Report on Midcap and Large Stock

7/31/2019 Report on Midcap and Large Stock

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Report on MIDCAPSTOCK and LARGCAP STOCK 2011

ABSTRACT

Volatility estimation is important for several reasons and for different people in the market.

Pricing of securities is supposed to be dependent on volatility of each asset. Mature markets/ Developed markets continue to provide over long period of time high return with low

volatility. Amongst emerging markets except India and China, all other countries exhibited

low returns (sometimes negative returns with high volatility). The third and fourth order

moments exhibit large asymmetry in some of the developed markets. Comparatively, Indian

market show less of skewness and Kurtosis. Indian markets have started becoming

informationaly more efficient. Contrary to the popular perception in the recent past,

volatility has not gone up. To achieve higher returns in the long run you have to accept

more short-term volatility. The focus is on finding the driver mechanism responsible for the

average rate of return determination and the corresponding risk metrics affecting in

measuring the risk. The 12 parameter taken into consideration for both MIDSTOCK and

LARGESTOCK company where this parameter has its impact in determining the return. Theinvestor would focus on investing in the portfolio which provide the better return and avoid

minimal loss and risk associated during the volatility in the market.

INTRODUCTION:

DATA AND RISK METRICS

Sample

The sample of 10 each of the company from Midstock and Large stock are taken to identify

the risk associated with respect to 12 variables taken into consideration which determine

the firm returns associated with its size, beta.

Data

The data set consists of all the BSE 100 firms trading its stock in MIDSTOCK and

LARGESTOCK of 10 company. The following is the detail

For midstock The period is selected from the year August 2005 August 2011monthly closed share price for finding the monthly return.

The company taken into consideration is as follow:

ASIAN PAINTS EXIDE


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GILLETTE GODREJ JINDAL CAPITAL NIRMA

P&G SINTEX VIJAYA BANK YES BANK

For LargestockThe period is selected from the year January 2005 January 2011monthly data.

The company taken into consideration is as follow:

AXIS BANK TATA CHEMICALS BHARATI AIRTEL GRASIM HUL L & T MAHINDRA & MAHINDRA NESTLE INDIA NTPC SAIL

Risk Metrics

A variety of risk metrics are used to explain the average returns.

One-Factor Market Model. Using the single-factor model, where Rmt denotes the return on

the market returns from BSE 100 index, the estimate regression:

Rit rft = + [Rmt rft] + eitwhere rft is the risk free rate of treasury bill in INDIA, and eit is the residual. Also, note that

emt = Rmt Avg(Rmt) is used .

= Cov (Rs; Rm) / Var (Rm). But the value is taken from the regression between The Rit

rft and Rmt rft.

SR (systematic risk) is the beta, in equation TR (total risk) is the standard deviation of company return _i. IR (idiosyncratic risk) is the standard deviation of the residual eit.

Size. For size (market capitalization), we take the natural log of average market capitalization

over the relevant period for each company. Size could be related to liquidity and the amount

of information available in the market, which are legitimate risk factors. We find that there is

little relation between the average international returns and size.


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Semistandard Deviations. The formula for semistandard deviation is:

A measure of dispersion for the values of a data set falling below the

observed mean or target value. Semideviation is the square root of semivariance, which is

found by averaging the deviations of observed values that have a result that is less than themean. The formula for semideviation is as follows:

Where:

n = the total number of observations below the mean

rt = the observed value

average = the mean or target value of a data set

In portfolio theory, semideviation evaluates the fluctuations in returns below the mean. It

provides an effective measure of downside risk for a portfolio. It's similar to standard

deviation, but it only looks at periods where the portfolio's return was less than the target or

average level. This allows investors to see how much loss can be expected from a portfolio,

instead of only looking at its expected fluctuations.

Semimean is the semistandard deviation with B = average returns for the market.

Semi-rf is the semistandard deviation with B = risk-free rate.

Semi-0 is the semistandard deviation with B = 0.

Downside Beta Measures. Down-_iw is the _ coefficient from the market model using

observations when company returns and market returns are simultaneously negative.

Down-_w is the _ coefficient from the market model using observations when company

returns are negative.

The Downside Beta 1(when company return are taken negative leaving market returnany value).

The Downside Beta 2(When both the company return and market return are takennegative).

Downside beta is both intuitively and theoretically appealing, and empirically can provide a

better risk measure than the regular beta

Value at risk. VaR is a value at risk measure. It is the simple average of returns below the

5th percentile level. The semi-variance is applicable only when portfolio return distribution

is non-symmetrical. When the portfolio return is normally distributed semivariance belowthe expected return is half the portfolios variance and hence variance may still be

used to quantify risk.


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Skewness. Skew is the unconditional skewness of returns. It is calculated by taking the Mean

divided by the [Standard Deviation of (ei)]. Skew 5%: [(Return at the 95th Percentile level

mean return)(Return at 5th Coskew1 represents coskewness definition 1. It is calculated by

(sum up ei _ em2)/T and divide by [square root of (sum of (ei 2)/T)] _ [(sum of (em2)/T)].

Coskew2 represents coskewness definition 2. It is calculated by (Sum up ei _ em2)/T anddivide by [standard deviation of(em)].

Spread. Kurt is the kurtosis of the return distribution.

REGRESSION ANALYSIS and INTERPRETATION.

Bivariate Regressions

These regressions examine the bivariate relation between the average returns and the average

risk measures. Comparing averages to averages over the same time period. This is consistent

with some of the early tests of asset pricing models. Time variation in the risk and returnsmeasures is very important. The second risk measure is total risk. Asset pricing theory says

that only systematic risk, or the part of variance that contributes to a well-diversified

portfolios variance, should be important. The 12 parameter are measured where the

correlation chart show the relationship between the risk associated and the interdependent

effecting the return.

Data obtained:

As it can been seen that average return of the firm range from 0.671 to 4.75 that means the

portfolio would provide the return above the mean of 2.03% on investment. If an assetscontributes positive skewness to a diversified portfolio then the assets will be valuable and

will have high price.

Chart 1. Correlation of MIDSTOCK Company.

COMPANIES Mean beta skewness kurtosis Stdev Variation semistand riskfree semistand market residual risk IR market cap in cr. downside beta1 downside beta2

ASIAN PAINTS 2.9699306 0.444833822 -0.239479904 0.530148036 7.532840039 56.74367905 7.818810764 8.208055859 6.417436456 9.298958709 0.456416704 0.392246428

EXIDE 2.057687204 0.722169238 -2.616709849 15.52680059 15.46814807 239.2636048 15.422559 14.16783182 14.12502772 8.632826997 0.718236879 -0.128074425

GILLETTE 1.979472998 0.638065378 1.569488343 9.63306911 11.21157993 125.6995245 11.20894951 10.10663245 9.685601635 8.078072472 0.638065378 0.374943657

GODREJ 1.044857287 0.36883705 -2.142891237 14.4173284 12.65860448 160.2402675 12.57679505 13.38809401 12.29837666 8.477304808 0.36883705 -0.282447988

JINDAL CAPITAL 4.752927145 2.673457159 2.685128758 8.44495248 40.29911679 1624.018814 40.22592995 35.54639387 32.49594941 1.594477243 0.525117999 0.635395995

NIRMA 0.671087845 0.361333991 0.221424859 5.525003869 13.61336325 185.3236591 13.5198031 14.33746201 13.30938319 7.974428332 0.357055631 0.131504497

P&G 1.536020897 0.342743338 1.33866973 3.797735983 7.773695349 60.43033937 7.766825137 9.215233795 7.18118711 8.137744404 0.349382423 0.057676034

SINTEX 0.992510206 1.710621633 -0.740213037 3.501796701 20.91421093 437.4042188 20.77277279 15.39025271 14.20001775 8.02407224 1.651270998 1.125256393

VIJAYA BANK 0.934403619 1.208405633 1.540619145 6.815446717 15.42302686 237.8697575 15.31909271 10.99412832 10.97702424 7.763960261 0.590906583 0.576507196

YES BANK 3.129269227 1.341488697 0.704412232 4.581920616 15.30863383 234.3542698 15.39661812 9.943818875 9.427335253 8.489083122 0.979884413 1.01082121


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From the table it is clearly can been seen that high correlation exists between as follow

Mean

beta : 0.599, Mean

Standard deviation : 0.575, Mean

Variation : 0.679, Mean

semistandard risk free: 0.596, Mean semistandard market return: 0.583, Mean residual

risk : 0.555

Betastandard deviation: 0.922, Betavariation: 0.88368, Betastandard risk free: 0.924,

BetaIR: 0.501 and inverse relation between BetaMarket capitalization: -0.553

Skewness is having high inverse correlation with market capitalization, standard deviation

show high correlation with variation, semistand risk free, semistand market returnand

residual risk(IR) above >0.95, and inverse correlation with Market capitalization of -0.630.

Chart 2. Mean vs Beta

Out of the 10 portfolio 6 of them give return above the mean average return that means the firm is

performing well in the market.

Chart 3. Mean Vs Skewness.

Mean beta sk ewness k urtosis Stdev Variation semistand risk free semistand mark et residual risk IR mark et cap in cr. downside beta1 downside beta2

1

0.598384106 1

0.407684507 0.447091844 1

-0.081398408 -0.068086628 -0.45336799 1

0.58749594 0.922854855 0.360463906 0.153224541 1

0.679761365 0.883683684 0.439725748 0.099163062 0.979350984 1

0.596506363 0.923949545 0.362698072 0.147998022 0.999925661 0.980729805 1

0.583377653 0.792120076 0.321650747 0.219462868 0.960828317 0.977097152 0.960881719 1

0.555422018 0.801367691 0.293583673 0.28016472 0.968993749 0.968807634 0.968458016 0.994697146 1

0.013792176 -0.552741166 -0.505734662 -0.2281151 -0.630786239 -0.577160366 -0.62317907 -0.594404907 -0.620146817 1

-0.066127923 0.462092566 -0.161646467 -0.196951129 0.221596175 0.083523649 0.219772296 0.007547788 0.036098412 -0.101189627 1

0.290382342 0.676344557 0.441368348 -0.588338577 0.383184028 0.315611833 0.385728674 0.155210945 0.158388566 -0.195447621 0.742579957 1

COMPANIES beta Mean

ASIAN PAINTS 0.444833822 2.9699306

EXIDE 0.722169238 2.057687204

GILLETTE 0.638065378 1.979472998

GODREJ 0.36883705 1.044857287

JINDAL CAPITAL 2.673457159 4.752927145

NIRMA 0.361333991 0.671087845

P&G 0.342743338 1.536020897

SINTEX 1.710621633 0.992510206

VIJAYA BANK 1.208405633 0.934403619

YES BANK 1.341488697 3.129269227

y = 1.0093x + 1.0165

R = 0.3581

0

0.5

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1.5

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2.5

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3.5

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4.5

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0 0.5 1 1.5 2 2.5 3

Mean

Beta


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If an assets contributes positive skewness to a diversified portfolio then the assets will be

valuable and will have high price.

Chart 4. Mean Vs Kurtosis

Chart 4. Mean Vs Standard Deviation

COMPANIES skewness Mean

ASIAN PAINTS -0.239479904 2.9699306

EXIDE -2.616709849 2.057687204

GILLETTE 1.569488343 1.979472998

GODREJ -2.142891237 1.044857287

JINDAL CAPITAL 2.685128758 4.752927145

NIRMA 0.221424859 0.671087845

P&G 1.33866973 1.536020897

SINTEX -0.740213037 0.992510206

VIJAYA BANK 1.540619145 0.934403619

YES BANK 0.704412232 3.129269227

y = 0.3087x + 1.9352

R = 0.1662

0

0.5

1

1.5

22.5

3

3.5

4

4.5

5

-3 -2 -1 0 1 2 3

ME

AN

SKEWNESS

COMPANIES kurtosis Mean

ASIAN PAINTS 0.530148036 2.9699306EXIDE 15.52680059 2.057687204

GILLETTE 9.63306911 1.979472998

GODREJ 14.4173284 1.044857287

JINDAL CAPITAL 8.44495248 4.752927145

NIRMA 5.525003869 0.671087845

P&G 3.797735983 1.536020897

SINTEX 3.501796701 0.992510206

VIJAYA BANK 6.815446717 0.934403619

YES BANK 4.581920616 3.129269227

y = -0.0217x + 2.1649

R = 0.0066

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0.5

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1.5

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2.5

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3.5

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0 5 10 15 20

MEAN

KURTOSIS


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When the total volatility of individual stock is decomposed into systematic volatility and

idiosyncratic volatility, it is clearly evident that idiosyncratic volatility has trended up.

Crosssectional regressions that the volatility of individual stocks maybe related to the amountof institutional ownership.

Chart 5. Mean vs Variation

The table clearly show the variation in the return of the stock to be above 50% that describe the

volatility of the market and the trend involve in recognizing the investment inorder to averse risk.

Chart 6. Mean Vs SemiStandard Deviation

COMPANIES Stdev Mean

ASIAN PAINTS 7.532840039 2.9699306

EXIDE 15.46814807 2.057687204

GILLETTE 11.21157993 1.979472998

GODREJ 12.65860448 1.044857287

JINDAL CAPITAL 40.29911679 4.752927145

NIRMA 13.61336325 0.671087845

P&G 7.773695349 1.536020897

SINTEX 20.91421093 0.992510206

VIJAYA BANK 15.42302686 0.934403619

YES BANK 15.30863383 3.129269227

y = 0.0803x + 0.7207

R = 0.3452

0

0.5

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1.5

22.5

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3.5

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4.5

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0 10 20 30 40 50

ME

AN

STDEV

COMPANIES Variation Mean

ASIAN PAINTS 56.74367905 2.9699306

EXIDE 239.2636048 2.057687204

GILLETTE 125.6995245 1.979472998

GODREJ 160.2402675 1.044857287

JINDAL CAPITAL 1624.018814 4.752927145

NIRMA 185.3236591 0.671087845

P&G 60.43033937 1.536020897

SINTEX 437.4042188 0.992510206

VIJAYA BANK 237.8697575 0.934403619

YES BANK 234.3542698 3.129269227

y = 0.0019x + 1.3765

R = 0.4621

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0 500 1000 1500 2000

MEAN

Variation


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Chart 7. Mean Vs Semi Standard Market Return




average level. This allows investors to see how much loss can be expected from a portfolio,

instead of only looking at its expected fluctuations. Thus there six portfolio meeting the

target.

COMPANIES semistand riskfree Mean

ASIAN PAINTS 7.818810764 2.9699306

EXIDE 15.422559 2.057687204

GILLETTE 11.20894951 1.979472998

GODREJ 12.57679505 1.044857287

JINDAL CAPITAL 40.22592995 4.752927145

NIRMA 13.5198031 0.671087845

P&G 7.766825137 1.536020897

SINTEX 20.77277279 0.992510206

VIJAYA BANK 15.31909271 0.934403619

YES BANK 15.39661812 3.129269227

y = 0.0819x + 0.6954

R = 0.3558

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0.5

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1.5

22.5

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3.5

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4.5

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0 10 20 30 40 50

MEAN

SEMISTAND RISKFREE

COMPANIES semistand market Mean

ASIAN PAINTS 8.208055859 2.9699306

EXIDE 14.16783182 2.057687204

GILLETTE 10.10663245 1.979472998

GODREJ 13.38809401 1.044857287

JINDAL CAPITAL 35.54639387 4.752927145

NIRMA 14.33746201 0.671087845

P&G 9.215233795 1.536020897

SINTEX 15.39025271 0.992510206

VIJAYA BANK 10.99412832 0.934403619

YES BANK 9.943818875 3.129269227

y = 0.0947x + 0.6692

R = 0.3403

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0 5 10 15 20 25 30 35 40

MEAN

SEMISTAND MARKET


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Chart 8. Mean vs Residual Risk

Chart 9. Mean vs Market capitalization

It show inverse relationship between the market size and average return that means small

firm give high return and large firm provide low return due to stability in the market in long

run.

COMPANIES residual risk IR Mean

ASIAN PAINTS 6.417436456 2.9699306

EXIDE 14.12502772 2.057687204

GILLETTE 9.685601635 1.979472998

GODREJ 12.29837666 1.044857287

JINDAL CAPITAL 32.49594941 4.752927145

NIRMA 13.30938319 0.671087845

P&G 7.18118711 1.536020897

SINTEX 14.20001775 0.992510206

VIJAYA BANK 10.97702424 0.934403619

YES BANK 9.427335253 3.129269227

y = 0.0968x + 0.747

R = 0.3085

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0.5

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0 5 10 15 20 25 30 35

MEAN

RESIDUAL RISK IR

COMPANIES market cap i n cr. Mean

ASIAN PAINTS 9.298958709 2.9699306

EXIDE 8.632826997 2.057687204

GILLETTE 8.078072472 1.979472998

GODREJ 8.477304808 1.044857287

JINDAL CAPITAL 1.594477243 4.752927145

NIRMA 7.974428332 0.671087845

P&G 8.137744404 1.536020897

SINTEX 8.02407224 0.992510206

VIJAYA BANK 7.763960261 0.934403619

YES BANK 8.489083122 3.129269227

y = -0.3777x + 4.8949

R = 0.4077

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MEAN

Market cap


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Chart 10. Mean Vs Downsidebeta 1.

Chart 11. Mean vs Downside Beta 2

Data of LARGECAP STOCK Companies:

COMPANIES downside beta1 Mean

ASIAN PAINTS 0.456416704 2.9699306

EXIDE 0.718236879 2.057687204

GILLETTE 0.638065378 1.979472998

GODREJ 0.36883705 1.044857287

JINDAL CAPITAL 0.525117999 4.752927145

NIRMA 0.357055631 0.671087845

P&G 0.349382423 1.536020897

SINTEX 1.651270998 0.992510206

VIJAYA BANK 0.590906583 0.934403619

YES BANK 0.979884413 3.129269227

y = -0.2133x + 2.1484

R = 0.0044

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1.5

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0 0.5 1 1.5 2

MEAN

DOWNSIDE BETA 1


ASIAN PAINTS 0.392246428 2.9699306

EXIDE -0.128074425 2.057687204

GILLETTE 0.374943657 1.979472998

GODREJ -0.282447988 1.044857287

JINDAL CAPITAL 0.635395995 4.752927145

NIRMA 0.131504497 0.671087845

P&G 0.057676034 1.536020897

SINTEX 1.125256393 0.992510206

VIJAYA BANK 0.576507196 0.934403619

YES BANK 1.01082121 3.129269227

y = 0.8075x + 1.6924

R = 0.0843

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-0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2

MEAN

DOWNSIDE BETA 2


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From the table it is evident that average return is 1.878 and for most of the firm the beta

value is greater than 1 with skewness being negative for six firm out of ten, A comparison of

a normal distribution with a distribution exhibiting positive excess kurtosis reveals the

following points. It is very interesting to note what happens when its move from a normal

distribution to a distribution with positive excess kurtosis. The effect of excess kurtosis is

therefore to increase the probability of very large moves and very small moves in the value

of the variable, while decreasing the probability of moderate moves.

Correlation matrix

From the table the average return has inverse correlation with market capitalization (56.4%),

the variation of beta is dependent on risk free rate and market return fluctuation in the

COMPANIES Mean beta skewness kurtosis Stdev Variation semistand riskfree semistand market residual risk IR market cap in cr. downside beta1 downside beta

AXIS BANK 1.9 1.1667118 -0.055667717 0.5947647 12.9354535 167.30219 15.6478411 11.1631397 3.85494 11.11413868 1.17184265 0.7659775

TA TA CHEMICA LS 2. 119341 1. 154348 -0. 38936 0. 774561 12. 59563 158. 6498 12.64122 12. 51145 13. 72893 10.98578526 1.152767 0.917226

B HA RATI A IRTE L 1. 335677 0. 613789 -1. 60299 5. 245336 11. 04104 121. 9045 10.97982 10. 98183 9. 286976 13.97674234 0.613789 0.212155

GRASIM 2.294001 1.056736 1.40014 9.141795 17.8651 319.1616 17.85565 17.74591 17.35462 12.18206155 1.067097 0.605653

HUL 1.293217 0.646184 0.156948 0.005262 8.367118 70.00866 8.347272 8.335239 8.426123 13.12100976 0.338109 0.215777

L & T 2.606749 1.421123 -0.51937 6.543247 16.54914 273.8739 16.59314 16.44947 17.43333 7.974428332 1.420798 0.877411

M AHINDRA & M AHINDRA 1. 636134 0. 868746 -0. 874 29 3. 185469 1 4. 05888 19 7. 6521 14 .0 3636 13. 9656 1 11. 9501 6 12.15898185 0.875309 0.380292

NES TLE INDIA 2. 924262 0. 403746 -0. 41256 0. 80287 6.951646 48.32538 7. 337472 6. 978396 6. 278001 11.97116209 0.42192 0.256054

NTPC 1.539925 0.631086 -0.25321 0.452286 8.291248 68.74479 8.304388 8.245268 8.301206 14.11898751 0.62811 0.457345

SAIL 2.578264 0.868746 0.439807 1.710894 14.9903 224.7092 15.05488 14.90186 12.10243 13.23417078 0.875309 0.661423

Mean beta sk ewness k urtosis Stdev Variation semistand risk free semistand mark et residual risk IR mark et cap in cr. downside beta1 downside beta2

Mean 1

beta 0.244720677 1

skewness 0.332345617 0.22280694 1

k urtos is 0. 181466058 0.394941111 0.186737147 1

S tdev 0. 296110884 0.79479141 0.372968143 0.72529458 1

V ar ia ti on 0 .3 53 71 44 81 0 .7 65 73 90 59 0 .4 39 38 10 14 0 .7 68 50 49 14 0 .9 91 95 71 26 1

semistand r isk free 0 .2958967 0.834681956 0.380104948 0.640529098 0.974125398 0.957980589 1

semistand market 0 .309903782 0.74541475 0.362472475 0.761306418 0.989102184 0.988153654 0.930668765 1

res idual r isk IR 0.301359084 0.569822782 0.286986986 0.733865987 0.754658528 0.783729131 0.602858014 0.833294415 1

market cap in cr . -0.564536695 -0.581859193 -0.287539366 -0.131069971 -0.441114496 -0.452761401 -0.499865959 -0.403181584 -0.279522608 1

downside beta1 0 .354120075 0.962070702 0.160188437 0.443683337 0.816691233 0.787998097 0.859065451 0.76780808 0.560146545 -0.543161339 1

downside beta2 0 .431865994 0.892513085 0.285932067 0.144249899 0.633915287 0.605608399 0.682734056 0.587218285 0.454275607 -0.511330298 0.9091674 1


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market. Thus the stock has interdependence on risk free rate and market return in

determining the investment in various portfolio to go with. It is better to have a diversifiable

portfolio in order to averse the risk.

Chart 12. Mean vs beta

The returns averaged 1.975 thus the beta value is more for the firm that providing high

return. The systematic risk associated with the fluctuation in the market, economic determine

the return on the various portfolio.

Chart 13. Mean vs skewness.

COMPANIES beta Mean

AXIS BANK 1.1667118 1.9

TATA CHEMICAL 1.154348 2.119341

BHARATI AIRTEL 0.613789 1.335677

GRASIM 1.056736 2.294001

HUL 0.646184 1.293217

L & T 1.421123 2.606749

MAHINDRA & MA 0.868746 1.636134

NESTLE INDIA 0.403746 2.924262

NTPC 0.631086 1.539925

SAIL 0.868746 2.578264

y = 0.4438x + 1.6308

R = 0.0599

0

0.5

1

1.5

2

2.5

3

3.5

0 0.5 1 1.5

Mean

Beta

Mean

Mean

Linear (Mean)

COMPANIES skewness Mean

AXIS BANK -0.055667717 1.9

TATA CHEMICAL -0.38936 2.119341

BHARATI AIRTEL -1.60299 1.335677

GRASIM 1.40014 2.294001

HUL 0.156948 1.293217

L & T -0.51937 2.606749

MAHINDRA & MA -0.87429 1.636134

NESTLE INDIA -0.41256 2.924262

NTPC -0.25321 1.539925

SAIL 0.439807 2.578264

y = 0.2384x + 2.0731

R = 0.1105

0

0.5

1

1.5

2

2.5

3

3.5

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

MEAN

SKEWNESS

y = 0.2384x + 2.0731

R = 0.1105

0

0.5

1

1.5

2

2.5

3

3.5

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

MEAN

SKEWNESS


13/18


If an assets contributes positive skewness to a diversified portfolio then the assets will be

valuable and will have high price. Thus there are six assets where the skewness is negative.

Chart 14. Mean vs Kurtosis

Chart 15. Mean vs Standard Deviation

COMPANIES kurtosis Mean

AXIS BANK 0.5947647 1.9

TATA CHEMICAL 0.774561 2.119341


GRASIM 9.141795 2.294001

HUL 0.005262 1.293217L & T 6.543247 2.606749

MAHINDRA & MA 3.185469 1.636134

NESTLE INDIA 0.80287 2.924262

NTPC 0.452286 1.539925

SAIL 1.710894 2.578264

y = 0.0332x + 1.9281

R = 0.0329

0

0.5

1

1.5

2

2.5

3

3.5

0 2 4 6 8 10

MEAN

KURTOSIS


14/18





average level. This allows investors to see how much loss can be expected from a portfolio,instead of only looking at its expected fluctuations. Thus there five portfolio meeting the

target. Thus require to diversify the portfolio to the extend of 50% to get better return.

Chart 16. Mean vs Variation

Chart 17. Mean vs Semistandard Risk free.

COMPANIES Stdev Mean

AXIS BANK 12.9354535 1.9

TATA CHEMICAL 12.59563 2.119341


GRASIM 17.8651 2.294001

HUL 8.367118 1.293217

L & T 16.54914 2.606749

MAHINDRA & MA 14.05888 1.636134

NESTLE INDIA 6.951646 2.924262

NTPC 8.291248 1.539925

SAIL 14.9903 2.578264

y = 0.0461x + 1.4526

R = 0.0877

0

0.5

1

1.5

2

2.5

3

3.5

0 5 10 15 20

ME

AN

STDEV

COMPANIES Variation Mean

AXIS BANK 167.30219 1.9TATA CHEMICAL 158.6498 2.119341


GRASIM 319.1616 2.294001

HUL 70.00866 1.293217

L & T 273.8739 2.606749

MAHINDRA & MA 197.6521 1.636134

NESTLE INDIA 48.32538 2.924262

NTPC 68.74479 1.539925

SAIL 224.7092 2.578264

y = 0.0022x + 1.6545

R = 0.1251

0

0.5

1

1.5

2

2.5

3

3.5

0 50 100 150 200 250 300 350

MEAN

Variation


15/18


Chart 18. Mean vs Semi Standard Market return

COMPANIES semistand riskfree Mean

AXIS BANK 15.6478411 1.9

TATA CHEMICAL 12.64122 2.119341


GRASIM 17.85565 2.294001

HUL 8.347272 1.293217

L & T 16.59314 2.606749

MAHINDRA & MA 14.03636 1.636134

NESTLE INDIA 7.337472 2.924262

NTPC 8.304388 1.539925

SAIL 15.05488 2.578264

y = 0.0449x + 1.4529

R = 0.0876

0

0.5

1

1.5

2

2.5

3

3.5

0 5 10 15 20

ME

AN

SEMISTAND RISKFREE

COMPANIES semistand market Mean

AXIS BANK 11.1631397 1.9

TATA CHEMICAL 12.51145 2.119341


GRASIM 17.74591 2.294001

HUL 8.335239 1.293217

L & T 16.44947 2.606749

MAHINDRA & MA 13.96561 1.636134

NESTLE INDIA 6.978396 2.924262

NTPC 8.245268 1.539925

SAIL 14.90186 2.578264

y = 0.0486 x + 1.433

R = 0.096

0

0.5

1

1.5

2

2.5

3

3.5

0 5 10 15 20

MEAN

SEMISTAND MARKET


16/18


Chart 19. Mean vs Residual Risk

Chart 20. Mean vs Market capitalization

Chart 21. Mean vs Downside beta1

COMPANIES residual risk IR Mean

AXIS BANK 3.85494 1.9

TATA CHEMICAL 13.72893 2.119341


GRASIM 17.35462 2.294001

HUL 8.426123 1.293217

L & T 17.43333 2.606749

MAHINDRA & MA 11.95016 1.636134

NESTLE INDIA 6.278001 2.924262

NTPC 8.301206 1.539925

SAIL 12.10243 2.578264

y = 0.0384x + 1.6051

R = 0.0908

0

0.5

1

1.5

2

2.5

3

3.5

0 5 10 15 20

MEAN

RESIDUAL RISK IR

COMPANIES market cap i n cr. Mean

AXIS BANK 11.11413868 1.9

TATA CHEMICAL 10.98578526 2.119341

BHARATI AIRTEL 13.97674234 1.335677

GRASIM 12.18206155 2.294001

HUL 13.12100976 1.293217

L & T 7.974428332 2.606749

MAHINDRA & MA 12.15898185 1.636134

NESTLE INDIA 11.97116209 2.924262

NTPC 14.11898751 1.539925

SAIL 13.23417078 2.578264

y = -0.1676x + 4.0481

R = 0.2782

0

0.5

1

1.5

2

2.5

3

3.5

0 5 10 15

MEAN

Market cap


17/18


Chart 22. Mean vs Downside beta2.

Conclusion:

Risk is inseparable from return. Every investment involves some degree of risk, which can be

very close to zero in the case of a Treasury security or very high for something such asconcentrated exposure to Sri Lankan equities or real estate in Argentina. Risk is quantifiable

both in absolute and in relative terms. A solid understanding of risk in its different forms can

help investors to better understand the opportunities, trade-offs and costs involved with

different investment approaches. As expected monthly average return and monthly volatility

across markets vary over time and space. Their divergencies are highly demonstrable.

While stock prices have risen sharply over the last year, on a monthly basis they have


AXIS BANK 1.17184265 1.9

TATA CHEMICAL 1.152767 2.119341


GRASIM 1.067097 2.294001

HUL 0.338109 1.293217

L & T 1.420798 2.606749

MAHINDRA & MA 0.875309 1.636134

NESTLE INDIA 0.42192 2.924262

NTPC 0.62811 1.539925

SAIL 0.875309 2.578264

y = 0.5746x + 1.5306R = 0.1254

0

0.5

1

1.5

2

2.5

3

3.5

0 0.5 1 1.5

MEAN

DOWNSIDE BETA 1


AXIS BANK 1.17184265 1.9

TATA CHEMICAL 1.152767 2.119341


GRASIM 1.067097 2.294001

HUL 0.338109 1.293217

L & T 1.420798 2.606749

MAHINDRA & MA 0.875309 1.636134

NESTLE INDIA 0.42192 2.924262

NTPC 0.62811 1.539925

SAIL 0.875309 2.578264

y = 0.5746x + 1.5306

R = 0.1254

0

0.5

1

1.5

2

2.5

3

3.5

0 0.5 1 1.5

MEAN

DOWNSIDE BETA 2


18/18


been usually stable. Firms make a good deal of their money from exploiting the bumps and

wrinkles in markets, which drive profits in derivatives, arbitrage and all kinds of market

making. The returns on portfolio of stocks (index) are more or less normally distributed.

because normal distributions are fully described by their mean and standard deviation, the

risk of such portfolios can indeed be measured with one number. Confronted with non-

normal distributions, however, it is no longer appropriate to use the standard deviation as the

sole measure of risk. In that case investors should also look at the degree of symmetry of

the distribution, as measured by its so-called skewness, and the probability of extreme

positive or negative outcomes, as measured by the distributions, `kurtosis. A symmetrical

distribution will have a skewness equal to zero, while a distribution that implies a relatively

high possibility of a large loss (gain) is said to exhibit negative (positive) skewness. A

normal distribution has a kurtosis of 3, while a kurtosis higher than 3 indicates gain. Since

most investors are in it for the longer run, they strongly rely on compounding effects. This

means that negative skewness and high kurtosis are extremely undesirable features as onebig loss may destroy years of careful compounding. Higher order movements, skewness and

kurtosis, provide additional information about he nature of return distribution. Negative

skewness and high kurtosis are extremely harmful to investors (long only).

Reference:

http://www.efmaefm.org/efma2006/papers/310329_full.pdf

http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2005/wp11-05.pdf
http://www.efmaefm.org/efma2006/papers/310329_full.pdfhttp://www.efmaefm.org/efma2006/papers/310329_full.pdfhttp://www.buseco.monash.edu.au/ebs/pubs/wpapers/2005/wp11-05.pdfhttp://www.buseco.monash.edu.au/ebs/pubs/wpapers/2005/wp11-05.pdfhttp://www.buseco.monash.edu.au/ebs/pubs/wpapers/2005/wp11-05.pdfhttp://www.efmaefm.org/efma2006/papers/310329_full.pdf

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