1. 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

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

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. Methodology (1/2)
Data Collected From
Sector
Companies Chosen

5. Methodology (2/2)
Statistical
Method =
Multivariate
Regression Financial
Beta
Analysis Operational
Beta
Variability
in Top-Line
Time Period =
19
Quarters, rangi
ng from
Q1FY09 to
Market Beta Q3FY13

6. Results and Findings (1/5)
Regression Statistics Coefficients
Multiple R 0.850949625 Intercept -6.98610615
X Variable 1 (Change -8.7922E-05
R Square 0.724115265
in Sales)
Adjusted R Square 0.668938318 X Variable 2 -7.320170644
Standard Error 0.104435141 (Operational Beta)
X Variable 3 16.3833858
Observations 19
(Financial Beta)
If the company runs high on
Financial Beta => high positive
leverage, the market treats it as risky
impact Operational Beta => slightly
thus shooting up the beta, but as soon
negated this effect
as the company uses this for capital
investments, the market perceives it as
valuable thus bringing down the beta.

7. Results and Findings (2/5)
Regression Statistics Coefficients
Multiple R 0.81932292 Intercept
1.603783483
R Square 0.671290046 X Variable 1
Adjusted R (Change in Sales) -0.000123863
Square 0.605548056 X Variable 2
Standard Error 0.041504947 (Operational Beta) -0.576842317
Observations X Variable 3
19
(Financial Beta) 0.178052804
If the company takes loans and invests it in capital
Financial Beta => small positive assets, the market treats it as a good sign, as the
impact Operational Beta => market beta reduces. As seen from the equation, a
relatively 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. Results and Findings (3/5)
Coefficients
Regression Statistics Intercept -0.5720710142
Multiple R 0.804529506
X Variable 1 -0.000488088
R Square 0.647267725
(Change in Sales)
Adjusted R 0.57672127
X Variable 2 2.084508382
Square
(Operational Beta)
Standard Error 0.129115189
X Variable 3 -0.201410293
Observations 19 (Financial Beta)
From the Regression Equation, we can
Financial Beta => small negative conclude that the market believes that it is
impact 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. 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 be
Operational Beta => relatively higher
seen from the fact that the Operational Beta
positive effect has a co-efficient of 1.2. Any loans taken from
the company would not significantly affect the
Market Beta.

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 highly
negative impact on the Market Beta
underperforming and has a huge potential
followed 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. 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. 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. 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