applicability of logistic regression analysis in...
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APPLICABILITY OF LOGISTIC REGRESSION ANALYSIS IN PREDICTING
FINANCIAL DISTRESS FOR FIRMS LISTED AT THE NAIROBI SECURITIES
EXCHANGE
BY
WARUTERE JOSPHINE NYAKIO
REG NO. D61/63326/2011
A RESEARCH PROJECT SUBMITTED IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE AWARD OF THE DEGREE OF MASTER OF
BUSINESS ADMINISTRATION, UNIVERSITY OF NAIROBI
NOVEMBER 2013
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DECLARATION
This research project is my original work and has not been presented for award of degree
or examination in any other University.
Signed ………………………………… Date ………………………..
WARUTERE JOSPHINE NYAKIO
This research project has been submitted for examination with my approval as the
University supervisor.
Signed ………………………………… Date ………………………..
DR. JOSIAH ADUDA
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DEDICATION
I would like to dedicate this work to my parents Mr. and Mrs. Charles Warutere Gicheru
who have invested and believed in me over the years and helped me to appreciate hard
work, dedication and responsibility. To my sisters and brother, I am greatly indebted for
your concern and prayers.
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ACKNOWLEDGEMENT
First and foremost, I give special thanks to God for His unrelenting love, wisdom,
strength and grace that enabled me to complete this research work successfully. I also
wish to thank my family and friends for offering moral support and encouragement
throughout the duration of the study.
I sincerely appreciate my supervisor, Dr. Josiah Aduda who supervised the writing of this
research study. Through his guidance, encouragement, prompt feedback and patience, I
was able to complete this study.
The successful completion of this study received valuable contribution from my college
mates with whom we held useful and insightful discussions throughout the course that
further opened up my mind to practical experiences at work places and businesses. All
these were relevant and helped me in appreciating practical application for the course. I
pray that God blesses their lives in amazing ways.
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ABSTRACT
Logistic regression combines independent variables to estimate the probability that a
particular event will occur, i.e. a subject will be a member of one of the groups defined
by the dichotomous dependent variable. If the probability for group membership in the
modeled category is above some cutoff point, the subject is predicted to be a member of
the modeled group. If the probability is below the cutoff point, the subject is predicted to
be a member of the other group.
This study assesses the probability of firm failure, a year before failure, using logistic
regression model that was developed by Ohlson (1980). The study utilized secondary
data collected from Capital Markets Authority and Nairobi Securities Exchange. The
required data was collected from financial statements of a sample of sixteen companies;
ten of which were in good financial health and six of which were financially distressed.
The study covered a range of 14 years from 1997 to 2011.
The results of this study show that logistic regression analysis is applicable in 9 out of 10
firms analyzed which indicates a 90% successful application of the Ohlson (1980) model
used in the study. The model is found to be successful in predicting business failure one
year before it occurs. The study concludes that the logistic regression analysis model
developed by Ohlson (1980) is applicable in predicting financial failure of companies and
is a useful tool for investors in the Kenyan market.
This study contributes to the literature by expanding the application of Ohlson (1980)
logit model to Kenya publicly listed companies. It provides applicable measures for
predicting firm delisting events in Kenya’s stock markets. The study recommends that the
assessment be extended is to test bankruptcy prediction models to the non-listed,
relatively smaller turnover sized firms where the incidence of business failure is greater
than larger corporations.
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TABLE OF CONTENTS
DECLARATION .......................................................................................................... ii
DEDICATION ............................................................................................................. iii
ACKNOWLEDGEMENT ............................................................................................iv
ABSTRACT ................................................................................................................... v
CHAPTER ONE ............................................................................................................ 1
INTRODUCTION ....................................................................................................................... 1
1.1 Background to the Study ............................................................................................... 1
1.1.1 Financial Distress ................................................................................................... 2
1.1.2 Logistic Regression Analysis .................................................................................. 4
1.1.3 Logistic Regression Analysis and Financial Distress ............................................... 4
1.1.4. Financial Distress in Nairobi Securities Exchange .................................................. 6
1.2 Research Problem.......................................................................................................... 7
1.3 Research Objective ...................................................................................................... 10
1.4 Value of the Study ....................................................................................................... 10
CHAPTER TWO ......................................................................................................... 13
LITERATURE REVIEW ............................................................................................................. 13
2.1 Introduction ................................................................................................................ 13
2.2 Empirical Literature ..................................................................................................... 13
2.3 Review of Local research on predicting financial distress ................................... 16
2.4 Early warning signals of financial distress ..................................................................... 19
2.5 The recovery process ................................................................................................... 20
2.6 Summary of the Literature Review............................................................................... 21
CHAPTER THREE ..................................................................................................... 22
RESEARCH METHODOLOGY ................................................................................................... 22
3.1 Introduction ................................................................................................................ 22
3.2 Research Design .......................................................................................................... 22
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3.3 Population ................................................................................................................... 22
3.4 Sample Design ............................................................................................................. 23
3.5 Data Collection ............................................................................................................ 24
3.6 Data Analysis ............................................................................................................... 24
3.7 Data Validity and Reliability ......................................................................................... 26
CHAPTER FOUR ....................................................................................................... 27
DATA ANALYSIS AND PRESENTATION OF FINDINGS .................................... 27
4.1 Introduction ........................................................................................................... 27
4.2 Data Presentation .................................................................................................. 27
4.2.1 Total Kenya Limited .................................................................................................. 28
4.2.2 British American Tobacco Limited ............................................................................. 28
4.2.3 East African Breweries Limited.................................................................................. 29
4.2.4 Nation Media Group ................................................................................................. 29
4.2.5 Car & General (Kenya) Limited .................................................................................. 29
4.2.6 East African Cables Limited ....................................................................................... 30
4.2.7 Sameer Africa Limited ............................................................................................... 30
4.2.8 Sasini Tea and Coffee Limited ................................................................................... 30
4.2.9 Crown Berger Limited ............................................................................................... 31
4.2.10 Scangroup Limited .................................................................................................. 31
4.2.11 CMC Holdings Limited ............................................................................................. 32
4.2.12 Uchumi Supermarkets Limited ................................................................................ 32
4.2.13 A. Baumann & Company Limited............................................................................. 33
4.2.14 Kenya Orchards Limited .......................................................................................... 33
4.2.15 Theta Tea Factory ................................................................................................... 33
4.2.16 Dunlop Kenya Limited ............................................................................................. 34
4.3 Summary and Interpretation of the findings ........................................................ 34
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CHAPTER FIVE ......................................................................................................... 37
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS ................................ 37
5.1 Summary ..................................................................................................................... 37
5.2 Conclusions ................................................................................................................. 38
5.3 Policy Recommendations ............................................................................................. 39
5.4 Limitations of the study ............................................................................................... 40
5.5 Suggestions for further study ....................................................................................... 41
REFERENCES ............................................................................................................ 43
APPENDICES ............................................................................................................. 50
Appendix 1: TOTAL KENYA LIMITED ................................................................................... 50
Appendix 2: BRITISH AMERICAN TOBACCO LIMITED .......................................................... 51
Appendix 3: EAST AFRICANBREWERIES LIMITED ................................................................ 52
Appendix 4: NATION MEDIA GROUP .................................................................................. 53
Appendix 5: CAR & GENERAL (KENYA) LIMITED .................................................................. 54
Appendix 6: EAST AFRICAN CABLES LIMITED ...................................................................... 55
Appendix 7: SAMER AFRICA LIMITED ................................................................................. 56
Appendix 8: SASINI TEA AND COFFEE LIMITED ................................................................... 57
Appendix 9: CROWN BERGER LIMITED............................................................................... 58
Appendix 10: SCANGROUP LIMITED .................................................................................. 59
Appendix 11: CMC HOLDINGS LIMITED .............................................................................. 60
Appendix 12: UCHUMI SUPERMARKETS LIMITED ............................................................... 61
Appendix 13: A. BAUMANN & COMPANY LIMITED............................................................. 62
Appendix 14: KENYA ORCHARDS LIMITED .......................................................................... 63
Appendix 15: THETA TEA FACTORY .................................................................................... 64
Appendix 16: DUNLOP KENYA LIMITED .............................................................................. 65
Appendix 17: Companies listed on the NSE ........................................................................ 66
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LIST OF ABBREVIATIONS
CMA- Capital Markets Authority
EBITDA- Earnings before interest, tax, depreciation, and
amortization
GNP- Gross National Product
JSE- Johannesburg Securities Exchange
LA- Logit Analysis
LRA- Logistic Regression Analysis
MDA- Multiple Discriminant Analysis
NSE- Nairobi Securities Exchange
RP- Recursive Partitioning
UA- Univariate Analysis
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CHAPTER ONE
INTRODUCTION
1.1 Background to the Study
The prediction of firm financial distress has been of considerable interest to accountants
and financial economists over the last three decades (Altman, 1993). The continual
development of conceptually richer and more accurate forecasting models is of
importance to regulators, practitioners and academics (Shumway, 2001). Distress
forecasts are now widely used for a range of purposes including monitoring of the
solvency of financial and other institutions by regulators, assessment of loan security,
going concern evaluations by auditors, the measurement of portfolio risk and the pricing
of defaultable bonds, credit derivatives, and other securities exposed to credit risk (Scott,
1981).
In the field of corporate finance, any individual or organization (investor or credit
institution) that examines the possibility of knitting up a relationship with a firm would
be interested in determining its performance and viability, and predicting any possible
problems that the firm may face in its operations. Prediction of financial distress therefore
is a much needed technique in determining business failure.
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1.1.1 Financial Distress
Financial distress is a broad concept that comprises several situations in which firms face
some form of financial difficulty. The most common terms used to describe these
situations are bankruptcy, failure, insolvency and default. These terms provide a slightly
different definition connected with the specific interest or condition of the firms under
examination. Altman (1993) provided a complete description and definition of these
terms. Bankruptcy identifies mostly with the legal definition of financial distress. As
pointed out by Theodossiou et al. (1996), many financially distressed firms never file for
bankruptcy due to acquisition or privatization, whereas healthy firms often file for
bankruptcy to avoid taxes and expensive lawsuits.
Altman (1993) defines failure as the situation where the realized rate of return on
invested capital, with allowances for risk consideration, is significantly and continually
lower than prevailing rates of similar investments. This is a term of an economic sense
and does not indicate the discontinuity of a firm. Insolvency illustrates a negative
performance indicating liquidity problems. Insolvency in a bankruptcy sense indicates
negative net worth. Finally, default refers to a situation where a firm violates a condition
of an agreement with a creditor and can cause a legal action. All these situations result in
a discontinuity of the firm’s operations, unless proper measures are employed. To
overcome the differences among these definitions, the more general term “financial
distress” will be used throughout this study to describe the situation where a firm cannot
pay its creditors, preferred stock shareholders, suppliers, etc., or the firm goes bankrupt
according to the law.
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The signs of potential business failure are evident before the actual failure occurs. As
long as liquidity in capital markets and credit risk appetite amongst lending institutions
are high, investors’ expectations are positive. Thus many firms use borrowed funds to
expand and grow their businesses. In pursuit of growth, many highly leveraged firms are
usually able to renegotiate debt covenants and obtain additional funding. Thus, the very
high volume of liquidity continues to keep potentially struggling companies afloat for a
while. As soon as companies have reached a certain level of leverage but do not perform
to their business plans, default can happen even in a booming economic environment.
Thus, the early prediction of distress is essential for investors, managers, shareholders,
the government, suppliers, customers, employees and lending institutions who intend to
protect their financial investments from occurrence of losses.
O’Leary (2001) argues that Prediction of bankruptcy is probably one of the most
important business decision-making problems. Affecting the entire life span of a
business, failure results in a high cost from the collaborators (firms and organizations),
the society, and the country’s economy (Ahn, Cho, and Kim, 2000). Thus, the evaluation
of business failure has emerged as a scientific field in which many academics and
professionals have studied to find other optimal prediction models, depending on the
specific interest or condition of the firms under examination.
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1.1.2 Logistic Regression Analysis
Logistic Regression Analysis (logit analysis) involves the determination of conditional
probabilities of variables in a sample using the logistic regression model (logit model).
Logistic regression allows one to predict a discrete outcome, such as group membership,
from a set of variables that may be continuous, discrete, dichotomous, or a mix of any of
these. Generally, the dependent or response variable is dichotomous, such as
presence/absence or success/failure.
Logistic regression combines independent variables to estimate the probability that a
particular event will occur, i.e. a subject will be a member of one of the groups defined
by the dichotomous dependent variable. If the probability for group membership in the
modeled category is above some cutoff point, the subject is predicted to be a member of
the modeled group. If the probability is below the cutoff point, the subject is predicted to
be a member of the other group. For any given case, logistic regression computes the
probability that a case with a particular set of values for the independent variable is a
member of the modeled category.
1.1.3 Logistic Regression Analysis and Financial Distress
Ohlson (1980) pioneered the application of Logistic Regression Analysis in prediction of
bankruptcy. He described the Logit model as a non-linear transformation of the linear
regression and a technique that weights independent variables and assigns a score. The
logit approach incorporates non-linear effects and uses the logistic cumulative
distribution function to maximize the joint probability of default for the distressed firms
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and the probability of non-failure for the healthy companies in a sample. Much of the
early research in the area of financial distress focused on MDA and then in later years on
Logit Analysis (LA).
Logit analysis is known to overcome some problems associated with MDA, the most
popular technique in bankruptcy studies. Some of the shortcomings as identified by
Ohlson include: first, there are certain statistical requirements imposed on the
distributional properties of the predictors e. g. the variance- covariance matrices of the
predictors should be the same for both groups (failed and non-failed firms); secondly, the
output of the application of an MDA model is a score which has little intuitive
interpretation since it is basically an ordinal ranking (discriminatory) device; thirdly, the
matching procedure whereby failed and non-failed firms are matched according to
criteria such as size and industry tend to be somewhat arbitrary. Ohlson argues that it
would be more fruitful to include variables as predictors rather than to use them for
matching purposes. Logit analysis seeks to estimate the probability that a firm belonging
to a pre-specified population fails within some pre-specified time period; without having
to make assumptions regarding prior probabilities of bankruptcy.
The logit model however suffers a shortcoming with respect to data collection of
bankrupt firms. According to Ohlson, realistic evaluation of a model’s predictive
relationships requires that the predictors are (would have been) available for use prior to
the event of failure. The shortcoming arises because annual audited reports would not be
publicly available at the end of the financial year; since audit takes place the following
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year. The timing issue can be expected to be serious for firms which have a large
probability of failure in the first place. Another researcher who used conditional
probability models, and more specifically Logit Analysis, to predict financial distress was
Zavgren (1985). She argued that models which generated a probability of failure were
more useful than those that produced a dichotomous classification as with the MDA.
1.1.4. Financial Distress in Nairobi Securities Exchange
The Nairobi Securities Exchange (NSE) is licensed and regulated by the Capital Markets
Authority (CMA). It has the mandate of providing a trading platform for listed securities
and overseeing its Member Firms. Currently, there are 62 listed firms in various sectors
of the economy. A number of companies have, over the past three decades, faced
financial difficulties leading to suspension of their stocks, delisting and restructuring. The
most recent cases of financial distress among firms listed on the NSE are those faced by
Uchumi Supermarkets and CMC Motors. During the early 2000’s Uchumi encountered
financial difficulties and was placed in receivership in June 2006. Uchumi’s shares were
suspended from trading at the bourse in mid 2006 after it plunged into financial
difficulties following an aggressive expansion plan that pushed it into the loss making
territory. A receiver manager was appointed and undertook recovery strategies that saw
the supermarket chain return to profitability. The shares were re-listed in 2011 following
improved performance and a profit trend for three years.
The shares of CMC Motors, on the other hand, were suspended from trading at NSE in
2011 following non-adherence to corporate governance guidelines spelt out by the
Capital Markets Authority (irregular appointment of a chairman). Thereafter, conflict of
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interest and fraud were leveled against certain directors of CMC Holdings causing panic
in the capital markets. The Authority suspended trading of the shares at the NSE in
September 2011, to pave way for investigations. The company’s performance begun
deteriorating thereby reporting losses. Subsequently, CMC Holdings lost car dealership
franchise from Land Rover. CMC Holdings remains suspended, pending resolution of the
corporate governance challenges. Additionally, City Trust was suspended from trading in
March 2013, pending the conclusion of a merger between City Trust Limited (CTL) and
Investment and Mortgages (I&M). Other firms whose shares continue to be suspended
include Hutchings Biemer, A.Baumann& Co and Access Kenya.
Over the last 20 years a number of firms have been listed through initial public offerings,
additional offers and listings by introduction. Over the same period, there has also been
companies that were delisted from NSE due to failure to meet listing obligations whereas
others were delisted as a result of restructuring and takeovers. This development shows
that even as CMA strives to attract more listings, the causes for delistings should be
addressed.
1.2 Research Problem
Prediction of financial distress is a worthwhile exercise in business. The prediction and
analysis of corporate financial performance is a crucial phenomenon in a developing
country like Kenya. It would therefore be very useful to have a reliable prediction model,
particularly in the lending, investment and capital markets sectors. Recently, there has
been a significant increase in interest in business failure prediction from both industry
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and academia. Statistical business failure prediction models attempt to predict the failure
or success of a business. A number of approaches for predicting financial distress do exist
but the most popular ones include multiple discriminant analysis and Logit analysis.
In 2006, Aziz and Humayon (2006) compiled an extensive literature review on 46 articles
reporting 89 empirical studies of predicting corporate bankruptcy. The authors analysed
the accuracies of three different predictive models namely statistical models (comprised
the Multiple Discriminant Analysis and Logistic Regression Analysis); Artificial
Intelligent Expert System (AIES) models (comprised Recursive Partitioning and Neural
Networks) and Theoretical models (primarily consists of entropy theory). Of the
reviewed literature, 64% of all authors used statistical techniques, 25% of the authors
used AIES models and 11% the authors used theoretical models.
According to the study more than 60% of past research on the topic has used financial
ratios, 33% a mixture of financial ratios and other macro -economic and industrial
variables and the remaining 7% used cash flow information. Even the definition of
bankruptcy is not consistent between the different researchers. What is evident from this
literature survey is that there is little consensus on the most appropriate predictive
variables, the best predictive technique or even the best year prior to failure (Muller,
2008). Because of the differences in opinions, there is a good reason to resort to
alternative methods of predicting financial distress; perhaps a method that would
combine the variables tested by the researchers over the past three decades.
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Logistic Regression Analysis is one of the statistical models of predicting financial
distress. One of the popular models was developed by Ohlson in 1980. Ohlson’s O-Score
model weights nine independent variables and assigns a score. However, unlike
discriminant analysis, this method estimates the probabilities of default for each company
in a sample. It overcomes the restrictive assumptions of multiple discriminant analysis
that provides an output which is a single dichotomous and which does not offer an
indication of the probability of default. Based on the probability of financial distress,
Ohlson was able to predict failure, for companies one year prior to failure to an accuracy
level of 96.1%.
Another researcher who used conditional probability models, and more specifically Logit
Analysis, to predict financial distress was Zavgren (1985). Zavgren argued that one of the
advantages of the technique is that it overcomes the issues of non-normality of the
sample. Here the author modelled a matched pair of 45 “failed” and “non-failed”
companies where the Logit Analysis provided a probability of failure and not a
dichotomous result. The author was able to show a clear distinction between probability
of failure on “non-failed” and “failed” companies. She argued that models which
generated a probability of failure were more useful than those that produced a
dichotomous classification as with the Multiple Discriminant Analysis.
Locally, Keige (1991) conducted a study on business failure prediction using
discriminant analysis. He concluded that financial ratios can be used to predict business
failure. Bwisa (2010) tested the applicability of Altman’s revised Z-Score model and
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found it to apply to 60% of the sample tested. Kiragu (1993), in his study on prediction of
corporate failure using price adjusted accounting data concluded that 9 ratios had high
corporate failure predictive ability and the most critical ratios were found to be liquidity
and debt service ratios. These studies differ from this research in that none has used the
probabilistic approach to prediction of failure. This study will adopt the Logit Analysis to
determine whether the model can be used in Kenya to predict failure among listed
companies. The researcher will focus on listed companies as secondary data is readily
available.
1.3 Research Objective
The objective of this research study was to evaluate the appropriateness of Logistic
Regression Analysis technique in predicting business failure in Kenya.
1.4 Value of the Study
The findings of this study will be useful to various stakeholders including the following
among others:
Potential investors who wish to make investment decisions in certain companies will
benefit from understanding financial distress and particularly the statistical variables to
look out for. This will assist them in identifying early warning signals in a company that
is facing financial difficulties and thus make an informed investment decision. Similarly,
shareholders who wish to monitor current performance and predict future performance of
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the companies in which they have invested can use the findings of this study to make
disposal or investment decisions.
Financial institutions that lend money to companies will need to evaluate the financial
health of such companies in order to determine their debt repayment capability and thus
the probability of default and other inherent risks. Banks use models that are built using
selected financial ratios to generate credit ratings for firms. Credit ratings are useful in
determining the strength of the company and also a measure of probability of default.
The analysis of financial ratios assists in determining financing needs as well as the
ability to repay debt.
Suppliers who wish to enter into business relationships with potential buyers can use the
model to make credit policy decisions. Suppliers would be most interested in a model that
evaluates working capital patterns as supplies form a core component of working capital
(Current Assets minus current liabilities). The model would be used to evaluate the
strength of cashflows. Hence, credit policy decisions such as creditor days, trade
discounts, late payment penalties, and cash trading can be made based on results of
default model.
The academicians and researchers will also benefit from further insights into the
predictive ability of the logit model which may form the basis for further research. The
findings of this study may generate further research questions that may be investigated
further. They can further evaluate applicability of Logit analysis to non-listed firms,
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financial and investment firms, small businesses among others. The financial health of
firms deemed to be financially strong can be confirmed through the use of Logit analysis.
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CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
The chapter begins with a review of theories in financial distress, followed by a
discussion of the empirical studies that have been conducted by other researchers in the
subject of financial distress. The review of theories and empirical studies will lay
emphasis on the type of technique used, the number of companies involved in the
respective research and the overall predictive accuracy of the particular research. The
factors that lead to business failure are also discussed in this chapter.
2.2 Empirical Literature
Zavgren (1985) modeled a matched pair of 45 failed and non-failed companies where the
Logit Analysis provided a probability of failure and not a dichotomous result. The author
was able to show a clear distinction between probability of failure on non-failed and
failed companies. She argued that models which generated a probability of failure were
more useful than those that produced a dichotomous classification as with the MDA.
Based on the probability of financial distress, the author was able to predict failure for
companies two years prior to failure to an accuracy of 95.5%.
Ohlson (1980) researched the probabilistic prediction of bankruptcy using Logistic
Regression Analysis. Ohlson argued that the use of conditional Logistic Regression
Analysis does not require any assumptions regarding the prior probabilities of bankruptcy
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and distribution of the independent variables. Ohlson identified four main factors that
were statistically significant in predicting the probability of failure within one year of
failure. These were: the size of the company, a measure of financial structure, a measure
of performance and a measure of current liquidity. Ohlson considered 2058 non-failed
and 100 failed firms. Based on the probability of financial distress, the author was able to
predict failure, for companies one year prior to failure to an accuracy of 96%. The higher
the O-Score the higher the risk of default. Ohlson finds that a cutoff of 0.038 minimizes
the sum of Type I and Type II estimation errors in his sample. A type I error occurs if the
O-Score is less than the cutoff point but the firm is bankrupt. If the O-Score is greater
than the cutoff point but the firm is non-bankrupt, this is a Type II error. Ohlson reports
that size of the company appears to be the most significant predictor of financial distress.
Beaver (1966) compared the financial ratios of 79 failed firms with the ratios of 79
matched firms up to five years before the 79 firms actually failed. The ratios examined
included: Cash flow (EBITDA) /total debt; Net income / total assets; Total debt / total
assets; Working capital / total assets; Current ratio; No credit interval and Total assets.
Here Beaver used Portfolio Analysis (of ratios indicating how the ratios changed as the
company progressed towards financial distress) to predict failure and Univariate Analysis
(UA) to classify companies as failed or non-failed. Beaver showed that the technique
accurately classified 78% of the sample companies five years prior to failure. The
research completed by Beaver concluded that the cash flow to debt ratio was the single
best indicator of bankruptcy.
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Altman (1968) developed a multivariate insolvency model based on Multiple
Discriminant Analysis (MDA). Altman combined a number of ratios and developed an
insolvency prediction model known as the Z-score. The Altman Z-score model is a linear
model which weighs five inputs according to the predetermined coefficients and
determines an overall Z-score. This overall Z-score per company forms the basis for
classification of the firms as failed or non-failed. In the model developed by Altman, the
Z-score indicator provided a forecast of whether the company would enter into distress
within a two year period.
Aziz and Lawson (1989) utilized cash flow information based on the operating cash flow
model of Lawson to predict financial distress. The authors used the Z-score model, Zeta
score model, Logit Analysis model and a mixed model to predict financial distress on 49
matched companies between 1973 and 1982. The overall comparative classification and
predicative accuracy on the hold-out sample between the Z-score model, Zeta score
model, Logit Analysis and mixed model were 77.4%, 92.8%, 76.3% and 82.8%
respectively. The authors argued that operating cash flows were important variables to
predict financial distress.
Blum (1974) proposed a model to quantify the probability of the point at which a
company is considered failing by analyzing the financial and market data of failed firms.
He developed the failing company model (FCM) to aid the antitrust division of the
Justice Department in assessing the probability of business failure (Altman, 1993). The
sample of Blum consisted of 115 firms that failed from 1954 to 1968 with a minimum of
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Usd 1 million in liabilities at the time of failure, paired with 115 non-failed firms on the
basis of industry, size, and year. By constructing FCM, Blum selected 12 variables to
measure the cash flow parameters with three common factors underlying the cash flow
framework; liquidity, profitability, and variability. By employing the multiple
discriminant analysis, the overall accuracy rates on average was 94% when failure
occurred within one year after the most recent statement date, 80% for the prediction two
years prior to failure, and the accuracy declined to 70% for three years prior to failure.
Steyn-Bruwer and Hamman (2006) utilised Recursive Partitioning to classify industrial
companies which were experiencing financial distress. Their model was based on income
statement, balance sheet and cash flow information from 352 industrial companies listed
on the Johannesburg Securities Exchange (JSE) between 1997 and 2002. The authors
concentrated on cash flow information by arguing that financial distress was a cash
related phenomenon. They concluded that three ratios emerged as being the most
important classifiers. These were the size of the company (Log TA/GDP), cash flow from
operations to sales and Cumulative cash flow from operating activities to sales.
2.3 Review of Local research on predicting financial distress
Kiragu (1993) carried out a study on the prediction of corporate failure using price
adjusted accounting data. He used a sample consisting of 10 failed firms and 10 non
failed firms. Financial ratios were computed from price level adjusted financial statistics.
The discriminant model developed showed that 9 ratios had high corporate failure
predictive ability. These ratios were Times Interest Coverage, Fixed Charge Coverage,
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Quick Ratio, Current Ratio, Equity to Total Assets, Working Capital to Total Debt,
Return on Investments to Total Assets, Change in Monetary Liabilities, and Total Debt to
Total Assets. The most critical ratios were found to be Liquidity and Debt Service Ratios.
The results were consistent with the finance theory relating to the firm’s risk. The firm
has to maintain sufficient liquidity in order to avoid insolvency problems. It also needs to
generate sufficient earnings to meet its fixed finance charges. The results however
differed from earlier studies done by Altman (1968) and Kimura (1980) who had
concluded that liquidity ratios were not of any significance in bankruptcy prediction.
Both had indicated that efficiency and profitability ratios were the most important.
Keige (1991) conducted a study on business failure prediction using discriminate
analysis. He concluded that ratios can be used to predict company failure. However, the
types of ratios that would best discriminate between failing companies and successful
ones tend to differ from place to place. In Kenya current ratio, fixed charge coverage,
return on earning to total assets, and return on net worth can be used successfully in
predicting failure for a period up to 2 years before it occurs. Keige concludes that
stakeholders should pay attention to liquidity, leverage and activity ratios.
Sitati (2010) carried out a study to evaluate the applicability of Altman’s revised model in
predicting financial distress in Kenya. This study assessed whether Edward Altman’s
financial distress prediction model could be useful in predicting business failure in
Kenya. He concluded that Edward Altman’s financial distress prediction model is an
accurate prediction on firms quoted at Nairobi Securities Exchange. On eight out of the
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ten failed firms there is 80% validity for the model. On ten non-failed firms, nine of them
proved that Edward Altman’s financial distress prediction model was correct representing
a 90% validity of the model.
Taliani (2010) conducted a study aimed at developing a discriminant model incorporating
stability ratios that can be used to predict financial distress in Commercial Banks in
Kenya and to identify critical financial ratios with significant predictive ability. The
findings of the study provide evidence that the stability of financial ratios has an impact
on the ability of the firm to continue as a going concern. Profitability ratios offer a
reasonable measure of management effectiveness in firm value creation; leverage /
indebtedness ratios provide historical reasons for firm failure while liquidity ratios
constitute a measure of firms' solvency. The model attained 70% and 100% correct
classification in year 1 and in year 3 respectively.
Maina and Sakwa (2012) carried out a survey of the financial distress status of firms
listed on the Nairobi Securities Exchange (NSE) using the Z-score model in order to
assist in suggesting policy implications. The study established that the firms listed in NSE
do not always exhibit a healthy financial position. The authors further stated that it is
generally assumed that firms within the same sector should not differ in terms of financial
position since they operate within the same economic environment. The findings however
indicate that the financial status of these firms differ from one company to the other.
They suggested that this is because the financial health of a company is affected by
various factors such as management style and capacity, government policies, stock
19
ratings, current legal affairs and largely depends on how each firm is capable of coping
with such factors. They therefore concluded that the financial position of listed firms
differs from sector to sector and firms in the same sector do not necessarily exhibit
similar financial strength.
2.4 Early warning signals of financial distress
Signs of potential financial distress are evident long before the actual failure occurs. In
the run up to business failure, companies are likely to be on the receiving end of specific
types of negative news. Whether this is reporting of poor financial results, impending
administration issues, factory closures or redundancies, early warning signs should be
identifiable in advance (Ritchie, 2012). The most important signals about financial
distress can be received from the analysis of financial ratios of a company.
Accounting based indicators of financial distress are still very popular among researchers
and practitioners and are widely used as selection criteria. Despite the critique that
financial ratios are past oriented and cannot capture the future dynamics and prospects of
the company as a going concern, they perform well in models predicting financial distress
and probability of default. Denis and Denis (1995) identify financial distress when a
company experiences losses (negative pre-tax operating income or net income) over at
least three consecutive years. Results of their empirical analysis of the dividend policy in
financial distress show that after a company enters into financial distress, it usually
experiences cash flow problems and is unable to pay dividends. Therefore, rapid and
aggressive dividend reductions together with consecutive negative income can be used in
order to determine financial distress situation.
20
2.5 The recovery process
The managerial response to default is very crucial and implies the undertaking of
distressed restructuring in order to prevent legal bankruptcy filing. Restructuring is a
complex mechanism and encompasses many aspects of a distressed firm, such as its
assets, creditors, shareholders, employees, management, and retirees (Datta and Datta
1995, 15). Most of the restructuring processes run simultaneously. Researchers identify
four main types of distressed restructuring namely: financial, governance, asset, and
labour. The first two types of restructuring are used for the short-term elimination of the
aftermath of default; the last two are applied to achieve the middle-term objectives of the
recovery process.
Another approach to classifying distressed restructuring is to look at the restructuring
dependent on the possible activities for improvement on the asset or the liabilities side of
the balance sheet. First, a company pursues distressed debt reorganization in order to
stabilize the fluctuation in liquidity. It negotiates with suppliers, employees, creditors,
and other stakeholders about the conditions of the reorganization (Gilson, 1990;
Nothardt, 2001). Typical actions for improvements on the assets side of the balance sheet
are asset sales, mergers, capital expenditure reductions, and layoffs (Asquith et al.,1994,
625).
21
2.6 Summary of the Literature Review
The studies conducted on prediction of financial distress show that financial statements
can be reliably used to predict financial distress. Thus, this study will also utilize the
information contained in the financial statements. The study will evaluate Ohlson’s O-
Score model to determine whether it may be adopted in the assessment of probability of
default in Kenya. The researcher, however, would like to emphasize that there is very
little consensus on which ratios most accurately point to signs of distress as prior studies
have been carried out using a varied range of financial ratios. By using financial results
and taking into account the size of the firm, Ohlson’s Logit model will be utilized to
investigate its predictive ability. A major assumption of this research report (as
mentioned by Laitinen (1991: 649) is the fact that the failure process of companies is
based on the systematic deterioration of financial ratios.
22
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 Introduction
In this chapter, the researcher outlined the methods that were employed to achieve the
objectives of the study as outlined in chapter one. The chapter begins with the research
design; then discusses the study population, the sample data, data collection methods and
data analysis. Data validity and reliability were also discussed.
3.2 Research Design
The study was descriptive in nature. A descriptive study is one in which information is
collected without alterations to the source data. The descriptive study involved data
collection from existing records. The method observes the descriptive statistics of a
phenomenon from which deductions can be made. This method was chosen on the basis
that the researcher examined and extracted information from documents that contain
participants’ data. The data then was analysed to make deductions. The method ensures
that data is collected with precision which defines its reliability; accuracy which defines
validity and with minimal error.
3.3 Population
For purposes of this study, the population was all firms listed on the Nairobi Securities
Exchange. This source was selected as data is publicly available and reliable as it was
drawn from audited financial statements. Secondly, Nairobi Securities Exchange has been
characterised by distressed companies, failed companies as well as surviving companies.
23
This enabled the researcher use descriptive observation of the data collected to achieve
the objective of this study.
3.4 Sample Design
Sampling is the process of choosing participants for a research study. It involves
choosing a small group of participants that will represent a large group. The study was
designed to ensure that enough participants were identified to generate useful information
that can be representative of the group represented.
The study sample comprised companies that were delisted from NSE, those whose stocks
were suspended from trading and surviving companies between 1996 and 2011.
The sample comprised failed firms and non-failed firms. Failed firms were those firms
whose stocks were delisted or suspended from the Nairobi Securities Exchange between
1996 and 2011. For purposes of this study, the event of being delisted or suspended was
treated as a clear signal of firm failure. The sample of failed firms comprised a mix of
firms delisted and suspended from trading. Although Ohlson (1980) only considered
industrial firms, this study expands the sample to non-industrial firms.
Non-failed firms were the surviving firms between 1996 and 2011. The qualifying factor
in this category was that the firm must not have been delisted or suspended over the
period under focus. Firms in the banking, insurance and investment sectors were not
included in the sample. Ohlson (1980) excluded financial institutions from the study as
entities in the financial and investment sector are structurally different and have
bankruptcy environment.
24
3.5 Data Collection
The study adopted secondary data. This was obtained from financial reports of listed
companies. The data was obtained from Nairobi Securities Exchange and Capital Markets
Authority. The secondary data comprised the following items: Total assets, Total
Liabilities, working capital (calculated as total current assets minus total current
liabilities), Net income, current assets and Funds from operations.
3.6 Data Analysis
In this study, Ohlson’s O-Score Model was used to analyze the data. This model was
appropriate for Kenyan firms listed on the various market segments on NSE as they
comprise manufacturing, commercial and industrial firms used in the original study by
Ohlson. Ohlson (1980) identified four main factors that were statistically significant in
predicting the probability of failure within one year of failure. These were: the size of the
company, a measure of financial structure, a measure of performance and a measure of
current liquidity. He used the logit model and US firms to develop an estimate of the
probability of failure for each firm. Ohlson selected nine independent variables that he
thought should be helpful in predicting bankruptcy. The O-Score formula:
O-Score = -1.32 – 0.407LOGX1 + 6.03 X2 – 1.43X3 + 0.08X4 – 1.72X5 – 2.37X6 – 1.83X7 + 0.285X8 – 0.521X9
The score is transformed into a probability using the formula below:
Probability of Failure = P-score = e o-score / 1+ e o-score
The nine variables are defined below:-
25
X1 = total assets / GNP price level index; Where GNP price-level index = (Nominal
GNP/Real GNP)
X2 = total liabilities / total assets
X3 = working capital / total assets
X4 = current liabilities / current assets
X5 = 1 if total liabilities > total assets, else 0
X6 = net income / total assets
X7 = funds from operations / total liabilities
X8 = 1 if a net loss for the last two years, 0 otherwise
X9 = Change in net income
The cut-off point of p = 0.5 was used, consistent with Ohlson. Non-bankrupt firms lay
below the cutoff point and bankrupt firms lay above the cutoff value.
Consistent with Ohlson, estimates were computed for the logistic model using the
predictors defined above. The O-score was transformed into a probability using logistic
transformation (P-score = e o-score / 1+ e o-score) whereby the cut-off point used was 0.5.
Hence; P > 0.5 indicated that a firm was in distress or at risk of distress and P < 0.5
indicated a safe firm. The model predicted bankruptcy within one year of financial
distress. To aid in the analysis of data, SPSS Regression model was adopted.
26
3.7 Data Validity and Reliability
The data used in this study was obtained from financial statements drawn from the period
1996 to 2011. Reliability of the data was derived from the fact that listed firms apply
uniform accounting standards and uniform presentation format. Hence all variables were
subjected to similar accounting treatment. Further, the information was obtained from
custodial sources namely Nairobi Securities Exchange and Capital Markets Authority
ensuring that records were available. The data was also valid as the population exhibited
the attributes required in this study. Such attributes included presence of failed
companies, suspended companies and the existence of surviving companies.
27
CHAPTER FOUR
DATA ANALYSIS AND PRESENTATION OF FINDINGS
4.1 Introduction
This chapter focuses on presentation and analysis of findings of the study, obtained from
secondary sources. The objective of this study was to assess the applicability of logistic
regression analysis in predicting financial distress for firms listed on the NSE.
4.2 Data Presentation
Data for a total of 16 firms were collected between 1997 and 2011. The researcher
targeted 10 financially distressed firms between 1997 and 2011. Out of the targeted failed
firms, data was available for six firms in the commercial and services sector, automobiles
and Accessories, agricultural sector and Manufacturing and Allied Sector. These included
Uchumi Supermarkets, CMC Holdings, A. Baumann & Co, Kenya Orchards, Dunlop
Kenya (now known as Olympia Capital Holdings) and Theta Tea Factory. The response
rate was 60%. On the surviving firms, the researcher targeted 10 firms in the
manufacturing and allied sector, construction and allied sector, energy and petroleum
sector, commercial and services sector, automobiles and accessories sector and the
agricultural sector. The response rate was 100%.
Nine independent variables, weighted by coefficients, were computed and presented in
appendix 1 to 16. To assess the applicability of Ohlson (1980) Logit model in predicting
financial distress in Kenya, the variables were computed by identifying listed firms that
28
had been placed on receivership, suspended from trading and de-listed from NSE and
comparing these to variables derived from surviving firms. This study considered these
signals as clear signs of occurrence of financial distress in firms. Ohlson’s (1980) logit
model that tests probability of default, one year prior to occurrence was applied. The
model estimates probability of default (p-score) where a cut-off point of 0.5. Firms
falling below cut-off are safe while those falling above cut-off are at risk of default. For
the financially distressed firms, the year prior to de-listing or suspension from stock
trading is critical as the model assesses probability of default one year prior to its
occurrence.
4.2.1 Total Kenya Limited
Total Kenya was analysed from 2007 to 2011 and had default probability score of 0.46 in
2007 and 0.62 in 2008. From 2009 to 2011, the probability score was below 0.5, the set
distress point. The results show that the company was at low risk of failure in all years
examined except 2008. However, the position improved over the subsequent years. From
Ohlson (1980) logit model, any firm with p-score below the cut-off point of 0.5 is at low
risk of failure. Total Kenya was at low risk of failure and was classified as a healthy
company. Thus this proves the validity of Ohlson (1980) logit model in predicting
distress probability in Kenya (see appendix 1).
4.2.2 British American Tobacco Limited
From the study’s findings, British American Tobacco had a default probability score
between 0.13 and 0.4 between 2007 and 2011. According to Ohlson (1980), a firm whose
29
p-score falls below 0.5 is considered to be at low risk of default. The company was not
placed on receivership nor its shares suspended from trading at NSE. This confirms that
Ohlson (1980) logit model can be applied in assessing probability of distress among
Kenyan companies (see appendix 2).
4.2.3 East African Breweries Limited
East African Breweries had probability scores ranging between 0.018 and 0.365 between
2007 and 2011. According to Ohlson (1980) logit model, companies with probability
scores below 0.5 are considered to be at low risk of failure than those whose probability
scores occur above the cut-off point. This implied that East African Breweries had a very
low probability of failure and hence the model is proven to be applicable locally (see
appendix 3).
4.2.4 Nation Media Group
In year 2007 to 2011, Nation Media Group had probability scores ranging between 0.02
and 0.08. These p-scores were below the cut-off point of 0.5 implying low risk of default.
The company is classified as one of the non-failed firms and hence confirms the validity
of Ohlson (1980) logit model in Kenya (see appendix 4).
4.2.5 Car & General (Kenya) Limited
The probability scores for Car & General range between 0.56 and 0.66 for year 2007 to
2011. The analysis conducted showed that the company was at high risk of financial
30
distress as the scores fell above cut off point of 0.5. The company is classified as non-
failed and hence a type ii error exists. This is because Ohlson (1980) logit model
classified the company as having been in distress while distress signals such receivership,
suspension, delisting, bankruptcy did not occur (see appendix 5).
4.2.6 East African Cables Limited
The findings of this study show that in year 2007, East African Cables had a p-score
value of 0.63 which was above the model’s cut-off point of 0.5. In 2008 to 2011, the p-
scores for the company improved and ranged between 0.18 and 0.45 indicating a low risk
of failure. Since the scores were above the cut-off point, it implied that Ohlson (1980)
logit model was applicable in predicting financial distress in Kenya (see appendix 6).
4.2.7 Sameer Africa Limited
The results show that Sameer Africa, a company listed on NSE’s Automobiles and
accessories sector had distress probability scores ranging between 0.045 and 0.18 for the
years 2007 to 2011. The scores were below the model’s default cut-off point of 0.5 and
hence proved the applicability of Ohlson (1980) logit model in Kenya (see appendix 7).
4.2.8 Sasini Tea and Coffee Limited
In year 2007 to 2011 Sasini Tea & Coffee scored 0.1 and 0.35 on Ohlson (1980) logit
model. The p-score values were decreasing year on year implying that financial
performance improved over the years and this is seen to have reduced the risk of business
31
failure. The p-scores were below the cut-off point identified as 0.5 in Ohlson (1980)
distress prediction model. Sasini is classified as a non-failed company and hence this
proves the validity of Ohlson (1980) logit model in predicting business failure in Kenya
(see appendix 8).
4.2.9 Crown Berger Limited
Crown Berger was analysed for year 2007 to 2011. In year 2008, the distress probability
score was 0.767. However, the company’s p-score significantly improved to 0.04 in
2009. In 2010 and 2011, the score was higher at 0.36 and 0.46 respectively but below the
cut-off point of 0.5. The company is classified as non-failed with no occurrence of
distress signals. This proves applicability of Ohlson (1980) logit model in predicting
financial distress in Kenya as p-scores were below 0.5 (see appendix 9).
4.2.10 Scangroup Limited
Scangroup Limited was assessed from 2007 to 2011. Its p-score was 0.603 in 2007 and
improved to levels ranging between 0.3 and 0.1 between 2008 and 2011. At a p-score of
0.603 in 2007 and an improvement to 0.26 the following year, it indicates that the
company may have employed strategies to improve financial performance. It was
observed that some variables in the model such as total assets increased by 114% in 2008,
working capital and profitability also improved from 2008 and beyond. These contributed
to improvement of the p-score. This research study revealed that Scangroup was at low
risk of business failure for years 2008 to 2011 as the p-scores were below 0.5. This
32
confirms that Ohlson (1980) logit model is applicable in Kenya in predicting business
failure (see appendix 10).
4.2.11 CMC Holdings Limited
From the data analysis carried out on CMC Holdings, the p-score deteriorated year on
year from year 2006 to 2010. The p-score recorded in 2006 was 0.346. It declined to 0.4
in 2007 and 2008 and further to 0.61 in 2009. In 2010, the year prior to suspension of its
shares trading on NSE the p-score was 0.627. This showed clear signals of business
failure and proves that Ohlson (1980) logit model is applicable in predicting the
probability of financial distress locally (see appendix 11).
4.2.12 Uchumi Supermarkets Limited
The findings of the study indicate that Uchumi Supermarkets had a score of 0.999 in
2005 hence a high probability of default as the score was above Ohlson (1980) model’s
cut-off point of 0.5. A year later, Uchumi’s shares were suspended from trading at NSE.
In years 2006 to 2008, the p-scores remained high at 0.99 implying that the company
continued in financial distress. Later in 2009 and 2010, the p-score improved to 0.51 and
0.25 respectively showing that Uchumi’s risk of business failure had reduced. As the
distress signal occurred one year before suspension from the stock market, Ohlson (1980)
logit model is confirmed to be applicable in predicting business failure in Kenya (see
appendix 12).
33
4.2.13 A. Baumann & Company Limited
A .Bauman & Company scored 0.24 and 0.41 in 2003 and 2004 respectively. The p-score
sharply deteriorated to 0.968 in 2005, a year before the stock was suspended. The
company’s score remained high at 0.66 and 0.86 in 2006 and 2007 respectively; implying
that it was still in the distressed status. Ohlson(1980) logit model is therefore proved to be
applicable in assessing business failure locally (see appendix 13).
4.2.14 Kenya Orchards Limited
Kenya Orchards was analyzed for the years 2003 to 2007. It had p-scores oscillating
between 0.96 and 0.98 throughout the period examined. This implies that the company
was at high risk of business failure. To confirm this, its shares remain suspended from
trading; an event that signals distressed state. In this case, Ohlson (1980) logit model is
confirmed to be applicable in Kenya as a tool for predicting probability of default (see
appendix 14).
4.2.15 Theta Tea Factory
The default probability score for Theta Tea Factory was above the cut-off point of 0.5
throughout the period examined (1997 to 2001). From 1997 to 1999, the company’s p-
score was at 0.9 before improving to 0.6 in 2000 and deteriorating further to 0.84 in 2001,
the year it was de-listed. This result confirms validity of Ohlson (1980) logit model in
predicting financial distress in Kenya (see appendix 15).
34
4.2.16 Dunlop Kenya Limited
Dunlop is classified as one of the failed firms at NSE. Its p-score was 0.21 and 0.37 in
year 2004 and 2005 respectively; implying no signals of business failure as the scores fell
below the distress cut-off point of 0.5. In year 2006 and 2007 the p-score deteriorated to
0.90 and 0.95 respectively showing that the firm was facing high risk of business failure.
During 2007, the firm entered distress state as its financial performance declined as a
result of discontinuation of certain lines of business. This firm eventually changed its
business and also its name to Olympia Capital Holdings in 2007 to reflect its mainstay
business. The fact that the company’s p-score was above cut-off point one year before
occurrence of distress event implies that Ohlson (1980) logit model is applicable locally
in predicting financial distress (see appendix 16).
4.3 Summary and Interpretation of the findings
The researcher’s sample comprised ten non-failed firms and six failed firms on the NSE.
Failed firms were those whose stocks had been delisted or suspended from trading at
NSE and non-failed firms were the surviving firms whose stocks were trading and had
not faced failure signals such as delisting and suspension from trading. The ten non-failed
firms in the sample included Total Kenya, British American Tobacco, East African
Breweries, Nation Media Group, Car & General Kenya, East African Cables, Sameer
Africa, Sasini Tea & Coffee, Crown Berger and Scangroup. The six failed firms in the
35
sample included CMC Motors, Uchumi Supermarkets, A. Bauman & Company, Kenya
Orchards, Theta Tea Group and Dunlop Kenya.
Data was collected from each company’s audited Statement of Financial Position, Income
statement and cash flow statement for five years. Using a set of nine financial variables,
O-score was computed. The O-score was then transformed into probabilities using
logistic transformation function, P-score = e o-score / 1+ e o-score. The cut-off point used was
0.5. Hence; P > 0.5 indicated that a firm was in distress or at high risk of distress and P <
0.5 indicated a safe firm.
Out of the six failed firms that were assessed, 100% had their probability scores above
the cut-off point of p = 0.5, one year prior to occurrence of financial distress of each of
the firms. This means that they were all financially distressed during the respective
periods examined. One of the failed firms, representing 17% of the failed firms analyzed,
showed an improvement of its probability score to levels below 0.5 after four years of
suspension from trading. This implied that the firm had put in place survival strategies
that improved its performance; hence a conclusion that failed firms can be revived if
rescue measures are taken by management.
Of the ten non-failed firms that were assessed, 90% had probability scores below the cut-
off point of 0.5; implying that they were classified as financially healthy firms. Their
scores remained in the risk free zone in the period examined. This meant that they
continued to trade as healthy firms and their risk of failure was low. 10% of the non-
36
failed firms (one firm) exhibited type a two error. This means that the probability score
was greater than the cut-off point of 0.5 for all the five years examined but the firm was
not de-listed or suspended. This implied that not all firms deemed healthy exhibit
satisfactory or healthy financial position and these require closer monitoring.
The results of this research study compare to empirical studies carried out by Zavgren
(1985) in that it is possible to show a clear distinction between the probability of failure
on non-failed and failed companies through the use of logit analysis and identified cut-off
points. The study also compares to a local study by Keige (1991) whereby he concluded
that financial ratios can be used to predict company failure. The Ohlson (1980) logit
model used in this study is applied by deriving nine ratios from the financial statements
of the sixteen companies assessed. Finally, Maina & Sakwa (2012), in their survey of
firms listed on NSE with an aim of suggesting policy implications used the Z-score
model and established that firms listed on NSE do not always exhibit a healthy financial
position. This compares to this research study in that 10% of the firms tested (one firm)
was classified as non-failed while the Ohlson (1980) model classified it as failed.
37
CHAPTER FIVE
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
5.1 Summary
The objective of the study was to evaluate the applicability of Logistic Regression
Analysis technique (Logit Analysis) in predicting business failure in Kenya. Over the last
20 years a number of firms have been listed through initial public offerings, additional
offers and listings by introduction. Over the same period, there has also been companies
that were delisted from NSE due to failure to meet listing obligations and deteriorating
financial performance whereas others were delisted as a result of restructuring and
takeovers. This development shows that even as CMA strives to attract more listings, the
causes for delistings should be investigated and addressed.
To achieve the objective, the researcher obtained financial data from a sample of sixteen
listed firms on the NSE between 1997 and 2011. The data collected included total assets,
total liabilities, current assets, current liabilities, net income and funds from operations.
The data was obtained from each firm’s audited statement of financial position, income
statement and cash flow statement. The audited financial statements were obtained from
Capital Markets Authority and Nairobi Securities Exchange. The data was used to
calculate nine financial variables that were weighted by co-efficients to work out o-
scores. The o-scores of each firm were then transformed into a probability score known
as p-score using logistic transformation function; P-score = e o-score / 1+ e o-score whereby
the cut-off point used was 0.5. P-score was used to classify the firms as being at a high
or low risk of default.
38
The findings of this research study confirmed that when a firm’s p-score is above the cut-
off point of 0.5, the risk of failure is high and when a firm’s p-score is below the cut-off
point of 0.5, the risk of failure is low. Out of the six failed firms that were analyzed in
this study, Ohlson (1980) logit model was applicable to an accuracy level of 100% as
events of distress occurred one year after scoring above cut-off point. This confirms
validity of the model in Kenya.
5.2 Conclusions
The study assessed Ohlson’s model using data from Kenyan companies listed on NSE.
The results showed that signals of financial distress were detectable through subjecting
financial results to distress checks such as the logit model developed by Ohlson. Out of
the six firms assessed, Ohlson’s model was 100% applicable as their p-scores were above
0.5 a year prior to delisting or suspension. The results of the study hence confirmed that
delisting is a predictable event.
This study contributes to the literature by expanding the application of Ohlson (1980)
logit model to Kenya publicly listed companies. It provides applicable measures for
predicting firm delisting events in NSE. In this regard, the study provides a framework to
set listing obligations for companies as far as going concern concept is concerned. The
findings of the study can be applied by NSE and CMA to set trigger points for events of
financial distress. In order to protect the interests of shareholders, the regulators may
impose corrective measures on companies striking the set distress trigger points. For
example, companies whose p-scores are close to the cut-off point (say from 0.45 and
39
above) should be warned of business failure if corrective measures are not taken. The
regulators would then impress upon such companies to devise and commit to remedial
actions before next reporting dates.
The study concludes that signs of financial distress occur before the actual occurrence of
distress and hence there is need for companies to adopt methods of distress checks within
their management reporting framework. This is a conclusion derived from the tests done
on healthy firms whereby 40% were classified under default risk zone of p > 0.5 as each
had a p-score > 0.5 for a single year’ yet they were not declared bankrupt, delisted,
suspended or placed under receivership the following year. The study concludes that such
firms may set own trigger points for the p-score and once signs of distress are first
detected, measures should be put in place to redeem the financial performance. Such
measures would be determined by identifying the individual deteriorating variable from
the model and addressing its component. For example measures addressing improvement
of liquidity, profitability, funds from operations and working capital would assist in
improving the p-score.
5.3 Policy Recommendations
The study recommends that an assessment should be conducted on all listed companies to
check on their general health. This would be aimed at ensuring that listing obligations
continue to be fulfilled as well as assure shareholders of the performance of their
investments.
40
The study also recommends that a survey should be done on listed companies to establish
those that internally analyze key performance ratios and determine whether actions are
taken based on the results of such analysis. Results of such a survey would form the basis
of recommendations to incorporate, in the management reports, an analysis of key ratios
targeted at predicting the occurrence of financial distress of the firms. Such information
would help the management apply survival measures and strategies to revive the failing
firms through turn around strategies before the occurrence of financial distress.
The study also recommends that Nairobi Securities Exchange and Capital Markets
Authority continuously monitor the financial progress of the listed firms and advice them
appropriately on ways to revive the failing companies in order to avoid firms’ liquidation.
The study further recommends that listed firms establish financial distress prediction
reporting to analyze the financial distress status of the firms and advice appropriately
where financial distress is likely to occur so that appropriate measures can be taken to
prevent liquidated state.
5.4 Limitations of the study
The findings of this study were limited to 6 distressed firms between 1996 and 2011 due
to constraints in obtaining data for delisted companies. Financial statements for delisted
entities were not obviously available at CMA and NSE as most of the firms did not
comply with the listing obligation requiring submission of audited financial statements on
annual basis.
41
The occurrence of events leading to financial distress was limited to signals derived from
financial data of companies. However, there are other factors that may lead to business
failure such as catastrophes, economic conditions, social factors and mismanagement.
Financial distress keeps on changing from period to period depending on prevailing
economic situations and demand on the capital market. The findings therefore may not
reflect the true prediction of financial distress across the firms listed for a period of 17
years since some firms are delisted and listed again depending on their performance on
NSE.
The study was further limited as it did not focus on all market segments as defined by
NSE. The findings were limited to selected manufacturing, commercial, automobiles,
agricultural and construction sectors.
5.5 Suggestions for further study
The study recommends a further study to be undertaken on all listed firms, excluding
banks and insurance firms to confirm the applicability of Ohlson’s logit model in Kenya.
This study focused on a sample of 10 listed firms and generalized the findings to similar
listed firms.
Another research area that could be extended is to test bankruptcy prediction models to
the non-listed, relatively smaller turnover sized firms where the incidence of business
42
failure is greater than larger corporations.
Another research could be carried out to come up with a model suitable for investigating
the financial distress among financial institutions, investment and insurance companies
since the Ohlson (1980) logit model is not recommended for these institutions.
A similar study should also be carried out on firms currently listed at the NSE
incorporating non-qualitative but measurable aspects such as the nature of audit opinion,
composition of the board, inflation rates and number of years in business. This would
assist in arriving at an all inclusive score in determining distress status of firms.
43
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APPENDICES
Appendix 1: TOTAL KENYA LIMITED
AMOUNT IN MILLIONS 2007 2008 2009 2010 2011
Total Assets 12,513.00 14,527.00 31,528.00 30,376.00 35,198.00 X1 (total assets / GNP price level index) 13,311.70 22,557.86 53,519.05 52,519.28 69,165.12
Total Liabilities 7,761.00 9,509.00 18,588.00 17,520.00 22,983.00 X2 (total liabilities / total assets) 0.6202 0.6546 0.5896 0.5768 0.6530
Working Capital (CA-CL) 2,014.00 2,255.00 2,203.00 2,595.00 2,356.00
X3 (working capital / total assets) 0.1610 0.1552 0.0699 0.0854 0.0669
Current Liabilities 7,761.00 9,509.00 18,588.00 17,520.00 22,983.00 Current Assets 9,775.00 11,764.00 20,745.00 20,115.00 25,339.00 X4(current liabilities / current assets) 0.7940 0.8083 0.8960 0.8710 0.9070
X5 (1 if TL > TA; else, 0) 0 0 1 1 1
Net Income 524 704 483 916 -71 X6 (net income / total assets) 0.0419 0.0485 0.0153 0.0302 -0.0020
Funds from Operations 2454 -453 377 6011 -2006 X7 (funds from operations / total liabilities)
0.3162 -0.0476 0.0203 0.3431 -0.0873
X8 (1 if net loss for last 2 years, else 0) 0 0 0 0 0
X9 (Change in net income) 0.078 0.343 -0.314 0.896 -1.078
O-score -0.1436 0.4919 -1.3478 -2.7010 -0.3706 e o-score 0.8662 1.6355 0.2598 0.0671 0.6904 Probability (p-score) 0.4642 0.6206 0.2062 0.0629 0.4084
51
Appendix 2: BRITISH AMERICAN TOBACCO LIMITED
AMOUNT IN MILLIONS 2007 2008 2009 2010 2011 Total Assets 9,270.00 10,307.00 10,553.00 11,121.00 13,751.00 X1 (total assets / GNP price level index) 9,861.70 16,004.95 17,913.81 19,227.91 27,021.12
Total Liabilities 4,576.00 5,414.00 5,881.00 6,008.00 7,339.00 X2 (total liabilities / total assets) 0.4936 0.5253 0.5573 0.5402 0.5337
Working Capital (CA-CL) 449 223 -90 697 1639
X3 (working capital / total assets) 0.0484 0.0216 -0.0085 0.0627 0.1192
Current Liabilities 3,544.00 4,400.00 4,334.00 4,107.00 5,341.00 Current Assets 3,993.00 4,623.00 4,244.00 4,804.00 6,980.00 X4(current liabilities / current assets) 0.8876 0.9518 1.0212 0.8549 0.7652
X5 (1 if TL > TA; else, 0) 0 0 0 0 0
Net Income 1,386.00 1,700.00 1,478.00 1,767.00 3,098.00 X6 (net income / total assets) 0.1495 0.1649 0.1401 0.1589 0.2253
Funds from Operations 1663 2386 1578 2214 3869 X7 (funds from operations / total liabilities)
0.3634 0.4407 0.2683 0.3685 0.5272
X8 (1 if net loss for last 2 years, else 0) 0 0 0 0 0
X9 (Change in net income) 0.154 0.227 -0.13 0.196 0.753
O-score -1.0667 -1.1340 -0.3521 -0.9800 -1.9049 e o-score 0.3442 0.3218 0.7032 0.3753 0.1488 Probability (p-score) 0.2560 0.2434 0.4129 0.2729 0.1296
52
Appendix 3: EAST AFRICANBREWERIES LIMITED
AMOUNT IN MILLIONS 2007 2008 2009 2010 2011 Total Assets 31,106.00 33,255.00 35,832.00 38,421.00 49,712.00 X1 (total assets / GNP price level index) 33,091.49 51,639.12 60,825.13 66,428.86 97,685.56
Total Liabilities 10,256.00 11,137.00 12,464.00 14,468.00 22,824.00 X2 (total liabilities / total assets) 0.3297 0.3349 0.3478 0.3766 0.4591
Working Capital (CA-CL) 9,899.00 8,667.00 9,509.00 5,675.00 811.00 X3 (working capital / total assets) 0.3182 0.2606 0.2654 0.1477 0.0163
Current Liabilities 8204 8868 9432 11684 15509 Current Assets 18103 17535 18941 17359 16320 X4(current liabilities / current assets) 0.4532 0.5057 0.4980 0.6731 0.9503
X5 (1 if TL > TA; else, 0) 0 0 0 0 0 Net Income 6,133.00 9,184.00 8,609.00 8,838.00 9,014.00 X6 (net income / total assets) 0.1972 0.2762 0.2403 0.2300 0.1813
Funds from Operations 8,552.00 9,309.00 8,661.00 12,203.00 8,878.00 X7 (funds from operations / total liabilities) 0.8339 0.8359 0.6949 0.8434 0.3890
X8 (1 if net loss for last 2 years, else 0) 0 0 0 0 0
X9 (Change in net income) 0.137 0.496 -0.063 0.027 0.02 O-score -3.6547 -3.9931 -3.3176 -3.2721 -1.6816 e o-score 0.0259 0.0184 0.0362 0.0379 0.1861 Probability (p-score) 0.0252 0.0181 0.0350 0.0365 0.1569
53
Appendix 4: NATION MEDIA GROUP AMOUNT IN MILLIONS
2007 2008 2009 2010 2011 Total Assets 5,898.00 6,723.00 6,573.00 7,975.00 8,816.00 X1 (total assets / GNP price level index) 6,274.47 10,439.63 11,157.72 13,788.56 17,323.70
Total Liabilities 2,162.00 2,408.00 1,858.00 2,598.00 2,694.00 X2 (total liabilities / total assets) 0.3666 0.3582 0.2827 0.3258 0.3056
Working Capital (CA-CL) 1,719.00 1,855.00 1,997.00 2,524.00 3,324.00
X3 (working capital / total assets) 0.2915 0.2759 0.3038 0.3165 0.3770
Current Liabilities 1,895.00 2,173.00 1,769.00 2,553.00 2,531.00 Current Assets 3,614.00 4,028.00 3,766.00 5,077.00 5,855.00 X4(current liabilities / current assets) 0.5243 0.5395 0.4697 0.5029 0.4323
X5 (1 if TL > TA; else, 0) 0 0 0 0 0
Net Income 1,076.00 1,296.00 1,119.00 1,538.00 2,007.00 X6 (net income / total assets) 0.1824 0.1928 0.1702 0.1929 0.2277
Funds from Operations 1,629.00 910.00 1,519.00 2,449.00 1,714.00 X7 (funds from operations / total liabilities)
0.7535 0.3779 0.8175 0.9426 0.6362
X8 (1 if net loss for last 2 years, else 0) 0 0 0 0 0
X9 (Change in net income) 0.44 0.205 -0.137 0.374 0.305
O-score -3.0701 -2.4023 -3.4881 -3.8293 -3.5695 e o-score 0.0464 0.0905 0.0306 0.0217 0.0282 Probability (p-score) 0.0444 0.0830 0.0297 0.0213 0.0274
54
Appendix 5: CAR & GENERAL (KENYA) LIMITED
AMOUNT IN MILLIONS 2007 2008 2009 2010 2011 Total Assets 2,043.00 2,750.00 3,214.00 3,871.00 5,562.00 X1 (total assets / GNP price level index) 2,173.40 4,270.26 5,455.79 6,692.85 10,929.50
Total Liabilities 1,156.00 1,622.00 1,906.00 2,315.00 3,642.00 X2 (total liabilities / total assets) 0.5658 0.5898 0.5930 0.5980 0.6548
Working Capital (CA-CL) 306 415 510 639 383
X3 (working capital / total assets) 0.1498 0.1509 0.1587 0.1651 0.0689
Current Liabilities 966.00 1,414.00 1,681.00 2,039.00 3,105.00 Current Assets 1,272.00 1,829.00 2,191.00 2,678.00 3,488.00 X4(current liabilities / current assets) 0.7594 0.7731 0.7672 0.7614 0.8902
X5 (1 if TL > TA; else, 0) 0 0 0 0 0
Net Income 175 215 198 238 289 X6 (net income / total assets) 0.0857 0.0782 0.0616 0.0615 0.0520
Funds from Operations (171.00) (74.00) (155.00) 95.00 77.00 X7 (funds from operations / total liabilities)
-0.1479 -0.0456 -0.0813 0.0410 0.0211
X8 (1 if net loss for last 2 years, else 0) 0 0 0 0 0
X9 (Change in net income) 0.268 0.229 -0.079 0.202 0.214
O-score 0.5087 0.3842 0.6134 0.2281 0.6844 e o-score 1.6631 1.4684 1.8468 1.2563 1.9825 Probability (p-score) 0.6245 0.5949 0.6487 0.5568 0.6647
55
Appendix 6: EAST AFRICAN CABLES LIMITED
AMOUNT IN MILLIONS 2007 2008 2009 2010 2011 Total Assets 3,209.00 3,043.00 3,543.00 4,519.00 4,994.00 X1 (total assets / GNP price level index) 3,413.83 4,725.24 6,014.27 7,813.23 9,813.36
Total Liabilities 2,107.00 1,677.00 1,883.00 2,272.00 2,719.00 X2 (total liabilities / total assets) 0.6566 0.5511 0.5315 0.5028 0.5445
Working Capital (CA-CL) 793 784 452 397 334
X3 (working capital / total assets) 0.2471 0.2576 0.1276 0.0879 0.0669
Current Liabilities 1,435.00 1,189.00 1,247.00 1,399.00 2,074.00 Current Assets 2,228.00 1,973.00 1,699.00 1,796.00 2,408.00 X4(current liabilities / current assets) 0.6441 0.6026 0.7340 0.7790 0.8613
X5 (1 if TL > TA; else, 0) 0 0 0 0 0
Net Income 417 463 296 184 315 X6 (net income / total assets) 0.1299 0.1522 0.0835 0.0407 0.0631
Funds from Operations -221 1155 762 445 413 X7 (funds from operations / total liabilities)
-0.1049 0.6887 0.4047 0.1959 0.1519
X8 (1 if net loss for last 2 years, else 0) 0 0 0 0 0
X9 (Change in net income) 0.463 0.11 -0.36 -0.378 0.712
O-score 0.5426 -1.4907 -0.5284 -0.1944 -0.4860 e o-score 1.7204 0.2252 0.5895 0.8233 0.6151 Probability (p-score) 0.6324 0.1838 0.3709 0.4516 0.3808
56
Appendix 7: SAMER AFRICA LIMITED
AMOUNT IN MILLIONS 2007 2008 2009 2010 2011 Total Assets 3,162.00 3,076.00 3,005.00 3,087.00 3,125.00 X1 (total assets / GNP price level index) 3,363.83 4,776.48 5,101.01 5,337.34 6,140.72
Total Liabilities 1,200.00 941.00 723.00 919.00 875.00 X2 (total liabilities / total assets) 0.3795 0.3059 0.2406 0.2977 0.2800
Working Capital (CA-CL) 1,178.00 1,255.00 1,469.00 1,364.00 1,523.00 X3 (working capital / total assets) 0.3725 0.4080 0.4889 0.4419 0.4874
Current Liabilities 1,048.00 812.00 606.00 796.00 754.00 Current Assets 2,226.00 2,067.00 2,075.00 2,160.00 2,277.00 X4(current liabilities / current assets) 0.4708 0.3928 0.2920 0.3685 0.3311
X5 (1 if TL > TA; else, 0) 0 0 0 0 0 Net Income 119 151 158 57 97 X6 (net income / total assets) 0.0376 0.0491 0.0526 0.0185 0.0310 Funds from Operations -100 51 338 79 -78 X7 (funds from operations / total liabilities) -0.0833 0.0542 0.4675 0.0860 -0.0891
X8 (1 if net loss for last 2 years, else 0) 0 0 0 0 0
X9 (Change in net income) 6.409 0.269 0.046 -0.639 0.702 O-score -4.2315 -1.8801 -3.0579 -1.5131 -2.1193 e o-score 0.0145 0.1526 0.0470 0.2202 0.1201 Probability (p-score) 0.0143 0.1324 0.0449 0.1805 0.1072
57
Appendix 8: SASINI TEA AND COFFEE LIMITED
AMOUNT IN MILLIONS 2007 2008 2009 2010 2011 Total Assets 3,825.00 6,195.00 8,000.00 9,060.00 9,462.00 X1 (total assets / GNP price level index) 4,069.15 9,619.74 13,580.0
7 15,664.4
9 18,593.1
1 Total Liabilities 870.00 2,079.00 2,335.00 2,570.00 2,699.00 X2 (total liabilities / total assets) 0.2275 0.3356 0.2919 0.2837 0.2852
Working Capital (CA-CL) 267.00 9.00 637.00 709.00 660.00 X3 (working capital / total assets) 0.0698 0.0015 0.0796 0.0783 0.0698
Current Liabilities 260.00 361.00 406.00 519.00 583.00 Current Assets 527.00 370.00 1,043.00 1,228.00 1,243.00 X4(current liabilities / current assets) 0.4934 0.9757 0.3893 0.4226 0.4690
X5 (1 if TL > TA; else, 0) 0 0 0 0 0 Net Income (41.00) 1,266.00 760.00 994.00 450.00 X6 (net income / total assets) -0.0107 0.2044 0.0950 0.1097 0.0476 Funds from Operations -110 86 353 404 497 X7 (funds from operations / total liabilities) -0.1264 0.0414 0.1512 0.1572 0.1841
X8 (1 if net loss for last 2 years, else 0) 0 0 0 0 0
X9 (Change in net income) -1.17 31.87 -0.3997 0.3079 0.547
O-score -0.6127 -17.9740 -1.6188 -2.1027 -2.1340
e o-score 0.5419 0.0000 0.1981 0.1221 0.1184 Probability (p-score) 0.3514 0.0000 0.1654 0.1088 0.1058
58
Appendix 9: CROWN BERGER LIMITED
AMOUNT IN MILLIONS 2007 2008 2009 2010 2011 Total Assets 1,526.00 1,948.00 1,858.00 1,972.00 2,215.00 X1 (total assets / GNP price level index) 1,623.40 3,024.90 3,153.97 3,409.53 4,352.54
Total Liabilities 610.00 1,126.00 1,021.00 1,070.00 1,163.00 X2 (total liabilities / total assets) 0.3997 0.5780 0.5495 0.5426 0.5251
Working Capital (CA-CL) 387.00 346.00 403.00 488.00 497.00 X3 (working capital / total assets) 0.2536 0.1776 0.2169 0.2475 0.2244
Current Liabilities 609.00 1,030.00 923.00 992.00 1,072.00 Current Assets 972.00 1,376.00 1,326.00 1,480.00 1,569.00 X4(current liabilities / current assets) 0.6265 0.7485 0.6961 0.6703 0.6832
X5 (1 if TL > TA; else, 0) 0 0 0 0 0 Net Income 74.00 28.00 152.00 149.00 129.00 X6 (net income / total assets) 0.0485 0.0144 0.0818 0.0756 0.0582
Funds from Operations 207 -215 508 342 118 X7 (funds from operations / total liabilities) 0.3393 -0.1909 0.4976 0.3196 0.1015
X8 (1 if net loss for last 2 years, else 0) 0 0 0 0 0
X9 (Change in net income) 0.17 -0.62 4.42 -0.02 -0.134 O-score -1.3531 1.1925 -3.0877 -0.5398 -0.1551 e o-score 0.2584 3.2953 0.0456 0.5829 0.8563 Probability (p-score) 0.2054 0.7672 0.0436 0.3682 0.4613
59
Appendix 10: SCANGROUP LIMITED
AMOUNT IN MILLIONS 2007 2008 2009 2010 2011 Total Assets 1,754.00 3,761.00 3,933.00 8,010.00 8,490.00 X1 (total assets / GNP price level index) 1,865.96 5,840.17 6,676.30 13,849.07 16,683.10
Total Liabilities 1,149.00 1,682.00 1,567.00 4,431.00 4,135.00 X2 (total liabilities / total assets) 0.6551 0.4472 0.3984 0.5532 0.4870
Working Capital (CA-CL) 466.00 1,903.00 1,658.00 2,878.00 3,981.00
X3 (working capital / total assets) 0.2657 0.5060 0.4216 0.3593 0.4689
Current Liabilities 1,146.00 1,678.00 1,555.00 4,240.00 3,798.00 Current Assets 1,612.00 3,581.00 3,213.00 7,118.00 7,779.00 X4(current liabilities / current assets) 0.7109 0.4686 0.4840 0.5957 0.4882
X5 (1 if TL > TA; else, 0) 0 0 0 0 0 Net Income 244.00 316.00 401.00 641.00 911.00 X6 (net income / total assets) 0.1391 0.0840 0.1020 0.0800 0.1073
Funds from Operations 63.00 (148.00) 306.00 1,013.00 187.00 X7 (funds from operations / total liabilities)
0.0548 -0.0880 0.1953 0.2286 0.0452
X8 (1 if net loss for last 2 years, else 0) 0 0 0 0 0
X9 (Change in net income) 0.245 0.295 0.269 0.599 0.421
O-score 0.4184 -1.0338 -1.7771 -1.0555 -1.2891 e o-score 1.5195 0.3557 0.1691 0.3480 0.2755 Probability (p-score) 0.6031 0.2624 0.1447 0.2582 0.2160
60
Appendix 11: CMC HOLDINGS LIMITED
AMOUNT IN MILLIONS 2006 2007 2008 2009 2010 Total Assets 7,814.00 9,325.00 12,023.00 13,294.00 14,668.00 X1 (total assets / GNP price level index) 8,312.77 9,920.21 18,669.59 22,566.68 25,360.57
Total Liabilities 4,272.00 5,263.00 7,089.00 8,020.00 9,212.00 X2 (total liabilities / total assets) 0.5467 0.5644 0.5896 0.6033 0.6280
Working Capital (CA-CL) 2,195.00 2,620.00 3,265.00 3,328.00 3,437.00
X3 (working capital / total assets) 0.2809 0.2810 0.2716 0.2503 0.2343
Current Liabilities 3,862.00 5,006.00 6,848.00 7,560.00 8,788.00 Current Assets 6,057.00 7,626.00 10,113.00 10,888.00 12,225.00 X4(current liabilities / current assets) 0.6376 0.6564 0.6771 0.6943 0.7189
X5 (1 if TL > TA; else, 0) 0 0 0 0 0
Net Income 434.00 618.00 927.00 540.00 407.00 X6 (net income / total assets) 0.0555 0.0663 0.0771 0.0406 0.0277
Funds from Operations 912.00 320.00 66.00 (382.00) (302.00) X7 (funds from operations / total liabilities)
0.2135 0.0608 0.0093 -0.0476 -0.0328
X8 (1 if net loss for last 2 years, else 0) 0 0 0 0 0
X9 (Change in net income) 0.276 0.424 0.5 -0.417 -0.246
O-score -0.6352 -0.3814 -0.2969 0.4512 0.5191 e o-score 0.5298 0.6829 0.7431 1.5702 1.6806 Probability (p-score) 0.3463 0.4058 0.4263 0.6109 0.6269
61
Appendix 12: UCHUMI SUPERMARKETS LIMITED
AMOUNT IN MILLIONS 2005 2006 2007 2008 2009 2010 Total Assets 1,854.00 1,491.00 1,584.00 1,608.00 2,440.00 3,154.00 X1 (total assets / GNP price level index)
2,238.40 1,586.17 1,685.11 2,496.94 4,141.92 5,453.18
Total Liabilities 2,968.00 2,222.00 2,576.00 2,633.00 2,621.00 1,614.00 X2 (total liabilities / total assets)
1.6009 1.4903 1.6263 1.6374 1.0742 0.5117
Working Capital (CA-CL) (1,640.0) (933.00) (214.00) (574.00) (760.00) (100.00)
X3 (working capital / total assets)
-0.8846 -0.6258 -0.1351 -0.3570 -0.3115 -0.0317
Current Liabilities 2,192.00 1,420.00 978.00 1,453.00 1,801.00 1,294.00
Current Assets 552.00 487.00 764.00 879.00 1,041.00 1,194.00 X4(current liabilities / current assets)
3.9710 2.9158 1.2801 1.6530 1.7301 1.0838
X5 (1 if TL > TA; else, 0) 1 1 1 1 1 0
Net Income (1,227.0) (751.00) (257.00) 95.00 421.00 865.00 X6 (net income / total assets) -0.6618 -0.5037 -0.1622 0.0591 0.1725 0.2743
Funds from Operations (523.00) (821.00) (685.00) (33.00) 425.00 236.00
X7 (funds from operations / total liabilities)
-0.1762 -0.3695 -0.2659 -0.0125 0.1622 0.1462
X8 (1 if net loss for last 2 years, else 0)
1 1 1 0 0 0
X9 (Change in net income) -0.76 0.39 0.66 1.37 3.43 1.05
O-score 9.4036 7.7240 6.5617 5.2642 0.0597 -1.0866 e o-score 12131 2262.019 707.4608 193.2962 1.0615 0.3374 (p-score) 0.9999 0.9996 0.9986 0.9949 0.5149 0.2523
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Appendix 13: A. BAUMANN & COMPANY LIMITED
AMOUNT IN MILLIONS 2003 2004 2005 2006 2007 Total Assets 383.00 387.00 189.00 155.00 138.00 X1 (total assets / GNP price level index) 413.02 446.59 228.19 164.89 146.81
Total Liabilities 101.00 123.00 42.00 51.00 69.00 X2 (total liabilities / total assets) 0.2637 0.3178 0.2222 0.3290 0.5000 Working Capital (CA-CL) 77.00 28.00 55.00 39.00 38.00 X3 (working capital / total assets) 0.2010 0.0724 0.2910 0.2516 0.2754
Current Liabilities 73.00 88.00 26.00 34.00 52.00 Current Assets 150.00 116.00 81.00 73.00 90.00 X4(current liabilities / current assets) 0.4867 0.7586 0.3210 0.4658 0.5778
X5 (1 if TL > TA; else, 0) 0 0 0 0 0 Net Income (7.00) (10.00) (128.00) (42.00) (35.00) X6 (net income / total assets) -0.0183 -0.0258 -0.6772 -0.2710 -0.2536 Funds from Operations -3 26 -52 -18 -21 X7 (funds from operations / total liabilities) -0.0297 0.2114 -1.2381 -0.3529 -0.3043
X8 (1 if net loss for last 2 years, else 0) 1 1 1 1 1
X9 (Change in net income) 0.86 -0.42 -1.18 0.67 0.17 O-score -1.1076 -0.3470 3.4391 0.6638 1.8202 e o-score 0.3303 0.7068 31.1592 1.9421 6.1733 Probability (p-score) 0.2483 0.4141 0.9689 0.6601 0.8606
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Appendix 14: KENYA ORCHARDS LIMITED
AMOUNT IN MILLIONS 2003 2004 2005 2006 2007 Total Assets 124.00 119.00 116.00 113.00 92.00 X1 (total assets / GNP price level index) 133.72 137.32 140.05 136.43 97.87
Total Liabilities 103.00 114.00 105.00 104.00 81.00 X2 (total liabilities / total assets) 0.8306 0.9580 0.9052 0.9204 0.8804
Working Capital (CA-CL) -1 6 6 -26 -17 X3 (working capital / total assets) -0.0081 0.0504 0.0517 -0.2301 -0.1848
Current Liabilities 50 40 42 50 40 Current Assets 49 46 26 24 23 X4(current liabilities / current assets) 1.0204 0.8696 1.6154 2.0833 1.7391
X5 (1 if TL > TA; else, 0) 0 0 0 0 0 Net Income -11 -16 -8 -9 -3 X6 (net income / total assets) -0.0887 -0.1345 -0.0690 -0.0796 -0.0326
Funds from Operations 1 -7 3 5 6 X7 (funds from operations / total liabilities) 0.0097 -0.0614 0.0286 0.0481 0.0741
X8 (1 if net loss for last 2 years, else 0) 1 1 1 1 1
X9 (Change in net income) 0.01 -0.455 0.5 0.437 0.667
O-score 3.4022 4.5620 3.4894 4.0484 3.4730 e o-score 30.0303 95.772 32.7657 57.3037 32.2338 Probability (p-score) 0.9678 0.9897 0.9704 0.9828 0.9699
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Appendix 15: THETA TEA FACTORY
AMOUNT IN MILLIONS 1997 1998 1999 2000 2001 Total Assets 192.00 433.00 300.00 724.00 553.00 X1 (total assets / GNP price level index) 202.11 466.94 346.20 874.11 588.30
Total Liabilities 164.00 402.00 234.00 403.00 320.00 X2 (total liabilities / total assets) 0.8542 0.9284 0.7800 0.5566 0.5787
Working Capital (CA-CL) (30.00) 15.00 (26.00) (17.00) (10.00) X3 (working capital / total assets) -0.1563 0.0346 -0.0867 -0.0235 -0.0181
Current Liabilities 164.00 362.00 234.00 403.00 245.00 Current Assets 134.00 377.00 208.00 386.00 235.00 X4(current liabilities / current assets) 1.2239 0.9602 1.1250 1.0440 1.0426
X5 (1 if TL > TA; else, 0) 0 0 0 0 0 Net Income 7 4 9 7 5 X6 (net income / total assets) 0.0365 0.0092 0.0300 0.0097 0.0090
Funds from Operations -111 -90 -120 117 -73 X7 (funds from operations / total liabilities) -0.6768 -0.2239 -0.5128 0.2903 -0.2281
X8 (1 if net loss for last 2 years, else 0) 0 0 0 0 0
X9 (Change in net income) 0.01 -0.428 1.25 -0.22 -0.285
O-score 4.3606 3.8295 2.7812 0.5166 1.6956 e o-score 78.3036 46.0418 16.1382 1.6762 5.4500 Probability(p-score) 0.9874 0.9787 0.9417 0.6263 0.8450
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Appendix 16: DUNLOP KENYA LIMITED
AMOUNT IN MILLIONS 2004 2005 2006 2007 2008 Total Assets 311.00 277.00 797.00 788.00 1,090.00 X1 (total assets / GNP price level index) 358.89 334.43 847.87 838.30 1,159.57
Total Liabilities 112.00 97.00 603.00 230.00 415.00 X2 (total liabilities / total assets) 0.3601 0.3502 0.7566 0.2919 0.3807 Working Capital (CA-CL) 52.00 36.00 (24.00) 82.00 251.00 X3 (working capital / total assets) 0.1672 0.1300 -0.0301 0.1041 0.2303
Current Liabilities 91.00 83.00 496.00 194.00 338.00 Current Assets 143.00 119.00 472.00 276.00 589.00 X4(current liabilities / current assets) 0.6364 0.6975 1.0508 0.7029 0.5739
X5 (1 if TL > TA; else, 0) 0 0 0 0 0 Net Income 39 23 23 -147 34 X6 (net income / total assets) 0.1254 0.0830 0.0289 -0.1865 0.0312 Funds from Operations 27 10 -33 -48 -100 X7 (funds from operations / total liabilities) 0.2411 0.1031 -0.0547 -0.2087 -0.2410
X8 (1 if net loss for last 2 years, else 0) 0 0 0 1 1
X9 (Change in net income) 0.44 -0.41 0 -5.32 0.478 O-score -1.3436 -0.5381 2.2093 3.0331 -0.1513 e o-score 0.2609 0.5839 9.1091 20.7606 0.8596 Probability (p-score) 0.2069 0.3686 0.9011 0.9540 0.4623
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Appendix 17: Companies listed on the NSE
AGRICULTURAL Eaagads Ltd Kapchorua Tea Co. Ltd Kakuzi Limuru Tea Co. Ltd Rea Vipingo Plantations Ltd Sasini Ltd Williamson Tea Kenya Ltd COMMERCIAL AND SERVICES Express Ltd Kenya Airways Ltd Nation Media Group Standard Group Ltd TPS Eastern Africa (Serena) Ltd Scangroup Ltd Uchumi Supermarket Ltd Hutchings Biemer Ltd TELECOMMUNICATION AND TECHNOLOGY AccessKenya Group Ltd Safaricom Ltd AUTOMOBILES AND ACCESSORIES Car and General (K) Ltd CMC Holdings Ltd Sameer Africa Ltd Marshalls (E.A.) Ltd BANKING Barclays Bank Ltd CFC Stanbic Holdings Ltd Diamond Trust Bank Kenya Ltd Housing Finance Co Ltd Kenya Commercial Bank Ltd National Bank of Kenya Ltd NIC Bank Ltd Standard Chartered Bank Ltd
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Equity Bank Ltd Co-operative Bank of Kenya Ltd. I&M Holdings Limited INSURANCE Jubilee Holdings Ltd Pan Africa Insurance Holdings Ltd Kenya Re-Insurance Corporation Ltd British American Investments CIC Insurance Group Liberty Kenya Holdings INVESTMENT Trans-Century Limited Olympia Capital Holdings ltd Centum Investment Co Ltd MANUFACTURING AND ALLIED B.O.C Kenya Ltd British American Tobacco Kenya Ltd Carbacid Investments Ltd East African Breweries Ltd Mumias Sugar Co. Ltd Unga Group Ltd Eveready East Africa Ltd Kenya Orchards Ltd A.Baumann CO Ltd CONSTRUCTION AND ALLIED Athi River Mining Bamburi Cement Ltd Crown Berger Ltd E.A.Cables Ltd E.A.Portland Cement Ltd ENERGY AND PETROLEUM Kenol Kobil Ltd Total Kenya Ltd KenGen Ltd Kenya Power & Lighting Co Ltd