Capital Structure of Select Companies in Indian Automobile

www.theinternationaljournal.org > RJSSM: Volume: 03, Number: 11, March-2014 Page 243

Capital Structure of Select Companies in Indian Automobile Industry:

A SEM Approach

Dr R SARAVANAN

Director, School of Management, Sri Krishna College of Technology

( V.L.B.Janakiammal College of Engineering & Technology )

Kovaipudur, Coimbatore- 641 042, India

K GOWRI

Ph.D Research Scholar, Bharathiar University, Coimbatore-46, India

ABSTRACT

Capital structure is one of the most complex areas of financial decision making due to inter

relationship with other financial decision variables. In finance, the capital structure is the most

debatable topic and continues to keep researchers pondering. A good capital structure helps to gain

attractive profit, and the absence of a proper capital structure affect the debt position as well as the

leverage which leads to great financial risk. A restructuring of capital will be all suggested for poor

profit generating Industries and loss making Industries. For sound capital structure, the companies

struggle while raising funds is whether to raise debts or equity, because the debt and equity are the two

principal sources of finance for a company. The tough problem facing companies which raising funds,

so there arises an inconclusive debate on this issue. Hence, an attempt is made in this study to ascertain

the impact of various determinants so that appropriate capital structure could be designed by the

companies in order to make themselves competitive and cost effective. The analysis of the Structural

Equation model suggests that the measured variables are influenced to determine the capital structure

of select companies in Indian Automobile industry except STDR NDTS and OPR.

KeyWords: Capital structure, Determinants of Capital structure, Automobile industry.

INTRODUCTION

Structural equation models (SEMs) report findings in three different ways. Understanding the

way statistical significance is reported requires understanding the terminology of the model itself.

Within the graphical display of the model there are boxes and arrows. Boxes represent observed data

and the arrows represent assumed causation. Within the model a variable that receives a one-way

directional influence from some other variable in the system is termed "endogenous", or is dependent.

A variable that does not receive a directional influence from any other variable in the system is termed

as "exogenous" or is independent. The capital structure decision is crucial for business organizations.

The capital structure decision is important because of the need to maximize returns of the firms, and

because of the impact, such a decision has on the firm’s ability to deal with its competitive

environment. Therefore, it is important to test the relationship between capital structure and the

profitability of the firm to make sound capital structure decisions. Hence, the capital structure

determinants have been used to analyse the capital structure of select companies in Indian Automobile

Industry under SEM approach.

REVIEW OF PREVIOUS STUDIES

One of the main factors that could influence the firm’s performance is capital structure. Since

bankruptcy costs exist, deteriorating returns occur with further use of debt in order to get the benefits

of tax deduction. Therefore, there is an appropriate capital structure beyond which increases in

bankruptcy costs are higher than the marginal tax-sheltering benefits associated with the additional


substitution of debt for equity. Firms are willing to maximize their performance, and minimize their

financing cost, by maintaining the appropriate capital structure or the optimal capital structure. Harris

and Raviv (1991) argued that capital structure is related to the trade-off between costs of liquidation

and the gain from liquidation to both shareholders and managers. So firms may have more debt in their

capital structure than is suitable as it gains benefits for both shareholders and managers. However, as

stated in the previous literature, underestimating the bankruptcy costs of liquidation or reorganization,

or the aligned interest of both managers and shareholders, may lead firms to have more debt in their

capital structure than they should (see, for example, Harris and Raviv, 1991). Krishnan and Moyer,

(1997) found a negative and significant impact of total debt to total equity (TD/TE) on return on equity

(ROE). Another study by Gleason, Mathur and Mathur, (2000) found that firms capital structure has a

negative and significant impact on firms performance measures return on assets (ROA), growth in

sales (Gsales), and pre tax income (Ptax). Therefore, high levels of debt in the capital structure would

decrease the firm's performance.

Hunjra et al., (2011) in their study entitled “Patterns of Capital Structure and Dividend Policy

in Pakistani Corporate Sector and their Impact on Organization Performance”, analyzed the

determined of patterns of capital structure decisions and dividend policy as well as their level of

application in Pakistani corporate sector and also checked the impact of capital structure and dividend

policy on organization performance. The study concluded that capital structure decisions were being

properly practiced while dividend policy was a major concern in most of the organizations. The study

also concluded that there was a significant and positive relationship between capital structure decision,

dividend policy and organization performance.

San and Heng (2011) in their article entitled “Capital Structure and Corporate Performance of

Malaysian Construction Sector”, studied the relation of capital structure with performance of the firm

in the Malaysian construction industry in the aftermath of financial crises of 2007-08 that badly

affected most of the economies of the world including Malaysia. They found that the financial crises

do not show any major impact on the performance of construction industry because of the large scale

development work going on the country. Weak relation exists between leverage and performance

measured by assets returns, equity returns and profitability in the Malaysian construction industry

including small, medium and large size companies.

OBJECTIVE

To analyse the factors determining the capital structure of select companies in Indian

Automobile Industry under SEM approach.

SCOPE OF THE STUDY

The present study has been analyzed to design an appropriate capital structure to make

competitive and cost effective of select companies for a period of the study which was extended over

ten years from 2002-2003 to 2011-2012.

SELECTION OF SAMPLE

The Study takes into account of the following top ten Automobile companies in the year 2013 were

purposively selected.

1. Maruti Suzuki India Ltd

2. Hyundai Motors India Ltd

3. Tata Motors Ltd

4. Ashok Leyland

5. Mahindra & Mahindra Ltd

6. Toyota Ltd

7. General Motors India Private Ltd

8. Ford Motor Company

9. Honda Cars India Ltd.,

10. Nissan Motor India Private Ltd.

http://www.hyundai.com/in/en/main/

http://www.mahindra.com/

http://www.toyotabharat.com/


DATA AND SOURCES OF DATA

The study was based mainly on secondary data. The following data needed for the study were

collected from the official Directory of the Bombay Stock Exchange and Corporate Data base

(PROWESS) of the Centre for Monitoring Indian Economy (CMIE).

ANALYSIS OF CAPITAL STRUCTURE

STRUCTURAL EQUATION MODELING (SEM)

Structural Equation Modeling is a very general statistical modeling technique, which is widely

used in the behavioural sciences. It can be viewed as a combination of factor analysis and regression or

path analysis. The interest in SEM is often on theoretical constructs, which are represented by the

latent factors. The relationships between the theoretical constructs are represented by regression or

path coefficients between the factors. The structural equation model implies a structure for the co-

variances between the observed variables, which provides the alternative name covariance structure

modeling. However, the model can be extended to include means of observed variables or factors in

the model, which makes covariance structure modeling a less accurate name.

RESEARCH MODEL AND HYPOTHESIS FORMULATION

The research hypotheses have been defined on the basis of the constructs outlined above and

using previous research on assessing the capital structure capital structure of select companies in

Indian Automobile Industry. The Chart No.1 is a graphic presentation of the developed hypothetical

model. On the basis of above presented model, the following hypotheses are proposed.

HYPOTHESIS OF THE STUDY

The following “selected variables are positively correlated with the capital structure of select

companies in Indian Automobile Industry”. The manifest and latent variables are exhibited in table

no.1.

CO-VARIANCE MATRIX

In probability theory and statistics, a covariance matrix (also known as dispersion matrix or

variance covariance matrix) is a matrix whose element in the i, j position is the covariance between the

i th

and j th

elements of a random vector (that is, of a vector of random variables). Each element of the

vector is a scalar random variable, either with a finite number of observed empirical values or with a

finite or infinite number of potential values specified by a theoretical joint probability distribution of

all the random variables. Intuitively, the covariance matrix generalizes the notion of variance to

multiple dimensions. As an example, the variation in a collection of random points in two-dimensional

space cannot be characterized fully by a single number, nor would the variances in the x and y

directions contain all of the necessary information; a 2×2 matrix would be necessary to fully

characterize the two-dimensional variation.The Co-variance matrix of select Automobile

manufacturing companies are presented in Table No.2.

DISCUSSION OF THE RESULT

Table No.3 and Chart No.3 infers that the following measured variables LTDR, TDR, CI,

PROF, AGE, SIZE, TANG, ROE, ROCE, RONW, GPR, NPR, OCR and CPR are influenced with

latent variable of successful operation and have positive relationship with the significant at 1 percent

and 5 percent level of determining the capital structure of select companies in Indian Automobile

industry and the negative relationship with STDR, NDTS and OPR. The analysis of the model

suggests that almost all the measured variables are influenced to determine the capital structure of

select companies in Indian Automobile industry except STDR, NDTS and OPR.

The table No.4 reveals that the regression coefficient of the exogenous variables. It is noted

that the critical ratio of the following LTDR, TDR, CI, PROF, AGE, SIZE, TANG, ROE, ROCE,

RONW, GPR, NPR, OCR and CPR manifest variables are above the table value 2.962 and it is

significant at 1 percent level. Among the selected variables, almost all the variables are influenced to

http://en.wikipedia.org/wiki/Probability_theory

http://en.wikipedia.org/wiki/Statistics

http://en.wikipedia.org/wiki/Matrix_(mathematics)

http://en.wikipedia.org/wiki/Covariance

http://en.wikipedia.org/wiki/Random_vector

http://en.wikipedia.org/wiki/Euclidean_vector

http://en.wikipedia.org/wiki/Random_variable

http://en.wikipedia.org/wiki/Scalar_(mathematics)

http://en.wikipedia.org/wiki/Joint_probability_distribution

http://en.wikipedia.org/wiki/Variance


determine the capital structure of select companies in Indian Automobile industry except three

variables named STDR, NDTS and OPR. These are not influenced to determine the capital structure of

the select companies.

Table no.5 indicates that the model fit indices of Automobile industry. The entire test has the

range of 0 to 1. The comparative fit index (CFI) scored 0.712, normed fit index (NFI) scored 0.823,

relative fit index (RFI) scored 0.769, incremental fit index (IFI) scored 0.825, parsimonious normed fit

Index (PNFI) scored 0.622, parsimony comparative fit index (PCFI) scored 0.789, Tucker Lewis index

(TLI) scored 0.597, and the Root Mean Squared Error of Approximation (RMSEA) secured 0.031 that

indicates a close fit of the model.

FINDINGS

The following LTDR, TDR, CI, PROF, AGE, SIZE, TANG, ROE, ROCE, RONW, GPR,

NPR, OCR and CPR manifest variables are above the table value 2.962 and it is significant at 1

percent level. Among the selected variables, these variables are influenced to determine the capital

structure of select companies in Indian Automobile industry. And the other variables STDR, NDTS

and OPR are not influenced to determine the capital structure of the select companies.

SUGGESTIONS

Based on the findings of this study, it is concluded that the capital structure of the firm influences

profitability. The results suggest that profitable firms depend more on debt as their main financing option.

Although interest on debt is tax deductable, a higher level of debt increases default risk, which in turn,

increases the chance of bankruptcy for the firm. Therefore, the firm must consider using an optimal capital

structure by including debt. It is “The best” debt / equity ratio for the firm, which in turn, will minimize the

cost of capital, i.e., the cost of financing the company’s operations. In addition, it will reduce the chances of

bankruptcy.

The prime objective of every firm is to maximize the profit to improve better returns to its

stakeholders and to increase faith in the intrinsic value of the firm. The company can find out the ratios

which consistently discriminate the more profitable companies from less profitable. The latter

companies shall take the necessary cost reduction measures to improve their profitability. Cost control

measures should be applied to control the cost. As cost decides the profit of the concern, different

elements of cost should be verified and find out which cost is going up and take remedial measures to

control cost.

CONCLUSION Capital structure/leverage level of the firm determined by several factors. Proper capital

structure leads the firm to achieve the better performance and ensures the sustainability in its

operation. Even though there are several factors contribute to the institutional performance,

determinants of the capital structure play an important role. If the suggestions given in this study are

properly carried out the concern can have a bright future. These concerns can concentrate more on cost

control and Economic Production. The study is a rewarding exercise to the researcher and he will be

delighted if the findings and suggestions are incorporated by the policy makers of these select

companies in Indian Automobile Industry.


CHART N0.1

RESEARCH MODEL SPECIFICATION


TABLE No.1

VARIABLES SPECIFICATION FOR CAPITAL STRUCTURE ANALYSIS

MANIFEST AND LATENT VARIABLES FOR SEM

Manifest variables Latent variables

Long-term Debt Ratio LTDR

Capital Structure(CS) Short-term Debt Ratio STDR

Total Debt Ratio TDR

Capital Intensity CI

Capital structure

determinants(CAPSD)

Profitability PROF

Age AGE

Size SIZE

Non-debt tax Shields NDTS

Tangibility TANG

Return on Equity ROE

Investment Ratios(IR) Return on Capital Employed ROCE

Return on Networth RONW

Gross Profit Ratio GPR

Profitability of the Firm (PROFT) Net Profit Ratio NPR

Operating Profit Ratio OPR

Operating Cost Ratio OCR

Cash Profit Ratio CPR



CHART No.2

UNSTANDARDISED ESTIMATES


CHART No.3

STANDARDISED ESTIMATES


TABLE No.3

TESTING OF HYPOTHESES – STANDARDISED ESTIMATES

Hypotheses Hypothetical

Relationship

Result

H1: There is a positive impact of LTDR and the Capital

structure of Select Automobile companies in India. Positive Confirmed

H2: There is a negative impact of STDR and the Capital

structure of Select Automobile companies in India. Negative

Not

Confirmed

H3: There is a positive impact of TDR and the Capital structure

of Select Automobile companies in India. Positive Confirmed

H4: There is a positive impact of CI and the Capital structure

of Select Automobile companies in India. Positive Confirmed

H5: There is a positive impact of PROF and the Capital


H6: There is a positive impact of AGE the Capital structure of

Select Automobile companies in India. Positive Confirmed

H7: There is a positive impact of SIZE and the Capital


H8: There is a positive impact of NDTS and the Capital


Not

Confirmed

H9: There is a positive impact of TANG and the Capital


H10: There is a positive impact of ROE and the Capital


H11: There is a positive impact of ROCE and the Capital


H12: There is a positive impact of RONW and the Capital


H13: There is a positive impact of GPR and the Capital


H14: There is a positive impact of NPR and the Capital


H15: There is a positive impact of OPR and the Capital


Not

Confirmed

H16: There is a positive impact of OCR and the Capital


H17: There is a positive impact of CPR and the Capital


Chi-square = 27752.9, Degrees of freedom = 113, Probability level = .000


TABLE No.4

REGRESSION WEIGHTS FOR CAPITAL STRUCTURE ANALYSIS

LISREL MAXIMIM LIKELIHOOD ESTIMATES

Latent

Variables

Measured

Variables

Estimates

SE

R2

CR

P

CS <--- LTDR 10.773 .366 0.05 29.439 ***

CS <--- STDR 10.000 .255 -0.88 1.33 0.02

CS <--- TDR 12.103 1.879 0.82 6.441 ***

CAPSD <--- CI 5.409 .243 0.92 22.244 ***

CAPSD <--- PROF 6.719 1.010 0.87 6.656 ***

CAPSD <--- AGE 72.545 .772 0.95 93.952 ***

CAPSD <--- SIZE 3.864 .221 0.20 17.472 ***

CAPSD <--- NDTS 3.818 .182 -0.10 1.071 0.022

CAPSD <--- TANG 4.818 .243 0.32 19.831 ***

IR <--- ROE 6.682 1.790 0.39 3.732 ***

IR <--- ROCE 12.379 2.475 0.61 5.001 ***

IR <--- RONW 52.409 6.358 1.26 8.243 ***

PROFT <--- GPR 3.045 .290 0.21 10.488 ***

PROFT <--- NPR 2.591 .306 0.09 8.461 ***

PROFT <--- OPR 2.864 .249 -1.24 1.511 0.024

PROFT <--- OCR 85.545 1.659 0.24 51.566 ***

PROFT <--- CPR 4.091 .160 0.26 25.578 ***

***- Significant at 1% level

TABLE No.5

MODEL FIT INDICES

Sl.

No

Model Fit Indices Calculated

Value

Acceptable Threshold Levels

1 Comparative Fit Index(CFI) 0.721 0-1

2 Normed Fit Index (NFI) 0.823 0-1

3 Relative Fit Index (RFI) 0.769 0-1

4 Incremental Fit Index (IFI) 0.825 0-1

5 Parsimonious Normed Fit Index

(PNFI))

0.622 0-1

6 Parsimony Comparative Fit Index

(PCFI)

0.789 0-1

7 Tucker Lewis Index (TLI) 0.597 0-1

8 Root Mean Squared Error of

Approximation (RMSEA)

0.031 0.05 or less would indicate a close fit of

the model

REFERENCES

1. Abdel Shahid, S. (2003), “Does Ownership Structure Affect Firm Value? Evidence from The

Egyptian Stock Market”, Working Paper, [online], (www.ssrn.com).

2. Ahmed Imran Hunjra, Muhammad Bilal, Haroon Shafi, Ikram Ullah and Kashif-Ur Rehman,

“Patterns of Capital Structure and Dividend Policy in Pakistani Corporate Sector and their Impact on

Organization Performance”, African Journal of Business Management Vol. 5, Issue 27, pp. 11060-

11067, 2011.

3. Almazan A. and Molina C.A. (2005). “Intra-industry capital structure dispersion”. Journal of

Economics & Management Strategy, Vol. 14, No.2. 263-297.

4. ASE (2002), Amman Stock Exchange, 2002, Fourth Annual Report, (Amman, Jordan).

http://www.ssrn.com/


5. Ang, J. S., R. A. Cole, and Lin, J. W. (2000), “Agency Costs and Ownership Structure”,

Journal of Finance 55, 81-106.

6. Barclay, M. J., and Smith, C. W. (1995), “The Maturity Structure of Corporate Debt”, Journal

of Finance 50, 609-32.

7. Bevan A.A. and Danbolt J. (2002). “Capital structure and its determinants in the UK – A

decompositional analysis”. Applied Financial Economics, Vol.12, 159-170.

8. Bradley, M., G. A. Jarrell, and Kim, E. H. (1984), “On the Existence of an Optimal Capital

Structure: Theory and Evidence”, Journal of Finance 39, 857-878.

9. Breusch, T., and Pagan, A. (1980), “The Lagrange-Multiplier Test and its Applications to

Model Specification in Econometrics”, Review of Economic Studies 47, 239–253.

10. Brick, I. E., and Ravid, S. A. (1985), “On the Relevance of Debt Maturity Structure”, Journal

of Finance 40, 1423–37.

11. Chakravarthy, B. S., (1986), “Measuring Strategic Performance”, Strategic Management

Journal 7, 437-58.

12. Demsetz, H., and K. Lehn, (1985), “The Structure of Corporate Ownership: Causes and

Consequences”, Journal of Political Economy 93, 1155-1177.

13. DeAngelo, H. and Masulis, R. (1980). “Optimal capital structure under corporate and personal

taxation”. Journal of Financial Economics, 8, 3–30.

14. De Miguel, A. and Pindado J. (2001). “Determinants of capital structure: new evidence from

Spanish panel data”, Journal of Corporate Finance, Vol. 7, 77-99.

15. Durand, R., and R. Coeurderoy, (2001), “Age, Order of Entry, Strategic Orientation, and

Organizational Performance”, Journal of Business Venturing 16, 471-94.

16. Fisher, F. M., and J. McGowan, (1983), “On the Misuse of Accounting Rates of Return to Infer

Monopoly Profits”, American Economic Review 73, 82-97.

17. Gleason, K. C., L. K Mathur, and I. Mathur, (2000), “The Interrelationship between Culture,

Capital Structure, and Performance: Evidence from European Retailers”, Journal of Business

Research, 50, 185-191.

18. Greene, W. H., (2003). Econometrics Analysis (Prentice Hall, New York).

19. Hatfield G.B., Cheng L.T.W. and Davidson W.N. (1994). “The determination of optimal

capital structure: the effect of firm and industry debt ratios on market value”, Journal of

Financial and Strategic Decisions, Vol. 7, No. 3

20. Harris, M., A. Raviv, (1991), “The Theory of Capital Structure”, Journal of Finance 46, 297–

355.

21. Kraus, A., and R. Litzenberger, (1973), “A State-Preference Model of Optimal Financial

Leverage”, Journal of Finance 28, 923-931.

22. Krishnan, V. S., and R. C. Moyer, (1997), “Performance, Capital Structure and Home Country:

An Analysis of Asian Corporations”. Global Finance Journal 8, 129-143.

23. Lauterbach, B., and A. Vaninsky, (1999), “Ownership Structure and Firm Performance:

Evidence from Israel”, Journal of Management and Governance 3, 189-201.

24. Long, W. F., D. J. Ravenscraft, (1984), “The Misuse of Accounting Rates of Return:

Comment”, American Economic Review 74, 494-500.

25. Mehran, H., (1995), “Executive Compensation Structure, Ownership, and Firm Performance”,

Journal of Financial Economics 38, 163-184.

26. Modigliani, F., and M. Miller, (1958), “The Cost of Capital, Corporation Finance and the

Theory of Investment”, American Economic Review 48, 261-297.

27. Modigliani, F., and Merton H. Miller, (1963), “Corporate Income Taxes and the Cost of

Capital: A Correction”, The American Economic Review 53, 433-443.

28. Morck, R., A. Shleifer, and R. Vishny, (1988), “Management Ownership and Market

Valuation: An Empirical Analysis”, Journal of Financial Economics 20, 293-315.

29. Ozkan, A., (2002), “The Determinants of Corporate Debt Maturity: Evidence From UK Firms”,

Applied Financial Economics 12, 19–24.


30. Reese, J. S., and W. R. Cool, (1978), “Measuring Investment Centre Performance”, Harvard

Business Review 56, 28- 46.

31. San O.T. and Heng T.B., “Capital Structure and Corporate Performance of Malaysian

Construction Sector”, International Journal of Humanities and Social Science, Voi.1 No.2.

2011, pp:28-36, 2011.

32. Schiantarelli, F., and A. Sembenelli, (1999), The Maturity Structure of Debt: Determinants and

Effects on Firms’ Performance? Evidence from the United Kingdom and Italy, Policy Research

Working Paper Series, The World Bank.

33. Stohs, M. H., and D. C. Mauer, (1996), “The Determinants of Corporate Debt Maturity

Structure”, Journal of Business 69, 279-312.

34. Titman S. and Wessels R. (1988). “The determinants of capital structure choice”, Journal of

Finance, Vol. 43, No.1, 1-19

35. Wiwattanakantang Y. (1999). “An empirical study on the determinants of the capital structure

of Thai firms”. Pacific-Basin Finance Journal, Vol. 7, 371-403.

36. Zhou, X., (2001), “Understanding the Determinants of Managerial Ownership and the Link

Between Ownership and Performance: Comment”, Journal of Financial Economics 62, 559-

571.

Documents

Capital Structure of Select Companies in Indian Automobile