Incorporated In The Capm Model

Published Date: 02 Nov 2017

5.1 INTRODUCTION

The study aims to see whether there exists a relationship between firm specific investor sentiments and actual returns that a stock gives. Thereafter such firm specific characteristics are incorporated in the CAPM model to see whether it is able to give a better value of alpha. Alpha is the difference between the actual returns and expected returns over a specified period; in this case it is one year. The study is conducted for three periods, the pre recession period- for which the data for the year 2007 is considered, the recession period- for which the data of 2009 is considered and the post recession period- for which the data for 2012 is considered. The purpose is to see how the role of investor sentiments varies in these three periods.

Investors have different perceptions or sentiments regarding various attributes of a firm. An investor might believe that a firm having a small market capitalization will be able to generate him abnormal returns on the notion that information on smaller firms is not available to the public and this sentiment makes then invest in the stock. Since investor sentiments question the major assumption of efficient market hypothesis that investors are rational, presence of these noise traders (who invest on the basis of sentiments) disable the rationality of other investors and it may lead to a herd behaviour causing many investors to invest in such a stock. This leads to increase in the price of that stock enabling the noise trader to earn abnormal returns over and above what he could have normally made by investing just on the basis of fundamental and technical analysis. This increase in the stock prices due to sentiments on market capitalization is called the size effect. It represents a financial market anomaly i.e. the returns are not as per the EMH theory but investors are able to generate superior returns.

As per the methodology of this study, regression analysis is done to see the extent to which investor sentiments on firm specific characteristics (market capitalization, PE ratio, Price to Book ratio, EPS) have an impact on the actual returns for the three years defined above and then a conditional CAPM equation is formed which includes not only the market premium but also the firm characteristics to get the value of expected returns.

5.2 DATA ANALYSIS

The data for the study has been extracted from the Prowess database. It consists of data on NIFTY 50 companies for the last 10 years, the details of which have been described in research design under chapter 3. NIFTY 50 companies have been used since it covers all the industries and thus gives a fair idea as to whether the sentiments play an important role in all the industries or only some particular sectors.

5.2.1 DESCRIPTIVE STATISTICS OF THE SECONDARY DATA

The following table depicts the descriptive statistics of the secondary data for the three years taken into the analysis.

Table 5.1: Descriptive statistics of the secondary data

Pre recession period (2007)

 

N

Minimum

Maximum

Mean

Std. Deviation

Market cap.

50

2.63

118.56

23.49

27.33

Price to Book ratio

50

0.8

17.33

4.47

3.48

PE ratio

50

6.19

50.02

20.66

10.46

EPS

50

-0.39

146.13

36.57

28.69

Recession period (2009)

 

N

Minimum

Maximum

Mean

Std. Deviation

Market cap.

50

3.93

136.6

29.49

32.53

Price to Book ratio

50

0.88

17.71

4.32

3.13

PE ratio

50

6.8

51.16

20.39

9.95

EPS

50

-0.22

132.06

38.52

29.44

Post recession period (2012)

 

N

Minimum

Maximum

Mean

Std. Deviation

Market cap.

50

9.21

218.04

44.25

46.74

Price to Book ratio

50

1.07

18.54

4.48

3.21

PE ratio

50

7.08

48.78

21.41

9.17

EPS

50

-0.35

125.86

41.1

28.99

Source: Secondary Data

INTERPRETATION

The descriptive statistics show wide variability in the factors for the three periods. Market capitalization for the pre recession period varies from 2.63 units to 118.56 units. Similar range is seen for the other periods as well. However the mean for all the three periods is more inclined towards the minimum value showing that there are more of small to medium size companies in the list. The standard deviation is highest for the post recession period since in 2010 coal India was listed in the NSE with the highest average market capitalization. The largest company by size i.e. Reliance Industries’ average was less than that of Coal India.

Similar volatility is found in other factors as well. The least deviation is seen in Price to Book ratio with a standard deviation of just 3.48 from the mean for the pre recession period. The mean for all the factors, expect PE ratio, is inclined more towards the minimum value. For PE it is more or less normally distributed.

INFERENCE

From the above descriptive statistics it is clear that there exists high volatility in all the factors which are taken as independent variables to conduct the study. The mean is highest for all the factors in the post recession period suggesting that high sentiments enveloped the market leading to high variability in the price of the shares, thus affecting the PE and Price to Book ratio of a firm.

CONCLUSION

It can be concluded that the high volatility in the firm characteristics makes it important for these variables to be incorporated in the CAPM to get a more relevant value of the expected return. This high volatility also causes difficulty in describing the relation of these independent variables on the dependent variable. The sudden change in one of the factors may distort the degree of dependency of the independent variables in the dependent variable.

5.2.2 REGRESSION ANALYSIS

Regression analysis is a process of constructing a mathematical model that is used to predict the dependence of a variable on another or a set of other variables. The variable on which the effect is to be seen is called the dependent variable and the variable(s) whose effect on the dependent variable is to be seen is called the independent variable. For the research a set of four independent variables are taken and their impact is determined on the dependent variable for three time periods, before recession (2007), during recession (2009) and after recession (2012). The independent variables are market capitalization, Price to Book ratio, PE ratio and EPS. The dependent variable is the actual returns generated by the NIFTY 50 stocks over these three periods.

Table 5.2: Model summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

0.913

0.833

0.786

0.170

2

0.303

0.092

0.001

0.348

3

0.822

0.675

0.589

0.130

Note: Model 1- Pre recession period (2007), Model 2- Recession period (2009) and Model 3- Post recession Period (2012).

Source: Secondary Data

INTERPRETATION

R is the square root of the R squared value and is the correlation between the observed and predicted value of the dependent variable (actual returns). For the pre recession period, the value of R is 0.913 showing good extent of correlation between the observed and the predicted values of the dependent variable. For the recession period the R value is as low as 0.303 showing very low correlation between the predicted and observed value of the dependent variable. For the post recession period, the value of R is 0.822 again depicting better correlation between the observed and the predicted value of the dependent variable than what is there during the recession period.

R squared: It is a statistical tool that represents what proportion of the dependent variable can be explained by the independent variable(s). This is an overall assessment of the strength of the association and does not reveal the degree to which any particular independent variable is associated with the dependent variable.

The R squared value for the pre recession period is 0.833 depicting that there is high strength of correlation between the independent and the dependent variable. For the recession period, this value goes down to 0.092. This is because during this period the entire market was down and the returns generated did not depend on firm specific characteristics. The market was more dominated by the systematic risk which is given by Beta. Thus firm specific sentiments did not affect the negative returns given by the companies under the study. For the post recession period, sentiments again played a role in determining asset returns since the value of R squared shows high strength of correlation between the independent and the dependent variable. The value for the post recession period is 0.675.

Adjusted R squared: This value is an adjustment to the value of R squared and shows the extent to which additions of any extraneous variables add any significant predictability to the dependent variable. For the pre recession period the value of adjusted R squared is 0.786, for the recession period it is 0.001 and for the post recession period the value of the adjusted R squared is 0.589. This shows that there can be other factors, apart from the ones taken into consideration in the study that can impact the actual returns (dependent variable) which can be taken into consideration as well.

Table 5.3 ANOVA table

 

Sum of Squares

Df

Mean Square

F

P

Model 1

Regression

2.015

4

0.504

17.505

0.000

Residual

0.403

14

0.029

 

 

Total

2.418

18

 

 

 

Model 2

Regression

0.49

4

0.122

1.012

0.413

Residual

4.84

40

0.121

 

 

Total

5.33

44

 

 

 

Model 3

Regression

0.531

4

0.133

7.795

0.001

Residual

0.255

15

0.017

 

 

Total

0.786

19

 

 

 

Note: Model 1- Pre recession period (2007), Model 2- Recession period (2009) and Model 3- Post recession Period (2012).

H0: Investor sentiments on firm specific characteristics do not have an impact on the actual returns. H1: Investor sentiments on firm specific characteristics affect the actual returns.

Source: Secondary Data

INTERPRETATION

F test is a test used in regression analysis to see whether the model is statistically significant or not. For this the p value of the Anova table is seen. If the p value of zero to 3 decimal places, the model is proved to be statistically significant. From the Anova table it is clear that for the pre recession period (2007), the p value is less than the significant level taken at 5%. The value is not less than 5% for the year 2009 i.e. the recession period since there exists no relationship between the firm specific investor sentiments (the independent variables) and the actual returns (the dependent variable). For the post recession period (2012) the p value is again less than 5% depicting that the model is statistically significant.

Therefore as per the data and the analysis, the null hypothesis gets rejected for the pre recession period and the post recession period thereby accepting the alternate hypothesis and thus proving that investor sentiments have an impact on the actual returns given by a company. However for the year 2009 i.e. the recession period, the null hypothesis gets accepted since during the recession period there is no relationship between the investor sentiments for the firm specific factors and the actual returns.

Table 5.4 Coefficients of the variables

 

Unstandardized Coefficients

Standardized Coefficients

T

Sig.

 

B

Std. Error

Beta

Model 1

(Constant)

1.994

0.57

 

3.499

0.004

Market cap.

-0.387

0.174

-0.422

-2.23

0.043

Price to Book ratio

0.436

0.114

0.446

3.831

0.002

PE ratio

-0.491

0.219

-0.397

-2.245

0.041

EPS

-0.091

0.067

-0.174

-1.371

0.192

Model 2

(Constant)

0.066

0.167

 

0.395

0.695

Market cap.

0.002

0.002

0.153

0.952

0.347

Price to Book ratio

-0.032

0.02

-0.325

-1.606

0.116

PE ratio

0.004

0.007

0.105

0.51

0.613

EPS

-0.002

0.002

-0.144

-0.873

0.388

Model 3

(Constant)

1.385

0.373

 

3.712

0.002

Market cap.

-0.134

0.048

-0.52

-2.789

0.014

Price to Book ratio

0.167

0.053

0.535

3.138

0.007

PE ratio

-0.346

0.11

-0.484

-3.137

0.007

EPS

-0.032

0.032

-0.166

-0.988

0.339

Note: Model 1- Pre recession period (2007), Model 2- Recession period (2009) and Model 3- Post recession Period (2012).

Source: Secondary Data

INTERPRETATION

The first variable for all the three years is constant. This is the dependent variable and represents the Y-intercept i.e. the height of the regression line from where it cuts the Y axis. It depicts the value of the actual returns when all other variables i.e. the independent variables are zero. The other coefficients against the independent variables shown under the column B shows the relationship between the independent variables on the dependent variables. For the year 2007 the B value or coefficient of market cap is -.387. This means that for every 1 unit change in market capitalization of a company, the actual returns change by 0.387 units. The negative sign depicts an increase relationship between the two variables. An increase in one leads to a fall in the other. Other variables in all the years follow the same suit.

The regression equation for the pre recession period (2007) can be written as:

Actual returns = 1.994 – 0.134 Market capitalization + 0.436 Price to Books ratio – 0.491 PE ratio – 0.091 EPS.

The regression equation for the recession period (2009) can be written as:

Actual returns = 0.066 + 0.002 Market capitalization - 0.032 Price to Books ratio + 0.004 PE ratio – 0.002 EPS.

The regression equation for the post recession period (2012) can be written as:

Actual returns = 1.385 – 0.387 Market capitalization + 0.167 Price to Books ratio – 0.346 PE ratio – 0.032 EPS.

5.2.3 INCORPORATING FIRM CHARACTERISITCS IN THE CONDITIONAL CAPM

Since it is proved that investor sentiments with respect to form specific characteristics affect the actual returns, incorporating them in the asset pricing models will enable an investor to get a better estimate of the expected return. There the traditional CAPM is used to create a conditional CAPM in which these firm specific sentiments are included.

The new equation for the conditional CAPM is:

Expected returns = Rf + B1 (Market return – Rf) + B2 (Market capitalization) + B3 (Price to Book ratio) + B4 (PE ratio) + B5 (EPS) + B6 (Investor sentiment index)

Investor sentiment index represent sentiment which investors have for the entire economy and not specific to a firm. For this Consumer Confidence Index (CCI) of India has been used as a proxy and directly added to the model. Each firm specific characteristic has been separately tested to see their impact on the expected return. This in turn leads to four separate conditions, each condition testing one characteristic.

Condition 1:

Expected returns = Rf + B1 (Market return – Rf) + B2 (Market capitalization) + B6 (Investor sentiment index)

Condition 2:

Expected returns = Rf + B1 (Market return – Rf) + B3 (Price to Book ratio) + B6 (Investor sentiment index)

Condition 3:

Expected returns = Rf + B1 (Market return – Rf + B4 (PE ratio) + B6 (Investor sentiment index)

Condition 4:

Expected returns = Rf + B1 (Market return – Rf) + B5 (EPS) + B6 (Investor sentiment index)

The expected return for each condition is calculated for each of the three periods and then the value is subtracted from the actual returns given by the stocks in the same period thereby giving the value of alpha. This value of alpha is compared with the alpha value as per the traditional CAPM which does not take into consideration investor sentiments in the equation.

Table 5.5: Value of Alpha under conditional CAPM for Pre recession period (2007)

 

Unconditional CAPM

Conditional CAPM (Multifactor Model)

 

 

 

 

Condition 1

Condition 2

Condition 3

Condition 4

Company Name

Actual return

Expected return

Alpha

Expected Return

Alpha

Expected Return

Alpha

Expected Return

Alpha

Expected Return

Alpha

A C C Ltd.

-6.00%

12.13%

-18.13%

7.36%

-13.36%

35.72%

-41.72%

10.45%

-16.45%

11.71%

-17.71%

Ambuja Cements Ltd.

3.29%

10.96%

-7.67%

17.25%

-13.96%

25.54%

-22.25%

31.65%

-28.36%

10.32%

-7.03%

Asian Paints

18.04%

9.24%

8.80%

15.14%

2.90%

16.91%

1.13%

14.52%

3.52%

10.15%

7.89%

Axis Bank Ltd.

37.73%

11.99%

25.74%

18.76%

18.97%

34.28%

3.45%

19.11%

18.63%

20.82%

16.92%

Bank Of Baroda

-6.72%

14.25%

-20.97%

38.33%

-45.06%

8.70%

-15.43%

39.77%

-46.49%

22.77%

-29.49%

BHEL

0.87%

11.95%

-11.08%

11.70%

-10.84%

26.03%

-25.16%

5.27%

-4.40%

10.53%

-9.67%

BPCL

-28.81%

12.90%

-41.71%

8.01%

-36.83%

16.25%

-45.07%

11.83%

-40.64%

12.48%

-41.29%

Bharti Airtel

85.08%

11.68%

73.40%

14.33%

70.75%

19.63%

65.45%

-1.31%

86.39%

12.72%

72.36%

Cipla Ltd.

-64.24%

11.41%

-75.65%

10.67%

-74.91%

29.74%

-93.98%

3.39%

-67.63%

9.88%

-74.12%

Dr. Reddy'S Laboratories

-48.77%

10.96%

-59.72%

6.81%

-55.58%

14.71%

-63.47%

10.69%

-59.46%

10.21%

-58.98%

G A I L Ltd.

-16.94%

12.72%

-29.65%

12.52%

-29.45%

42.99%

-59.93%

34.87%

-51.81%

28.41%

-45.35%

Grasim Industries Ltd.

1.48%

11.54%

-10.07%

20.09%

-18.62%

35.27%

-33.80%

24.42%

-22.94%

14.92%

-13.44%

H C L Technologies

-55.46%

15.15%

-70.61%

8.36%

-63.82%

19.59%

-75.05%

22.53%

-77.99%

43.48%

-98.94%

H D F C Bank

23.24%

9.83%

13.41%

13.78%

9.46%

33.79%

-10.56%

15.71%

7.52%

14.56%

8.68%

Hero Motocorp

-22.65%

10.42%

-33.07%

19.51%

-42.16%

24.53%

-47.18%

7.21%

-29.86%

16.06%

-38.72%

Hindalco Industries Ltd.

-28.68%

11.54%

-40.22%

5.00%

-33.68%

26.97%

-55.65%

26.77%

-55.45%

9.02%

-37.70%

Hindustan Unilever Ltd.

-24.56%

10.73%

-35.29%

15.62%

-40.18%

20.91%

-45.47%

9.26%

-33.82%

4.74%

-29.30%

HDFC Ltd.

13.69%

10.55%

3.14%

15.32%

-1.63%

24.76%

-11.07%

16.10%

-2.41%

14.74%

-1.05%

I C I C I Bank

44.87%

11.27%

33.60%

14.58%

30.29%

33.93%

10.93%

14.57%

30.30%

19.07%

25.79%

I D F C Ltd.

25.68%

13.66%

12.01%

-60.71%

86.39%

-261.3%

286.99%

-162.98%

188.65%

-127.67%

153.35%

I T C Ltd.

-22.55%

9.96%

-32.51%

13.71%

-36.26%

22.28%

-44.83%

7.76%

-30.30%

11.86%

-34.40%

Infosys Ltd.

-32.29%

12.99%

-45.28%

14.85%

-47.14%

8.85%

-41.14%

7.85%

-40.15%

12.97%

-45.26%

Jaiprakash Associates Ltd.

14.93%

13.48%

1.45%

18.24%

-3.31%

27.97%

-13.03%

7.44%

7.49%

23.83%

-8.90%

Jindal Steel & Power Ltd.

25.41%

13.71%

11.70%

24.76%

0.64%

38.87%

-13.46%

28.21%

-2.81%

15.97%

9.43%

Kotak Mahindra Bank Ltd.

71.67%

12.31%

59.36%

16.68%

55.00%

22.69%

48.99%

15.19%

56.49%

18.18%

53.50%

Larsen & Toubro Ltd.

-33.40%

11.77%

-45.17%

16.40%

-49.80%

26.57%

-59.97%

8.16%

-41.56%

15.13%

-48.53%

Lupin Ltd.

-40.21%

13.12%

-53.34%

32.49%

-72.70%

40.20%

-80.41%

22.15%

-62.36%

27.52%

-67.73%

Mahindra & Mahindra Ltd.

24.46%

11.99%

12.46%

18.82%

5.63%

31.16%

-6.71%

16.29%

8.16%

17.42%

7.03%

Maruti Suzuki India Ltd.

-6.19%

13.30%

-19.49%

16.26%

-22.44%

69.83%

-76.01%

35.96%

-42.15%

8.87%

-15.06%

N T P C Ltd.

12.04%

11.41%

0.64%

14.17%

-2.13%

27.15%

-15.10%

-1.57%

13.61%

28.81%

-16.76%

Oil & Natural Gas Corpn. Ltd.

-32.83%

12.26%

-45.10%

15.99%

-48.82%

45.26%

-78.09%

35.22%

-68.05%

16.88%

-49.71%

Punjab National Bank

0.81%

13.44%

-12.63%

25.32%

-24.51%

46.02%

-45.22%

28.10%

-27.29%

17.69%

-16.88%

Ranbaxy Laboratories

-18.61%

11.14%

-29.75%

-1.24%

-17.37%

53.16%

-71.77%

10.81%

-29.42%

9.72%

-28.33%

Reliance Industries Ltd.

72.29%

12.99%

59.30%

15.94%

56.35%

32.41%

39.88%

26.16%

46.13%

15.32%

56.96%

Reliance Infrastructure

-19.21%

11.05%

-30.26%

10.06%

-29.27%

22.74%

-41.95%

18.62%

-37.82%

16.79%

-36.00%

Sesa Goa Ltd.

32.60%

12.54%

20.06%

20.66%

11.93%

30.86%

1.74%

21.39%

11.21%

15.78%

16.81%

Siemens Ltd.

-80.78%

10.91%

-91.69%

9.63%

-90.41%

18.02%

-98.80%

8.62%

-89.40%

14.95%

-95.73%

State Bank Of India

2.68%

12.22%

-9.54%

18.73%

-16.05%

44.49%

-41.81%

27.85%

-25.17%

19.02%

-16.35%

Sterlite Industries (India) Ltd.

-73.20%

12.31%

-85.51%

18.99%

-92.19%

27.19%

-100.4%

12.13%

-85.33%

#DIV/0!

#DIV/0!

Sun Pharmaceutical Inds. Ltd.

22.91%

10.19%

12.72%

14.70%

8.21%

25.56%

-2.65%

15.93%

6.98%

13.17%

9.74%

TCS Ltd.

-35.59%

10.24%

-45.82%

12.28%

-47.87%

10.13%

-45.71%

23.01%

-58.59%

15.84%

-51.43%

Tata Motors Ltd.

-21.85%

11.77%

-33.62%

19.57%

-41.42%

34.76%

-56.61%

19.53%

-41.39%

16.62%

-38.47%

Tata Power Co. Ltd.

-12.55%

12.76%

-25.31%

30.11%

-42.66%

37.09%

-49.64%

24.96%

-37.51%

49.58%

-62.13%

Tata Steel Ltd.

-16.19%

12.81%

-28.99%

20.86%

-37.05%

31.15%

-47.34%

18.79%

-34.98%

18.07%

-34.26%

Ultratech Cement Ltd.

13.04%

11.36%

 

18.94%

 

 

 

 

 

 

 

Wipro Ltd.

-0.05%

14.88%

-14.93%

18.45%

 

9.89%

 

11.67%

 

12.77%

 

Note: Condition 1: Incorporating market capitalization in CAPM, condition 2: Incorporating P/B ratio in CAPM, condition 3: Incorporating PE ratio in CAPM and condition 4: Incorporating EPS in CAPM.

Source: Self computed.

Table 5.6: Value of Alpha under conditional CAPM for Recession period (2009)

 

 

Unconditional CAPM

Conditional CAPM (Multifactor Model)

 

 

 

 

Condition 1

Condition 2

Condition 3

Condition 4

Company Name

Actual return

Expected return

Alpha

Expected Return

Alpha

Expected Return

Alpha

Expected Return

Alpha

Expected Return

Alpha

A C C Ltd.

-30.47%

-29.88%

-0.59%

-24.78%

-5.69%

-15.32%

-15.15%

-25.65%

-4.82%

-29.05%

-1.42%

Ambuja Cements Ltd.

-41.68%

-27.85%

-13.83%

-24.01%

-17.66%

-19.28%

-22.40%

-13.92%

-27.75%

-26.46%

-15.22%

Asian Paints

-34.47%

-11.16%

-23.30%

-8.92%

-25.55%

-6.04%

-28.42%

-6.33%

-28.14%

-11.25%

-23.21%

Axis Bank Ltd.

-47.46%

-44.93%

-2.53%

-43.26%

-4.20%

-35.41%

-12.06%

-38.55%

-8.92%

-47.45%

-0.02%

Bank Of Baroda

-17.29%

-46.97%

29.68%

-34.95%

17.66%

-85.39%

68.10%

-25.51%

8.22%

-49.35%

32.05%

BHEL

-26.72%

-38.42%

11.70%

-37.09%

10.37%

-31.81%

5.09%

-41.01%

14.29%

-35.72%

9.00%

BPCL

-7.95%

-30.69%

22.74%

-30.12%

22.16%

-25.24%

17.29%

-24.79%

16.84%

-28.46%

20.50%

Bharti Airtel

-24.27%

-27.03%

2.77%

-25.12%

0.85%

-20.47%

-3.80%

-32.99%

8.72%

-29.22%

4.95%

Cairn India Ltd.

-17.85%

-33.54%

15.69%

-47.65%

29.81%

-30.00%

12.15%

#DIV/0!

#DIV/0!

34.96%

-52.81%

Cipla Ltd.

0.02%

-22.15%

22.17%

-25.73%

25.75%

-7.44%

7.46%

-10.53%

10.55%

-19.69%

19.71%

D L F Ltd.

-74.09%

-62.84%

-11.25%

-56.75%

-17.34%

-50.00%

-24.09%

-49.24%

-24.86%

-24.88%

-49.21%

Dr. Reddy'S Laboratories Ltd.

-17.09%

-16.86%

-0.23%

-8.79%

-8.30%

-11.99%

-5.11%

-13.28%

-3.81%

-16.04%

-1.05%

G A I L Ltd.

-42.27%

-31.10%

-11.17%

-32.24%

-10.04%

-5.71%

-36.57%

-18.47%

-23.81%

-22.13%

-20.14%

Grasim Industries Ltd.

-38.56%

-31.51%

-7.06%

-26.23%

-12.33%

-20.16%

-18.41%

-18.95%

-19.62%

-29.61%

-8.95%

H C L Technologies

-59.70%

-29.88%

-29.82%

-21.29%

-38.41%

-20.67%

-39.03%

-22.26%

-37.45%

-9.04%

-50.66%

H D F C Bank

-26.88%

-33.54%

6.66%

-32.55%

5.67%

-8.34%

-18.54%

-23.79%

-3.09%

-33.51%

6.63%

Hero Motocorp

54.22%

-14.82%

69.05%

-11.95%

66.18%

-1.20%

55.42%

-9.33%

63.56%

-12.93%

67.16%

Hindalco Industries Ltd.

-68.47%

-49.82%

-18.66%

-40.37%

-28.10%

-102.12%

33.64%

-33.97%

-34.51%

-47.58%

-20.89%

Hindustan Unilever Ltd.

3.80%

-18.49%

22.29%

-15.22%

19.03%

-15.85%

19.65%

-12.34%

16.14%

-34.61%

38.41%

HDFC Ltd.

-40.66%

-35.17%

-5.49%

-32.70%

-7.96%

-22.84%

-17.81%

-29.18%

-11.48%

-35.65%

-5.01%

I C I C I Bank Ltd.

-56.75%

-49.41%

-7.33%

-46.54%

-10.20%

-56.34%

-0.40%

-42.05%

-14.69%

-45.84%

-10.91%

I D F C Ltd.

-64.17%

-48.60%

-15.58%

-44.07%

-20.10%

-41.24%

-22.93%

-37.73%

-26.44%

-118.43%

54.26%

I T C Ltd.

-10.38%

-20.11%

9.74%

-17.70%

7.32%

-9.21%

-1.16%

-14.22%

3.84%

-17.84%

7.46%

Infosys Ltd.

-8.06%

-15.64%

7.58%

-14.23%

6.18%

-6.59%

-1.47%

-9.08%

1.03%

-11.74%

3.69%

Jaiprakash Associates Ltd.

-62.88%

-63.25%

0.37%

-59.56%

-3.32%

-53.69%

-9.19%

-58.02%

-4.86%

-55.14%

-7.74%

Jindal Steel & Power Ltd.

-41.76%

-57.14%

15.38%

-57.53%

15.77%

-52.57%

10.81%

-54.31%

12.55%

-54.15%

12.39%

Kotak Mahindra Bank

-54.91%

-56.33%

1.42%

-54.32%

-0.60%

-49.02%

-5.89%

-53.09%

-1.82%

-49.35%

-5.56%

Larsen & Toubro Ltd.

-77.89%

-41.68%

-36.21%

-39.29%

-38.59%

-33.06%

-44.82%

-35.27%

-42.62%

-38.60%

-39.28%

Lupin Ltd.

37.32%

-20.93%

58.25%

-3.96%

41.28%

0.51%

36.82%

-12.79%

50.11%

-15.85%

53.18%

Mahindra & Mahindra Ltd.

-44.96%

-39.65%

-5.32%

-35.28%

-9.68%

-28.85%

-16.11%

-32.15%

-12.81%

-35.41%

-9.55%

Maruti Suzuki India Ltd.

-5.70%

-29.88%

24.18%

-32.79%

27.09%

14.11%

-19.81%

-7.74%

2.04%

-33.54%

27.84%

N T P C Ltd.

-8.52%

-24.59%

16.07%

-23.35%

14.83%

-19.27%

10.75%

-24.68%

16.16%

-91.10%

82.58%

Oil & Natural Gas Corpn. Ltd.

-20.43%

-30.69%

10.26%

-27.77%

7.34%

-5.13%

-15.30%

-11.22%

-9.21%

-26.03%

5.60%

Power Grid Corpn. Of India

-2.75%

-26.62%

23.88%

126.84%

-129.59%

130.19%

-132.93%

32.62%

-35.37%

-226.26%

223.51%

Punjab National Bank

-19.36%

-40.46%

21.10%

-33.48%

14.11%

-39.33%

19.96%

-26.53%

7.17%

-39.76%

20.39%

Ranbaxy Laboratories Ltd.

-62.21%

-24.18%

-38.02%

-11.37%

-50.83%

-9.16%

-53.05%

#NUM!

#NUM!

#NUM!

#NUM!

Reliance Industries Ltd.

-32.71%

-38.42%

5.72%

-36.65%

3.95%

-29.07%

-3.64%

-35.48%

2.77%

-38.47%

5.77%

Reliance Infrastructure Ltd.

-58.78%

-53.89%

-4.90%

-50.23%

-8.55%

-48.22%

-10.57%

-45.09%

-13.69%

-55.04%

-3.74%

Sesa Goa Ltd.

-96.84%

-48.19%

-48.65%

-48.22%

-48.62%

-39.07%

-57.77%

-34.03%

-62.81%

-45.75%

-51.09%

Siemens Ltd.

-56.51%

-36.39%

-20.12%

-32.13%

-24.37%

-31.09%

-25.42%

-32.90%

-23.61%

-32.35%

-24.16%

State Bank Of India

-33.32%

-38.42%

5.11%

-36.10%

2.78%

-28.69%

-4.63%

-27.58%

-5.73%

-37.89%

4.58%

Sterlite Industries (India) Ltd.

-49.95%

-54.29%

4.34%

-51.81%

1.86%

-46.52%

-3.43%

-50.94%

0.99%

#DIV/0!

#DIV/0!

Sun Pharmaceutical Inds. Ltd.

-9.59%

-11.16%

1.57%

-9.79%

0.20%

1.38%

-10.97%

-5.38%

-4.21%

-10.77%

1.18%

Tata Consultancy Services Ltd.

-33.55%

-17.67%

-15.88%

-13.91%

-19.64%

-13.22%

-20.33%

-12.22%

-21.33%

-11.31%

-22.24%

Tata Motors Ltd.

-71.05%

-37.20%

-33.84%

-30.84%

-40.21%

-41.87%

-29.17%

-28.53%

-42.52%

-33.16%

-37.88%

Tata Power Co. Ltd.

-34.39%

-40.87%

6.47%

-38.64%

4.25%

-30.98%

-3.41%

-35.60%

1.21%

-42.04%

7.65%

Tata Steel Ltd.

-70.35%

-50.63%

-19.71%

-45.97%

-24.38%

-64.90%

-5.44%

-41.41%

-28.93%

-47.82%

-22.53%

Ultratech Cement Ltd.

-29.72%

-27.03%

-2.69%

-17.47%

-12.26%

-18.27%

-11.45%

-23.11%

-6.62%

-26.96%

-2.77%

Wipro Ltd.

-43.09%

-27.44%

-15.65%

-25.66%

-17.43%

-21.16%

-21.94%

-20.34%

-22.75%

-23.61%

-19.48%

Note: Condition 1: Incorporating market capitalization in CAPM, condition 2: Incorporating P/B ratio in CAPM, condition 3: Incorporating PE ratio in CAPM and condition 4: Incorporating EPS in CAPM.

Source: Self computed.

Table 5.7: Value of Alpha under conditional CAPM for Post recession period (2012)

 

Unconditional CAPM

Conditional CAPM (Multifactor Model)

 

 

 

 

Condition 1

Condition 2

Condition 3

Condition 4

Company Name

Actual return

Expected return

Alpha

ExpectedReturn

Alpha

Expected Return

Alpha

ExpectedReturn

Alpha

ExpectedReturn

Alpha

ACC Ltd.

26.51%

-5.72%

32.23%

-2.75%

29.27%

15.42%

11.09%

-2.87%

29.38%

-3.71%

30.23%

Ambuja Cements Ltd.

16.82%

-4.66%

21.49%

-1.84%

18.66%

8.63%

8.20%

6.23%

10.59%

-3.21%

20.03%

Asian Paints Ltd.

28.36%

0.77%

27.59%

3.02%

25.33%

6.48%

21.87%

4.22%

24.13%

2.93%

25.43%

Axis Bank Ltd.

-18.35%

-12.55%

-5.80%

-9.68%

-8.68%

6.59%

-24.94%

-4.06%

-14.29%

-11.25%

-7.10%

Bajaj Auto Ltd.

14.73%

-7.47%

22.20%

-3.12%

17.85%

28.56%

-13.83%

52.64%

-37.91%

-0.07%

14.80%

Bank Of Baroda

-17.48%

-10.10%

-7.39%

-7.79%

-9.69%

3.58%

-21.07%

17.59%

-35.07%

-8.78%

-8.70%

Bharat Heavy Electricals Ltd.

-37.63%

-9.22%

-28.41%

-6.73%

-30.90%

-2.43%

-35.20%

-9.43%

-28.20%

-6.22%

-31.41%

Bharat Petroleum Corpn. Ltd.

14.45%

-3.96%

18.41%

-3.88%

18.32%

9.75%

4.70%

1.44%

13.01%

-1.83%

16.28%

Bharti Airtel Ltd.

-5.46%

-3.61%

-1.84%

-1.67%

-3.79%

0.81%

-6.26%

-5.92%

0.47%

-3.79%

-1.66%

Cairn India Ltd.

-4.86%

-7.29%

2.43%

-3.92%

-0.94%

7.07%

-11.93%

-6.74%

1.88%

51.42%

-56.28%

Cipla Ltd.

-5.15%

-0.11%

-5.04%

-0.87%

-4.28%

11.34%

-16.48%

11.10%

-16.25%

-1.97%

3.18%

D L F Ltd.

-24.87%

-20.78%

-4.09%

-15.66%

-9.22%

-11.82%

-13.06%

-7.95%

-16.92%

-30.68%

5.81%

Dr. Reddy'S Laboratories Ltd.

7.67%

0.59%

7.08%

2.67%

5.00%

8.89%

-1.22%

4.37%

3.30%

5.53%

2.16%

G A I L

-18.91%

-2.91%

-16.00%

-3.05%

-15.87%

17.61%

-36.52%

5.59%

-24.51%

3.34%

-22.25%

Grasim Industries Ltd.

6.93%

-6.24%

13.17%

-0.79%

7.71%

18.32%

-11.40%

4.29%

2.63%

-3.97%

10.90%

H C L Technologies

1.11%

-6.94%

8.05%

-3.76%

4.86%

3.45%

-2.34%

1.98%

-0.87%

1.48%

-0.37%

H D F C Bank Ltd.

10.80%

-8.34%

19.14%

-6.05%

16.85%

25.21%

-14.41%

5.20%

5.60%

-5.60%

16.40%

Hero Motocorp

29.31%

1.29%

28.01%

3.97%

25.34%

11.47%

17.84%

6.82%

22.48%

3.60%

25.71%

Hindalco Industries Ltd.

-38.13%

-15.00%

-23.13%

-7.84%

-30.29%

-25.77%

-12.37%

0.53%

-38.67%

-18.01%

20.12%

Hindustan Unilever Ltd.

42.82%

0.24%

42.58%

1.97%

40.86%

2.98%

39.84%

4.97%

37.86%

-5.31%

48.13%

HDFC Finance Corp. Ltd.

-3.98%

-7.64%

3.66%

-5.08%

1.10%

9.10%

-13.08%

-0.49%

-3.49%

-4.32%

0.34%

I C I C I Bank Ltd.

-20.25%

-16.40%

-3.84%

-13.24%

-7.01%

3.89%

-24.14%

-4.56%

-15.69%

-13.91%

-6.34%

I D F C Ltd.

-12.68%

-15.88%

3.20%

-10.17%

-2.51%

6.95%

-19.63%

-1.93%

-10.75%

-24.47%

11.80%

I T C Ltd.

24.60%

-0.46%

25.06%

1.62%

22.99%

6.60%

18.00%

4.25%

20.35%

1.72%

22.88%

Infosys Ltd.

-11.57%

0.24%

-11.81%

1.69%

-13.26%

9.78%

-21.35%

7.86%

-19.43%

3.91%

-15.48%

Jaiprakash Associates Ltd.

-11.96%

-21.66%

9.70%

-11.57%

-0.39%

-10.30%

-1.66%

-17.42%

5.46%

-16.70%

4.74%

Jindal Steel & Power Ltd.

-21.82%

-17.63%

-4.19%

-14.71%

-7.11%

-7.37%

-14.45%

-12.00%

-9.82%

-15.51%

-6.31%

Kotak Mahindra Bank Ltd.

19.11%

-17.98%

37.09%

-15.81%

34.93%

-5.69%

24.80%

-14.67%

33.78%

-5.37%

24.48%

Larsen & Toubro Ltd.

-20.76%

-12.90%

-7.86%

-9.92%

-10.83%

-2.92%

-17.84%

-5.96%

-14.80%

-9.35%

-11.41%

Lupin Ltd.

27.09%

-1.86%

28.95%

0.35%

26.74%

18.09%

9.00%

4.29%

22.80%

2.41%

24.67%

Mahindra & Mahindra Ltd.

-0.02%

-10.97%

10.95%

-8.32%

8.30%

6.88%

-6.90%

-4.02%

4.00%

-7.40%

7.38%

Maruti Suzuki India Ltd.

7.00%

-4.14%

11.14%

-3.79%

10.79%

43.70%

-36.70%

10.85%

-3.85%

-3.85%

10.85%

N T P C Ltd.

-15.72%

-3.79%

-11.93%

-1.31%

-14.41%

9.96%

-25.68%

-4.30%

-11.42%

9.73%

-25.45%

Oil & Natural Gas Corpn. Ltd.

-7.88%

-6.77%

-1.11%

-4.06%

-3.82%

14.71%

-22.59%

12.58%

-20.46%

-3.05%

-4.82%

Power Grid Corpn. Of India

6.13%

-5.02%

11.15%

1.16%

4.98%

15.16%

-9.02%

-0.43%

6.56%

-2.80%

8.94%

PNB

-23.72%

-9.04%

-14.68%

-6.37%

-17.35%

8.88%

-32.61%

8.83%

-32.55%

-7.41%

-16.31%

Ranbaxy Laboratories Ltd.

5.34%

-4.14%

9.48%

9.36%

-4.02%

2.15%

3.19%

-1.15%

6.49%

-0.82%

6.17%

Reliance Industries Ltd.

-28.45%

-9.40%

-19.06%

-7.22%

-21.23%

-1.05%

-27.40%

-0.75%

-27.70%

-9.75%

-18.70%

Reliance Infrastructure Ltd.

-14.89%

-18.68%

3.80%

-11.13%

-3.76%

-22.27%

7.38%

-11.37%

-3.52%

-18.00%

3.11%

Sesa Goa Ltd.

-33.17%

-12.90%

-20.27%

-7.88%

-25.30%

-6.10%

-27.08%

4.59%

-37.77%

-10.54%

-22.63%

Siemens Ltd.

-13.80%

-10.10%

-3.70%

-5.46%

-8.34%

-3.26%

-10.53%

-6.36%

-7.44%

-6.51%

-7.29%

State Bank Of India

-24.19%

-10.80%

-13.39%

-8.44%

-15.75%

6.94%

-31.13%

-5.13%

-19.06%

-8.66%

-15.53%

Sterlite Industries (India) Ltd.

-36.07%

-15.88%

-20.19%

-11.61%

-24.46%

-8.53%

-27.54%

-12.45%

-23.61%

-17.29%

-18.77%

Sun Pharmaceutical Inds. Ltd.

28.80%

2.34%

26.46%

3.81%

24.99%

17.58%

11.22%

7.00%

21.80%

4.12%

24.69%

TCS

-1.28%

-0.46%

-0.82%

1.74%

-3.01%

4.87%

-6.15%

5.83%

-7.10%

-1.07%

-0.20%

Tata Motors Ltd.

10.25%

-12.02%

22.27%

-9.06%

19.31%

8.10%

2.15%

-10.76%

21.01%

-8.45%

18.70%

Tata Power Co.

-23.03%

-9.04%

-13.99%

-5.49%

-17.54%

2.46%

-25.49%

-3.36%

-19.67%

-5.66%

-17.37%

Tata Steel Ltd.

-24.19%

-16.58%

-7.61%

-11.57%

-12.61%

-18.36%

-5.82%

3.79%

-27.97%

-14.34%

-9.85%

Ultratech Cement Ltd.

33.85%

-3.79%

37.64%

-3.17%

37.02%

7.55%

26.30%

0.18%

33.67%

-2.48%

36.33%

Wipro Ltd.

-8.35%

-4.49%

-3.86%

-3.34%

-5.01%

3.24%

-11.59%

3.30%

-11.65%

0.99%

-9.34%

Note: Condition 1: Incorporating market capitalization in CAPM, condition 2: Incorporating P/B ratio in CAPM, condition 3: Incorporating PE ratio in CAPM and condition 4: Incorporating EPS in CAPM.

Source: Self computed.

5.2.4 HYPOTHESIS TESTING

5.2.4.1 MARKET CAPITALIZATION

The regression coefficient for the effect of market capitalization on the actual returns shows a negative correlation for the pre and the post regression period. This shows as the market capitalization falls, the actual returns tend to go up. Thereby the null hypothesis is rejected and the size effect is captured in the equation for these two periods. However for the recession period, the null hypothesis is accepted thereby revealing that during the recession period (2009), there was no relation between the sentiments regarding market capitalization and actual returns.

Investors perceive that for small market capitalization companies much information is not available to the investors so noise traders tend to invest in these stocks so as to earn abnormal returns. This lead to herd behaviour and the stock prices tend to go up. Hence it depicts the impact of noise trader sentiments on market cap that affects the stock returns.

Incorporating market capitalization on the CAPM reveals the following results:

Pre recession period: 59% companies show better value of alpha than the unconditional CAPM out of which 75% are small cap.

Recession period: Only 40% companies show better value of alpha out of which 45% are small cap.

Post recession period: 56% of the companies show better value of alpha than the unconditional CAPM out of which 72% are small to mid cap.

Thus the results reveal that investor sentiments regarding market capitalization has an impact on the actual returns and therefore incorporating it in the asset pricing model helps in deriving a better estimate of the expected return and therefore enables an investor to make better decisions before investing in a stock.

5.2.4.2 PRICE TO BOOK RATIO

The regression coefficient for the effect of Price to Book ratio on the actual returns shows a positive correlation for the pre and the post regression period. This show as the Price to Book ratio falls, the actual returns also fall. Thereby the null hypothesis is rejected for these two periods. However for the recession period, the null hypothesis is accepted thereby revealing that during the recession period (2009), there was no relation between the sentiments regarding Price to Book ratio and the actual returns. Though there exists a relationship between the Price to Book ratio and actual returns, investors do not really analyze this ratio before investing. Sentiments for this characteristic is vague and thus it is seen that when this firm specific sentiment is incorporated in the asset pricing model, it does not reveal a better value of alpha than what is revealed in the traditional unconditional asset pricing model.

5.2.4.3 PE RATIO

The regression coefficient for the effect of PE Ratio on the actual returns shows a negative correlation for the pre and the post regression period. This shows as the PE ratio falls, the actual returns tend to go up. Thereby the null hypothesis is rejected and the value effect is captured in the equation for these two periods. However for the recession period, the null hypothesis is accepted thereby revealing that during the recession period (2009), there was no relation between the sentiments regarding PE ratio and actual returns.

Investors perceive that a stock having high PE ratio is overvalued. Noise traders who trade on noise as if it is profitable information believes that high PE ratio gives low returns and vice versa. This is called value effect. Thus for a stock with high PE ratio these noise traders tend to sell it under the perception that it does not give high returns. This distorts the decision of the rational investors to take the decision based on technical and fundamentals and hence lead to a herd behaviour thereby selling the stock. Hence the prices tend to go down. This shows that PE ratio has an impact on the actual returns and thereby incorporating it in the conditional asset pricing model enables an investor to take better and informed decisions.

Incorporating PE ratio in the CAPM successfully captures the value effect and reveals the following results:

Pre recession period: 60% companies show better value of alpha than the unconditional CAPM out of which 70% have high PE ratio.

Recession period: Only 30% of the companies show better returns of alpha for the conditional CAPM model.

Post recession period: 54% companies show better value of alpha than the unconditional CAPM out of which 60% have high PE ratio.

5.2.4.4 EARNINGS PER SHARE

The regression coefficient for the effect of EPS on the actual returns shows a negative correlation for the pre and the post regression period. This shows as the EPS falls, the actual returns tend to go up. Thereby the null hypothesis is rejected and the value effect is captured in the equation for these two periods. However for the recession period, the null hypothesis is accepted thereby revealing that during the recession period (2009), there was no relation between the sentiments regarding PE ratio and actual returns.

Incorporating PE ratio in the CAPM successfully captures the value effect and reveals the following results:

Pre recession period: 61% companies show better value of alpha.

Recession period: 38% companies show better value of alpha.

Post recession period: 58% companies show better value of alpha.

5.3 CONCLUSION

The data analysis proves that all the independent variables taken into consideration have an impact on the actual returns. The investor sentiments on firm specific characteristics causes the stock prices to move up or down based on what the investors perceive about these firm specific factors. Incorporating market capitalization in the conditional asset pricing model helps in determining the size effect thus successfully capturing one of the financial market anomalies to be proved in the study. Similarly by incorporating PE ratio in the conditional asset pricing model helps to determine the value effect thereby capturing the other financial market anomaly.

Other than that it is also proved that investor sentiments regarding these firm specific characteristics have a different impact in different time periods. The analysis shows that during the year 2007 and 2012, which is the pre-recession and post recession periods, the impact of investor sentiment can be seen in the asset prices. However, during the year 2009, which is the recession period, the impact of the sentiments relating to these firm characteristics is hardly visible. This is because during the recession period, the entire market was facing a down turn and was dominated by the market risk, or systematic risk, which is common to all industries and is not specific to a particular firm. Therefore the asset prices do not show the impact of investor sentiments since they are firm specific.

CHAPTER VI

FINDINGS, CONCLUSION AND SUGGESTIONS

6.1 FINDINGS

Though efficient market hypothesis is a widely accepted theory to determine the behaviour of stock price movement and returns, the study show that there are factors which impact these movements that are not revealed by the EMH. These are the investors’ sentiments about the companies whose shares they invest in.

The most important finding of the study is that leaving out the aspects of behavioural finance in determining asset prices will give a distorted picture of the returns that a stock is expected to generate.

The study reveals that investor sentiments on various firm specific characteristics like market capitalization, Price to Book value ratio, PE ratio and EPS have an impact on the returns generated by the firm. There are noise traders present in the market who trade on their beliefs on these firm specific characteristics and thus it deters the abilities of the investors who trade on the basis of fundamental and technical analysis to take a rational decision. This deviate the prices of the shares from their fundamental values.

The sentiments on the basis of which the investors trade have different impact in different time periods. In the study three time periods are taken into consideration- the pre recession period (2007), the recession period (2009) and the post recession period (2012). The analysis reveals that during the recession period the firm specific investor sentiment was subdued by the overall market risk and that these sentiments did not enable the traders to earn abnormal returns.

The study reveals that unconditional CAPM, which takes into consideration only the market risk, or the systematic risk, does not show the true value of the expected returns. The conditional CAPM, which takes into consideration the size, PE ratio, P/B ratio and EPS of the company, outperforms the unconditional or traditional CAPM.

Incorporating investor sentiments in the asset pricing model helps to capture size and value effect. These represent financial market anomalies which crop due to the noise traders present in the market. They believe that firms with low market capitalization or high PE ratio give superior returns than a company with high market capitalization or low PE ratio. Therefore incorporating the impact of these sentiments in the asset pricing models like CAPM successfully capture these financial market anomalies thereby giving better value of the expected returns.

6.2 CONCLUSION

The study talks about the role of investor sentiments in asset pricing. It questions the basic assumption of efficient market hypothesis that investors behave rationally when making investment decisions. It also questions the assumption that markets are efficient and that all the historic and present information on a company is already revealed in the stock prices and it is impossible for an investor to make abnormal returns. The point that the study tries to prove is that investors are not rational and that human psych plays an important role while investors make investment or trading decisions. Over the years the impact of behavioural finance has paced and it today it cannot be ignored while determining the stock prices movement. Apart from the results revealed from technical and fundamental analysis, the role of investors’ sentiments on the stock prices has to be necessarily considered in decision making or predicting the price movement.

The presence of noise traders in the market prove that markets are not efficient and that it is possible to earn superior returns. Their sentiments about the firm specific characteristics like market capitalization, PE ratio, P/B ratio make them take decisions against the rational decisions as per the fundamentals and technical. This deters the rational investors, who trade on the basis of fundamentals, to take opposite position that that of these noise traders and thus the stock prices does not move in tandem with the technical’s. The regression analysis done in the study proves the negative relationship between the actual return and the investor sentiment on market cap and PE ratio thereby proving that there exists financial market anomalies and that it makes sense to incorporate these effects in the asset pricing models to derive a better value of the expected returns. This will enable the investors to make better and informed decisions while investing in shares. By incorporating investor sentiments on various firm specific characteristics in three time periods taken into consideration show the impact of these factors on the expected return and give a lower alpha value for the pre and post recession periods. This shows that impact of sentiments is different in different time periods. Therefore it is important to take into account the macro economic factors along with the role that these sentiments play in determining asset prices.

6.3 SUGGESTIONS

The study shows the impact of investor sentiments on firm specific characteristics have on the returns generated by a firm. The traditional asset pricing models like capital asset pricing model, the FF model, etc do not show the impact of these sentiments on the asset prices. These models only take into account the market risk, called the systematic risk, into the equation. They reveal that the excess returns that a stock gives depend on the beta of the stock, i.e. the degree of relationship between the returns that the market gives and the returns of a firm.

In order to understand the volatility in the stock prices and the trend of their movement, the impact of these sentiments has to be taken into consideration so as to come to a better conclusion. In order to make better investment decisions, incorporating investor sentiments in the asset pricing models will give a better estimate of the returns that a stock is expected to give. Stock prices do not always move in tandem with the market and taking only beta as a factor to determine the premium over the market does not give a true picture. The perception of the investors causes prices to deviate from their fundamental values.

What is suggested in the study is that investor sentiments should also be considered while making investment decisions and that they should not be seen in isolation. The analysis shows that during the recession period, i.e. during 2009, the role of investor sentiments on firm specific characteristics was over ridden by the marker risk. This means that it is not only the investor sentiments that will help the investor to make rational decisions but it alleviates the possibility of making unprofitable decisions. Thus what is suggested is that though market risk, covered under the asset pricing models do give a fair amount of information on the returns expected, backing it up with the investor sentiments on these stocks will foster the decision making capacity of the investors to make better investment decisions.

6.3.1 SCOPE FOR FURTHER STUDIES

Though the study has to some extent managed to explain the role that investor sentiments play in asset pricing, there are various interesting facts that this study has not taken into account. Further studies on these factors could further enhance our understanding on the impact if sentiments on stock prices.

The study takes into consideration only four variables that could impact the investment decisions of investors. However there are various other factors that might have an added impact on their perception on a particular stock. For example, the management competency of a firm can be one of the factors, changes in the board of directors or some key employees of the firm etc. taking these factors can further give better estimates of the expected returns of a company.

Secondly, the study shows the impact of sentiments on firm specific characteristics individually. Taking all the factors together in the asset pricing model by considering the impact of one factor on another factor might change the values of the expected returns.

The proxy taken for investor sentiment in the study is Consumer Confidence Index published by Neilson. Though this index gives a good estimate of the sentiment of the people for the economy on the whole, it does not specifically talks about the perception of the investors on the stock markets. Construction of a risk factor which can capture the investor sentiments on stock markets can give a better estimate of the returns.

Fourthly, the study takes into account investments done for one year only. Thus it would be interesting to see the relationship between investor sentiments and stock prices and firm characteristics for a longer investment horizon.

6.4 LIMITATIONS OF THE STUDY

The study has taken the CCI as a proxy for investor sentiments. The index considers the peoples sentiments for the economy as a whole and not specifically investors’ sentiments on the stock market.

The study takes into account only four firm specific characteristics to show the impact of investor sentiments on returns. Other factors can also have an important impact as well like management competency.

The study gas been done for three time periods i.e. 2007 (pre recession period), 2009 (recession period) and 2012 (post recession period). It does not show whether sentiments play role in determining asset prices in other years as well which were not close to the recession period.

The study is specific to Indian stock market and only domestic factors are taken into account to study the impact of investor sentiments on stock prices. For example: what would be the impact if market participation of foreign institutional investors is high or the impact of sentiments in other internationally recognized stock markets are taken into consideration as well.

BIBLIOGRAPHY