A Study on Comparative Analysis of Risk and Return with ...

Volume:01, Number:04, August-2011 Page 101 www.theinternationaljournal.org

A Study on Comparative Analysis of Risk and Return with reference to

Selected stocks of BSE Sensex index, India.

P.Karthika,

Lecturer,

Department of Management Studies,

Maharaja Prithvi Engineering College,

Avinashi, Coimbatore-641 654,

Tamilnadu, India.

Dr. P. Karthikeyan,

Assistant Professor,

School of Management studies,

Kongu Enginerring College,

Perundurai, Erode-638 052,

Tamilnadu, India.

Abstract

The study aims to compare stocks of selected companies from different sectors like

Information Technology, Automobiles, Banking, Pharmaceuticals, and Oil Sectors in the

form of their risk, return and liquidity. The study also creating awareness about Stocks among

the investors to invest in the particular sectors. The risk/return relationship is a fundamental

concept in not only financial analysis, but in every aspect of life. If decisions are to lead to

benefit maximization, it is necessary that individuals/institutions consider the combined

influence on expected return or benefit as well as on risk/cost. The requirement that expected

return/benefit be commensurate with risk/cost is known as the "risk/return trade-off" in

finance. It discusses the trade-off using beta and standard deviations, coefficient of

correlation tools and provides a method for quantifying risk.


Introduction

Risk and return plays a key role in most individual investors‟ decision making

process. Every investor wants to avoid risk and maximize return. In general, risk and return

go hand in hand. If an investor wishes to earn higher returns than the investor must appreciate

that this will only be achieved by accepting a commensurate increase in risk. Risk and return

are positively correlated; an increase in one is accompanied by an increase in the other.

Investment decisions, therefore, involve a tradeoff between risk and return, which is

considered to be central to the investment decision making. In today‟s environment, it is

prudent for a rationale investor to look into the real interest on an investment as the inflation

is moving out of the gear. While investors like return they abhor risk. This necessitates for

optimization of risk and reward. Share provides investment opportunities depending on

investor‟s risk, return expectations.

Review of Literature

Treynor (1965) developed a methodology for evaluating the fund performance called

reward to volatility measure. In his path breaking study, Sharpe (1966) developed reward to

variability measure and found 11 funds reported superior performance out of 34 funds to that

of DJIA. Jensen‟s (1968) devised a measure based on CAPM and reported that mutual funds

did not appear to achieve abnormal performance when transactions cost were considered.

Fama (1972) developed a methodology for evaluating investment performance of managed

portfolios. He suggested that overall performance could be broken down into several

components.

Gupta (1974) evaluated the performance of select mutual funds categorized in terms

of their broad investment objectives for the period 1962-71.He reported that all fund types

outperformed the market irrespective of choice of market index and performance measure.

Jayadev (1996) evaluated the performance of two growth -oriented mutual funds on the basis

of monthly returns compared to benchmark returns over a study period of 21 months (June

1992 to March 1994). He employed risk- adjusted performance, measures suggested by

Jensen, Treynor and Sharpe for evaluation. He found that both the funds were poor in earning

better returns either adopting market timing strategy or in selecting under – priced securities.

Further, the study concluded that the two growths -oriented funds have not performed better

in terms of total risk and were not offering advantages of diversification and professionalism

to the investors.

Sehgal and Jhanwar (2008) evaluated the performance of 60 growth and growth –

income mutual fund schemes in India from January 2000 to December 2004.They examined

both the stock selection skills and the timing abilities of the sample fund managers and

argued that multi-factor benchmarks provide better selectivity and timing measures compared

to one-factor CAPM as they control for style characteristics such as size, value and

momentum. It found that the evidence on selectivity improved marginally when higher

frequency data such as daily returns are used instead of monthly returns. Zabiulla (2010)

examined the investment performance of twelve selected sector funds during April 2006 to

July 2009 using high frequency data. The study revealed that performance measured in terms

of downside and relative risk criteria revealed that almost all the schemes posted poor


performance. The study concluded that time tested models alone cannot give a fair view of

the fund manager‟s competence skills in delivering abnormal returns; downside risk measures

could definitely augment the performance evaluation framework of managed portfolios.

Statement of the Problem

In the current economic scenario, interest rates are falling and fluctuate, it reflects

in the share market has put more investors in the trouble. One finds it difficult to take

decision on investment. This is primarily, because investments are risky in nature and

investors have to consider various factors before investing in investment avenues. Therefore

the study aims to compare stocks of various companies from different sectors like

Information Technology, Automobiles, Banking, Pharmaceuticals, and Oil Sectors in the

form of their risk, return & liquidity.

Objectives of the Study

1. To compare selected Companies BSE sensex stocks in respect with their risk

& return

2. To analyze the performance of Stocks on selected companies with respect to

their Fundamentals.

3. To find the Volatility of Shares using Beta.

4. To find out the relationship between risk and return of selected companies

Stocks.

Data and Their Sources

The study is based on comparing Stocks risk and return of ten Companies (TCS,

Maruti, SBI, Sun Pharma, ONGC & ACC, Bharathi Airtel Ltd, ITC, L&T, and Tata Power)

among SENSEX 30 companies in respect to their risk, return, beta and standard deviation.

However, with the objective and scope of the study in mind, it is decided to study on return

series of selected stocks. Monthly closing prices of the selected scripts are to be collected

from http://www.bseindia.com website. In order to avoid bias, at least three years monthly

data is decided to be necessary. The reference period is from Jan, 2008 to May 2011.

Statistical Tools used for Calculation of Volatility

Beta (β)

Beta measures the non diversifiable risk. Beta shows how the price of a security

responds to market forces. In effect, the more responsive the price of security is to changes in

the market, the higher will be its beta. Beta is calculated by relating the returns on security

with the return for the market. It can positive or negative. Stock Beta is a calculation or

measurement of volatility or risk of a stock trading on the stock market. It is the fluctuation in

stock prices and the market in general. Some stocks have greater risk than others, and thus

carry higher Stock Betas.

Beta for each stock calculated using daily opening and closing share price of each

company corresponding daily Bombay Stock Exchange Sensex. First, rate of returns of

companies and Bombay Stock Exchange Sensex are calculated. The calculations as follows:


Rate of return = (closing share price –opening share price)/ opening share price*100

Computation of Beta:

β=N∑XY-(∑X) (∑Y)/ N (∑X²) - (∑X) ²

Standard Deviation (S.D)

This is the most commonly used measure of risk in fiancé. Its square also is widely

used to find out the risk associated with a security. Standard deviation is a statistical term that

measures the amount of variability or dispersion around an average. Standard deviation is

also a measure of volatility. Generally speaking, dispersion is the difference between the

actual value and the average value. The larger this dispersion or variability is, the higher the

standard deviation. The smaller this dispersion or variability is, the lower the standard

deviation. Chartists can use the Standard Deviation to measure expected risk and determine

the significance of certain price movements.

Standard Deviation= (X-X1)²/N

Coefficient of Correlation

Coefficient of Correlation is a statistical technique, which measures the degree or

extent to which two or, more variables fluctuate with reference to one another. Correlation

analysis helps in determining the degree of relationship between two variables but correlation

does not always imply cause and effect relationship.

The Coefficient of Correlation is essentially the covariance taken not as an absolute

value but relative to the standard deviations of the individual securities. It indicates, in effect,

how much x and y vary together as a proportion of their combined individual variations,

measured by SD of x multiplied by SD of y.

R= N / [{(NΣY²)-(ΣY) ²} {NΣX²- (ΣX) ²}]


Table 1: Stock & Market Return of selected companies from Jan 2008 to May 2011

Month Mar

ket

Retu

rn

TCS MARU

TI

SBI SUNPHAR

MA

ONG

C

AC

C

BHARTIAR

TL

ITC LN

T

TATA

POWE

R Jan-08 -

13.1

7

-

17.8

2

-15.55 -

9.19 -6.52 -

20.85

-

24.3

8

-14.41 -

7.92

-

12.1

8

-12.98

Feb-08 -

1.36

-

0.87 2.87 -

3.49 8.01 0.33 1.78 -5.11 1.23 -

4.78 8.95

Mar-08 -

9.19

-

6.74 -3.54 -

23.4

6

0.95 -2.64 5.64 -1.54 4.01 -

13.5

8

-14.89

Apr-08 9.61 12.8

3 -10.51 10.2

6 18.21 3.55 -

9.14 8.02 6.06 -

1.53 16.82

May-08 -

6.52

10.6

7 1.93 -

19.6

4

-4.30 -

17.29

-

13.6

4

-4.11 -

2.83

-

1.64 -6.44

Jun-08 -

18.8

6

-

16.4

6

-19.21 -

23.3

5

-3.96 -6.57 -

21.0

7

-18.73 -

13.8

2

-

27.4

0

-22.56

Jul-08 6.50 -

3.06 -5.72 26.3

2 1.45 22.06 10.8

7 10.69 0.59 18.3

6 10.58

Aug-08 3.56 -

2.00 18.19 0.54 5.33 3.36 -

3.00 6.14 1.67 0.77 -8.61

Sep-08 -

10.7

7

-

18.1

8

7.03 6.52 0.54 0.54 9.22 -5.42 -

0.13

-

5.31 -13.71

Oct-08 -

24.7

5

-

19.7

8

-19.25 -

25.0

3

-24.54 -

35.72

-

19.7

7

-17.53 -

18.2

3

-

35.4

6

-25.04

Nov-08 -

10.9

4

-

0.35 -7.61 -

5.90 -5.91 -1.17 -

18.7

5

-0.59 8.44 -

12.9

4

-4.41

Dec-08 5.29 -

15.9

8

-2.60 17.6

5 -0.02 -4.62 13.9

2 5.94 -

1.41 6.08 11.38

Jan-09 -

3.05 5.56 8.76 -

10.9

9

0.32 -2.49 5.19 -11.93 4.17 -

11.3

1

1.39

Feb-09 -

4.80

-

5.21 19.93 -

9.99 -5.51 4.72 6.12 1.54 2.21 -

12.1

5

-3.79

Mar-09 10.7

9

13.6

8 16.73 5.60 10.79 13.99 9.21 0.13 0.71 12.3

0 7.03

Apr-09 17.0

1

23.3

4 5.25 18.3

4 15.83 11.00 11.6

2 19.73 2.16 29.3

5 14.90

May-09 25.7

0

16.6

3 23.68 43.7

8 -6.95 34.23 16.8

7 7.85 -

3.39

57.0

5 18.42

Jun-09 -

1.71

-

42.6

9

2.94 -

7.09 -11.30 -

10.33

-

3.74 -12.91 1.84 10.4

4 5.00

Jul-09 8.02 38.5

3 32.08 4.38 5.49 9.14 15.9

6 -48.70 30.5

7

-

4.52 13.03

Aug-09 -

0.18 0.00 0.82 -

4.49 -0.42 1.32 -

8.41 3.45 -

7.93 3.23 -0.85

Sep-09 9.15 15.9

8 17.33 24.7

6 18.58 -1.96 1.02 -2.44 -

0.86 6.60 0.79

Oct-09 -

7.51 1.00 -16.98 0.50 -1.24 -3.59 -

8.70 -32.84 9.51 -

6.55 2.38

Nov-09 6.87 9.95 10.76 2.20 6.97 5.63 7.65 5.16 1.10 3.03 0.44

Dec-09 3.05 7.72 -1.29 0.73 2.80 -2.28 9.40 8.87 -

3.15 3.79 2.07

Jan-10 -

6.38

-

2.03 -12.28 -

9.14 -4.46 -7.42 0.09 -7.12 -

0.30

-

16.0

7

-5.45

Feb-10 0.55 3.47 5.52 -

3.38 7.59 -0.26 5.84 -8.59 -

7.27 9.95 -6.71

Mar-10 6.63 2.32 -3.99 4.47 15.46 -2.27 2.53 10.76 12.8

2 2.61 12.65

Apr-10 0.02 -

2.66 -10.00 10.2

1 -12.64 -4.08 -

5.11 -5.27 0.19 -

1.65 -1.31

May-10 -

3.38

-

1.85 -3.36 -

0.99 5.59 10.95 -

9.22 -11.83 6.83 1.79 -5.22

Jun-10 4.47 1.23 14.81 1.86 7.38 10.59 7.04 0.10 7.63 10.7

3 3.04

Jul-10 1.07 12.4

5 -15.27 9.34 -1.62 -5.15 -

4.43 16.69 1.23 0.12 0.75

Aug-10 0.33 -

0.25 3.37 9.72 -0.60 7.53 4.85 6.53 -

47.5

3

0.36 -8.94

Sep-10 11.3

3 9.31 13.67 16.6

4 14.06 4.28 19.2

2 11.39 8.77 12.3

5 10.56

Oct-10 -

0.31

13.2

4 6.87 -

3.04 3.81 -7.96 -

1.47 -11.01 -

4.36

-

1.12 2.46

Nov-10 -

3.71 2.25 -8.62 -

6.05 -79.17 -5.44 -

1.03 9.38 -

0.75

-

3.85 -7.90

Dec-10 5.01 9.13 -0.10 -

6.24 7.27 3.06 8.87 0.82 1.16 1.49 4.81

Jan-11 -

11.1

2

-

0.67 -12.39 -

6.68 -10.45 -9.00 -

8.16 -11.12 -

7.31

-

17.5

3

-9.40

Feb-11 -

3.27

-

3.82 -4.98 -

0.75 -5.45 -

77.32

-

2.51 2.51 3.24 -

7.39 -7.63

Mar-11 8.14 5.67 3.94 4.41 3.12 5.99 10.2

3 7.81 6.02 7.01 14.10

Apr-11 -

1.68

-

1.64 5.75 1.21 5.46 6.15 2.71 6.40 5.26 -

4.12 -2.03

May11 -

3.75

-

0.57 -7.01 -

18.2

7

1.94 -8.87 -

7.60 -1.28 0.03 1.54 -6.97


Risk & Return relationship Analysis

TCS: The relationship between risk and return of TCS from Jan 2008 to may 2011 is

discussed here. The table shows that market return is negative. The TCS stock whose returns

vary more than the market's returns over time.

Stock Return 52.34

Market Return -3.31

Beta(β) 0.87

Standard

Deviation(S.D) 11.55

Variance 133.31

Coefficient of

Correlation® 0.61

Table 2: Risk & Return relationship of TCS

The above table shows that, the Company is less volatile & less risky as the beta value is

less than 1.i.e. β=0.87 The Co-efficient of Correlation of the sector is showing positive

correlation. It means that when market return increases, the return from this sector also

increases. Since the value of S.D is comparatively less, it indicates that this sector is

comparatively less volatile. Hence the company will yield good return.

Chart 1: Stock and Market Return of TCS


MARUTI:

The stock return of maruti is 41.96% during the period of Jan 2008 to may 2011.The table 3

shows that, the Company is less volatile & less risky as the beta value is less than 1.i.e.

β=0.80. The Co-efficient of Correlation of the sector is positive correlation. Since the value

of S.D and variance is comparatively low, it indicates that this stock is comparatively less

volatile.

Table 3: Risk & Return relationship of MARUTI

SBI:

The stock return of SBI is 22.27% during the period of Jan 2008 to may 2011.The table 4

shows that, the Company is more volatile than the market & more risky as the beta value is

greater than 1.i.e. β=1.19. The Co-efficient of Correlation of the sector is positive correlation.

Since the value of S.D and variance is comparatively high, it indicates that the SBI is more

deviate from is average.

Stock Return 22.27

Market Return -3.31

Beta(β) 1.19

Standard Deviation(S.D) 11.82

Variance 139.66

Coefficient of Correlation® 0.81

Table 4: Risk & Return relationship of SBI

SUN PHARMA:

The stock return of sun Parma is -18.11% during the period of Jan 2008 to may 2011.The

table 5 shows that, the Company is less volatile & less risky as the beta value is less than

1.i.e. β=0.66. A Stock's Beta is the middle line between minimizing the risk of undertaking

investment activities and maximizing the returns gained. The Co-efficient of Correlation of

the sector is positive correlation. Since the value of S.D and variance is comparatively high,

it indicates that the stock is more deviate from is average.

Stock Return -18.11

Market Return -3.31

Stock Return 41.96

Market Return -3.31

Beta(β) 0.80


Variance 118.09



Beta(β) 0.66


Variance 160.10


Table 5: Risk & Return relationship of SUN PHARMA

ONCG:

The stock return of Oil & Natural Gas Corporation is -78.86% during the period of Jan 2008

to may 2011.The table 6 shows that, the Company is more volatile than the market & more

risky as the beta value is near to 1.i.e. β=0.98. So the stock is moving in proportion with the

market. The Co-efficient of Correlation of the sector is positive correlation. Since the value

of S.D and variance is comparatively high, it indicates that stock is more deviate from is

average

Stock Return -78.86

Market Return -3.31

Beta(β) 0.98


Variance 179.56


Table 6: Risk & Return relationship of ONCG

ACC:

The stock return of ACC is 15.74% during the period of Jan 2008 to may 2011.The table 7

shows that, the Company is less volatile than the market & less risky as the beta value is less

than 1.i.e. β=0.82. The Co-efficient of Correlation of the sector is positive correlation. Since

the value of S.D and variance is comparatively high, it indicates that the stock is more deviate

from is average.

Stock Return 15.74

Market Return -3.31

Beta(β) 0.82

Standard Deviation(S.D) 10

Variance 100.08


Table 7: Risk & Return relationship of ACC


BHARTI AIRTEL:

The stock return of Bharti Airtel is -82.59% during the period of Jan 2008 to may 2011.The

table 8 shows that, the Company is less volatile than the market & less risky as the beta value

is greater than 1.i.e. β=0.62. It gives the moderate return. The Co-efficient of Correlation of

the sector is positive correlation. Since the value of S.D and variance is comparatively high,

it indicates that stock is more deviate from is average.

Stock Return -82.59

Market Return -3.31

Beta(β) 0.62


Variance 127


Table 8: Risk & Return relationship of BHARTI AIRTEL

ITC:

The stock return of ITC is 0.23% during the period of Jan 2008 to may 2011.The table 9

shows that, the Company is less volatile than the market & less risky as the beta value is less

than 1.i.e. β=0.33. The Co-efficient of Correlation of the sector is positive correlation. Since

the value of S.D and variance is comparatively high, it indicates that stock is more deviate

from is average.

Stock Return 0.23

Market Return -3.31

Beta(β) 0.33


Variance 101.50


Table 9: Risk & Return relationship of ITC

LNT:

The stock return of LNT is -2.13% during the period of Jan 2008 to may 2011.The table 10

shows that, the Company is more volatile than the market & more risky as the beta value is

greater than 1.i.e. β=1.39. The Co-efficient of Correlation of the sector is positive correlation.

Since the value of S.D and variance is comparatively high, it indicates that stock is more

deviate from is average.

Stock Return -2.13

Market Return -3.31

Beta(β) 1.39


Variance 152.64


Table 10: Risk & Return relationship of LNT


TATA POWER:

The stock return of TATA POWER is -13.28% during the period of Jan 2008 to may

2011.The table 12 shows that, the Company is moving in the same direction with the market

because the beta value is near to 1.i.e. β=0.94.. The Co-efficient of Correlation of the sector is

positive correlation. Since the value of S.D and variance is comparatively less, it indicates

that stock is less deviate from is average.

Stock Return -13.28

Market Return -3.31

Beta(β) 0.94


Variance 96.10


Table 11: Risk & Return relationship of TATA POWER

SUMMARY OF THE COMPANY’S STATISTICAL VALUES:

Company Name

Stock

Return

Beta(β) Standard

Deviation

(S.D)

Coefficient of

Correlation®

Tata Consultancy Services

Ltd. 52.34 0.87 11.55 0.61

Maruti Suzuki India Ltd. 41.96 0.80 10.87 0.62

State Bank of India 22.27 1.19 11.82 0.81

Sun Pharmaceutical

Industries Ltd. -18.11 0.66 12.65 0.41

Oil & Natural Gas

Corporation Ltd. -78.86 0.98 13.40 0.56

ACC Ltd 15.86 0.82 10.00 0.73

Bharti Airtel Ltd. -82.59 0.62 11.27 0.46

ITC Ltd. 0.23 0.33 10.07 0.29

Larsen & Toubro Ltd. -2.13 1.39 12.35 0.88

Tata Power Co. Ltd. -13.28 0.94 9.80 0.86

Table 12: company’s statistical values

The table 12 shows the stock return, beta, standard deviation and coefficient of

correlation of ten companies. During the period Jan 2008 to May 2011 the market returns


percentage in negative even though The TCS, Maruti Suzuki, SBI, ACC are proving return

percentage in positive. All the companies‟ coefficient of correlation is positive in value it

implies that the market value is increase the stock value also increase.

Beta comparison:

Stock Beta is a calculation or measurement of volatility or risk of a stock trading on

the stock market. It is the fluctuation in stock prices and the market in general. Some stocks

have greater risk than others, and thus carry higher Stock Betas.

Chart 2: Beta values of the companies from Jan 2008 – May 2011

A Beta of 1 show the stock is moving in proportion with the market. A Beta Greater

than 1 shows the stock is more volatile than the market. A Beta Less than 1 shows the stock is

less volatile than the market. The companies like LNT and SBI has high beta value compare

to all other stocks which are high risky and provide high return. A Stock's Beta is the middle

line between minimizing the risk of undertaking investment activities and maximizing the

returns gained. For example, LNT Beta is 1.39. This means the stock is 1.39 times as volatile

as the market.

The market provides a -3.31% return from Jan 2008 – May 2011 to ordinary

investors, the ITC stock with a Beta of 0.33 will provide a 0.23 return (lower risk, lower

return!). However, if the market provides a negative return, then the Stock with a Beta of 0.33

will provide a positive return.

Coefficient of correlation comparison:

A coefficient of correlation is a mathematical measure of how much one number

(such as a share price) can expect to be influenced by changes in another (such as an index).

It is closely related to covariance. A correlation coefficient of 1 means that the two numbers

are perfectly correlated: if one grows so does the other, and the change in one is a multiple of

the change in the other.


Chart 3: Beta values of the companies from Jan 2008 – May 2011

In this study all ten company‟s stock has the positive value as well as those values is

less than 1. A non-zero correlation coefficient means that the numbers are related, but unless

the coefficient is either 1 or -1 there are other influences and the relationship between the two

numbers is not fixed. So if you know one number you can estimate the other, but not with

certainty. The closer the correlation coefficient is to zero the greater the uncertainty, and low

correlation coefficients means that the relationship is not certain enough to be useful. The

LNT and TATA POWER have the high correlation value which indicates that the stocks

movement is correlated with market.

Findings:

The computed values of beta show that Pharmaceutical, Housing related and

Automobile sectors are the most defensive (least risky) sectors whereas banking,

power, construction and oil and gas sectors are the most aggressive (most risky)

sectors.

That is banking, construction and oil and gas have the maximum market risk of

running a business whereas Pharmaceutical, Housing related and automobile sectors

has the least risk. The results show that the risk of construction and Banking sectors

are the largest, it means that they are the most aggressive sectors

The table 12 shows that the SBI, ONCG, LNT, and TATA POWER have the beta,

S.D, Variance and correlation is high. These companies have high risk and high return

in the market.

The ACC, TCS, MARUTI SUZUKI have the beta, S.D, Variance and correlation is

moderate. These companies have moderate risk and average return in the market.

The companies like SUN PHARMA, BHARTI AIRTEL, and ITC have the beta, S.D,

Variance and correlation is less. These companies have less risk and less return in the

market.


Due to the highest β value among the selected 10 companies, the systematic risk of

L&T was the highest during the study period. Hence it indicates that the returns from

L&T are higher than the market. Also L&T has got the highest value of standard

deviation and variance. This indicates that L&T is highly volatile.

ITC is having the least „β‟ value. This indicates that ITC has the lowest systematic

risk among the stocks during the study period. Also ITC has got the least value of

standard deviation and variance. This indicates that ITC is less volatile.

Suggestion:

If an investor has a desire for bearing high risk, then he can go for the companies with

higher beta values. He can invest on the shares of SBI, ONGC, LNT, and Tata Power

where the values of beta are highest.

If an investor does not have an enthusiasm for bearing risk, then he can go for

companies with beta values less than one. For new investors who wish to invest in the

capital market, it is better to invest for long term as the sexsex is experiencing the

huge fluctuations. Also the market is highly volatile.

The investor who desire to bear risk and wants to yield high return, they invest in

LNT and SBI as the beta, S.D, Variance and correlation is high. Hence this will yield

high return.

The stock market is characterized by the trade-off between risk and return. The higher

the risk the investor is willing and able to take, the higher the potential rewards from

the investment. Therefore, if a particular investment offers you high returns, it is an

indication that it will come with a high risk burden.

As part of the selection process, investor should determine the risk level of the stock

as well as their risk tolerance. If they are looking for high returns they should be able

to meet high potential losses as well.

There is no safe investment that will provide investors with high returns over a short

period of time. Therefore, investor should direct their resources toward long-term

investments that are more likely to reward you for the patience with high returns.

Conclusion:

As a whole the stock market is sometime highly volatile. Cyclical sectors like Banking

and Power sectors are having high risk. The non cyclical sectors like Pharmaceutical,

Housing related, FMCG having low risk. The cyclical sectors are those sectors which

generally move with the performance of the entire economy and the products of which are

highly price elastic and income.

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 company‟s fundamentals will give you a


much better picture of the potential long-term risk. The stocks may not be a safe but for a risk

adverse investor and for a risk taker the reward may he heavy in the short run, than in the

long run.

References

o Attilio Meucci “Exercises in Advanced Risk and Portfolio Management”, August 15

2010 Last version available at http://ssrn.com/abstract=1447443.

o Doron Avramov”Stock Return Predictability and Model Uncertainty” April 17, 2001

available at ssrn.com

o Ackermann, Carl, Richard McEnally, and David Ravenscraft, 1999, The performance

of hedge funds: Risk, returns, and incentives, The Journal of Finance 54, 833-874.

o Fama, Eugene and Ken French, 1993, Common risk factors in the returns on stocks

and bonds, Journal of Financial Economics.

Website:

http://www.lariba.com/knowledgecenter/articles/pdf/Risk%20and%20Return%20of%

20Islamic%20Stock%20Indexes.pdf

http://www.citefin.com/375-beta-calculation.html

http://www.investopedia.com/articles/04/012104.asp

http://ssrn.com/abstract=1447443

http://www.lariba.com/knowledgecenter/articles/pdf/Risk%20and%20Return%20of%20Islamic%20Stock%20Indexes.pdf

http://www.lariba.com/knowledgecenter/articles/pdf/Risk%20and%20Return%20of%20Islamic%20Stock%20Indexes.pdf

http://www.citefin.com/375-beta-calculation.html

http://www.investopedia.com/articles/04/012104.asp

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