130245157365_75

7/27/2019 130245157365_75

1/11

JM INTERNATIONAL JOURNAL OF FINANCE DECEMBER, 2010

JMACADEMYITM Page 65

An Analysis of the Profitability and Liquidity Position of the Select Software

Companies in India

Dr.G.Kavitha

Asst. Professor of Commerce, Government Arts College, Coimbatore

Dr.L.Manivannan

Associate professor and Research Advisor, Erode Arts College, Erode

Abstract The prosperity of any country depends on the performance of its

corporate sector. In the liberalized economic policy regimes, the corporate

sector has been assigned a major role as the driver of growth and development

process of the Indian economy. Information technology industry has come to

occupy a major chunk of the corporate sector. From the day of economic

liberalization till recent days, Indias information technology industry has

registered a phenomenal growth in terms of numerical strength of software

companies as well as investors. The industry contributes a lions share to the

growth of the corporate sector in terms of employment generation, revenue andentrepreneurial skills. Its amazing performance resulting in high rate of return to

investors has attracted investors in domestic and abroad. Financial performance

analysis is a process of evaluating the performance of the companies to find out

its financial and operational efficiency using the financial statements. Through

financial statement analysis the firms financial position can be determined.

Financial analysis involves selection, evaluation, analysis and interpretation of

financial variables. The risk and return of the companies have to be carefully

analysed to evaluate their performance and to serve the needs of the interested

parties.

Keywords : Operating Profit, Net Sales, Net Worth

Introduction

he Information technology industry consists of all software companies which provide software

solutions to problems relating to business management namely production, marketing, finance

and human resource. Information technology has created a revolutionary change in the Indian

economic scenario. Today software has come to stay as part and parcel of human life. The importance of

T

7/27/2019 130245157365_75

2/11

JMACADEMY OF IT & MANAGEMENT


software application was felt more after the globalization of the economy. This industry has created a

revolution by employing large number of people, earning huge profit and joining the club of Multi-

National Corporations. The Indian software companies hold the distinction of advancing the country into

the new-age economy. The growth momentum attained by the overall economy since the late 1990s to a

great extent can be owed to the Information Technology sector, well supported by a liberalized policy

regime with reduction in telecommunication cost and import duties on hardware and software. Today,

India is the world leader in information technology and business outsourcing.

Financial and Operational efficiencyA Theoretical Framework

Efficiency involves seeking a good balance between financial system in terms of resources such as time,

money, infrastructure, materials, and the achievement of an organization's goals and objectives.

Efficiency of a company is reflected in the financial statements of a company i.e., a companys efficiency

can be evaluated only by analyzing its financial performance. A company is said to be efficient if it makes

optimum utilization of resources and achieves its goal of wealth maximization. In other words, the

achievement of goals in an economic way is termed as efficiency. Economic or financial efficiency refers

to the production and distribution of goods at the lowest possible cost which in turn will increase the

profits of the organization. Carrying out all the operational activities in the most effective manner is

referred as operational efficiency. If the organizations handle the issues such as fund acquisition, resource

allocation, asset utilisation and other functional operations efficiently, then it can achieve its objectivessmoothly.

Financial performance analysis is a process of evaluating the performance of the companies to find out its

financial and operational efficiency using the financial statements. Through financial statement analysis

the firms financial position can be determined. Financial analysis involves selection, evaluation, analysis

and interpretation of financial variables. The risk and return of the companies have to be carefully

analysed to evaluate their performance and to serve the needs of the interested parties.

Statement of the problem

In recent years, Information technology industry has come to occupy a major chunk of the corporatesector. From the day of economic liberalization till recent days, Indias information technology industry

has registered a phenomenal growth in terms of numerical strength of software companies as well as

investors. Its amazing performance resulting in high rate of return to investors has attracted investors from

abroad. Foreign Direct Investment and Foreign Institutional Investments have increased considerably in

Information Technology industry. Indian software companies cater to the software requirements directly

and through Business Process Outsourcing. As a result the country has become a knowledge centre of the

world.

7/27/2019 130245157365_75

3/11



This situation raises the following issues:

(1) Is the performance of Indian software companies sustainable?(2) Whether all the software companies in India perform equally well?(3) What are the factors influencing their performance?

The present study will find the answers to the above questions apart from giving policy implications for

the betterment of software companies in India.

Objectives

The study is focused on fulfilling the following objectives:

1. To evaluate the overall financial performance of select software companies in India.2. To identify the determinants of operational efficiency of the selected software companies.

Significance of the study

The Indian software industry has been the growth engine of the Indian economy. Hence, the benefits of

the present study are varied. It would help the management understand the strengths and weakness of a

company. It would help the policy makers decide on policy implications. It would be useful to thecreditors in assessing the credit worthiness of a company to grant credit. This study will benefit the

investors in making better investment decisions. The results of the financial analysis are also useful to the

bankers, employees, researchers, academicians, customers and public in knowing the financial condition

of the companies.

Methodology

Sampling design

The study is confined to the Indian Software Industry. The data for the study have been collected mainly

from the prowess database. From the total population of 396 software companies, the sample companieswere selected based on the following criteria:

1) The company should have been listed with any of the stock exchanges in India.2) The companies which have the required financial data for a continuous period of 10 years have

been selected, for this study

7/27/2019 130245157365_75

4/11



Sources of data

The data required for the study are secondary in nature. The main source of the data for the present study

is the Prowess database of the Centre for Monitoring Indian Economy and Capital line plus corporate

database. Both the database provides financial information that includes financial statements, ratios, funds

flow statements, cash flow statements, share prices, etc. of various corporate companies belonging to

different industries. Other sources of data were annual published reports of the companies.

Period of study

The period of study has been confined to one decade, from 1st

April 1997 to 31st

March 2007. In order to

facilitate easy reference, the financial year from 1st

April 1997 to 31st

March 1998, has been referred to as

1998, the financial year from 1st

April 1998 to 31st

March 1999, has been referred to as 1999 and so on.

Classification of the sample companies

The sample companies have been classified into different groups based on their size, year of

establishment and region to have an overall idea of the sample distribution and to enable group wise

analysis.

Classification based on size of the companies

As size plays a very important role in influencing the financial performance of the companies, it has been

considered as an important criterion for classifying the companies. In order to classify the companies into

three groups based on the size as small, medium and large percentile values of Net Sales have been used.

The companies for which the average sales have been more than the 70th

percentile are classified as large,

less than 30th

percentile are small and between 30th

and 70th

percentile are classified as medium sized. Out

of the 65 selected software companies, 20 (30.77%) companies are small sized, 25 (38.46%) companies

are medium sized and 20 (30.77%) companies are large sized.

Classification based on the companys year of establishment

Considering the year of establishment as the year of birth and 31st

March 2007 as the concluding year, the

sample companies are classified as old, moderately old and new. Companies established upto 1986 are

classified as old, companies established after 1992 are classified as new and the companies established

from 1987 and 1992 are classified as moderately old. It has been found that out of 65 selected software

companies 8 (12.31%) companies are old, 18 (27.69%) companies are moderately old and 39 (60%)

companies are new.

7/27/2019 130245157365_75

5/11



Classification based on the region / location of the companies

The Sample companies have been classified into two groups based on the states in which they have been

incorporated, namely, companies in the Northern region and companies in the Southern region. The states

of Andhra Pradesh, Karnataka, Kerala and Tamil Nadu have been categorized as Southern Region and the

remaining states, as Northern Region. It has been found that 41 companies (63.08%) are located in

northern region and the rest of the 24 companies (36.92%) are located in southern region.

Determinants of Operational Efficiency of the Select Software Companies

An attempt has been made to study the relationship between operating profit and its determinants among

the more efficient, moderate and less efficient group of companies. Multiple Regression models were

used to identify the vital internal financial variables which influence the operating profit. Operating profit

is used as the dependent variable representing the earnings power of the selected companies. After

making preliminary correlation analysis ten independent variables were identified. They are Net Sales,

Total Assets, Current Assets, Current Liabilities, Long term debt, Fixed assets, Operating expenses, Net

working capital, Employee Cost and Retained Earnings. Based on t value of the model the significance

of these variables was tested and reported for the three classified groups i.e., more efficient, moderately

efficient and less efficient group of companies. To ascertain the relationship between the operating profit

and its determinants, the following null hypothesis (H0) and alternate hypothesis (Ha) have been

constructed.

H0 = There is no significant influence of the independent variables on the Operating Profit H a = There is

significant influence of the independent variables on the Operating Profit

Determinants of operational efficiency of the less efficient companies

The relationship between the Operating Profit and the variables Net Sales, Total Assets, Current Assets,

Current Liabilities, Long term debt, Fixed assets, Operating expenses, Net working capital, Employee

cost and Retained earnings among the less efficient companies has been ascertained using multiple

regression analysis. Table.2 shows the results of the stepwise multiple regression models of thedeterminants of Operating Profit (EBIT) of the less efficient companies.

This regression analysis examines the strength of the relationship between the dependent variable

operating profit and independent variables taken together and the impact of these variables on the

earnings of the less efficient companies. From the table 2.1.1, it can be observed that the value of multiple

correlations is 0.504 and the R2 value is 0.254. The model explains 25% variations in its operating profit.

The regression coefficient values of the independent variables indicate the extent of their relationship with

the dependent variables. Both positive and negative values of regression coefficient have been observed.

7/27/2019 130245157365_75

6/11



The F ratio, with its value 8.700 shows that the regression model with the simultaneous effect of all the

independent variables is statistically significant at one percent level.The T values reveal that Net worth

and Fixed assets influence Operating profit at one percent level of significance. Moreover the variable,

long term debt negatively influences the operating profit at one percent level of significance. As the F

ratio proves that the regression model is statistically significant, the null hypothesis is rejected.

Table 2 Regression Analysis -Less efficient companies

Dependent Variable: Operating profit (EBIT)

B Std. Error t Sig. Correlations

(Constant) .890 .422

Net sales -0.0254 .069 -.372 Ns .062

Net worth -.331 .067 -4.956 ** -.001

Current assets 1.122 2.216 .506 Ns -.064

Current Liabilities -1.302 2.208 -.590 Ns -.040

Long term debt -.620 .127 -4.865 ** -.030

Fixed assets .468 .037 12.520 ** .511

Operating Expenses -.712 .498 -1.431 Ns -.032

Net Working Capital -.901 2.215 -.407 Ns -.053

Employee Cost .247 .347 .711 Ns .042

Retained Earnings -0.03212 .018 -.174 Ns .020

R R Square F Sig.

0.504 0.254 8.700 **

** Significant at 1% level respectively

Determinants of the operational efficiency of the moderately efficient companiesTable 3 depicts the results of the stepwise multiple regression analysis for the moderately efficientcompanies.

The relationship between the Operating Profit and the variables Net Sales, Total Assets, Current Assets,

Current Liabilities, Long term debt, Fixed assets, Operating expenses, Net working capital, Employee

cost and Retained earnings among themoderately efficient companies has been ascertained using multiple

regression analysis. The regression analysis examines the strength of the relationship between the

dependent variable Operating Profit and independent variables taken together and the impact of these

variables on the earnings of the moderately efficient companies.

7/27/2019 130245157365_75

7/11



Table 3 Regression Analysis - Moderately efficient companies


BStd. Error

Errort Sig. Correlations

(Constant) 1.588 8.204

Net sales .371 .117 3.170 ** .382

Net worth -.383 .181 -2.116 * .334

Current assets -1.035 9.540 -.109 Ns .313

Current Liabilities .915 9.546 .096 Ns .211

Long term debt -9.106E-02 .152 -.600 Ns .130

Fixed assets -.253 .075 -3.363 * .225

Operating Expenses -4.134 1.502 -2.752 ** .238

Net Working Capital 1.221 9.540 .128 Ns .328

Employee Cost .548 .341 1.605 Ns .266

Retained Earnings .535 .186 2.884 ** .364

R R Square F Sig.

0.673 0.453 16.483 **

* ,** Significant at 5% and 1% level respectively

From table 3, it can be observed that the value of multiple correlations is 0.673 and R2 value is 0.453. The

adjusted R2

is 0.45 which indicates that 45.3 per cent variation is explained by selected independent

variables. The extent of relationship between the dependent and independent variables is explained by the

regression coefficients. Both positive and negative values of regression coefficients indicate the extent of

relationship with the dependent variables. The value of the F ratio is 16.483 which shows that the

regression model with the simultaneous effect of all the independent variables is statistically significant atone percent level. The T values reveal that Net worth and Fixed Assets influence operating profit at 5 %

level of significance. Net sales, Operating Expenses, and Retained Earnings are the variables which

influence the operating profit at 1 % level of significance. Operating expenses negatively influence the

operating profit. Since F ratio with its value at 1 percent significance level proves that the model is

valid, the null hypothesis is rejected at 1 per cent level.

7/27/2019 130245157365_75

8/11



Determinants of operational efficiency of the more efficient companies

Table 4 depicts the results of the stepwise multiple regression analysis for the moderately efficient

companies.

The regression analysis examines the strength of the relationship between the dependant variable

Operating Profit and independent variables taken together and the impact of these variables on the

earnings of the more efficient companies. The coefficient of multiple correlations, with its value 0.987

shows a high positive correlation of independent variables taken together with dependent variables.

Table.4 Regression Analysis - More efficient companies


B Std. Error t Sig. Correlations

(Constant) -2.496 8.416

Net sales 0.08234 .048 1.725 Ns .969

Net worth -.368 .207 -1.774 Ns .979

Current assets 0.07291 .908 .080 Ns .973

Current Liabilities -.148 .912 -.162 Ns .896

Long term debt -.242 .147 -1.648 Ns .150Total fixed assets .116 .092 1.271 Ns .956

Operating Expenses 0.0009168 .152 .006 Ns .814

Net Working Capital 0.0193 .906 .021 Ns .904

Employee Cost .189 .092 2.054 * .971

Retained Earnings .470 .214 2.194 * .980

R R Square F Sig.

0.987 0.973 651.407 **

*, ** Significant at 5% and 1% level respectively

7/27/2019 130245157365_75

9/11



The R2 value is 0.973 which indicates that 97.3 per cent variation in operating profit is explained by

selected independent variables. The regression coefficients of the independent variables have shown the

extent of their relationship, both positive and negative with the dependent variable operating profit. The

F ratio value of 651.407 shows that the regression model with the simultaneous effect of all the

independent variables has been statistically significant at one percent level. The T values reveal that

Employee Cost and Retained Earnings influences operating profit at 5 % level of significance. Since F

ratio with its value at 1 percent significance level proves that the model is valid, the null hypothesis is

rejected at 1 per cent level.

Summary and finding

Determinants of Operating profit of the selected software companies

In order to find out the factors influencing the operating Profit, multiple regression model was

constructed for all the three groups.

In case of less efficient companies the Net worth and Fixed assets positively influence the

operating profit. Long term debt negatively influences the operating profit.

The Operating profit of the moderately performing companies has been positively influenced by

the factors such as Net worth and Fixed Assets at 5 % level of significance. Net sales, Retained

Earnings are the variables which influence the operating profit at 1 % level of significance.

Operating expenses negatively influence the operating profit.

In case of more efficient companies the employee cost and the Retained earnings positively

influence the operating Profit. All the other variables do not influence the operating profit.

Suggestions

Based on the above findings and conclusions the following suggestions are made:

1. Most of the Indian software companies have used their own funds in running the business i.e.,their capital structure comprises more of owners equity and very less debt capital. Hence it has

been suggested that those companies should avail cost advantage by making use of leveraged

funds to provide value addition to their business turnover.

2. Companies should be very conscious in utilizing their assets and other financial resources in orderto increase their return on investment.

3. More efficient companies can retain a major portion of their earnings instead of paying out asdividends. This would help them increase their business performance as the funds can be used in

new areas of business.

7/27/2019 130245157365_75

10/11



4. The companies should try to maintain good relations with foreign countries to position itself withforeign countries to enhance their export earnings.

5. Care should be taken by the Indian software firms to improve the after sales services which inturn will fetch them new customers globally.

6. With the growing scarcity for trained manpower, the salaries in the software industry have beenrising (at par with MNCs) at the rate of 30 per cent per annum. In addition, the employers are

offering other incentives such as stock options and are spending too much to make the working

environment more attractive for their workers. The rising salaries have reduced the margin of the

advantage in wage cost that Indian software companies enjoyed initially. Hence, the softwarecompanies can rethink and restructure the salary levels and adopt standardized salary procedures.

7. The Government can come forward to set up new Software Technological Parks. Tax incentivesand tax holidays can be given to those companies which start their business in STPIs.

8. The Government should help the software firms achieve high standards of corporate governance,risk management, security and an adequate intellectual property framework to promote the

business standard.

Conclusion

The Government of India should take initiatives to establish India as a trusted hub for professional

services. NASSCOM can act as a mediator to achieve this. The Indian IT Industry, for more than two

decades, has shared high growth and led into countrys economy boom and also an increase in

consumption power. However currently we have our own competitors emerged and today they are

significantly grown their market in this industry. One of the key competitors for this service industry

is China, as they also have surplus human resources. The reason they are found as key competitor is

their trade mission Produce in mass, with low margin (ie, low profit) and earn more. Currently

china is a growing Red alert for Indian IT Industry. In the recent past the Government of China

addressed their weaknesses by improving the communications, open policies for trade and renewed

education systems. Our Government should initiate necessary steps to rectify the deficiencies of the

Indian software industry and help them to grow.

References[1] Agarwal J.D., Sagarika Ghosh.1994.Quantitative Techniques for financial analysis. Indian Institute of

finance, New Delhi, pp.53-62.

[2] Arthue.J.Keown, John D.Martin, William Petty.Jand David F.Scott.Jr 2001, foundation of Finance The

Logic and Practice of Financial Management Third Edition, Prentice Hall.

[3] Douglas R.Emery and JohnD.Finnerty, Corporate Management, Prentice Hall Inc.,1997.

[4] Khan M Y and Jain P K, Financial Management Text, Problems and cases, Tata McGraw Hill

Publishing Company Ltd., NewDelhi,4th edition ,2004.

7/27/2019 130245157365_75

11/11



[5] Pandey I M, Financial Management, Vikas Publishing House Pvt.Ltd.,9th edition,2005.[6] Prasanna Chandra, Financial Management Theory and Practice, Tata McGraw Hill Publishing

Company Ltd., NewDelhi,4th edition ,2004

[7] Prasanna Chandra, Financial Management Theory and Practice, Tata McGraw Hill Publishing

Company Ltd.,New Delhi 6th edition

[8] Sharma R.K & Shashi K.Gupta, Management Accounting, Kalyani Publishers, Ninth edition

[9] Dr.S.N.Maheswari, Financial Management, Principles and Practice, Sultan Chand and Sons, Tenth edition,

2005.

[10] Kuchaal,S.C., Financial Management, An analytical and conceptual approach.,8 th EditionChaitanya

Publishing House, Allahabad, 1982.

[11] Walker E.W.Essential Financial Management, Prentice Hall of India Pvt Ltd., New Delhi, 1976.

[12] Van Horne C James., Financial Management and Policy, Prentice Hall of India Pvt Ltd.., New

Delhi,110001 Eleventh Edition 2002.

[13] Weston J. and Brigham, Management Finance, Holt Rinehart and Winston, 1976.

130245157365_75

Documents