
However, the neglect of specific or errors in applied
factor analysis is not always justified. The presence of a
variable with a high specific or error variance component can
be an indication that this variable is probably related to
variables not yet involved in the study.
If, however, the variable with the high specific or error
variance component proves to be important, then other, new,
variables should be added.
115
As mentioned previously, factor analysis aims in fact at
the analysis of the common factors f and their corresponding
coefficients, which we call factor loading. The practical
working model of factor analysis expressed in normalised
observation is therefore:
Z -a f +a f+ + a f (i1,2,3,. ..,n)it il it i2 2t im mt
Where b and c of model (5) are assumed to be zero.
In matrix notation this is
Z = AF
or in detail
IZ , ,Z 1 la „a (I f , ,f I
I 11 1TI 1 11 iml I 11 iTI
1 1=1 11 1
12 , , Z 1 la „a lif , If 1
1 nl nTI 1 nl nmll ml mT1
Where
Z = The matrix of the normalised ratios Z (i=1,..,n, t=1,..,T)it
A = The matrix of factor loadings a (i=1,.. ,n, j=1,..,m)ij
F = The matrix of factors f with elements f (j=1,..,m,t=1,..,T)
Substituting (Z=AF) in (R=ZZ / /T) gives us the relation
between the correlation matrix R of the normalised ratios Zit
and the matrix of the factor loadings A.
R = ZZ 1 /T = AF(AF) / /T = A(FF / /T)A 1 = AA/
The product FF I /T is a matrix of the correlation
coefficients between the factors themselves. As these factors
are also in normalised form the product-matrix is:
116
FF 1 = Z:f f l = TRf fit=1 jt jt
According to condition (a) the factors f are not correlated
thus Rf f i becomes an identity matrix so that
ii
FF /TI and FF / /T I
Equation (R=AA 1 ) shows that the product of AA / again
reproduces a correlation matrix.
4.5 FACTOR EXTRACTION
After the correlation matrix R has been computed, the next
step in the factor analysis is to determine how many factor
constructs are needed to account for the pattern of values
found in R. This is done through a process called 'factor
extraction' which constitutes the third major step in a factor
analysis. This process involves a numerical procedure that
uses the coefficients in the entire R matrix to produce a
column of coefficients relating the ratios included in the
factor analysis to a hypothetical factor construct variable.
The procedure usually followed is to "extract" factors from
the correlation matrix R until there is no appreciable
variance left, that is, until the "residual" correlations are
all so close to zero that they are presumed to be of
negligible importance. There are many methods of extracting a
factor but they all end up with a column of numbers, one for
each ratio, that represent the "loading" of the ratios on that
117
factor. These loadings represent the extent to which the
ratios are related to the hypothetical factor. For most
factor extraction methods, these loadings may be thought of as
correlations between the ratios and the factor. The most well
known factor extraction are Thurston's (1947) centroid method,
Hotelling's (1933) iterative procedure and more recently by
Francis (1965) known as Q. R. method. If a ratio has an
extracted factor loading of .7 on the factor, then its
correlation is to the extent of .7 with that hypothetical
factor construct. Another ratio might have a substantial
negative loading on the factor, indicating that it is
negatively correlated with the factor construct.
To reproduce the R matrix exactly with real data ordinarily
requires as many factors as there are data variables. It is
usually possible, however, to reproduce approximately the R
matrix with AA' where A has a number of common factors m such
that m is considerably smaller than n, the number of ratios in
R. For example
.16
.32
.28
.24
.32
.64
.56
.48
.28
.56
.49
.42
.24
.48
.42
.36
=
.41
.81x[.4
•71
.61
.8 .7 .6]
A
Methods of factor extraction, designed to produce the A
matrix, usually seek to account for as much of the total
extracted variance as possible on each successive extracted
factor. That is, a factor is sought at each step for which
the sum of squares of the factor loadings is as large as
possible.118
The total variance extracted in a factor analysis is
represented by the sum of the computed communalities that is
m2where m is the number of common factors. All the data
il ij
variable variance is not ordinarily extracted. In our case each
ratio has a variance of 1, so the total ratios variance that
could theoretically be extracted is n x 1, or n, where n is the
n2number of ratios. If a represent the sum of
squares of
i=1 ik
loading on factor k, the proportion of the total extracted
variance due to factor k is obtained by dividing this total by
the sum of the communalities.
Since n is the total variance for all ratios combined, that
is, the sum of the diagonal elements of R, then dividing the
sum of the communalities by n gives the proportion of the
total variance that is accounted for by common factors.
After the first factor has been determined, its
contribution to reproducing the R matrix is removed from R by
the operation R R-A A i , where A represents the first factor1 11 1
vector (a ,a ,....,a ) and A l is the transpose of A , R is11 21 n1 1 1 1
called the residual matrix after extraction of factor 1 or
the first residual matrix. It contains the residual
correlations after the contribution to those correlations by
factor 1 has been removed. If one factor is insufficient to
119
reproduce the correlations in R, then R will have same1
values which are substantially different from zero. If this
is the case, another factor will be extracted from the first
residual matrix by the equation R =R -A A l . Thus, the second2 1 22
extracted factor is removed from the first factor residuals.
In general, this process is continued, extracting the mth
factor from the residuals left after taking out factor m-1,
until the residuals are too small to yield another factor.
Since at each step as much variance is extracted as possible,
the successive factors become smaller and smaller from first
to last as shown by the sum of squares of the loadings in the
successive column of A. This initial A matrix does not
represent the final factor solution, however. These factors
are "rotated" from their original positions by methods which
are explained in the following section.
4.6 FACTOR ROTATION
Factor analysis in general and factor extraction methods in
particular do not provide a unique solution to the matrix
equation R AA'. One of the reasons is that the R matrix is
only approximately reproduced in practice and experimenters
may differ on how closely they feel they must approximate R.
This will lead to their using different numbers of factors.
Also, different methods of determining A may give slightly
different results. An even more important reason for lack of
unique solutions, however, is the fact that even for A
120
matrices of the same number of factors, there are infinitely
many different A matrices which will reproduce the R matrix
equally well.
a a
Comrey (1973) considered
V v
the following:
11 12 11 12a a 'cos a sin a 1 V v21 22 xl I= 21 22
a a 1-sin a cos a I V v31 32 31 32
a a V v41 42 41 42
A
V
The schematic matrix operation may be expressed as a matrix
equation
If R-AA I , then it is also true that R=VV • since if we
transpose the product AV, it may be rewritten as
(AV) 1 =V 1
Since the transpose of a product is the product of
transpose in reverse order, then
- V/
VV 1 = A A Al Ai
But ISM, included in the middle of the matrix product
gives an identity matrix, as follows:
1
cos a sin a
-sin a cos a
xlcos a -sin al = 11 01
isin a cos al 10 11
The reason for this is that the diagonal terms of the product
121
2 2matrix are equal to cos a+sin a, which equals 1 for all 'a' and
the off-diagonal elements are equal to sin a cos a -sin a cos a
which equals zero. As a result the above equation simplifies to
R = AA'
Since multiplying by an identity matrix does not alter the
matrix, that is
AIA I = AA'
As long as the matrix is of such a form that ACV= I,
then A A will reproduce the R matrix as well as A itself.
Since the value of 'a' is not specified, this means that there
are as many "matrices that will do this as there are value of
'a'.
This particular A matrix was of size 2 x 2, or order 2,
because only two factors were involved in the A matrix. If
there had been three factors the A matrix, the A matrix
required would be of size 3 x 3. In general, if there are m
factors in the A matrix, the "matrix will be of size m x m.
Any such (\matrix must meet the following requirements.
1) the sums of squares of the rows must equal 1
2) the sums of squares of the co umn must equal 1
3) the inner product of one row by another row must
equal zero for all pairs of non identical rows
4) the inner product of one column by another column
must equal zero for all pairs of non-identical
columns.
If these conditions are met, then An, . I = CV A. If
122
these conditions are not met, then AA' is not equal to the
identity matrix and A A will not substitute for A in
reproducing the R matrix in the same way that A will. The
values in a given column of A will be different from those
of A itself. This means that different constructs are
involved. Matrix A represents one set of constructs for
accounting for the data. Matrix A A represents a different
set of constructs which account for the data equally well in
the mathematical sense that both reproduce the R matrix
equally well. The rotational process in factor analysis
involves finding a matrix A such that AV will represent an
optimum set of constructs for scientific purposes.
"Since what is optimum for one investigator may not
be optimum for another, this particular phase of the
factor analytic process provides a fertile source of
differences among investigators in the way they view
the data. But, just as the artist, the engineer,
the geologist, and the farmer may all describe a
given piece of real estate accurately in very
different ways, so can various transformations of
'A' provide equally accurate but different
descriptions of a body of data."
With the advent of high speed computers analytical
solutions for the rotation problem were made feasible. The
Quartimax-type methods were firstly developed by Saunders
(1953) in which the focus was on simplifying the rows of the
pattern matrix A. Each variable would have high loadings on
the fewest possible factors and zero or close to zero loading
123
on the others. Several other methods were suggested by Mulaik
(1972) involving oblique as well as orthogonal rotation.
4.7 THE KAISER VARIMAX METHOD
The Varimax (1959) method is based on the idea that the
interpretable factor has high and low but few intermediate-
sized loadings. Such a factor would have a large variance of
the squared loadings since the values are maximally spread
out. Using the square of the formula for the standard
deviation, the variance of the squared loadings on factor j
may be symbolised as follows:
2 n 2 2 2 n 2 2Sd = 1/nZ:(a ) - 1/n (==a )
i=1 ij i=1 ij
The variance should be large for factors, so an orthogonal
solution is sought where V is a maximum, V being defined as
follows:
m 2V = Sd
j=1 j
In practice, V is not maximised in one operation. Rather
factors are rotated with each other systematically, two at a
2 2time in all possible pairs, each time maximising Sd +Sd . After
i j
each factor has been rotated with each of the other factors,
completing a cycle, V is computed, and another cycle is begun.
124
These cycles are repeated until V fails to get any larger. The
fundamental problem, then, is to find an orthogonal
transformation matrix A that will rotate two factors such that
2 2Sd +Sd for the rotated factors will be as large as possible.
i j
Consider the following:
x
x
x
xn
11
22
33
y
y
y
y
V1
n
'cos a-sin al
x I 1='sin a cos al
A
X Yii
X Y22
X Y33
. .X Yn n
V2
Where V1 is a matrix of factor loadings to be rotated to
2 2maximise Sd + Sd, V2 is the matrix of factor loadings for which
2 2Sd +Sd is a maximum, and A is the orthogonal transformationii
matrix that will accomplish this desired rotation. The values
in V1 are known. The values in V2 are not. The values in V2,
however, are functions of the angle 'a' and the values in V1 as
follows:
X = x cos a + y sin a
Y = -x sin a + y cos a
2 2 2 2tan 4a = 2[n::(x - y )(2xy) - 7_1(x - y ) ( 2xy)]/n
2 22 2 2 22 26::[(x - y ) - ( 2xY) ]} - {[:— (x - y )] [(2xY)] }
The value of 'a' must chosen such that the above equation
125
is maximised. To ensure that the value of 'a' gives a maximum
rather than a minimum or a point of inflection, however,
requires that the second derivative of above equation with
respect to 'a' shall be negative when evaluated at 'a'. The
angle of rotation that will accomplish this result may be
determined as follows:
tan 4a = sin 4a/cos 4a = num/denom
The angle 4a will be in the first quadrant if both
numerator and denominator of above equation are positive, and
the angle of rotation will be 'a' itself. If both numerator
and denominator are negative, the tangent will still be
positive but the required angle of rotation is
-1/4(180-4a) = -(45-a)
Since the sin and cos are both negative in the third
quadrant. If the numerator is negative and the denominator is
positive, the angle 4a will be in the fourth quadrant and the
angle of rotation will be -a. Finally, if the numerator of
the above equation is positive and the denominator is
negative, 4a will be in the second quadrant and the angle of
rotation will be
1/4(180-4a) = (45-a)
If we assume that:
2 2A - (x - y )
2 2 2B = [(x - y )]
C = 2xy
126
2D = (2xy)
then we can provide Table 4.7.1 as follow
Table 4.7.1 varimax rotation of two(x,y) factors
A AC A- D 1
.578 .562 .0182 .6497 .0003 .4221 .0118 -.4218 1
.531 .344 .1636 .3653 .0268 .1334 .0598 -.1066 1
.687 -.422 .2939 -.5798 .0864 .3362 -.1704 -.2498 1
.765 -.484 .8267 -.7405 .1232 .5483 -.2509 -.4251 1
1.8 26 76 7 1-.30531.2367 11.440 -.3587 I -1.2033 1
Tan 4a = 2[4(-.3587)-(.8267)(-.3053)]/4((-1.2033)}-
{(.8267)- (-.3053) } = .47374
The absolute value of tan 4a is .47374 giving an angle of
0 ,
25 2 / for 4a. The angle 'a' is 6 20'. With a negative
a0denominator, the angle of rotation will be (45 - a) or 38 41/
which is rounded(39) numerator and denominator negative, the
transformation matrix is as follows:
.578 .56201
.531 .34401 1.7771 .62931
l x 1 1=
.687 -.4221 1-.629 .77711
.765 -.4841
.10 .80
.20 .60
.80 .10
.90 .11
0
Where Cos 39 = .7771 and Sin 39 = .6293
The varimax rotation tends to possess invariance property.
This fact is pointed out by Harman (1970) when he states that,
although varimax factors do not have a greater explanatory
meaning than those obtained from other methods, those:
"obtained in a sample will have a greater likelihood of
portraying the universe of varimax factors".
So in our case, if we want to analyse and verify the
remaining ratios by factor analysis, and to find out the most
wanted and the most significant ratios among the whole 27
ratios, we should compute their Varimax rotated factors. This
has been done by using the Fortran computer language tother
with the Statistical Package for Social Sciences (SPSS).The
following tables show the highest rotated factors for each
ratio.
128
TABLE 4.7.2 RATIOS WITH THE HIGHEST VARIMAX ROTATED FACTORS
AFTER ROTATION WITH KAISER NORMALIZATION
'RATIOS 119711 1 I I I I I I I 1 I
172 173 174 175 176 177 178 179 180 181 182 183 184 185 1
I I I I I I I I I I I
R1I
.84 .641.481.7 1.811.751.851.741.851.911.831.781•721•921•951
R2I
.76 .771-.91.841-.91-.61-.81-.71.991-.91-.91-.71.711.791.791
R5I 1.7 1.511.8 1.811.861.731.781.811.881.851•931•881•681.691
R7 .7 .431.711.431.601.601.321.291.341.351.591.3 1.791.881.651
R26I
.76 .811.841.831.8 1.851.741.831.831.831.841.861.681.761-.71
R29 J .9 I.99L 61.651.981•771.711.831.081.311.8 1.691.611.921.551
R30 I •79 .651.881.8 1•431.5 1.731.731.351.461.411.791.451.361.821
R33 .47 .741-.31.341.331.611.481.461.081.121.121.081.131.171.771
R46 1 -.9 1-.91-.81-.81.7 1-.81-.81-.71.711-.61-.61.8 1.8 1-.81-.91
R50 1 .84 1.861.781.8 1.841.891.9 1.941.741.891.911.811•871.831•971
R51 1 -.3 1-.31-.21-.21-.21-.21-.21.111.141-.21-.21-.11.1 1-.21-.21
R57 1 -.5 1-•31•9 1-.21.731.821.791.681-.91.811.881.741.5 1.351-.61
R58 1 -.6 1-.61.971-.91-.71.811•561.591-.81.721.771.951-.61.861-.71
R59I - .8 1-.71-.81-.7I--51-.81--71--71-.61-.51-.81-.71-.51-.61-.81
R73 1 .52 1.791.881.881.891.891.851.851.851.821.851.851.871.861.941
R75 1 .41 1.371•351-.31-.41-.51.3 1.331.791-.41-.41-•41-.41•441.451
R77 1 .17 1.241-.11-.11-.11.111.191-.3 .131.091-.11.171.051.211.291
R80 1 .79 1.821.871.951-.81.721.7 1•761.8 1.651.6 1.721.551.931.831
R82 1 -.6 1-.71-.71-.71.811-.71-.61-.51-.51.6 1.721.641.431.461•591
R83 1 .66 1.841.991.571.571.511.631.671.831.831.551.971.891.881.771
R84 1 .71 1.721.8 1.791-.71.761.821.871-.81.871.861- . 71- .8 1 .7 1.691
R87 1 .93 1.881.831.9 1.891.861.821.821.851.831.821.8 2 1 .89 1 .91.91(
R98 1 .35 1.281.3 1.281.661.321.311.351-.31.331.321.221-.71.281.651
129
1 R101 1 .74 1.971.471.041.941-.71.441.6 1.231.251.8 1.741.351. 1.411
1 R103 1 .83 1.791-.91.651.7 1.711.831.831.981.761.811-.91.761.931.881
1 R105 1 -.6 1-.51.6 1-.61.761-.51-.51-.41.621-.41-.51.651.481-.51-.61
1 R121 1 -.3 1-.31-.51-.31-.41-.41.251-.41-.41-.41-.31-.31-.41.511-.41
1 1 1 1 1 1 1 1 1 I 1 1 1 1
If we assume that
A = 1.00 - .90
B = .90 - .80
C = .80 - .70
D - .70 - .60
E - .60 - .50
F= .50- .00
Then we can illustrate the above table as follow
Table 4.7.3 Transforming the Table 4.7.2
+
I I 1 1 1 1 1 1 1 1 1 1 1 1'RATIOS 11971 172 173 174 175 176 177 178 179 180 181 182 183 184 185+
1 11111111 1 I 1R1 I B ID FICIBIC BIC BIA BIC CIAIA
R2 I C IC AIBIAID BIC AIA BIC CIBB
R5 I B IC EIBIBIB CIB BIB BIA BICC
R7 I C IF CIFIDID FIF FIF EIF BIAD
R26ICIB BIB BIB CIB BIB B B D CC
PAl DID III C I BFIFI B C D lAPE
R30 I BID I AIB FIE I CIC I FIF I F B FIFB
R33 F1C FIF FID FIF FIF F F FIFC
R46 A BBB CIBIBIC CID DIBIB BA
R50 I B I BBB lBIAIAIA CIA AIBIB I BA
R51 F FFF FIFIFIF F F F FIF FF
R57 I E IFAFICIBBIDABBCEFD
R58 D DABICIBEIDBCCADBC
R59 B D B CIE1B C DIE E DID EDD
R73 E I B I ABIBIA B I BIB I B I BIB I B I BA
R75 F F F FIFIE F FIB FIFIF F FIF
R77 F F F FIFIF F FIFIFIFIF F FIF
R80 I BIB I B IIICI CI C1BID IDI C I E IAIB
R82 DIC C CIBIC E EIEID CID FIFD
R83 I D IB I AIEIEIE I DIDIBIB I EIA I AIAB
R84 C IC BIBICIC BIBIBIBIBICIBICC
R87 I A IAI BIAIA1B I BIBIBIBIBIBIAIAA
R98 F IF FIFIDIF FIFIFIFIFIFICIFD
R101 I C IA FIFIAIC FIDIFIFIBICIFIFIF
131
I R103
1
I R105
1
I R121
1
1
1
+
B IBIAIDICI C IBI B I A I C I B I A I C 1 1A1
D IEIDIDICIEIFIFIDIFIEID1F1E1E1
F IFIFIFIFIFIFIFIFIFIFIFIFIEIF1
1 1 1 1 1 1 1 1 1 1 1 1
+
After subtracting the ratios with low rotated factors from
the whole ratios we have the following ratios which can be
considered as significant ratios with high reliability and
high stability.
R = NI/SALES1
R - NI/SF2
R = NI/(TA-SF)5
R = SALES/(TA-SF)26R = INVENT/CA46R = CL/TA50R = CASH/CA73R = CA/CL80
R = (CA-INVENT)/TA84R = CASH/(TA-CL)87
R = (CA-CL)/SF103
4.8 INTERPRETATION OF FACTOR ANALYTIC RESULTS
The usual procedures followed in factor iterpretation are
deceptively simple. Those ratios with high factor loadings
are considered to be "like" the factor in some sense and those
with zero or near zero loadings are treated as being "not
132
like" the factor, whatever it is. Those ratios that are
"like" the factor, that is, have high loadings on the factor,
are examined to find out what they have in common that could
be the basis for the factor that has emerged. High loading in
both the positive and negative direction are considered. If a
ratio were to correlate perfectly with a factor, it would
ordinarily be considered identical with the factor in what it
measures. Since ratios are not perfectly reliable, they can
not correlate perfectly with a factor, of course, but with a
factor loading of .90 would indicate a total overlap in true
variance between the ratio and the factor.
A question that frequently arises is how high the
correlation between a ratio and a factor must be before it can
be regarded as significant for interpretive purposes? There
can be no answer to this question in any precise statistical
sense since there is not available at the present time any
statistical test that can establish the significance level of
a rotated factor loading. The loading of a given ratio on a
factor can be altered easily by rotating the factor a little
closer to or a little farther away from the particular ratio
vector in question. A crude index of the sability of a given
ratios for interpretive purposes is the square of the
correlation between the factor and the ratios.
A fairly commonly used cutoff level for orthogonal factor
loadings is .30, that is, no ratio with a factor loading below
.30 is listed among those ratios defining the factor. A
squared value (.30) gives .09, which indicates that a ratio
correlating with the factor less than .30 has less than 10
133
EXCELLENTVERY GOOD
.71 5040.63
30.55
.45 20
.32 10
GOOD
FAIR
POOR
percent of its variance in common with the factor. The other
90 plus percent lies elsewhere, in specific and common factors
plus error. Whereas loadings of .30 and above have commonly
been listed among those high enough to provide some
interpretive value, such loadings certainly can not be relied
upon to provide a very good basis for factor interpretation.
Table 4.8.1 Scale of ratio-factor correlation
1 ORTHOGONAL FACTOR LOADING 'PERCENT OF VARIANCE' RATING
4.9 CONCLUSION
Courtis (1978) and Laurent (1979) have looked at ways of
reducing the number of ratios in use without losing
significant amounts of information. One of the important
result of these studies was that there is a significant degree
of correlation between different ratios and that one or two
ratios selected from each area should be sufficient at least
for the initial review of the firm's performance. One of the
best techniques which can be used to study the correlation
between the ratios is factor analysis that enable management
134
to choose the most significant and reliable ratios among the
others.
This chapter has described the prime difficulty of using
ratios which is deciding which ratio to use. However, it has
been established that this problem is overcome by the use of
factor analysis.
135
CHAPTER 5
DEVELOPING A FINANCIAL MODEL
o COMPADIES' waymmInn
136
CHAPTER 5: DEVELOPING FINANCIAL MODEL OF COMPANIES' PERFORMANCE
5.1 FACTOR SCORE ESTIMATION
There are different methods of factor score estimation
using multivariate analysis which were described in detail by
Anderson (1958), Duckworth (1968), Goodman (1970), overall &
Klett (1972), Dunn & Clark (1974), Harvis (1975), Afifi & Azen
(1979) and Linderman, Merenda & Gold (1980).
Comrey (1973) has employed multiple regression methods to
estimate factor scores, using the following basic equation:
Zf =b2 4112 +1)2+ bi 1 li 22i 33i n ni
where
Zf is a standard score of factor f for subject i
Z is a standard score of ratio 1 for subject iii
2 is a standard score of ratio 2 for subject i2i
b is the standard regression coefficient for ratio i
The standard scores on the n ratios (in our case 27) used
to predict the factor scores are known, these ratios could
consist of all the ratios in the factor analysis, in which
case many of the bi weights would be very low because their
loadings on the factor would be low, or the ratios included
could be a subset of these, restricted to only those with
137
loadings above a selected cut off point. This development,
however, will presume that all ratios are being used.
Equation (5.1.1) is like the standard multiple regression
equation where n ratios are being used to predict a single
criterion variable. To obtain the bi weights for this
equation, it is sufficient to know the correlations among the
ratios and the correlation of the ratios with the criterion,
that is, the validity coefficients. In the application to the
problem of estimating factor scores, the factor scores become
the predicted criterion scores, the ratios in the factor
analysis are the predictors, and the orthogonal factor
loadings or oblique structure coefficients, are the validity
coefficients. The unknown bi weights are obtained through the
solution of the following normal equations derived using the
principle of least squares:
b +br +br + + b r =r1 212 313
n ln if
br +b+br+ +br =r (5.1.2)121 2 323
n 2n 2f
br +br +b + + b r =r131 232 3
n 3n 3f
br +br +br+ +b =r1 n1 2n2 3n3 n nf
The above equation may be expressed in matrix form as
Rb = rf (5.1.3)
Where R is the matrix of known correlations among ratios 1
through n in Eq.(5.1.2), b is a column vector containing the
unknown bi weights, and rf is a column vector of correlations
138
between the ratios and the factors, that is, orthogonal factor
loadings of oblique structure coefficients. Provided the
matrix R has an inverse, Eq.(5.1.3) and hence Eqs.(5.1.2) may
be solved as follows:
-1b R rf
(5.1.4)
Thus, the column of bi weights to be used in Eq.(5.1.1) for
predicting the factor scores from the ratio scores is obtained
by multiplying the inverse of the matrix of correlations among
the ratios by the column vector of correlations of the ratios
with the factors.
In our case, the factor score coefficients(b) are computed
by the SPSS(1975) for all the 27 ratios (n=27) for each of 530
companies and 14 years of activities (7420 cases) as follows:
139
TABLE 5.1.1 FACTOR SCORE COEFFICIENTS
I. -1-
I I I I I I I I I
R Fl 1 F2 1 F3 1F4 1 F5 1 F6 1 F7 1 F8 1F9 1 F10 1 Fll 1
-V
I I I1
I I I I I
1R1 .04411.12011.04851.44811-.0481.04391-.0671-.1811.09491-.0941-.0591
1R2 -.0831.08971-.3771.30961-.0211.11021-.0351.00521.03711.66511-.0131
1R5 .00551-.0651.04031.51201-.0171.08431-.0171.17361-.2461.09301-.0031
1R7 1-.0011.02751-.0011.01671.00181-.0251.00871-.0141-.1791-.0051.05801
1R261-.0431-.1011-.0241.04101.01091.11991-.0311.5110i.1561i-.061i-.024i
1R291-.0001-.0051-.0021.00841.00551.01061-.0181.00011-.0101.03251.31411
1R301.00481.00731.00121.04151-.0011.01631-.0011-.0061.01281.10111.01501
1R331.00481.00791.00561-.0001-.0041-.0071.02971-.0031-.0121.10311-.0091
1R461.06531.37691-.0131.00091-.0041.29501.38121.10721-.0721.01471.03641
1R501-.0201.45101-.0301.08791.05141.49841-.4201.03331.13821.13391-.0181
1R511-.0031-.0031.00511-.0091-.0061-.0151-.0141.04701.00641-.1241-.0431
1R571-1.121.13611-.6571.25341-.0041.11621.11031-.0891-.2261.62931-.0041
1R581-.0121-.1351.22161.00131-.0441-.0091-.0481-.0041.23661.67261.04001
1R591-.0161-.1011.01681.01751-.0001-.0391-.0751.15361.18861-.1871.02941
1R731-.0151-.0061-.0451-.0241.54021.04151.03381-.0291.02871.05961-.0211
1R751.00101.00311.00021-.0091-.0061-.0111.00581-.0111-.0071.03021-.0121
1R771-.0001.00281.00111-.0011-.0001-.0041-.0021.00501-.0061-.0141.00191
1R801.01451.01071.02361-.0511.01641.19831.54781.07131.18801.20571.07131
1R821-.0221-.0021-.0101.06591.00451.14261.01991-.3461.36611-.1481.08071
1R831-.1721.19481.56871.11181.01891.00961.20881-.0381-.3411-.2761-.0011
1R841.09531.61241.05801.08041-.1251-.8341.23351.32441.28751-.0071.04581
1R871.01421-.0161.00611-.0321.49221.14541.03451-.0241-.0791-.0331-.0081
1R981.00191-.0261-.0041.00521-.0001.05451-.0181-.0051.17811.00601-.0081
11011-.0031-.0011.00011-.0031.00501.00261-.0131.00291-.0341-.0241.36291
140
11031-.0701.09111-.1891-.0601.02131.03601.19001-.1371-.3371.1014 00311
11051-.0091.03001.00721.05541.01241.10481-.0371.06681.15801-.0131.00061
11211-.0111-.0281.00561-.0001-.0171-.0291.01531.00991.04241.22601-.0441
I I I I I I I I I I I+ +
5.2 BUILDING COMPOSITE FACTOR SCORES FROM THE FACTOR-SCORE COEFFICIENT
After the final solution is obtained we may wish to have
composite scales built that represent the theoretical
dimensions associated with the respective factors. The factor
scores for the individual data cases are calculated from the
factor-score coefficient matrix.
As SPSS specified, the factor-score coefficient matrix (F)
is:
F = (A IA) A (5.2.1)
Where A is the rotated factor pattern matrix and A i is the
transpose of A. In our case the factor-score coefficient
matrix F has been calculated from the:
F = S R (5.2.2)
Where S is the rotated factor structure matrix and R is the
correlation matrix. A composite scale (factor score) is then
built for each factor in the final solution. For each data
case a vector of factor f is calculated:
f = Fz (5.2.3)
Where F is the factor-score coefficient matrix and z is the
vector of standardised values of the ratios which have been
factor analysed.
141
For example, from the factor-score coefficient matrix in
Table (5.1) we may construct a case's factor-score fl, which
is a composite scale representing Factor 1, as follows:
f = .0441z - .0371z + .0055z - .001z - .043z - .000z +1 1 2 5 7 26 29
.0048z + .0048z + .0653z - .02z - .003z - 1.12z -30 33 46 50 51 57
.012z - .016z - .015z + .001z -.000z + .01145z -58 59 73 75 77 80
.022z - .172z + .0953z + .0142z +.0019z - .003z -82 83 84 87 98 101
.07z - .009z - .011z103 105 121
Where z represents the standardised values of ratios, or
z = (R - mean of R )/standard deviation of R1 1 1 1
Note that the composite factor-score variables produced by
SPSS include a term for each variable in the factor analysis.
It has been customary to build factor scores employing only
those variables that have substantial loadings on a given
factor. By this shorter method we can modify the above
equation to:
f = - 1.11449z1 57
f = .61235z2 84
f = .5687423 83
f = .44807z + .512z4 1 5
142
f = .54025z + .49215z5 73 87
f = .49844z6 50
f = .38124z + .54781z + .19001z7 46 80 103
f = .51104z8 26
f = -.17859z + .18855z + .36611z + .15803z + .17812z9 7 59 82 105 98
f = .66508z + .67259z + .10113z + .22604z - .0144z +
10 2 58 30 121 77
.03017z + .10307z - .124z75 33 51
f - .31409z + .30982z11 29 101
By adding all the above equations together we will have:
f +f+f+f+f+f+f+f+f+f +f=1 2
.44807z
3 4
+ .66508z
5 6 7 8
+ .512z - .17859z
9 10
+ .51104z
11
+ .31409z +1 2 5 7 26 29
.10113z +.10307z +.38124z + .49844z - .12400z - 1.1145z +30 33 46 50 51 57
.672602 + .18855z + .5403z + .0302z - .0144z + .5478z +58 59 73 75 77 80
.36611z + .56874z + .6124z + .49215z + .17812z - .3098z +
82 83 84 87 98 101
.19z + .15803z + .56874z103 105 121
Let Y be the name of the total values of factor scores (f)
and substitute the standardised score of ratios (z) with their
initial and original values, then we have:
Y = .44807(R - .04)/.0537 + .66508(R - .1063)/.5458 + .5121 2
143
(R - .1116)1.1209 - .17859(R - .2196)/3.0873 +.51104(R -3.0922)5 7 26
/1.7032 + .31409(R - .1534)15.8897 + .38124(R - .4517)/.1749 +29 47
.49844(R - .3785)1.1413 + .67259(R - .7736)/.7898 + .18855(R -50 58 59
.7355).6126+.54025(R -.0631)/.0978+.54781(R - 1.7276)/.7931 +73 80
.3661(R - .4456)/.2759+.56874(R - 1.5767)/6.2481+ .61235(R -82 83 84
.319)/.1273+.19(R -.468)/1.1866+.30982(R -3.412)/28.862- 1.1145103 101
(R -.2585)/2.2488+.10113(R -.0398)/.0571 +.22604(R - .0322)157 30 121
.0221+.49215(R -.06)1.112 - .0144(R - 1.2021)/22.951 + .1580387 77
(R -1.4809)/2.1847+.17812(R -.983)14.5205+ .03017(R - .0207)105 98 75
.0755 + .10307(R - 1.4741)/7.2896 - .124(R - 1.0119)/.023933 51
Or simply
Y = 8.344R +1.218R +4.235R -.0578R +.300R +.0533R +1.77R +1 2 5 7 26 29 30
.014R +2.18R -2.969R -5.188R -.496R +.852R +.308R +33 46 50 51 57 58 59
5.524R +.4R -.0006R +.691R +1.327R +.091R +4.81R +73 75 77 80 82 83 84
4.394R +.0394R +.011R +.16R +.072R +10.23R -1.98987 98 101 103 105 121
By eliminating the ratios with low loadings which have been
discussed in Chapter 4 (Table 4.3) from the above equation
then we have:
Y = 8.344N1/SALES + 1.218NI/SF + 4.235NI/(TA - SF) +
.3SALES/ (TA - SF) - 2.969CL/TA + 5.524CASH/CA
+ .691CA/CL + 4.81(CA - INVENT)/TA + .16(CA -
144
CL)/SF + 4.394CASH/(TA - CL) - 1.989
(5.2.4)
Where
NI = NET INCOME
SF = SHAREHOLDERS' FUND
TA = TOTAL ASSETS
CA =, CURRENT ASSETS
CL = CURRENT LIABILITIES
INVENT = INVENTORY
5.3 TESTING THE EFFECTIVENESS OF THE MODEL
From initial 600 UK companies, 53 companies have been
chosen randomly to test the effectiveness of the model. First
of all it should be noted that the mean value of Y is zero.
It means that all the companies with Y score above and higher
than zero are classified as the going concern or well
performing companies, and those with Y-score lower than zero
are classified as the poor performing group of companies some
of which are actually failing.
One of the simplest ways of testing th model is to find
out how well the model can classify those companies whose data
were used to construct the model, then doing the same test for
the other companies as well and finally compare the results or
testing both groups simultaneously. This can be done by
computing the Y-scores for all the 53 companies which include
20 of initial 600 companies under investigation and 33
companies out of the model constructing companies, then
145
classify them according to their Y-scores. On the other hand
the companies with positive Y-scores are classified as the
going concerns and those with negative Y-scores as failed or
poor performing companies, then compare the results with the
actual cases. The results are shown below:
Table 5.3.1 Classification of companies performance
1 1 FAILED1 CLASSIFICATION 1 NO OF I I GOING 1
I 1 COMPANIES RECEIVERSHIP 1 OTHERS I 1
1+
1 I I
+
1 1
1 FAILED I 18 13 2 31I GOING 1 35 o 0 35 1
1 1+ +
One of the 2 "others" had a sort of compulsory liquidation
and the second one had a voluntary liquidation, the 3 going
concern in failed group had some other drastical changes
because of the financial difficulties. As it can be seen from
the above table the results are quite good and it can be said
at this stage the model has a high and considerable
effectiveness in measuring the companies' performance.
The second method and one of the import nt ways of testing
the effectiveness of the model is to plot the Y-score against
time and compare it with actual profitability, working
capital, and liquidity of the companies. We can classify the
ten ratios comprising the model into three separate groups as
follows:
a) profitability ratios
146
1) NI/SALES
2) NI/SF
3) NI/(TA-SF)
4) SALES/(TA-SF)
b) working capital ratios
5) CA/CL
6) CL/TA
7) (CA-CL)/SF
C) LIQUIDITY RATIOS
8) CASH/CA
9) CASH/(TA-CL)
10) (CA-INVENT)/TA
By multiplying the coefficient of the above three group
ratios by their mean values then dividing the total values for
each group by the total values of the three groups, we can
identify the total variance of each group in the whole model.
The results of above computation are
1) profitability 30%
2) working capital 37%
3) liquidity 33%
This means that the model almost contains the same
percentage of variance for each of the three important factors
of the company's performance.
The model was applied to all 53 companies selected from the
Exstat tape accessible at computer centre of University of
Bradford and the 'Y-value' for all of them was computed for
each year (for which data was available) and plotted against
147
time. In the following pages, we have compared the 'Y-value'
as a performance index with three main factors; profitability,
working capital, and liquidity(cash position) for each of the
53 companies, using Simple Plot(1985) which is available at
University of Bradford. The aim of this exercise was to
demonstrate the effectiveness of the model in measuring
performance, and to see how it responds when changes occur to
these three important financial dimensions. This sort of
comparison can be done for well, fair and poor performing
companies separately as follows:
5.3.1 DEMONSTRATION OF THE MODEL'S EFFECTIVENESS ON WELL PERFORMING COMPANIES
The General Electric Co is well known, and accepted as a
well performing company. Figure 5.3.1 comprises four graphs.
The top left graph is a plot of General Electric's 'Y-value'
from 1973 until 1984. The top right graph is a plot of the
same company's profitability over time, while the bottom left
graph shows the company's cash position and the bottom right
graph the company's working capital position. All these
financial dimensions are plotted over the same time period as
the 'Y-values'. The 'Y-value' as well a profitability, and
cash position is rising while the working capital is static.
This means that in General Electric Co the performance (Y-
value) is responding quickly to any changes occurring in
companies cash position and profitability if working capital
remains unchanged.
5.3.2 DEMONSTRATION OF THE MODEL'S EFFECTIVENESS ON FAIR PERFORMING COMPANIES
According to the classified performances in Chapter 6
(6.1), the Anglia Television Group is generally accepted as a
fair performing company for which the relevant graphs are
presented in Figure 5.3.17. As it can be seen from
performance graph, it was well performing from 1972 to 1978
and then rapidly deteriorates from 1978 to 1984. At the same
time the three other financial dimensions are falling as well.
This means that the performance of Anglia Television Group is
declining when companies' profitability, cash position and
working capital are falling. But still its performance is
above the safety level.
5.3.3 DEMONSTRATION OF THE MODEL'S EFFECTIVENESS ON POOR PERFORMING COMPANIES
Burrell & Co is one of the failed companies whose
performance has been analysed. The relevant graphs are shown
in Figure 5.3.46. Its 'Y-value' as well as its profitability,
cash position and working capital is falling. This means that
Burrell & Co was failing because its profitability ,cash
position and working capital were declining which is affected
its 'Y-value'. In fact this assumes a negative value in 1979
indicating that the company has a failed company financial
profile. Burrell's historic performance led to a receiver
being appointed on 4th of August 1980.
The same sort of evaluation can be applied to the other
companies and the results demonstrate the effectiveness of the
149
model. The main conclusion is that in most well performing
companies, the performance model is rising and all of them are
well above the ideal level. In fair performance companies
the performance model is going up and down but they all are
above the safety level. And in poor performing companies the
performance model for all of them is declining and its overall
performance is below the safety level.
150
6.0
5.5
0.200
0.175
0.150
1
//n\ //.\\ /4 \\
0.401
0.35
0.30
0.10
0.1
1974- 1976 1978 1980 1982 1984YEAR
2.0 N.
1.5
(_)
z:1 . 0 _ -
cc •-••••
0.5
1974' 19b 1978'1980 1982t. 1984'YEAR
0.22
E4.5
4.0
3.5
3.0
0.100
/'0.075
0.050
1974 1976'197EI 198d 1982r1984'YEAR
1974 '.1976 1978'1980'1982' 1984'YEAR
GENERAL ELECTRIC CO
Figure 5.3.1 Testing the Effectiveness of the Model
Y VALUE --NI/SFILES
-NI/SF-NI/TA-SF
--CASH/SF-CASH/TA-CL CA/CL
-{i1C/SFCL/TA
151
0.35
0.30-
0.406-
ilf\f \
\/\\-)0.10
1/4
1/4 1/4
*L-j• 0.25
0.5
0.05
0.2
0.20
// I2.0
F--'ET 1.5
'-' 1 . 0 /
1974 1.976 1.978 1980 1.982 1984YEAR
1974 1976 1978 1380 1982 1984YEAR
2
1974 1976 1978 1980 1982: 1984'YEAR
0.05-
1974' 1976 1978 1980 1982 1984YEAR
COALITE GROUP
Figure 5.3.2 Testing the Effectiveness of the Model
tz
Y VALUE---
-NI/SF
-NI/TA-SF
CRSH/SFCRSH/TA-CL
------CF1/CLWC/SF CL/TA
152
4.5
w
V,-
1.0 ,
' '
1972. 1974 1976 1978 1980 1982. 1984YEAR
1972 197.4 .1976 1978 1980 1982 1984YEAR
0.06
0.04
-
972. 1.974 1976 1978 1980 1982 1984__YEAR
0.1
0.14
0.12
0.10
01-11
0..
r0.08tr)cc
0.06
0.04
3.5
3.
2.T
0.cc
1.5
1.0
0.5
1972.197.4 1976 1978 1980 1982 198.4YEAR
ALLIED TEXTILE CO PLC
Figure 5.3.3 Testing the Effectiveness of the Model
Y VALUE -NI/SALES -NI/SF
-NI/TA-SF-------CASH/SF
-CASH/TA -CL----CA/CL
-WC/SF CL/TA
153
7. Q
6.5
6.0
1.0
13-- 4.5
4 .0-
3.0
0.40
0.35"
./ •
7-\\ ,--\
0.30
0.25-
a. 0.20-
0.15
0. 10-
0. 05-1974'1976 1978'1980'1982'1984'1974 1976'1978 1980 1982 1984
YEAR YEAR
0.5\\
0.4-
./2.5"
/IEtr10.3- ILi
// -
/I
1.5
0.2 I //"/ °
\\1.0
0.1- I Jj11
0.5
S.S.
1974 1976 1976' 1980.1982 1984'YEAR YEAR
1574 1976 1978. 1980 1.982 1984
BRITISH HOME STORES PLC
Figure 5.3.4 Testing the Effectiveness of the Model
VALUE
- - - - - - NI/SALES-NI/SF-NI/TA-SF
— —CASH/SF-CASH/TA-CL
— — CA/CL-WC/SF
154
0.30
0.18
0.16
0.14
g0.12
(I) 0.08L_1
0.06
0.04
0.02 1.0
4.5-
4.0
3.5
3.0
cm „
fN, i \, ,f
I \-/' "i ,
\‘‘%
6
I
5 I0.z,, ,i)
c. , ,, , i n\, , 1,-
0.15 /......_.., A ! / ----/\o / \ \\L/ \
0.10 \ ,. ,,,..
2 s„,„,‘ ....„ ,, _ ,_, , . ,• _.0.05
,ii /
\
1972 1974 1976 1978 1980 1982 1984YEAR
• 1972 1974- 1976 197d 198d 1982: 1984'YEAR
BELL (ARTHUR) & SONS PLC
Figure 5.3 . 5 Testing the Effectiveness of the Model
Y VALUE -NI/SALES -N1/SF
-N1/TR-SF
-CASH/TA-ft CA/CL
-112/SF
0.25
1972 1974 1976 197d mid 19E4 1984'1972: 1974' 1976 1978 1980 198 1984YEAR YEAR
155
0.14'
0.12-
0.200"
0.175'
m0.150"
'&10 .125-417)o_nt
0.100E9
0.075
0.050
0.5
972 1.974- 1976 1978 1980 19821 1984YEAR V_0 025
0_04'
In rAJ/
\‘‘: v\
/ .
1n ... s- n,--,
n t \". \, I \i \,1 ...
1972 1974 1976 1978 1980 1982 1984YEAR
1972.- 1974 1976 1978 1980 19821 1984YEAR
972 1974 1976 1976 19ed 1982 196.4'YEAR
WELLCOME FUNDAT I ON
Figure 5.3.6 Testing the Effectiveness of the Model
Y VALUE -NI/SALES
-NI/SF-NI/TA-SF
-CASH/TA-CL---CIA/CL
-WC/SF -CL/TA
156
F-
4.0
3.5
0.40
0.35
0.30
:1125
0.15
0.10'
0.05
0.00f1
0.007
0.006
20.005
co
20.004'
ccin
'O.003
0.002-
/tr\ \,/
/-‘/
\J /
/
1972 1974 1976' 1978' 198d 1982 1984YEAR
97 1974 1976k 1978' 1980 1982 1984YEAR
1972 1974 1976 1978 1980 1982 1984YEAR
1974 1976' 1978' 198d 1982 1984YEAR
BENFORD
Figure 5.3.7 Testing the
CONCRETE MACHINERY PLC
Effectiveness of the Model
Y VALUE -NI/SALES
-NI/SF-NI /TR-SF
--------CASH/SF-CASH/TA-CL
--- - -CA/CL-IC/SF
157
5.5'"
5.0
14. 0
3.0
0.250-
0.225-
0.20T
0.175-
(20.150
D A 25
0.1.00
0.075
0.050
(\\ 20.200F.----„, j:\
0.250F
)
0.225
/
'
-..7'-]
i 1:
E 0.150-
0.125'
/5.
0.075-
1974 1976 1976 1980 1982: 1984YEAR
1974 197d 1978'198d 1982 1984YEAR
2.50-
/-- -'-- N/ .
/ \ / \/
/-N \_-
\ //
1974 1976' 1978' 198d 1382' 1984'YEAR
O. 75-1
0.50 7-`----"Y
1974 197d 1978' 198d 1982'1984YEAR
BEECHAM GROUP PLC
Figure 5.3.8 Testing the Effectiveness of the Model
VALUE- - - -N I /SFILES -NI/SF
-NI/TA-SF----CASH/g
-CASH/TA-CL-----CA/CL
-1./JC/SF
158
0.35-
0.36
0.25
:k- 30.20
60_4.0a-0.15
0.10
0.05
6.0
5.5
5_0t.L1
g4.5
3.5
3_0
2.5
1974 1976 1978 1980 1982 1984YEAR
\
_
1974 1976'1978' 1986 1982 1984'YEAR
1.0
0.8
er.-j
- 0.6LiCD
FL; 0.4
0.2
197A 197G -1 43 1-9811----1-9E-1•984,'YEAR
0-,2
(LT
• _
LE)
ct
0.2
0.1
1974.- 1976'1978'1980'1982' 1984'YEAR
MARKS & SPENCER
Figure 5.3.9 Testing the Effectiveness of the Model
Y VALUENI/SALES-NI/SF-NUTA-SFCASH/SF-CASWITFCL
-111C/SF-CUM
159
2.0
1.5
972. 1974. 1976'1978'1980'1982' 1984YEAR
0.200
0.175
6 0.150
0. 100
0.075
lr
12
i \I\ 0Z
0. 18'
DO
n"
,
n \ 7
0.14
-.-\ /\ / \/j/I. t
-1'1On0..10 \\ \ i
.
/
\ '
t
E %1
008 ‘V/k \t / . -- \ //
0.04-
1972 1974' 1976 1978' 198d 1982 19E4YEAR
0.18'
//-
0.06' /
2.00-
f' .-
1 \ 1.
A1,N
50 - ......,.t i .....-
1 1 --1 1.25'
/ 7.1-7 -,\ ,,./1 I'cc\ 1.00
IL)
ii 1 cct.-,.
/ .It..% ,......
0.75
/'
%.,:1 1 cc
..- J /.
' 9 0.50'...--
0.25"
0.050 972 1974'1976 1978'1980k1982 1984'YEAR
0-.25-0.025
1912.- 1974 1976 197B 1980 1982. 1 .984YEAR
PEARSONS
Figure 5.3.10 Testing the Effectiveness of the Model
Y VALUE- NI/SALES
-NI/SF-NI/TA-SFCASH/SF
-CF1SH/TR-CL— CA/CL
-111C/SF --CUTR
160
70.4
0.35
0.30
:EcEl 0.25
Lj
1974: 197e 1978' 198d 1982: 1984:1.974 1976 1978 1.980 1982 1984YEARY.EAR
_
\
1980:1982: 1984
0. 25-
0. 20:-
cnCCLJ
0.10
0.05-
11\I-...' 0-
Ct
2 .0-
1 . 0
_
—
/
V
0.5
1974 1979 1978:
19 -4 1979. 1979 i980: 1982': 1984'
0-. 5YEAR
RACAL ELECTRONICS
Figure 5.3.11 Testing the Effectiveness of the Model
1 VALUE -NI/SALES
— -NI /SF-NI/IA-SF
—CASH/SF- CRSII/TR-CL
— — — CA/CL-NC/SF CL/ TA
161
F-
0.3C
/
•••
_
1974 1976'1978'1980'1982' 1984'YEAR
r
/\\ / \_,--
4.0
3.5
0.25
LL13.0
ILES2.50_
'21 _17;0.20
cc-0.15
2.0
0.10
1.50.05
1974 19767, 197d-1980' 198 1984'YEAR
0.200-
1.8
0.175-
0.150-
.90.125
Ro.100-
'0.075
0.050
0.02.5
Ifn
"
0.8
1974 1976'1978'1980'1982'1984'YEAR
1974 1976 1978 1980 1982 1984YEAR
BPB INDUSTRIES PLC
Figure 5.3.12 Testing the Effectiveness of the Model
Y VALUE -NI/SALES------NI/SF
-NI/TA-SF-------CASH/SF
-CASH/TA-CL CA/CL
-WC/SF
162
5.0
4,5
tA.1 4.0
I3.5'
'at' 3.0
1_5
1974 1976' 1978 1980 1982 1984YEAR
1974 1976 1978 1980 1982 1984YEAR
\ , --- , .\ / / \\ / ,--- \ /---_-- -' . ,
\ /\ /
1974.1976'1978 1980 1982: 1984'YEAR
,\/
/a.0 / \ / \
// 1 / \
,--... / \ /_,L.5' ," ----...... F-Im
L:
ES,9 1.0z
--\'..\'115-
1974 1976 1978 1980'1982'1984YEAR
0-.5
0.1 0"
ALLIED COLLOIDS PLC
Figure 5.3.13 Testing the Effectiveness of the Model
Y VALUE -NI/SALES 411/SF
-NI/TA-SF
-CASH/TA-CL— CA/CL
-WC/SF -CL/TA
163
0.30
0.25
PE
8E 0. 15- \)/
0.10
4.0
2.. d
1 .5-
Th
0..01
19721 1.974 1976'1978'1980 1982YEAR
0.05'
0.04L
i)\
9721 1974 1976 . 1978:YEAR
1988' 19821 1984'
, IE. /•175' / \ /cm / \_„..•z.
1.50
mi=ic ..1.25
t/
1.00\
.. ._0.75'
0.509721 1974'1976 1978'1980'198211984'YEAR
1984
a.4.
2_75
2.50-
2.25-
2_00-
197211974'
I
1
1976'
I\
I 1
\ 1V
1978'YEAR
f\11 \
\
ASH & LACY PLC
Figure 5.3.14 Testing the Effectiveness of the Model
Y VALUE -NI/SALES--NI/SF
-NI/TA-SF---CASH/SF-CASH/TR-n_ CA/CL
-WC/SF
164
6.0
5.5
5.0
4-5
/
0.6-
0.4N.
1974 1976 1978 1980 1982 1984
1971 1976 1978'1980'1982'1984'YEAR
YEAR
BOOTS CO PLC (THE)
Figure 5.3.15 Testing the Effectiveness of the Model
Y VALVE -NI/SALES- --111/SF
-N I /TFE-SF---CASH/SF
-CRSH/TR-CL CA/CL
-111C/SF
165
/ '.- ---..N./ \ /----,, \
\ /\ /1.6-
1974 1976 1978 1980 1982 1984YEAR
0.35t
I
0.30(
=0.25 (\
0.206`SE
0.15
0.10
0.051974 1976 1978 1980 1982 1984
YEAR
/ /-\.--\\
- -KW" • '-74 197 978 1980
0.6
0.4
„
0-.4
76 1978 1980 1982 1984YEAR 0-.6
5
0.05-
i-
6_l \
La,... / I_.cL.
z 1 ,5 )0.02 / ii, (/ \\
\J , ,, ,,
\ f_
0.01.... ... •
,
_,,
0.04-
1974 1976'197d 1980'1982 1984YEAR YEAR
1974 1976 1978 198d 1982'1984
BRITISH GAS CORPORATION
Figure 5.3.16 Testing the Effectiveness of the Model
Y VALUE
- ------ -NI/SALESNI/SF-NI/TA-SFCASH/SF-CASH/TA-CL
-WC/SF
166
0.50
0.45-
8-0.40
•0.35
0.30
1E0.25
-0.20
1972 1974 1976 1978 1980 1982 1984YEAR
0.4
0.40
0.30
E0.25
0.m
0.15
0.10
0.05
0.15
0.10
0.05
2.5
•-
/-\/
1972 1974 1976
/-•
1.878YEAR
1980
_ _ _
972! 1974 1976!19?8 1980 1982 1984YEAR
1982! 1984
1972 1974 1976 1978 1980 1982 1.984YEAR
ANGLIR
Figure 5.3.17 Testing the
TELEVISION GROUP PLC
Effectiveness of the Model
-
URLUE- - - - -NI/SRLES
-NI/SF
-NI/TA--SFIRSH/SF
-CRSH/TR-CL CA/CL
-MC/SF CL/TA
167
2.25-
2.00-
0.75-
0.50-
0.1
YEAR97 1978 1981 198
- ' 10 , •, .....„
\ s- - - I ‘\\, 711r 0- . 1
,.!I
\,._i L__/
\ I0-.3
0-.4 \J
0-.5
0.050
0.045-
0.040-
I'
I
I , \I
• I l\,0.035-
c%
liE0.030-ocn
0... _
0.025 /I \\
cut' 1
_
Li0.020- II
0.010-!I_
_IL\0.0,5- i
0.005" -.1. '.-.-
\\ , r. -
/
1
1
7
972 1974 1976 1978 198d 1982' 1.972 1974 1976' 1978' 198d 1982'YEAR
YEAR
GOODYEAR TYRE & RUBBER CO.
Figure 5.3.18 Testing the Effectiveness of the Model
Y VALUE----- .- NI/SALES -N1/SF
-NI/TA-SF
-CASH/TA-CL CA/CL
-WC/SF
168
0.18
0.16
0.14
R0.08
[1.12
0.02
197 1974 1976'1978 198( 1982' 1984 197 1974 1976'1978'198d 19E2 1984YEAR YEAR
n
1.4N z\ _ __
/N
\ '
1.2 \. /
0.16-
0.14
=co60.M
0.0 6 / 7/ ---z0.04 0.4
972'1974 1976 1978'198d 198 1984 1972' 1974 1976' 1978 1980'1982' 1984YEAR
YEAR
BABCOCK INTERNATIONAL PLC
Figure 5.3.19 Testing the Effectiveness of the Model
VALUE -NI/SALES
-NI/SF-NI/TB-SF
-------CRSH/SF-CASH/TA-CL CA/CL
-WC/SF
169
2.0-` / NJ
972 1974 19/6 1978 1980 1982 1984YEAR
0.12-
0.04-
1972 1974 1976 1978 1980 1982: 1984'YEAR
1972 1974 1976 1.918 1980 1982 1984YEAR
972 1974 1976 1978 1980 1982 1984YEAR
APV HOLDINGS PLC
Figure 5.3.20 Testing the Effectiveness of the Model
Y VALUE -NI/SALES
-NI/TA--SF
-CRSH/TR-CL— ClitCL
-WC/SF CL/TA
170
1972 1974 197d 1978 198 •1982. 198.4YEAR
972. 197.4 1976 19714 1980 1982. 198.4YEAR
0.200-
0.175
>_ 0.150
iTa.125-
0.100-
0.075-
0.050
0.025-
2.5
2.0
0.5-
0.06-
0.05-z-:
in 0.04
3 0.03
0.02-
0.01-
1.6-
Li
0.8
0.6-
!A / \._ _ /
/ I \
/ --I
\
I/
, ----", \
X %
0.07
0.4-1972 1974 1976 197d 1980 1982. 1984'
YEAR YEAR1972 1974'1976 1978'198d 1982'198.4
AULT & WIBORG GROUP PLC
Figure 5.3.21 Testing the Effectiveness of the Model
Y VALUE -NI/SALES -N1/SF
-NI/TA-SF— —CASH/SF
-CASH/TA-CL— — — — CA/CL
-WC/SF
171
0.200-
0.175-
0.150-LU
:1
0.125-(7.1 PELJ- -
fa- N.100
0.05-
1972. 1974- 1976' 1978 1980'1982'19841YEAR
0.25-
0.20-
,,„
•• n•
3.0
2.. 5
0.5
/
-
1974 1976 1973 1980 1982. 1984—YE
'-
1972 1974 1976'197g.1980'1982'1984'YEAR
0.075
0.050-
0.025
172 1974 1976'1978'1981 1982. 1984YEAR
AL.BR.IGHT & WILSON LTD
Figure 5.3.22 Testing the Effectiveness of the Model
VALUE- - - - - - -+11/SALES
— -NI/SF-NI/TA-SF
— —CASH/SF-CASH/TA--CL
— — CR/CL-WC/SF CL/ TA
172
0.200
0.175
972 1974 1976 1978 1980 1982 1984YEAR
0.250
0.225-
0.200-
2,0.175
E2
0,_
coC) Ii \\!=.0.150-
ml1125(r,m
It tI 1.\
c..)
0.100 III irt_
0.075-/ j) \ 11
Ii
I
, 9, / t---•,..._.../...,"
0.025 972 1974 1976' 197d 198d 1982 1984
YEAR
I
II I
I/11
II \II
1980 1982 1984
_tFE1.2
co
0.6-
0 .4--•
0.2- \,/
97 1974 1976' 1978' 198d 1982' 1984YEAR
\\.
0 150
FEV \ \\'6E1- '0.125
'60.100
\j‘--K)0.075-
0.050
0.025-
1.8
1.6
1.4
/
972 1974 1976 1978YEAR
1
BARROW HEPBURN GROUP PLC
Figure 5.3.23 Testing the Effectiveness of the Model
Y VALUE -NI/SALES
-NI/SF-NI/TA-SF
--------CASH/SF-CASH/TA--CL cripa
-WC/SF
173
F-
fj
\J0-.50-
0.9
0.8
D0.6
r-(.710.5
en' 0.4Li
0.3
0.2
0.1
/ \
/ 1 /'' \/
/ I
I
I
972! 1974 1976,...----1478. 1980
•
1\
1982' 19E14'
0.5
,,/ \,
0.45i ' .-____J. 1
iI
I0.40 ./ 1
›.-
7' 10,35
if
.I Ed
FCE 0.30 /i \I
20.25/ / I
/ \-- I
4
/
0.20
\ ,7--- .---/ / ... --
1972 1974 1976 1978 1980 1982 1984YEAR
1.972 1974 1976 1978 1980 1982 1984YEAR
1972:.1974 1976'1978'1980 1982 1984YEAR
PLEASURAN4 PLC
Figure 5.3.24 Testing the Effectiveness of the Model
Y 'VALUE -NI/SALES
--NI/SF-NI/TA-SF
—CASH/SF-CASH/TA-CL
-WC/SF —CL!TA
174
YEAR97 1974 1976 197 1980 198
0.1
e-. 1
97 1974 1976 74-4... 1980 198
/Th\)
0-.4
0-. 5
0.06-
Ii\ji
\
/s,
0.05
6L.7). 0.04
'60.03
0.02-
0. 01- 972 19 1976 197e- --4380 182EA
1972 1974 1976 1978'YEAR
1.980'19820-.2
1.4
1.2 \
N_ j 1.0 ,.__. /— --
1--m \ /— — N
eff 0.8 \JN\s)
pmF., 0.6
C)- 0.4
0.2
BRITISH RAILWAYS BOARD
Figure 5.3.25 Testing the Effectiveness of the Model
Y VALUE -NI/SALES
-NI/SF-NI/TA-SF
----CASH/g-CASH/TA-CL
— — CA/CL41C/SF
175
1 .6-
1 .4-
1.2-
1 . 0-
0.8
0.6
0.4
0.2
972'1974'1 76'1.978' 1988 1.982 1.98.4YEAR
0-.2
0.20-
0.05
/
1
372 1974 1 .978 1980YEAR
1982. 1984'
0-..25
0-.50-
n0.08
L71-
1"0.06
Gul/r\
0.04 ()\ )
-_!-/\jj/0.02
0.12
0.10
1.50
I\
I`, I. Lou-I! V \ 1I
i()\ I \ Ii \ e 0351....
i tIt
%i \ (-) 0.50-i CM
Z
.E. 0.25cz,
- - •'
•••.-
972'1974'1976 1978 198d 1982'. 1984'YEAR
1972.1974'1978 1978'1980' i.se 18841YEAR
ANCHOR CHEMICAL GROUP PLC
Figure 5.3.26 Testing the Effectiveness of the Model
Y UFLUE -NI/SALES
-NI/SFt'11/TA-SF
--CASH/SF-CASH/TA--CL
- ----CA/CL-WC/SF CL/TA
176
1.5
1.0
2.0
0.8-
0.6-
0.250/e\3. -
0.225- II
3.0 0.200- II
I
0.175-
!]0.150-
2'0.125-u_
E0.100-
0.075
0.0511
0.025-
1974 1976 1978 1980 1982 1984YEAR
./
1974 1976 1978' 1980 198 1984YEAR
I\I
—\ I' 1 \1
\ I \ 1\
\ / \ 1 k-/
\ ...... ‘.....„-\
0.07,'
/ 1
Ili : I\
0.06
zo 1
2//11/1
i
r-o.o5
= / / \ c,C-),0.03 i
0.02 \ /74r 1978 1980 1982 1984
YEAR1974 1976' 1978 198d 1982 1984
YEAR
BAKER PERKINS HOLDINGS PLC
Figure 5.3.27 Testing the Effectiveness of the Model
Y VALUE -NI/SALES------NI/SF
-NI/TA-SF--------CASH/SF
-CASH/TR-CL CR/CL
-WC/SF
177
9 2 13 1 6 1978 1986' 1982 1984YEAR
72 1374 1976 1978 1980 1982 1984YEAR
3 .0-
2.5
2.0
1.5
_F.5 0.5
0.4
0.3
-1.0
/
_r _
cca-
C260.8
0.6
0.4
0.2z
/\/
972 1974 1976 1978 1980 1982 1984YEAR
0-.1
1.4-
-1.5
0.10
0.09 t‘i
0.08
0.071
z r1r
Q\ \
ii
0.06 ;I 1 i
=
U)(LE, 0.04
11 \\\ n
CL 0.05
0.03 \i
0.02\\
0.01‘
1972 1974 1976 1978 1980 1982 1984YEAR
FORD MOTOR CO. LTD.
Figure 5.3.28 Testing the Effectiveness of the Model
VALUE -NI/SALES--NI/SF
-NI/TA-SF---CASH/SF
-CASH/TA-CL-----CA/CL
-61C/SF
178
0.20
0.18
0.16-
0.14
r1-0.08
0.06
0.04
0.02-
-
0.0175
0.0050
0.0025-
/i
0.0157
'\iZ
.90.0125I—
U7
20.0100-
(-1' 0.0075-
....,/ \ liVi" \
\ ' \
0.75-
0.50-
0.5
972 1974 1376 1978YEAR
0-.25
0-.50
1980 1982 1984
3.5
3.0
1.5
1 011972. 197.4.- 1976 1978 1980 1982 1984
YEAR1972 1974 1976 1978'1980'1982"1_984'
YEAR
0.02.00
cc
cLCC
1974 1976 1978'YEAR
:\i i/111
U \\
II 11 (--- \,
ili V1 \1980'1982'1984'
1.75"\
1.50 \ /
1.25-
1.00-
ADAM & GIBBON PLC
Figure 5.3.29 Testing the Effectiveness of the Model
Y UALUE- - - - - -N1fSFILES
— -NI/SF-NI/TA-SF
— —CASH/SF-CASH/TA-CL
— CR/CL-WC/SF CL/TA
179
0.14
0.12-
(I-)50.06-
0.04-
1I 0
8"_
\ ,-, y/- ' - / 0 .02 /.,...--- ---t-. , 0.5
7 fi 1 \
1.0
0.m(--'1.m
ixC)
\2..5 I \
24 \
,--..n
n
n
/.•n /
1972 1974'1978 1978 1980'1982, 1.984 1972'. 1974 1976 1978 1980 1982 1984
YEAR
-N,
YEAR
n
2
1
0.30-
0.25-
..---
1 \ /
/ \ /
1 % /
0.05- n /....-
1972 1974-1976'1978'1.986 1982 .1.9E14YEAR
972 197+ 1976 1978'1900 1982 1984YEAR
ARRITAGE SHANKS GROUP LTD
Figure 5.3.30 Testing the Effectiveness of the Model
Y VALUE- - - - - - -NI/SALES
-N1/SF
-NI/TA-SFCASH/SF-CASH/TA-CL CA/CL
-WC/SF
180
F-
I', 1 k1
1\
0.05-
3.5-
3.0.-
2.. 5
1.5-
1 .0-
0 .5-
197.4:
ip.. \
/ k
1 \ /
1976'
i/
/
\
i1
n
--
1978'198d.YEAR
C n i/ N •
...\ i
1982
/ ---. .....
\
1924'
. -..\
1
1
1974" 1976' 1978'1380' 1982". 1984'YEAR
0.10-
5.0-
4.5
4.0-
1974 1976 1978 1980 1982. 1.984YEAR
0.02T
F.0.02C
0_
(T)0.015CC
0. 0 10
0.005
1974'1976 1978'19El8' 1982 1984'YEAR
0.30-
0.25
ATKINS BROTHERS PLC
Figure 5.3.31 Testing the Effectiveness of the MOdel
Y VALUE -NI/SALES— -N1 /SF
-NI/TA-SF— —CASH/SF
- -CASH/TA-CL CA/CL
-WC/SF
181
1.50
1.25
1. 00
°- 0.50
0-.050.25
972 1974 1976 1978 1980YEAR
0-.25
0-.101984
0-.15
0.15
0.1.0
>_ 0..0517-
972 1974 1976 1978YEAR
nu -1-'3
2.0
972 1974 1.976 1978 1980'1982',38\4,YEAR
1.5
-
0.250-
0.225-
0.200-
60.175-
I--'&10.150-
Ei0.5 112
C)
a_-
0.100- II \
Lt
iI/
%
0.075-
/ \71; •0.050-—
N;.--'•
0.025-'52"
1972 1974 1974 1974 1980 1982 1984YEAR
DUNLOP HOLDINGS PLC
Figure 5.3.32 Testing the Effectiveness of the Model
Y VALUE -NI/SALES
-NI/SF-NI/TA-SF
-------CASH/SF-CASH/TA-CL CA/CL
-WC/SF
182
4.0-
3.5-
t;:;3.0
630.125
0. 100
0.075
0.050
0.025
0.200
0.175
015T
0.25
0.20
_
R0.15
CT,
0.10-
0.05
1972 1974 1976' 1978YEAR
1981i 1982'1984 197 1974 1976 1978 1980 1982 1984YEAR
'•••n\
0.4-
1.5-
Ai1\ //r—\ \
I 1
\ _I/ -.
1k
/i j/
...\_ \ I/ ..- n \\
f\.__--/ // ‘
\t\ r.
1972 1974 1976 197d 198d 1982 1984YEAR YEAR
--1972 1974 1976 1978 1980 1982 1984
BARNO INDUSTRIES PLC
Figure 5.3.33 Testing the Effectiveness of the Model
Y VALUE -NI/SALES
-NI/SF-NI/TA--SF
-------CRSH/SF-CASH/TA--CL CA/CL
-WC/SF
183
1 .4-
t±j1.2-
0.14
0.12
0.10 \
cc
0.08
:7. 0.06 - -S.
A- 0.04
\J
rN.,/
0.02
1972 1974 197d 1978' 198d 1982 1984YEAR
0.10
;\
0. 8-
0 .6-
1.6
0-.02
1.8-
1.6-
1.4-
cc
cc
_
0.8
0. 6
0 .
YEAR
/ \ / \ \/ \
/ \\
,/
-- /\ /\.1
"Th
972 1974 1976 1978 19
2/r\\
982 19840 .4-
0.09
I
=0. 1
0.08
07
;0.05 /f.\\
t,10.M
I
0.04 t
0.03
0.02
0.011972 1974 1976 1978 1 .980 1982 1984
YEAR1972' 1974 1976 1978'1980 1982 1984
YEAR
BBA GROUP PLC
Figure 5.3.34 Testing the Effectiveness of the Model
Y VALUE -NI/SALES------NI/SF
411/TA-SF-------CASH/SF
-CASH/TA--CL CA/CL
-WC/SF
V--
1 84
0.30-
0.25'
>-0.M
0.1
0.14 1
0.12
n=0.1061
dt 0.081
83
0.06 \\Li
0.04
0.02
1974 1976 1978 1980 1982 1984YEAR
0-.2
1974 1976 1978 1980 '1982 13 ?4YEAR
ie.-0-.4
0-,6
1974 1976 1978 1980 1982 1984YEAR
0.10
0.05
1.0
0.8
0.6
- _
n
1974 1976
/..-
1978 1980YEAR
—
1982 1984
BRTLEYS OF YORKSHIRE PLC
Figure 5.3.35 Testing the Effectiveness of the Model
Y VALUE -NI/SALES
-NI/SF-NJ/TA--SF
-----CASH/SF-CASH/TA-CL CA/CL
-WC/SF
185
F-
972: 1974' 1976:1978'1980 1982:1984YEAR 1.972 1974 1976 1978 1980 1982 1984
YEAR
1.75'
0.75'
0.50'
0.25"
0.091..6
0.08
0.07
g30.05CL
0.03
0.02"
0.01
rA
\./""\/I
197 1974' 1976:1978'1980 1982 1984YEAR
1974" 1976'1978 1980 1982 1984YEAR
BEMROSE CORPORATION PLC
Figure 5.3.36 Testing the Effectiveness of the Model
Y VALUE -NI/SALES
-NI/SF-NI/TA-SF
-------CASH/SF-CASH/TA-CL CA/CL
-WC/SF
186
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0 1
- -
0.10
0.05 s'‘
0-.05
0-.10
0-.15
972 1974 1976 1978 1980 1982 194YEAR
•-••
1974"1976 1.978 1980'1982'1984YEAR
972 1974 1976 1978 1980 1982 198YEAR
0.18'
A0.16- t
I 1.8
I\ FE 1
0.14-
.6P0.12'
II \ ESuD' i %=.0 10 1 \'
2 1-4
=
\
LJcn0.00-i \
I k
/,\
li1
\;
011.2`
\\I
0.06/
\ ''N iI1/ V \II V\ Hi-
0-047---\ i
------. \. 0.8
1972: 1974 1976 1978 1980 19E2 1984 197iYEAR
BESTOBELL PLC
Figure 5.3.37 Testing the Effectiveness of the Model
Y OLLIE -----NI/SALES•- -NI/SF
-NI (TA-SF--CRSH/SF
-CASH/TA-CL CR/CL
-WC/SF
,/ \
/ \/ \
- - ----\ 1 \ / \\ / \ / \\,.\., \
187
0.30
2.0
0.25
1.5
,-. 0.201=1,mm
o 1 . 0 F-
DILL 0.15Lálj_
IT
0.5
1972 1973 1974 19/5 1976 19/7 1978 19/YEAR 0.05
1972 1973 1974 1975 1976 1977 1918 1979YEAR
0.10
2.0
- -
1972 1973 1974 1975 1976 1977 1978 1379
1972 1973 1974 1915 1976 1.977 1978 1.979 0- .5-
YEAR
0.09
0.08 /
I 1
ii\I
i/f0.07
cp
z 0.06 /
F.r.ii
7'..\'6;0.05(7)a_ ill
/ \
0. I //037. \
ai 0.04ct
0.02 / / A /0.01 //
BROCKS GROUP OF CO LTD
Figure 5.3.38 Testing the Effectiveness of the Model
`t VALUE
- - - - -N USRLES441/SF-NI/TA-SF
— —CRSH/SF-CASH/TA-CL
— — — — CA/CL
-WC/SF CUTR
188
3.5
1.97 -1974: 1976 -1978YEAR
0.15-
0.05'
1972 1974- 1976 1978 N o— ATYEAR
— —
\
1972 1974 1976 1978 1980YEAR
Y VALUE -NI/SALES -NI/SF
-NI/TA--SF— —CASH/SF
-CASH/TA-CL— — CA/CL
-WC/SF CL/TA
189
0.18-
‘‘. I \c\ r
• /
II \---i \\30.
t/i
0.02
19721 .197‘. 1976 1978 1980 (3-.5-YEAR
0.16'
STONE PLRTT INDUSTRIES PLC
Figure 5.3.39 Testing the Effectiveness of the Model
3.5
3.0
2.5
cu 2.0E 1.5
1972 1974 1976 1978 1980YEAR
0.5
1972 1974 1976 1978 1980YEAR
0-.1
0-.2
0.3
0.2
0.05- 1.8-
0.06-2.0-
/i
E, O. 04-1—
/\
\
CC
_r 1 . 6- /:*:) 1.4
cn
10.03-/ r‘`,\ 1 g 1.2C)
Ln '-'ct
icc
(_) r ' ‘-jf
0.02/0.8
0.01- 0.6
0.4 ,197i 1974 1976' 1976' 1980' 1972
YEAR1974 1.976' 1978' 1986'
YEAR
BRITISH AIRWAYS
Figure 5.3.40 Testing the Effectiveness of the Model
Y VALUE- - - - - NJ/SALES
-NI/TA-SF—. —CASH/SF
-CASH/TA-CL CA/CL
--11JC/SF
0-.5
190
3.0.25r
3.0
2. b 0.20
1.0
0.5 0..05
1972 1974 1976 978 1980YEAR
0-. 5
1976
1971i 1980YEAR
Y VALUE -NI/SALES - -NI/SF
-NI/TA-SF
-CASH/TA-CLCA/CL
-11.1C/SF--CUM
191
n nn
n -1972' 1974 - 1976'- 194
YEAR
-1.0 0- . 05-
0.061
r-N\ \ ,.....„ ...... n ....n •n• ....n.
/ ‘ If
/ \
%
\ \ .--..e-
ISLJ LO
1
\ %ii
\ 2
ICn
n ..............
9 O. 5'
197 1974
0-.. 51980
VINERS
Figure 5.3.41 Testing the Effectiveness of the Model
(T) 0..03ctLi
r\0.02
//11‘.
„,.//
0.01
197 1974 1976 1978'YEAR
F-
0.20
0.18
0.16
0.14
;720.12 \I'6;
R- 0.10ltu/
0.0G
50.08
0.04
0.02k
1972 1974 1976YEAR
1978 1980
0-.05
1.6/
/1.4 / I
/ I1.2-
0.4
0.2
197 1976 1978' 1980'YEAR
_
0.20
uu r=LJz --I 0.15
E'mcc
E
1972 1974 19 1978 1980°-
tr 0.1022oL
YEAR 0.05
- - --
1972 1974 1976 874-- 1980, /
BLACKNAN & CONRAD
Figure 5.3.42 Testing the Effectiveness of the Model
-Y VALUE -NI/SALES
-NI/SF-NI/TA-SF
--------CASH/SF-CASH/TA-CL CA/CL
-WC/SF
YEAR
192
1.75
1.50
0.05
N,
1.25
e 1.00
--, --,
/
,.../\-.
1972 1973 194 1975' 1976 1977 1976 19.1iYEAR
...•••••
0.75
0.25
0-.25
0-.50
0.1
•••.
0.10
YEAR1973 1974 1975 1976 1977 197 100,5
itnrs /1197.
g
--J;30-.05 ii
L o—. Lo Im F--
Et-, 0-.5R
-L.0 CL 0-'. 15 I
-1.50-.20 I
0-.25
0-.30
u,t 11( \\ct II
0_
;::: , I ' \ 1.
'..jo..136
0.10 0
0.02" II
il
0. 04
o_oe''i \
'. 11
1\
1 \
'11
/‘,...._./ 1972 1973' 1974 1975 1976 197f 134 1.34
YEAR
AP1ALGAMATED INDUSTRIALS
Figure 5.3.43 Testing the Effectiveness of the Model
Y VALUE -NI/SALES
-NI/SF-NI/TR-SF
--------CRSH/SF-CASH/TA-CL
— — — — —CR/CL-WC/SF
-------CL/TA
193
0.20
0.15
0.10
e:n 0.05
LT:1973 1974 1975 1976 1977 19 1979 1 80
YEAR
0-.5
1973 1974 1 1976 1977 '980YEAR
2.0
1.5
1 .0
I0.5
0-.05
0-.10
0-.15
1.
g
1973 1974 19/5 19/6 19/7 19/8 1979 1980YEAR
0.0200
0.0175'
0.0150-
I‘20.0125c-ZTri
20.0100\ //(\
u-) \cc(-10.0075-
0.0050'
0.0025- kt:
0-.5-1973 1974 1975 1976 1977 1978 1979 1980YEAR
BLACKWOOD, PlORTON 8, SONS
Figure 5.3.44 Testing the Effectiveness of the Model
Y VALUE
- - — NI/SALES----- -NI/SF
-NI/TA-SFCASH/SF
- CASH/TA-CL— CR /CL
- 111C/SF CL/TA
19 4
1972 1973 1974 1975 1976 1977 1i 1919YEAR 1972 1973 19i+ 1915 196 12TT 19 J979
0-.05
IERR
.\\:‘
0-.10
0.25
0_
0.040
A
0.035
0.030
6 i \to±0.020 ili \
0.0258a_L,' -_ii \
0.015 i
:
0.010 \i
0.0(6
1972. 1974 1974 1975 1974 197i 1978 ' 1974 0-.5YEAR
972 19(3 1974 1975 19/6 1977 1918 1919YEAR
BURRELL 8, CO.
Figure 3.3.46 Testing the Effectiveness of the Model
Y VALU -NI/SALES
------NIISF-Nian-sF
----- --CASH/SF- CASH/TA-a
- WC/SF
195
0.05
0 - . 05a
1972 1974 1976 1978 990 0-.15YEAR
\\
_ _ _ YEAR197 19-74 ". -1-976 19-7 198
1972T 1976 1978 1980YEAR
0.14
0.'
0.12
10'
..r=t°
±:\2i0.06
0.04'
E0.08
0.02
1972 1974
-1976'
1978 1.980YEAR
PICKLES (WILLIAM & CO.
Figure 5.3.45 Testing the Effectiveness of the Model
0-.20
0-.25
Y VALUE----- - NI/SALES -NI/SF
-NI/TA-SF--------CASN/SF
-CASH/TA-CL CA/CL
-WC/SF CL/TA
196
0.1
0.10
0.05
0-.15
0-.20
0-.25
0-.30
\1. (5-
1.50-
1.25-
0.02.00
0.0175-
0.015(1 i\I'
It
\( ,-. \ "4 i .00'
% !---,20.0125-
ul20.01007=U/
'0.0075-
0.0050-
0.0025-
- 1976 1978.. 1980.- 0-. 50-)1(
.------..
197,1
-----....
197d 1978 1980
-..._.---
-.25- YEAR
0
1I
7
\\
0. 75-
-....--"---- - -\,
,,.ES
1I ''\I
__,,/
0.
0.
50
25
__
' \
I. .
YEAR
CAWDAW INDUSTRIAL HLDGS
Figure 5.3.47 Testing the Effectiveness of the Model
Y VALUE------- -NI/SALES
-NI/SF
-NI/TA-SFCASH/SF
-CASH/TA-CL—•--•--CR/CL
-WC/SF CL/TA
197
O. 2
0.1
1973 1974 1975 1976 1977 197 197\9 198
1973 1974 1975 1976 1977 1978 1979 980 50-.1YEAR
0.2
11111.1
1973 1.974 1975 19761 1977 1978 1979 1980
YEAR
n
0.14
0.12
0.10
-.0.08
(-c2 0.06
0.04
0.02
( 1973' 1974 1975' 1976. 1977 1978' 1974 1980)
YEAR
1.
0-. 5-
RIRFIX INDUSTRIES
Figure 5.3.48 Testing the Effectiveness of the Model
Y VALUE- - - - - -N I /SALES -NI/SF
-NI/TA-SF— —CASH/SF
-CASH/TA-CL— — — CR/CL
-WC/SF CL/TA
198
E0.020'i=7
cz)
=0.015=Ln
0.010'
L.50-
0.75'
0.50"
1972 1974 JO 1979
1.25
1 .00'
0.030
0.025'
0.005-
ii
ii ii
II.Ir\\
/
1972'1974 1979 1379 1986YEAR
OXLEY PRINTING GROUP
Figure 5.3.49 Testing the Effectiveness of the Model
V VALUE -NI/SALES -NI/SF
-NI/TA-SF-------CASH/SF
-CASH/TA-CL— CA/CL-IC/SF CL/TA
199
4
1974 L976 1978 1.980YEAR
0.225-
0.200-
0.175
or= 0.150174ID-0.125
ct .'0.100-
O. 075''
0.050-
0.025-
4 VALUE- - -N I /S ALES
• -NI/SF-NI/TA-SF
— —CASH/SF-CASH/TA-CLCA/CL
-WC/SF CL/TA
200
\
/ \
/ \.-- \/ / \
3 - 6' / \ / \
/ \ / \c—cs 2.5`I 1 I \
i \F--
\ . \cct
.... ___)
L5
_
0.5
19JEL.- • 1980'YEAR
• 1974' 1976 1978 1980'YEAR
LESNEY PRODUCTS & CO.
Figure 5.3.50 Testing the Effectiveness of the Model
98
0-.
0-.4-
O. _
197 1974 1976 197
r---------- 2.0\
/ --_--/ /\
0.030 -
0.025- I \
1.5 \
z . ILO
\..jcE
\\
o I \ S 0.5 \\
p0.020- .L-.-- \t
_------a..
LT, 0_015' 1- n .--!CtI-3 C\ / ‘ \--/..-. \ \ 5) 19---- ---- YE94
1
\_
197274 16
978 _ 1_N
BOo
0.010 i
•
0-.5
0.005"/
xi -1.0
1974 • 1976'1978' 1980YEAR
RICHARDS & WF1LLINGTON INDUSTRIE URLIJE
7- - - - - -N1/511115NI/SF
-NI/TR-SF— —CASH/SF
-CASH/TA-CL—CA/CL
-1.dC/SF CL/TA
Figure 5.3.51 Testing the Effectiveness of the Model
201
YEAR
1972 1974 1976 1978 1980YEAR
0.30-
0.25
,T)0.15Li
0.10-
/— \\ / N-
\,/
1972: 1974! • 1976 1978. 188dYEAR
.—\
/ \/// /.\
2.0\/7 \i \
—lca
F---
a..'; 1.5
/ \ %
c_)
1_0/
I
\\% i
\k
0.05
- 0-.5
1972 1974 1976 137E1 198OYEAR
2.5
NORVIC SECURITIES
Figure 5.3.52 Testing the Effectiveness of the Model
Y VALUE
-NI/SF
NI/TA-SF
CASH/SFCASH/TR-ELCR/CL
-WC/SF CL/TA
202
0- .. 5
0-.6
0-.7
• 197 4i- 1976' is7d /980'YEAR
0.02'
0-.5-
0.12'
0.10'
z.
p0.08-
J: 0.06uoLi
0. 04
2.0
/— —1374! •
, •1976 1978' 19e0'
YEAR
Y VALUE -NI/SALES -NI/SF
-NI/TA--SF--CRSH/SF
-CASH/TA--CL----CR/CL
-WC/SF --CUM
203
0- .1
/-\0.1
— —19-7 —i'80
j.-1r.10-.2
i CISccLL, -2 t 0-.31.`12_3 m
AUSTIN(F.)(LEYTON)
Figure 5.3.53 Testing the Effectiveness of the Model
5.3.4 OVERALL EFFECTIVENESS OF THE MODEL
To evaluate overall effectiveness of the model for all 53
companies we can look at the general trends of their four
graphs such as performance (Y-value), profitability, cash
position and working capital for each company and over the
time which were presented by Figure 5.3.1 to 5.3.53. If the
general trend over time is improving it is called 'up', if it
is constant over time it is called 'static' and if it is
declining then it is called 'down'. This demonstration for
all the companies is shown in Table 5.3.2, by considering that
P - profitability
CP = cash position
WC = working capital
AOP = actual overall performance
204
Table 5.3.2 Effectiveness of the Model
I IFigures P CP WC I AOP IY-valuelModel Effectiveness' +
5.3.1 I up I up Istatic I up up very good5.3.2 Istatic I up I up I up up very good
5.3.3 I up down up up I up I good
5•3•4 'static up down 'static up bad
5.3.5 I up I up I up I up up excellent
5.3.6 'static I up I up I up I up very good
5.3.7 I down up I up 'static 'static I bad
5.3.8 'static I up I up I up I up j very good
5.3.9 I down down I down I down I down excellent
5.3.10 I up I up I up I up I up excellent
5.3.11 I down down 'static I down I down very good
5.3.12 I up down 'static 'static I up I bad
5.3.13 'static I down 'static 'static 'static I very good
5.3.14 I up 'static I up I up I up I very good
5.3.15 down down 'static I down 1 down very good
5.3.16 up I down I down I down I up I very bad
5.3.17 down down 1 down I down I down I excellent
5.3.18 down down I up 'static I up I very bad
5.3.19 I up I up I up I up 1 up I excellent
5.3.20 down I up 'static 'static 'static I good
5.3.21 I down up I up I up I up I good
5.3.22 down I down I down I down I down I excellent
5.3.23 'static up 1 up 1 up 1 up very good
5.3.24 I up I up I up I up I up I excellent
205
excellen
very good
excellent
excellent
excellent
excellent
excellent
good
excellent
very good
very good
very good
excellent
excellent
very good
bad
excellent
excellent
excellentexc
very good
excellent
excellent
very good
excellent
very good
excellent
excellent
excellent
excellent+
5.3.25 I up up I up I up I up
5.3.26 'static up 'static 'static 'static
5.3.27 down down down down down
5.3.28 up up up I up up
5.3.29 down down down down down
5.3.30 down down down down down
5.3.31 down down down down down
5.3.32 down up down down down
5.3.33 down down down down down
5.3.34 down down 'static down down
5.3.35 I down1down 'static I down
1I down
5.3.36
5.3.37
'static
down
down
dodown
'static 'static 'static
down downI do down
5.3.38 down down I down down downdo
5.3.39 down down 'static down down
5.3.40 down 'static I up Istatic down
5.3.41 down down down down down
5.3.42
5.3.43
down
down
down
down
down
down
down
down
down
down
5.3.44 down down Istatic down down
5.3.45
5.3.46
5.3.47
5.3.48
5.3.49
5.3.50
down
down
down
down
down
down
down
I down
'static
I down
'static
I down
down
down
down
dodown
dodown
down
down
down
down
down
down
down
down
down
down
down
down
down
5.3.51 down I down1down down down
5.3.52 down I down1
down down down
5.3.53 down I down down down down
206
For classifying the effectiveness of the model the
following computations can be done, by this assumption that
the model has almost got 33% of profitability, 33% of cash
position and 33% of working capital.
1) if P + CP wc U = Y-value
then 33% 33% 33% 99% 11
So when the effectiveness is about 99% it is called 'excellent'
2) if P CP 1-t- + WC (static) = Y-value II
then 33% 33Z + 22% = 88%
When the effectiveness is about 88% it is called 'very good'.
3) if P CP + WC = Y-value
or P + CP (static) + WC (static) = Y-value
or P I + CP + WC (static) = Y-value (static)
then 33% 11 332 J1 11% = 77% J1
or 33% 22% + 22% = 77% 11
When the effectiveness
4) if P
or P
then
+
33%
is
CP
CP
+
77%
If
+
11% II
it
WC
WC
is called 'good'.
(static) = Y-value
= Y-value
22% = 66%
(static)
or 22% + 22% + 22% = 66%
207
When the effectiveness is around 66% it is called 'bad'.
5) if P + CP li wc 11 Y-value
then 33% + 11% + 11% if = 55Z fl
When the effectiveness is 55% it is called 'very bad'.
Overall effectiveness of the model is shown in Table 5.3.3.
Table 5.3.3 Overall Effectiveness of the Model
Effectiveness (1) No (2) I' (1)x(2)
excellent 26 99% 25.75 1very good 17 88% 14.96 1
good 4 77% 3.08 1
bad 4 66% 2.64 1
very bad 2 55% 1.10
total 53 47.52 1
If we divide 47.52 to 53 then we have 90%, which means that
the overall effectiveness of the model is 90% or its accuracy
to measure the companies' performance is about 90%.
208
5.4 CONCLUSION
In this chapter a financial model has been described which
was developed to measure companies' financial performance.
This model was applied to a sample of 53 companies. About 83
percent of failed companies and 100 percent of going concerns
were classified correctly. Of the 15 failed companies 13 have
gone into receivership and the other 2 were in serious
financial difficulties. At this stage it appears that the
model might be effective in measuring the companies'
performances.
A second aspect of the model is that it explains 30 percent
of profitability, 37 percent of working capital and 31 percent
of liquidity, which means that it almost explains the same
variance of the three main factors of the companies'
performance.
Finally by plotting the output from the model against time
for each company separately and comparing its trend with
companies' actual profitability, cash position and working
capital trends (pages 151-203), it is possible to demonstrate
the model's effectiveness in measuring the company's financial
performance. The main result of this visual analysis is that
the overall effectiveness of the model in identifying the
companies' strengths and weaknesses is about 90 percent.
209
CHAPTER 6
PERFORMANCE CLASSIFICATION AND COMPARISON
210
CHAPTER 6 : PERFORMANCE CLASSIFICATION AND COMPARISON
Measurements are taken to obtain either definitive
statements or information for the purpose of comparison. In
the analysis of financial data the comparative aspect is
foremost and it is the direction of trends that is important
in most cases. Consequently a standard of performance can not
be established in an absolute sense and in practically all the
measurements taken the previous year's results are used for
comparison.
6.1 CLASSIFICATION OF THE PERFORMANCES
The next step after testing the model is to use it to
classify companies. If we consider the model as the following
equation:
Y =a +aR +aR+ +a R0 11 22 10 10
and compute the Y-value for each company and each year
separately then we have for company A:
213.
• =b1971 1
• =b1972 2
• = b1973 3
• =b1985 n
Where n is the number of years, and b is the Y-value at the
end of each year. To compute the mean value of all Y-values
we need to divide the total value of 'Y' by the number of years
for each company.
mean = m = (b + b + b + +b )/n1 2 3
where m is the mean of Y-values. This mean value shows the
average performance of the company over the years, or how
effectively they were doing in the past. This mean value can
be used as a base for analysing and evaluating each year's
activity. It can be said that each Y-value above the mean
value is classified as the good and well performing an fur
all the Y-value's below the mean are classified as the poor
and bad performing companies.
So if we assume D as deviation of each year performance
from the mean or simply:
D = b -m
Then we can classify the company's performance into three
212
different categories such as:
1)
if D>0
and b >0
That such companies are classified as the well performing
companies.
2)if D<0
and b >0
Such companies are classified as the fair performing companies
3)if D<0
and b <0
Such companies are classified as the poor performing companies
By applying the above classification to our 53 sample
companies we have:
well performing companies 20
fair performing companies 15
poor performing companies 18
To identify cut-off lines to distinguish areas which specify
the above three categories, we can compute Z:b /m for eachj=1 nj
group separately where m is the number of companies in each
group and b is the terminal 'Y-value' for each company. Then
20well performing=Z:b /20=66.486/20=3.3243
j =1 nj
213
2.6
-.244
15
fair performing4--b /15=28.4912/15=1.899j=1 nj
18
poor performing=t=b /18=-38.5746/18=-2.143j=1 nj
And the cut-off lines are calculated as follows:
a) cut-off line between first and second
group=(3.3443+1.899)/2=2.6
b) cut-off line between second and third
group=(1.899-2.143)/2=-.244
So by the above calculations we can specify the cut-off
lines as follows:
If Y>2.6 well performing companies
If 2.6>Y>-.244 fair performing companies
If Y<-.244 poor performing companies
The above classifications can be shown as
well performing area
fair performing area
poor performing area
Figure 6.1.1 Classification of performing area
By applying the above classification criterion to the
sample companies which have been presented in Chapter 5 we
have:
214
Table 6.1.1 Applying the new classification to the sample
companies
CLASSIFICATION NO OF CO FAILED GOING 1
WELL 16 0 16 1FAIR 21 21 1
POOR 16 15 1 1
As we can see from the above table 100% of the well
performing companies are truly classified and 94% of the poor
companies have gone into receivership and the other 6% are in
serious difficulty. 100% of those classified as fair
performing companies are going concerns. Therefore, the model
can classify 98 percent of the whole sample companies
correctly.
The companies comprising the three classes are:
a) WELL PERFORMING COMPANIES
1) General Electric Co.
2) Coalite Group
3) Allied Textile Companies plc
4) British Home Stores plc
5) Bell (arthur) & Sons plc
6) Wellcome Fundation
7) Benford Concrete Machinery plc
8) Beecham Group plc
9) Marks & Spencer
215
10) Pearsons
11) Racal Electronics
12) BPB Industries plc
13) Allied Colloids plc
14) Ash & Lacy plc
15) Boots Co plc (The)
16) British Gas Corporation
b) FAIR PERFORMING COMPANIES
17) Anglia Television Group plc
18) Goodyear Tyre & Rubber Co (GB) Ltd.
19) Babcock International plc
20) APV Holdings plc
21) Ault & Viborg Group plc
22) Albright & Wilson Ltd.
23) Barrow Hepburn Group plc
24) Pleasurama plc
25) British Railways Board
26) Anchor Chemical Group plc
27) Baker Perkins Holdings plc
28) Ford Motor Co Ltd.
29) Adams & Gibbon plc
30) Armitage Shanks Group Ltd.
31) Atkins Brothers (hosiery) plc
32) Dunlop Holdings plc
33) Barno Industries plc
34) BBA Group plc
35) Batleys of Yorkshire plc
36) Bemrose Corporation plc
37) Bestobell plc
216
C) POOR PERFORMING COMPANIES
38) Brocks Group of Companies Ltd. (Receiver
appointed 3rd March 1981)
39) Stone Platt Industries plc (Receiver appointed
18th March 1982)
40) British Airways (On 12th March 1986, because of
difficulties on renewal of the UK/US Air Service
Agreement and other political reasons the
Government decided to privatise British Airways.
On 30th september 1986, British Helicopter
Ltd.(BAHL) was sold.)
41) Viners (Receiver appointed 16th November 1982)
42) Blackman & Conrad (Voluntary Liquidation 11th
February 1981)
43) Amalgamated Industrials (Compulsory liquidation
6th November 1981)
44) Blackwood, Morton & (HLDGS) (Receiver appointed
15th November 1981)
45) Pickles (William) & Co. (Receiver appointed
16th June 1982)
46) Burrell & Co. (Receiver appointed 4th August
1980)
47) Cawdaw Industrial HLDGS (Receiver appointed 22nd
February 1982)
48) Airfix Industries (Receiver appointed 29th
January 1981)
49) Oxley Printing Group (Receiver appointed 17th
August 1981)
217
50) Lesney Products & co. (Receiver appointed 11th
June 1982)
51) Richards & Wallington Industries (Receiver
appointed 15th July 1981)
52) Norvic Securities (Receiver appointed 15th July
1981)
53) Austin (F.)(Leyton) (Receiver appointed 31th
July 1982)
Or testing the model's effectiveness is classifying
companies it might be enlightening to compare its
effectiveness with those of other models that exist for this
purpose which is the subject of the following discussions.
6.2 FAILURE PREDICTION STUDIES
Business failures can be attributed to circumstances or
conditions that were known prior to make any major financial
commitments and could be easily identified. For some reason
whether enthusiasm or ignorance, management simply failed to
recognise the importance or existence of these failures.
The prediction of failure was stimulated in the USA by the
high rate of business failures during the Depression of the
1930s. Between 1930 and 1942, five studies were undertaken,
by Fitzpatrick (1932), Smith (1930), Smith and Winakor (1935),
Ramser and Foster (1931), and Merwin (1942). These studies
gave a good indication of the data which appeared to be
relevant to the prediction of failure. Some data were shown
to indicate serious weaknesses in a company several years in
218
advance of failure. The idea was that, for example if current
assets more than current liabilities, this was a sign of
strength. The lower the current assets, the greater the
weakness. The extremes could readily be identified, and a
cut-off point or range could be established from predictive
experience. Most of the data was in the form of ratios, and
those which appeared to be useful predictors were:
1) Current ratio=current assets/current liabilities
(two studies)
2) Acid test ratio=(current assets-inventories)/
current liabilities (two studies)
3) Net Worth to Fixed assets=(total assets-total
liabilities)/ Fixed assets (five studies)
4) Working capital to total assets=(current assets-
current liabilities)/total assets (four studies)
5) Net profit to net worth=Net profit/net worth
(three studies)
6) Net worth to total liabilities-(total assets-
total liabilities)/ total liabilities
The first two measuring the company's ability to meet
short-term liabilities. The others measuring the overall,
long and short term position of the company. The ratio
analysis has been considered as a tool in assessing current
and expected company performance, in relation to investment
decisions as well as more specially in relation to the
prediction of failure.
The next major study was by Tamari (1964) on Israeli data,
who found that six ratios in particular were good predictors
219
of failure, in some cases up to five year ahead, and all cases
in the year prior to failure:
1) current ratio, as above
6) net worth to total liabilities, as above
7) (sales+change in inventory)/inventory
8) sales/(current assets-current liabilities)
9) profit trend(fitting a trend line to profit
figures over the recent past)
10) sales/debtors
Tamari observed that a large proportion of the successful
companies in his sample had at least one weak ratio, some had
two and even three. He concluded that the analyst can not
rely on one ratio alone in measuring the degree of risk
associated with a company.
Beaver (1966) found that the best predictor of failure was
the hitherto untested longer-term ratio:
11) cash flow/total debt
12) net profit/total assets
13) total liabilities/total assets
Beaver tested the ability of financial ratios to predict
failure. He found that not only the financial ratios of
failed firms differ significantly from non failed firms, but
they deteriorated considerably during the five years prior to
failure. He also found that the mean of total debt over total
assets of the failed firms was 0.79, whereas that of the non
failed companies was 0.37. He should that the low ratio of
net profit over total assets is one of the three major
220
characteristics of the company failure.
Horrigan (1968) showed that in the 1930s, the first attempt
were made to test the utility of ratios by examining how
effective they were in predicting business failure.
Altman (1968) attempted to 'assess the analytical quality
of ratio analysis - a set of financial ratios was combined in
a discriminant analysis approach to the problem of corporate
bankruptcy prediction, by the use of multiple discriminant
analysis. Discriminant analysis aims at distinguishing
between two or more distinct populations on the basis of some
characteristics of their members, and the classification of
individual companies into one or other of the classifications,
in this case 'failing' and 'non-failing'.
Altman, like Beaver, selected a sample (thirty-three) of
solvent companies to 'pair' with (thirty-three) failed
companies. From twenty-two ratios, he selected five that
appeared to be most effective in predicting failure, and these
ratios were used to discriminate between failed and solvent
companies, using data from one to five years before failure.
The predictive ability of his'five-ratio' model declined in
proportion to the number of years prior to failure but was
able to predict fairly accurately up to two years ahead.
221
Altman assigned weights to each of his ratios, as below:
weights
(4) working capital/total assets 0.012
(14) Retained earning/total assets 0.014
(15) EBIT/total assets 0.033
(16) Market value of equity/book value of long-term
debt
0.006
(17) sales/total assets 0.999
These ratios were drawn together, with the weights assigned
to each of them to give an overall score, often called the Z
factor:
Z=(4)W +(14)W +(15)W +(16)W +(17)W4 14 15 16 17
According to Altman, a minimum Z score of 1.8 is necessary
to avoid failure, but only with a Z score of 3.0 or more is
the company fairly safe.
222
95%75%
48%
30%
Table 6.2.1 The Predictive Accuracy
+
+ Altman's predictive accuracy
within one yearwithin two year
within three years
within four or five years
Taffler (1977) has used a model to predict failure among UK
companies which has the following characteristics:
Z=C +(PBT/AVCL)C +(CA/TL)C +(CL/TA)C +(No Credit interval)C0 1 2 3 4
Where C is a constant which measures half the distance between0
the Z score of the failed and solvent companies, C ,....,C are1 4
the weights, (PBT/AVCL) is the ratio of profits before taxes to
average current liabilities and
'No credits' interval=(cash and market securities-current
liabilities)/projected daily operating expenditure
The weights C to C contributed 0.53, 0.13, 0.18 and 0.16,1 4
respectively, to the models operation. The failed/insolvent
cut-off point was found to be Z = -1.95.
Betts (1984) developed two models for identifying those
companies which are in danger of financial failure, using
published accounting data and multiple discriminant analysis.
223
On the basis of these studies, it appears that financial
ratios can be used as predictors of various events, and it is
likely that ratio analysis will become more useful in future.
6.3 COMPARISON OF THE MODEL WITH SIMILAR MODELS AND STUDIES
Although some studies have been undertaken to measure the
overall financial performance of companies, the main problem
with these studies is the proprietorial nature of the models,
which makes comparison of performances of different models
difficult. However, there are some similarities of the
present study with others which is presented in this section.
According to the previous studies such as Wall & Dunning
(1928), Tamari ( 1964), Smith (1965), Lev (1974), Altman
(1977), Hoshino (1982) and Taffler ( 1982), the current ratio
is a good indicator of company's success and failure. Tamari
also found from data of manufacturing companies in Israel in
1968 that 70 percent of failed companies had a current ratio
less than 1.0 whereas only 27 percent of non failed companies
had similar values.
In this study, it was found that the current ratio is
significantly related the company's financial performance. As
described in Table 6.3.1, 88 percent of well performing
companies had a current ratio in excess of the optimum
liquidity ratio which is 1.5 according to Richard (1964). 75
percent of poor performing companies had a current ratio less
than 1.5 which is called under liquidity and they all had
payment difficulties.
224
Table 6.3.1 A comparison of current ratios with differing
levels of company overall financial performance.
liquidity over optimal under paymentratio liquidity liquidity liquidity difficulties
R = CA/CL R>2.0 2.0>R>1.5 1.5>R>1.0 R<1.0
Well performing 57% 31% 6% 6%Fair performing 5% 48% 382 9%
Poor performing O Z 25% 372 38%
I
The variation in the current ratios overtime for particular
companies are depicted in the lower right graphs of Figures
5.3.1 to 5.3.53.
There are also nine studies which found that business
failure is usually linked with low or declining profitability.
The studies were those of Beaver (1966), Altman (1968), Haslem
and Langbrake (1971), Schoeffler (1974), Tamari (1977),
Taffler (1977), Bass (1978), Belhoul (1983) and Betts (1984).
As indicated in Table 6.3.2 the present study showed that
about 81 percent of well performing companies had excellent
and good profitability whereas 100 percent of poor performing
companies had low and deficient profitability with about 94
percent of them have already gone into receivership.
225
wellfair
poor
31%19%
O Z
19%57%
19%
50%19%
OZ
O Z5%
81%
Table 6.3.2 A comparison of profitability ratios with
differing levels of company overall financial performance
+ +
1 1 1 1
11 excellent 1 good 1 deficient I danger of 1
IR — NI/SFlprofitability 'profitability 'profitability I failure 1
R>0.15 I 0.15>R>0.10 I 0.10>R>0.0 I R<0.0
Another good indicator of business success or failure was
found to be company's cash position. This view is shared by
Beaver (1966), Blum (1969), Gonedes (1971), Deakin (1972),
Martin & Scott (1974), Pinches (1975), Mao (1976) and Belhoul
(1983).
Table 6.3.3 presents information on the cash position of
companies for varying levels of overall financial performance.
The variation of this ratio over time is shown at the lower
left of Figures 5.3.1 - 5.3.53 for various companies. In
summary nearly 56 percent of well performing companies had a
considerable margin of cash in hand to make immediate payments
whereas 100 percent of poor performing companies had problems
meeting immediate payments.
Table 6.3.3 The comparison of cash position ratios with
differing levels of company overall financial performance
+ +
1
'cash position lexcellent Ivery good good I bad very bad 1
1 R =. cash/SF I R>0.15 .15>R>.10 .10>R>.05 .05>R>.01 I R<.01 1
1
+ +
well 44% 6% 6% 25% 19% 1fair 5% 5Z 33% 33% 24% 1
poor O Z O Z OZ 44% 56% 1
+ +
The models output is also depicted in the top left corner
of Figures 5.3.1- 5.3.53. This output can be simultaneously
compared with the three main indicators of company financial
performance, ie. profitability, working capital and cash
position, for the 53 companies analysed.
By the above analysis it was found that the effectiveness
and accuracy of the model to measure companies' financial
performance whose data were used to construct the model is
about 91 percent which dropped to 88 percent when it was
applied to companies whose data was not used in the model's
construction. However, the overall effectiveness of the model
is about 90 percent which can be compared with some of the
other model's and studies accuracy regardless of their
specific purposes or different techniques or criterions used
for their construction. Some of these studies are summarised
in Table 6.3.4 as follow.
8072
75
86.5
90
86
81.7
84
77
98.5
94.4
97
85
90
Table 6.3.4 The Classification Accuracy of Some Financial
Performance Models
1 authors year area of study
'classification 1
1
accuracy 2'
1 Walter 119591 Smith 11965
1 Altman 11968
1 Haslem & 1
1 Longbrake 11971
1 Frank & 1
1 Weigandt 11971
1 Klekowsky 1
1 & Petty 11973
1 Blum (1974
1 White 11975
1 Schick & 1
1 Verbrugge 11975
1 Taffler 11977
'Gillingham 11980
1 Betts & 1
1 Belhoul 11982
1 Belhoul 11983
1 Betts 11984
financial characteristicscommon stock analysis
failure prediction
bank performance analysis
debt characteristics analysis
share price analysis
business failure analysis
shares analysis
company failure analysis
high performing companies
failure prediction analysis
'financial characteristics analysis
company failure analysis
profitability analysis
Finally the classification accuracy of this model is
compared with that of Betts' (1984) first model. Although,
228
Betts' model was designed for the restricted purpose of
identifying financially failing companies within a set of
going concerns, it was thought that such a comparison may be
useful, because the present model ought among other things be
able to identify failing companies.
The model was applied to 15 out of 23 failed companies that
Betts (1984) used in his study and it was able to classify
them as failed companies. It was then applied to another 6
companies which Betts defined as well known financially
healthy companies and again they were classified correctly.•
These companies are Allied Colloids Group Plc, Anglia
Television, Coalite Group, General Electric Co, Pleasurama Ltd
and Racal Electronics.
229
6.4 CONCLUSION
In this chapter the model was used to classify companies.
The average performance of the company over the years was used
as a base for analysing and evaluating each year's activity.
The cut-off line between well and fair performing companies
found to be Y = 2.6, and between fair and poor performing
companies was computed to be Y = -.244. These criteria were
applied to the sample 53 companies. 100 percent of well
performing companies and 94 percent of poor performing
companies were correctly classified.
At the end of this chapter the similarities between the
present model and other models were discussed in detail.
The main result of above comparison is that we have almost
used the same indicators of business success or failure as
others used in their models and studies. By comparing the
model's output with these main indicators of company's
financial performance, it reveals that the accuracy of the
model in measuring company's financial performance whose data
were used to construct the model is about 91 percent which
dropped to 88 percent when it was applied to companies whose
data was not used in the model's construction.
230
CHAPTER 7
PERFORMANCE STABILISATION
231
CHAPTER 7 : PERFORMANCE STABILISATION
The goals of performance stabilisation are not simply to
eliminate fluctuations in performance variables, but to force
variables to follow 'ideal' paths. For example a 5% profit
margin even if it is constant over time, is not acceptable.
Thus the goals of stabilisation might include reaching (and
then maintaining) a high rate of profitability, a high rate of
working capital and a low rate of leverage. Eliminating
fluctuations in the company performance is therefore a
secondary objective that becomes desirable only after the
performance has reached a 'healthy' steady state.
In other words, we would like variables such as current
assets, current liabilities, net income and cash as closely as
possible to follow a nominal or 'ideal' path throughout the
performance period.
7.1 PERFORMANCE STABILISATION
The structural model of stabilisation consists of four
equations which are extracted from five different factors.
The factors themselves are extracted by factor analysis
available on SPSSX at University of Bradford. The variables
are R1 to R10 the main 10 variables of Y-model and the number
of cases are 7420 (530 companies multiply by 14 years of
activity for each company). By applying the above package to
our variables we can construct the following equations:
232
F = .60132ZR + .4056ZR1 2 7
F = .49905ZR + .50836ZR2 5 8
F = .53485ZR + .4632ZR3 1 3
F = 1.07767ZR4 9
F = .82264ZR + .15715ZR5 6 10
Where the Z is the normalised value of ratios and is equal
to
Z = (variable - mean)/standard deviation
By replacing the actual variables in the above equation we
have
F = 1.1R + .34R - .2761 2 7
F = 5.1R + 4.54R - .5942 5 8
F = 9.96R + 3.83R - .8263 1 3
F = 7.627R - 2.8874 9
F = 1.04R + 1.23R - 2.195 6 10
And by substituting the real variables with the ratios we have
F = 1.1NI/SF + .34(CA-CL)/SF - .2761
F = 5.1CASH/CA + 4.54CASH/(TA-CL) - .5942
F = 9.96NI/SALES + 3.83NI/(TA-SF) - .8263
233
F = 7.627CL/TA - 2.8874
F = 1.04CA/CL + 1.23(CA-INVENT)/TA5
In Chapter 6 it was seen that well performing companies had
D?,0
and b - 140
b
Which means that for well performing companies the b score
in year n should be greater than or equal to M the mean value
of bn scores. So to get an ideal value we can write
b =M
or b =riff +mF +mF +mF +mF = Mn 1 2 3 4 5
where mF is equal to the mean value of the factor 11
On the other hand the mean values of factors are equal to
zero and their standard deviation is equal to one, which means
that
M 0
or simply
1.1NI/SF + .34(CA-CL)/SF .276 = 0
5.1CASH/CA + 4.54CASH/(TA-CL) - .594 = 0
9.96N1/SALES + 3.83NI/(TA-SF) - .826 - 0
7.627CL/TA - 2.887 = 0
1.04CA/CL + 1.23(CA-INVENT)/TA = 0
234
Finally the ideal values for four important and
controllable variables such as CA, CL, CASH and NI are
calculated as follows:
ideal CA = OCA = .55TA + .31INVENT
ideal CL = OCL = .378TA
ideal CASH = OCASH = .37TACA/(3.2TA + 4.5CA)
ideal NI
= ONI = .826SALES(TA-SF)/(9.96TA-
9.96SF+3.83SALES)
In this chapter the ideal path based on the above equations
together with its associated actual path which is based on
historical data are presented in graphical form. This is done
so as to make it possible to easily observe the general form
and characteristics of the ideal solution. We can consider
the positive direction (+) for each actual value above or
better than the ideal value and negative direction (-) for
each value below the ideal value (except for CL). By doing
the above classifications for all the cases which are the
whole set of sample companies the results can be shown in the
following tables.
235
Table 7.1.1 Comparison of Ideal Path with its Actual Path
COMPANY WORKING NET ICAPITAL I INCOME
CASH
General Electric Co + + +
Coalite Group + + +
Allied Textile Companies plc + +
British Home Stores plc + + +
Bell (Arthur) & Sons plc + + +
Wellcome Fundation + +
Benford Concrete Machinery plc + _
Beecham Group plc + + +
Marks &Spencer + + +
Pearsons
Racal Electronics
+
+
+
+
+
+
BPB Industries plc + + _
Allied Colloids plc + +
Ash & Lacy plc + + +
Boots Company plc (THE) + +
British Gas Corporation + +
Anglia Television Group plc +
Goodyear Tyre & Rubber Co (GB) Ltd. + -
Babcock International plc - +
APV Holdings plc - +
Ault & Wiborg Group plc + +
Albright & Wilson Ltd. + +
Barrow Hepburn Group plc - +
236
Pleasurama plc + +
British Railways Board + -
Anchor Chemical Group plc - + +
Baker Perkins Holdings plc -
Ford Motor Co. - -
Adams & Gibbon plc
Armitage Shanks Group Ltd. +
Atkins Brothers (hosiery) plc + _
Dunlop Holdings plc - - +
Barno Industries plc - -
BBA Group plc +
Batleys of Yorkshire plc -
Bemrose Corporation plc -
Bestobell plc
Brocks Group of Companies Ltd.
Stone Platt Industries plc -
British Steel Corporation -
British Airways
Viners
Blackman & Conrad
Amalgamated Industrials - -
Blackwood,Morton & Sons - -
Pickles (William) & Co -
Burrell & Co. - -
Cawdaw Industrial HLDGS
Airfix Industries -
Oxley Printing Group
Lesney Products & Co. - -
Richards & wallington Industries
237
238
. sTime
Type 2
. "-Time
Type 3
)1,-Time
Type 4
1
Norvic Securities
1 1
-
1 1
1
Austin (F.)(Leyton)
1
-
1 1-
1
As the above table shows, in well performing companies 100%
of working capital, 88% of the net income and 63% of the cash
are above ideal values and in poor performing companies,
100% of working capital, 100% of the net income and 100% of
cash are below their ideal paths. It means that all the
companies that have gone into financial difficulties and
receiverships were suffering from insufficient working
capital, lack of profit and shortage of cash and the majority
of well performing companies are doing very well in the above
three important dimensions.
7.1 PERFORMANCE IMPROVEMENT
From the quantitative model of the characterics of the
failing company, Argenti suggested what he called three
possible trajectories of failing company performance, which
are illustrated below:
Exce lent Ea) a)o 0O 00 Good d GE m
o 04-1 Poor clAp;-I ka) a)
fa4 1:14
Fai F-----..,,
TimeType 1
Type (1) failure should be preventable at the
planning stage. The company never performs satisfactorily,
and should probably never have been established.
Type (2) failure exhibits 'mercurial'
characteristics, with very high growth rates and other
performance measures, until some point at which the company
over-reaches itself and collapses equally dramatically. One-
man rule is, according to Argenti, a major feature of such
companies, and the prevention of collapse should take the form
of a moderating influence, preferably from inside the company
otherwise from the company's bankers and advisers.
Type (3) companies are probably typical of the long-
established business which has not 'moved with the times' and
has not recruited enough professional management from outside
the company. The dashed lines indicate what might happen if
the company were rescued by a management change or other
factor and in case of the non-failed company what might happen
if things start to go wrong.
Argenti's study is interesting because it relates a largely
qualitative approach of management alongside he quantitative
and statistical research studies, in an attempt to identify
common themes in the behaviour of failing companies.
In our case we can not do anything about the poor companies
because they have already failed to meet a certain level in
their activities, but we are able to keep the fair and well
performing companies at a good level or as close as possible
to their ideal paths to improve their further activities.
This can be effective when the other environmental factors are
239
Table 7.2.1 Performance Improvement Recommendations
COMPANY 1VARIABLESIFROM 19851 TO 1986(O00) (E, 000)
Allied Textile Companies plc CashI
1093I
1695British Home Stores plc CA
I116981 1219393.3
Bell (Arthur) & Sons plc CA 156976 1169387.3
Wellcome Fundation CAI
466500I489621
NI 31600I37121.4
Benford Concrete Machinery plc NII
474I
584
CashI
116I
856.3
Beecham Group plc CAI
1247800I1356095
Marks & Spencer CA 456600I1097287
Pearsons CAI
471434I511465
BPB CAI
215500I284238
Cash 3900I
15253
Allied Colloids plc Cash 915 3092.3
CA 48089I53810.1
Ash & Lacy plc CAI
15149I15904.2
Boots Co plc (THE) CAI674000 767872
CashI
9000 43307.1
British Gas Corporation CAJ2735500 110193209
CashI
30300 261667
Anglia Television Group plc NI 1868 2310
CashI
448I1458.8
Goodyear Tyre & Rubber Co Ltd. NII
1097 7208.1
Cash 1 593 5308.3
241
Babcock International plc CLI349000 1 26 54
NI j 24800 1 50803.4
APV Holdings plc CLI119606 1104406.6
NII
3890 1 17486.6
Ault & Wiborg Group plc NII
816 1 3329.8
Albright & Wilson Ltd. CAI197827 1225330.1
CashI
6517 12876.5
Barrow Hepburn Group plc CLI
11221 9333.2
NII
992I1642.9
Pleasurama plc CAI
15424I67990.6
British Railways Board CAI
859300I1139238
NI 97300 1124287.2
CashI
27100I
61619
Anchor Chemical Group plc CLI
6213I
4547
Baker Perkins Holdings plc CLI
84139I61614.4
NII
7999I10497.4
CashI
3984I6713.2
Ford Motor Co. CLI1297000 1223208
NII
45000 1179715.6
Cash 117000 1128949.1
Adams & Gibbon plc CAI
6682I6906.1
CL 4617I3917.6
NI 413I
808.3
CashI
2I
405
Armitage Shanks Group Ltd. CAI
45644I59128.1
CLI
36837I35482.5
Cash 1613134.4
Atkins Brothers (Hosiery) plc CAI
4914 5307.6
NI I 169 I 467.6
242
Cash 43 3 .4
Dunlop Holdings plc CL 808 396.1
NI 0 81.6
Barno Industries plc CL 7838 5079.6
NI 439 994.8
Cash 142 545.6
BBA Group plc CA 70644 77872.8
NI 1217 7548.5
Cash 1101 4445.2
Batleys of Yorkshire plc CA 18674 20150.8
CL 19087 10528.4
NI 1260 3336.4
Cash 2 1111.4
+
7.3 A GRAPHICAL ILLUSTRATION OF IDEAL PERFORMANCE
As stated in pages 232-235, there are four ideal equations
for four important and controllable financial variables such
as current assets, current liabilities, cash and net income.
In the following pages, the graphs for all the companies
are listed according to their classifications such as well,
fair, and poor performing companies. All the ideal paths are
indicated by '0' at the beginning of the variables, for
example OCA stands for ideal current assets which is plotted
against CA or actual current assets. If CA is greater than
OCA it is favourable but if it is lower than OCA it is not
favourable. For example, this kind of evaluation can be done
for three different classified companies as follows:
WELL PERFORMING COMPANIES
These companies are shown from Figure 7.3.1 to 7.3.16. If
we take a look at the General Electric Co (Figure 7.3.1) on
the top left hand side current assets is plotted with its
ideal path, on the top right hand side is the current
liabilities with its ideal path, on the bottom left hand side
is net income with its ideal path and on the bottom right hand
side is cash with its associated ideal path. As it can be
seen the current assets and current liabilities are better
than their ideal paths, which means that the working capital
of the company is at a good and satisfactory level. Company's
net income and its cash are both well above their ideal paths.
244
This evaluation shows that the General Electric Co is doing
very well in working capital, net income and cash, and the
overall performance is improving.
FAIR PERFORMING COMPANIES
These companies are illustrated from Figure 7.3.17 to
7.3.37. By looking at the Anglia Television Group plc (Figure
7.3.17) we can see that its current assets and current
liabilities are nearly the same as their ideal paths and its
net income and cash have gone under their ideal paths. This
means that the working capital is almost ideal and static,
while the net income and cash are both declining but they are
still above the safety level and overall performance is not
bad.
POOR PERFORMING COMPANIES
Poor performing companies are shown from Figure 7.3.38 to
7.3.53 and we choose the Burrell & Co as an example. This
company's current assets and current liabilities are worse
than their associated ideal values and its net income and cash
are also far below and worse than their ideal paths. This
means that this company's working capital, its net income and
its cash are declining sharply and the company is in danger of
failure and bankruptcy. In fact as indicated in 5.3.3 a
receiver was appointed for this company on 4th August 1980.
245
The difference between cash graph in Figures 7.3.1 to
7.3.53 and cash position graph of Figures 5.3.1 to 5.3.53 is
that the first one shows the total cash held by each company
at the end of each year and second one is equal to the ratio
of cash over shareholders fund and the ratio of cash over
total assets minus current liabilities. This is also
indicated in legend at the bottom right hand side of each
graphs.
246
1974 1976 1918 1980 1982 1984YEAR
1.2E6
4.0E5
1.0E6
0.4E6
1974 1976 1978 1980 1982 1984YEAR
0 .2E6 7 - • - - 'N....." / - ---.- --
/ _____ ---•------
1974 1976 1978 198d 1982 1984'YEAR
3.5E5
3.0E5
(75W -2.5E5
Lu
2.0E5
1.5E5-
1.0E5
0.5E5
(.--- Lni
; Z‘.. 5 0.6E6! //
GEACJGENERAL ELECTRIC CO
Figure 7.3.1 A Graphical Illustration of Ideal Performanc
1.0E6 0.2E6-
247
-CL—
-NI•- — OCASH
-CASH
4.0E61.8E6-
3.5EEi 1.6E6- / 1
'3.0E6/-- - - in 1 . 4E6- / ?'"1/ LLI
LLH-ui2.5E6
:1.2E6 / / /
//'T. 1-- /
//::j. 1.0E6 /
E / /'cc ,/2.0E6
/LI:10.8E6-
u a- /1 . 5E6- .
(--) 0 . 6 E G-/,.
0.4E6-
1974 1976 1978 198d 1982 1984'YEAR
1.8E5
1.6E5
1.4E5
E1.2E5-Ln
1.0E5Luz'
0.8E5L_)
80000-
40000-Li
70000-
Ln1u. 60000
a 30000-
20000-
-10000--
0.6E5
0.4E5-
0.2E5-
30000-18000
25000-16000
14000'20009-
12000'Li
— 10000-I--Li
(cf, 15090
7-/78000-10000
6000-
4000'
2000'
cn-ocn
-CL— —ON!
-NOCF1SH
-CASH
248
COALITE GROUP
COME
Figure 7.3.2 A Graphical Illustration of Ideal Performance
1974 1976 1978 1980 1982'1984'YEAR
1974 13 S 1378 1980 1982 1984'YEAR
1974 1976 1978 1980 1982 1984
1974 1976 1978 1980 1982 1984YEAR YEAR
•
I_TJ
Li
8000
6000-
4000-
7;"\
1984 1972 1974 1976
35000-
30000
U,
25000Lc:2 ("-x
u-)Li
-c.', 1;
14000-
12000
10000-
La=.I 20000a-.
Li
15000-
10000-
1972 1974 1976 1978 1980 1982YEAR
\I/
1978'1980 1982 1984'YEAR
2500
2250
2000
Li1750
1500
750
500-
250
CAOCR
——
— — °CASH--CASH
1-Z-I 1250
2250 ii \t \
..I2000
: 1/...\
r 1750- 1 s \
1 I
1 1500-\
/I
I. \ \/ ...," LTC 1250- I ,.. 1 /
Li I \I,'", ec
1-v
000 / , ......- 'K\
: I i
1000
•50
500
1972 1974 1976 1978 1980 1982 1984YEAR
972 1974 1976 1978 1980 1982 1984YEAR
ALLIED TEXTILE CO. PLC
Figure 7.3.3 A Graphical Illustration of Ideal Performance
21+9
200000-
175000-
1.2E5-
E.11.0E5
PIET!-21
=tri'i 0 . 6E5
Licr.
0.4E5
V2150000-
0.2E5
125000
100000-
75000-
50000-
25000-
35000-
30000-
1974 1976' 1978 1980YEAR
1982 1984
t/
I/
1974 1976 1978 1980'1982'1984'YEAR
70000
60000
u, 25000EiLi
—20000-Ui
15000-
10000-
5000-
50000
FA 40000-I Li
30000
20000
10000-
CA-OCR
-CL—
- -NI— — — OCFISH
-CASH
250
1974 1976' 1978'1980'1982'1984'YEAR
•
1974 1976 1978 1980 1982' 1984'YEAR
BRITISH HOW STORES
Figure 7.3.4 A Graphical Illustration of Ideal Performance
90000
80000-
1-13 70000-
---160000-22.
50000-
(XccLij 4 0000-Li
30000-
20000-
, /rr---\
J \ 1
1 ,6E5-
1,4E5-
1,2E5-LU
1,0E5-
0. 8E5Li
0.8E5-
0,4E5-
20000
17500
15000
(512500
Ig 10000
7500
5000
2500
972 1974 1976 1978 1980 1982 1984YEAR
—
1972 1974 1376 1978 1980 1982 1984YEAR
25000-
20000-
1,15: 15000-Li
10000-
5000-
-OCA
—— —0N1
-NI— NASH
-CASH
10000-_
1972 1974 1976 1978 1980 1982 1984 1972 19744 1976 1978 1980 1982I
19841YEAR YEAR
BELL (ARTHUR) & SONS PLC
Figure 7.3.5 A Graphical Illustration of Ideal Performance
251
3.0E5-
2.5E5
tr)LU
_
2.0E5'Er3cr.
1 . 5E5-u.;
LU
cc
1 . 0E5-
2.0E5e r
/1.5E5
1.0E50.5E5._
1972 1974 1976 1978 1980 1982 1984 1972 1974 1976 1978 1980 1982 1984YEAR YEAR
4.5E5
4.0E5
,3.5E5LU
_tcf-'. 3.0E5
35000
/30000-
1.0E5
0.9E5
0.8E5
0.7E5
1918
1980
1982
1984YEAR
CA-OCR
-CL— ---ON I
--NI— — — °DISH
-CASH
252
_ -
1972 1974 1976
/1
Lu 25000- ,,,i.'---JE -i 0.6E5U i I 2:
20000-• _ _....7t.._ Lt.)
E9 0.5E5z
/ //'./' 0.4E5
15000-V 0.3E5
410000- 0.2E5
0.1E55000
1972 1974 1976 1978 1980 1982 1984YEAR
WE.LLCOPIE FUNDAT ION
Figure 7.3.6 A Graphical Illustration of Ideal Performance
16000-
14000-
E 12000-
cc
'2 /0000-
cr.
Booci
8000-
" 7000
triLi;726000-
5000-
LEV 400C
• 1\\
\.1\
972 1974'1976'1978'1980 1982 19841YEAR
6000-
4000-
97i ' '1974 1976
1800-/
/60C//
/
1400
1978 1980 1982 1984' ' ' 'YEAR
•
I
\... I.LIJ
E, 1200-
1-- 1000
800-
600
400
3000-
900
800
1972 1974 1976 1978YEAR
' ' '1972 1974 1976 1978 1980 1982 1984YEAR
2000
1980 1982 1984
% 700-
600
tT) 500
400 ,/300
200
100 /
BENFORD CONCRETE MACHINERY PLC
Nigure 7.3.7 A Graphical Illustration of Ideal . Performance
CA-OCR
-GEL— -CL— I
-NI— KASH
-CASH
2 3
F-
1974 19761- 1978 1980 1982 1984'YEAR
8E5-
6E5
'fic'D 5E5
'lt 4E5
Li 3E5-
/
///
1.0E6c.n
LI;
cu)E 0.8E6
6 0.6E6
0.4E6
I .2E6
7E5-
2E5- Ji
1E5--0.2E6
1974 1976 1978 1980YEAR
1982 1984
2.5E51.6E5
1.4E5
CD
w 1.2E5
Z' 1.0E5
0.8E5
0.6E5
0.4E5
0.2E5
1974 1976 1978 1980 1982 1984YEAR
2.0E5
II/ LT 1.5E5/ cr
1.0E5
0.5E5
-'-
1974 1976 1978 1980 1982 1984YEAR
CA-ocn
-CL
— —ON]
-NI
— — — — OCASH
-CASH
BEECHAM GROUP PLC
Figure 7.3.8 A Graphical Illustration of Ideal Performance
254
7E5
1974 1976 1978 1980 1982 1984YEAR
1974'197E: 1978 198d 1982 1984'YEAR
1.0E6
o . 8E6
(.1)MU)
I- O. 6E6LU
crix
LU
0.4E6
0.2E6
1E5
6E5
r".". 5E5
-
4E5
IT-. 3E5LU
2E5
1.8E5
1.6E5
1.4E5
614.1.2E5LU
1.0E5-Lu
0.8E5
0.6E5-
0.4E5-
0.2E5
///V
/
U)cr
80000
70000
60000
50000
40000
30000
20000
10000-
1974'1976'1978'198d 1982'1984'YEAR YEAR
1974 1976 1978 1980 1982' 1984
rt••n
CA-OCR
— --CL—ONI
-NI– °CASH
-CASH
255
MARKS & SPENCER
Figure 7.3.9 A Graphical Illustration of Ideal Performance
4.0E5
E- 3.5E5
3.0E5
115: 2.5E5I.-.)
2.0E5
CA-OCA
— -CL— —ON!
-N1— — (KASH
- --CASH
5.0E5
4.5E5
1.5E5
,///'1.0E5
0.5E5 97i 1974'
v) 2.5E5-
if
212.0E5 r
cr.
ti! 1.5E5-f'
-1.0E5-
0.5E5-/
1976' 1978' 1980' 1982' 1984 972' 1974 1976 1978 1980 1982 1984YEAR YEAR
6000090000-
80000-50000
'7000T
u, 40000- 60000-
g!.450000-L)ICi] 30000-z 40000
30000-20000-
20000-
10000- 10000-
1972 1974 1976 1978 1980 1982 1984'YEAR
1972' 1974' 19761 1978 1980 1982 1984'YEAR
PEA RSONS
Figure 7.3.10 A Graphical Illustration of Ideal Performance
256
3.0E5-
8E5
7E5
6E5cr,
LU
a-.
I.T.J 4E5
3E5
2E5
1E5
1974 1976 1978 1980 1982 1984YEAR
4.5E5
I
1974 1976 1918 1980 1982 1984YEAR
70000-
60000-
650000
LU 40000
30000I.
1974'1976'1978'198d 1982 1984'YEAR
80000
'70000
60000
LU
1
50000-
/ 40000-.,
30000-
20000
10000-
1974 1976 1978 1980 1982 1984YEAR
20000-
10000-
CA-OCR
4.0E5
3.5E5
.t; 3.0E5
Fi 2.5E5..J
LT, 2.0E5cr.
(-) 1.5E5
1.0E5
0.5E5
— --CL— --ON1
-NI— — OCASH
-CASH
257
RACAL ELECTRONICS
Figure 7.3.11 A Graphical Illustration of Ideal Performance
1 .8E5-
EGET2.5E5
1.4E5
E. 1 . 2E5
1.0E5E, 1.5E5
1.0E5
0.5E5
50000
40000
-
1974 1976' 1978 1980YEAR
0. bE5
0 . 4 E 5
0 . E 519761-
,1974 11978 1980
YEAR
18000
1600C1 1 '. /
14000-i
12000 I
Eil0000 .
8000- 0'.../ t
6000-
4000j
2000--
1974 1976 1978 1980
1982 1984'
\
F.; 30000
20000
10000-
-
1974 1976 1978'1980 1982'1984'
/-
11982 1984 -
I
\I
1.982 1984
Fl
- - -
— --ON I-NI°CASH
-CASH
258
YEAR YEAR
BPB INDUSTRIES PLC
Figure 7.3.12 A Graphical Illustration of Ideal Performance
10000
1974 1976 1978 1980 1982 1984YEAR
10000- ,j'
5000-
1974 1979 1979 198d 1982 1984'YEAR
3000.
500-2000-
12000-
10000-
It' BOOT
LU
LT; 6000-
4000-
a:ccir)(--) 1500
1 000-•I ,r
,Ifii
\
2500.
2000
CA—ocn
50000- 30000-
III
u., 25000-40000
r u..1
/ I=•Luf—in
/ .•.,
Lnul .i:73! 20000-cc 30000 cc
:11LL.I.
cc LiZ 15000-R— 20000-
cl-.a
1974
1976 1979 1980 1982 1984 1974 1976 1979 198d 1982 1984YEAR YEAR
ALAFYALLIED COLLOIDS PLC
--CL
Figure 7.3.13 A Graphical Illustration of Ideal Performance--ONI
• -NI— OCASH
-CASH
259
16000
14000
12000
Ui
Lr)m10000
LU
E 6000
6000
4000
•••
1972 1974 1976 1978 1980 1982 1984YEAR
2250
2000
1750
U.1
51500Li
L:1250
1000
750I,•\
500
1972 1974 1976 1978 1980 1982 1984YEAR
9000-
8000-
226000-
.:1
:It 5000-='4000-
3000
2000-
1972 1974 1916'197d 198d 1982'1984'YEAR
900-
/- --
800-/ -/700 /...-
600 -/ tr\
m _ /1 %
"(2500 itt
\Li /
400- ,-// 1
300 - / I 1
./ ri-\t t
i
I
I
200-'1
1
\
100-%
!
1--1! 1 ,
1972 1974 1916 1978 1980 1982 1984'YEAR
ASH & LAP( PLC
Figure 7.3.14 A Graphical Illustration of Ideal Performance
CA-OCA -0CL
— -CL— 1
-N1- - OCRSH
-CASH
260
7E5-
6E5-
C̀inj 5E- 5-=In
4E- 5.
Li
3E5
2E5-
1E5-
4.0E5
3.5E5
3.0E5COcr
:12.5E5
uJI-
/
cig 2.0E5Li
1.5E5
1.0E5
1974'976'1978'1980 1982 1984
1974'1976'1978'1986' 1982'1984'YEAR YEAR
0.2E5,'''
,...- 1
.---- .--- --
k
CA-OCA
— -CL— —ON I
I— — — °CASH
-CASH
261
4.5E5
A
II i
/ 0.9E5
0.9E5-/ I \/ I
0.8E5-,/ 0.8E5 / I1
L.Li O. 7E5- 1- --...../-
0.7E5//-.-
1
La IItp /
I-..F-. 0 . 6E5-
It
.va2.7.: 0 . 6E5
/11
/ Li 5E50.
..." f n
0.4E5 I
0.3E5
0.2E5
r
0.1E5 -- -...„--
1.0E5-i1.0E5
,/'''
0.4E5- /,/ ---' ---• ''' .......„../
0.5E5
0.3E5- /
1
1974 1976 1978 1980 1982 1984YEAR
1974 1976 1978 1980 1982 1984YEAR
BONS CO PLC (THE)
Figure 7.3.15 A Graphical Illustration of Ideal Performance
7E6
6E6
(cf, 5E6
cio 4E6
1--5 3E6a:
2E6
1E6
0E4
0E4
0E4
I
1-Z' 0E4
0E4
19 6 1978 1980 1982 198:14YEAR
1974 197611978 1980 1982 19841T - 1
YEAR
-OCR
— - - EL— — -ON I
— — °CASH-CASH
262
1.0E7-
0.9E7-
0.8E7-
(MO. 7E7-
0.6E7-
E 0.5E7-a:
_
0.4E7
O. 3E7-
0.2E7-_ -
0.1E7-
1974 1976'1976 1980'1982'1984'YEAR
1974 19(6 1978 1980 1982 1984YEAR
BRITISH Gm CORPORATION
Figure 7.3.16 A Graphical Illustration of Ideal Performance
16000-
14000
4000
2000
1972 1974 1976'1973 198d 1982 1984YEAR
500-
3000
n2500
2000
crw(-31500
1000
CA-OCR
——ON1
-NI- — OCFISH
-CASH
263
22500
20000
17500
L1115000
5: 12500
Lc110000
7500
5000
2500
3500
3000-
2500-LJJ
2000
1500
1000
500
///' "
•
\ n ,
/ //V, //
it \ I,,
,,,
/-........„.. ___ -----
1972 1974 1976 1978 1980 1982 1984YEAR
/
/7
,7tr, 12000
r-: 7
1..0
10000-mcr. /
:11/
l- 8000-w IfExcr //a6000 --..../
197 1974 1976 1978 1980' 1982 1984 1972 1974 1976 1978 1980 1982 1984\ERR YEAR
ANGLIA TELEVISION GROUP PLC ANAB I
Figure 7.3.17 A Graphical Illustration of Ideal Performance
60000-
55000
50000-.-
45000
-7:140000
cg 35000-
30000-
25000-
90000-
80000-
Litc̀iLl 70000-
50000-
40000-
YEAR1.93 _ 1978 19.93814___
5000
5000
4000-
-
-5000
-10000
-15000
-KR
•
-CL—0N1
- OCASH-CASH
197 1974' 1976'1978'1980'1982'YEAR
I20000 -
1972 1974119161— 197811980 1982YEAR
1.1-; . I1
z
o I
Li , 1
.--. I I
1
LcT.3000-I 1 _
2000-
1000-
- •1972 1974 1976 1378 1980 19872r—
YEAR
GOODYEAR TYRE & RUBBER CO.
Figure 7.3.18 A Graphical Illustration of Ideal Performance
264
3.5E5
3.0E5-
• 2.5E5
•
-
n2.0E5-
1.5E5
1.0E5
•
-OCA
— —ON!-NI
— KASH-CASH
5.0E5-
4.5E5-
4.0E5-
IT 3.5E5-w
cc 3.0E5-
(g. 2.5E5
2.0E5-
1.5E5
1.0E5-,--r 0.5E5 —1972 1974 1976 1978 1980 1982 1984 1972 194 1916' —19 (81--1 -9-807- 198i - 19E141
YEAR YEAR
50000-
45000-
40000-
35000-LL., _s 30000zLi- 25000-
20000-
15000
10000
5000
/----
/_./
le—
/f\ /. \ ,/ /
F
i
,-- —/
1,
1
/-
7
/
/
,.,- Lt :in5
50000-
40000-
-30000
20000
10000
.--..--/—
•
.—
.../,
. r1 ,
1978YEAR
1972 1974 197 1978YEAR
1980 1982 1984 1972 1974 1976 1980'1982 1984---,
BABCOCK INFERNATIONHL PLC
Figure 7.3.19 A Graphical Illustration of Ideal Performance
265
200000
175000
tu-' 150000LUIn
,C17. 125000
S' 100000
75000
50000
25000
CFI-0CF1
— • -CL—ON1
- -N I- -- -- -- • - °CASH
-CASH
266
1972 1974 1976 1978 1980 1982 1984YEAR
1.2E5-
1.0E5-
/
0.4E5
0.2E5-
1972 1974 1976 19(8I
1980'1982' 1984'YEAR
LU
0.8E5-
cr.7:1
12 0 . 6E5-
(XC
18000 20000-/
16000/
18000-'I/
14000 / 16000-/
/
14000- iu., 12000- r"-- - -- - --'c=9
,,,'
i/u
,-. 10000 - 1/N., , • - \ \ =, 12000
c.n /
/L-- / f \ L' 10000cc
1-f '''' 8000-
/I 8000
.--- ,
4000-
/1 ' \\
/6000-
1 1 6000 _I__
..--- ." 4000-
2000-1972 1974 1978 1978 1980 1982 198411972 1974 1976 1978 1980 1982 1984YEAR YEAR
APV HOLDINGS PLC
Figure 7.3.20 A Graphical Illustration of Ideal Performance
30000
27500
25000
in 22500
in, 20000cr.
1—E, 17500
a 15000
12500
10000
7500
1972 1974 1976 1978 1980 1982 1984YEAR
5000-
1972 1974 1976•191E1'1980 198-2i1984'
YEAR
22500-
20000-(r)LLI
;217500-
7-n
IIc(z
(-) 000ci
7500-
3500
3000
Li.: 2500
;
I
It
\II
2000-
1750-
1500-
1250-
./
mu'Li
1000-,/
•750-
500- r
250 n
1972-1974 1976 1978 1980 1982 1984'YEAR
1Li
-2000LLH-
1500
1000
500 ,1972 1974 1976' 1978'1980 1982'1984'
YEAR
;
CA-OCA
-CL—ON 1
-NI• - - OCIISH
-CASH
267
AULT & WIBORG GROUP PLC
Figure 7.3.21 A Graphical Illustration of Ideal Performance
225000-
200000-
175000-
V! 150000-
Ln`n
cc 125000-
LL.1
Eg 100000-=
75000
50000-
25000
20000
17500
LEI. 15000t_)2::
12500
FA-0CF1
• — • -CL-- ---ON!
-N1
OEFISH-CASH
1.2E5
Ln.
..11 . f /
i1777.1.0E5
.C7 /
:721 0.8E5 /1-.
,
/u..1= ,.
E i
/0.6E5
/
...-/
0.4E5 r •
972 1974 1976 1978 1988 1982 1384'1972 1974 1976 1918 1980 1.982 1984YEAR
4000022500
35000-
10000-
7500
5000
1972'1974 1976 1978 1988 1982 198-4TYEAR
YEAR
5000-
1972 1974 1976 1378 1980 1982 1984YEAR
ti:
t
C.-- i300001
-\ / i
t 1
u, 250087 :
1 !
5 200001
t
11 I
15000
I / 100001
t jt
ALBRIGHT 8, WILSON LTD
Figure 7.3.22 A Graphical Illustration of Ideal Performance
268
35000
32500
30000
CO 27500u
T25000
E 22500:D
(-) 20000
17500
1972 1974 1976 1978 1980 1982 1984YEAR
r\
30000'
tri 25000-
520000
c
a )5000- 1
it \
\\
‘.*\\./-\
1972 1974 1976- 19(8 198-0( 1982 1984'YEAR
/--,10000 t'
15000
12500
CAocn
•- --OCL CL— —ONI
- °CASH-CASH
r.fl
4500- / \ 2500- ; 1' 1
4000- i \ 2250- ._/ t
/ \ /
3500 / 12000- /
/ 1
B 3000 1 I .-. 1750/
\
L.L.;
./ / \
Li 2500-z
7 \E 1500
//' .__.... 1 :Th1 ''
2000/ \1250
/
i - -,I ,,i\
/1000-
/
/1500
1 I" i ,-, \ 1
-I
s.... ...r......
„......n1_ 7 ____./ 750 i1000- .V'''. %
%',.//'-' n , 500 7 \..../ . v
1 1
• / %v
7 1972 1914. 19761- 1978 1980 1982 19841
YEAR19(2 19(4 19 (6 1v9E 116R - 19801— 1982 1984
, --- 1
BARROW HEPBURN GROUP PLC
Figure 7.3.23 A Graphical Illustration of Ideal Performance
269
60000-
50000
/
/
1980 1982 1984
//I\
\/I
../
r It
CRUCF1
• -CL— —ON!
NI— °CASH
-CASH
/
270
PLEASURAMA PLC
PL ABU
Figure 7.3.24 A Graphical Illustration of Ideal Performance
/ 45000
1 40000
ce.1,2) 40000-a-.
SLC. 30000
5,3 000-Ui
t; 30000Ec;cl; 25000..J
E 20000cL
20000-
10000-
uJ
1--
1000
Li 15000
10000
5000
5000-
cr.U
/2000.
-
4000 /
2000,--
1972 1974 1976 1978 1980 1982'1984'YEAR
14000 7000
12000-6000
10000 /
L-1 6000
in8F--. 8000 ,1 ,,,, 4000.
/
-}
/
//
,/ .
/
1972 1974 1976 1978
3000/
YEAR
,
..../ --------- ..---'
.... /
1972 1974 1976 1978 1980 1982 1984 1972 1974 1976 1980 1982 1984
..__ _ __ .... .__ .- -- --'
YEAR 1y90178R
IpicIU 19821
/
1972 1974 1976 1978 1980 1982YEAR.
60000
50000-
40000
CCW
30000
20000
10000
19 (2 1974 1916 19(8YEAR
,-***
•-•"'
198d 1982
1
:‘.
I j
1.4E6
0.6E6
0.4E6-
0.2E6-1972 1974 1976 1978
YEAR
1.0E5
0.5E5-
Li
/Li
•-•972 1974 1976 1978
YEARLi
0. • .5E 5-n
-1.0E5-
-1.5E5-
1980 1982
•
8E5
3E5-
2E5-
Li
9E51
1.2E6-
1.0E6tt..;tn
12-0.8E6Ui
Q:.
(-3=
-OCA CA
• -CL—UN!-NI
— KASH-CASH
BRITISH RAILWAYS BOARD
Figure 7.3.25 A Graphical Illustration of Ideal Performance
271
4
(..r)
cc5000
7:11.1CC:
Li 4000
-
972 1974' 1976' 1978' 1980' 1982' 1984YEAR
972 1974 1976' 1918' 1980' 1982' 1984YEAR
6000
2000
.1
/‘; 1
tiv....„-
,-- ;
1 1
1972 1974 ' 1976 1918 1980 1982' 1984YEAR
800
700
600
w. 500•=•
500
450-
400-
350-
300u-)ccLi 250
-.400
-300 /
/11
t ,
/
,j \I1I
,
200
100if
. ' —\ ; t
!1i
;1 ;
1I
\
972 1974 1976 197E1 1980YEAR
/\
1982 1984
InEICF1
—— --ON!
• -NI- ocnsH
-CASH
ANCHOR CHEMICAL GROUP PLC
Figure 7.3.26 A Graphical Illustration of Ideal Performance
272
1.0E5
0.9E5
1.1E5 80000
19 ./4 1976 1918 1980 1982 1984
1914'1916 1918 1980 1982 1984YEAR
YEAR
(."1-n 0.8E5
'20.7E5
70000
;:60000"
nalooT
E 0.6E5
0.5E5
0.4E5
0.2E5
30000
20000
10000
///
//
/
6000
5000
_4000
3000
2000
1000"
8000
./
6000
-
4000
2000
1914 1916'T 1918 1980 1982 1984YEAR YEAR
1914'1916'1918 198011982 1984
CII
-CL—
NI- — — ocnsH
-CASH
BAKER PERKINS HOLDINGS PLC
Figure 7.3.27 A Graphical Illustration of Ideal Performance
273
1.4E6.2.0E6-
. 1.8E6-
1.6E6-
uJ1 . 4E6-
cc
1.2E6-Li
1.0E6-
O. 8E6-1
1.2E6Li172.
*f_l 1 .
an:71
0.8E6Luz:
/1'a,
a 0.6E6-
0. 4E60.6E6-
0.4E6-
1972 1974 1976 1978 1980 1982 1984 972 1974 1976 19(8 1980 1982 1984'YEAR YEAR
I
e2,11.0E5-
J/' /7- \ 0.8E5-: ,
0E4
\ 0.6E5-%
\ 0.4E5-5E4
0.2E5
cn-0 CA
- - -0CL -CL
—ON!-N1
— °CASH-cnsn
5E41 .8E5-
0E4 1 . 6E5-
5E4I
\
\1.4E5-
11
%A/Li 1. 2E5-
(2 1974 1976 1978 1980 1982 1984YEAR 1972 1974 1916 1980T 1982 19841
YEAR
FORD MOTOR CO. LTD.
Figure 7.3.28 A Graphical Illustration of Ideal Performance
274
• 000
6000
r: 5000Cr7Cr)
E4000
3000
2000
_2500
Li
2000
1500
1 000
800
700-
50
1980 1982 1984
C71OCA
—— •—ON I
-NI- -- • - • - °CASH
-CASH
4500-
5000
4000Ui
3500--J
E 3000---J
/ /---, /
\s/
1972 1974 1976' 1978' 198d 1982' 1984YEAR
1972 1974 1976 1978 1980' 1982' 1984YEAR
/r 400
350
r \600 / \ / 300
Lu:w;'... 500-
z
// f\II\ /
250
i E 200in
/I
,."400 1501 /
/1
J 1 l'\\._,l
300 /(-----\100
--i I.200
1972 1974 1976 1978 1980 1982 1984
1972 1974 1976
1978YEAR
YEAR
AnAms a GIBBON
Figure 7.3.29 A Graphical Illustration of Ideal Performance
275
35000-
30000-
E 25000-
;FO:c
"615000
10000
50009-.? 1974 197d 1978'1980'1982'198'14 1972 1974 1976 1978 1980 1982 1984
YEAR YEAR
3000
2500/-**-
I/ ' A
n. r \,
2000 /
It / , mg..' / --- -- / 1
t 1 1 / u 1500 /s 1
I I /
7 s \/ / i I
I I
i I
/ \ t / 1000
. r I .....\ n'. ''.
..4""."-.. I Ili
.,,....,G, 500 1
I ,,
1972 1974 1976 1978 1980'1982k198:411972 1974 1976 1978 1980 1982 1984Y .YEAR YEAR
1000.1
3000-
2000 //-*
8000
7000-
6000
Ili; 4000-
CA-OCA
-CL— —ONI
-N1— — OCF1SH
-CASH
ARMITAGE SHANKS GROUP LTD
Figure 7.3.30 A Graphical Illustration of Ideal Performance
276
1974 1976 198I1986 1 -98211984T
YEAR1974 1976 1978 1980 1982 1984
YEAR
500
3000-
5000
2500-4500
3000
25001000
2000
300-
250/ - •
450
400
1974'1976 1978 1980'1982'1984'YEAR
a.:
350L.L.1
E:
g. 300
Ui1-,
250
200
150
100
--OCR
-CL— —ON I
-NI- OCRSH
-CASH
277
200-
150"
100I \
\
50-
1974'1976'1978'1980'1982 1984YEAR
tf‘
ATKINS BROTHERS PLC
Figure 7.3.31 A Graphical Illustration of Ideal Performance
F-
Li 5E5Lnmy'
4E5or.
a 3E5
1974 19761-1978.1980 1982 1.984YEAR
-OCA- --OCL
Cl—ONI
-- • - •- NI- ocnsH
cnsH
DUNLOP HOLDI\
Figure 7.3.32 A Graphical Illustration of Ideal Performance
GS PLC
1912 1974 1976'1978 1980 1982 1984YEAR
7E5
win _ ,--16E5-
5E52; \
,4E5., ,r t 1
3E5 , --- -)
ill1-.
cc 1%
2E5- /titil
1E5
1972 197; 1976r 19igF r---18811 —1982' -1984YEAR
8E4- 80000,
6E4-70000-
60000-
w 4E4 7
E \ 50000
•-•
u
• -21:
340000-
n 30000-972 1974 1976'1978 iseo' 1982'1921
YEAR20000
-2E410000-,
I.-4E4 1972
278
9000
8000
.n // f
900
800-
CAOCR
-CL— —OWL
-NI— • - OCASH
-CASH
7000
7000
4000
3000
En 60001.1/
LU
LU 3000
2000-
2000
1972 1974 1976 1978 1980 1982 1984jYEAR
1000-
11
I
700 / 114(Llj
Goo/iiiO
III
tj-. 500 721
400 //'
/ r300
200
1972.1974 1976 1978 1980 1982 1984'
YEAR
1°°°1972
.
1200-
100C
800
cr,LU
600
400
200- / k
./
1974 1976 1978YEAR
/
1980 1982
,
r
1984
\
1972 1974
1976 1378 1980 1982 1984'YEAR
BARNO INDUSTRIES PLC
Figure 7.3.33 A Graphical Illustration of Ideal Performance
279
H-
70000
60000
IL7.1Ln(efE' 50000
640000
30000
40000
Li) 35000
'&130000cc
ra,
Li _20000
15000
Li
/ 45000
20000
1972 1974 1976 1978 1980 1982 1984YEAR
1-9-772r 19761 — 19—?8T —190 1982 1984)YEAR
7000
6000
5000-
4500-
4000-
3500-
-CA-ocn
— -CL— —ONI
- -NI
—•— ocnsn
CASH
1...
4000 13000-
/
Li e i
— 3000-------;"
1--L1.1
.7<\
,.. _ 31̀: Pc 2500
, 1z I i /
z
2000
1 II./
1000
1I1 i ...
.1 ' \2,3ors
i5o0- , ,A i \.•/\, ..,. •
, ; ,‘
. , 1...-972 1914 1976 1978 198 1982 1984 1000 .- \ j ___ __ \ ,,
n. 500 /
YEAR ---.._
-1000 t f
1974' 1976 1978 1980 1982 1984'197YEAR
BBA GROUP PLC
Figure 7.3.34 A Graphical Illustration of Ideal Performance
280
8000
u 6000-
4000-
2000
1200
1000
800
1974 1976 1S8 1980YEAR
1982 1984
200
1974 1976 1978 1980 1982 1984YEAR
400
1974 1976 1978 1980 1982 1984'YEAR
CFI-0CF1
-CL
— —ON I
-NI
— — — • - °CASH- CASH
if 18000
16000
E 14000-
J '11!
2112000-/
:110000-
20000
17500
15000
cc`jri 12500
10000
• 7500
5000
2500
3000
2500
0 2000
1500
1000
500
II
•
1974 1976 1978 1980'1982'1984YEAR
BATLEYS OF YORKSHIRE PLC
Figure 7.3.35 A Graphical Illustration of Ideal Performance
281
25000
22500
u-)20000
Ls)In -cr. 17500
LcS15000-
12500-
10000
7500-
s's
1972 1974 1976 197d 1980'1982 1984YEAR
14000
ER 12000-It
, /1 ICYLLIa-.
2500/
/I 1
1 t
A /
1
2000 ?
1978 1980 1982 1984YEAR
"-•••
CA
-CL—
-N1•— — ()CASH
-CASH
18000
16000
C)1500 / I
I
11 En=
I ,17
/ 80C— 1
5
t
A
1000-
I—z
1000.
\ 600w/ ;
/ 7. \)1
k \.]I
40C/
500/ // Ill7 I', t 1
1
It 1 f
-zt .-. !
//'I 1\./ \200-
\n1972 1974'1976 1978'1980 1982 198411972 1974 1976 1978 1980 1982 19-8-4
YEAR
(-) 8000-
6000"
4000
1600
1200-
140C
972 1974 1976
YEAR
BEMROSE CORPORATION PLC
Figure 7.3.36 A Graphical Illustration of Ideal Performance
282
/
1982 1984
\- 2000-
- 4000-
70000-45000-
60000- Lutn 4000C
3500C50000
cr.
(SA 40000-
30000-
20000
6000
4000
g 2000
./ FE: 30000
1-0c, 25000
20000
15000
10000
',
-
972 1974 1976 1978YEAR
1988 1982' 1984
8000
7000
6000-
m 500Ccn
972 1974 1976' 1978YEAR
1980
CE
400C•972 1974'1976 1978 1980 1982'1914iYEAR
3000
2000
1000
1972 1974 1976 1978'1988 1982'1984'YEAR
BESTOBELL PLC
Figure 7.3.37 A Graphical Illustration of Ideal Performance
CA-OCR
- - - -• — -CL
—0N1-NI
— OCASH-CASH
283
800
700
w 600-
Ii
500-
LU
400
300
200-
I
1
I:
I
\
n
I%
\%
\
/ n
s ,
N\\
,
Aj
400-
350-
300-
250-Incc'200-
150-
100
50
l n
r /
/ \\
0CA CA
—L— --ON I
-NI• - • - ocnsH
-CASH
8000-
7000
6000cc
E 5000
Li4000-
3000
2000-
I 5000
4500-
LU
4000-
-cc 3500
:71
I-. _Ei 3000
i =(-) 2500- / II \ /Ix/ a-.
2000 ;
1500-(
1972 1973 1974 1975 1976 1977 1978 1979 1972 1973 1974 197 1976 1977' 1978' 19791YEAR YEAR
, 1,
, 7-\
\ ,
I,,
I/ '-' \\ ,,I/
. , , \
\... /(,, \ ___ i
1972 1973' 1974 1975 1976 197/ 1978 1979
1972 197:3 1974' 1975 1978 1977' 1978 1915YEAR YEAR
BROCKS GROUP OF CO. LTD
Figure 7.3.38 A Graphical Illustration of Ideal Performance
284
30000-
\
LT/CC
' 8000-
It 1980'„
1i
6000-/
4000- •
./
2000• ,
1972' • 1974'. 1976 • 1978 • 1980'YEAR
1980'
1972 • 1974 • 1976" • 1978 • 1980'YEAR
/N 16000
I`
14000-
1 12000-ti
110000-
20000
-OCA CA
— -CL— I
-NI— — OCASH
-CASH
285
1 .3E5-
1.2E5-
1.1E5-
co 1.0E5-
((1/.-)) O. 9E5-cr.
70000-//
/ t' / .. ...
"n...-..• •
-- .. ...'- •
\glmooT
50000..J
gre 40000
0.8E5crHx
Li (3..7E5-Li
0.6E5-
0.5E5-
0.4E5
1972 1974 1976 1978YEAR
10000
8000
6000
'...:-..;w 4000E.-. 2.-,/.C/L.)
2000I- -Li
1972' . 1974' • 1976' • 1978'
YEAR
-2000
-4000
-6000
STONE PLATT INDUSTRIES PLC
Figure 7.3.39 A Graphical Illustration of Ideal. Performance
F-
1.4E5-
0.8E5
80000Ui
n7000C
LIJ
cr._
1— 60000
0'.
a 50000-
- _
/ /
, ' 1/7 \* \ j/
.., 7....
9000
8000-
7000 I
i
/ i I
a, GOOC t(.17 r ,
20000
15000
10000
t-LI 5000Lic)
972 1974 1976 1978 1980 1 mYEAR I 5000
-5000 r
-10000\ 4000 .... '
-150003000
-20000 2000
.. • i' •- j.
-OCA CA
— --CL— --ON1
-Ni— KASH
-CASH
1.6E590000-
0.6E5
1972 1974 1976 1978 198030000-
1972.'
1974 1978 1978 1980YEAR YEAR
1972 1974' 197E; 1978 1980'YEAR
BRITISH AIRWAYS
Figure 7.3.40 A Graphical Illustration of Ideal Performance
286
5000-
U7
4000-
Cr:11nIl
-
2000-
10001974 1978 19801972 1976
YEAR1972 1974 •
1976 1978 nedYEAR
300
g.! 200/
100
1972'1974' 1976' 1979 1980YEAR
-100-
-200-
150
100-
50
500
400-
, •
}/
//'
n
450-
400-
350-
300-
ic2 250
200-
.,/
OCA CA
— • ft— ---owl
-N I-- • - NASH
287
•
1972'1974' 1976I
1978' 19807YEAR
VINERS
Figure 7.3.41 A Graphical Illustration of Ideal Performance
5000
4500
cr: 4000-
(nu'
3500-
3500
Lucn 3000-
;L::=;
300 (Fi
200
I OT
1972 1974 1976YEAR
-10T
\lr 198d
500
400
250
200
-OC11
— --ON I-NI
— -- • - — acnsH-CASH
288
/I \
/ ,- \rN / , , \, •
1 N-- /; '
u 3000-2000
ci200
1500
2000
1972'1974 1976' 1978'
1980 1972 1974 1976 1976 1980YEAR YEAR
1972 1974 1916' 1978' 1980YEAR
BLACKMAN & CONRAD
Figure 7.3.42 A Graphical Illustration of Ideal Performance
1972 1973 1974 1975 1976 1977 1918 1979YEAR
U.J
U/
L.cL.J
a'.
12000
11000
10000
9000
8000
7000
6000
5000
4000
3000
/
1
172 1973 1974 1975 1976 1977 1978 1919YEAR
1972 1973 1974 1915 1976 1977 1978 1979YEAR
800
600
w 400
— 200-LU-
-200
-400
-OCA CA
----— ----ON!
-NI— °CASH
-CASH
11000
10000-
9000
8000
7000-
6000
5000
4000
3000
1972 1973 1974 1975 1976 1977 1978 1979YEAR
AMAL1AP1ATED INDUSTRIALS
Figure 7.3.43 A Graphical Illustration of Ideal Performance
289
1—E5000ce`xa
4500-
4000
7000
6500-
tn6000
135500
(\ //1/
_ ,1/
S.
-OCA CA
— -CL— —ONI
-NI— — OCFISH
-CASH
290
14000
13000
12000 /
/ .(.1-)L now , ,u-) 1tr)cc
10000uJ
a 9000
8000
7000
YEAR
1000
750
500
LL.1 250-
1974 1974 151976 1977YEAR
-250
-500-
-750-
-1000-
1973 1974 1975 1976 1977 1978 1979 1980
700
600
500
(-c2 400
300
200-
100-
1973
../
1974 1975 1976 197? 1978 1979 1980YEAR
/
1n978 1/979 980
I•
1973' „ 1974 1975 1976 19T? 1978 1979 1980YEAR
BLACKWOOD,FlORTON & SONS
Figure 7.3.44 A Graphical Illustration of Ideal Performance
7/- 5000"
6000
5000
cE
4000
63000
2000
4500-
4000-Li.tu)
E 3500--J
300
•
0."
2500-
_(-) 2000
1500-,
loaf
1975 1976 1977 1978 1979YEAR
l'‘\ ------ 350-
nn
1 / \
..._./ ; 300-
‘mFE) 200-L)
‘
t 150-,t
100-
50-t
t
\ It 250-
1975 1976 1977 197k 1979YEAR
-OCR CA
——
-NIOCRSH
- -CASH
1972 1973 1974
400
. \
200 ,/
/
L.).1
C)L.,z.....
I--wz
1972 1973 1974
-200
-400
1972 1973 1974 1975 1976 1977 1978 1979YEAR
'-\\
(-\\\\
1972' 1973 1974 1975 1976 1977 1978 1979YEAR
BURRELL & CO.
Figure 7.3.45 A Graphical Illustration of Ideal Performance
291
6000-
5500
5000
Lr)117, 4500
nE 4000
2000-
1500-
\ 350-
300-
250-
/LT) 200-=Li
-400
-600
100-
50
-OCR CR
- --CL— —ON!
-NI-- °CASH
-CASH
3500-
3000-
2500-7
2000
4500
4000
1.-_11-; 3500
FE; 3000--J1-ZIeti 2500-rxa
// '....I \1 \
..--". I 1------- / \
...',........... - - ...
..."/ /
1974 197E 1978 1980'YEAR YEAR
1974 1976 1978' - 1980'
600
400
200YERP
1974, 7 1978, 1980,
- -L.1-200
U_1
CZ11
150
1
-800
1974 1916 197E1 1980'YEAR
CAWDAW INDUSTRIAL HLDGS
Figure 7.3.46 A Graphical Illustration of Ideal. Performance
292
30000 30000
25000 25000
1973 1974 1975 1976 1977 1978 1979 1980YEAR
3000
2000-
-1000
1974 1974 1975 1976 1977YEAR
- 1006
- 2000-
10000
1800-
1600
1400-
1200-
a:U.) 1000E
800
600-
400
-OCA CA
——ON]
-141OCASH
-CASH
U)nI-.
Vr). 20000r.Fc; 20000
i n
m
LT; I -aim z:= W15000-u 15000 a-.
La
10000
// I
. :
,/ 1 ,
(\
I
,
, . \l
1 I / I
1 .A,-- /./
\
III
!---- I I\I
I
5000 1973 1974 1975 1576 1971 1978 1974 1980
YEAR
200
,1974 1974 1975 1975 19T/ 1978 1974 1.980
YEAR
AIRFIX INDUSTRIES
Figure 7.3.47 A Graphical Illustration of Ideal Performance
293
1000
500
LUE.
Li 5006-.41
-W=--1000
-1500
-2000
-2500
• ••••"--.°
YEAR197 13 19
1972 1974 1976YEAR
1978 1980
1978 198
,
10000
9000
LU8000
Z
11000
7000
- - 6000LU
5000-Li
4000-
3000"
2000-
,
//,csd {Te4 (Re6' csee' ?see'
YEAR
-700 /
/
600
500
1972 1974 1976 137Ei
EgeGYEAR
12000
10000
)j 8000L11
LUccac- 6000
4000
-OCR CA
— --CL— --ONI
- --NI— °CASH
-CASH
OXLEY PRINTING GROUP
Figure 7.3.48 A Graphical Illustration of Ideal Performance
29L1.
40000
35000-
LU11) 50000 r- 30000-LU
'Er; -cr.cc 25000
E- 40000 .-J
cujcr. L.T., 20000u 30000
=11."
Lj 15000-
70000
60000
II I
20000
10000
10000-
-5000
1976 1978YEAR
1980 1974 1976 1978YEAR
1974 1980
4000
2000 - YEAR--11374--- 1976 197 19
-2000
- 4000
- 6000
- 8000
-10000
-12000
6000
5000-
a-: 4000v)LI
3000
2000-
1000
!! \
/ I -
1tI /.....'/ \ \
\ /I
\
1
\\.." I 1A %
,\
.-••• \
. ...• \ t
/
..."..." \,. /
,
1974 1976 1978 1980YEAR
LESNEY PRODUCTS & CO.
Figure 7.3.49 A Graphical Illustration of Ideal Performance
-OCR cn -OCL— -CL
— —ON1
OCFISH-CASH
295
25000-
10000-
•1972'1974' 1976'
YEAR1978 1980
f-1-125000-
_
ES. 20000
1-1-;cg 15000-
10000-"
1400-
1200-
1000
IT:800(ff.
Li
600
400
200
1972
/
- -
1974'
IN
,
1976YEAR
/
1978'
n
n
/
15000-
YEAR197 1974 1976
I
,
30000-
.„/•••n\
30000-
•1972 • 1974'1916' • 1978'1980
YEAR
RICHARDS & WALLINGTON INDUSTRIES
Figure 7.3.50 A Graphical Illustration of Ideal Performance
-OCR CFI • -0CL
— -CL— ---ON1
--N1- - KASH
-CASH
296
• ,
1972'1974' 1976 1978' 1980'YEAR
8009
7000
6000
criF-
in' 5000
cr.`n
o--.
4000or.cr'
3000
2000
1000
1972 1974 1976 1978YEAR
1980
5000
4500
4000tr)LL.1
_
3000a-.
2500
2000
1500-
1000-
YEAB..--1972, • 19- •---711376, , 1978, ., 1980,
1400
1200
1000-
800cr:
600
400
200- 1500-
500-
(-1 -500IFn111
- 1000-
/ f
ocn CFI
—-- —ON!
-NI-- • - ocnsH
-CASH
297
1972 1974 1976 • 1978 1980YEAR
NORVIC SECURITIES
Figure 7.3.51 A Graphical Illustration of Ideal Performance
3000
3500
2150
250d
FE, 2250-
:300O
cr.w
Ele. 2500:5'2'2000- ,cr.
Ci1750-
1500-
1250-'7/
2000
1500• 1974' 1976' 197E1 198d
YEAR• 1974 1976 1978 • 1980
YEAR
250-
I 1, \ : t1I
= 150 Icc
\
V)i\
1
1001 I 11
V
\150; n
-250
-500
-750
-1000
-1250
-1500
-1750
250
n
1974,1
/I-
200 I\/ I/
n
U!
./. 1
I
—— —ON!
-NIOCFISH
-CASH
298
1974
1976 1978
1980YEAR
AUSTIN (F.)(LEYTON)
Figure 7.3.52 A Graphical Ellustration of Ideal Performance
7.4 CONCLUSION
Factor analysis has been used to construct the ideal
equations as presented in this chapter. There are four
equations that can be used to calculate ideal values of
current assets, current liabilities, cash and net income for
each company separately and at the end of each year.
By applying these equations to the sample of companies, it
is found that in well performing companies about 84 percent of
performance variables are above their ideal values and 100
percent of these variables are below the ideal values in the
poor performing companies. This means that all the companies
that have had financial difficulties and have gone into
receivership were suffering from insufficient working capital
(managerial performance), lack of profit ( profitability) and
shortage of cash (liquidity), and the majority of well
performing companies are doing very well in the above three
important financial dimensions. This will also confirm the
earlier conclusion that the present model has almost explained
the same variance of three significant performance factors;
profitability, managerial performance and liquidity.
299
These equations could be also used to improve future
financial performance, and guide managers to provide better
plans. In the Marks & Spencer case (page 240), based on its
past performance and comparison of its actual performance with
associated ideal values, it is possible to detect that current
assets should have been increased in 1985 from their present
value of 456 million pounds. A more appropriate level would
be over 1 billion pounds.
300
CHAPTER 8
CONCLUSIONS AND RECOMMENDATIONS
301
CHAPTER 8 : CONCLUSIONS AND RECOMMENDATIONS
8.1 MAIN CONCLUSIONS
The main conclusions of the present study can be summarised
as below
1) The knowledge of financial ratios and its use as
a measuring tool provides the means of
controlling the success and stability of a
business.
2) A single ratio can not reflect every aspect of a
company's performance and sets of ratios are
proposed to allow a better evaluation of the
financial performance of company.
3) Three dimensions represented by profitability,
managerial and liquidity ratios jointly measure
nearly every aspect of a company's financial
performance. They can and do serve as tools for
detecting irregularities in managerial behaviour
and company performances. They also provide a
meaningful and quantitative representation of the
results of decisions and the effects of external
conditions.
302
4) The techniques available in the past were wholly
inadequate for proper analysis, and there is a
need for constructing a firm conceptual basis for
financial analysis; particularly, the
desirability of a shift from univariate to
multivariate financial analysis.
5) An important result of the past studies was that
there is a significant degree of correlation
between different ratios and one of the best
techniques which can be used to study the
correlation between the ratios is factor analysis
which enable management to choose the most
significant and reliable ratios.
6) The model developed using factor analysis
together with regression analysis to measure
companies' financial performance with the
following significant characteristics:
6.1) It explains nearly 30 percent of
profitability, 37 percent of working capital
and 33 percent of liquidity, which means that
it almost explains the same percentage of
variance of the three main indicators of the
companies financial performance.
6.2) Its overall effectiveness in identifying
companies strengths and weaknesses is about 90
percent.
6.3) It can correctly classify 100 percent of well
performing companies and 94 percent of poor
performing companies.
303
6.4) Its accuracy to measure companies' financial
performance whose data were used to construct
the model is about 91 percent which dropped to
88 percent when it was applied to companies
whose data was not used in the model's
construction.
7) The Ideal models which were constructed using
factor analysis for calculating ideal values of
current assets, current liabilities, cash and net
income, when applied to a sample companies, it
was found that in well performing companies about
84 percent of performance variables are better
than their associated ideal values, whereas 100
percent of these variables are worse than their
ideal values in poor performing companies.
8) These models could also be used to improve the
future financial performance, and guide managers
to provide better plans for their company.
Finally it appears that the model is able to measure and
summarise past performance and assist in ide tifying future
targets of financial performance. That is, it can be a useful
tool for financial planning and control.
304
8.2 RECOMMENDATION FOR FURTHER RESEARCH: DYNAMIC ASPECT OF RATIO
Saint Augustine says that we always are in the present.
The present has several dimensions, however: the present of
the past, the present of the present, the present of the
future. Thus, at any time our actions at every single instant
depend not only on our current state but on our memory of the
past and anticipation of the future. However, while there is
little that can be done to affect the past, there is still
time to influence the future by present actions. To do so, it
is essential that we relate the past and the future i,e,
construct a model of 'temporal change'. When a model of
temporal change is used for the purposes of selecting a
desired future sequence of states, this is called 'planning'.
Thus our ability to plan is strictly a function of our ability
to reconstruct the process of temporal change as a function of
discretionary acts. As it has been stated the main purpose of
this section is to seek a greater understanding of how the
special characters of time and change influence our ability to
construct dynamic financial models and how su h models have
improved our ability to cope with and manage time and change.
The management of time and change is, however, a very complex
task, requiring that we understand how and why change occurs
over time. To do this it is necessary that we first
understand the dynamic behaviour of financial ratios. Once
their behaviour over time is understood there may be a
possibility of controlling them to some extent.
305
It would appear that fruitful research could be carried out
into how financial ratios vary over time and into the extent
that the company can control future values of these ratios.
In this way it might be possible to control future company's
financial performance to a limited degree. Of course
companies are exposed to considerable influences outside of
their control and therefore the extent to which future
performance can be endogenously controlled will be limited.
The major objective of analysing dynamic financial ratios
is to predict future values of the ratios. The general
approach to such predictions is to search for dynamic patterns
in the historic behavior of the ratios; knowledge of such
patterns can then be used in the prediction process. This
approach to the prediction of ratios rests on the assumption
that the underlying process generating the ratios is stable
over time, that is, the process continues to operate as it did
in the past. Dynamic patterns in the behaviour of ratios can
be determined by various statistical techniques, such as
plotting the data on scatter diagrams, serial correlation
analysis, and various transformations of the original data.
The best prediction model to be used depends on the
statistical nature of the process generating the ratios.
However, most processes in business and finance are very
complex and, in many cases not even well understood because of
the large number of factors and the complex interactions
involved.
306
For example, a firm's net income is usually effected by
1) Economy-Wide factors such as interest rate and
price-level fluctuations.
2) Industrial factors such as a change in demand for
the industry's product.
3) Firm's factors such as firm size and quality of
management.
Consequently the financial analyst would be interested in a
set of techniques and methods which enable him to study and
explain the crucial effects of these factors in order to
improve the behaviour of the financial ratios.
307
APPENDICES
308
P (.19.8)
•4 (.59.4)(Centroid point)
P3 (. 8 ' .1)P4 ( ' 9 i° 1)X(Rotated factor I)
.0 n
APPENDIX 1: GEOMETRIC PRESENTATION OF THE FACTOR MODEL
To understand the basis of factor analysis which is a
complex statistical technique, Comrey(1973) find it helpful to
employ an additional medium of representation of factor model.
In the geometric representation of factor model, a data
variable may be represented as a vector in a space of as many
dimensions as there are common factors(Fig.A11). In this
case, the length of the vector is h, the square root of h, the
communality. It is also possible to represent the data
variables in spaces of higher order, adding also a dimension
for each specific and error factor. If all of these factors
are included, h rises to 1.0 for each data variable and so
does the vector length.
Figure AllY(Rotated factorII)
1.0
As an illustration, Fig.All represents four data variables
as vectors in a two dimensional space. A vector is a line
extending from the origin to some point in space. The
coordinates of the end points of the vector are given. There
are two coordinates for each point because the vector are
represented in two dimensions.
P (.2 6)
309
By the Pythagorean theorem, the length of each vector in
Fig.All is given by the square root of the sum of squares of
its coordinates:
h =2+(.8)
2= \/.01+.64
1
2\/(.2)
2h = +(.6) \0 .04+.362
2\/(.8)
2h = +(.1) \/74-T7E—3
2 2h = \/(.9) +(.1) \iv .81+.014
= \/7-E- = . 806
= V770T .632
= V7i3- . 806
= V782—.906
For more than two dimensions, vector length is given by
2 2 2 2h=\/a +a +a + +a
1 2 3
Where the a values are the coordinates of the vector with3.
respect to the m reference axes or dimensions For more than
three dimensions of course, it is impossible to visualise the
results.
310
The scalar product of two vectors may be defined as follows:
a21
a22
[a a a ...a ] x a = [c] (Al2)11 12 13 lm 23
a2m
Where c=a a +a a +....+a a is a constant. The values a11 21 12 22 lm 2m 11
a , a ,...,a represent the coordinates of first vector, and12 13 lm
the values a , a , a ,...,a are the coordinates for the21 22 23 2m
second vector. Thus, the scalar products of all possible pairs
of four test vectors in Fig.All are given as follows:
311
Pair Row Column Scalar product
1,2 (.1 .8) x 1.21 =(.1x.2) + (.8x.6) =(.02+.48) =.50
I
.61
1,3 (.1 .8) x 1. 8 1=(.1x.8) + (.8x.1) =(.08+.08) =.16
I
.11
1,4 (.1 .8) x 1.91 = (.1x.9) + (.8x.1) =(.08+.09) =.22
I
.11
2,3 (.2 .6) x 1 .8 1=(.2x.8) + (.6x.1) =(.16+.06) =.24
I
.11
2,4 (.2 .6) x 1.91 =(.2x.9) + (.6x.1) =(.18+.06) =.24
I
.11
3,4 (.8 .1) x 1.91 =(.8x.9) + (.1x.1) =(.72+.01) =.73
Lets L represent the cosine of the angle between vector i andij
coordinate axis j. The value L is called the direction cosineij
of vector i with respect to coordinate (factor) axis j. If aij
is the coordinate of data vector i with respect to factor axis
j, and h is the length of vector i, then
L = a /h , L = a /h , ...,L = a /h11 11 1 12 12 1 lm lm 1
or
a = hL ,a = hL , ....,a =hL11 11]. 12 112 lm 1 lm
and
a = hL ,a = hL , ....,a =hL21 221 22 122 2m 1 2m
Substituting these values in Eq. (Al2) gives the following
312
= [c]
or
and
representation of the scalar product of two vectors:
[h L h L .... h L ] xiii i i2 urn
h Ljjl
h Lj i2
h L
jiff'
hhL L +hhL L + ...+hhL L -ci j il jl i j i2 j2 i j im jm
h h [L L + L L + ...+ L L ] = c (A13)i j il jl i2 j2 im jm
A theorem from analytic geometry that will not be proved
here states that the inner product of the direction cosines
for two vectors equals the cosine of the angle between the
vectors. Thus Eq. (A13) becomes
h h cos v =c (A14)ii ij
The scalar product between vectors i and j is also equal to the
correlation between them. The proof proceeds from
r =aa +aa + ....+ aij il j1 i2 j2 im jm
Dividing both sides of this equation by h and h gives
J
r /h h =a /h .a /h + a /h .a /h +...+a /h .a /hij i j il i jl j i2 i j2 j im i jm j
=LL +LL + ...+LL = cos vil jl i2 j2 im jm ij
Or
r = h h cos v (A14)ij i j ij
313
The application of the law of cosine will show that the
scalar products computed all pairs of vectors in the series of
above equations are the same as the results of multiplying the
length of two vectors by the cosine of the angle between them
as on the right hand side of Eq. (A14). For two of the
vectors, P1 and P2, for example the law of cosines states that
2 2 2h h cos v = 1/2(h +h -c )12 12 1 2
Where c is the distance between the vector end points and is
given by
2 2c = \//(X -X ) + (Y -Y )
12 12
Where (X ,Y ) and (X ,Y ) are the coordinates for P and P11 22 1 2
respectively, in Fig. All. For the first two vectors
2 2c = \//(.1 -.2) + (.8-.6) = \/.o1+.04 = \/7(7)3
Then, h h cos v = 1/2(.65 +.40 -.05) =.50. This value is the12 12
same as that obtained for the scalar product of vector 1 and 2.
Using the squared lengths of the vectors and scalar
products for all non-identical pairs of vectors in Fig. All,
the correlation matrix with communalities, as shown in Table
A15, is obtained for the four data variables.
314
the vectors in Fig. All.
Figure Al2
Cent oid vector n
x
315
Table A15 : A Centroid Factor Analysis
1
2
3
4
1 2 3 4
=
1
2
3
4
I II
I
II
1 2 3 4(.65)
.50
.16
.17
.50
(.40)
.22
.24
.16
.22
(.65)
.73
.17
.24
.73
(.82)
.578-.562
.531-.344
.687 .422
.765 .484
.578
-.562
.531
-.344
.687
.422
.765
.484
A'
R A
In Table A15, R is the correlation matrix; A is a factor
matrix, or a matrix of extracted factors derived from R; and
A is the transpose of A.
The values in parentheses along the main diagonal of R are the
communalities h obtained by squaring the vector lengths h .ii i
The off-diagonal elements of R are the correlations r amongii
the fictitious data variables, derived as scalar products of
If the X and Y coordinates for the four vectors in Fig.
All are averaged, the average X coordinate would be .5 and the
average Y coordinate would be .4. These two coordinates
locate the centroid point Pc(.5, .4). Centroid factor
analysis derives the factor loadings by obtaining the
perpendicular projections of the test vectors onto a line
extending from the origin through the centroid point Pc as in
Fig. All. The perpendicular projection of vector 1 on the
centroid vector is shown as line OA in Fig. Al2. Then
Cos v =OA/OP =a /hlc 1 1 1
Where a is the factor loading, or projection of vector 1 on1
the centroid vector. Using these equalities, the expression for
the factor loading a becomes1
a = h Cos v1 1 lc
Cosine v here is the cosine of the angle between 1 and thelc
centroid vector. The scalar product between vector 1 and the
centroid vector is:
(.1 .8) x 1.51 = (.1 x .5) + (.8 x .4) = .37 = h h Cos v
II 1 c lc
1.41
.37 = h (h Cos v ) = h ac 1 lc c 1
a = .37/h1 c
h = j
2 2(.5) + (.4) = .6403
C
a = .37/.6403 = .5781
316
Performing this operation for the other three vectors in Fig.
All gives a = . 531, a = . 687, and a = . 7652 3 4
The contribution of factor I to the R matrix is removed by the
equation R = R - A A i . R , the matrix of residuals after the1 11 1
extraction of factor I, is reproduced exactly by the product of
the second factor times its transpose. Or
=
(.65)
.50
.16
.17
(.65)
.50
.16
.17
.50
(.40)
.22
.24
R
.50
(.40)
.22
.24
.16
.22
(.65)
.73
.16
.22
(.65)
.73
.17
.24
.73
(.82)
.17
.24
.73
(.82)
_
.578
.531
.687
.765
A1
.334
.307
.397
.442
x [.578
.307 .397
.282 .365
.365 .472
.406 .526
.531 .687
At1
.442
.402
.526
.585
.765]
=
(.316)
.193
-.237
-.272
R
.193
(.118)
-.145
-.166
-.237 -.272
-.145 -.166
(.179) .204
.204 (.234)
A Al11
-.5621I
-.3441I
.4221x [-.562I
.4841
-.344 .422 .484]
R
A Ai1 2 2
In matrix terms, R - A A i = O. Since R is reproduced exactly1 22 1
by the second factor multiplied by its transpose, no more than
two factors are necessary to account for the original R matrix.
317
The sum of the original communality values is
.65 + .40 + .65 + .82 = 2.52
The sum of squares of the first factor loadings 1.6737,
plus the sum of squares of the second factor loadings, .8463
also equals 2.52 showing that all of the common factor
variance was extracted. The first factor was approximately
twice as large as the second factor since 1.6737 is roughly
twice as much as .8462.
318
APPENDIX 2: FACTOR ROTATION
Factor analysis in general and factor extraction methods in
particular do not provide a unique solution to the matrix
equation R = An'. One of the reasons is that the R matrix is
only approximately reproduced in practice and experimenters
may differ on how closely they feel they must approximate R.
This will lead to their using different numbers of factors.
Also, different methods of determining A may give slightly
different results. An even more important reason for lack of
unique solutions, however, is the fact that even for A
matrices of the same number of factors, there are infinitely
many different A matrices which will reproduce the R matrix
equally well.
a a V V11 12 11 12
a a I Cos v Sin vi V V21 22 xi 1= 21 22
a a I-Sin v Cos vi V V31
a a32
V31
V32
41 42 41 42
A A V
AA= V
If R = AA' then R = VV / since if we transpose the product A
A as
(A 6) = V / or A IN = V/
VV I = A A A'A'
but " Ai , is
319
1 Cos v Sin vi 'Cos v -Sin v1 11 01
1 1 x 1 1 = 1 11-Sin v Cos 111 'Sin v Cos v1 10 11
A Ai I
since multiplying by an identity matrix does not alter the
matrix, that is AIA 1 = AA'
The angle between the centroid axis and the Y axis in Fig.
All may be obtained as follows : The Y-axis vector terminus
has coordinates (0,1). The scalar product with the centroid
is given by
h h Cos v = (0 1) x 1•51 = ( 0x.5) + (lx.4) = .4y c yc 1 1
e
1.41
Cos v = .4/h h = .4/(1.0 x .6403) = .625y c
0 ,Hence v = 51 19 and the Sin v is .781. The angle of the
centroid axis with the X axis in Fig. All is 90 - 511 19' or
384' 41I .
To obtain a configuration of points corresponding to those
in Figure All using the coordinates of the data points from
the matrix of factor loadings A in Table A15, it is necessary
to reverse the direction of factor II in matrix A. This is
done by changing the signs of all the loadings in factor II,
matrix A, Table A15. Any factor may be reversed in direction
in this manner at any stage in the factor analytic process
without affecting the property that AA 4 reproduces the matrix
R.
320
• P2
( . 531'
e 344 )
.6 .8 1.0
38-1 Lfit
-P
3(.687,-.422)
• P4(.765,-.484)x
If the signs on the loadings for factor II in Table A15 are
reversed in this manner, the coordinates for the four data
vector with respect to the centroid axes become P1(.578,
:562), P2(.531, .344), P3(.687, -.322) and P4(.765, -.484).
Plotting these data points with respect to centroid factor
axes I and II yields Fig. A13. Only the end points of the
data vectors are plotted here. This is a more useful practice
than drawing in the full vector from the origin to each data-
vector end point.
Rotation of factor I away from factor II by an angle of 38
41 will bring it into coincidence with the old X axis (see
Fig. A13). At the same time, rotation of factor II an
equivalent amount toward factor I into coincidence with the
old Y axis. The matrix operations in Table Al2 show how this
rotation is carried out, transforming the coordinates with
respect to centroid axes (after reversing axis II) into the
coordinates with respect to the original X and Y coordinate
axes, respectively.
Fig. A13 Rotation from centroids to original coordinate axesII
n
321
Table A16 : Rotation of the Centroid Axis
Unrotated factor1 Transformation 1 Rotated factor
1 1
I II I(X) II(Y)
.578 .562 1 .1 .8
.531 .344 1 .781 .6251 2 .2 .6x 1
1=
.687 -.422 1-.625 .7811 3 .8 .1
.765 -.484 4 .9 .1
A A V
322
APPENDIX 3: FACTOR EXTRACTION BY THE CENTROID METHOD
The centroid method of factor extraction (Thurstone, 1947;
Fruchter, 1954) is probably the best known of all methods of
factor extraction. The centroid method has the advantage of
being easily conceptualized in terms of the geometric model of
factor analysis.
In Fig. All the centroid point was located by averaging
all the data- vector X coordinates to get the X coordinate of
the centroid point and all the data-vector Y coordinates to
get the Y coordinate of the centroid point. If there had been
more than two dimensions involved, the third, fourth, and
other coordinates would have been averaged to get the
remaining coordinates of the centroid point. Imagine a new
set of coordinate axes at right angles to one another placed
in such a way that one of the axes goes through the centroid
point, as Fig. A13. Suppose that the coordinates of the data
vectors with respect to these new coordinate axes are known
(actually they are the centroid loadings given in Table Al2.
With these new coordinates, it would be possible to recompute
the coordinates of the centroid point with respect to the new
coordinate axes. Since one of the axes, say the first one,
goes right through the centroid point, however, the
coordinates of the centroid point with respect to the new axes
will be
323
in— E a ,0,0, 0 (1)n i=1 il
That is, since the centroid point falls on the first
coordinate axis, its coordinates with respect to all axes will
be zero. In Fig. A13 there are only two factors, hence only
two coordinates. Therefore, the coordinates of the centroid
point can be found by averaging the loadings with respect to
the new axes, the first of which goes through the centroid
point. These loadings are given in Table Al2. Thus,
averaging the coordinates in the first column gives 1/
4(.578+.531+.687+.765)=.64 as the first coordinate of the
centroid point. The second coordinate of the centroid point
is the average of the second column of the A matrix in Table
A16, that is, 1/4(.562+.344 —.422—.484) =0, which is in
accordance with Eq.(1). To use Eq.(1) for deriving an
expression for the centroid loadings, Eq.(2) serves as a
starting point:
r =a +a a +
+a a
(2)ij il i2 j2 im jm
Summing both sides of Eq.(2) over i gives
n n n nE. r = a 1: a + a E a + ....+ a /:: a (3)i=1 ij jl 1 =1 il J2 1=1 12 jm i =1 im
Summing both sides of Eq.(3) over j gives
n n n n n n n nE E r = Ea Ea + a La + ... + L a E a (4)j=1 i=1 ij j=1 jl i=1 il J=1 j2 i=1 12 j=1 Jm i=1 im
324
But
Z: a = a (5)j=1 jk i=1 ik
Since both terms in Eq. (5) are merely the sum of the entries
in the kth column of the A matrix of factor loadings.
Substituting (5) in (4), therefore, gives
2 n 2 n 2r =(Z: a ) +(E: a ) + +(Z.: a ) (6)
j=1 1-1 ij i-1 ii i-1 i2 i=1 im
By Eq. (1), however, all the sums on the right-hand side of Eq
(6) are zero except the first. Eq. (6) reduces, therefore, to
n n ii 2
r a )
(7)j=1 i=1 ij i=1
Also by Eq. (1) the sums of loadings for the second and
subsequent factors are zero, since the second and subsequent
coordinates of the centroid point derived from these sums are
zero, making Eq. (3) reduce to
Cr = a a (8)i -1 ij j1 i=1
Taking the square root of both sides of Eq.(7) and substituting
in Eq. (8) gives
n n= a \ Er
(9)i=1 ij j1 j=1 i=1 ij
Solving for a givesjl
r1=1 ij
a =
(10)jl
n n
rj=1 1=1 ij
325
Eq. (10) gives the formula for a centroid loading for
variable j on factor 1. To get the centroid factor loading
for data variable 1, for example, Eq. (10) calls for the
following steps:
1) Add up the entries in the first column of the
correlation matrix, including the diagonal cell.
2) Divide this number by square root of the sum of
all the entries in the entire correlation matrix,
including the diagonal cells.
For the second data variable, compute the sum of the
entries in the second column of the correlation matrix and
divide by the same square root term as for first data
variable. Continue this for all columns of the R matrix.
Thus, the computation steps involve computing the sums of the
columns of the R matrix, adding these column sums to get the
sum of all entries, taking the square root, and dividing this
square root into each column sum.
Applying these steps to the four-variable correlation
matrix in Table All gives the results in Table A17. The
contribution of factor 1 must be removed.
Table A17: Correlation Matrix
1 2 3 41 (.65) .50 .16 .172 .50 (.40) .22 .243 .16 .22 (.65) .73 T = E r = 6.564 .17 .24 .73 (.82)t 1.48 1.36 1.76 1.96 \J 2.56125a .578 .531 .687 .765i 1/\/Y— = . 3904
326
From the correlation matrix by the operation R-AlAq , as
shown before. The results of this operation give the matrix
R1, the matrix of first factor residuals shown in Table A18.
Note that in Table A18 the columns in each case add up to
zero, within the limits of rounding error. As a check, the
rows should be added also, to make sure that the row totals
equal the column totals as required for a symmetric matrix.
Table A18: First Factor Residuals
1234Sum
0
1(.316).193
-.237-.272.000
-.316
2.193
(.118)-.145-.166.000
-.118
3-.237-.145(.179).204.000
-.178
4-.272-.166.204
(.234).000
-.234
It was established in Eq. (1) that the sum of the loadings
on the second and subsequent centroid factors is zero, which
provides the basis for a check on the computations of the
first factor residuals used above. Since the sums of the
columns are zero, however, it is clearly impossible to apply
the steps used for computing the first centroid factor to the
matrix of residuals given in Table A18. It is necessary first
to carry out a process of reflecting the residuals to get rid
of as many negative signs as possible in the matrix of first
factor residuals.
327
First Factor Residuals after Reflecting Variable 1
1 2 3 41 (.316) -.193 .237 .2722 -.193 (.118) -.145 -.1663 .237 -.145 (.179) .2044 .272 -.166 .204 (.234)Sums withoutcommunalities .316 -.504 .296 .310
Reflected 1st Factor Residuals and 2nd Factor Calculations
T = 3.281
VT = 1.811351
1A117= .55207
1 2 3 41 (.316) .193 .237 .2722 .193 (.118) .145 .1663 .237 .145 (.179) .2044 .272 .166 .204 (.234)t 1.018 .622 .764 .877a .562 .343 .422 .484
328
COMPUTER PROGRAMS
329
COMPUTER PROGRAMS
C This program reads 23 financial data from EXSTAT tape
for all the British companies which have 14 years
available data.
Data
88 C2 B29
C31 C105 C114 C157 C115 C158 C49 C111 C91 C122 C43
C34 C57 C47 C48 C52 C50 C42 C123 C124 C151 C132 C106
End
Select
(B9 EQ EX AND B29 EQ 14)
END
C This program reads the above data and indicates
companies which have missing values.
Program MISSVAL
Dimension X(23), KX(23)
Character 35 company
Integer date, NY
Open (1, file= 'datal', status='old')
DO 10 I =1, 23
10 KX(1) = 0
20 Read (1„ END=99) Company, date, NY, (X(I), 1=1,
23)
DO 30 I=1, 23
30 If (X(I).GT.9999999999.0) KX(I)=KX(I)+1
DO 40 K=1, NY-1
330
Read (1, ) Company, date, NY, (X(I), I =1, 23)
DO 50 I=1, 23
50 If (X(I).GT.9999999999.0) KX(I)=KX(I)+1
40 Continue
GO TO 20
99 Write ( , ) (KX(I), I =1, 23)
STOP
END
C This program eliminates the missing values from the
extracted financial data.
Program ELMISVA
Dimension X(23)
Character 35 Company
Integer Date, NY
Open (1, file='datal', status='old')
30 Read (1„ END=99) Company, date, NY, (X(I), I=1,
23)
DO 20 I=1, 23
20 IF (X(I).GT.9999999999.0) GO TO 60
Write (2, 200) Company, date, NY, (X(I), I=1, 23)
200 Format (A35, 1X, 14, 1X, 12/14(5F15.0/))
60 Continue
DO 40 K=1, NY-1
Read (1, ) Company, date, NY, (X(I), I=1, 23)
DO 50 I =1, 23
50 IF (X(I).GT.9999999999.0) GO TO 80
40 Write (2, 200) Company, date, NY, (X(I), I=1, 23)
80 Continue
331
GO TO 30
99 STOP
END
C This program calculates ratios for all the cases based
on year by year activities from the original
financial data.
Program RATIOS
Character Company 35
Integer date, NY
Real Invent, NI
Open (1, File= 'tape2', status='old')
Rewind 1
Rewind 2
Rewind 3
Rewind 4
Rewind 5
Rewind 6
Rewind 7
Rewind 8
Rewind 9
Rewind 10
Rewind 11
Rewind 12
Rewind 13
Rewind 14
Rewind 15
Rewind 16
Rewind 17
332
10 Read (1, 200, END=99) Company, date, NY, Sales,
Invent, CA, CL, TA, TL, RE, Cash, FA, PS, NI, PBT,
TI, PD, CD, Depre, El, TT, OC, DC, Credits, SF,
Debts
R1 - NI/TA
R2 = NI/SF
R3 = NI/CA
R4 = NI/(TA-SF)
R5 = NI/SALES
R6 = NI/FA
R7 = (PBT+TI)/TA
R8 - (PBT+TI)/SALES
R9 = (PBT+TI)/SF
R10 = (PBT+DEPRE)/SF
R11 = SALES/TA
R12 = SALES/SF
R13 = SALES/CA
R14 - SALES/(TA-SF)
R15 = NI/(TA-CL)
R16 = (PD+CD)/(NI+DEPRE+EI)
R17 = (DEPRE+TI+TT)/(PS+0C+DC)
R18 = TI/SF
R19 = TI/(PBT+TI)
R20 = CD/NI
R21 = CA/CL
R22 = CL/SF
R23 = (CA-CL)/TA
R24 = CA/TA
R25 = CA/SALES
333
R26 = CA/SF
R27 = (CA-CL)/FA
R28 = (CA-CL)/SALES
R29 = (CA-INVENT)/CL
R30 = (CA-INVENT)/SALES
R31 = (CA-INVENT)/TA
R32 = (RE+DEPRE+EI)/(TA-CL)
R33 = DEBTS/SF
R34 = CASH/TA
R35 = CASH/SALES
R36 = CASH/CL
R37 = CREDITS/SF
R38 = TL/SF
R39 = SF/FA
R40 = (NI+DEPRE+EI)/(TA-SF)
R41 = (NI+DEPRE+EI)/CL
R42 = (NI+DEPRE+EI)/SALES
R43 = (NI+DEPRE+EI)/SF
R44 = (NI+DEPRE+EI)/TA
R45 = INVENT/SALES
R46 = INVENT/CA
R47 = INVENT/TA
R48 = INVENT/CL
R49 = INVENT/(TA-CL)
R50 = CL/CA
R51 = CL/TA
R52 = TL/CA
R53 = RE/SF
R54 = (CA-CL)/SF
334
R55 0 RE/TA
R56 0 CASH/CA
R57 0 SF/TA
R58 0 FA/SF
R59 = FA/TA
R60 = RE/NI
R61 (TL+PS)/TA
R62 = SF/(TA-SF)
R63 = CL/(TA-SF)
R64 = FA/(TA-CL)
R65 = OC/SF
IF (DATE.EQ.1971) IC = 1
IF (DATE.EQ.1972) IC = 2
IF (DATE.EQ.1973) IC = 3
IF (DATE.EQ.1974) IC = 4
IF (DATE.EQ.1975) IC = 5
IF (DATE.EQ.1976) IC = 6
IF (DATE.EQ.1977) IC = 7
IF (DATE.EQ.1978) IC = 8
IF (DATE.EQ.1979) IC = 9
IF (DATE.EQ.1980) IC = 10
IF (DATE.EQ.1981) IC = 11
IF (DATE.EQ.1982) IC = 12
IF (DATE.EQ.1983) IC = 13
IF (DATE.EQ.1984) IC = 14
IF (DATE.EQ.1985) IC = 15
WRITE (IC, 100) COMPANY DATE R1 R2 R3 R4 R5 R6 R7 R8
R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21
R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34
335
R35 R36 R37 R38 R39 R40 R41 R42 R43 R44 R45 R46 R47
R48 R49 R50 R51 R52 R53 R54 R55 R56 R57 R58 R59 R60
R61 R62 R63 R64 R65
N = NY
DO 20 I= 1, N-1
Read (1, 200, END=99) Company, date, NY, Sales,
Invent, CA, CL, TA, TL, RE, Cash, FA, PS, NI, PBT,
TI, PD, CD, Depre, El, TT, OC, DC, Credits, SF,
Debts
R1 = NI/TA
R2 = NI/SF
R3 = NI/CA
R4 = NI/(TA-SF)
R5 = NI/SALES
R6 = NI/FA
R7 = (PBT+TI)/TA
R8 = (PBT+TI)/SALES
R9 = (PBT+TI)/SF
R10 = (PBT+DEPRE)/SF
Rll = SALES/TA
R12 = SALES/SF
R13 = SALES/CA
R14 = SALES/(TA-SF)
R15 = NIRTA-CL)
R16 = (PD+CD)/(NI+DEPRE+EI)
R17 = (DEPRE+TI+TT)/(PS+0C+DC)
R18 = TI/SF
R19 = TI/(PBT+TI)
R20 = CD/NI
336
R21 = CA/CL
R22 = CL/SF
R23 = (CA-CL)/TA
R24 = CA/TA
R25 = CA/SALES
R26 = CA/SF
R27 = (CA-CL)/FA
R28 = (CA-CL)/SALES
R29 = (CA-INVENT)/CL
R30 = (CA-INVENT)/SALES
R31 = (CA-INVENT)/TA
R32 = (RE+DEPRE+EI)/(TA-CL)
R33 = DEBTS/SF
R34 = CASH/TA
R35 = CASH/SALES
R36 = CASH/CL
R37 = CREDITS/SF
R38 = TL/SF
R39 = SF/FA
R40 = (NI+DEPRE+EI)/(TA-SF)
R41 = (NI+DEPRE+EI)/CL
R42 = (NI+DEPRE+EI)/SALES
R43 = (NI+DEPRE+EI)/SF
R44 = (NI+DEPRE+EI)/TA
R45 = INVENT/SALES
R46 = INVENT/CA
R47 = INVENT/TA
R48 = INVENT/CL
R49 = INVENT/(TA-CL)
337
R50 = CL/CA
R51 ... CL/TA
R52 = TL/CA
R53 = RE/SF
R54 = (CA-CL)/SF
R55 = RE/TA
R56 = CASH/CA
R57 = SF/TA
R58 = FA/SF
R59 = FA/TA
R60 = RE/NI
R61 = (TL+PS)/TA
R62 = SF/(TA-SF)
R63 = CL/(TA-SF)
R64 = FA/(TA-CL)
R65 = OC/SF
IF (DATE.EQ.1971) IC = 1
IF (DATE.EQ.1972) IC = 2
IF (DATE.EQ.1973) IC = 3
IF (DATE.EQ.1974) IC = 4
IF (DATE.EQ.1975) IC = 5
IF (DATE.EQ.1976) IC = 6
IF (DATE.EQ.1977) IC = 7
IF (DATE.EQ.1978) IC = 8
IF (DATE.EQ.1979) IC = 9
IF (DATE.EQ.1980) IC = 10
IF (DATE.EQ.1981) IC = 11
IF (DATE.EQ.1982) IC = 12
IF (DATE.EQ.1983) IC = 13
338
IF (DATE.EQ.1984) IC = 14
IF (DATE.EQ.1985) IC = 15
20 WRITE (IC, 100) COMPANY DATE R1 R2 R3 R4 R5 R6 R7 R8
R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21
R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34
R35 R36 R37 R38 R39 R40 R41 R42 R43 R44 R45 R46 R47
R48 R49 R50 R51 R52 R53 R54 R55 R56 R57 R58 R59 R60
R61 R62 R63 R64 R65
GO TO 10
100 FORMAT (A35, 2X, I4/9(8(F9.4,1X)/))
200 FORMAT (A35, 1X, 14, 1X, 12/14(5F15.0/))
99 STOP
END
C This SPSS package reads the above ratios and analyse
them by Factor Analysis technique.
Run Name Factor Analysis
Data List Fixed (10)/
1 company 1-8 (A), year 38-41/
2 R1 TO R8 1-80/
3 R9 TO R16 1-80/
4 R17 TO R24 1-80/
5 R25 TO R32 1-80/
6 R33 TO R40 1-80/
7 R41 TO R48 1-80/
8 R49 TO R56 1-80/
9 R57 TO R64 1-80/
10 R65 1-10
N OF CASES UNKNOWN
339
FACTOR VARIABLES=R1 TO R65
OPTIONS 7,10,11
STATISTICS 1,2,4,5,6,7
This SPSSX package reads the selected ratios and
COMPUTE the Y-value for each company.
TITLE COMPANL
FILE HANDLE TAPE21
DATA LIST FILE=TAPE21 FIXED (3) /1 COMPANY 1-8 (A),
YEAR 13-16/
2 R1 TO R8 1-80/
3 R9 TO R10 1-20
COMPUTE Y = 8.344R1 + 1.218R2 + 4.235R3 + .3R4 +
5.524R5 + .691R6 + .16R7 + 4.394R8 - 2.969R9 +
4.81R10 - 1.989
FILE HANDLE KOBRA
PRINT OUTFILE = KOBRA/ COMPANY, Y (A8,F9.4)
EXECUTE
FINISH
C This program reads the KOBRA file and recode it company
by company.
program recode
character 10 comp, compl
integer year, c
open (3, file='kobra')
open (4, file='farhood')
compl= 'A. C. Cars'
c= 0
5 read (3, 10, end=999) comp, year, Y
340
If (compl.NE.comp) then
compl = comp
c = c+1
end if
write (4, 20) comp, year, Y, c
Go to 5
999 close (3)
close (4)
stop
10 format (A10, 14, 1X, F8.3)
20 format (A10, 14, 1X, F8.3, 1X, 13)
end
C This SPSSX package reads the Farhood file and plots Y-
value against years.
FILE HANDLE FARHOOD
DATA LIST FILE=FARHOOD/COMPANY 1-10 (A), YEAR 11-15,
Y 16-23, C 24-27
SORT CASES BY C
SPLIT FILE BY C
PLOT TITLE = 'PLOT YEAR AND RATIOS'
/VERTICAL =
/HORIZONTAL = YEAR
EXECUTE
FINISH
C This Fortran program reads the provided data and plots
four different groups of data on four different
scales in one page by the SIMPLE PLOT.
PROGRAM PLOT14
341
DIMENSION XARR(14), YlARR(14), Y2ARR(14), Y3ARR(14),
Y4ARR(14), Y5ARR(14), Y6ARR(14), Y7ARR(14),
Y8ARR(14)
REWIND 1
OPEN (UNIT=1,FILE='KOBRA')
READ (1, ) (XARR(I), YlARR(I), Y2ARR(I), Y3ARR(I),
Y4ARR(I), Y5ARR(I), Y6ARR(I), Y7ARR(I), Y8ARR(I),
I=1, 14)
CALL PAGE (20.0, 29.7)
CALL PICSIZ (8.0, 8.0)
CALL MARGIN (1.0)
CALL GROUP (2, 2)
CALL SCALES (1972.0, 1985.0, 1, 28.0, 45.0, 1)
CALL AXES7 ('YEAR', 'CURRENT ASSETS')
CALL BRKN CV (XARR, YlARR, 14, 6)
CALL BRKN CV (XARR, Y2ARR, 14, 0)
CALL SCALES (1972.0, 1985.0, 1, 67.0, 78.0, 1)
CALL AXES7 ('YEAR', 'CURRENT LIABILITIES')
CALL BRKN CV (XARR, Y3ARR, 14, -1)
CALL BRKN CV (XARR, Y4ARR, 14, -6)
CALL SCALES (1972.0, 1985.0, 1, 56.0, 9 .0, 1)
CALL AXES7 ('YEAR', 'NET INCOME')
CALL BRKN CV (XARR, Y5ARR, 14, -5)
CALL BRKN CV (XARR, Y6ARR, 14, 5)
CALL SCALES (1972.0, 1985.0, 1, 238.0, 458.0, 1)
CALL AXES7 ('YEAR', 'CASH')
CALL BRKN CV (XARR, Y7ARR, 14, -2)
CALL BRKN CV (XARR, Y8ARR, 14, 4)
CALL TITLE7 ('L', 'C', 'COMPANY'S NAME')
342
CALL SET KY ('L', 'R', 8, 6)
CALL LINE K7 (0, 'CA')
CALL LINE K7 (6, 'OCA')
CALL LINE K7 (-6, 'CL')
CALL LINE K7 (-1, 'OCL')
CALL LINE K7 (5, 'NI')
CALL LINE K7 (-5, 'ONI')
CALL LINE K7 (4, 'CASH')
CALL LINE K7 (-2, 'OCASH')
CALL ENDPLT
STOP
END
343
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344
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