capital structure policy_large cap_indian companies

7/28/2019 Capital Structure Policy_Large Cap_Indian Companies

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Capital Structure Policy of

Indian CompaniesFinance II Report21

stMarch, 2013

PGP I 2012-2014

Section A Group 3

Arpit Tandon 2012PGP063

Chandraprakash Sinha 2012PGP093

Navtej Verma 2012PGP221

Rishabh Singh 2012PGP306

Souvik Sinha Roy 2012PGP370

Yadav Rahul 2012PGP446


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Capital Structure Policy

Objectives

The purpose of this research study has been to empirically find evidence as to what factorsaffect the capital structure policy decisions of the Indian Companies. Companies differ not only

by their respective industries but also by their revenues, profits, cash flows and credit ratings.

When all these factors are combined, we get a clear picture of any corporate firm. Then only we

can analyze what sources of new funds do companies prefer. Whether they go by the pecking

order theory to utilize internal funds, or prefer MM theory of obtaining higher financial

leverage to obtain tax shields, or prefer issuing new securities in the capital markets.

Research Methodology

We collected ten years financial data of the top 100 companies by Market Capitalization listed

on the National Stock Exchange (NSE). The variables collected for these 100 companies were

Sales, EBIT, Operating cash flow, Credit ratings, Debt-Equity ratio, Interest coverage ratio,

market capitalization, EPS and promoter shareholding. Only top 100 companies were selected

because generally these companies are matured and have well defined financial policies.

There could be arguments that by selecting data of small and mid cap companies, our analysis

could cover a wide range of companies varying sharply in size. But we believe that in India, with

small caps and mid caps, management structures and corporate governance are not very welldefined. Thus, the data we get for analysis might not reflect any policy preferences, but rather

reactive choices to the changing macro environment of the industry. Hence, we decided to go

with the large and generally stable companies.


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Capital Structure variations across Industries

It is a well known fact that the business model and cost structures of specific industries have

the most significant impact on financial decisions of any firm. Here, we calculate mean, median,

variances and standard deviation of Debt-Equity ratio companies (on 10 years data from 2003

to 2012) across various industry groups to have a brief overview of the capital structuresfollowed in the different industry groups.

This is only a preliminary investigation which would further be backed by more rigorous

statistical analysis such as t-tests and multiple regression models.

Automobile sector has a mean Debt-Equity of 0.37, median of 0.27, and a SD of 0.31. Aslarge Automobile industry companies in India such as Tata Motors, Bajaj, Maruti are

matured companies, we would expect an optimal level of Debt Equity ratio of around

0.4 with relatively less variation.

Since debt for banks is simply their liabilities, we have not considered this for thepurpose of analysis.

Cement industry having a high fixed cost structure has mean Debt Equity ratio of around0.78 with lesser variation of 0.58. Perhaps, the regulatory issues of having to pay stiff

penalties because of anti competitive policies have led these companies to increase

their leverage to ward off any cash flow problems.

INDUSTRY Mean Median VarianceStandard

Deviation

Automobiles 0.371111 0.27 0.099542 0.31550265

Bank 0 0 0 0

Cement 0.7805 0.75 0.343748 0.58630066

IT 0.127429 0.005 0.292625 0.5409484

Finance 4.319091 4.465 4.513315 2.1244565

Oil & Refineries 0.490714 0.46 0.209734 0.45796728

Personal Care 0.2528 0.13 0.086151 0.29351522

Pharmaceuticals 0.536566 0.31 0.402447 0.63438732

Power 125.6373 0.54 937465.4 968.227954

Power(neglecting outlier) 0.639661 0.53 0.291224 0.53965176

Steel 1.3125 0.955 2.125553 1.45792749

Overall 8.403447 0.18 58229.11 241.307077

Overall(neglecting outlier) 0.639617 0.18 1.461188 1.20879624


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Information technology is a knowledge based industry with very low levels of fixedcosts. Their low mean Debt Equity ratio of 0.12 is in line with the expectations. The

interesting part to note is that their median is extremely low at 0.005 while mean is still

0.12. This reflects the fact that only 1-2 companies because of firm specific reasons have

obtained leverage while the rest are mostly zero debt companies.

Finance sector, (Non Banking Financial Institutions) have a high mean Debt Equity ratioof around 4.3. The companies in this sector are trying to increase their balance sheets in

order to become significant and potential choices to obtain banking licenses which the

government plans to give in the next financial year.

Oil & Refineries in spite of being a very high fixed cost industry has an optimum meanDebt-Equity ratio of 0.49 and median of 0.46. Most of the largest companies in this

sector such as ONGC, IOC, and HPCL are PSUs which are risk averse and prefer not toface financial distress costs. The major private player Reliance Industries again is a very

stable business house, so leverage ratio seems the most appropriate for this industry.

Personal Care or FMCG industry has a low mean Debt Equity ratio of 0.25 and median ofeven lower 0.13. Again, the businesses such as HUL, P&G, and ITC are well established

and respected for their impeccable management ability. There has been no significant

event requiring the companies to expand capacity drastically, thus requiring high levels

of leverage.

Pharmaceutical industry generally requires high levels of cash for Research &Development and drug licensing activities. Hence, they have good levels of leverage of

around 0.53 with lower median of 0.31.

Power industry being a high fixed cost industry with long cash cycles is expected to havehigher levels of debt as reflected in their mean Debt-Equity ratio of 0.63. Reliance Power

in 2003 had abnormally high level of Debt-Equity ratio of 7,500 due to which the mean

and variance data is getting corrupted. The reason for this outlier is not known to us,

though we would suppose the company had just started their operations and plannedto install huge capacity in Uttar Pradesh. In the later years, Reliance Power has brought

Debt-Equity ratio well below 1. Hence, for the purpose of analysis we have ignored the

outlier value.

Steel has a high level of mean Debt-Equity ratio of around 1.3. This could be the result ofsome major Mergers & Acquisitions taking place in this sector. Moreover, to gain


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economies of scale and operating efficiencies, steel companies are quickly trying to

ramp up their size. This might have led to higher levels of debt as compared to other

high fixed cost industries.

The overall mean Debt Equity ratio (neglecting the outlier value) is around 0.63, median of 0.18

and standard deviation of 1.2. Since, the variation represented by SD is quite high, we would

like to base our assumptions on the more reliable median value. Mean value is getting skewed

to the higher side only because of few companies whose leverage could be high due to firm

specific reasons. The low median value clearly indicates that Indian companies in genera prefer

to be risk averse and not take high levels of debt. The factors and reasons for such attitude

would be explored by further statistical tests.

Data Analysis through T- Tests

Purpose of conducting t-tests

The t-test assesses whether the means of two groups are statisticallydifferent from each other.

This analysis is appropriate whenever you want to compare the means of two groups. Consider

the three situations shown in Figure. The first thing to notice about the three situations is

that the difference between the means is the same in all three. But, you should also notice that

the three situations don't look the same -- they tell very different stories. The top example

shows a case with moderate variability of scores within each group. The second situation shows

the high variability case. The third shows the case with low variability. Clearly, we would

conclude that the two groups appear most different or distinct in the bottom or low-variability

case. Why? Because there is relatively little overlap between the two bell-shaped curves. In the

high variability case, the group difference appears least striking because the two bell-shaped

distributions overlap so much.


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This leads us to a very important conclusion: when we are looking at the differences between

scores for two groups, we have to judge the difference between their means relative to the

spread or variability of their scores. The t-test does just this.

T-Tests across Industry Groups

We have conducted two tailtests with a confidence level of 95%. If the P- value (highlighted in

green) is below (1-0.95=0.05) 0.05, then the t-test says that mean of one group is statistically

significantly different from the mean of the other group.

Debt-Equity ratio of Finance industry is significantly greater than the rest of the industrygroups. P-value is extremely low, well below the significance level of 0.05.

Mean of Debt-Equity ratio in Steel industry is again significantly higher than that of thepersonal care industry with p-value of 0.02, well below 0.05.

An interesting finding is that the mean of Debt-Equity ratio in Oil exploration industry issignificantly below than that of the Oil Refineries. The qualitative reasons behind this

observation are not known to us.


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When comparing Automobile industrys mean of Debt-Equity to the rest of the industry,p-value is above0.05. Though mean value of automobile sector is less than the mean of

rest of the industry groups but due to high variance in rest of industry, t-test says that

this difference in means is not significantly large.

Mean Debt-Equity levels in IT sector are significantly less than the means in Finance andPower sector as shown by p-values of less than 0.05.

Though, we made these observations earlier also while analyzing the mean and mediandata, t-tests provides us a rigorous statistical tool to empirically verify the observations.

T-tests across various parameters

The above test shows that Debt-Equity ratios or financial leverage policy decisions arenot statistically different when comparing companies by market capitalization. As shown

above, neither Top 5% vs rest of the industry group, nor Bottom 5% vs rest of industry

group and any significant difference in leverage. Though the means of the two groups

are different at first sight, but due to high variances, t-tests fails to prove the difference.

Similar explanation can be given when comparing the mean Debt-Equity ratios ofcompanies by their Net Sales or Revenues. An interesting observation is that, though the

mean Debt-Equity ratio in bottom 5% is very low at 0.02 as compared to 0.54 of the rest

of industry group, but due to very high variance, t-test does not show any significant

difference.


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Operating Cash flow seems to have some impact on the Capital Structure policydecisions of the firms. As shown, mean of bottom 20% (by Operating Cash flow) is

significantly higher than the rest of the industry group. This is in line with the Pecking

Order theory which says that companies should prefer internal funds if available

because they generally cost the less. So, the firms with lower operating cash flows are

taking higher levels of debt to fund their business operations.

Data Analysis through Multiple Regression

Multiple regression provides a statistical tool to predict a dependent variable (such as Debt

Equity ratio) through multiple independent variables (such as Sales, EBIT, Market Capitalization,

Promoter Shareholding)

R Square denotes that percentage of variability in the dependent variable that can be

accounted by the correlation with the independent variables combined. Coefficients denote the

change in dependent variable when one unit of independent variable is changed. T- Stat

denotes the strength of the predictor variable and is generally considered to be better than

coefficient because it incorporates the error considerations. P-value denotes the percentage of

chance involved in the respective predictor variable. (1-p) denotes the predictive power, higher

the predictive power, stronger the correlation between the predictor variable and the

predicted variable.


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Regression Statistics

R Square 0.037185337

Coefficients Standard Error t Stat P-value

Intercept 0.418845058 0.274496955 1.525864 0.13075618

Market Cap -1.89059E-06 1.86009E-06 -1.0164 0.31232375

Sales 2.33335E-06 1.55614E-06 1.499451 0.13746178

EBIT -3.52117E-06 9.81815E-06 -0.35864 0.72075428

Promoter

Shareholding 0.001520097 0.004189359 0.362847 0.71761978

R Square value is around 0.037, which implies that only 3.7% of the variability in theDebt-Equity ratio of top 100 Indian companies in our sample can be attributed to the

independent variables, namely Market Capitalization, Sales, EBIT and Promoter

Shareholding. This is a very low value, which emphasizes upon the fact that no definiteconclusion can be drawn upon which factors strongly influence the capital structure

policy decisions of the Indian companies.

As market capitalization of the firm increases, its Debt-Equity ratio goes down as shownby the negative correlation. This could be because as the firm grows and matures, it has

internal sources of funding and risks associated with financial leverage increase

significantly. But 31% is due to chance, which means the predictive power is low, around

69%. Even t-stat is only around 1. In the industry, t-stat of 1.96 and above s considered

to be a strong predictor variable.

Sales are directly proportional to Debt Equity ratio which makes sense. As the firmgrows, it needs to invest in new assets and working capital for which additional funds

would be required. In this case predictive power is high, nearly 87% which a high t-stat

of 1.49. This shows a relatively strong relationship between sales and Debt Equity ratio.

EBIT is negatively correlated with Debt Equity, that is, as the firm increases its operatingprofits, lesser debt it takes. This is consistent with the pecking order theory which says

that internal funds should be preferred over debt and other external sources of funding.But the predictive power of this variable is very low, around 28% and a low t-stat. This

makes it difficult for us to assume that this relationship between EBIT and Debt Equity

would hold in other cases also.


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Promoter Shareholding is positively related to the Debt Equity ratio. A promoter holdinga large percentage of shares would prefer financial leverage as this would help him

achieve higher returns on equity. But again very low predictive power, around 29% and

low t-stat makes it difficult to give strong empirical evidence for our inference.

Conclusion

1. Indian companies in general prefer to have lower levels of debt as reflected in theirlower Debt-Equity ratios. Exceptions can always be there, and in crisis time or for major

acquisitions, companies can take on more debt. Generally, the optimal level of debt-

equity ratio is considered to be 40% but the median value for Indian companies comes

to around 18%.

2. High fixed cost industries such as Steel, Power, and Cement have much higher levels ofdebt than companies in FMCG and IT domains. Upfront expenditure in manufacturing is

high due to setting up factories etc. which required heavy investment. While, companies

in IT are mostly people based with variable cost structures hardly any upfront

investment. Moreover, IT sector is highly uncertain due to which lower debt levels are

preferred.

3. Most of the Indian companies unlike their US counterparts have large share ofpromoters. These promoters also play a major role in the management of the company.

Many policy decisions including capital structure depends on the personal preferences

of the promoters. Risk averse promoters prefer lower levels of debt while young

aggressive promoters prefer to use higher financial leverage and improve their returns

on equity.

4. Uncertainties in business environment, volatile markets, not fully developed capitalmarkets and frequent changes in government regulations force companies in developing

countries like India to assume lower debt levels to reduce their risk to financial distress.

Many large Indian firms are of the pre liberalization era where capital markets were

small in size and not many funding options were available. Hence, the companiespreferred not to be using high financial leverage.


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Exhibits

Debt Equity Ratio for 100 Indian companies (2011-12 annual data)

Name of Company Debt Equity Ratio

Tata Consultancy Services Ltd 0.00

Oil & Natural Gas Corpn Ltd 0.02Reliance Industries Ltd 0.44

ITC Ltd 0.01

Coal India 0.38

Infosys Ltd 0.00

HDFC Bank 0.00

State Bank of India 0.00

NTPC 0.66

ICICI Bank Ltd 0.00

Bharti Airtel Ltd 0.29

Wipro Ltd 0.22

Hindustan Unilever Ltd 0.00

Tata Motors Ltd 0.81

Larsen and Toubro 0.36

Sun Pharmaceuticals Industries Ltd 0.01

MMTC Ltd 3.40

Indian Oil Corporation Ltd 1.13

Axis Bank Ltd 0.00

Cairn India 0.04

BHEL 0.01

Hind Zinc 0.00

NMDC Ltd 0.00

Jindal Steel 1.42

Power Grid Corp 2.10

Bajaj Auto 0.04

GAIL 0.19

Tata Steel 0.55

Mahindra and Mahindra 0.27

UltraTechCement 0.35

Kotak Mahindra 0.00

Maruti Suzuki 0.05

DLF 1.30

Hero Motocorp 0.19

Sterlite Industries 0.23

HCL Tech 0.17

Reliance Power 0.05

Idea Cellular 0.90


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Oil India 0.03

Ranbaxy Labs 0.00

SAIL 0.46

Asian Paints 0.06

Bank of Baroda 0.00

Dr Reddys Laboratories Ltd 0.25

Cipla 0.03

Ambuja Cements 0.01

Adani Ports 0.93

Punjab National Bank 0.00

Bosch 0.07

BPCL 1.45

Grasim 0.09

Power Finance 5.45

Lupin 0.30

Adani Enterprise 0.12

Godrej Consumer 0.13

Oracle Financial 0.00

Hindalco Industries Ltd 0.38

NHPC 0.63

Siemens 0.00

Tata Power 0.64

United Spirits 0.72

IDFC 3.56

ACC 0.08

Canara Bank 0.00

Bank of India 0.00

Rural Electrification Corporation 5.81

Titan Industries Ltd 0.03

Dabur India LTD 0.22

GlaxoSmithKline Pharmaceuticals 0.00

GlaxoSmith Con 0.00

JSW Steel 0.78

Castrol 0.00

Cadila Health 0.40Jaiprakash Asso 2.00

Colgate-Palmolive India Ltd 0.00

Shree Cements 0.76

IndusInd Bank 0.00

United Breweries 0.62

Marico 0.56


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Reliance Communications 0.66

L&T Finance Holdings ltd 0.07

Satyam 0.01

Sesa Goa 0.19

Glenmark 0.39

Divis Labs 0.02

Shriram Transport Finance Company 3.95

Cummins India ltd 0.00

Container Corp 0.00

Tech Mahindra 0.34

Yes Bank 0.00

Neyveli Lignite 0.34

Pidilite Ind 0.22

Sun TV Network LTD 0.00

Zee Entertaintainment Enterprises Ltd 0.00

ABB 0.00

Union Bank 0.00

Wockhardt 2.09

Bharti Infratel 0.00

Nestle 0.00

capital structure policy_large cap_indian companies

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