A Study on Beta Stability in the Indian Stock Market

 

Mrs. Soumya Shetty1, Dr. Janet Jyothi Dsouza2

1Assistant Professor, Dept. of Business Administration, Shreedevi Institute of Technology, Jnana Gangothri Campus Kenjar, Mangalore

2Assistant Professor, Dept. of Business Studies, Ballari Institute of Technology and, Management, Ballari

*Corresponding Author E-mail: shettysoumya8509@gmail.com, janetjyothidsouza@gmail.com

 

ABSTRACT:

Measurement of risk plays an important role for any investment decisions because risk can be eliminated to some extent but not completely. The degree of risk varies from one sector to other sector based on company’s internal and external influences. The risk has been categorised as unsystematic risk (company risk) or systematic risk (market risk). Most of the investors are taking beta as the measurement to know the market risk for the any of the investment securities. The purpose of this paper is to find out the stability of beta for different sectors of Indian economy over last 10 years. The period of analysis covered for this study is September 2007 to October 2017. For CPSE indices data available only from July 2008 and Infrastructure indices from May 2014 and for India manufacturing indices from June 2015. In this study I have used regression techniques to find out risk factor as well as descriptive statistics to know data structure. The sector wise indices from Bombay stock exchange of India are used for the purpose of analysis. The sectors  which have been considered for the study are Auto, Bankex ,Capital goods, Consumer durables, energy, Fast moving capital goods ,consumer goods, Finance, Healthcare,  India infrastructure index, Information technology, Industrials, S and P BSE Technology, S and P BSE Telecommunication, S and P BSE Utilities, S and P BSE Carbonex, S and P BSE Greenex, S and P BSE Quality index, S and P BSE PSU, S and P BSE Oil  and Gas, S and P BSE Realty, S and P BSE Mid cap, S and P BSE Small CAP, S and P BSE Basic materials, S and P BSE CPSE, Large cap. It is found that Capital goods, Consumer durables, Finance, India infrastructure Index, Industrials, S and P BSE Telecommunication, S and P BSE Greenex, S and P BSE PSU, S and P BSE Realty, S and P BSE Mid cap, S and P BSE Small CAP, S and P BSE Basic materials are aggressive stocks indices and rest all are the defensive sectors.

 

KEYWORDS: Beta, Risk, Investment, Indices.

 

 

 

1. INTRODUCTION:

Measurement of risk as well as its analysis in one of the significant requirement for the investors to take right investment decisions. Therefore to know the risk about each sector depends on its internal and external factors. Based on nature of different sectors risk pattern is varies. It is found that there are two different types of risks namely systematic risk (market risk) and unsystematic risk.

 

Systematic risks are that kind of risk which cannot be avoidable by the investors or invisible or uncontrollable in nature. Unsystematic risks are those which can be avoidable by taking necessary controlling actions or taking precautions it can be minimised to greater extent and systematic risk cannot be avoidable as it is entirely dependent on changing scenario of market. India has two major stock exchanges namely Bombay stock exchange and national stock exchange which attracted most of the investors to meet investors financial goal. This exchange helps the investors in terms of providing all market related information easily and freely which makes the investors to take quick investment decisions. It also widened its market to the foreign investors there by encouraging investments in the country as well as the different sectors which brings the tremendous growth to the Indian economy.Foreign institutional investors  also contributed and expanded their  interest in the stock market due to this p;ayers there is high demand and scope in  the security market.Meanwhile as sectors are growing in the stock market which in turn increase risk levels in the various sectors too.Therefore judgement of selecting the best investments makes the investors to have deeper and upgraded knowledge in the field of the stock market. From past fifty decades there is a  so many experiments  and facts are contributed by the researchers, so that this helps the investors to broaden their investment horizon .

 

Sectorial indices will have stocks in respect to its sectors and indices will disseminates the information about overall   price movement of the sectors. The computation of indices is done taking into consideration of its base periods as well as base index value. The movement of the index reflects the total free float market value of all the stocks in the index relative to respective base market capitalisation value. For the study purpose around 25 sectors has  been considered such as Auto, Bankex ,Capital goods, Consumer durables, energy, Fast moving capital goods ,consumer goods, Finance, Healthcare,  India infrastructure index, Information technology, Industrials, S and P BSE Technology, S and P BSE Telecommunication, S and P BSE Utilities, S and P BSE Carbonex, S and P BSE Greenex, S and P BSE Quality index, S and P BSE PSU, S and P BSE Oil  and Gas, S and P BSE Realty, S and P BSE Mid cap, S and P BSE Small CAP, S and P BSE Basic materials, S and P BSE CPSE, Large cap. It is found that Capital goods, Consumer durables, Finance, India infrastructure Index, Industrials, S and P BSE Telecommunication, S and P BSE Greenex, S and P BSE PSU, S and P BSE Realty, S and P BSE Mid cap, S and P BSE Small CAP, S and P BSE Basic materials are aggressive stocks indices and rest all are the defensive sectors. The period of analysis covered for this study is September 2007 to October 2017 and this is of ten years study. The data for this is taken from BSE Website. We analyse whether there is a any significant correlation between different sectorial indices and S and P BSE 500 index. This paper is grouped as follows: Section 2 provides information on literature review, section 3 provides about significance of the study, section 4 discusses on hypotheses of the study, section 5 is about samples and data, section 6 gives data about results and analysis and finally in the section 7 we have drawn conclusion to the topic.

 

2. LITERATURE REVIEW:

Sharpe (1964) observed that there is a positive relationship between the expected return and market risk and proposed the famous Capital Asset Pricing Model (CAPM). Capital asset pricing model (CAPM) has given the concept of beta which even after so many opinions it is considered as a reliable measurement of market risk by the existing literature. It is also found that many researcher as well as academicians have used CAPM model as the standard return model to estimate beta and its relative returns. CAPM justifies the fact of higher the beta, higher is the stock return of the respective investors with the assumption of stock return should be higher than the risk free asset. Michael (1968) found out in the study of beta stability that mutual fund Stocks are more stable than stock returns. John et. al. (1968) analysed portfolio algorithm and considered sample data of 470 scripts listed in standard and poor’s index for the period 1958-1967 and created around 40 portfolios and with help of regression equations output been realised. Beaver et. al.(1970) conducted the study about some variables like dividend pay-out, risk, earnings, variability, growth, financial variability, liquidity and its effect on systematic risk and found that except size of the firm all other above mentioned variables has the strong positive correlation on the systematic risk. Levitz (1974) and Levy (1971) analysed that portfolio betas are more stable than individual betas. Similar type of studies been done by Blume (1971) found that portfolio betas are comparatively more stable than stock betas. Fischer Black (1972) considered stocks of New stock exchanges and study period is from 1931 to 1965 after grouping stocks into 10 portfolio results are revealed as higher returns are obtained from risky portfolio as well as these stocks seemed overpriced and lower returns are obtained from low risky portfolio and seemed to be under priced. Gonedes (1973) analysed that relationship between accounting beta and stock beta. The study took random sample data of 99 firms ,listed on New York stock exchanges ,study period was between 1946-1967 and found that there is an null hypotheses exists between the variables. Eugene and James (1973) analysed the relationship between average return and the risk for NYSE common stocks. the sample size taken for the study was 1000 securities and its monthly return percentage has been considered. It is observed that there was a positive relationship between return and risk. Baesel (1974)  analysed that the stock market conditions are influencing factors for the stability or instability of stock betas. Jack  (1975) investigated the study and took sample size of 113 large mutual fund companies returns. The study period was between 1060 to 1968.The two variables considered for the study was namely standard deviation and skewness and observed that standard deviation as a measure of risk has a greater weightage to take investment decisions by the investors than skewness which is used less as a parameter for the investment decisions. Uri et. al. (1975) examined  the relationship between the firms risk and leverage, size, dividend record. The sample size used for study was 1000 securities from largest US Industrial corporations and observed from regression results that firms size and leverage are important determinants of its risk and the dividend record proved to be important factors of the firm’s equity risk. Cheng et. al.(1976) explored the study of validity of CAPM to verify whether the risk and return relationship was positive. For this study he took 75 random securities from NYSE from the period 1967 to 1976 and observed that 33.33% of the individual securities and 66.67% of the random portfolios have non-linear CAPM. Basu (1977) investigated that stocks with low price earnings ratio had higher average return than stocks with high price earnings ratio. The sample data for the study took from compustat file of NYSE during the period from September 1956 and for these study1400 industrial firms has been considered. McDonald (1985) tested that maximum likelihood methods are more relevant application than least square method for the estimation of beta stability. Jaggannathan (1996) examined that stock market conditions affect the debt equity component of the company and this makes beta instability for the company stocks. Gupta and Mallik(1996) investigated that there are instability of beta in empirical test. Vipul (1999) examined the causes for instability of betas and found that size of firm as well as liquidity factors is also highly influencing factors for the instability. Chawla (2001) analysed about stability of beta and hypothesis of alternative hypotheses of instability of beta has chosen. Campbell (2004) found that large stocks have low risk with high return and small stocks have high risk with low return. Lewellen J and Nagel S (2006) examined the stability of beta causes of variability in stock returns and found that variability of returns has the greater impact on the firm as well as stock return. Haddad (2007) analysed market risk and concluded that systematic risk varies from time to time.  Monica and Singhania (2008) analysed validity of CAPM. This study comprised with 320 scripts listed on Bombay stock exchanges. Monthly closing price of the scripts has been considered. The period for the study taken from 1998 to 2004.Using regression analysis results has been proved as positive to CAPM. They also investigated stationarity of beta and found that beta is based on time factor. Sarmah and Sarma (2008) analysed stock instability for short period of time and found that estimation of beta stability will results with beta instability. Ahmed (2008) examined the relationship between stock prices and internal and external economic variables such as exports, industrial production, foreign direct investments, money supply, exchange rates, and money supply. The study period is from 1995 to 2007 and observed that except interest rates activities all other variables have positive influences on stock prices. Shailendra (2012) explored the study on beta instability over market phases in Bombay stock exchanges. The objective of this study was to know the stability of beta in different market phases. This study has been conducted from the year 2007 to 2011.For this study monthly returns data of top 15 stocks has been considered. This study used the value weighted BSE 100 INDEX   series to assess the market performance. It was found that there was beta instability over the market phases as well as beta values of the stocks have been fluctuated significantly. This indicates that the variations of the stock returns are mainly depend on market situations i.e. bearish or bullish. Thus the results reject null hypotheses. Shanmugasundram and Benedict (2013) conducted the study of risk of risk variability in the Indian sectorial indices and Nifty market. The Study has done for 8 years from 2004 to 2012 and found that there is no significant difference in variability of risk of sectorial indices but there is more differences in the average return of each sectorial indices. Mallikarjunappa  and Vasanth (2013) analysed stability of beta by applying adf and found that only few stocks has stability of beta and most of the securities falling under instability of betas. Terceno et. al. (2014) has done a study on stability of beta coefficients of sectors and sub sectors portfolios in an uncertain environment. The objective of this was to determine the systematic risk by using all market information. The calculations of betas of sectors and subsector has done by using fuzzy regression techniques and it was found that fuzzy regression is consistent with that reported in traditional econometric studies on beta stability. The relevance of this verification is that the more stable beta is, the more confident the predications are. Harish and T .Mallikarjunappa (2015) examined the study of beta stability in the Indian capital market. The objective of this paper to know whether betas are stable across the time or not. They used the data of 14 financial years from 2000 to 2014 and selected the samples for the study from S and P BSE Sensex Index. The estimation of CAPM betas by using 100 days estimation period of 30 stocks. The collection of data for this has been taken from the centre for monitoring Indian economy Pvt.ltd. The hypotheses of the study were to have the stability of beta on the individual stocks and portfolios. For this they constructed the three portfolios to know the stability of beta and with the help of chow beak point test and found that the subprime crisis of 2008 impacts 47% and do not impact 53% of individual stocks betas. The chow test results also shows that the crisis of 2008 affects less on portfolios compared to individual stocks and portfolio construction positively influences the stability of the beta in the crisis period. Dr. Prem chandran (2016) conducted the study on the sectorial predictability of Risk and return in India. For his study he took daily closing prices of the 10 selected indices of NSE for the period from April 2006 to March 2016.The fluctuations of returns are estimated by using standard deviation and beta. The benchmark for the comparison was taken as Nifty index. In this study it is observed that during this time period Sectors such as Realty, Metal, Bank and financial services, Industrials has high risk and return and on the other hand sectors like FMCG, Pharma IT Media and Auto sectors had lower returns with low risk.  Guha et al. (2016) has done their study on Measurement of risk Vs return of sectorial Indices. The objective of this paper was to evaluate the performance of the different sector based index of NSE and to find the positive relationship between variables of sectorial indices as well as its market benchmark Nifty. This study also fulfilled the second objective that is of factor analysis was performed among the eleven sectorial indices to determine the underlining influence of the sectorial indices on nifty. The important computations were undertook for this study was measurement of return per volatility and return per unit of risk and it was observed that in terms of above mentioned measurement parameters FMCG sectors has been performed better. Realty, Metal, IT are highest sensitive sectors. Pharma and Auto are the most defensive sectors. Further factor analysis suggests that there is one factor for analysis. Patjoshi (2016) examined on competitive risk return analysis of BSE with selected Banking stocks in India. The study examines the correlation between risk and return of the sensex and banking sectors of BSE 30. This study is based on secondary data. The data for the analysis has taken from the BSE Website over a period of 15yeras. The study period was from January.1.2001 to December.31.2015. In this study they analysed and tested the hypotheses of presence and absence of risk return trade-off in the Indian equity markets. The various methods used to get the results are correlation, regression, descriptive statistics and t-test. This study taken into consideration of 4 major banking stocks of BSE 30 (HDFC, ICICI, AXIX BANK AND SBI) and it was found that in terms of risk sensex is having less risk comparing to other selected banking sectors. In terms of beta Axis bank is having a more sensitive to its benchmark and all other banking sectors are less variation in their returns. As per the measurement of return per volatility HDFC bank stocks ranks higher than other bank stocks returns. Dash (2017) investigated on testing the stationarity of beta for banking sectors stocks in Indian stock markets. The study was performed using a sample of 14 banking sector stocks listed on the BSE, India, over a study period of 10 years. The study period is sub grouped as different phases based on its overall market trends, i.e; stagnant phase, growth phase, boom phase, depression phase and steady phases. The analysis was performed through the application of univariate ANCOVA. The results indicate that the betas were relatively stationary over the different market regimes for all except one of the sample stocks. This suggests that beta can be taken to be stationary for banking stocks in Indian stock market. The study of risk return relation of various sectorial indices has done after considering information from BSE sectorial indices and in the study BSE market taken as benchmark for analysis purpose.

 

3. SIGNIFICANCE OF THE STUDY:

CAPM model proves that there is a positive correlation between risk and return. The objective of this study is to know the beta stability of various sectorial indices in respect to its market return (S and P 500index). Even though many studies have exhibited about beta stabilities in different perspectives, still there is a need to prove the accurate results for the same. The historical price of a sectorial price plays a very significant role to estimate the beta analysis. Therefore analysis of various sectorial indices with market return results to understand risk and return concept in better ways. The critics argue that due to uncertainty in the stock market, systematic risk is one of unpredictable risk, so estimation of degree of risk is cumbersome task.

 

This study makes the investors to know the beta performance of 25 different sectorial indices in relation to its market benchmark S and P BSE Index from last 10 years period; this makes the investors to take decisions on investment after going through its beta variability status as well as knowing accurate information about systematic and unsystematic risk. This will also give knowledge to understand the market risk as well company risk behaviour in different sectors over a period of 10 years.

 

4. HYPOTHESES OF THE STUDY:

Null hypothesis-there is no significance differences between betas of various sectorial indices as well as market return index that are S  and  P BSE 500.

 

5. SAMPLE AND DATA:

The sample consists of S and P BSE500 sectors listed in Bombay Stock Exchanges. The main purpose to choose from this index of   S andP BSE500 based sectors is that, it will give the true picture of the market and analysing these sectors will be results with accurate outcomes. The data comprises of dates of September 2007 to September 2017.The dates of individual sectors are collected from BSE Website.  For CPSE indices dates are available only from July 2008 and for infrastructure indices from May 2014 and for India manufacturing indices are from June 2015.

 

The total sample consists of 25 sectorial indices. We use monthly closing prices of sample sectors for the calculation of returns and S and P BSE 500 index is taken as market proxy. If beta of sector is greater than one as compared to its market beta, then those sectors are considered as aggressive sectors and if beta of sector is less than one than its market beta, then those sectors are considered as defensive sectors, if beta is equal to zero, then those sectors are neutral to its variability to returns.

 

6. METHODOLOGY:

The framework of this study is to compute risk and return of various sectorial indices in relation to S and P BSE500 market index. For the qualitative analysis, the empirical evidences have done by applying suitable formulas. The return of each sectorial index was calculated as follows:

 

    P1-P0

[-----------] X 100

     P0

 

 

P1-Price of today

P0-Price of yesterday

Beta for the various sectors is calculated as follows:

 

Ri = α + βi Rm + ei

 

              Cov (Ri Rm)

β = -------------------------

                      σ2 m

 

Market-return- is the independent variable

sectorial indices are the dependent variable

 

Rm- return of market (S and P 500)

Ri-expected return of selected sector

α is the intercept of the linear regression relationship between Ri and Rm

βi is the slope of the linear regression relationship between Ri and Rm

 

 

Table 1. Descriptive Statistics of Monthly Data of Selected Sectors.

 

Mean

Max

Mini

Std.Dev

Coeff Of Variation

Skweness

Kurtosis

S and P BSE 500

0.847

33.32

-27.10

7.189

8.491

0.078

4.673

Auto

1.558

31.79

-26.92

7.8

5.064

0.023

2.711

Bankex

0.376

45.26

-100.000

13.35

35.492

-3.238

26.98

Capital Goods

0.370

50.737

-33.67

10.04

6.3794

0.860

5.335

Consumer durables

1.589

56.92

-29.23

10.14

27.17

0.815

7.792

Energy

0.660

28.91

-31.76

7.83

11.85

-0.066

3.107

FMCG

1.400

21.01

-16.70

5.00

3.57

-0.119

2.522

Finance

1.25

44.40

-23.63

9.155

7.87

0.650

3.99

Health Care

1.25

15.58

-24.33

5.982

4.751

-0.980

2.85

Infra

0.805

15.36

-13.36

3.71

5.07

-0.079

1.86

IT

0.91

20.5

-21.97

7.517

8.175

-0.114

0.597

Industrials

0.764

52.18

-35.13

10.038

13.138

0.8956

6.287

Technology

0.554

17.11

-18.282

6.56

11.84

-0.15

0.777

Telecom

-0.048

22.10

-31.38

8.685

-177.6

-0.32

1.14

Utilities

0.413

33.82

-28.64

8.718

21.06

0.40

3.04

Carbonex

0.72

13.148

-10.363

4.617

6.406

0.12

0.141

Greenex

1.468

33.13

-11.056

11.53

7.854

1.45

5.618

PSU

0.38

43.72

-26.91

8.58

22.20

5.64

0.88

Oil and Gas

0.70

28.11

-31.45

7.815

11.06

-0.143

2.77

Realty

-0.06

79.30

-43.62

15.56

-233.2

1.169

5.53

Mid Cap

0.99

43.90

-33.30

8.52

8.599

0.240

6.62

Small Cap

0.96

51.91

-32.49

9.77

10.15

0.745

6.57

Basic Materials

0.87

42.27

-35.88

9.589

12.79

0.123

3.67

CPSE

0.29

34.41

-26.77

6.84

23.31

0.59

-4.72

Large Cap

0.778

29.11

-25.18

6.75

8.67

0.056

3.75

India Manufacturing Index

0.730

8.64

-6.88

3.71

5.07

-0.326

-0.056

Source: Computed

 

 

 

7. RESULTS AND ANALYSIS:

The purpose of this study is to know the risk return relationship and stability of beta of various sectorial indices on Bombay stock exchanges.Table 1 shows the discretive results of various sectorial indices and market return from the year 2007 September to 2017 september.In the case of mean values it is understood that Auto sector has the highest mean percentage followed byconsumer durables,fmcg,finance,health care,greenex and it implies that these sectors are significantly contributing highest return in relation to its market return.Hence,investors will have greater scope of earnings for their investment ,if they desire to invest in above said sectors. In the case of standard deviation as well as coefficient of variation (CV) of the rate of return gives the information about total risks of the different sectors. It is understood that from the table 1 CV is more in the sectors like Bankex, CPSE, PSU, which indicate that risk or variability in the return from last ten years are more in these sectors and least return variation in sectors like India Manufacturing index, Infrastructure, Carbonex, FMCG, Large cap. It implies that bases on risk-reward ratio as well as investors nature of  risk taking ability ,they can decide either to select highest risk investment avenues or lowest risk investment alternatives.

 

We have also provided data of kurtosis distribution. In kurtosis if the values are more than positive 3 is considered as more risky investments and these risks are due to its external factors and it is termed as leptokurtic and it will appear as flat tails with peaked kurtosis in the distribution. If the kurtosis is less than 3 then it appears as thin tails with flat kutosis.On the other hand there a platykurtic this gives values in the negative figure. If the values of kurtosis distribution are zero then it is called as Mesokurtic kurtosis. From the above data Bankex, Consumer durable, Industrials, Midcap, Small Cap indices has the leptokurtic kurtosis distribution and India manufacturing index ,CPSE comes under the purview of platykurtic kurtosis distribution.

 

Skewness gives the information about data whether it is skewed positively or negatively. If data are more of positive value then right tail is longer than the left in the distribution and if negative data’s are more, then left tail is longer than right.From the above table results shows the positive skweness because positve tail is greater than the negative tails.

 

The results of Beta, Alpha and R2 are given in table 2.

 

Table 2. Results of regression analysis

Sl.

No

Sectors

Alpha (α)

BETA (β)

R2

1

Auto

0.765

0.953

0.759

2

Bankex

0.277

0.136

0.0052

3

Capital goods

0.128

0.929

0.518

4

Consumer durables

0.568

1.2065

0.731

5

Energy

-0.487

0.730

0.536

6

FMCG

0.55

0.352952

0.311

7

Finance

0.235

1.206

0.893

8

Health care

0.562

0.443

0.338

9

Infrasturcture

-0.442

1.178599

0.893

10

IT

0.212

0.449

0.220

11

Industrials

-0.928

1.076

0.710

12

Technology

-0.238

0.504

0.364

13

Telecommunications

-1.035

0.627

0.364

14

Utilities

-0.877

0.821

0.547

15

Carbonex

0.640

0.365

0.326

16

Greenex

-0.033

1.007

0.946

17

PSU

-0.524

1.076

0.811

18

Oil and gas

-0.1004

0.952

0.768

19

Realty

-1.704

1.934

0.798

20

Mid cap

0.020

1.145

0.934

21

Smallcap

-0.400

0.749

0.762

22

Basic materials

-0.720

1.015

0.684

23

CPSE

-0.4182

0704

0.506

24

Large cap

-0.010

0.931

0.984

25

India Manufacturing Index

0.686

0.117

0.0166

Source: Computed

 

 

Alpha is the measurement tool to know the excess return on investment in relation to its beta coefficient over the market index. If alpha is lesser than zero then, it indicates that there is a lesser return on investment as compared to its beta co-efficient, on the other hand if alpha is greater than zero, then this investment is considered as better performed. Based on this from the above table it is clear that Auto sector indices is having a alpha followed by India manufacturing index, Carbonex, consumer durables, healthcare, FMCG and underperformed sector wise indices are Large Cap, Mid Cap, Greenex Technology respectively.It implies that based on investors return  and return relation estimation they can take the decision of choosing the right investment decisions

 

From the above  table 2 computed data it is understood that Realty sector is most aggressive sectors followed by consumer durables indices, finance indices, infrastructure indices, mid cap indices, basic materials indices, industrials indices, public sector units’ indices, greenex sector indices. It indicates that these sectorial indices are more volatile than S and P BSE 500 indices. On the other hand there is a most defensive sector such as India manufacturing indices, Bankex indices. These sectors has less fluctuations in its prices as well as its respective returns comparing to its benchmark index S and P BSE 500.Beta is calculated by using formula in the Ms Excel i.e. slope function of two variables (sectors returns, market returns).This makes the investors to choose the right sector for investments based on their attitude towards risk.

 

R2 is the statistical measurement to find out the how closely data are fitted in the regression line. For this study we  have calculated the R2 with the help of regression analysis and from the above table it understood that most of the sectors are having the good fit to the regression line .i.e Large cap, (0.98), Greenex (0.94), Mid Cap (0.93), PSU (0.893), Finance (0.893) respectively and least percentage of R2 scored sectors are Bankex (0.0052), India manufacturing Index (0.0166), IT (0.220), FMCG(0.311) and Carbonex (0.326) respectively.This attractes the most of the investors to invest in the most filtted sectors and it assures with guaranteed return.

 

Following presented tables disseminates information about systematic and unsystematic risk. The calculation of systematic risk is as follows:

 

Table 3. Results of Total risk

SL.N0

sectors

Systematic risk

Unsystematic risk

1

Auto

46.93

14.83

2

Bankex

0.933

177.52

3

Capital Goods

44.64

49.48

4

Consumer Durables

75.2

27.63

5

Energy

27.54

28.47

6

FMCG

6.52

17.25

7

Finance

75.19

9.00

8

Health Care

10.14

23.65

9

Infra

71.78

6.08

10

IT

10.43

44.03

11

Industrials

59.86

29.21

12

Technology

13.14

27.40

13

Telecom

20.33

51.12

14

Utilities

34.83

34.36

15

Carbonex

6.88

14.35

16

Greenex

52.47

2.03

17

PSU

59.87

13.89

18

Oil and Gas

46.91

14.16

19

Realty

193.44

48.82

20

Mid Cap

67.84

4.75

21

Small Cap

29.05

10.83

22

Basic Materials

53.31

29.40

23

CPSE

25.63

23.08

24

Large Cap

44.88

0.68

25

India Manufacturing Index

0.713

14.25

 

From the above table it understood that Realty sector, Consumable sectors, Infrastructure sectors are having a highest percentage of market risk respectively and least market risk affected sectors are Indian manufacturing Index, Bankex, FMCG andCarbonex respectively. On the other hand with high unsystematic risks are Bankex, Telecommunication, Capital goods sectors respectively and least percentage of unsystematic involved sectors are Large cap, Greenex, Mid Cap, Infrastructure and Finance respectively.

 

This implies that by knowing the performance of different sectors total risk nature from the last ten years, investors can analyse the level of risk for the future, based on this investors can take the decision of investment.

 

8. CONCLUSIONS:

In the overall study of beta stability in the Indian stock market we found that there a significant differences between sectorial indices and S and P BSE Index. In regard to this Realty sector indices is the most aggressive sectors and India Manufacturing indices is most defensive sectors. In terms of R2 parameter the large cap indices has the 0.98 fit in the regression line. It indicates that the model explains most of the variability of the predicted data is around its mean and least fit to the regression line is the Bankex. Finally systematic risk as well as unsystematic risk prediction has the greater contribution for the investors to take right investment decisions in time of their needs based on nature of risk taking ability of the investors (i.e. risk averser or risk seeker). In this study we concluded that Realty sector has the highest market risk and India manufacturing index has the lowest market risk, on the contrary Bankex has the more unsystematic risk and Large cap indices has the lowest rate of unsystematic risk.

 

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Received on 21.03.2018          Modified on 03.05.2018

Accepted on 03.06.2018           ©A&V Publications All right reserved

Asian Journal of Management. 2018; 9(3):1077-1084.

DOI: 10.5958/2321-5763.2018.00171.3