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Security & Portfolio Management

Models of BETA

calculation on EquitySAPM Assignment 3

4 Sectors - 12 Companies 3 Models of BETA Calculation

Pros/Cons

GHOUTHAM.U

8/19/2011

Roll No 2010c23

SCMHRD 2010-12

MBA Finance

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Name: Ghoutham.U (2010C23)

SCMHRD MBA Finance- SAPM Course

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TABLE OF CONTENTS

1) Introduction 3

2) Definition of BETA 3

3) Beta Calculation Methods 5

4) Preliminaries & Calculation Example 6

5) Beta & Risk 8

6) Finding Beta 8

7) Problems with Beta 9

8) How To Use Beta 9

9) Regression Alpha & Beta Squared 9

10) Workings & Data 11

11) References 45

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Introduction:

In finance, the Beta() of a stock or portfolio is a number describing the relation of its returns with those of

the financial market as a whole.

An asset has a Beta of zero if its returns change independently of changes in the market's returns. A positive betameans that the asset's returns generally follow the market's returns, in the sense that they both tend to be above their

respective averages together, or both tend to be below their respective averages together. A negative beta means

that the asset's returns generally move opposite the market's returns: one will tend to be above its average when the

other is below its average.

The beta coefficient is a key parameter in the capital asset pricing model (CAPM). It measures the part of the asset's

statistical variance that cannot be removed by the diversification provided by the portfolio of many risky assets,

because of the correlation of its returns with the returns of the other assets that are in the portfolio. Beta can be

estimated for individual companies using regression analysis against a stock market index.

Definition:

The formula for the beta of an asset within a portfolio is

where ra measures the rate of return of the asset, rp measures the rate of return of the portfolio, and cov( ra,rp) is

the covariance between the rates of return. The portfolio of interest in the CAPM formulation is the market

portfolio that contains all risky assets, and so the rp terms in the formula are replaced by rm, the rate of return of

the market.

Beta is also referred to as financial elasticityor correlated relative volatility, and can be referred to as a

measure of the sensitivity of the asset's returns to market returns, its non-diversifiable risk, its systematic risk, or

market risk. On an individual asset level, measuring beta can give clues to volatility and liquidity in the

marketplace. In fund management, measuring beta is thought to separate a manager's skill from his or her

willingness to take risk.

The beta coefficient was born out of linear regression analysis. It is linked to a regression analysis of the returnsof a portfolio (such as a stock index) (x-axis) in a specific period versus the returns of an individual asset (y-axis)

in a specific year. The regression line is then called the Security characteristic Line (SCL).

a is called the asset's alpha and a is called the asset's beta coefficient. Both coefficients have an

important role in Modern portfolio theory.
http://en.wikipedia.org/wiki/Financehttp://en.wikipedia.org/wiki/Stockhttp://en.wikipedia.org/wiki/Portfolio_(finance)http://en.wikipedia.org/wiki/Financial_markethttp://en.wikipedia.org/wiki/Capital_asset_pricing_modelhttp://en.wikipedia.org/wiki/Variancehttp://en.wikipedia.org/wiki/Diversification_(finance)http://en.wikipedia.org/wiki/Correlationhttp://en.wikipedia.org/wiki/Regression_analysishttp://en.wikipedia.org/wiki/Stock_market_indexhttp://en.wikipedia.org/wiki/Rate_of_returnhttp://en.wikipedia.org/wiki/Covariancehttp://en.wikipedia.org/wiki/Elasticity_(economics)http://en.wikipedia.org/wiki/Elasticity_(economics)http://en.wikipedia.org/wiki/Volatility_(finance)http://en.wikipedia.org/wiki/Assethttp://en.wikipedia.org/wiki/Riskhttp://en.wikipedia.org/wiki/Systematic_riskhttp://en.wikipedia.org/wiki/Volatility_(finance)http://en.wikipedia.org/wiki/Liquidityhttp://en.wikipedia.org/wiki/Linear_regressionhttp://en.wikipedia.org/wiki/Security_characteristic_linehttp://en.wikipedia.org/wiki/Alpha_(finance)http://en.wikipedia.org/wiki/Modern_portfolio_theoryhttp://en.wikipedia.org/wiki/Modern_portfolio_theoryhttp://en.wikipedia.org/wiki/Alpha_(finance)http://en.wikipedia.org/wiki/Security_characteristic_linehttp://en.wikipedia.org/wiki/Linear_regressionhttp://en.wikipedia.org/wiki/Liquidityhttp://en.wikipedia.org/wiki/Volatility_(finance)http://en.wikipedia.org/wiki/Systematic_riskhttp://en.wikipedia.org/wiki/Riskhttp://en.wikipedia.org/wiki/Assethttp://en.wikipedia.org/wiki/Volatility_(finance)http://en.wikipedia.org/wiki/Elasticity_(economics)http://en.wikipedia.org/wiki/Covariancehttp://en.wikipedia.org/wiki/Rate_of_returnhttp://en.wikipedia.org/wiki/Stock_market_indexhttp://en.wikipedia.org/wiki/Regression_analysishttp://en.wikipedia.org/wiki/Correlationhttp://en.wikipedia.org/wiki/Diversification_(finance)http://en.wikipedia.org/wiki/Variancehttp://en.wikipedia.org/wiki/Capital_asset_pricing_modelhttp://en.wikipedia.org/wiki/Financial_markethttp://en.wikipedia.org/wiki/Portfolio_(finance)http://en.wikipedia.org/wiki/Stockhttp://en.wikipedia.org/wiki/Finance

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For an example, in a year where the broad market or benchmark index returns 25% above the risk free rate,

suppose two managers gain 50% above the risk free rate. Because this higher return is theoretically

possible merely by taking a leveraged position in the broad market to double the beta so it is exactly 2.0, we

would expect a skilled portfolio manager to have built the outperforming portfolio with a beta somewhat less

than 2, such that the excess return not explained by the beta is positive. If one of the managers' portfolios

has an average beta of 3.0, and the other's has a beta of only 1.5, then the CAPM simply states that the

extra return of the first manager is not sufficient to compensate us for that manager's risk, whereas the

second manager has done more than expected given the risk. Whether investors can expect the second

manager to duplicate that performance in future periods is of course a different question.

Security market line

The SML graphs the results from the capital asset

pricing model (CAPM) formula. The x-axis represents

the risk (beta), and the y-axis represents the expected

return. The market risk premium is determined from

the slope of the SML.

The relationship between and required return is

plotted on the security market line(SML) which shows

expected return as a function of . The intercept is the

nominal risk-free rate available for the market, while

the slope is E(Rm) Rf. The security market line can

be regarded as representing a single-factor model of the asset price, where Beta is exposure to changes in

value of the Market. The equation of the SML is thus:

It is a useful tool in determining if an asset being considered for a portfolio offers a reasonable

expected return for risk. Individual securities are plotted on the SML graph. If the security's risk versus

expected return is plotted above the SML, it is undervalued because the investor can expect a greater

return for the inherent risk. A security plotted below the SML is overvalued because the investor would

be accepting a lower return for the amount of risk assumed.

The Security Market Line
http://en.wikipedia.org/wiki/Benchmark_(surveying)http://en.wikipedia.org/wiki/Leverage_(finance)http://en.wikipedia.org/wiki/Alpha_(finance)http://en.wikipedia.org/wiki/Security_market_linehttp://en.wikipedia.org/wiki/File:SecMktLine.pnghttp://en.wikipedia.org/wiki/File:SecMktLine.pnghttp://en.wikipedia.org/wiki/Security_market_linehttp://en.wikipedia.org/wiki/Alpha_(finance)http://en.wikipedia.org/wiki/Leverage_(finance)http://en.wikipedia.org/wiki/Benchmark_(surveying)

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One distinct advantage of calculating the beta yourself is that you can gauge its reliability by

calculating the coefficient of determination, or as it is better known, the r-squared. This is a powerful

tool that can gauge how well your beta measures risk. The range of this statistic is from one to zero. A

measurement of one shows perfect correlation and a zero shows there is no correlation. The closer

the r-squared is to one, the more reliable your beta is.

Another unknown is what method is being using to calculate the beta. There are two ways of doing

this: regression and the capital asset pricing model (CAPM). CAPM is used more commonly in

academic finance; investment practitioners more often use the regression technique. This allows for

a better explanation of returns pertaining to the market rather than a theoretical explanation of the

overall return of an asset, which takes interest rates as well as market returns into account.

Inevitably, along with the previously proclaimed advantages, there are also disadvantages to doing it

yourself. The main disadvantage is the time involved. Calculating beta yourself takes longer than just

obtaining it through a website, but this time can be significantly cut down by using programs such as

Microsoft Excel or Open Office Calc.

The Preliminaries & Calculation

Once you have decided on a time frame that aligns itself with your investment time horizon and have

chosen an appropriate index, you can then move on to gathering the data. There are many sources

for historical data on the internet. Look for historical prices of each equity to find the appropriate

date information matching your chosen time horizon. On some sites, you will have the option to

download the information as a spreadsheet. Choose this option and save the spreadsheet. Do the

same for our chosen index as well.

Copy both of the closing price columns into a new spreadsheet. They should be in order from newest

to oldest from top to bottom, respectively. To obtain the correct format for calculation we must

convert these prices into return percentages. To do this we just take the price from today minus the

price from yesterday and divide the answer by the price of yesterday. The result is the percentage

change. Below is a picture showing this in Excel. Figure 1 is of the formulas and Figure 2 is of the

result.
http://www.investopedia.com/terms/r/r-squared.asphttp://www.investopedia.com/terms/c/correlation.asphttp://www.investopedia.com/terms/r/regression.asphttp://www.investopedia.com/terms/c/capm.asphttp://www.investopedia.com/terms/c/closingprice.asphttp://www.investopedia.com/terms/c/closingprice.asphttp://www.investopedia.com/terms/c/capm.asphttp://www.investopedia.com/terms/r/regression.asphttp://www.investopedia.com/terms/c/correlation.asphttp://www.investopedia.com/terms/r/r-squared.asp

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Figure 1: Formulas

Figure 2: Results

The calculation of beta through regression is simply the covariance of the two arrays divided by the

variance of the array of the index. The formula is shown below.

Beta = COVAR (E2:E99,D2:D99)/VAR(D2:D99)

One advantage we discussed earlier is the ability to gauge the reliability of your beta. This is done by

calculating the r-squared. From here we input the two arrays containing the percentage changes.

Below is this formula in Excel.

R-Squared = RSQ(D2:D99,E2:E99)
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The Bottom Line

Although calculating your own betas can be time-consuming compared to using service-providedbetas, they provide the investor a better look at risk through personalization. In addition, we can also

gauge the reliability of this risk measurement by calculating its r-squared. These advantages are a

valuable tool to an investment arsenal and should be used by any serious investor.

Beta Calculated

The number is calculated for you (thank goodness) using regression analysis. The whole market,

which for this purpose is considered the S&P 500, is assigned a beta of 1. There is no single index

used to calculate beta, although the S&P 500 is probably the most common proxy for the market as a

whole.

Stocks that have a beta greater than 1 have greater price volatility than the overall market and aremore risky.

Stocks with a beta of 1 fluctuate in price at the same rate as the market.

Stocks with a beta of less than 1 have less price volatility than the market and are less risky.

Beta and Risk

Of course, there is more to it than that. Risk also implies return. Stocks with a high beta should have

a higher return than the market. If you are accepting more risk, you should expect more reward.

For example, if the market with a beta of 1 is expected to return 8%, a stock with a beta of 1.5 shouldreturn 12%. If you dont see that level of return, then the stock is not a good investment possibility.

Stocks with a beta below 1 may be a safer investment (at least by this one measure) and you shouldexpect a lower return.

Beta seems to be a great way to measure the risk of any stock. If you look a young, technology stocks,

they will always carry high betas. Many utilities on the other hand, carry betas below 1.

Finding Betas

You can also compare a stocks beta to its sector to get a picture of whether the stock is out of line

with its peers.

You can find a stocks beta through a number of online services such as offered byReuters.
http://www.investor.reuters.com/StockEntry.aspx?target=/stockshttp://www.investor.reuters.com/StockEntry.aspx?target=/stocks

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You have to register (its free) to get to the level of detail where you can find a stocks beta among thelisted ratios.

Problems with Beta

While the may seem to be a good measure of risk, there are some problems with relying on betascores alone for determining the risk of an investment.

Beta looks backward and history is not always an accurate predictor of the future. Beta also doesnt account for changes that are in the works, such as new lines of business or

industry shifts. Beta suggests a stocks price volatility relative to the whole market, but that volatility can be

upward as well as downward movement. In a sustained advancing market, a stock that isoutperforming the whole market would have a beta greater than 1.

How to Use Beta

Investors can find the best use of the beta ratio in short-term decision-making, where price volatility

is important. If you are planning to buy and sell within a short period, beta is a good measure of risk.

However, as a single predictor of risk for a long-term investor, the beta has too many flaws. Carefulconsideration of a companys fundamentals will give you a much better picture of the potential long-term risk.

Regression, Alpha, R-Squared

One use of CAPM is to analyze the performance of mutual funds and other portfolios - in particular,to make active fund managers look bad. The technique is to compare the historical risk-adjustedreturns (that's the return minus the return of risk-free cash) of the fund against those of anappropriate index, and then use least-squares regression to fit a straight line through the data points:
http://www.moneychimp.com/glossary/regression.htmhttp://www.moneychimp.com/glossary/regression.htm

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Each data point in this graph shows the risk-adjusted return of the portfolio and that of the indexover one time period in the past. (For example, you might make a graph like this with twenty datapoints, showing the annual returns for each of the past twenty years.)

The general equation of this type of line is

r - Rf = beta x ( Km- Rf) + alpha

where r is the fund's return rate, Rfis the risk-free return rate, and Km is the return of the index.

Note that, except for alpha, this is the equation for CAPM - that is, the beta you get from Sharpe'sderivation of equilibrium prices is essentially the same beta you get from doing a least-squaresregression against the data. (Also note that alpha and beta are standard symbols that statisticians useall the time for this type of regression; Sharpe and his followers weren't trying to be obscure, as somepeople like to believe.)

Beta is the slope of this line. Alpha, the vertical intercept, tells you how much better the fund didthan CAPM predicted (or maybe more typically, a negative alpha tells you how much worse it did,probably due to high management fees).

The quality of the fit is given by the statistical number r-squared. An r-squared of 1.0 would meanthat the model fit the data perfectly, with the line going right through every data point. Morerealistically, with real data you'd get an r-squared of around .85. From that you would conclude that85% of the fund's performance is explained by its risk exposure, as measured by beta. (Then you'dpunch your fist in the air and say "And the other 15% is due to pure luck!" MPT never believes ininvestor skill: an investment's behavior equals that of its asset class, minus management fees, plus-or-minus unpredictable luck.)
http://www.moneychimp.com/glossary/r_squared.htmhttp://www.moneychimp.com/glossary/r_squared.htm

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WORKINGS & DATA:

ULTRATECH Cements

Ultratech

Beta 0.414578176

Debt/Equity

Ratio 0.38

Tax Rate 30%

Unlevered Beta 0.327470913

Levered Beta/(1+(1-Tax)*D/E)

Date Avg.Price

NIFTY StockPricereturn

NSEReturns

201108 1,012.09 5072.95

2.53% -7.46%201107 987.09 5482 -0.86% -2.93%

201106 995.69 5647.4 -3.54% 1.57%

201105 1,032.25 5560.15 -3.87% -3.29%

201104 1,073.84 5749.5 4.05% -1.44%

201103 1,032.02 5833.75 8.00% 9.38%

201102 955.57 5333.25 -6.56% -3.14%

201101 1,022.62 5505.9 -5.25% -10.25%

201012 1,079.32 6134.5 -3.36% 4.64%

201011 1,116.87 5862.7 1.04% -2.58%

201010 1,105.37 6017.7 8.80% -0.20%201009 1,015.98 6029.95 13.09% 11.62%

201008 898.39 5402.4 5.32% 0.65%

201007 852.99 5367.6 -10.00% 1.04%

201006 947.81 5312.5 0.26% 4.45%

201005 945.33 5086.3 -14.73% -3.63%

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201004 1,108.63 5278 0.02% 0.55%

201003 1,108.36 5249.1 11.73% 6.64%

201002 992 4922.3 1.94% 0.82%

201001 973.08 4882.05 11.21% -6.13%

200912 875.03 5201.05 13.84% 3.35%

200911 768.68 5032.7 -5.07% 6.81%

200910 809.72 4711.7 7.07% -7.32%

200909 756.27 5083.95 1.49% 9.05%

200908 745.15 4662.1 -0.11% 0.55%

200907 745.97 4636.45 4.33% 8.05%

200906 715.02 4291.1 15.10% -3.55%

200905 621.2 4448.95 11.92% 28.07%

200904 555.03 3473.95 13.55% 15.00%

200903 488.8 3020.95 15.84% 9.31%

200902 421.97 2763.65 8.18% -3.87%200901 390.05 2874.8 15.87% -2.85%

200812 336.64 2959.15 4.71% 7.41%

200811 321.49 2755.1 -20.93% -4.52%

200810 406.58 2885.6

SUMMARY OUTPUT

Regression Statistics

Mul ti pl e R 0.946805286

R Squa re 0.896440249

Adjusted R Square 0.893302075

Standard Error 80.86460149

Observations 35

ANOVA

df SS MS F ignificance F

Re gre ss ion 1 1867932.6 1867932.6 285.65662 8.173E-18

Residual 33 215789.76 6539.0838

Total 34 2083722.3

Coe ff icients tandard Erro t Stat P- value Low er 95% Uppe r 95% ow er 95.0% Upper 95.0%

Intercept -251.5100956 65.702361 -3.8280222 0.0005467 -385.18255 -117.83764 -385.18255 -117.83764

NIFTY 0.225571012 0.0133463 16.901379 8.173E-18 0.1984177 0.2527243 0.1984177 0.2527243

Beta Coefficient 0.225571012

Fixed Cost 5447.78

Variable Cost 6872.95

Business Risk Beta 0.182675151

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ACC Cements

Date Avg.Price


NSEReturns

201108 995.06 5072.95 1.55% -7.46%

201107 979.92 5482 -1.00% -2.93%

201106 989.78 5647.4 -1.47% 1.57%

201105 1,004.58 5560.15 -9.68% -3.29%

201104 1,112.30 5749.5 9.24% -1.44%

201103 1,018.23 5833.75 3.25% 9.38%

201102 986.17 5333.25 -3.61% -3.14%

201101 1,023.12 5505.9 -2.49% -10.25%

201012 1,049.21 6134.5 -0.06% 4.64%

201011 1,049.83 5862.7 4.63% -2.58%

201010 1,003.35 6017.7 2.81% -0.20%

201009 975.88 6029.95 14.77% 11.62%

201008 850.3 5402.4 2.11% 0.65%

201007 832.74 5367.6 -3.12% 1.04%

201006 859.54 5312.5 -0.34% 4.45%

201005 862.48 5086.3 -7.41% -3.63%

201004 931.51 5278 -3.98% 0.55%201003 970.09 5249.1 9.46% 6.64%

201002 886.24 4922.3 -3.90% 0.82%

201001 922.21 4882.05 9.98% -6.13%

200912 838.56 5201.05 12.02% 3.35%

200911 748.61 5032.7 -4.10% 6.81%

200910 780.6 4711.7 -2.70% -7.32%

200909 802.3 5083.95 -2.53% 9.05%

200908 823.13 4662.1 1.87% 0.55%

200907 808.02 4636.45 -0.34% 8.05%

200906 810.79 4291.1 19.22% -3.55%

200905 680.07 4448.95 9.62% 28.07%

200904 620.39 3473.95 12.91% 15.00%

200903 549.44 3020.95 0.70% 9.31%

200902 545.61 2763.65 9.14% -3.87%

200901 499.93 2874.8 6.97% -2.85%

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200812 467.34 2959.15 5.97% 7.41%

200811 441.02 2755.1 -14.43% -4.52%

200810 515.39 2885.6 -14.29% -26.41%

200809 601.32 3921.2 1.96% -10.06%

200808 589.76 4360 9.07% 0.62%

200807 540.74 4332.95 -12.58% 7.24%

200806 618.52 4040.55 -10.88% -17.03%

200805 694.03 4870.1 -14.31% -5.73%

200804 809.96 5165.9 2.58% 9.11%

200803 789.57 4734.5 1.98% -9.36%

200802 774.21 5223.5 -11.33% 1.67%

200801 873.15 5137.45 -17.09% -16.31%

200712 1,053.13 6138.6 -0.80% 6.52%

200711 1,061.57 5762.75 -7.26% -2.34%

200710 1,144.63 5900.65 1.83% 17.51%200709 1,124.04 5021.35 12.63% 12.49%

200708 997.98 4464 -6.00% -1.43%

200707 1,061.68 4528.85 27.16% 4.88%

200706 834.94 4318.3 -3.99% 0.52%

200705 869.6 4295.8 12.42% 5.09%

200704 773.53 4087.9 0.29% 6.97%

200703 771.29 3821.55 -23.28% 2.04%

200702 1,005.39 3745.3 -5.37% -8.26%

200701 1,062.46 4082.7 -0.86% 2.93%

200612 1,071.68 3966.4 2.22% 0.30%

200611 1,048.40 3954.5 6.53% 5.62%

200610 984.13 3744.1 3.51% 4.34%

200609 950.76 3588.4 7.00% 5.11%

200608 888.53 3413.9

ACC

Beta 0.445918314

Debt/Equity Ratio 0.09

Tax Rate 30%

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AMBUJA CEMENTS

Date Avg.Price


NSEReturns

201108 129 5056.6 0.97% -7.76%

SUMMARY OUTPUT


Mul ti pl e R 0.665761259

R Squa re 0.443238054



Observations 61

ANOVA

df SS MS F Significance F

Re gre ss ion 1 902172.0786 902172.08 46.969886 4.78707E-09

Residual 59 1133239.979 19207.457

Total 60 2035412.058

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept 238.1831209 91.90650639 2.5915806 0.0120224 54.27862642 422.08762 54.2786264 422.0876154

NIFTY 0.132208495 0.019290772 6.853458 4.787E-09 0.093607749 0.1708092 0.09360775 0.170809241


Fixed Cost 3282.16



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201107 127.76 5482 -4.98% -2.93%

201106 134.46 5647.4 -2.25% 1.57%

201105 137.55 5560.15 -9.37% -3.29%

201104 151.77 5749.5 15.60% -1.44%

201103 131.29 5833.75 8.07% 9.38%

201102 121.49 5333.25 -7.08% -3.14%

201101 130.75 5505.9 -6.42% -10.25%

201012 139.72 6134.5 -6.55% 4.64%

201011 149.52 5862.7 6.08% -2.58%

201010 140.95 6017.7 1.53% -0.20%

201009 138.82 6029.95 16.56% 11.62%

201008 119.1 5402.4 5.28% 0.65%

201007 113.13 5367.6 -2.14% 1.04%

201006 115.6 5312.5 5.38% 4.45%

201005 109.7 5086.3 -8.60% -3.63%201004 120.02 5278 3.82% 0.55%

201003 115.6 5249.1 11.02% 6.64%

201002 104.13 4922.3 -2.09% 0.82%

201001 106.35 4882.05 8.64% -6.13%

200912 97.89 5201.05 12.19% 3.35%

200911 87.25 5032.7 -6.73% 6.81%

200910 93.55 4711.7 -5.72% -7.32%

200909 99.23 5083.95 -2.24% 9.05%

200908 101.5 4662.1 5.53% 0.55%

200907 96.18 4636.45 0.62% 8.05%

200906 95.59 4291.1 13.85% -3.55%

200905 83.96 4448.95 6.37% 28.07%

200904 78.93 3473.95 15.60% 15.00%

200903 68.28 3020.95 -2.78% 9.31%

200902 70.23 2763.65 -0.50% -3.87%

200901 70.58 2874.8 9.44% -2.85%

200812 64.49 2959.15 14.34% 7.41%

200811 56.4 2755.1 -7.59% -4.52%

200810 61.03 2885.6

-25.21% -26.41%200809 81.6 3921.2 -2.16% -10.06%

200808 83.4 4360 5.05% 0.62%

200807 79.39 4332.95 -7.19% 7.24%

200806 85.54 4040.55 -19.96% -17.03%

200805 106.87 4870.1 -8.47% -5.73%

200804 116.76 5165.9 -3.92% 9.11%

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200803 121.53 4734.5 2.35% -9.36%

200802 118.74 5223.5 -9.92% 1.67%

200801 131.82 5137.45 -11.46% -16.31%

200712 148.89 6138.6 1.22% 6.52%

200711 147.09 5762.75 0.19% -2.34%

200710 146.81 5900.65 2.29% 17.51%

200709 143.53 5021.35 10.42% 12.49%

200708 129.99 4464 -0.64% -1.43%

200707 130.83 4528.85 14.59% 4.88%

200706 114.17 4318.3 -3.89% 0.52%

200705 118.79 4295.8 6.11% 5.09%

200704 111.95 4087.9 4.19% 6.97%

200703 107.45 3821.55 -19.43% 2.04%

200702 133.36 3745.3 -5.48% -8.26%

200701 141.09 4082.7 1.55% 2.93%200612 138.93 3966.4 3.10% 0.30%

200611 134.75 3954.5 11.73% 5.62%

200610 120.6 3744.1 4.76% 4.34%

200609 115.12 3588.4 5.48% 5.11%

200608 109.14 3413.9 5.64% 8.61%

200607 103.31 3143.2 12.40% 0.48%

200606 91.91 3128.2

AmbujaBeta 0.543358288


Tax Rate 30%



Unlivered Beta/(1+(Fixed Cost/ Variable Cost))

fixed cost 2832.44

variable cost 3271.02

direct risk beta 0.287180824

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SUMMARY OUTPUT


Multiple R 0.524070778

R Square 0.27465018



Observations 62

ANOVA

df SS MS F

Significance

F

Regression 1 0.1345774 0.1345774 22.7187081 1.234E-05

Residual 60 0.3554184 0.0059236

Total 61 0.4899958

Coefficients

Standard

Error t Stat P-value Lower 95% Upper 95%

Lower

95.0%

Upper

95.0%

Intercept 0.00326245 0.0098632 0.3307697 0.741970822 -0.0164669 0.0229918 -0.0164669 0.0229918

NSE Returns 0.552265801 0.1158661 4.7664146 1.23365E-05 0.3204991 0.7840325 0.3204991 0.7840325

regression beta 0.552265801

EIH Hotels

Date Avg.Price


NSEReturns

201108 88.05 5056.6 2.11% -7.76%

201107 86.23 5482 2.05% -2.93%

201106 84.5 5647.4 1.22% 1.57%

201105 83.48 5560.15 -4.63% -3.29%

201104 87.53 5749.5

9.44% -1.44%201103 79.98 5833.75 -7.80% 9.38%

201102 86.75 5333.25 -9.30% -3.14%

201101 95.64 5505.9 -6.24% -10.25%

201012 102 6134.5 -3.14% 4.64%

201011 105.31 5862.7 -11.10% -2.58%

201010 118.46 6017.7 -5.03% -0.20%

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201009 124.73 6029.95 5.36% 11.62%

201008 118.38 5402.4 7.59% 0.65%

201007 110.03 5367.6 4.73% 1.04%

201006 105.06 5312.5 -1.97% 4.45%

201005 107.17 5086.3 -5.61% -3.63%

201004 113.54 5278 5.06% 0.55%

201003 108.07 5249.1 -0.82% 6.64%

201002 108.96 4922.3 -9.15% 0.82%

201001 119.93 4882.05 -1.58% -6.13%

200912 121.85 5201.05 4.37% 3.35%

200911 116.75 5032.7 -0.06% 6.81%

200910 116.82 4711.7 10.74% -7.32%

200909 105.49 5083.95 5.40% 9.05%

200908 100.09 4662.1 -2.35% 0.55%

200907 102.5 4636.45 -11.26% 8.05%200906 115.5 4291.1 12.83% -3.55%

200905 102.37 4448.95 1.54% 28.07%

200904 100.82 3473.95 23.33% 15.00%

200903 81.75 3020.95 -9.10% 9.31%

200902 89.93 2763.65 -7.44% -3.87%

200901 97.16 2874.8 -7.70% -2.85%

200812 105.27 2959.15 32.77% 7.41%

200811 79.29 2755.1 -14.76% -4.52%

200810 93.02 2885.6 -29.13% -26.41%

200809 131.25 3921.2 10.05% -10.06%

200808 119.26 4360 9.49% 0.62%

200807 108.92 4332.95 0.76% 7.24%

200806 108.1 4040.55 -12.05% -17.03%

200805 122.91 4870.1 -5.04% -5.73%

200804 129.43 5165.9 1.07% 9.11%

200803 128.06 4734.5 -16.08% -9.36%

200802 152.6 5223.5 -7.54% 1.67%

200801 165.04 5137.45 5.75% -16.31%

200712 156.06 6138.6

27.17% 6.52%200711 122.72 5762.75 7.13% -2.34%

200710 114.55 5900.65 15.13% 17.51%

200709 99.5 5021.35 5.35% 12.49%

200708 94.45 4464 2.27% -1.43%

200707 92.35 4528.85 3.57% 4.88%

200706 89.17 4318.3 1.10% 0.52%

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20 | P a g e

200705 88.2 4295.8 2.14% 5.09%

200704 86.35 4087.9 3.86% 6.97%

200703 83.14 3821.55 -1.74% 2.04%

200702 84.61 3745.3 -6.55% -8.26%

200701 90.54 4082.7 -2.79% 2.93%

200612 93.14 3966.4 3.82% 0.30%

200611 89.71 3954.5 -0.36% 5.62%

200610 90.03 3744.1 -3.73% 4.34%

200609 93.52 3588.4 16.84% 5.11%

200608 80.04 3413.9 7.60% 8.61%

200607 74.39 3143.2 6.79% 0.48%

200606 69.66 3128.2

EIH

Beta 0.500562421


Tax Rate 30%



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Hotel LEELA VENTURES

Date Avg.Price

NIFTY Stock Pricereturn

NSEReturns

201108 43.23 5072.95 -2.81% -7.46%

201107 44.48 5482 14.64% -2.93%

201106 38.8 5647.4 -3.29% 1.57%

201105 40.12 5560.15 -3.56% -3.29%

201104 41.6 5749.5 9.13% -1.44%

201103 38.12 5833.75 -1.14% 9.38%

201102 38.56 5333.25 -13.15% -3.14%201101 44.4 5505.9 -1.57% -10.25%

201012 45.11 6134.5 -7.90% 4.64%

201011 48.98 5862.7 -10.11% -2.58%

201010 54.49 6017.7 -0.60% -0.20%

201009 54.82 6029.95 9.79% 11.62%

fixed 922.72

variable 134.74

d ire ct ri s k b eta 0.045138661

SUMMARY OUTPUT


Multipl e R 0.426294143

R Square 0.181726696


Sta nd ard Erro r 0.092581088

Observations 62

ANOVAdf SS MS F Significance F

Regres si on 1 0.114213163 0.1142132 13.325134 0.000550788

Res idual 60 0.51427547 0.0085713

Total 61 0.628488632


Intercept 0.00298193 0.011864408 0.2513341 0.8024146 -0.020750419 0.02671428 -0.020750419 0.02671428

NSE Returns 0.508768363 0.139374813 3.6503609 0.0005508 0.22997723 0.787559495 0.22997723 0.787559495

regression beta 0.508768363

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201008 49.93 5402.4 1.53% 0.65%

201007 49.18 5367.6 4.71% 1.04%

201006 46.97 5312.5 -4.16% 4.45%

201005 49.01 5086.3 -1.43% -3.63%

201004 49.72 5278 4.59% 0.55%

201003 47.54 5249.1 2.17% 6.64%

201002 46.53 4922.3 -6.72% 0.82%

201001 49.88 4882.05 12.60% -6.13%

200912 44.3 5201.05 15.30% 3.35%

200911 38.42 5032.7 1.83% 6.81%

200910 37.73 4711.7 5.48% -7.32%

200909 35.77 5083.95 11.85% 9.05%

200908 31.98 4662.1 2.50% 0.55%

200907 31.2 4636.45 -11.56% 8.05%

200906 35.28 4291.1 35.90% -3.55%200905 25.96 4448.95 17.25% 28.07%

200904 22.14 3473.95 25.80% 15.00%

200903 17.6 3020.95 -4.56% 9.31%

200902 18.44 2763.65 -3.66% -3.87%

200901 19.14 2874.8 -2.05% -2.85%

200812 19.54 2959.15 -13.00% 7.41%

200811 22.46 2755.1 -17.58% -4.52%

200810 27.25 2885.6 -13.33% -26.41%

200809 31.44 3921.2 -2.99% -10.06%

200808 32.41 4360 3.61% 0.62%

200807 31.28 4332.95 -19.30% 7.24%

200806 38.76 4040.55 -15.72% -17.03%

200805 45.99 4870.1 4.10% -5.73%

200804 44.18 5165.9 4.35% 9.11%

200803 42.34 4734.5 -15.69% -9.36%

200802 50.22 5223.5 -15.81% 1.67%

200801 59.65 5137.45 -10.92% -16.31%

200712 66.96 6138.6 17.91% 6.52%

200711 56.79 5762.75

21.16% -2.34%200710 46.87 5900.65 -0.93% 17.51%

200709 47.31 5021.35 8.29% 12.49%

200708 43.69 4464 -15.15% -1.43%

200707 51.49 4528.85 -4.51% 4.88%

200706 53.92 4318.3 -5.04% 0.52%

200705 56.78 4295.8 -0.68% 5.09%

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23 | P a g e

200704 57.17 4087.9 7.40% 6.97%

200703 53.23 3821.55 -11.71% 2.04%

200702 60.29 3745.3 -5.32% -8.26%

200701 63.68 4082.7 -1.85% 2.93%

200612 64.88 3966.4 1.80% 0.30%

200611 63.73 3954.5 -1.77% 5.62%

200610 64.88 3744.1 -3.14% 4.34%

200609 66.98 3588.4 20.45% 5.11%

200608 55.61 3413.9 10.29% 8.61%

200607 50.42 3143.2 -7.62% 0.48%

200606 54.58 3128.2 -28.99% 1.86%

200605 76.86 3071.05 -1.73% -13.68%

200604 78.21 3557.6

Leela Hotel

Beta 0.422475579


Tax Rate 30%



SUMMARY OUTPUT



R Square 0.099026849

Adjusted R

Square 0.084495024


Observations 64

ANOVA

df SS MS F

Significance

F

Regression 1 0.085176689 0.085177 6.814481 0.011323211

Residual 62 0.774960632 0.012499

Total 63 0.860137321

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25 | P a g e

201101 97.75 5505.9 3.89% -10.25%

201012 94.09 6134.5 -5.60% 4.64%

201011 99.67 5862.7 -1.48% -2.58%

201010 101.17 6017.7 -3.06% -0.20%

201009 104.36 6029.95 2.07% 11.62%

201008 102.24 5402.4 -1.64% 0.65%

201007 103.94 5367.6 2.33% 1.04%

201006 101.57 5312.5 -6.10% 4.45%

201005 108.17 5086.3 1.17% -3.63%

201004 106.92 5278 9.28% 0.55%

201003 97.84 5249.1 9.11% 6.64%

201002 89.67 4922.3 -11.63% 0.82%

201001 101.47 4882.05 8.61% -6.13%

200912 93.43 5201.05 10.71% 3.35%

200911 84.39 5032.7 8.54% 6.81%200910 77.75 4711.7 10.28% -7.32%

200909 70.5 5083.95 8.68% 9.05%

200908 64.87 4662.1 1.72% 0.55%

200907 63.77 4636.45 -9.85% 8.05%

200906 70.74 4291.1 19.45% -3.55%

200905 59.22 4448.95 19.61% 28.07%

200904 49.51 3473.95 36.05% 15.00%

200903 36.39 3020.95 -4.59% 9.31%

200902 38.14 2763.65 -12.00% -3.87%

200901 43.34 2874.8 2.95% -2.85%

200812 42.1 2959.15 -11.85% 7.41%

200811 47.76 2755.1 -16.66% -4.52%

200810 57.31 2885.6 -22.23% -26.41%

200809 73.69 3921.2 -8.15% -10.06%

200808 80.23 4360 -5.98% 0.62%

200807 85.33 4332.95 -13.41% 7.24%

200806 98.54 4040.55 -15.21% -17.03%

200805 116.21 4870.1 3.00% -5.73%

200804 112.82 5165.9

-0.50% 9.11%200803 113.39 4734.5 -8.33% -9.36%

200802 123.69 5223.5 -8.81% 1.67%

200801 135.64 5137.45 -0.15% -16.31%

200712 135.84 6138.6 6.04% 6.52%

200711 128.1 5762.75 1.97% -2.34%

200710 125.63 5900.65 3.14% 17.51%

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200709 121.81 5021.35 2.08% 12.49%

200708 119.33 4464 -10.27% -1.43%

200707 132.99 4528.85 -0.84% 4.88%

200706 134.11 4318.3 2.42% 0.52%

200705 130.94 4295.8 -2.05% 5.09%

200704 133.68 4087.9 5.35% 6.97%

200703 126.89 3821.55 -8.30% 2.04%

200702 138.37 3745.3 -3.65% -8.26%

200701 143.61 4082.7 1.58% 2.93%

200612 141.38 3966.4 3.37% 0.30%

200611 136.77 3954.5 6.42% 5.62%

200610 128.52 3744.1 3.97% 4.34%

200609 123.61 3588.4 8.38% 5.11%

200608 114.05 3413.9 8.87% 8.61%

200607 104.76 3143.2 7.57% 0.48%200606 97.39 3128.2 -20.89% 1.86%

200605 123.11 3071.05 -2.99% -13.68%

200604 126.91 3557.6

Indian Hotels

Beta 0.47997408


Tax Rate 30%



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SPICEJET

Date Stock

Price

BSE_SENSEX Stock

Price

Return

Market

Return

8/1/2011 22.55 16469.8 -28.64% -9.49%

7/1/2011 31.6 18197.2 -3.95% -3.44%

6/1/2011 32.9 18845.9 -19.16% 1.85%

5/2/2011 40.7 18503.3 -0.73% -3.31%

4/1/2011 41 19136 7.05% -1.59%

3/1/2011 38.3 19445.2 0.79% 9.10%

2/1/2011 38 17823.4 -37.60% -2.75%

1/3/2011 60.9 18327.8 -24.72% -

10.64%

12/1/2010 80.9 20509.1 -1.52% 5.06%

11/1/2010 82.15 19521.3 -2.95% -2.55%

10/1/2010 84.65 20032.3 13.17% -0.18%

SUMMARY OUTPUT


Mul ti pl e R 0.423615079

R Squa re 0.179449735



Observations 64

ANOVA

df SS MS F Significance F

Re gre ss ion 1 0.109939334 0.1099393 13.55905181 0.000486814

Residual 62 0.502707623 0.0081082

Total 63 0.612646957


Intercept -0.007981067 0.011321849 -0.7049262 0.483496033 -0.03061312 0.014650985 -0.03061312 0.014650985

X Variable 1 0.487592716 0.132416635 3.6822618 0.000486814 0.222895636 0.752289796 0.222895636 0.752289796

Beta 0.487592716

Variable Cos t 448.14

Fixed Cost 1117.36

Bus iness risk Beta 0.086142552

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9/1/2010 74.8 20069.1 4.47% 11.67%

8/2/2010 71.6 17971.1 14.74% 0.58%

7/1/2010 62.4 17868.3 6.94% 0.95%

6/1/2010 58.35 17700.9 2.46% 4.46%

5/3/2010 56.95 16944.6 -9.46% -3.50%

4/1/2010 62.9 17558.7 7.89% 0.18%

3/2/2010 58.3 17527.8 4.76% 6.68%

2/1/2010 55.65 16429.6 0.09% 0.44%

1/4/2010 55.6 16358 -2.03% -6.34%

12/1/2009 56.75 17464.8 21.26% 3.18%

11/3/2009 46.8 16926.2 26.32% 6.48%

10/1/2009 37.05 15896.3 3.93% -7.18%

9/1/2009 35.65 17126.8 34.78% 9.32%

8/3/2009 26.45 15666.6 25.36% -0.02%

7/1/2009 21.1 15670.3 9.33% 8.12%6/1/2009 19.3 14493.8 -18.67% -0.90%

5/4/2009 23.73 14625.3 66.18% 28.26%

4/1/2009 14.28 11403.3 6.01% 17.46%

3/2/2009 13.47 9708.5 -2.60% 9.19%

2/2/2009 13.83 8891.61 -1.21% -5.65%

1/1/2009 14 9424.24 -11.95% -2.31%

12/1/2008 15.9 9647.31 30.11% 6.10%

11/3/2008 12.22 9092.72 8.14% -7.10%

10/1/2008 11.3 9788.06 -48.64% -

23.89%

9/1/2008 22 12860.4 -19.41% -

11.70%

8/1/2008 27.3 14564.5 -2.33% 1.45%

7/1/2008 27.95 14355.8 12.25% 6.64%

6/2/2008 24.9 13461.6 -23.38% -

17.99%

5/2/2008 32.5 16415.6 -25.03% -5.04%

4/1/2008 43.35 17287.3 6.25% 10.50%

3/3/2008 40.8 15644.4 -37.28% -

11.00%2/1/2008 65.05 17578.7 -10.83% -0.40%

1/1/2008 72.95 17648.7 -16.10% -

13.00%

12/3/2007 86.95 20287 65.46% 4.77%

11/1/2007 52.55 19363.2 -4.63% -2.39%

10/1/2007 55.1 19838 -5.97% 14.73%

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9/3/2007 58.6 17291.1 2.27% 12.88%

8/1/2007 57.3 15318.6 -2.96% -1.49%

7/2/2007 59.05 15551 7.07% 6.15%

6/4/2007 55.15 14650.5 -9.59% 0.73%

5/3/2007 61 14544.5 37.23% 4.84%

4/2/2007 44.45 13872.4 -0.78% 6.12%

3/1/2007 44.8 13072.1 -8.38% 1.04%

2/1/2007 48.9 12938.1 -16.98% -8.18%

1/2/2007 58.9 14090.9 6.51% 2.21%

12/1/2006 55.3 13786.9 4.93% 0.66%

11/1/2006 52.7 13696.3 16.34% 5.67%

10/3/2006 45.3 12961.9 3.19% 4.07%

9/1/2006 43.9 12454.4 0.92% 6.46%

8/1/2006 43.5 11699.1 9.71% 8.89%

7/3/2006 39.65 10743.9 -17.48% 1.27%6/1/2006 48.05 10609.3 -26.13% 2.03%

5/1/2006 65.05 10398.6 -12.39% -

13.65%

4/3/2006 74.25 12042.6 3.34% 6.76%

3/1/2006 71.85 11280 -4.58% 8.77%

2/1/2006 75.3 10370.2 1.83% 4.54%

1/23/2006 73.95 9919.89

SpiceJet

Beta 1.492625913


Tax Rate 30%

Unlevered Beta 0.220769992Levered Beta/(1+(1-Tax)*D/E)

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HPCL

Date Avg.

Price

NIFTY Stock Price

return

NSE Returns

201108 387.3 5072.95 -2.38% -7.46%

201107 396.75 5482 4.14% -2.93%

201106 380.99 5647.4 1.25% 1.57%

201105 376.28 5560.15 3.42% -3.29%

201104 363.84 5749.5 8.99% -1.44%

201103 333.84 5833.75 -1.25% 9.38%

201102 338.06 5333.25 -9.28% -3.14%

201101 372.63 5505.9 -8.32% -10.25%

201012 406.46 6134.5 -10.88% 4.64%

201011 456.09 5862.7 -9.26% -2.58%

201010 502.61 6017.7 -5.45% -0.20%

201009 531.56 6029.95 9.01% 11.62%

201008 487.62 5402.4 7.28% 0.65%

201007 454.53 5367.6 23.55% 1.04%

201006 367.9 5312.5 8.96% 4.45%

SUMMARY OUTPUT



R Square 0.404025323


Standard Error 0.156333108Observations 67

ANOVA

df SS MS F gnificance F

Regression 1 1.076951 1.076951 44.06504 7.52E-09

Residual 65 1.588603 0.02444

Total 66 2.665554

Coefficients ndard Err t Stat P-value ower 95%Upper 95%ower 95.0 pper 95.0%

Intercept -0.014916373 0.019267 -0.77419 0.441627 -0.0534 0.023563 -0.0534 0.023563

X Variable 1 1.515241457 0.228263 6.63815 7.52E-09 1.05937 1.971113 1.05937 1.971113

Beta 1.515241457


Fixed Cost 1354.47


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31 | P a g e

201005 337.64 5086.3 10.58% -3.63%

201004 305.34 5278 -6.97% 0.55%

201003 328.23 5249.1 -6.24% 6.64%

201002 350.08 4922.3 -6.38% 0.82%

201001 373.94 4882.05 -2.99% -6.13%

200912 385.48 5201.05 10.60% 3.35%

200911 348.53 5032.7 -5.44% 6.81%

200910 368.57 4711.7 -6.76% -7.32%

200909 395.29 5083.95 10.84% 9.05%

200908 356.62 4662.1 9.36% 0.55%

200907 326.11 4636.45 2.81% 8.05%

200906 317.19 4291.1 9.33% -3.55%

200905 290.13 4448.95 9.59% 28.07%

200904 264.74 3473.95 1.94% 15.00%

200903 259.7 3020.95 -11.18% 9.31%200902 292.39 2763.65 6.54% -3.87%

200901 274.44 2874.8 8.98% -2.85%

200812 251.82 2959.15 14.04% 7.41%

200811 220.82 2755.1 3.07% -4.52%

200810 214.24 2885.6 -8.49% -26.41%

200809 234.12 3921.2 6.52% -10.06%

200808 219.78 4360 5.98% 0.62%

200807 207.37 4332.95 2.57% 7.24%

200806 202.17 4040.55 -17.48% -17.03%

200805 244.99 4870.1 -2.56% -5.73%

200804 251.42 5165.9 -6.87% 9.11%

200803 269.98 4734.5 -6.13% -9.36%

200802 287.6 5223.5 -9.34% 1.67%

200801 317.24 5137.45 -0.21% -16.31%

200712 317.91 6138.6 16.53% 6.52%

200711 272.82 5762.75 9.57% -2.34%

200710 248.99 5900.65 0.62% 17.51%

200709 247.45 5021.35 2.90% 12.49%

200708 240.48 4464 -7.75% -1.43%200707 260.69 4528.85 -3.78% 4.88%

200706 270.94 4318.3 -6.51% 0.52%

200705 289.81 4295.8 13.24% 5.09%

200704 255.92 4087.9 0.25% 6.97%

200703 255.29 3821.55 -10.97% 2.04%

200702 286.73 3745.3 -5.30% -8.26%

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200701 302.78 4082.7 9.86% 2.93%

200612 275.61 3966.4 -11.50% 0.30%

200611 311.43 3954.5 1.74% 5.62%

200610 306.11 3744.1 4.35% 4.34%

200609 293.34 3588.4 18.62% 5.11%

200608 247.29 3413.9 11.44% 8.61%

200607 221.9 3143.2 -12.30% 0.48%

200606 253.02 3128.2 -23.02% 1.86%

200605 328.7 3071.05 1.30% -13.68%

200604 324.49 3557.6

Beta 0.290756077

Debt/Equity

Ratio 1.98

Tax Rate 30%


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GAIL

Date NSEReturns

(Gail) AdjClose

GailReturn

201108 -7.46% 446.5 -3.77%

201107 -2.93% 464 5.45%

201106 1.57% 440 -0.68%

201105 -3.29% 443 -6.74%

201104 -1.44% 475 3.35%201103 9.38% 459.6 7.63%

201102 -3.14% 427 -9.05%

201101 -10.25% 469.5 -8.30%

201012 4.64% 512 4.92%

201011 -2.58% 488 -0.41%

201010 -0.20% 490 3.27%

201009 11.62% 474.5 3.71%

SUMMARY OUTPUT



R Square 0.388162999



Observations 65

ANOVA

df SS MS F gnificance F

Regression 1 133564.9 133564.9 39.9686 2.99E-08

Residual 63 210530 3341.747

Total 64 344095

Coefficients ndard Err t Stat P-value ower 95% pper 95%ower 95.0 pper 95.0%

Intercept 97.85800076 35.06119 2.791063 0.006945 27.79379 167.9222 27.79379 167.9222

NIFTY 0.047318383 0.007485 6.322073 2.99E-08 0.032362 0.062275 0.032362 0.062275


Fixed Cost 9169.94



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201008 0.65% 457.53 5.70%

201007 1.04% 432.87 -6.21%

201006 4.45% 461.53 2.98%

201005 -3.63% 448.19 6.27%

201004 0.55% 421.75 4.30%

201003 6.64% 404.36 2.54%201002 0.82% 394.33 -6.13%

201001 -6.13% 420.07 2.59%

200912 3.35% 409.45 -0.64%

200911 6.81% 412.1 21.54%

200910 -7.32% 339.07 -3.70%

200909 9.05% 352.11 6.87%

200908 0.55% 329.49 1.71%

200907 8.05% 323.95 14.62%

200906 -3.55% 282.63 -3.49%

200905 28.07% 292.86 17.06%

200904 15.00% 250.17 5.64%

200903 9.31% 236.82 17.93%

200902 -3.87% 200.81 5.67%

200901 -2.85% 190.04 -3.86%

200812 7.41% 197.67 5.00%

200811 -4.52% 188.26 -8.54%

200810 -26.41% 205.83 -47.29%

200809 -10.06% 390.47 3.81%

200808 0.62% 376.15 7.78%

200807 7.24% 349 13.14%

200806 -17.03% 308.47 -18.00%

200805 -5.73% 376.18 -8.49%

200804 9.11% 411.07 2.38%

200803 -9.36% 401.52 0.73%

200802 1.67% 398.61 -0.76%

200801 -16.31% 401.66 -21.21%

200712 6.52% 509.77 28.11%

200711 -2.34% 397.92 3.49%

200710 17.51% 384.49 8.59%

200709 12.49% 354.07 22.81%

200708 -1.43% 288.3 -8.06%

200707 4.88% 313.58 9.01%

200706 0.52% 287.67 1.62%

200705 5.09% 283.08 3.02%

200704 6.97% 274.78 12.04%

200703 2.04% 245.26 -5.61%

200702 -8.26% 259.85 0.58%

200701 2.93% 258.34 7.55%

200612 0.30% 240.2 0.04%

200611 5.62% 240.1 4.30%

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200610 4.34% 230.21 -2.68%

200609 5.11% 236.55 1.19%

200608 8.61% 233.76 7.55%

200607 0.48% 217.36 -5.20%

200606 1.86% 229.29 6.57%

200605 -13.68% 215.15

Beta 0.934177874

D/E 0.12

Tax Rate 30%

UnleveredBeta 0.861787707


Regression (Beta Calculation)SUMMARYOUTPUT



R Square 0.554555993Adjusted RSquare 0.547253632


Observations 63

ANOVA

df SS MS FSignificance

F

Regression 1 0.397191039 0.397191 75.942015 2.62E-12

Residual 61 0.319041486 0.0052302

Total 62 0.716232525

CoefficientsStandard

Error t Stat P-value Lower 95%Upper95% Lower 95.0% Upper 95.0

Intercept 0.007166839 0.009197954 0.7791775 0.4388876 -0.0112256 0.0255593 -0.011225607 0.0255592

NSE Returns 0.949245259 0.10892746 8.7144716 2.62E-12 0.7314313 1.1670592 0.731431342 1.1670591

Beta Coeffecient 0.949245259


Fixed Cost 2885.44

Business Beta 0.772509009

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Business Risk Beta= Unlevered Beta/(1+(Fixed Cost/Variable Cost))

BPCL

Date NSEReturns

AdjClose

BPCLReturn

201108 -7.46% 688.8 4.68%

201107 -2.93% 658 1.55%

201106 1.57% 647.95 2.33%

201105 -3.29% 633.2 0.99%

201104 -1.44% 627 2.77%

201103 9.38% 610.1 9.53%

201102 -3.14% 557 -8.48%

201101 -10.25% 608.6 -7.37%

201012 4.64% 657 -2.79%

201011 -2.58% 675.85 -7.28%

201010 -0.20% 728.9 -2.85%

201009 11.62% 750.25 -0.02%

201008 0.65% 750.4 19.45%

201007 1.04% 628.2 -3.38%

201006 4.45% 650.18 12.76%

201005 -3.63% 576.62 12.97%

201004 0.55% 510.41 0.58%

201003 6.64% 507.47 -7.65%

201002 0.82% 549.52 -7.19%

201001 -6.13% 592.08 -5.08%

200912 3.35% 623.78 6.79%

200911 6.81% 584.12 16.92%200910 -7.32% 499.61 -12.09%

200909 9.05% 568.32 13.75%

200908 0.55% 499.61 8.89%

200907 8.05% 458.8 10.25%

200906 -3.55% 416.16 -7.54%

200905 28.07% 450.08 20.46%

200904 15.00% 373.62 3.00%

200903 9.31% 362.73 -4.16%

200902 -3.87% 378.46 -0.26%

200901 -2.85% 379.43 3.98%

200812 7.41% 364.91 3.86%200811 -4.52% 351.36 26.92%

200810 -26.41% 276.83 -20.30%

200809 -10.06% 347.34 20.17%

200808 0.62% 289.03 -7.36%

200807 7.24% 312 47.38%

200806 -17.03% 211.7 -38.89%

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200805 -5.73% 346.45 -11.49%

200804 9.11% 391.43 1.24%

200803 -9.36% 386.65 -13.46%

200802 1.67% 446.8 30.77%

200801 -16.31% 341.67 -31.08%

200712 6.52% 495.75 33.16%200711 -2.34% 372.29 12.46%

200710 17.51% 331.04 -4.45%

200709 12.49% 346.45 20.26%

200708 -1.43% 288.08 -3.21%

200707 4.88% 297.63 -5.62%

200706 0.52% 315.36 -5.75%

200705 5.09% 334.59 8.39%

200704 6.97% 308.69 10.07%

200703 2.04% 280.44 -2.73%

200702 -8.26% 288.32 -13.64%

200701 2.93% 333.84 8.38%

200612 0.30% 308.03 -1.82%

200611 5.62% 313.73 -13.53%

200610 4.34% 362.82 9.44%

200609 5.11% 331.51 1.31%

200608 8.61% 327.21 15.72%

200607 0.48% 282.77 -6.59%

200606 1.86% 302.73 -18.88%

200605 -13.68% 373.19 -5.37%

394.36

Beta 0.808180238

D/E 1.7

Tax 30%

UnliveredBeta 0.369032072

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Regression Beta

SUMMARY OUTPUT



R Square 0.238517954



Observations 64

ANOVA

df SS MS FSignificance

F

Regression 1 0.3117272 0.3117272 19.420173 4.238E-05

Residual 62 0.9952067 0.0160517

Total 63 1.3069339

CoefficientsStandard

Error t Stat P-value Lower 95%Upper95%

Lower95.0%

Upper95.0%

Intercept 0.011309953 0.0159301 0.709974 0.4803821 -0.0205339 0.0431538-

0.0205339 0.04315376

NSE Returns 0.821008496 0.1863035 4.4068325 4.238E-05 0.448593 1.193424 0.448593 1.19342396


Business RiskBeta 0.348361356


Fixed Cost 8656.1


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KINGFISHER

Date Avg.Price


NSEReturns

201108 28.9 5072.95 -16.47% -7.46%

201107 34.6 5482 -13.17% -2.93%

201106 39.85 5647.4 -5.68% 1.57%

201105 42.25 5560.15 -3.76% -3.29%

201104 43.9 5749.5 8.53% -1.44%

201103 40.45 5833.75 7.29% 9.38%

201102 37.7 5333.25 -25.49% -3.14%

201101 50.6 5505.9 -23.68% -10.25%

201012 66.3 6134.5 -0.97% 4.64%

201011 66.95 5862.7 -12.48% -2.58%

201010 76.5 6017.7 3.52% -0.20%

201009 73.9 6029.95 24.83% 11.62%

201008 59.2 5402.4 12.33% 0.65%

201007 52.7 5367.6 5.82% 1.04%

201006 49.8 5312.5 11.41% 4.45%

201005 44.7 5086.3 -10.24% -3.63%201004 49.8 5278 6.41% 0.55%

201003 46.8 5249.1 -5.07% 6.64%

201002 49.3 4922.3 -21.12% 0.82%

201001 62.5 4882.05 -1.65% -6.13%

200912 63.55 5201.05 21.05% 3.35%

200911 52.5 5032.7 17.85% 6.81%

200910 44.55 4711.7 -20.23% -7.32%

200909 55.85 5083.95 15.63% 9.05%

200908 48.3 4662.1 -5.85% 0.55%

200907 51.3 4636.45 1.99% 8.05%

200906 50.3 4291.1 -19.90% -3.55%

200905 62.8 4448.95 63.97% 28.07%

200904 38.3 3473.95 15.19% 15.00%

200903 33.25 3020.95 3.42% 9.31%

200902 32.15 2763.65 -8.40% -3.87%

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200901 35.1 2874.8 -13.55% -2.85%

200812 40.6 2959.15 45.00% 7.41%

200811 28 2755.1 -13.18% -4.52%

200810 32.25 2885.6 -46.96% -26.41%

200809 60.8 3921.2 -24.00% -10.06%

200808 80 4360 0.25% 0.62%

200807 79.8 4332.95 33.00% 7.24%

200806 60 4040.55 -48.12% -17.03%

200805 115.65 4870.1 -19.63% -5.73%

200804 143.9 5165.9 18.53% 9.11%

200803 121.4 4734.5 -28.29% -9.36%

200802 169.3 5223.5 1.29% 1.67%

200801 167.15 5137.45 -39.68% -16.31%

200712 277.1 6138.6 14.22% 6.52%

200711 242.6 5762.75 59.40% -2.34%200710 152.2 5900.65 4.18% 17.51%

200709 146.1 5021.35 4.36% 12.49%

200708 140 4464 -0.67% -1.43%

200707 140.95 4528.85 2.51% 4.88%

200706 137.5 4318.3 -5.95% 0.52%

200705 146.2 4295.8 40.58% 5.09%

200704 104 4087.9 12.74% 6.97%

200703 92.25 3821.55 -26.55% 2.04%

200702 125.6 3745.3 -13.59% -8.26%

200701 145.35 4082.7 6.52% 2.93%

200612 136.45 3966.4 -1.83% 0.30%

200611 139 3954.5 23.45% 5.62%

200610 112.6 3744.1 9.11% 4.34%

200609 103.2 3588.4 25.47% 5.11%

200608 82.25 3413.9 9.89% 8.61%

200607 74.85 3143.2 -12.76% 0.48%

200606 85.8 3128.2

Leela Hotel

Beta 1.906632252


Tax Rate 30%

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SUMMARY

OUTPUT



R Square 0.560165357



Observations 62

ANOVA

df SS MS F

Significance

F

Regression 1 1.65508162 1.6550816 76.414903 2.693E-12

Residual 60 1.299548829 0.0216591

Total 61 2.954630449

Coefficients

Standard

Error t Stat P-value Lower 95%

Upper

95%

Lower

95.0%

Upp

95.0

Intercept -0.015582828 0.018861751

-

0.8261602 0.4119864 -0.0533119 0.0221463

-

0.0533119 0.022

NSE Returns 1.937888518 0.221686761 8.7415618 2.693E-12 1.494449 2.3813281 1.494449 2.381



Fixed Cost 4273.89

Business Risk 0.236355461

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JET AIRWAYS

Date Avg.Price


NSEReturns

201108 373.5 5072.95 -16.06% -7.46%

201107 444.95 5482 -6.52% -2.93%

201106 476 5647.4 6.58% 1.57%

201105 446.6 5560.15 -7.01% -3.29%

201104 480.25 5749.5 6.96% -1.44%

201103 449 5833.75 7.98% 9.38%

201102 415.8 5333.25 -24.61% -3.14%

201101 551.5 5505.9 -27.81% -10.25%

201012 764 6134.5 -4.99% 4.64%

201011 804.1 5862.7 -1.81% -2.58%

201010

818.956017.7

0.25% -0.20%201009 816.9 6029.95 13.30% 11.62%

201008 721 5402.4 6.13% 0.65%

201007 679.35 5367.6 27.90% 1.04%

201006 531.15 5312.5 3.74% 4.45%

201005 512 5086.3 -4.93% -3.63%

201004 538.55 5278 12.20% 0.55%

201003 480 5249.1 18.58% 6.64%

201002 404.8 4922.3 -20.19% 0.82%

201001 507.2 4882.05 -9.10% -6.13%

200912 558 5201.05 23.59% 3.35%

200911 451.5 5032.7 20.40% 6.81%

200910 375 4711.7 13.98% -7.32%

200909 329 5083.95 24.62% 9.05%

200908 264 4662.1 2.72% 0.55%

200907 257 4636.45 14.76% 8.05%

200906 223.95 4291.1 -26.57% -3.55%

200905 305 4448.95 56.41% 28.07%

200904 195 3473.95 14.17% 15.00%

200903

170.83020.95

20.71% 9.31%200902 141.5 2763.65 -20.39% -3.87%

200901 177.75 2874.8 -11.63% -2.85%

200812 201.15 2959.15 57.76% 7.41%

200811 127.5 2755.1 -18.27% -4.52%

200810 156 2885.6 -61.48% -26.41%

200809 405 3921.2 -14.02% -10.06%

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200808 471.05 4360 -2.88% 0.62%

200807 485 4332.95 16.85% 7.24%

200806 415.05 4040.55 -23.00% -17.03%

200805 539 4870.1 -1.82% -5.73%

200804 549 5165.9 -1.86% 9.11%

200803 559.4 4734.5 -23.84% -9.36%

200802 734.55 5223.5 -0.74% 1.67%

200801 740 5137.45 -25.48% -16.31%

200712 993 6138.6 19.49% 6.52%

200711 831 5762.75 -5.45% -2.34%

200710 878.9 5900.65 -3.16% 17.51%

200709 907.6 5021.35 13.07% 12.49%

200708 802.69 4464 10.28% -1.43%

200707 727.88 4528.85 -10.49% 4.88%

200706 813.22 4318.3 6.40% 0.52%200705 764.29 4295.8 7.19% 5.09%

200704 713.03 4087.9 13.35% 6.97%

200703 629.04 3821.55 5.94% 2.04%

200702 593.77 3745.3 -21.70% -8.26%

200701 758.33 4082.7 23.65% 2.93%

200612 613.29 3966.4 -8.37% 0.30%

200611 669.32 3954.5 13.09% 5.62%

200610 591.83 3744.1 -7.99% 4.34%

200609 643.19 3588.4 20.72% 5.11%

200608 532.78 3413.9 5.72% 8.61%

200607 503.97 3143.2 -13.98% 0.48%

200606 585.87 3128.2 -22.67% 1.86%

200605 757.64 3071.05 -18.37% -13.68%

200604 928.16 3557.6

Leela Hotel

Beta 1.697657256


Tax Rate 30%



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SUMMARY OUTPUT



R Square 0.558631567

Adjusted R

Square 0.551512722

Standard

Error 0.132388674

Observations 64

ANOVA

df SS MS F

Significance

F

Regression 1 1.375363711 1.375364 78.47221 1.3E-12

Residual 62 1.086659189 0.017527

Total 63 2.4620229

Coefficients

Standard

Error t Stat P-value Lower 95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept

-

0.009463767 0.016645862 -0.56854 0.571725 -0.04274 0.023811 -0.04274 0.0238108

NSE Returns 1.724604197 0.194684552 8.858454 1.3E-12 1.335435 2.113773 1.335435 2.1137731

Beta

Coeffecient 1.724604197

Variable

Cost 413656

Fixed Cost 702436

Business

Risk 0.136014033


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References:

Websites:

1) http://www.moneychimp.com/articles/risk/regression.htm

2) http://finance.yahoo.com/

3) http://www.moneycontrol.com

4) http://www.capitaline.com

5) www.fundmanagersoftware.com/help/def_beta_calculation.html

6) http://www.traderji.com/technical-analysis/20267-beta-calculation.html

Journals & Books:

1) Investments by Zvi Bodie and Alex Kane

2) BETA-Wiley Risk Mgmt book
http://www.moneychimp.com/articles/risk/regression.htmhttp://www.moneychimp.com/articles/risk/regression.htmhttp://finance.yahoo.com/http://finance.yahoo.com/http://www.moneycontrol.com/http://www.moneycontrol.com/http://www.capitaline.com/http://www.capitaline.com/http://www.fundmanagersoftware.com/help/def_beta_calculation.htmlhttp://www.fundmanagersoftware.com/help/def_beta_calculation.htmlhttp://www.fundmanagersoftware.com/help/def_beta_calculation.htmlhttp://www.fundmanagersoftware.com/help/def_beta_calculation.htmlhttp://www.fundmanagersoftware.com/help/def_beta_calculation.htmlhttp://www.fundmanagersoftware.com/help/def_beta_calculation.htmlhttp://www.traderji.com/technical-analysis/20267-beta-calculation.htmlhttp://www.traderji.com/technical-analysis/20267-beta-calculation.htmlhttp://www.traderji.com/technical-analysis/20267-beta-calculation.htmlhttp://www.fundmanagersoftware.com/help/def_beta_calculation.htmlhttp://www.capitaline.com/http://www.moneycontrol.com/http://finance.yahoo.com/http://www.moneychimp.com/articles/risk/regression.htm

sapm ass 3

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