Measuring the Risk Profile of Companies in the Indian Auto Sector


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Measuring the Risk Profile of Companies in the Indian Auto Sector

  1. 1. Analyzing the Risk Profile ofCompaniesIshpreet Singh – 12P139 Karan Jaidka – 12P141Lucky Sharma – 12P145 Prabhat Singh– 12P154Vignesh Patil – 12P177 Viswanath Kuppa – 12P180 PGPM – Section C – Group 9
  2. 2. Agenda• Objectives of the Study• Methodology• Results and Findings• Conclusions• Limitations of the Study• References
  3. 3. 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!
  4. 4. Methodology (1/2) Data Collected FromSector Companies Chosen
  5. 5. Methodology (2/2)StatisticalMethod =MultivariateRegression Financial BetaAnalysis Operational Beta Variability in Top-Line Time Period = 19 Quarters, rangi ng from Q1FY09 to Market Beta Q3FY13
  6. 6. Results and Findings (1/5) Regression Statistics CoefficientsMultiple R 0.850949625 Intercept -6.98610615 X Variable 1 (Change -8.7922E-05R Square 0.724115265 in Sales)Adjusted R Square 0.668938318 X Variable 2 -7.320170644Standard Error 0.104435141 (Operational Beta) X Variable 3 16.3833858Observations 19 (Financial Beta) If the company runs high onFinancial Beta => high positive leverage, the market treats it as riskyimpact Operational Beta => slightly thus shooting up the beta, but as soonnegated this effect as the company uses this for capital investments, the market perceives it as valuable thus bringing down the beta.
  7. 7. Results and Findings (2/5) Regression Statistics CoefficientsMultiple R 0.81932292 Intercept 1.603783483R Square 0.671290046 X Variable 1Adjusted R (Change in Sales) -0.000123863Square 0.605548056 X Variable 2Standard Error 0.041504947 (Operational Beta) -0.576842317Observations X Variable 3 19 (Financial Beta) 0.178052804 If the company takes loans and invests it in capitalFinancial Beta => small positive assets, the market treats it as a good sign, as theimpact Operational Beta => market beta reduces. As seen from the equation, arelatively higher negative effect 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.
  8. 8. Results and Findings (3/5) Coefficients Regression Statistics Intercept -0.5720710142Multiple R 0.804529506 X Variable 1 -0.000488088R Square 0.647267725 (Change in Sales)Adjusted R 0.57672127 X Variable 2 2.084508382Square (Operational Beta)Standard Error 0.129115189 X Variable 3 -0.201410293Observations 19 (Financial Beta) From the Regression Equation, we canFinancial Beta => small negative conclude that the market believes that it isimpact Operational Beta => relatively beneficial for the company to take up loans.higher positive effect 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.
  9. 9. Results and Findings (4/5) Coefficients Regression Statistics Intercept Multiple R 0.850377451 -0.59749226 R Square 0.723141809 X Variable 1 Adjusted R (Change in Sales) 1.67634E-06 Square 0.66777017 X Variable 2 Standard Error (Operational Beta) 1.204518921 0.096529801 X Variable 3 Observations 19 (Financial Beta) 0.01837394 The market perceives the company as stable.Financial Beta => positive impact The current installed capacity of the company is good enough for the market. This can beOperational Beta => relatively higher seen from the fact that the Operational Betapositive effect has a co-efficient of 1.2. Any loans taken from the company would not significantly affect the Market Beta.
  10. 10. Results and Findings (5/5) Regression Statistics Coefficients Multiple R 0.795463819 Intercept 2.994950916 R Square 0.632762687 X Variable 1 (Change 0.000133853 Adjusted R 0.559315224 in Sales) Square X Variable 2 -0.896301524 Standard Error 0.125192257 (Operational Beta) Observations 19 X Variable 3 -0.681609227 (Financial Beta)Operational Beta had a high This shows that company is highlynegative impact on the Market Beta underperforming and has a huge potentialfollowed by Financial Beta. 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.
  11. 11. 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.
  12. 12. 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.
  13. 13. 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••••••••