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Journal of Management (JOM) Volume 5, Issue 4, July – August 2018, pp. 153–165, Article ID: JOM_05_04_017

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HERDING BEHAVIOUR AND SIZE OF THE

FIRMS: EVIDENCE FROM THE INDIAN STOCK

MARKET

Batchu Satish

Research Scholar, Department of Commerce,

Central University of Karnataka, Kalaburagi, India

Dr. Padmasree K

Professor, Department of Commerce,

Central University of Karnataka, Kalaburagi, India

ABSTRACT

This paper examines the relationship between herding behaviour and firm size in

Indian stock market using a sample of listed companies on National Stock Exchange

of India during the 2003 – 2017. The study applied the methodology proposed by the

Chang, Cheng and Khorana (2000), to examine the herding behaviour at asymmetric

nature, i.e., rising and declining movements for daily and weekly data for different

firm size i.e., small cap, mid cap and large-cap stocks. To capture the global financial

crisis effect on herding behaviour the study time frequency divided into three phases,

i.e., pre financial crisis during the financial crisis and post financial crisis and then

examined the herding behaviour in Indian stock market. Also, the study applied the

ADF statistics to check the unit root for the observations.

Key words: Herding behaviour, Cross Sectional Absolute Deviation, rising market and

declining market.

Cite this Article: Batchu Satish and Dr. Padmasree K, Herding Behaviour and Size of

the Firms: Evidence from the Indian Stock Market, Journal of Management, 5(4),

2018, pp. 153–165.

http://www.iaeme.com/JOM/issues.asp?JType=JOM&VType=5&IType=4

1. INTRODUCTION

Incorporation of human psychology into finance has initiated a big debate in finance literature

how an investors make investment decision. Many of behavioural economists expressed their

evidence that investors in the financial market influenced by the many behavioural factors,

among those factors herding behaviour is one of the important behavioural bias and many

researches empirically proved that a significant impact of herding behaviour on the financial

markets. In finance, herding behaviour has been defined as "a group of investors trading in the

same direction over a period of time"(Nofsinger & Sias, 1999)1. As in psychology, herding is

Herding Behaviour and Size of the Firms: Evidence from the Indian Stock Market


defined when persons change their concealed opinion to go with more strongly with the

openly uttered beliefs of others. Herding behaviour can be explained in two types, rational as

well as irrational. In rational herding, herding behaviour can defined as a tendency of

investors irrationally ignore their own analysis, information and confirm to the market

consensus, even if they do not agree with that (Christie, & Huang, 1995)2, on other side

irrational herding mainly happens to defend reputational concern of an investor. As investors

normally ignore their own available information and starts imitate the decision of other

investors by assuming that decisions made by other investor are more useful and they had

access to better source of information.

1.1. Importance of Size Factor in Herding behaviour

When examining the how investors employ funds to invest in stocks, it is necessary to know

the both investors anticipation of return and also risk craving. Once investor clears on these, it

will be a much feasible way for investors to allocate funds between the varieties of the

category of stocks. In this research, the study analysed the herding behaviour of stock returns

among the small mid and large capitalisation stocks. First, the study separates the sample into

different size according to their market capitalisation.

Though there are number of studies analysed the herding behaviour in Indian stock

market, the research of herding behaviour concerning to size of the stocks not conducted so

far. Further examining the relationship between sizes of the stocks in Indian stock market

make us a superior understanding of the dynamics of the financial market and how the

investors make decision with respect to the different capitalisation stocks.

This study aim is to detect the herding behaviour in Indian stock market concerning to

size of stocks listed on Indian stock exchange (NSE)

The reminder of this article is organised in the following manner. Section II presents the

review of literature for analysing the previous studies. Section III describes the methodology

and data descriptions. Section IV reports the analysis and discussion and Section V deals with

the finding & conclusion.

2. REVIEW OF LITERATURE

Many Researches in behavioural finance have focused on the relationship of investors’

financial decision-making behaviour with the variable viz., volatility, trading volume and

stock prices; also they attempt to test the relationship with all of these variables. Following

are some of the selected reviews pertaining to the herding behaviour.

For empirical analysis of herding behaviour (Christie, & Huang, 1995)2,in their study

proposed a new method called Cross-Sectional Standard Deviation(cssd) as a measure of

dispersion to investigate herding behaviour in an aggregate market by observing at how

returns on individual stock returns move around the market return during the large price

swings. The study was taken under the rationale that, under the market stress, individual

investor suppresses their beliefs and takes investment decision according to the market

consensus. The study applied return dispersion method on daily and monthly data for New

York Stock exchange and Amex firms during the year from 1962 to 1988. The study revealed

that dispersion increases significantly during the periods of large price movements and this

results were consistent with the rational asset pricing models, hence, this method is failed to

detect the herding behaviour. Later (Chang, Cheng, & Khorana, 2000)3developed a new

method based on the Cross-Sectional Return Dispersion(CSAD) of return which is a extended

version of (Christie, & Huang, 1995)2. The study examined the herding behaviour in some

Batchu Satish and Dr. Padmasree K


global stock markets and found that the results of the herding behaviour in the global markets

are mixed. In developed markets, no evidence of herding behaviour was found. However,

found evidence of herding behaviour in the developing stock markets like South Korea and

Taiwan. The study also noted that the proposed methodology of (Christie, & Huang, 1995)2

was more stringent, as it requires extreme return data to examine the herding behaviour. (Tan,

Chiang, Mason, & Nelling, 2008)4examined the herding behaviour of listed A and B share

stocks in Chinese stock market. In China A share market dominated by domestic investors

and B share market is by the foreign Institutional Investors, and the research found that the

study has detected the herding behaviour for both markets, also study analysed the this

behaviour in rising and declining pattern of market, High volume and low volume state as

well high volatility and low volatility of stocks and found evidence that herding behaviour

was presented both rising and declining and it is more intensity when market return is rising,

trading volume high and high volatility. With respect to Indian context (Lakshman, Basu, &

Vaidyanathan, 2013)5 examined the role of herding behaviour in Indian stock by taking into

consideration of FII’s and Mutual fund investors trading pattern. The results of the study

showed that mutual fund investors trading pattern leads to herding behaviour and no evidence

of herding found among the FII’s in Indian stock market. Also, the study noted that herding

behaviour was found increasing during the 2003-2005 and it started to decrease in later years,

this is because of financial crisis of 2008 affect on MFs and FIIs. In other study (Tayde &

Rao, 2011)6examined the herding behaviour among the foreign Institutional Investors in

Indian markets. The study found significant evidence of herding behaviour among FIIs on

different phases of market and these results were opposite to Lakshman et al. (2011) study.

With the use of methodologies proposed by the (Christie, & Huang, 1995)and (Chang et al.,

2000) in 2011, (Lao & Singh)7examined the existence of herding behaviour of stocks listed on

Indian and Chinese stock markets over the period. The results of the study provided the

evidence of herding behaviour is presented in listed stocks for both market and this behaviour

is depends on the different market conditions which could impact the market return over the

period. In Chinese stocks this behaviour was found when trading volume is high and return of

the market was shown declining trend and Indian market, it was exhibited when the market

return shows increasing state. However, Herding behaviour seems to be irrelevant at the level

of trading volume in India, unlike Chinese markets. Finally the study concludes that in both

the markets herding behaviour is stronger due to the presence of global financial crisis.

(Garg & Gulati, 2013)8investigated the presence of herding behaviour in Indian Stock

Market with consideration of daily, weekly and monthly data frequency of the return of the

stocks listed in CNX 500 during the period 2000-2013. (Christie, & Huang, 1995), and

(Chang et al., 2000) measures were employed in the study. The result provides that Indian

stock market not follow the herding behaviour. The existence of herd behaviour was also

investigated during increasing market state, decreasing market state, high volume state, low

volume state, pre-crisis, during the crisis and post-crisis. The results of the study states that

herding behaviour not exhibited in Indian stock market during the mentioned period. It seems

that Investors behaviour is entirely rational in Indian Stock Market as the same way by

considering daily and monthly returns of stocks listed on Indian stock market (Garg & Jindal,

2014)9 examined the herding behaviour during the 2000-2012.the study does not find any

evidence of herding behaviour among the stocks listed on the Indian market for mentioned

time.

(Chen, 2013)10

evaluated the herding behaviour in global stock markets. He found that

strongest evidence of herding behaviour in almost all stock markets. In addition to that, the

study also detected that herding behaviour is more apparent in the developed market than

other markets. The study also found that herding behaviour was presented in both Up and



down markets for some countries and Investors tend to be more herd in response to the bad

news regarding the stock market rather than the good news as the same direction (Houda &

Mohamed, 2013)11

have examined the investors herding behaviour in 28 stock markets during

up and down market by using (Christie, & Huang, 1995)and (Chang et al., 2000)

methodology. The study found that no indication of herding behaviour existed among stocks

traded in the markets. However in some markets, the herding behaviour was detected during

the market return shown declining state and investors of these markets posed to the

asymmetric reaction with respect to the superior and awful news in the market.

(Ramadan, 2015)12

examined herding behaviour in Amman Stock Exchange(ASE) by use

of Cross-Sectional Absolute Deviation of returns approach; found that herding behaviour

identified among investors in ASE also the study noted that investors of the ASE follow the

market characteristics not with the elements risk and return of Individual stocks..

From the available resources it was found that herding behaviour was less explored in the

developing countries, very few studies have been conducted so far in emerging countries

particularly in India. The studies were not reaching to any conclusive results also. And no

study was found to examine the herding behaviour of Indian stocks listed on the NSE with an

emphasize on size of the stock.

3. METHODOLOGY AND DATA DESCRIPTION

This section explains the methodology developed by the (CCK, 2000) to detect the existence

of herding behaviour of stocks listed in the NSE from 2003 to 2017. According to (Chang et

al., 2000) provided evidence of herding behaviour more likely present for the period of

relatively significant price movements. They have proposed the following model to detect the

herding in stock market.

CSADt = α + β1|Rmt|+ β2R2

mt+et (1)

CSADt indicates the return dispersion

CSADt=

∑ | | (2)

Where N is the number of stocks in the portfolio, Rit is the return of stock i at time t and

Rmt is the equally weighted average of all stock returns in the portfolio at time t. In equation

1; |Rmt| represents the absolute value of market return, β1 is the coefficient of |Rmt|, R2m,t is

the square of |Rmt|,β 2 is the coefficient of R2mt, β2is the coefficient of herding behaviour, if

it comes as significantly negative it will indicate the existence of herding behaviour in the

market.

To capture the asymmetric effects, the following equations have been used to identify the

existence of herding behaviour during upward and downward market situations. Extreme

market movements may lead to herding behaviour as investors may follow the actions of

others when market is either increasing or decreasing state.

CSADtup

= α + β1up

|Rmtup|+ β2up

R2

mtup+et Rmt>0 (3)

CSADtdown

= α + β1down

|Rmtdown |+ β2down

R2

mt down +et Rmt<= 0 (4)

Where, in equation 3 |Rmtup| represents the absolute market return in the rising state.

β1up is the coefficient of |Rmtup|, R2mt is the square of Rmt , β2up is the coefficient of R2mt

also it is the coefficient for herding behaviour if it comes the negative value and significant.

Equation4|Rmtdown| represents the absolute market return in the declining state. β1down is



the coefficient of |Rmtdown|, R2mt is the square of Rmt , β2downis the coefficient of R2mt

also it is the coefficient for herding behaviour if it comes the negative value and significant.

The returns of the stocks is UP trend when it Rm value is greater than zero (and it is down

trend when Rm value is equal and less than zero.

Augmented Dickey-Fuller statistics applied to validate the stationarity of the various data

series and found that series is stationarity at level. Tests like Jarque-Bera, Durbin-Watson test,

were also applied to check the normality, the autocorrelation of the error terms.

3.1. Data Description

Data was collected from the secondary sources. CMIE PROWESS data base is used to collect

the data. Daily and weekly observations of stocks listed in the NSE from Jan 1st 2003 to Dec

31, 2017 were collected for the present study. The study has 3680 daily and 780 weekly

observations. The stock return Rit is calculated by using the log formula ln (Pt/Pt-1)*100. The

data series is divided into according to the size of the stocks and then assign the each stock

into different capitalisation which they fall under. Since the study has conducted for a long

period to capture the financial crisis effects on herding behaviour, the study time frequency

divided into three phases, i.e., pre financial crisis during the financial crisis and post financial

crisis and then examined the herding behaviour in Indian stock market

4. ANALYSIS AND DISCUSSION

Table 1 & 2 provides the descriptive statistics on daily and weekly market returns (Rm) and

CSAD for different market capitalisation stocks listed on National Stock Exchange of India.

For daily observations, the average return for the entire sample is 0.0182 with ranges of

0.0087 to 1.1169 while the mean of market returns for weekly observation is 0.0020. The

equity market returns in Indian stock market provides more volatility with a standard

deviation of 0.0159 (1.59 percentage), while the standard deviation of weekly market return is

0.0362 (3.6 percentage) which is highly volatile nature. The average value of market returns

for different capitalisation stocks are similar in nature, i.e., Small Cap is 0.0003, mid-cap is

0.0005, and the large cap is 0.0004. While large-cap stocks do exhibit the high magnitude of

volatility 0.0281 (2.81percentage) than the small cap (1.6 percentage) and mid-cap (1.6

percentage) respectively. Mean value of market returns of weekly observations for different

capitalisation stocks are 0.0020 (Whole data), 0.0017 (small cap), 0.0026 (mid-cap) and

0.0021 (large cap), the standard deviation for whole observation is 3.6 percent and small cap

(4.0 percent) provides the evidence of high volatility than mid (3.6 percent) and large cap

(2.9) respectively.

Also, Table 1&2 provides a preliminary description of CSAD values. The mean values of

CSAD for daily and weekly observations are 0.0182, 0.0403. Average values vary among the

size of the stocks, it means the mean of small-cap stocks is 0.0219, mid-cap stocks is 0.0173,

and large-cap stocks is 0.0157, and these CSAD values are lesser than the weekly data points,

that is mean of small cap is 0.0451, 0.0374 for mid-cap and 0.0334. Concerning the volatility

small, mid and large cap stocks positioned with same values. The statistics of the Jarque-Bera

test is very high for both CSAD and RM, indicating that stock returns differ significantly from

the normal distribution. Jarque-Bera statistics is a test statistic to test whether the return of the

series is normally distributed. The Jarque-Bera statistic measures the variation of the

skewness and kurtosis of the series with those from the normal distribution. The value of

skewness and kurtosis mean that the distributions of these variables have a leptokurtic feature.

It implies that the subsequent test of OLS has to take into account this violation of normality

assumption. To check the stationarity of the various series, Augmented Dickey-Fuller test is



applied. The significant t statistics of CSAD is -18.020 (000), and RM is -54.111(000)

confirm that series is stationary at level with intercept, thereby rejects the null hypothesis, i.e.,

non-stationary series. Moreover, the data is stationary for all size of stocks listed on Indian

stock market. From the ADF values of Table 1&2, the data can be interpreted that, all the

series in the model follows the stationary hypothesis at level with intercept.

Table 1 Descriptive Statistics daily observations

Observations Whole Data Small-Cap Mid-Cap Large-Cap

csad Rm csad Rm csad Rm csad Rm

Mean 0.0182 0.0004 0.0219 0.0003 0.0173 0.0005 0.0157 0.0004

Min 0.0087 -0.2563 0.0105 -0.1151 0.0068 -0.2048 0.0042 -1.0677

Max 1.1169 0.2905 0.9046 0.2595 1.1950 0.3400 1.2857 1.0445

STD 0.0261 0.0159 0.0225 0.01609 0.0280 0.0161 0.0303 0.0281

Skewness 40.5640 0.3278 32.5106 0.0810 40.0430 1.2634 40.0998 -0.7344

Kurtosis 1701.9409 64.1341 1247.350 26.1641 1675.198 70.3990 1678.956 1084.921

Jarque-bera 4.45e+08 572508.3 2.38e+08 82122.45 4.29e+08 696568.0 4.31e+08 1.79e+08

ADF -18.020

***

-54.113

***

-21.1867

***

-48.1034

***

-18.2956

***

-52.9685

***

-17.9502

***

-88.2297

***

*** At one percent significant level

- Where ADF refers to the Augmented Dickey-Fuller to determine whether data is stationary or not

Table 2 Descriptive Statistics Weekly Observations

Observations Whole Data Small-Cap Mid-Cap Large-Cap

csad Rm csad Rm csad Rm csad Rm

Mean 0.0403 0.0020 0.0451 0.0017 0.0374 0.0026 0.0334 0.0021

Min 0.0000 -02139 0.0000 -0.2248 0.0000 -0.2134 0.0000 -0.1795

Max 0.0898 0.2128 0.1876 0.2229 0.1130 0.2424 0.1131 0.1448

STD 0.0097 0.0362 0.0136 0.0403 0.0110 0.0362 0.0117 0.0290

Skewness 1.3030 -0.5278 2.7119 -0.3930 1.5425 -0.5205 1.9086 -0.7906

Kurtosis 6.4766 7.0737 21.1787 6.4813 7.7928 8.2953 9.3710 7.2132

Jarque-bera 616.7824 578.5281 11756.20 416.0902 1061.328 951.4003 1801.959 661.5671

ADF -7.6743

***

-15.7860

***

-9.4613

***

-14.1580

***

-17.6688

***

-13.6999

***

-17.4597

***

-16.1772

***


- Where ADF refers to the Augmented Dickey-Fuller to determine whether data is stationary or not

Following figures show the relationship between Cross-sectional absolute deviations of

return and equally weighted average market returns for different market capitalisation during

2003 to 2017



To examine the herding behaviour based on size, the study divides the sample of stocks

listed on National Stock exchange of India into based market capitalisation during 2003 to

2017. Then we assign each stock in our sample into an appropriate size, i.e., small, mid and

large capitalisation stocks. Table 3. Provides the regression results of the examination of

herding behaviour concerning the small capitalisation stock for daily observations. The study

revealed that herding behaviour has not found in any of case for small-cap stocks during the

period. As a negative coefficient implies the herding behaviour, this research pays the

particular attention to all coefficients (β2) in the regression model. The results of the

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

-0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25

CSAD

Rm

CSAD versus Rm (with least squares fit)

Y = 0.0220 - 0.136X

0

0.2

0.4

0.6

0.8

1

1.2

-0.2 -0.1 0 0.1 0.2 0.3

CSAD

Rm

CSAD Vs Rm [mid-stock]

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

-1 -0.5 0 0.5 1

CSAD

RM

CSAD versus RM (with least squares fit)

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2

CSAD

Rm

CSAD versus Rm [weekly small cap]

Y = 0.0451 + 0.0367X

0

0.02

0.04

0.06

0.08

0.1

0.12

-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25

CSAD

Rm

CSAD versus Rm [weekly Mid cap]

Y = 0.0375 - 0.0290X

0

0.02

0.04

0.06

0.08

0.1

0.12

-0.15 -0.1 -0.05 0 0.05 0.1 0.15

CSAD

Rm

CSAD versus Rm [weekly large cap]



regression, coefficients (β2) of regression estimation for market returns found the positive and

significant. Moreover, the study divided into different phases (pre-crisis, during crisis and

post-crisis) to analyse the impact of financial crisis on herding behaviour, the results repeated

same coefficients (β2) of estimation show positive and significant for all observations it

means, the study has not detected the herding behaviour during different phases.

4.1. Asymmetric Effect of Market Return

To investigate the whether herding behaviour has an asymmetric in the up and down market.

The research set up a non-linear regression model by examining the asymmetric effects Rmt>0

and Rmt<0. The estimates of regression in Table 3, shows that the estimated coefficient (β2)

for both up and down market are positive and statistically significant at the level. In this

study, the small capitalisation stocks not provided at any evidence of herding behaviour when

the market it goes up and goes down.

Overall in Table 2 provided supportive evidence that herding behaviour is not found

among the small capitalisation stocks which listed on the Indian stock market. Investors of

these stocks have a rational choice and support to the CAPM assumptions.

Table 3 Regression Results for Cross-Sectional Absolute Deviation of return and Market Return on

Aggregate Market level and Pre, during and Post Crisis [2003-2017] for NSE listed small

capitalisation stocks.


mt+et

CSADtup

= α + β1up

|Rmtup|+ β2up

R2

mtup+et Rmt>= 0

CSADtdown

= α + β1down

|Rmtdown |+ β2down

R2

mt down +et Rmt< 0 Variabl

e Small Cap Pre (2003-2017) During(2008-2012) Post(2013-2017)

ALL Rising

Declinin

g ALL Rising

Declinin

g ALL Rising

Declinin

g ALL Rising

Declini

ng

Α 0.0143 0.0125 0.0169 0.0188 0.0226 0.0205 0.0148 0.0145 0.0138 0.0157 0.0166 0.0150

β1 0.6381 0.81929 0.1479 0.0612 -1.0396 -0.3372 0.5711 0.4857 0.6803 0.3470 0.1432 0.4534

β2 2.1427

***

0.9508

***

13.4275

***

4.3323

***

60.4492

***

25.0326

***

1.7832

***

1.8223

***

0.3053

***

5.3303

***

11.0177

***

4.1120

***

Adj.R^2 0.4318 0.1979 0.3480 0.4652 0.1942 0.3322 0.9437 0.9448 0.9127 0.8806 0.7950 0.9220

D.W 1.7002 1.9629 1.9404 1.8718 1.9329 1.9282 2.1224 2.1032 2.0407 2.0694 2.0480 2.0292

R^2 0.4323 0.1987 0.3488 0.4669 0.1964 0.3348 0.9439 0.9451 0.9134 0.8809 0.7962 0.9226


In Table 4. Provides the regression results of the examination of herding behaviour

concerning the mid capitalisation stocks for daily observations. The results of the regression,

coefficients (β2) of regressors for market returns found the positive and statistically

significant, results of the study reports that the results of the test shows herding behaviour has

not found in any of case for mid-cap stocks during the period. As like small capitalisation

stocks the study divided into different phases (pre-crisis, during crisis and post-crisis) to

analyse the impact of financial crisis on herding behaviour, the results repeated same

coefficients (β2) of regressors positive and statistically significant for all observations it

means, the study has not detect the herding behaviour during different phases.


To analyse the whether herding behaviour has an asymmetric in the up and down market. The

research set up a non-linear regression model by examining the asymmetric effects Rmt>0 and



Rmt<0. The estimates of regression in Table 4, shows that the estimated coefficient (β2) for

both up and down market are positive and statistically significant at the level except the down

market in during crisis phase. During crisis period, Indian market faces the significant impact

of global financial crisis. As a result the investors of the mid-cap stocks in the market mimic

the other when the market down trends to avoid the massive losses from the market. The

coefficient of estimation in the model during the financial crisis shows contrary (βdown=-

0.9926) and statistically significant.

Table 4 Regression Results for Cross-Sectional Absolute Deviation return and Market Return on

Aggregate Market level and Pre, during and Post Crisis [2003-2017] for NSE Mid capitalisation

stocks.


mt+et

CSADtup

= α + β1up

|Rmtup|+ β2up

R2

mtup+et Rmt>= 0

CSADtdown

= α + β1down

|Rmtdown |+ β2down

R2


e Mid Cap Pre (2003-2017) During(2008-2012) Post(2013-2017)

ALL Rising

Declinin

g ALL Rising

Declinin

g ALL Rising

Declinin

g ALL Rising

Declini

ng

α 0.0121 0.0077 0.0221 0.0257 0.0059 0.0280 0.0147 0.0143 0.0123 0.0133 0.0141 0.0129

β1 0.4386 0.9763 -1.0889 -1.4627 -0.5755 -1.7236 0.1660 0.1742 0.3891 0.1650 0.0133 0.2269

β2 2.6868

***

0.34199

***

25.4695

***

35.9429

***

40.9260

***

32.8808

***

0.7167

***

0.7007

***

-0.9926

***

1.7920

***

6.7998

***

1.1539

***

Adj.R^2 0.3928 0.1978 0.7271 0.8462 0.9455 0.8798 0.6775 0.7919 0.4754 0.3789 0.3782 0.3563

D.W 1.7664 1.9886 1.6731 1.5826 2.4914 1.5699 2.0852 2.0625 1.8198 2.1279 2.1203 1.6379

R^2 0.3933 0.1986 0.7275 0.8465 0.9456 0.8803 0.6786 0.7932 0.4733 0.3804 0.3807 0.3588


In Table 5. Provides the regression results of the examination of herding behaviour

concerning the large capitalisation stocks for daily observations. The results of the regression,

coefficients (β2) of regressors for market returns found the positive and statistically

significant, results of the study reports that the results of the test shows herding behaviour has

not found in any of case for large-cap stocks during the period. As like small and mid

capitalisation stocks the study divided into different phases (pre-crisis, during crisis and post-

crisis) to analyse the effects of financial crisis on herding behaviour, except during financial

crisis, the results repeated same coefficients (β2) of regressors positive and statistically

significant for all observations as small and mid capitalisation stocks. During the financial

crisis the coefficient (β2= -0.1012) of regression estimation provided the negative and

statistically significant, it means during the financial crisis the study has detected the herding

behaviour.



research set up a non-linear regression model by examining the asymmetric effects Rmt>0 and

Rmt<0. The estimates of regression in Table 5, shows that the estimated coefficient (β2) for

both up and down market are positive and statistically significant at the level except the down

market in during crisis phase and post-crisis phase. During crisis period, Indian market faces

the significant impact of global financial crisis. As a result the investors of the large cap

stocks in the market mimic the other when the market down trends to avoid the massive losses

from the market. The coefficient of estimation in the model during the financial crisis shows



negative (βdown=-0.1007) and statistically significant and post-financial crisis phase the

coefficient (βdown=-1.0730) is negative but significant level is relatively low.

Table 5 Regression Results for Cross-Sectional Absolute Deviation return and Market Return on

Aggregate Market level and Pre, during and Post Crisis [2003-2017] for large capitalisation stocks.


mt+et

CSADtup

= α + β1up

|Rmtup|+ β2up

R2

mtup+et Rmt>= 0

CSADtdown

= α + β1down

|Rmtdown |+ β2down

R2


e Large cap Pre (2003-2017) During(2008-2012) Post(2013-2017)

ALL Rising

Declinin

g ALL Rising

Declinin

g ALL Rising

Declinin

g ALL Rising

Declini

ng

α 0.0120 0.0132 0.0120 0.0133 0.0141 0.0137 0.0112 0.0130 0.0113 0.0106 0.0116 0.0102

β1 0.3147 0.1304 0.3598 0.2826 0.1633 0.2824 0.4371 0.1669 0.4368 0.3211 0.078 0.4464

β2 0.8415

***

1.0385

***

0.7799

***

0.8725

***

1.0085

***

0.8512

***

-0.1012

***

0.7514

***

-0.1007

***

1.7118

**

7.269

***

-1.0730

**

Adj.R^2 0.9727 0.9898 0.9645 0.9900 0.9948 0.9861 0.5401 0.8361 0.5402 0.2500 0.3544 0.2717

D.W 1.6179 2.095 1.7772 1.7711 2.0704 1.8815 1.6741 2.1271 1.6764 1.6660 2.036 1.9126

R^2 0.9727 0.9889 0.9646 0.9901 0.9948 0.9862 0.5409 0.8372 0.5410 0.2513 0.3581 0.2745

In Table 6, 7 and 8. The results of the CCK (2000)’s linearity model presents the results of

weekly observations for aggregate and different phases of the market during 2003-2007 for

small, mid and large capitalisation stocks. Table 6 represents the regression results of small

capitalisation stocks. From regression result, the study found significant evidence of herding

behaviour for whole market (β2= -0.2547) and post-financial crisis on down market phase

(β2= -0.4919) and the coefficients are statistically significant. From Table 7, regression

results provided the significant of herding behaviour for mid capitalisation stocks. The results

estimated coefficients at the aggregate level (β2= -0.2858), pre-crisis (β2= -0.3709) and

during the crisis (β2= -0.4112) shows negative and statistically significant nature. In Table 8,

in linearity model, the coefficients of estimates show positive and significant at an aggregate

level with weekly frequency of data the results of the large-cap stocks do not follow the

herding behaviour.



research used linear regression model by examining the asymmetric effects Rmt>0 and Rmt<0.

The estimates of regression in Table 6, shows that the estimated coefficient (β2) for both up

and down market are positive and statistically significant at the level except the post-crisis

down market phase. On post-crisis down market, the coefficient of regression estimate

provided negative (βdown=-0.4919) results, and it is statistically significant, and evidence of

herding behaviour found only above case. For mid-cap stock in Table 7, the asymmetric

effect has found during the financial crisis, regression coefficients (βUP=-0.0941) and (βdown=-

0.5170) found negative and statistically significant. During the financial crisis, mid-cap stocks

face the herd behaviour and it is more during the down market trend as the coefficient of

(βdown) is negative.

With large-cap stocks, asymmetric effect is found during the financial crisis on down

market trend, during this period the coefficient of regression model show negative (βdown=-

0.1007). Moreover, the evidence of herding behaviour is detected in only during financial

crisis downmarket trend.



Table 6 Regression Results for Cross-Sectional Absolute Deviation return and Market Return on the

weekly Market level and Pre, during and Post Crisis [2003-2017] for NSE listed small capitalisation

stocks.


mt+et

CSADtup

= α + β1up

|Rmtup|+ β2up

R2

mtup+et Rmt>= 0

CSADtdown

= α + β1down

|Rmtdown |+ β2down

R2


e

Small cap Pre (2003-2017) During(2008-2012) Post(2013-2017)

ALL Rising

Declinin

g

ALL Rising

Declinin

g

ALL Rising

Declinin

g

ALL Rising

Declini

ng

α 0.0356 0.0371 0.0357 0.0423 0.0434 0.0426 0.0358 0.0354 0.0368 0.0355 0.0356 0.0287

β1 0.3193 0.2927 0.2725 0.1912 0.1934 0.0136 0.3333 0.3395 0.2827 0.1338 0.1937 0.2946

β2 -

0.2547

***

0.5336

**

-0.2281

(no sig)

-0.4046

(no sig)

0.4061

(no sig)

0.6378

**

2.7407

***

2.5944

***

3.7194

***

0.0904

(no sig)

-0.0261

(no sig)

-0.4919

***

Adj.R^2 0.5373 0.5735 0.4621 0.4007 0.4582 0.2228 0.8976 0.8909 0.8809 0.6313 0.5582 0.5937

D.W 2.0832 2.0094 2.0741 2.0452 2.0181 2.0001 1.9666 1.9563 2.0256 2.0471 2.0079 1.6242

R^2 0.5397 0.5775 0.4683 0.4101 0.4729 0.2521 0.8992 0.8938 0.8854 0.6376 0.5724 0.5999

*** At one percent significant level, ** At five percent significant level, * At ten percent significant

level


weekly Market level and Pre, during and Post Crisis [2003-2017] for NSE listed mid capitalisation

stocks.


mt+et

CSADtup

= α + β1up

|Rmtup|+ β2up

R2

mtup+et Rmt>= 0

CSADtdown

= α + β1down

|Rmtdown |+ β2down

R2


e

Mid cap Pre (2003-2017) During(2008-2012) Post(2013-2017)

ALL Rising

Declinin

g

ALL Rising

Declinin

g

ALL Rising

Declinin

g

ALL Rising

Declini

ng

α 0.0302 0.0323 0.0316 0.0344 0.0362 0.0368 0.0267 0.0299 0.0261 0.0297 0.0298 0.0294

β1 0.2656 0.1750 0.1857 0.2597 0.2420 0.1290 0.3370 0.2076 0.3739 0.0997 0.0749 0.1850

β2 -0.2858

***

0.0295

(no sig)

0.6757

**

-0.3709

**

-1.0790

(no sig)

0.7144

(no sig)

-0.4112

***

-0.0941

**

-0.5170

***

1.6210

***

1.7028

***

0.6997

(no sig)

Adj.R^

2

0.4400 0.4418 0.4040 0.3117 0.2710 0.2613 0.5028 0.5600 0.5600 0.3313 0.3336 0.3673

D.W 2.1270 2.0686 1.6747 2.0577 2.0352 1.7567 1.6841 1.9364 1.9364 1.5589 1.6832 1.9882

R^2 0.4421 0.4468 0.4075 0.3197 0.2893 0.2766 0.5067 0.5667 0.5667 0.3389 0.3420 0.3929


level




weekly Market level and Pre, during and Post Crisis [2003-2017] for large capitalisation stocks.


mt+et

CSADtup

= α + β1up

|Rmtup|+ β2up

R2

mtup+et Rmt>= 0

CSADtdown

= α + β1down

|Rmtdown |+ β2down

R2


e Large cap Pre (2003-2017) During(2008-2012) Post(2013-2017)

ALL Rising

Declinin

g ALL Rising

Declinin

g ALL Rising

Declinin

g ALL Rising

Declini

ng

α 0.0279 0.0122 0.0120 0.0341 0.0141 0.0137 0.0257 0.0130 0.0113 0.0281 0.0115 0.0102

β1 0.2115 0.2508 0.3598 -0.0163 0.1633 0.2824 0.3599 0.1669 0.4368 -0.1388 0.0806 0.4464

β2 1.0452

***

0.9244

***

0.7799

***

2.6161

***

1.0085

***

0.8512

***

-0.0610

(no sig)

0.7514

***

-0.1007

**

8.0077

***

7.3321

***

-1.0745

**

Adj.R^2 0.2608 0.9824 0.9646 0.1773 0.9959 0.9861 0.3838 0.8361 0.5402 0.2304 0.3340 0.2717

D.W 16227 1.6254 1.7772 1.6461 2.0704 1.8815 1.5693 2.1271 1.6764 1.8671 2.1488 1.9126

R^2 0.2627 0.9824 0.9824 0.1837 0.9948 0.9862 0.3886 0.8372 0.5410 0.2363 0.3368 0.2745


level

5. FINDINGS

Secondary data used to examine the herding behaviour in Indian stock market during the 1st

Jan, 2003 to 31st Dec, 2017 by analysing the cross sectional variability of stock return and

market return. Chang, Cheng and Khorona (2000) methodology is applied and perform the

regression analysis. The following findings were list out from the regression analysis

At aggregate data level herding behaviour has not detected among the small

capitalisation stock during the investigation period but on weekly data, the behaviour

has found for whole period and post crisis down market period

While analysing the mid capitalisation stocks, the herding behaviour is existed on only

during financial crisis down market trend. In case of weekly data the herding

behaviour is found at pre and during crisis period.

Among the large cap stocks, herding behaviour is absent all phases except during the

financial crisis, during financial crisis period the herd behaviour is detected with

whole data and down market trend and same results were repeated at weekly

observations.

6. CONCLUSIONS

This study examine the presence of herding behaviour in national stock exchange of India by

segregating the listed stocks into different capitalisation i.e., small, mid and large

capitalisation stock during the 2003 to 2017. Also, the study divided the time frame into three

phases as pre crisis, during crisis and post crisis period. Empirical results provided the

evidence that herding behaviour is existed among mid and large cap during the crisis and rest

of the phases the market follows the traditional finance assumptions. When time frame

segregated into daily and weekly frequency, herding behaviour is present on the small

capitalisation stock during financial crisis, also the results were same for large capitalisation

stock as like daily observations. Moreover, in Indian stock market the herding behaviour is

strong on during the financial crisis.



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