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The Impact of Corporate Governance on the FinancialPerformance : A Study of Nifty Companies

Minor Project Report submitted to

Prof Surendra S Yadav & Prof PK Jain

by

Ajay Kumar Dhamija (2010SMZ8205)

Department of Management Studies

Indian Institute of Technology, DelhiNovember 2010

Executive Summary

Corporate governance is the set of processes, customs, policies, laws, and institutions affectingthe way a corporation (or company) is directed, administered or controlled. The contemporaryliterature highlights the importance of various corporate governance for the financial perfor-mance of the firm. These studies so far have focussed on the effect of composite corporategovernance index on the firm performance. The current study attempts to find out the individ-ual effect of each corporate governance variable on the firm performance.The firm performance is measured by Tobin’s Q and Return on Asset (ROA). After the literaturesurvey, the corporate variables considered as independent variables for this study are duality ofchairman’s role, board size, proportion on institutional investors, concentrated ownership, au-dit committee chairman, percentage of non-executive and independent directors in the board .Besides these, other variables like firm size in terms of turnover and gearing are also enteredinto the regression in order to control their effect on dependent variables. Tobin’s Q is found tobe significantly correlated with Duality of chairman’s role (r = -0.22,p < 0.01), Debt to TotalCapital ratio (r = -0.33, p < 0.001) and Firm’s Turnover (r = -0.15, p < 0.05). The ROA issignificantly correlated with Duality of chairman’s role (r = -0.28, p < 0.05) and Debt to TotalCapital ratio (r = -0.44, p < 0.05). Panel unit root tests revealed absence of unit roots in thetime series. Panel data regression - fixed effect models show that Duality of chairman’s roleand Debt to Total Capital ratio are significant predictors of Tobin’s Q and ROA. In addition,Firm’s Turnover is a significant predictor of Tobin’s Q, but not of ROA. Debt to Total Capitalratio is highly significant (p<.001) predictor of both Tobin’s Q and ROA. The predictor vari-ables explain 36.9 % of the variance of Tobin’s Q and 32.1 % of thevariance of ROA. Grangertests do not show the causality relationship between any pair of variables. Neither institutionalinvestors nor concentration of ownership exert any influence on firm performance. All the firmshave non-executive director as chairman of the audit committee, so effect of this variable is notdetermined.

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Acknowledgment

I would like to express my deep sense of gratitude to Prof Surendra S Yadav and Prof PKJain for their invaluable help and guidance during the course of project. I am thankful to themfor constantly encouraging me by giving their constructive and critical evaluation on my work.

Ajay Kumar Dhamija (2010SMZ8205)November 2010Department of Management Studies, IIT Delhi

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Contents

1 Introduction 1

2 Literature Review 52.1 Role of Independent Directors . . . . . . . . . . . . . . . . . . . . . . . . . . 62.2 Dual Role of CEO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.3 Board Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.4 Audit Committee, Ownership Concentration and other variables . . . . . . . . 8

3 Objective and Methodology 103.1 Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103.2 Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.2.1 Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103.2.2 Dependent Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . 123.2.3 Independent Variables . . . . . . . . . . . . . . . . . . . . . . . . . . 133.2.4 Empirical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153.2.5 Data Source and Data Types . . . . . . . . . . . . . . . . . . . . . . . 153.2.6 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

4 Results 204.1 Description of Overall Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204.2 Description of Firm-wise Data . . . . . . . . . . . . . . . . . . . . . . . . . . 224.3 Panel Data Unit Root Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244.4 Inter-correlation Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244.5 Panel Data Regression Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5 Summary and Recommendation 305.1 Conclusion and Managerial Implication . . . . . . . . . . . . . . . . . . . . . 305.2 Limitations of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Appendix 33

A Firm-wise summary of corporate governance 33

iv

List of Figures

1.1 Corporate Governance Framework . . . . . . . . . . . . . . . . . . . . . . . 2

4.1 ABB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

5.1 Firm Size (FSIZE) vs Gearing (DTC) . . . . . . . . . . . . . . . . . . . . . 31

Firm-wise summary of corporate governance . . . . . . . . . . . . . . . . . . . . 33

A.1 ABB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

A.2 Ambuja Cements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

A.3 ACC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

A.4 BHEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

A.5 BPCL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

A.6 Bharti Airtel Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

A.7 CIPLA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

A.8 CAIRN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

A.9 DLF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

A.10 GAIL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

A.11 HCL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

A.12 Hero Honda Motors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

A.13 Hindalco Industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

A.14 Hindustan Unilever Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

A.15 Idea Cellular . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

A.16 Infosys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

A.17 ITC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

A.18 Jaiprakash Associates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

A.19 Jindal Steel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

A.20 Larsen and Toubro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

A.21 Mahindra and Mahindra . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

A.22 Maruti Suzuki Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

A.23 NTPC Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

A.24 ONGC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

A.25 Power Grid Corporation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

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LIST OF FIGURES

A.26 Ranbaxy Laboratories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59A.27 Reliance Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . 60A.28 RIL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61A.29 Reliance Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62A.30 Reliance Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63A.31 Siemens Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64A.32 SAIL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65A.33 Sterlite Industries Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66A.34 Sun Pharmaceuticals Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67A.35 Suzlon Energy Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68A.36 TCS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69A.37 Tata Motors Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70A.38 Tata Power Co Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71A.39 Tata Steel Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72A.40 Unitech Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73A.41 Wipro Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

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List of Tables

4.1 Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4.2 ABB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4.3 Panel Unit Root Tests - Independent Variables . . . . . . . . . . . . . . . . . . 24

4.4 Panel Unit Root Tests - Independent Variables . . . . . . . . . . . . . . . . . . 24

4.5 Panel Unit Root Tests - Dependent Variables . . . . . . . . . . . . . . . . . . . 24

4.6 Intercorrelation Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4.7 Fixed Effects Model - Regression for TQ . . . . . . . . . . . . . . . . . . . . . 28

4.8 Fixed Effects Model - Regression for ROA . . . . . . . . . . . . . . . . . . . . 29

Firm-wise summary of corporate governance 33A.1 ABB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

A.2 Ambuja Cements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

A.3 ACC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

A.4 BHEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

A.5 BPCL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

A.6 Bharti Airtel Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

A.7 CIPLA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

A.8 CAIRN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

A.9 DLF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

A.10 GAIL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

A.11 HCL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

A.12 Hero Honda Motors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

A.13 Hindalco Industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

A.14 Hindustan Unilever Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

A.15 Idea Cellular . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

A.16 Infosys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

A.17 ITC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

A.18 Jaiprakash Associates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

A.19 Jindal Steel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

A.20 Larsen and Toubro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

A.21 Mahindra and Mahindra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

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LIST OF TABLES

A.22 Maruti Suzuki Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55A.23 NTPCLtd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56A.24 ONGC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57A.25 Power Grid Corporation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58A.26 Ranbaxy Laboratories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59A.27 Reliance Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60A.28 RIL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61A.29 Reliance Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62A.30 Reliance Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63A.31 Siemens Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64A.32 SAIL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65A.33 Sterlite Industries Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66A.34 Sun Pharmaceuticals Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67A.35 Suzlon Energy Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68A.36 TCS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69A.37 Tata Motors Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70A.38 Tata PowerCo Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71A.39 Tata Steel Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72A.40 Unitech Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73A.41 Wipro Ltd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

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1

Introduction

Corporate governance is the set of processes, customs, policies, laws, and institutions affectingthe way a corporation (or company) is directed, administered or controlled. Corporate gover-nance also includes the relationships among the many stakeholders involved and the goals forwhich the corporation is governed. The principal stakeholders are the shareholders, the boardof directors, employees, customers, creditors, suppliers, and the community at large. An impor-tant theme of corporate governance is to ensure the accountability of certain individuals in anorganization through mechanisms that try to reduce or eliminate the principal-agent problem.Another facet of corporate governance focuses on the impact of a corporate governance systemon economic efficiency, with a strong emphasis on shareholders’ welfare. Corporate governanceis a relationship among stakeholders that is used to determine and control the strategic direc-tion and performance of organizations, identify the ways to ensure that strategic decisions aremade effectively and to establish order between the firm’s owners and its top-level managers.The interrelationship among stakeholders and various internal and external factors which con-trol the modern corporations are delineated in the corporate governance framework shown inFigure 1.1.

The prevalence of financial scandals, the world over, highlight the increasing importance ofcorporate governance. The major facets of corporate governance are transparency and disclo-sure, control and accountability, and the appropriate form of board structure that may preventsuch scandals. The relationship between corporate governance reforms and recession (Kota &Tomar, 2010) is cyclical, where corporate regulations and restructuring follow after corporatecollapse during recession (Clarke, 1998). Recent financial collapses like financial meltdown,European Crisis etc have highlighted the importance of corporate governance for effective riskmanagement and the role it should play in restoring the trust of shareholders. Shareholdershave often ignored the excessive leverage for the sake of short term gains and the regulatorsalso fail to rein in the financial institutions who operate with too little capital, excessive lever-age,liquidity risk and poor mortgage lending practices. In this regard, corporate governanceneeds strengthening for stability and soundness of corporate in specific and financial system in

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Figure 1.1: Corporate Governance Framework

general.

Board members should update their knowledge on financial issues and risk-management tofulfill their functions. Boards should conduct annual appraisal of their performance and report toowners. Proper risk management frameworks, processes, and implementation practices shouldbe established.

Corporate Governance in India

In 1996, the Confederation of Indian Industry (CII) took initiative for first institutional evalu-ation of corporate governance with an objective to develop a code for corporate governance.The 1999 Kumar Mangalam Birla Committee on Corporate Governance made recommenda-tions delineating the responsibilities and obligations of boards and management for good gov-ernance and emphasizes the rights of shareholders. The 2000 Task Force on Corporate Ex-cellence through Governance recommended the phased implementation of essential measures,depending upon the size and the capabilities of the companies and market requirements. TheAdvisory Group on Corporate Governance: Standing Committee on International FinancialStandards and Codes 2001, gave special importance to audit committees and the appointmentof truly independent directors to raise the quality of board deliberations and performance. In2002, the Reserve Bank set up the Consultative Group of Directors of banks and financial in-

2

Introduction

stitutions recommended for making the role of the Board of Directors more effective with aview to minimizing risks and overexposure. The Naresh Chandra Committee on CorporateAudit and Governance Committee in 2002 recommended for changes in the statutory auditor-company relationship, the procedure for appointment of auditors and determination of auditfees, independence of auditing functions. The SEBI Committee on Corporate Governance in2003 discussed issues related to audit committees, audit reports, independent directors, relatedparties, risk management, directorships and director compensation, codes of conduct and finan-cial disclosures. Finally, the Naresh Chandra Committee II on Regulation of Private Companiesand Partnerships was constituted to suggest a scientific and rational regulatory environment.

Clause 49 (Kota & Tomar, 2010) of the Listing Agreement, which deals with CorporateGovernance norms that a listed entity should follow, was first introduced in the financial year2000-01 based on recommendations of Kumar Mangalam Birla committee. After these rec-ommendations were in place for about two years, SEBI, in order to evaluate the adequacy ofthe existing practices and to further improve the existing practices set up a committee underthe Chairmanship of Mr Narayana Murthy during 2002-03. The Murthy committee, after hold-ing three meetings, had submitted the draft recommendations on corporate governance norms.After deliberations, SEBI accepted the recommendations in August 2003 and asked the StockExchanges to revise Clause 49 of the Listing Agreement based on Murthy committee recom-mendations. This led to widespread protests and representations from the Industry thereby forc-ing the Murthy committee to meet again to consider the objections. The committee, thereafter,considerably revised the earlier recommendations and the same was put up on SEBI website on15th December 2003 for public comments. It was only on 29th October 2004 that SEBI finallyannounced revised Clause 49, which will have to be implemented by the end of financial year2004-05. These revised recommendations have also considerably diluted the original MurthyCommittee recommendations. Areas where major changes were made include:

• Independence of Directors to determine independence of a director.

• Whistle Blower policy

• Performance evaluation of nonexecutive directors

• Mandatory

• training of non-executive directors

Some important changes, which have been incorporated are as follows. Five new clauseshave been added to determine independence of a director. Two third of the members of Auditcommittee shall be independent directors as against the present requirement of majority beingindependent. A new requirement has been provided for obtaining prior approval of shareholders

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for payment of fees/compensation to non-executive directors. The CEO/CFO Certification is anew requirement and is based on Sarbanes Oxley Act of USA. The CEO or the Complianceofficer can now sign the compliance report. The annual corporate governance report shoulddisclose adoption or non-adoption of non-mandatory requirements. The revised Clause onlyrequires CEO and CFO to certify to the Board the annual financial statements in the prescribedformat.

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2

Literature Review

Garay & González (2008) constructed a corporate governance index (CGI) for publicly-listedfirms that is free of self-selection and self-reported bias and showed that an increase of 1 percent in the CGI results in an average increase of 11.3 per cent in dividend payouts, 9.9 per centin price-to-book, and 2.7 per cent in Tobin’s Q. These findings are robust after considering thepotential endogeneity of our regression variables. Kam & Joanne (2008), using a sample ofFortune 200 companies and defining top executives of other publicly traded firms as expert-independent directors and controlling for firm specifics, board features, and individual directorcharacteristics, found that the presence of expert-independent directors on board and in the au-dit committee enhances firm value. Chen et al. (2008) showed that earnings-return associationsfor foreign registrants without audit committees are significantly lower compared with those oftheir US-matched firms which are required to establish audit committees. This result was evenmore pronounced after the introduction of new audit committee rules in 1999 aimed at increas-ing the responsibilities of audit committees. In addition, earnings-return associations of foreignregistrants were found to increase following the establishment of audit committees. Overall,their results were consistent with the idea that the establishment of audit committees may berelated to higher earnings-return associations.Li et al. (2008) showed that ownership concentration, state ownership, ultimate owner, inde-pendent directors and auditors’ opinion turn out to be negatively associated with the probabilityof financial distress, while administrative expense ratio is positively related with the likelihoodof financial distress. Managerial ownership did not appear to be a significant determinant.Ehikioya (2009) suggested that ownership concentration had a positive impact on performance.Although the results revealed no evidence to support the impact of board composition on perfor-mance, there was significant evidence to support the fact that CEO duality adversely impactedfirm performance. The result also suggested firm size and leverage also impacted the firm per-formance. A new variable, identified as more than one family member on the board, was foundto have an adverse effect on firm performance. Abdullah (2004) suggested neither board in-dependence nor leadership structure nor the joint effects of these two had any relation with the

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2.1 Role of Independent Directors

firm performance. The structure of boards were largely found to be independent of managementand absence of any dominant person.Dwivedi & Jain (2005) reviewed the international literature on corporate governance and firmperformance and investigated the relationship in the Indian context, taking into account the en-dogeneity in the relationship. Governance parameters included board size, directors’ sharehold-ing, institutional and foreign shareholding, while the fragmentation in shareholding is capturedby public shareholding. They showed that a higher proportion of foreign shareholding is as-sociated with increase in market value of the firm, while the Indian institutional shareholders’association is not statistically significant. A weak positive association was also found betweenboard size and firm value. Directors’ shareholding has a non-linear negative relationship withfirm value, while the public shareholding has a linear negative association.

2.1 Role of Independent Directors

Empirical evidence on the association between outside independent directors and firm perfor-mance is mixed. One of the views is based upon the agency theory and the other is the manage-rial ’hegemony’. The board of directors, appointed by shareholders and other key stakeholders,is fiduciary of the shareholders, not the management. Hence, where the objectives of manage-ment differ from those of the shareholders, non-executive directors must represent the interestsof the ultimate owners. This is where the independence of ’directors’ comes into picture.

The agency perspective of corporate governance arises from the incentive problems (Sloan,2001) that created by the separation of management and ownership in corporations. Managerstake decisions to enhance their incentives and not to maximize shareholders’ wealth. This gaverise to corporate control mechanisms (Bushman & Smith, 2001) , which discipline the managersto act in the shareholders’ interests.

The independent and non-executive directors, are the hallmark of internal governance sys-tem in order to reduce agency problems, through mitigating incentives for managers to actopportunistically (Mather & Ramsay, 2003; Keasey et al., 1997; Fama & Jensen, 1983). Theevidence of independent directors protecting the shareholders wealth, when there are agencyproblems has been revealed by Brickley & James (1987); Byrd & Hickman (1992); Peasnellet al. (2000); Solomon & Solomon (2004); McCabe & Nowark (1992); Fernandes & Fran-sisco (2008); Mura (2007); Chin-Jung & Ming-Je (2007); Schellenger et al. (1989); Elloumi &Gueyie (2001); OSullivan & Wong (1999)

Beasley (1996) found that outside independent directors reduce the likelihood of financialstatement fraud. Scherrer (2003) found out that outside directors provide invaluable access toresources and information. Unlike inside or executive directors, they do not have the concernsfor their employment or career opportunities, thereby protecting shareholders’ interests. These

6

Literature Review

studies indicate that outside independent directors can monitor and control management betterthan insider ones.

On the other hand, as per hegemony theory, the board of directors is argued to be incapableof fulfilling its supervisory role and of protecting shareholders’ wealth. The outside directorsare especially valued for their ability to advise and to make business and personal relationshipsstronger more than for their ability to monitor. Vancil (1987) is skeptical about the ability ofoutside directors to make independent judgement due to the dominant role played by CEOs inselecting outside directors.

These directors (Conyon & Peck, 1998) either hold no shares or insignificant numbers ofshares in the firm, so their incentives to monitor management is low. Keasey et al. (1997)is skeptical about outside directors’ effectiveness to supervise management and their indepen-dence. A criticism of non-executive directors (Carter & Lorsch, 2004) is that they are overbusy with other commitments and are only involved with the company business on a ’part-time’basis, which adversely affects firm performance. In short, there is no consistent evidence thatindependent boards perform better.

2.2 Dual Role of CEO

The CEO is a full-time post and has responsibility for the day-to- day running of the companyas well as setting, and implementing corporate strategy. In contrast, the post of the chairman isnormally part-time and the his job is to ensure that the board works effectively. He is responsiblefor monitoring and evaluating the performance of the executive directors, including the CEO.

Agency theorists say that CEO duality affects firm performance negatively (Chen et al.,2005) and, therefore, the roles of CEO and chairperson should be separated (Higgs, 2003).Combining these two roles as a sign of a dominant CEO, renders the board ineffective in moni-toring managerial opportunism (Daily & Dalton, 1993; Messier, 2000) and signals the absenceof separation of decision control and decision management (Fama & Jensen, 1983). As a re-sult, CEO duality enhances CEO entrenchment and reduces board independence (Finkelstein &DAveni, 1994; AF & S, 2004) so that fewer independent directors will be appointed to the board.This eventually leads to corporate decline. In contrast, stewardship theorists counter-argue thatCEO duality affects firm performance positively. The managers are inherently trustworthy andare good stewards of company resources (Fama & Jensen, 1983).Elsayed (2007) indicated that the impact of CEO duality on corporate performance was foundto vary across industries, a result that was supportive of both agency theory and stewardshiptheory. In addition, when firms were categorized according to their financial performance, CEOduality attracted a positive and significant coefficient only when corporate performance was low.Jackling & Johl (2009) provided some support for aspects of agency theory as a greater propor-tion of outside directors on boards were associated with improved firm performance. The notion

7

2.3 Board Size

of separating leadership roles in a manner consistent with agency theory was not supported. Forinstance, the notion that powerful CEOs (duality role, CEO being the promoter, and CEO beingthe only board manager) have a detrimental effect on performance was not supported.

In addition to financial incentives, managers are also motivated by such non-financial mo-tives as job satisfaction, advancement and recognition, respect for authority, and work ethic.They seek intrinsic satisfaction by performing challenging work. CEO duality could promotea unified and strong leadership with a clear sense of strategic direction. Because the CEO hasknowledge of the business and industry and knows how to run the company, combining thesetwo roles can help in making timely and optimal decisions (Brickley et al., 1997; Donaldson,1990). Jackling & Johl (2009) suggested support for resource dependency theory. The find-ings suggested that larger board size had a positive impact on performance thus supporting theview that greater exposure to the external environment improves access to various resourcesand thus positively impacted the performance. The study however failed to support the resourcedependency theory in terms of the association between frequency of board meetings and perfor-mance. Similarly the results showed that outside directors with multiple appointments appearedto have a negative effect on performance, suggesting that "busyness" did not add value in termsof networks and enhancement of resource accessibility.

Hence, evidence on the duality-performance relationship is still mixed and inconclusive.

2.3 Board Size

Brickley et al. (1997) opines that large boards (beyond seven or eight) can be less effective thansmaller boards. Lipton & Lorsch (2005) state that the norms of behaviour in most boardroomsare dysfunctional because directors rarely criticize the policies of the top managers. They rec-ommend limiting the membership of boards to ten, with a preferred size of eight or nine. They,in a way, suggest that even if a board’s capacity for monitoring increases with the board size,the benefits are outweighed by such costs as slower decision-making, less candid discussions ofmanagerial performance, and biases against risk-taking. The idea is that when boards get to betoo big, agency problems increase and the board becomes more symbolic and less a part of themanagement process (Lipton & Lorsch, 2005; Eisenberg et al., 1998).

2.4 Audit Committee, Ownership Concentration and othervariables

Using a sample of Fortune 200 companies and defining top executives of other publicly tradedfirms as expert-independent directors and controlling for firm specifics, board features, andindividual director characteristics, Kam C. Chan & Li (2008) find the presence of expert-

8

Literature Review

independent directors on board and in the audit committee enhances firm value. Using a panel ofmanufacturing firms listed on the first section of the Tokyo Stock Exchange from 1980 through2005, (Hu & Izumida, 2008) find that ownership concentration has a significant effect on con-temporary and subsequent corporate performance. Specifically, a U-shaped relation of concen-tration to performance is consistent with the expropriation effect and monitoring effect of largeshareholders. However, the study fails to find that changes in performance are accompanied bychanges in ownership concentration due to the relatively illiquid securities market and stableshareholding arrangement in Japan.

9

3

Objective and Methodology

3.1 Objective

To study the impact of Corporate Governance on the Financial Performance of Nifty companies

3.2 Research Methodology

3.2.1 Sample

NIFTY companies are taken as sample representing all the sectors of the Indian Economy. Thefinance and banking companies are excluded out of the sample in order to eliminate possibledistortions caused by government regulations. The data has been collected from the last fiveyears annual reports of the companies. The year end stock price has been taken from NSE.Firm heterogeneity, unobserved by the econometrician, simultaneously affects both the firm’sownership structure and its performance (Himmelberg et al., 1999; Coles et al., 2007). In across-sectional regression of performance, the presence of such unobserved heterogeneity islikely to generate a degree of correlation between independent variables and the error term,leading to biased estimates. A solution to this problem is the use of panel data. The sampleconsists of 205 observations (41 firms * 5 years). As the study uses lags to examine the causalrelationship between the variables of interest, the unbalanced panel for Granger causality testsis limited to firms observed over at least four consecutive years, resulting in a total of 164observations (41 firms).

NIFTY Stocks

NIFTY consists of 50 top stocks from different sectors of NSE. They are listed based on theMarket capitalization. Market capitalization = numbers of shares * last price traded. Thismakes Reliance industries the heaviest script on the index. It has close to 10% weightage on the

10


indexes. The market capitalization for Reliance industries is Rs.295,088 crore. As the value ofshares increases and decreases the companies move in and out of the index e.g. Satyam - Theday Raju made the confession the script fell from Rs 200 to Rs 20 and the market capitalizationfor the company was reduced to just 10% and so that gave way for new script Reliance capital.Below is the list by October 2010.

1. ABB Ltd : ELECTRICAL EQUIPMENT

2. ACC Ltd : CEMENT AND CEMENT PRODUCTS

3. Ambuja Cements Ltd : CEMENT AND CEMENT PRODUCTS

4. Axis Bank : Banks

5. BHEL : ELECTRICAL EQUIPMENT

6. Bharat Petroleum Corporation Ltd(BPCL) : REFINERIES

7. Bharti Airtel Ltd : TELECOMMUNICATION - SERVICES

8. Cairn India Ltd : OIL EXPLORATION/PRODUCTION

9. Cipla Ltd : PHARMACEUTICALS

10. DLF Ltd : CONSTRUCTION

11. GAIL (India) Ltd : GAS

12. HCL Technologies Ltd : COMPUTERS - SOFTWARE

13. HDFC Bank Ltd : BANKS

14. Hero Honda Motors Ltd : AUTOMOBILES - 2 AND 3 WHEELERS

15. Hindalco Industries Ltd : ALUMINIUM

16. Hindustan Unilever Ltd : DIVERSIFIED

17. Housing Development Finance Corporation Ltd(HDFC) : FINANCE - HOUSING

18. IDFC: Finance (Including NBFCs)

19. ITC Ltd : CIGARETTES

20. ICICI Bank Ltd : BANKS

21. Idea Cellular Ltd : TELECOMMUNICATION - SERVICES

22. Infosys Technologies Ltd : COMPUTERS - SOFTWARE

23. Jindal Steel: Iron and Steel

24. JP Associate: Construction & Engineering

25. Kotak Bank: Banks

26. Larsen & Toubro Ltd : ENGINEERING

27. Mahindra & Mahindra Ltd : AUTOMOBILES - 4 WHEELERS

28. Maruti Suzuki India Ltd : AUTOMOBILES - 4 WHEELERS

29. NTPC Ltd : POWER

30. Oil & Natural Gas Corporation Ltd(ONGC) : OIL EXPLORATION/PRODUCTION

31. Power Grid Corporation of India Ltd : POWER

11


32. Punjab National Bank : BANKS

33. Ranbaxy Laboratories Ltd : PHARMACEUTICALS

34. Reliance Communications Ltd : TELECOMMUNICATION - SERVICES

35. Reliance Industries Ltd : REFINERIES

36. Reliance Infrastructure Ltd : POWER

37. Reliance Capital : Finance (including NBFCs)

38. Reliance Power Ltd : POWER

39. Siemens Ltd : ELECTRICAL EQUIPMENT

40. State Bank of India : BANKS

41. Steel Authority of India Ltd(SAIL): STEEL AND STEEL PRODUCT

42. Sterlite Industries (India) Ltd : METALS

43. Sun Pharmaceutical Industries Ltd : PHARMACEUTICALS

44. Suzlon Energy Ltd : ELECTRICAL EQUIPMENT

45. Tata Consultancy Services Ltd(TCS) : COMPUTERS - SOFTWARE

46. Tata Motors Ltd : AUTOMOBILES - 4 WHEELERS

47. Tata Power Co. Ltd : POWER

48. Tata Steel Ltd : STEEL AND STEEL PRODUCTS

49. Unitech Ltd : CONSTRUCTION

50. Wipro Ltd : COMPUTERS - SOFTWARE

3.2.2 Dependent Variables

The firm performance measures used are Tobin’s q (TQ) and ROA. Tobin’s q Ratio, is the mar-ket value of a company’s assets divided by their replacement value. Replacement value beingthe current cost of replacing the firms assets. This ratio is named after Nobel Economics Lau-reate James Tobin of Yale University. He hypothesized that the combined market value of allthe companies on the stock market should be about equal to their replacement costs. In otherwords, the ratio of all the combined stock market valuations to the combined replacement costsshould be around one. A low Q (between 0 and 1) means that the cost to replace a firm’s assetsis greater than the value of its stock. This implies that the stock is undervalued. Conversely, ahigh Q (greater than 1) implies that a firm’s stock is more expensive than the replacement costof its assets, which implies that the stock is overvalued.Q has been employed to explain a number of diverse corporate phenomena, such as cross-sectional differences in investment and diversification decisions Jose et al. (1986), the relation-ship between managerial equity ownership and firm value McConnell & Servaes (1990), therelationship between managerial performance and tender offer gains Lang et al. (1989), invest-ment opportunities and tender offer responses Lang et al. (1989), and financing, dividend andcompensation policies Smith & Watts (1992). Tobin’s q is given as

12


T Q =Total market value o f the f irm

Average total assets=

MVS + PS + DT A

Where MVS = Market value of all outstanding shares = firm’s Stock price ∗ Outstanding sharesTA = Firm’s assets, i.e. cash,receivables, inventory and plant book valuePS is the liquidating value of preferred stockD = Debt = (AVCL - AVCA)+ AVLTDWhere AVCL = Accounting value of the firm’s Current LiabilitiesAVLTD = Accounting value of the firm’s Long Term DebtAVCA = Accounting value of the firm’s Current Assets = Cash + Inventories + Receivables

ROA (return on assets)is an indicator of how profitable a company is relative to its totalassets. ROA gives an idea as to how efficient management is at using its assets to generate earn-ings. Calculated by dividing a company’s annual earnings by its total assets, ROA is displayedas a percentage. Sometimes this is referred to as "return on investment".

ROA =EAT + Interest − Tax Advantage on Interest

T A

Because of data unavailability, the study uses the simplified version of Tobin’s Q, i.e., us-ing book value to measure market value of debt and replacement value. Tobin’s Q estimatescorporate performance from a forward looking perspective and reflects what management willaccomplish, while ROA estimates performance from a backward-looking perspective and re-flects what the management has accomplished

3.2.3 Independent Variables

1. PNER: the percentage of non-executive directors on the board of directors. It is definedas the number of non-executive directors divided by the total number of directors on theboard of the company. The coefficient’s expected sign is positive, i.e., the higher theproportion, the more independent is the board in making decisions. This implies bettercompany performance, measured by the tobin’s q and ROA ratio.

2. PIND: the percentage of independent directors on the board of directors. It is defined asthe number of independent directors divided by the total number of directors on the boardof the company. The coefficient’s expected sign is positive, i.e., the higher the proportion,the more independent is the board in making decisions. This implies better companyperformance, measured by the tobin’s q and ROA ratio.

3. DUAL: a binary variable representing CEO’s who also double up as the chairman of the

13


board of directors. This variable takes the value of one if the CEO/Managing Directorperforms the dual role; otherwise it takes a value of zero. The coefficient’s expectedsign is negative. This is because the effectiveness of the board as an internal governancedevice will be perceived to have been compromised by the roles not being separated. Onthe other hand, a unity of command structure can motivate the CEO to strive for excellentperformance. If this is the case, the coefficient’s sign is expected to be positive.

4. CACNE: a binary variable representing the chairman of the audit committee. If the chair-man of the audit committee is a nonexecutive director, the variable takes the value ofone; otherwise, this variable takes a value of zero. This serves to test the degree of inde-pendence of the audit committee. An independent chairman is expected to contribute to amore rigorous regime of monitoring and therefore improves performance of the company.

5. BSIZE: the total number of directors in the board of a company. Cohesiveness of theboard members and having diverse expertise and experience may enhance the financialperformance. Unwieldy group on the other hand may be detrimental to the financialperformance.

6. CONCEN - measures the proportion of concentrated ownership. It is measured by themarket value of top 10 shareholders or the promoter’s share whichever is higher. Thehigher the proportion, the greater is the monitoring role of large owners. This is in linewith agency theory which hypothesized that greater ownership would reduce agency costsand hence improve performance. The coefficient is expected to be positive.

7. INST - measures the proportion of large institutional investors. The higher the proportion,the greater is the monitoring role of institutional investors. It also implies that managersof companies would be under pressure to perform to the expectations of institutionalinvestors. The coefficient is expected to be positive.

8. FSIZE: the size of the company in terms of the turnover (gross sales). It is hypothesizedthat size has a positive influence on the performance of the firm due to various reasons likediversification, economies of scale, access to cheaper sources of funds etc. The coefficientis expected to be positive.

9. AGE: the age of the firm, calculated by the natural logarithm of the difference betweenthe year of study and the year of incorporation. It is hypothesized that older firms areconsidered more efficient than younger firms because of the effect of the learning curveand survival bias.

10. DTC: the debt to total capital ratio, measures the financial leverage of the firm. It is agearing ratio and is defined as long-term borrowings/debt divided by the total sharehold-ers ordinary fund plus long-term debt. The coefficient is expected to be positive since

14


greater borrowing imply that lenders/banks will be expected to play a greater monitoringrole. A high debt to total capital ratio generally means that a company has been aggres-sive in financing its growth with debt. It is hypothesized that, if a firm uses debt to financethe increased operations, the firm could potentially generate more earnings than it wouldhave without this outside financing.

The study has included all the pertinent variables of corporate governance, which have beenshown to be significant for the firm performance by the literature survey. So far the studieson Nifty companies have demonstrated the the composite index of corporate governance ispositively related to the firm performance. This study measures the individual effect of eachcorporate governance variable on the firm performance.

3.2.4 Empirical Model

T Qit = α0 + β01 PNERit + β02 PINDit + β03 DUALit + β04 CACNEit + β05 BS IZEit

+ β06 CONCENit + β07 INS Tit + β08 FS IZEit + β09 AGEit + β10 DTCit (3.1)

ROAit = α1 + β11 PNERit + β12 PINDit + β13 DUALit + β14 CACNEit + β15 BS IZEit

+ β16 CONCENit + β17 INS Tit + β18 FS IZEit + β19 AGEit + β20 DTCit(3.2)

3.2.5 Data Source and Data Types

The data sources are the Annual reports 2006-2010. Dependent Variable is on ratio scale andindependent variables are either dichotomous variables or on ratio scale.

3.2.6 Analysis

The data collected were for the period 2006-2010 (five years) and involved 41 companies. Thedata were analyzed using R software.

Panel Data Unit Root Test

The unit root test is a test of stationarity of the time series. If the time series is

Yt = ρYt−1 + ut , −1 ≤ ρ ≤ 1 (3.3)

where ut is a white noise. In the case of unit root (i.e. ρ = 1), Yt becomes a random walkmodel without a drift, which is a non-stationary process. So, when we regress Yt on its lagged

15


values Yt−1 and find out if the estimated ρ is statistically equal to 1? If it is, then Yt is non-stationary. This is the general idea behind unit root tests. Various tests have been developedfor a time series like the Augmented Dicky Fuller (ADF) test and the Phillips-Peron (PP) UnitRoot Test etc. The literature gives account of many different types of competing tests to test outthe unit root of panel data. Though the firms have panel data for only five years of data, whichin itself is relatively short period to apply the unit root tests, three different types of unit roottests namely Levin, Lin and Chut; Im, Pesaran and Shin W-stat and CADF can be applied to thedata.

Granger Causality Test

In a bivariate framework, y is said to be Granger-caused by x if the prediction of y improveswhen the lagged values of x are taken into account. The literature (Hurlin & Venet, 2001)suggests that Granger causality tests based on panel data have higher accuracy than those basedon individual time series. Hurlin & Venet (2001) developed the Granger causality approach toa panel data model with fixed coefficients. Using a simplified form of their method, the studyconsiders a VAR (p) process in a panel data context. For each cross-section unit i and timeperiod t, he following model with lag length p is estimated

yi,t =

p∑k=1

γkyi,t−k +

p∑k=1

βkxi,t−k + Vi,t (3.4)

Unobservable heterogeneity is controlled in the model through individual effects αi and theeffects of macroeconomic changes through temporal effects ωt. The error term in model 3.4thus has been transformed into Vi,t = αi + ωt + εi,t, where εi,t ∼ i.i.d.(0, σ2

ε ). The autoregressivecoefficients γk and the regression coefficients betak are assumed to be identical ∀i ∈ [1,N].Conditional on the assumption that the lag length p is correctly specified, the variable xi,t failsto Granger cause yi,t if all coefficients of lagged xi in model 3.4 are not statistically significant.

Panel Data Regression

Panel data sets generally include sequential blocks or cross-sections of data, within each ofwhich resides a time series. Apart from the variable number, the data structure confers uponthe variables two dimensions. They have a cross-sectional unit of observation, which in thiscase is firm i. They have a temporal reference, t, in this case the year. The error term hastwo dimensions, one for the country and one for the time period. In this study, there are fortyone firms and five years of time. Even though time is nested within the cross-section in thisexample, but under some circumstances the cross-sections may be nested within time. If thereare no missing values, the data set is called a balanced panel, but if there are missing values, thedata set is referred to as an unbalanced panel, which is the data for current study as well.

16


The advantages of using Panel Data re as follows

• These can take heterogeneity explicitly into account by allowing for individual specificvariables.

• These give more informative data, more variability, less collinearity among variables,more degrees of freedom and more efficiency.

The usual equation is written as

Yit = β1 + β2X2it + β3X3it + uit, E(uit) ∼ N(0, σ2) (3.5)

where i stands for the ith cross-sectional unit and t for the tth time period. Estimation dependson the assumptions we make about the intercept, slope coefficients and the error term. Thereare several possibilities

1. Pooled Regression : All the coefficients constant across time and individuals and usualOLS regression is applied in this case.

2. The Fixed Effect or Least Square Dummy Variable (LSDV) regression model : In-dividuality of each firm is taken into account to let the intercept vary but still assumethat slope coefficients are constant across firms. Taking three independent variablesXi, i = 1..3, we write the model as

Yit = β1i + β2X2it + β3X3it + uit, E(uit) ∼ N(0, σ2) (3.6)

It is called fixed effect because each firm’s intercept does not vary over time.

3. The Random Effect Model / Error Components Model (ECM) : Instead of treating β1i

as fixed, we assume that it is a random variable with a mean value β1.

Yit = β1i + β2X2it + β3X3it + uit

β1i = β1 + εi (3.7)

where εi is a random error term with a mean value of zero and variance σ2ε

So the firms (total 41 in this study) are a drawing from a much larger universe of suchcompanies and that they have a common mean value for the intercept (= β∩1). and theindividual differences in the intercept values of each company are reflected in the errorterms εi. So

17


Yit = β1i + β2X2it + β3X3it + εi + uit

= β1i + β2X2it + β3X3it + wit

where wit = εi + uit (3.8)

The usual assumptions are

εi ∼ N(0, σ2ε ), uit ∼ N(0, σ2

u) (3.9)

E(εiuit) = E(εiε j) = E(uisuit) = E(uitu jt) = E(uitu js) = 0 (3.10)

(3.11)

that is, individual error terms components are not correlated with each other and are notautocorrelated across both cross section and time series units. As a consequence of theseassumptions

E(wit) = 0 (3.12)

var(wit) = σ2ε + σ2

u (3.13)

(3.14)

Now if σ2ε = 0, there is no difference between this model and pooled regression model

and we can simply pool all the units. The error term wit is homoscedastic. However it canbe shown that wit and wis are correlated; that is, the error terms of a given cross-sectionalunit at two different points in time are correlated. The correlation coefficient is

r(wit,wis) =σ2ε

σ2ε + σ2

u(3.15)

(3.16)

If we do not take this correlation structure into account, and estimate by OLS, the resultingestimators will be inefficient. The most appropriate method will be generalized leastsquares (GLS).

If T (number of time series data) is large and N (the number of cross-sectional units) issmall, there is likely to be little difference in the values of parameters estimated by FEM andECM. When N is large and T is small, the estimates obtained by the two methods can differsignificantly. If we strongly believe that the cross sectional units are not random drawings fromthe larger sample, we use FEM. However, if the cross sectional units are regarded as randomdrawings, then ECM is appropriate.

18


Hausman Test : Hausman (Hausman, 1978) gave a test to choose between FEM and ECM.The null hypothesis underlying the test ia that FEM and ECM estimators do not differ substan-tially. The test statistic has an asymptotic χ2 distribution. If the null hypothesis is rejected, theconclusion is that ECM is not appropriate and that we may be better off using FEM, in whichcase statistical inferences will be conditional on the εi in the sample.

19

4

Results

4.1 Description of Overall Data

As observed in Table 4.1, Tobin’s Q values range from -0.77 to 32.89, with an average value of3.66. The average ROA is 18 per cent, with a standard deviation of 16 per cent. The averageboard size is around 12. The average percentages of non-executive directors and independentdirectors in the board are 67% and 50% respectively. This is in line with the clause 49 of thelisting agreement. The average proportion of institutional investors is 27.60% and the stockholding of top 10 shareholders or of the promotor group (ie concentration) is rather high ie55.87%. The average gearing (measured as debt to asset ratio) is comfortable at 0.26. Forall the firms the chairman of the audit committee as non-executive, so the variable CACNE isremoved from further analysis. The ShapiroUWilks test tests the null hypothesis that a samplecame from a normally distributed population. The test is significant for all variables, which isbecause the sample is preselected being NIFTY firms only. The panel data regression can bevery well applied to get the estimates and considering the central limit theorem, the standarderrors can be used for predictions.

20

Results

Tabl

e4.

1:Su

mm

ary

Stat

istic

s

BSI

ZE

PNE

RPI

ND

INST

DT

CFS

IZE

CO

NC

EN

MV

ST

QR

OA

Num

bero

fVal

ues

185

185

185

184

203

203

188

181

203

203

Num

bero

fNU

LL

Val

ues

00

00

245

00

00

Val

ues

Not

Ava

ilabl

e20

2020

212

217

242

2M

inim

umV

alue

40.

270.

180.

530.

000.

005.

2231

58.1

2-0

.77

-0.1

3M

axim

umV

alue

221.

000.

8956

.42

0.91

2004

00.0

098

.19

3626

84.5

232

.89

1.17

Ran

ge18

0.73

0.71

55.8

90.

9120

0400

.00

92.9

735

9526

.40

33.6

61.

30Su

m21

8512

6.65

95.9

750

77.6

153

.55

3980

644.

2510

503.

0498

1059

3.02

743.

3237

.40

Med

ian

120.

670.

5028

.01

0.24

1049

7.25

61.7

828

825.

652.

340.

16M

ean

11.8

10.

680.

5227

.60

0.26

1960

9.08

55.8

754

202.

173.

660.

18SE

ofM

ean

0.26

0.01

0.01

0.96

0.02

2033

.89

1.63

4604

.10

0.29

0.01

95%

CIo

fMea

n0.

510.

020.

021.

890.

0340

10.3

73.

2190

84.9

50.

580.

02St

anda

rdD

evia

tion

3.52

0.15

0.13

12.9

90.

2328

978.

4122

.29

6194

1.79

4.19

0.16

Coe

ffof

Var

iatio

n0.

300.

210.

250.

470.

871.

480.

401.

141.

140.

85Sk

ewne

ss0.

330.

110.

420.

050.

573.

37-0

.49

2.40

3.15

2.63

2SE

Cri

teri

onof

Skew

ness

0.91

0.32

1.17

0.15

1.66

9.87

-1.3

96.

649.

247.

71K

urto

sis

-0.1

3-0

.55

0.48

-0.7

4-0

.64

13.0

6-0

.59

6.74

14.4

311

.63

2SE

Cri

teri

onof

Kur

tosi

s-0

.18

-0.7

70.

68-1

.04

-0.9

419

.22

-0.8

49.

3821

.24

17.1

2Sh

apir

oW

ilks

(SW

)Sta

tistic

0.98

0.98

0.96

0.98

0.92

0.59

0.95

0.71

0.71

0.79

Sign

ifica

nce

ofSW

0.02

0.03

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

21

4.2 Description of Firm-wise Data

4.2 Description of Firm-wise Data

The plots of all the variables (i.e. BSIZE, PNER, PIND, INST ,DTC, FSIZE, CONCEN, MVS,TQ and ROA) for all 41 firms are depicted along with the summary statistics in Appendix A.We can see wide variation among the firms for all the variables in the plots. This can be verifiedby summary statistics too. As an illustration figure 4.1 and the associated table show thatthe board size was initially 7, then rose to 11 and finally settled at 10. The proportion of non-executive directors varied between 0.78 & 0.91, and the average value was 0.83. The proportionof independent directors varied between 0.45 & 0.57, and the average value was 0.54. ABB isnot leveraged company and debt to total capital ratio was initially at 0.01 and later fell to zero.The turnover (FSIZE) increased from 2964 Cr in Mar 2006 to 6309 Cr in Mar 2010, and theaverage value was 5262 Cr. The concentration of ownership was constant throughout at 52.11%.The proportion of shares held by institutional investors increased from 25.24% in Mar 2006 to34.25% in 2010, and the average value was 32.75% . The market capitalization was 8561 Cr inMar 2006 and 16698 Cr in Mar 2010, and the average value was 16555 Cr. The Tobin’s Q ratio(average value 10.04) and ROA (average value 25%) were always higher than the Combinedaverage of the all firms. The Tobin’s Q ratio was 8.714 in Mar 2006 and 6.22 in Mar 2010. TheROA was 24.36% in Mar 2006 and 15.33 % in Mar 2010. The similar figures and the tables forall the 41 companies are given in the Appendix.

22

Results

Figure 4.1: ABB

Table 4.2: ABB

BSIZE PNER PIND INST DTC FSIZE CONCEN MVS TQ ROAMedian 10.00 0.80 0.57 34.25 0.00 5930.31 52.11 16698.38 8.74 0.27

Mean 9.40 0.83 0.54 32.75 0.00 5262.83 52.11 16555.98 10.04 0.25SE of Mean 0.68 0.02 0.02 1.91 0.00 717.16 0.00 3887.60 2.49 0.03

95% CI of Mean 1.88 0.07 0.07 5.30 0.01 1991.17 0.00 10793.72 6.90 0.07Stand Dev 1.52 0.05 0.05 4.26 0.01 1603.63 0.00 8692.95 5.56 0.06

Coeff of Var 0.16 0.06 0.10 0.13 1.37 0.30 0.00 0.53 0.55 0.23

23

4.3 Panel Data Unit Root Tests

4.3 Panel Data Unit Root Tests

Though the firms have panel data for only five years of data, which in itself is relatively shortperiod to apply the unit root tests, three different types of unit root tests namely Levin, Lin andChut; Im, Pesaran and Shin W-stat and CADF are applied and all the tests give absence of unitroots in all the time series by rejecting the null hypothesis.

Table 4.3: Panel Unit Root Tests - Independent Variables

Method BSIZE PNER PIND INST

Statistic prob Statistic prob Statistic prob Statistic probLevin, Lin and Chut 96.200 0.000 -30.892 0.000 -59.783 0.000 -89.267 0.000Im, Pesaran and Shin W-stat 82.841 0.000 72.564 0.000 -77.062 0.000 -23.898 0.000CADF -5.217 0.000 -4.453 0.000 -5.263 0.000 -3.005 0.000

Table 4.4: Panel Unit Root Tests - Independent Variables

Method DTC FSIZE CONCEN MVS

Statistic prob Statistic prob Statistic prob Statistic probLevin, Lin and Chut 75.733 0.000 -22.455 0.000 -19.929 0.000 -99.565 0.000Im, Pesaran and Shin W-stat -33.402 0.000 62.617 0.000 94.950 0.000 68.676 0.000CADF -5.913 0.000 -5.434 0.000 -4.633 0.000 -3.182 0.000

Table 4.5: Panel Unit Root Tests - Dependent Variables

Method TQ ROA

Statistic prob Statistic probLevin, Lin and Chut -8.994 0.000 15.998 0.000Im, Pesaran and Shin W-stat -79.815 0.000 -56.943 0.000CADF -4.633 0.000 -6.176 0.000

4.4 Inter-correlation Matrix

The correlation table (Table 4.6) shows that Tobin’s Q is significantly correlated with DUAL (r= -0.22,p < 0.01), DTC(r = -0.33, p < 0.001) and FSIZE (r = -0.15, p < 0.05). The correlationwith DTC is negative(p<.05) whereas with FSIZE is highly positive (p<.001). The ROA issignificantly correlated with DUAL (r = -0.28, p < 0.05) and DTC (r = -0.44, p < 0.05). Boththe performance ratios Tobin’s Q and ROA are significantly positively correlated (r = 0.71, p< 0.001) as expected. Apart from this other significant (and expected) correlations are foundbetween CONCEN and INST (r = -0.74, p< .001); PIND and PNER (r = 0.54, p< .001) andFSIZE and MVS (r = 0.55, p< .001). The percentage of concentrated ownership and percentageof institutional investment are negatively correlated because increase in one reduces the room

24

Results

for increases in other, as total percentage is constant i.e. 100%. The percentage on independentdirectors and that of non-executive directors are correlated because all independent directors arenon-executive directors also. The firm’s turnover and market capitalization are related becauselarger firms tend to have larger turnovers.

25

4.4 Inter-correlation Matrix

Table4.6:

IntercorrelationM

atrix

BSIZ

EPN

ER

PIND

DU

AL

INST

DT

CFSIZ

EC

ON

CE

NM

VS

TQ

BSIZ

EPN

ER

-0.20**PIN

D-0.30***

0.54***D

UA

L0.11

-0.38***-0.14

INST

0.23**0.12

0.04-0.05

DT

C0.12

-0.11-0.15*

0.13-0.06

FSIZE

0.16*-0.22**

-0.17*0.32***

-0.050.10

CO

NC

EN

-0.15*-0.13

-0.17*0.11

-0.74***-0.01

-0.03M

VS

0.17*-0.19*

-0.020.25***

-0.25***-0.10

0.55***0.09

TQ

-0.100.02

0.09-0.22**

0.03-0.33***

-0.15*0.03

0.08R

OA

0.04-0.02

0.04-0.28***

0.11-0.44***

-0.06-0.05

0.000.71***

p<

.001:"***"

p<

.01"**

"p

<.05

"*"

26

Results

4.5 Panel Data Regression Analysis

The problem of multicollinearity rarely exist in the panel data. After seeing the correlationmatrix, we can see that the correlation among the variables is very low and in one one case itis -0.74, that is between CONCEN and INST. This is because, when one variable tends to havehigher percentage of total number of shares, the other variable tends to have lower. The ruleof thumb is that if the pair wise or zero order correlation coefficients between two regressorsis high say in excess of 0.8, then multicollinearity is a serious problem. In our study all thepairwise correlations are much less than 0.8. Multicollinearity is also suggested if R2 is highand we have only a few significant t ratios. If R2 is high, say, in excess of 0.8, the F test inmost cases will reject the hypothesis that the partial slope coefficients are simultaneously equalto zero, but the individual t tests will show that none or very few of the partial slope coefficientsare statistically different from zero. The present study doesn’t find very high value of R2. //

The OLS regression that was done earlier produced relatively low R2 value and low Durbin-Watson statistics. An examination of the F-test and its P-value clearly indicated that the OLSmethod was not appropriate. This is because the data under study is panel data or sometimesreferred as pooled data and consists of a combination of time series and cross-sectional data.Such data requires the use of panel data regression models in order to obtain meaningful results.There are two most frequently used estimation techniques to address these problems, namelythe Fixed Effects Model (FEM) and the random effects model (REM). Since T( the number oftime series data) is small and N( the number of cross sectional units) is large, the estimatesobtained by the two methods can differ significantly. The assumptions underlying the randomeffects model ia that the innovations are a random drawing from a much larger population. Theassumption that 41 NIFTY firms are a random sample is not tenable. The Hausman specifi-cation test confirmed the superiority of the fixed effect models over the random effect models.Also, another variable was added, namely FSIZESQ (square of FSIZE) to test the possibility ofa quadratic/curvilinear relationship involving the size of a company.

Our further discussion, hence, only involve the fixed effects model. The results are summa-rized in Table 4.7 and Table 4.8.

Using TQ as the dependent variable, three significant variables are obtained namely DUALat <5% level (-0.4087,p < 0.00678), DTC at at the <0.1% level (-0.0144,p < 0.000052) andFSIZESQ (square of FSIZE) at <10% level (-0.0644,p < 0.097512). The interpretation of thebeta coefficient is that, if beta coefficient is 0.5 then every time the independent variable changesby one standard deviation, the estimated outcome variable changes by half a standard deviation,on average. For example, here if DTC decreases by 0.23 (st dev of DTC) , then TQ will increaseby 4.19 (i.e. st dev of TQ) * 0.23 = 0.9637. If the dual role chairman changes to two separate

27

4.5 Panel Data Regression Analysis

Table 4.7: Fixed Effects Model - Regression for TQ

Independent Variable Coefficient Std. Error t-value Pr(>|t|)BSIZE 0.0011 0.0025 0.4444 0.657486PNER 0.0997 0.0691 1.4318 0.154520PIND -0.0082 0.0790 -0.1045 0.916960

DUAL -0.4087 0.1486 -2.7495 0.00678 **DTC -0.0144 0.0034 -4.1774 0.000052 ***INST 0.0018 0.0014 1.2956 0.197341

FSIZE 0.0000 0.0000 0.9410 0.348381FSIZESQ -0.0644 0.0386 -1.6663 0.097512 .

AGE 0.0107 0.0696 1.5468 0.12373CONCEN 0.0013 0.0009 1.3212 0.188671

Total Sum of Squares: 19.24Residual Sum of Squares: 11.681

R-Squared: 0.39304Adj. R-Squared: 0.36902

F-statistic(10,169): 10.9362 0.00000 ***LM heteroscedasticity test 0.04 *Hausman specification test 0.004 **

Durbin-Watson statistic 1.642p < .001 : "***" p < .01 "**" p < .05 "*" p < .1 "."

persons performing roles of CEO and chairman respectively (DUAL changes from 1 to 0 ie itdecreases by 1.4142 St dev since its st dev is 0.7071), the TQ will increase by 1.4142 * 0.4087* 4.19 = 2.421751. The estimated value of the Adjusted R2 is 0.369, which implies that about36.9% of the variation in TQ could be explained by the model. Though the effect is found to behighly significant, the F-statistic (10.9362) being significant at the 0.1% level, the overall modelis not very strong since, the explanatory power of the model (i.e. Adjusted R2) is not very high.The probability level of significance of the Lagrange’s Multiplier (LM) heteroscedasticity test is0.04. The null hypothesis for LM test is no heteroscedasticity. The data is in fact heteroscedas-tic. The consequence of this will be that although corrected standard errors can be found butthe estimator will no longer be Best linear unbiased estimator (BLUE). The Probability level ofsignificance that the fixed effect model is not superior to the corresponding random effect modelbased on the Hausman specification test (null hypothesis) is 0.004. The Durbin-Watson statisticis 1.642, not very far from 2.

Using ROA as the dependent variable, two significant variables are obtained namely DUALat <5% level (-0.1350,p < 0.004977) and DTC at at the <0.1% level (-0.1273, p < 0.00092). Theestimated value of the Adjusted R2 is 0.321, which implies that about 32.1% of the variation inROA could be explained by the model. Though the effect is found to be highly significant, the

28

Results

Table 4.8: Fixed Effects Model - Regression for ROA

Independent Variable Coefficient Std. Error t-value Pr(>|t|)BSIZE -0.00003 0.0026 -0.0122 0.99024PNER 0.1076 0.0696 1.5468 0.12373PIND -0.0480 0.0794 -0.6046 0.54626

DUAL -0.1350 0.0472 -2.8554 0.004977 **DTC -0.1273 0.0377 -3.3736 0.00092 ***INST 0.0017 0.0014 1.2406 0.21641

FSIZE 0.0000 0.0000 -0.6664 0.50604FSIZESQ 0.0000 0.0000 0.9410 0.68722

AGE -0.0004 0.0006 -0.6250 0.53278CONCEN 0.0009 0.0009 1.0668 0.28755

Total Sum of Squares: 34.792Residual Sum of Squares: 22.896

R-Squared: 0.34207Adj. R-Squared: 0.32105

F-statistic(10,168): 8.72863 0.00000 ***LM heteroscedasticity test 0.03 *Hausman specification test 0.002 **

Durbin-Watson statistic 1.768p < .001 : "***" p < .01 "**" p < .05 "*" p < .1 "."

F-statistic (8.72863) being significant at the 0.1% level, the overall model is not strong since,the explanatory power of the model (i.e. Adjusted R2) is not high. The probability level ofsignificance of the LM heteroscedasticity test is 0.03. The Probability level of significance asper Hausman specification test (null hypothesis) is 0.002. The Durbin-Watson statistic is 1.768.

Granger Causality Test :As per Granger test, none of the variable is found to have a causal effect on any of the

dependent variable.

29

5

Summary and Recommendation

5.1 Conclusion and Managerial Implication

The three independent variables which were found to be significant are:

• The dominant role of the CEO and chairman of the board

• Gear

• Size of company

The results of the latter two variables are in accordance with the finance literature. Thesetwo variables (gear and size) can actually be seen as control variables in this study. The resultsrelating to negative coefficient of gearing (borrowing) can be explained as follows : an increasein borrowing burdens the companies with interest payments which are directly charged to theincome statement, thus reducing profits. However, according to the literature on corporategovernance, high borrowing encourages the banks and creditors to monitor the company andtherefore is expected to contribute to higher profits through investments in value-enhancingprojects. The results obtained clearly indicate that this is not the case in India. Banks do notperform such a value-enhancing role in India.The second significant result relating to size is important for Indian corporate literature becausesize has been found positively related to TQ and hence earnings.

The Figure 5.1shows that even though size matters when it comes to earnings, there is alimit, and a corporation which has become too large can suffer reduced earnings. This can beexplained in terms of managers favoring expansion at the expense of the profitability of thecompany, CEO’s inability to exert control and his lack of technical expertise to run larger firms.The current international corporate trend is to focus on a company’s core business and get ridof unrelated businesses of which top managers know very little.

30

Summary and Recommendation

Figure 5.1: Firm Size (FSIZE) vs Gearing (DTC)

The third significant variable (DUAL) is the only corporate governance variable, which isfound related to to firm performance. The existence of a dominant personality where the CEO isalso the chairman of the board may have negative influence on company performance in India.A strong dominant CEO may be essential for a developing economy where the system may bedependent on a few powerful corporate players to push for performance in these companies. Ina more mature economy, a dominant CEO may be less important. It seems that Indian Economyis maturing on its way, whereby separation of CEO and Chairman encourages better governanceand hence financial performance.

We have found that neither institutional investors nor concentration of ownership exert anyinfluence on firm performance. This shows lack of shareholder activism (through their votingpower) in the companies, they had invested in. It seems that the role of institutional investorsmay be limited to monitoring without intervention for short term objectives.

5.2 Limitations of the Study

1. Only NIFTY firms are considered in the study. Out of NIFTY firms banking and financialservices firms ar excluded from the sample in order to avoid the distortion of the results.

31

5.2 Limitations of the Study

2. The accuracy of the results is limited to the accuracy of information in the Annual reportsof the firms.

3. Some of the firms do not have the relevant information in their annual reports for someyears, especially year 2005-07. This has lead to construction of the unbalanced paneldata. This however may not limit the study in a serious way, as the panel data regressioncan take care of this.

32

Appendix A

Firm-wise summary of corporategovernance

33

Figure A.1: ABB

Table A.1: ABB


Mean 9.40 0.83 0.54 32.75 0.00 5262.83 52.11 16555.98 10.04 0.25SE of Mean 0.68 0.02 0.02 1.91 0.00 717.16 0.00 3887.60 2.49 0.03


Coeff of Var 0.16 0.06 0.10 0.13 1.37 0.30 0.00 0.53 0.55 0.23

34

Firm-wise summary of corporate governance

Figure A.2: Ambuja Cements

Table A.2: Ambuja Cements


Mean 14.00 0.71 0.39 44.79 0.14 6274.39 35.29 16714.17 3.56 0.26SE of Mean 0.77 0.06 0.04 3.40 0.06 837.70 7.89 2993.35 0.68 0.04


Coeff of Var 0.12 0.19 0.23 0.17 0.95 0.30 0.50 0.40 0.42 0.37

35

Figure A.3: ACC

Table A.3: ACC


Mean 13.00 0.92 0.55 35.20 0.19 6886.07 65.02 14864.14 1.34 0.21SE of Mean 0.32 0.00 0.01 0.89 0.06 1039.15 0.00 3040.47 0.88 0.05


Coeff of Var 0.05 0.00 0.04 0.05 0.64 0.30 0.00 0.35 1.32 0.48

36


Figure A.4: BHEL

Table A.4: BHEL


Mean 16.00 0.63 0.50 27.20 0.02 23370.20 68.00 84089.75 2.95 0.09SE of Mean 0.00 0.00 0.00 0.49 0.01 3475.44 0.00 13330.99 0.29 0.01


Coeff of Var 0.00 0.00 0.00 0.04 1.22 0.33 0.00 0.35 0.22 0.13

37

Figure A.5: BPCL

Table A.5: BPCL


Mean 11.00 0.55 0.36 29.29 0.56 119505.40 68.79 14150.80 0.62 0.09SE of Mean 0.32 0.01 0.02 0.58 0.03 10589.84 1.99 1291.19 0.02 0.02


Coeff of Var 0.06 0.05 0.12 0.04 0.13 0.20 0.06 0.20 0.08 0.57

38


Figure A.6: Bharti Airtel Ltd

Table A.6: Bharti Airtel Ltd


Mean 16.40 0.87 0.50 28.56 0.26 24905.94 61.46 127014.17 6.08 0.23SE of Mean 0.75 0.01 0.00 0.57 0.05 4663.05 4.17 13898.72 1.20 0.01


Coeff of Var 0.10 0.03 0.00 0.04 0.39 0.42 0.15 0.24 0.44 0.13

39

Figure A.7: CIPLA

Table A.7: CIPLA


Mean 9.00 0.67 0.67 30.49 0.12 4217.13 21.38 20491.21 4.58 0.18SE of Mean 0.00 0.00 0.00 1.12 0.03 446.63 0.11 2061.78 0.81 0.02


Coeff of Var 0.00 0.00 0.00 0.08 0.52 0.24 0.01 0.22 0.40 0.20

40


Figure A.8: CAIRN

Table A.8: CAIRN

BSIZE PNER PIND INST DTC FSIZE CONCEN MVS TQ ROAMedian 10.00 0.70 0.42 16.78 0.00 98.66 66.84 52259.08 1.64 -0.00

Mean 10.00 0.74 0.47 16.97 0.01 125.32 66.26 50173.48 1.54 -0.00SE of Mean 0.41 0.06 0.06 0.32 0.01 67.01 1.65 5046.65 0.16 0.00


Coeff of Var 0.08 0.15 0.24 0.04 2.00 1.07 0.05 0.20 0.20 -1.95

41

Figure A.9: DLF

Table A.9: DLF


Mean 12.00 0.58 0.50 11.08 0.62 2621.88 84.35 74418.34 2.07 0.09SE of Mean 0.00 0.00 0.00 1.77 0.10 818.99 2.33 31638.74 1.19 0.02


Coeff of Var 0.00 0.00 0.00 0.36 0.37 0.70 0.06 0.74 1.28 0.36

42


Figure A.10: GAIL

Table A.10: GAIL


Mean 10.60 0.45 0.45 24.08 0.10 20313.08 69.79 33497.10 2.17 0.19SE of Mean 0.51 0.03 0.03 1.83 0.02 1886.88 0.87 5953.00 0.24 0.00


Coeff of Var 0.11 0.13 0.13 0.17 0.35 0.21 0.03 0.40 0.25 0.06

43

Figure A.11: HCL

Table A.11: HCL


Mean 7.80 0.82 0.82 24.40 0.08 4234.16 70.94 19579.94 5.06 0.25SE of Mean 0.37 0.03 0.03 0.63 0.04 368.29 1.08 2464.25 0.70 0.02


Coeff of Var 0.11 0.08 0.08 0.06 1.26 0.19 0.03 0.28 0.31 0.20

44


Figure A.12: Hero Honda Motors

Table A.12: Hero Honda Motors


Mean 16.20 0.74 0.49 35.90 0.05 2488.98 26.00 19836.18 6.97 0.41SE of Mean 0.49 0.01 0.01 0.36 0.01 634.62 0.00 2562.58 0.80 0.06


Coeff of Var 0.07 0.02 0.03 0.02 0.61 0.57 0.00 0.29 0.26 0.33

45

Figure A.13: Hindalco Industries

Table A.13: Hindalco Industries


Mean 10.00 0.90 0.58 31.32 0.30 18691.59 31.38 20361.02 1.02 0.10SE of Mean 0.00 0.00 0.02 1.77 0.03 1580.52 2.12 4560.53 0.16 0.01


Coeff of Var 0.00 0.00 0.08 0.13 0.24 0.19 0.15 0.50 0.35 0.32

46


Figure A.14: Hindustan Unilever Ltd

Table A.14: Hindustan Unilever Ltd


Mean 9.67 0.55 0.48 29.75 0.06 16125.20 51.76 52142.78 15.42 0.86SE of Mean 0.33 0.05 0.02 0.19 0.03 1806.70 0.33 1937.55 6.43 0.11


Coeff of Var 0.06 0.17 0.07 0.01 1.20 0.25 0.01 0.06 0.93 0.27

47

Figure A.15: Idea Cellular

Table A.15: Idea Cellular


Mean 11.25 0.84 0.44 16.77 0.58 6960.16 53.24 22904.01 2.32 0.11SE of Mean 0.48 0.02 0.04 3.64 0.09 1783.58 2.38 2680.52 0.77 0.01


Coeff of Var 0.09 0.06 0.16 0.43 0.34 0.57 0.09 0.23 0.74 0.26

48


Figure A.16: Infosys

Table A.16: Infosys


Mean 15.00 0.60 0.53 41.66 0.00 15845.80 16.40 94748.51 3.88 0.32SE of Mean 0.00 0.00 0.00 1.22 0.00 2251.04 0.12 20413.12 1.25 0.02


Coeff of Var 0.00 0.00 0.00 0.06 0.32 0.01 0.43 0.72 0.11

49

Figure A.17: ITC

Table A.17: ITC


Mean 13.40 0.73 0.58 50.81 0.01 21374.25 58.02 79313.62 6.58 0.26SE of Mean 0.51 0.02 0.03 0.32 0.00 1711.86 0.18 6422.29 0.57 0.01


Coeff of Var 0.09 0.07 0.10 0.01 0.39 0.18 0.01 0.18 0.19 0.08

50


Figure A.18: Jaiprakash Associates

Table A.18: Jaiprakash Associates


Mean 19.20 0.61 0.52 36.32 0.68 5606.91 45.08 19521.97 1.92 0.08SE of Mean 0.37 0.01 0.02 0.88 0.00 1309.17 0.92 5147.08 0.31 0.01


Coeff of Var 0.04 0.04 0.07 0.05 0.01 0.52 0.05 0.59 0.36 0.28

51

Figure A.19: Jindal Steel

Table A.19: Jindal Steel


Mean 13.40 0.61 0.46 27.15 0.54 5851.75 58.87 37862.15 5.20 0.15SE of Mean 1.54 0.02 0.04 0.87 0.02 1083.94 0.10 7846.97 0.85 0.01


Coeff of Var 0.26 0.09 0.20 0.07 0.09 0.41 0.00 0.46 0.36 0.20

52


Figure A.20: Larsen and Toubro

Table A.20: Larsen and Toubro


Mean 17.00 0.53 0.53 51.59 0.28 25946.40 5.22 69410.83 1.20 0.20SE of Mean 0.00 0.00 0.00 1.80 0.02 4349.21 0.00 30051.43 0.82 0.00


Coeff of Var 0.00 0.00 0.00 0.05 0.15 0.37 0.00 0.61 1.54 0.04

53

Figure A.21: Mahindra and Mahindra

Table A.21: Mahindra and Mahindra


Mean 12.00 0.75 0.67 51.93 0.33 13678.36 24.76 19396.84 3.18 0.18SE of Mean 0.00 0.00 0.00 1.46 0.04 1886.06 1.30 3562.80 0.46 0.02


Coeff of Var 0.00 0.00 0.00 0.06 0.25 0.31 0.12 0.41 0.33 0.28

54


Figure A.22: Maruti Suzuki Ltd

Table A.22: Maruti Suzuki Ltd


Mean 11.80 0.58 0.35 35.01 0.07 21802.64 58.32 28692.24 3.28 0.19SE of Mean 0.58 0.03 0.01 1.93 0.02 2981.92 2.52 3217.67 0.40 0.02


Coeff of Var 0.11 0.10 0.04 0.12 0.51 0.31 0.10 0.25 0.27 0.20

55

Figure A.23: NTPC Ltd

Table A.23: NTPCLtd


Mean 14.40 0.50 0.40 7.44 0.35 37051.74 89.78 147478.38 1.93 0.11SE of Mean 1.08 0.02 0.05 0.42 0.01 3544.61 0.28 11292.69 0.07 0.00


Coeff of Var 0.17 0.07 0.29 0.13 0.07 0.21 0.01 0.17 0.08 0.06

56


Figure A.24: ONGC

Table A.24: ONGC


Mean 13.60 0.59 0.40 10.24 0.17 58201.11 90.39 167123.52 1.77 0.24SE of Mean 0.98 0.11 0.04 2.44 0.01 2668.54 1.98 42090.99 0.47 0.00


Coeff of Var 0.16 0.41 0.22 0.53 0.10 0.10 0.05 0.56 0.60 0.04

57

Figure A.25: Power Grid Corporation

Table A.25: Power Grid Corporation


Mean 14.00 0.50 0.21 6.80 0.65 5011.45 86.36 45308.18 0.89 0.07SE of Mean 0.02 793.66 0.17 0.01

95% CI of Mean 0.05 2203.55 0.48 0.01Stand Dev 0.04 1774.67 0.39 0.01

Coeff of Var 0.06 0.35 0.44 0.17

58


Figure A.26: Ranbaxy Laboratories

Table A.26: Ranbaxy Laboratories


Mean 15.40 0.75 0.57 28.81 0.48 4352.82 46.66 15922.52 3.17 0.04SE of Mean 0.68 0.02 0.01 5.07 0.05 188.66 7.05 1544.19 0.52 0.04


Coeff of Var 0.10 0.05 0.04 0.39 0.23 0.10 0.34 0.22 0.37 2.31

59

Figure A.27: Reliance Communications

Table A.27: Reliance Communications


Mean 4.75 1.00 0.79 19.95 0.31 11237.92 67.14 70833.79 1.35 0.05SE of Mean 0.25 0.00 0.01 0.35 0.08 2840.84 0.24 20492.89 0.63 0.02


Coeff of Var 0.11 0.00 0.03 0.03 0.58 0.57 0.01 0.58 1.04 0.75

60


Figure A.28: RIL

Table A.28: RIL


Mean 13.00 0.65 0.57 27.24 0.32 138697.00 16.40 253984.58 1.69 0.10SE of Mean 0.32 0.02 0.02 0.62 0.01 18353.68 0.00 47633.02 0.22 0.01


Coeff of Var 0.05 0.08 0.09 0.05 0.09 0.30 0.00 0.42 0.29 0.29

61

Figure A.29: Reliance Infrastructure

Table A.29: Reliance Infrastructure


Mean 8.00 0.60 0.50 42.48 0.39 5969.15 43.33 19402.27 1.18 0.07SE of Mean 0.00 0.06 0.00 1.90 0.02 1023.64 5.83 4863.43 0.28 0.00


Coeff of Var 0.00 0.23 0.00 0.10 0.10 0.38 0.30 0.56 0.54 0.10

62


Figure A.30: Reliance Power

Table A.30: Reliance Power


Mean 6.00 0.83 0.56 5.17 0.00 0.45 86.49 53089.10 2.01 0.01SE of Mean 0.00 0.00 0.06 0.32 0.00 0.45 1.71 21536.30 1.25 0.00


Coeff of Var 0.00 0.00 0.18 0.11 2.24 0.03 0.70 1.39 0.97

63

Figure A.31: Siemens Ltd

Table A.31: Siemens Ltd


Mean 11.80 0.63 0.45 26.25 0.00 6598.61 69.54 15583.95 9.56 0.34SE of Mean 1.02 0.02 0.02 0.51 0.00 1163.84 1.54 2614.38 1.84 0.02


Coeff of Var 0.19 0.07 0.11 0.04 0.39 0.05 0.38 0.43 0.10

64


Figure A.32: SAIL

Table A.32: SAIL


Mean 17.00 0.47 0.36 11.25 0.22 42394.11 85.82 66780.32 2.16 0.23SE of Mean 2.37 0.06 0.06 0.32 0.03 2862.44 0.00 15235.82 0.43 0.03


Coeff of Var 0.31 0.27 0.35 0.06 0.35 0.15 0.00 0.51 0.44 0.30

65

Figure A.33: Sterlite Industries Ltd

Table A.33: Sterlite Industries Ltd


Mean 7.80 0.60 0.40 14.96 0.26 11957.29 66.82 84121.02 4.46 0.08SE of Mean 0.73 0.04 0.04 1.76 0.04 1044.42 5.18 51212.43 1.46 0.01


Coeff of Var 0.21 0.15 0.23 0.26 0.34 0.20 0.17 1.36 0.73 0.42

66


Figure A.34: Sun Pharmaceuticals Ltd

Table A.34: Sun Pharmaceuticals Ltd


Mean 7.20 0.56 0.56 22.60 0.18 2045.46 64.00 25489.71 5.56 0.19SE of Mean 0.20 0.01 0.01 1.38 0.11 262.19 0.46 3536.10 0.39 0.02


Coeff of Var 0.06 0.06 0.06 0.14 1.37 0.29 0.02 0.31 0.16 0.24

67

Figure A.35: Suzlon Energy Ltd

Table A.35: Suzlon Energy Ltd

BSIZE PNER PIND INST DTC FSIZE CONCEN MVS TQ ROAMedian 7.50 0.56 0.56 18.24 0.31 5380.37 59.45 9150.04 -0.14 0.15

Mean 7.50 0.56 0.56 18.24 0.35 5374.55 59.45 9150.04 0.07 0.11SE of Mean 1.50 0.12 0.12 4.13 0.09 775.06 6.37 2182.97 0.33 0.07


Coeff of Var 0.28 0.29 0.29 0.32 0.56 0.32 0.15 0.34 10.28 1.32

68


Figure A.36: TCS

Table A.36: TCS


Mean 9.60 0.75 0.60 15.37 0.01 18032.70 78.65 107263.54 11.00 0.41SE of Mean 1.12 0.05 0.05 1.81 0.00 2238.64 1.76 17239.04 2.44 0.03


Coeff of Var 0.26 0.15 0.17 0.26 0.00 0.28 0.05 0.36 0.50 0.14

69

Figure A.37: Tata Motors Ltd

Table A.37: Tata Motors Ltd


Mean 11.40 0.80 0.42 36.15 0.44 30921.22 61.69 11633.77 0.96 0.15SE of Mean 0.40 0.02 0.04 0.78 0.04 2461.39 2.00 6864.74 0.27 0.03


Coeff of Var 0.08 0.05 0.19 0.05 0.18 0.18 0.07 1.32 0.62 0.46

70


Figure A.38: Tata Power Co Ltd

Table A.38: Tata PowerCo Ltd


Mean 12.25 0.74 0.44 45.94 0.35 5947.61 32.92 21072.89 1.39 0.08SE of Mean 1.18 0.05 0.04 0.86 0.02 549.70 0.57 5744.18 0.39 0.00


Coeff of Var 0.19 0.13 0.18 0.04 0.12 0.21 0.03 0.55 0.62 0.06

71

Figure A.39: Tata Steel Ltd

Table A.39: Tata Steel Ltd


Mean 11.80 0.71 0.59 41.02 0.42 22537.26 45.31 37767.60 1.48 0.16SE of Mean 1.11 0.01 0.04 2.45 0.06 1915.32 0.85 8681.14 0.33 0.04


Coeff of Var 0.21 0.04 0.16 0.13 0.33 0.19 0.04 0.51 0.51 0.51

72


Figure A.40: Unitech Ltd

Table A.40: Unitech Ltd


Mean 9.75 0.62 0.52 15.43 0.68 1852.27 64.66 24985.76 3.60 0.13SE of Mean 0.25 0.02 0.02 6.82 0.08 331.58 6.97 7991.53 1.34 0.03


Coeff of Var 0.05 0.06 0.06 0.88 0.25 0.40 0.22 0.64 0.83 0.51

73

Figure A.41: Wipro Ltd

Table A.41: Wipro Ltd


Mean 8.40 0.78 0.78 6.85 0.16 17259.96 72.92 67520.94 4.01 0.24SE of Mean 0.93 0.04 0.04 0.79 0.06 2380.43 0.00 27739.78 0.92 0.03


Coeff of Var 0.25 0.11 0.11 0.26 0.81 0.31 0.00 0.92 0.51 0.25

74

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