Dissertation Anagh

8/3/2019 Dissertation Anagh

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A DISSERTATION ON

THE MESUREMENT OF RISK-

BETA OR ABSOLUTE VOLATILITY?

SUBMITTED TOWARDS FULFILLMENT OF

POST GRADUATE DIPLOMA IN

BUSINESS MANAGEMENT

(Approved by AICTE, Govt. of India)

(Equivalent to MBA)

Under the Guidance of: Submitted By:

Dr. VIDYA SEKHRI ANAGH RASTOGI

Chairperson Finance PGDM (2009-11)

IMS Ghaziabad BM09032

Institute of Management Studies

C-238, Bulandshahr Road, Lal Quan

Ghaziabad 201009


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TO WHOMSOEVER IT MAY CONCERN

This is to certify that the project entitled, BETA OR ABSOLUTE VOLATILITY A

TECHNICAL ANALYSIS? is submitted by Anagh Rastogi, student of PGDM (Full

Time) 2009-2011 batch, IMS Ghaziabad for the fulfillment of the requirements for the

award of two year Post Graduate Diploma in Business Management is a bonafied

record of the work done by him under my guidance from and that this has not been

submitted by him for any other Degree or Diploma.

Dr. Vidya Sekhri

CHAIRPERSON FINANCE

IMS, GHAZIABAD


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DECLARATION

I the Student of PGDM (2009-11) of Institute of Management Studies, Ghaziabad

hereby declare that the project on BETA OR ABSOLUTE VOLATILITY A

TECHNICAL ANALYSIS? has been done under the guidance of Dr. VIDYA

SEKHRI.

Anagh Rastogi

(Candidates name & Signature)

This is to certify that the above project submitted is correct to the best of myknowledge.

Dr. Vidya Sekhri

Chairperson Finance


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ACKNOWLEDGEMENT

Every accomplishment requires a lot of efforts, hard work, support and blessings. This

project is no exception. This project would not have been possible without the support

and cooperation of a lot of people.

I am grateful to my project guide Dr. Vidya Sekhri, who provided me her constructive

ideas and advice at every stage of this project. Her expertise helped me a lot in

accomplishing the objectives of the project.

Last but not the least I would like to thank my family and friends for their support and

blessings without which I would not have succeeded.

Thank You,

Anagh Rastogi

(BM-09032)


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TABLE OF CONTENT

CONTENTS PAGE NO.

1. ABSTRACT 6

2. INTRODUCTION 7

3. LITERATURE REVIEW 8

4. OBJECTIVE OF THE STUDY 9

5. RESEARCH METHODOLOGY 10

6. WHAT IS THE BETA AND A.V 11

7. THE VALUE OF A.V, R, BETA OF ALL THE COMPANIES(CALCULATED)

- TABLE ANALYSIS OF ABOVE CALCULATED A.V, RAND BETA

16

8. ANALYSIS (Certain Tests) 19

9. LIMITATIONS 25

10. CONCLUSION 26

11. BIBLIOGARHY 27

12. ANNEXURE

- 50 NIFTY COMPANIES WITH SYMBOLS

28


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ABSTRACT

There are plenty of soothsayers/financial wizard offering advice and strategies to

investors. However, to play safe and manage their funds optimally, they need

organized information, logical reasoning backed by scientific methods and

techniques. There are two ways to see the stock exchange volatility, Beta or absolute

volatility. Now the question is which gives the perfect result as per as volatility is

concern. In the paper emphasises is given to find out that, Is there any difference

between the results given by absolute volatility and beta of a share.


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INTRODUCTION

This dissertation report is all about Beta or Absolute Volatility that how should I

constrain my portfolio's BETA OR ABSOLUTE VOLATILITY in order to achieve

market neutrality?

That is the practical question to be answered.

Market neutrality is a very useful feature, and is well worth pursuing. The value of a

fund to an investor is partly based on the return that it generates, and partly based on

its correlation to the rest of the investor's portfolio. The lower the correlation, the

more valuable it is? But the question is should we just go for beta and correlation or

that other thing which is absolute volatility. In my dissertation report, I have tried my

level best to have an answer to this question.


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LITERATURE REVIEW:

Published studies that have examined Beta and Absolute volatility in the stock market

appear to be limited.

According to Keppler M. (1990) if you ask investors what risk they assume when

buying stocks, they likely will respond, Losing money. Modern portfolio theorists

do not, however, define risk as a likelihood of loss, but as volatility, which are

determined using statistical measures of variance such as standard deviation and beta.

While standard deviation is a measure of absolute volatility that shows how much an

investments return varies from its average return over time, beta is a measure of

relative volatility that indicates the price variance of an investment compared to the

market as a whole.

Burns P. (2003) suggested that Simulations are performed which shows the

difficulty of actually achieving realized market neutrality. Results suggest that

restrictions on the net value of the fund are particularly ineffective. A negative

correlation that is-market negativity, is proposed as a more reasonable target, both on

theoretical and practical grounds. Random portfolios, portfolios that obey given

constraints but are otherwise unrestricted, prove themselves to be a very effective tool

to study issues such as this.

Cotter, John (2004) suggested that the use of absolute return volatility has many

modeling benefits. Volatility modeling is a key issue for the finance industry from an

academic and practitioner perspective. This is understandable given the importance

that volatility plays in risk management and the development of accurate risk

measures. To illustrate, successful market risk management requires the use of

accurate risk measures such as minimum capital requirements.


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OBJECTIVE OF THE STUDY:

1. Is there any difference between the results given by absolute volatility and

beta of a share?

2. Should we shift from beta to absolute volatility for better understanding the

share and market performance?

3. Is there any significant role of correlation of a particular share and market in

the end result of volatility?


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RESEARCH METHODOLOGY:

- Technical Analysis

- Sample Size- 50 Nifty Shares

- Time duration - one year (1 February. 2010 31 January. 2011)

- Test correlation, t-test, chi-square test, regression and few more.


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that the asset inversely follows the market; the asset generally decreases in value if

the market goes up and vice versa

Do you know your stocks beta?

If you do, what does it mean and should you be concerned?

Beta is one of the most used and misused of the financial ratios. First off, lets review

what a beta is, then look at how you can use it in a meaningful way.

The beta is a measure of a stocks price in relation to the rest of the market. In other

words, how does the stocks price move relative to the overall market?

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.


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Few Examples:

Stock Beta = 2

Market % Return Individual Stock % Return

10% (10% x 2) = 20%

-8% (-8% x 2) = -16%

If the market provides a 10% return to ordinary investors, the stock with a Beta of 2

will provide a 20% return (higher risk, higher return!). However, if the market

provides a negative 8% return, then the Stock with a Beta of 2 will provide a -16%

(higher risk, probability of lower returns!).

Here's another example, with a stock that has a Beta of 0.5

Stock Beta = 0.5

Market % Return Individual Stock % Return

10% (10% x 0.5) = 5%

-8% (-8% x 0.5) = -4%

If the market provides a 10% return to ordinary investors, the stock with a Beta of 0.5

will provide a 5% return (lower risk, lower return!). However, if the market provides

a negative 8% return, then the Stock with a Beta of 2 will provide only a -4% loss,

(lower risk, lower returns!).


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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. Careful consideration of a companys fundamentals will give you a much better

picture of the potential long-term risk.

Problems with Beta

While the may seem to be a good measure of risk, there are some problems with

relying on beta scores alone for determining the risk of an investment.

1. Beta looks backward and history is not always an accurate predictor of the

future.

2. Beta also doesnt account for changes that are in the works, such as new lines

of business or industry shifts.

3. 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 is outperforming the whole market would have

a beta greater than 1.

Absolute volatility (A.V):

Absolute volatility is equal to standard deviation of share divided by standard

deviation of market. Above we have written that = r * share / market.

Here: r = correlation between share and market and the balance share / market theabsolute volatility.

In other words A.V = share / market


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The value of A.V, r, Beta of all the companies are given below:

COMPANY

SYMBOL

S.D OF

SHARE

VARIANCE

OF SHARE

S.D OF

MARKET

ABSOLUTE

VOLITILITY

(A.V) R r2 BETA

(S.D OF

SHARE / S.D

OF

MARKET)

ABB 0.03096 0.000958522 0.02451 1.2630 0.657 0.432 0.830

ACC 0.03061 0.000936972 0.02451 1.2487 0.644 0.414 0.804

AMBUJACEM 0.03383 0.001144469 0.02451 1.3801 0.608 0.369 0.838

AXISBANK 0.04008 0.001606406 0.02451 1.6350 0.771 0.594 1.260

BHEL 0.03008 0.000904806 0.02451 1.2271 0.797 0.636 0.978

BPCL 0.02841 0.000807128 0.02451 1.1590 0.369 0.136 0.428

BHARTIARTL 0.04669 0.002179956 0.02451 1.9047 0.500 0.250 0.953

CAIRN 0.03394 0.001151924 0.02451 1.3846 0.704 0.496 0.975

CIPLA 0.02415 0.000583223 0.02451 0.9852 0.467 0.218 0.460

DLF 0.05525 0.003052563 0.02451 2.2539 0.708 0.502 1.596

GAIL 0.02904 0.000843322 0.02451 1.1847 0.568 0.323 0.673

GRASIM 0.03112 0.000968454 0.02451 1.2695 0.614 0.377 0.780

HCLTECH 0.04411 0.001945692 0.02451 1.7994 0.608 0.370 1.094

HDFCBANK 0.02696 0.000726842 0.02451 1.0998 0.758 0.575 0.834

HEROHONDA 0.02651 0.00070278 0.02451 1.0815 0.514 0.264 0.556

HINDALCO 0.04077 0.001662193 0.02451 1.6632 0.715 0.511 1.189

HINDUNILVR 0.02048 0.00041943 0.02451 0.8355 0.400 0.160 0.334

HDFC 0.03734 0.001394276 0.02451 1.5233 0.762 0.580 1.160

ITC 0.02393 0.000572645 0.02451 0.9762 0.553 0.305 0.539

ICICIBANK 0.04421 0.001954524 0.02451 1.8035 0.826 0.682 1.490

IDEA 0.03686 0.00135866 0.02451 1.5037 0.762 0.580 1.146

INFOSYSTCH 0.02649 0.00070172 0.02451 1.0806 0.632 0.399 0.683

IDFC 0.04541 0.002062068 0.02451 1.8525 0.718 0.515 1.329

JPASSOCIAT 0.05416 0.002933306 0.02451 2.2094 0.769 0.592 1.700


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COMPANY

SYMBOL

S.D OF

SHARE

VARIANCE

OF SHARE

S.D OF

MARKET

ABSOLUTE

VOLITILITY

(A.V) r r2 BETA

JINDALSTEL 0.06711 0.004503752 0.02451 2.7377 0.436 0.190 1.194

LT 0.03581 0.001282356 0.02451 1.4608 0.843 0.711 1.232

M&M 0.03991 0.001592808 0.02451 1.6281 0.667 0.445 1.087

MARUTI 0.02861 0.000818532 0.02451 1.1671 0.553 0.306 0.646

NTPC 0.02411 0.000581292 0.02451 0.9836 0.672 0.451 0.661

ONGC 0.02976 0.000885658 0.02451 1.2140 0.729 0.532 0.885

POWERGRID 0.02761 0.000762312 0.02451 1.1263 0.646 0.417 0.727PNB 0.03194 0.001020164 0.02451 1.3030 0.685 0.469 0.892

RANBAXY 0.03923 0.001538993 0.02451 1.6004 0.492 0.242 0.787

RELCAPITAL 0.05076 0.002576578 0.02451 2.0707 0.798 0.636 1.652

RCOM 0.04751 0.0022572 0.02451 1.9381 0.786 0.617 1.523

RELIANCE 0.03546 0.001257412 0.02451 1.4466 0.854 0.730 1.236

RELINFRA 0.04911 0.002411792 0.02451 2.0034 0.812 0.660 1.628

RPOWER 0.03441 0.001184048 0.02451 1.4037 0.765 0.586 1.074SIEMENS 0.03937 0.001549997 0.02451 1.6061 0.649 0.421 1.042

SBIN 0.03416 0.001166906 0.02451 1.3935 0.822 0.675 1.145

SAIL 0.04531 0.002052996 0.02451 1.8484 0.824 0.679 1.523

STER 0.04671 0.002181824 0.02451 1.9055 0.754 0.568 1.437

SUNPHARMA 0.02995 0.000897003 0.02451 1.2218 0.385 0.148 0.470

SUZLON 0.05611 0.003148332 0.02451 2.2890 0.669 0.447 1.530

TCS 0.04569 0.002087576 0.02451 1.8639 0.437 0.191 0.815

TATAMOTOR 0.04724 0.002231618 0.02451 1.9271 0.643 0.414 1.240

TATAPOWER 0.02986 0.00089162 0.02451 1.2181 0.714 0.510 0.870

TATASTEEL 0.04811 0.002314572 0.02451 1.9626 0.742 0.550 1.456

UNITECH 0.06112 0.003735654 0.02451 2.4933 0.663 0.440 1.653

WIPRO 0.03168 0.001003622 0.02451 1.2924 0.633 0.401 0.818

VARIANCE OF MARKET of market is = (S.D OF MARKET) 2 = (0.02451) 2

= 0.000600898


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Following table shows the analysis of above calculated A.V, r and Beta.

HIGH A.V HIGHBETA

LOW A.VHIGH BETA

HIGH A.V LOWBETA

LOW A.V LOWBETA

AXISBANK ABB CIPLA

BHARTIARTL ACC HINDUNILVR

BHEL AMBUJACEM ITC

CAIRN BPCL NTPC

DLF GAIL

HCLTECH GRASIM

HINDALCO HDFCBANK

HDFC HEROHONDA

IDEA INFOSYSTCH

IDFC MARUTI

ICICIBANK POWERGRID

JPASSOCIAT RANBAXY

JINDALSTEL SUNPHARMA

LT TCS

M&M TATAPOWER

ONGC WIPRO

PNB

RELCAPITAL

RCOMRELIANCE

RELINFRA

RPOWER

SIEMENS

SBIN

SAIL

STER

SUZLON

TATAMOTORS

TATASTEEL

UNITECH

TOTAL

30 0 16 4


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ANALYSIS (Certain Tests)

HIGH V.R

(no. of shares)

LOW V.R

(no. of shares)

HIGH BETA 30 0

LOW BETA 16 4

So there are 30 shares of High V.R and High Beta, 0 of Low V.R but High Beta, 16 of

Low Beta but High V.R and finally 4 of Low V.R and Low Beta.

There are many benefits of A.V over the beta. First of all let us see the relation of A.V

with beta.

Chi-Square Tests of A.V and beta of 50 Shares:

Crosstabs

Case Processing Summary

CasesValid Missing Total

N Percent N Percent N Percent

A_V *BETA

50 100.0% 0 .0% 50 100.0%

A_V * BETA Cross tabulationCount

BETA

0 1 Total

0 4 0 4A_V

1 16 30 46

Total 20 30 50


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Symmetric Measures

ValueAsymp. Std.

ErrorApprox.

TbApprox.

Sig.ExactSig.

Interval by

Interval

Pearson's R.377 .093 2.817 .007c .017

Ordinal byOrdinal

SpearmanCorrelation

.377 .093 2.817 .007c .017

N of Valid Cases 50

a. Not assuming the nullhypothesis.

b. Using the asymptotic standard error assuming the null hypothesis.

c. Based on normal approximation.

Chi-Square Tests

Value dfAsymp. Sig.

(2-sided)Exact Sig.(2-sided)

Exact Sig.(1-sided)

PointProbabili

ty

Pearson Chi-Square 7.094a 1 .008 .017 .017

ContinuityCorrectionb

4.522 1 .033

N of Valid Cases 50

a. 2 cells (50.0%) have expected count less than 5. The minimum expectedcount is 1.52.

b. Computed only for a 2x2 table

c. The standardized statistic is 2.637.

Degree of freedom=1

Level of significant= 5%

H0= there is no significant relation between A.V and Beta

H1=there is significant relation between A.V and Beta

2

tab, 0.05,1 = 3.841

2

cal = 7.094

Now, 2cal >2tab, (7.094 > 3.841)

So, 2 cal isaccepted.H1is accepted which means there is significant relation between A.V and Beta,


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The Chi-Square Test analysis shows thatthere is significant relation between A.V

and Beta. Although the factors by which, these two has been calculated are different.

The difference is ofcorrelation (r). But still according to Chi-Square Test analysis

Regression Test on A.V and Beta of 50 Shares:

Descriptive Statistics

MeanStd.

Deviation N

BETA 1.037040 .3670012 50

AV 1.548586 .4304356 50

Correlations

BETA AV

BETA 1.000 .835PearsonCorrelation AV .835 1.000

BETA . .000Sig. (1-tailed)

AV .000 .

BETA 50 50N

AV 50 50

Model Summary

Change Statistics

Model R R Square Adjusted RSquare Std. Error ofthe Estimate R SquareChange F Change df1 df2

Sig.F

Change

1 .835a .697 .691 .2039436 .697 110.676 1 48 .000

a. Predictors: (Constant), AV

Strength of association = R Square = 0.691

That signifies the proportion of the total variation in beta that account for by the

variation Of A.V.Now here it means in Beta, the total variation of A.V is 0.69


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Coefficients

UnstandardizedCoefficients

StandardizedCoefficients

Model B Std. Error Beta T Sig.(Constant) -.066 .109 -.604 .5491

AV .712 .068 .835 10.520 .000

a. Dependent Variable: BETA

Correlation between A.V & Beta = 0.835

Correlation between A.V & r = 0.22057662

Correlation between Beta & r = 0.69865757

Regression equation = BETA = 0.712 * A.V + (-0.066)

Or 0.712 * A.V - 0.066

This data is of last one year from here if we know the value of A.V for any share we

can easily calculate the value of Beta but main problem with beta is that it is 0.712 *

A.V - 0.066, Now can we take Beta as a true measure of volatility?

For this let us try further with other tests to prove that A.V is better than Beta


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The common thing between A.V and Beta is r:

Now there can be 3 types of r between stock market and a particular share:

Case 1

r = 1

Case 3

r < 1

Case 2

r > 1

Case 4

r 0.5

Now let us take some examples to prove that A.V is more powerful tool to judge

any shares volatility then Beta.

We know, Beta= r * share / market.

A.V = share / market

It means Beta = r * A.V

1st

case:Now if, r = 1,and A.V is 1.29 or 1 or 0.85 etc.

Than Beta in this case is equal to A.V because r = 1 and Beta = r * A.V, than in this

case what is the use of Beta.

2nd

case:Now if, r >1,and A.V is 0.5 or 0.75 or 1 or 1.5 etc.

Than beta in this case will be always more then A.V, means the Beta value is showing

exaggerate value of volatility.


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3rd

case:Now if, r


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LIMITATIONS

1. I have taken 50 nifty shares only it is possible that it may not give the true

picture of total shares in the market.

2. The time duration of my study is one year (1 February. 2010 - 31 January.

2011); this also may not give the actual picture.

3. This study is technical analyses study not a fundamental one, so there are

chances that it may not give the correct picture.

4. The test, tool, analysis and recommendations regarding Beta or A.V are truly

personal, it maybe possible that some people dont agree with this.

5. These findings are based on the assumption that, If Beta is 0.85 then it is a low

beta, otherwise high Beta and if A.V is less then 1 then its a low A.V,

otherwise it is a high A.V.


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CONCLUSION

BETA is a measure the check the volatility of a share but when we see its comparison

with the ABSOLUTE VOLATILITY, the result is in front of us. Absolute volatility

is giving much accurate result than Beta. We have seen in Chi-Square Test analysis

that there is significant relationship between Beta and A.V. after proving the

relationship between Beta and A.V; I have proved in Regression test analysis that A.V

is telling the right volatility than Beta, if we know the value of A.V for any share we

can easily calculate the value of Beta but main problem with beta is that it is 0.712 *

A.V - 0.066, Now can we take Beta as a true measure of volatility? Than in the

further analysis when I have taken correlation (r) as a base I have again proved that

the value given by A.V is far more reliable the value given by Beta. There is no end

for this discussion that whether A.V is good or Beta is Better but one thing is clear

from this that A.V is better and reliable than Beta.


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BIBLIOGARHY

Cotter, John University College Dublin, Absolute Return Volatility (2004)

Michael Keppler,Risk is Not The Same as Volatility (November 1990)

Patrick Burns, Does my beta look big in this? (15th July 2003)

Financial Management by Khan and Jain.

Portfolio Management, Volume 6, CFA, Level 1,2011

Quantitative Techniques, Volume 6, CFA, Level 1, 2011.

www.burns-stat.com/pages/Working/betalookbig.pdf

www.kamny.com/load/publications/p03_eng.pdf

www.moneycontrol.com

www.nse.com

www.ucd.ie/bankingfinance/docs/wp/COTTER5.PDF


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ANNEXURE

THESE ARE THE 50 NIFTY COMPANIES ON WHICH ANALYSIS HAS

BEEN DONE:

COMPANY NAME INDUSTRY SYMBOL

ABB Ltd. ELECTRICAL EQUIPMENT ABB

ACC Ltd.

CEMENT AND CEMENT

PRODUCTS ACC

Ambuja Cements Ltd.

CEMENT AND CEMENT

PRODUCTS AMBUJACEM

Axis Bank Ltd. BANKS AXISBANK

Bharat Heavy Electricals

Ltd. ELECTRICAL EQUIPMENT BHEL

Bharat Petroleum

Corporation Ltd. REFINERIES BPCL

Bharti Airtel Ltd.

TELECOMMUNICATION -

SERVICES BHARTIARTL

Cairn India Ltd.

OIL

EXPLORATION/PRODUCTION CAIRN

Cipla Ltd. PHARMACEUTICALS CIPLA

DLF Ltd. CONSTRUCTION DLF

GAIL (India) Ltd. GAS GAIL

Grasim Industries Ltd.

CEMENT AND CEMENT

PRODUCTS GRASIM

HCL Technologies Ltd. COMPUTERS SOFTWARE HCLTECH

HDFC Bank Ltd. BANKS HDFCBANK

Hero Honda Motors Ltd.

AUTOMOBILES - 2 AND 3

WHEELERS HEROHONDA

Hindalco Industries Ltd. ALUMINIUM HINDALCO

Hindustan Unilever Ltd. DIVERSIFIED HINDUNILVR


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Housing Development

Finance Corporation Ltd. FINANCE HOUSING HDFC

I T C Ltd. CIGARETTES ITC

ICICI Bank Ltd. BANKS ICICIBANK

Idea Cellular Ltd.

TELECOMMUNICATION -

SERVICES IDEA

Infosys Technologies Ltd. COMPUTERS SOFTWARE INFOSYSTCH

Infrastructure

Development Finance Co.

Ltd. FINANCIAL INSTITUTION IDFC

Jaiprakash Associates Ltd. DIVERSIFIED JPASSOCIATJindal Steel & Power Ltd. STEEL AND STEEL PRODUCTS JINDALSTEL

Larsen & Toubro Ltd. ENGINEERING LT

Mahindra & Mahindra

Ltd. AUTOMOBILES - 4 WHEELERS M&M

Maruti Suzuki India Ltd. AUTOMOBILES - 4 WHEELERS MARUTI

NTPC Ltd. POWER NTPC

Oil & Natural GasCorporation Ltd.

OILEXPLORATION/PRODUCTION ONGC

Punjab National Bank BANKS PNB

Ranbaxy Laboratories Ltd. PHARMACEUTICALS RANBAXY

Reliance Capital Ltd. FINANCE RELCAPITAL

Reliance Communications

Ltd.

TELECOMMUNICATION -

SERVICES RCOM

Reliance Industries Ltd. REFINERIES RELIANCEReliance Infrastructure

Ltd. POWER RELINFRA

Reliance Power Ltd. POWER RPOWER

Siemens Ltd. ELECTRICAL EQUIPMENT SIEMENS

State Bank of India BANKS SBIN

Steel Authority of India

Ltd. STEEL AND STEEL PRODUCTS SAIL


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Sterlite Industry Ltd. METALS STER

Sun Pharmaceutical

Industries Ltd. PHARMACEUTICALS SUNPHARMA

Suzlon Energy Ltd. ELECTRICAL EQUIPMENT SUZLON

Tata Consultancy Services

Ltd. COMPUTERS SOFTWARE TCS

Tata Motors Ltd. AUTOMOBILES - 4 WHEELERS TATAMOTORS

Tata Power Co. Ltd. POWER TATAPOWER

Tata Steel Ltd. STEEL AND STEEL PRODUCTS TATASTEEL

Unitech Ltd. CONSTRUCTION UNITECH

Wipro Ltd. COMPUTERS SOFTWARE WIPRO

The share price of these 50 shares and market has been collected from

www.nseindia.com from 1 February. 2010 - 31 January. 2011, time duration is one

year.

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Dissertation Anagh