Measuring the Risk Profile of Companies in the Indian Auto Sector

Analyzing the Risk Profile of Companies

Ishpreet Singh – 12P139 Karan Jaidka – 12P141

Lucky Sharma – 12P145 Prabhat Singh– 12P154

Vignesh Patil – 12P177 Viswanath Kuppa – 12P180

PGPM – Section C – Group 9

Agenda

•Objectives of the Study•Methodology•Results and Findings•Conclusions•Limitations of the Study•References

Objectives of the Study

Importance of Beta as a measure of Risk

Establishing a Relationship between Beta and Fundamental Factors

Identifying Fundamental Factors affecting Market Beta

To test the impact of these Fundamental Factors on Beta in the Indian context empirically through Multivariate Regression Analysis

Research papers written by stalwarts like Dr. Aswath Damodaran on this subject made us want to further the study!

Methodology (1/2)

Sector

Companies Chosen

Data Collected From

Methodology (2/2)

Market Beta

Variability in Top-Line

Operational Beta

Financial Beta

Time Period = 19 Quarters, ranging from Q1FY09 to Q3FY13

Statistical Method = Multivariate Regression Analysis

Results and Findings (1/5)

Regression Statistics

Multiple R 0.850949625

R Square 0.724115265

Adjusted R Square 0.668938318

Standard Error 0.104435141

Observations 19

Coefficients

Intercept -6.98610615

X Variable 1 (Change in Sales)

-8.7922E-05

X Variable 2 (Operational Beta)

-7.320170644

X Variable 3 (Financial Beta)

16.3833858

Financial Beta => high positive impact Operational Beta => slightly negated this effect

If the company runs high on leverage, the market treats it as risky thus shooting up the beta, but as soon as the company uses this for capital investments, the market perceives it as valuable thus bringing down the beta.



Multiple R 0.81932292R Square 0.671290046Adjusted R Square 0.605548056Standard Error 0.041504947Observations 19

Coefficients

Intercept1.603783483

X Variable 1 (Change in Sales) -0.000123863X Variable 2 (Operational Beta) -0.576842317X Variable 3 (Financial Beta) 0.178052804

Financial Beta => small positive impact Operational Beta => relatively higher negative effect

If the company takes loans and invests it in capital assets, the market treats it as a good sign, as the market beta reduces. As seen from the equation, a unit increase in Operational Beta and a unit increase in Financial Beta would reduce the Market Beta by 0.4 approximately. Also, another interesting feature is that the intercept is 1.6. Thus, it would take high values of Operational Beta to reduce the Market Beta to less than 1.




R Square 0.647267725

Adjusted R Square

0.57672127


Observations 19

Coefficients

Intercept -0.5720710142


-0.000488088


2.084508382


-0.201410293

Financial Beta => small negative impact Operational Beta => relatively higher positive effect

From the Regression Equation, we can conclude that the market believes that it is beneficial for the company to take up loans. However, this should not be invested in Capital Assets; rather, the company should use the capital to fund its Working Capital requirement. This is evident from the high coefficient of Operational Leverage.



Multiple R 0.850377451R Square 0.723141809Adjusted R Square 0.66777017Standard Error 0.096529801Observations 19

Coefficients

Intercept-0.59749226

X Variable 1 (Change in Sales) 1.67634E-06X Variable 2 (Operational Beta) 1.204518921X Variable 3 (Financial Beta) 0.01837394

Financial Beta => positive impact Operational Beta => relatively higher positive effect

The market perceives the company as stable. The current installed capacity of the company is good enough for the market. This can be seen from the fact that the Operational Beta has a co-efficient of 1.2. Any loans taken from the company would not significantly affect the Market Beta.




R Square 0.632762687

Adjusted R Square

0.559315224


Observations 19

Coefficients

Intercept 2.994950916


0.000133853


-0.896301524


-0.681609227

Operational Beta had a high negative impact on the Market Beta followed by Financial Beta.

This shows that company is highly underperforming and has a huge potential for growth. This can be seen from the fact that the market is treating the capital expansion and financial leverage as a positive as the risk is coming down.

Conclusions

• The explained variance of all the 5 regression models are ranging from 65% to 75% which shows that the 3 identified fundamental factors are decently explaining the change in beta.

• The co-efficient of these 3 factors in all the 5 models have not been consistent which shows that these factors are not industry specific but are company specific

• From the co-efficient it can be concluded that change in sales has a negligible impact when compared to accounting betas.

• There are many other qualitative factors which explain the unexplained variance (remaining 25-30%) in this model but since the scope of the project has been restricted to quantitative analysis only these 3 factors have been considered.

• This empirical study can be used for investment decisions in these stocks. While arriving at intrinsic value of a stock beta plays a crucial role and through this model one can estimate the future beta.

Limitations of the Study

• This is not a generalized model. It is a company-specific model. Developing an individual model for every company right from scratch in the Indian context is a highly laborious task

• Since the model relies on quarterly betas, the model needs to constantly updated

• The market betas calculated have been done on a quarterly basis for the last 19 quarters only. This is not a very standard method of calculating betas.

• Only 5 companies in the Indian automobile sector have been considered for the purpose of this study. The study can be extended to cater to many more companies across sectors and borders.

References

• Annie Yates and Colin Firer (1997), The Determinants of the Risk Perceptions of Investors

• Fransesco Franzoni (2008), The Changing Nature of Market Risk• Jiri Novak and Dalibor Petr (2010), CAPM Beta, Size, Book-to-

Market, and Momentum in Realized Stock, Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague

• Aswath Damodaran, Estimating Risk Parameters, Stern School of Business

• http://www.aceanalyser.com/• http://www.moneycontrol.com/stocksmarketsindia/• http://www.bseindia.com/• http://www.heromotocorp.com/en-in/investors/quarterlyresults• http://www.mahindra.com/Investors/Mahindra-and-Mahindra/Resource• http://www.escortsgroup.com/investor-information.html• http://www.tvsmotor.in/investor-home.asp• http://www.ashokleyland.com/performance-reports

Documents

Measuring the Risk Profile of Companies in the Indian Auto Sector