Analysing existence of herding behaviour in various stock markets--a theoretical framework.
Purohit, Harsh ; Satija, Vibha Dua ; Saxena, Sakshi 等
[ILLUSTRATION OMITTED]
Introduction
Traditionally, financial economists have based their work on the
standard assumption that investors are fully rational, self-interested
and maximizers of expected utility. But still financial markets across
the world have been observed volatile many times so it becomes
imperative to find out the reasons for volatile market behaviour. Over
the years, understanding the movements of stock markets have become very
complicated task. In the recent years, researchers in the stock markets
have introduced a new field of research known as behavioural finance. In
this researcher helps in analysing various behavioural anomalies that
arises while making investment decisions. According to them apart from
facts and figures the behaviour of stock market investors also matters a
lot. Therefore, understanding the behaviour of market participants has
become a major challenge to academicians and practitioners. Majority of
studies have documented that the concept of rationality and the
Efficient Market Hypothesis in finance have major shortcomings in
modelling real life stock returns. The Efficient Market Theory (E.Fama
1970) assumes that investors form rational expectations of future prices
and discount all market information into expected prices in the same
way. However, these assumptions of rationality underpinning the
efficient market hypothesis are often challenged in reality as the
observed returns displays various behavioural biases such as
overconfidence, optimism, conservatism, hindsight, herding, overreaction
to chance, errors of preferences, regret of omission & commission,
regret & risk taking in many markets. Irrationalities in investment
behaviour have been the reason behind major booms and busts in the
market. Herding is one such behavioural anomaly which defies the
efficient market hypothesis (EMH). The herding behaviour describes a
group of individuals who act to imitate the decisions of others or
market in general without paying any attention to their own beliefs or
information (Bikchandani and Sharma, 2000). Therefore, the present study
is an attempt to find out the existence of herding in the stock markets
across the world as well as India with the help of extant studies.
Concept of Herding
Herding means when the individual investors suppress their own
private beliefs and imitate the market consensus, it has significant
impact on security prices. Consequently, prices deviate from fundamental
value, and the risk and return characteristics of stock prices get
impacted. There are few causes of herding behaviour:
* Imperfect information makes investor view the actions of other
investors as a signal that they possess certain information which they
themselves do not have. Imitation of such behaviour seems justifiable as
they try to benefit from the supposed unknown information.
* Reputational concern stems from uncertainty about the skill and
ability of the investment manager to pick the right stocks. Therefore,
if the manager and other investment professional care to avoid
reputational scrutiny they may seek the comfort of trading in a group.
* Compensation structures that link performance to compensation may
lead to herd behaviour. An efficient contract that maximizes the
weighted sum of the principal's and agent's utility also links
compensation to a performance benchmark. The agent has the incentive to
imitate the benchmark in that the optimal investment portfolio moves
closer to the benchmark's portfolio after the agent observes the
benchmark's actions. In effect the agent herds the benchmark (Maug
and Naik, 1996).
Herding behaviour of investors is defined as the tendency to
accumulate on the same side of the market, is often viewed as a
significant threat for the stability and the efficiency of financial
markets (Hirshleifer and Teoh (2003) and Hwang and Salmon (2004)). It
refers to the human tendency to imitate the behaviour of others, which
leads to a group of people acting in a similar way. It is broadly
defined to include any similarity or convergence in behaviour brought
about by the interaction of individuals or firms (Hirshleifer and Teoh,
2003).The term "herding" describes the correlation of investor
behaviour resulting from imitating other investors' trading
activity. This correlation in activity may stem from informational
cascades, as the observation of prior trades can be so informative that
investors are willing to ignore their own current private information in
trading. Such kind of behaviour leads a group of investors to move in
the same direction, pushing stock prices further away from their
economic fundamentals, causing price momentum and excess volatility
(Bikhchandani et al. 1992; Nofsinger and Sias 1999).
While elaborating more about such behaviour, various researchers
have distinguished between intentional herding and unintentional or
spurious herding. Unintentional herding is mainly fundamental driven and
arises because institutions may examine the same factors and receive
correlated private information, leading them to arrive at similar
conclusions regarding individual stocks (Hirshleiferet al., (1994). The
primary reasons for rational herding are incentives for fund managers,
shared preferences for particular stocks, and common reactions to the
same news (Griffin et al., 2003), leading to efficient outcomes.
Moreover, professionals may constitute a relatively homogenous group:
they share a similar educational background and professional
qualifications and tend to interpret informational signals similarly. In
contrast, intentional herding is more sentiment driven and involves the
imitation of other market participants, resulting in simultaneous buying
or selling of the same stocks regardless of prior beliefs or information
sets. This type of herding can lead to asset prices failing to reflect
fundamental information, exacerbation of volatility, and destabilization
of markets, thus having the potential to create, or at least contribute,
to bubbles and crashes on financial markets, (Morris and Shin (1999) and
Persaud (2000)).
Access to information and information asymmetries attracts
uninformed investors to imitate the behaviour of other investors.
(Goodfellow et al., 2009). The herding behaviour of financial analysts
in relation to stock characteristics known as 'Characteristic
herding' phenomenon also affects investors' behaviour (Lin et
al., 2011). Size effects and the development of the market and state of
the market also contribute to herding behaviour of investors'
(Kremer et al., 2011). Herding behaviour in emerging markets can be seen
at the time of rising markets, high trading volume, and high volatility.
The present study is an attempt to examine various studies existing
about herding behaviour amongst investors' in various stock
markets.
Objectives of the Study
* To study the herd behaviour with the extant studies conducted in
India and abroad.
* To assess whether existence of herding behaviour in Indian stock
market corroborates with the studies done before.
To assess the herding behaviour in Indian stock market, data, i.e.
closing prices, market index was collected from CMIE Prowess database
for the period 2006-2013 for Nifty 50 Index. The existence of herding
behaviour has been in two time periods, one for the whole time period,
i.e. 2006-13 and another 2008-2013 to investigate the existence of
herding behaviour in India during and post subprime crisis.
Review of Extant Studies
A growing body of work has been developed over the years, which
have examined the herding behaviour across different scenarios. It is
tabulated as given in Table I.
Various studies indicated in table 1 supports herding behaviour.
Chiang et al. (2011) in his results shows that dynamic herding behaviour
is significantly correlated with state variables such as current stock
returns, conditional stock-return variance, and global stock volatility.
Kapusuzoglu (2011) provided evidence that herding behaviour exists in
the ISE National-100 index on both rising and falling days. Chiang et
al. (2010) documented that herding exists in all Asian markets, but not
in the US market. In the study conducted by Caporale et al. (2008),
herding is found during the stock market crisis of 1999. Investor
behaviour seems to have become more rational since 2002, owing to the
regulatory and institutional reforms of the Greek equity market and the
intense presence of foreign institutional investors. Hachicha et al.
(2008) in his study proposed a new herd measure which is based on the
cross-sectional dispersion of beta to detect the prevalence of herding
phenomenon towards the market. The findings of the study concluded that
the herd phenomenon consists of three essential components: constant
term of herding which signals the existence of the phenomenon whatever
the market conditions are, the error term relative to the anticipations
of the investors concerning the totality of assets, and the third
component highlights that the current herding depends on the previous
one. On the other hand, the findings of the study Khoshsirat et al.
(2011) does not provide significant effect of the considered periods on
herding behaviour existence in the exchange.
Various studies have shown in their results that Investors are more
likely to herd when selling rather than buying stocks, meaning that
herding activity appears to be more pronounced in falling markets
vis-a-vis rising markets.
Herding In Indian Stock Market
Extensive research has investigated the level of herding behaviour
in developed as well as emerging markets. The findings are contradictory
from country to country (refer table 1). Very few studies have been
conducted in India on herding till date. This study is an attempt to
find out the existence of herding in the Indian stock market with the
help of available studies.
Table II provides studies done on Indian stock market. Prosad et
al. (2012) in his study validates that herding behaviour exists in the
period stress. Lao et al. (2011) in his study also supports the results.
GokhanSonaer (2011) found that, during the herding quarter, stocks
bought intensely by herds outperform stocks sold intensely by herds.
While, Lakshman et al. (2011) does not found much existence of herding
behaviour in Indian stock market.
Behavioural finance in Asian markets being known as emerging
markets is an important topic worth studying. Majority of the studies
have taken care of developed markets. Anecdotally, individual investors
in Asia are often viewed as mere gamblers. Some researchers have
provided a useful literature review from the psychology and finance
literatures on the behaviour of Asian people and how these behaviours
might affect their investment decision making. Asia is an interesting
place to study behavioural finance because of the different levels of
capitalism and financial market experience of its participants.
Methodology Used to Examine the Herding Behaviour in India
Studies which focuses directly on the behaviour of the investors
requires detailed and explicit information on the trading activities of
the investors and the changes in their investment portfolios. Examples
of such herd measures are the LSV measure by Lakonishok, Shleifer and
Vishny (1992) and the PCM measure by Wermers (1995). The second category
of studies views herding behaviour as a collective buying and selling
actions of the individuals in an attempt to follow the performance of
the market or any other economic factors or styles. Here, herding is
detected by exploiting the information contained in the cross-sectional
stock price movements. Christie and Huang (1995), Chang, Cheng and
Khorana (2000) and Hwang and Salmon (2001, 2004) are contributors of
such measures. Majority of the studies referred in the table 1 are based
on the model developed on CSSD by Christie and Huang (1995).
Cross-sectional standard deviation (CSSD) and Cross-sectional Absolute
deviation (CSAD) among individual firm returns within a particular group
of securities have been employed.
CSSD denotes the cross-sectional standard deviation of stock return
rates from the market return rate in the period. Cross-sectional
standard deviations (CSSD), used as a measure of return dispersion, is
formulated as follows:
CSSDt = [square root of [N.summation over (i=1)] [([r.sub.i,t] -
[r.sub.p,t]).sup.2]/N-1]
where n is the number of firms in the aggregate market portfolio,
[r.sub.i,t] is the observed stock return on firm i for day t and
[r.sub.p,t] is the cross-sectional average of the n returns in the
market portfolio for day t. This measure can be regarded as a proxy to
individual security return dispersion around the market average.
CSS[D.sub.t] = [varies] + [[beta].sub.1] [D.sub.t.sup.L] +
[[beta].sub.2] [D.sub.t.sup.u] + [e.sub.t] equation (2)
In equation 2, [[beta].sub.1] is the coefficient of
[D.sub.t.sup.L], [[beta].sub.2] is the coefficient of [D.sub.t.sup.u].
The dummy variables in regression equation (2) are used as explanatory
variables to differentiate the periods of market stress from normal
periods, taking into consideration that market stress occurs when
aggregate returns lie in upper or lower tail of return distribution. So
that, [D.sub.t.sup.L] = 1 if, on day t Rm,t lies in lower tail of return
distribution and 0 otherwise. [D.sub.t.sup.U] = 1 if, on day t Rm,t lies
in upper tail of return distribution and 0 otherwise. Upper and lower
tails were determined at 90 percent, 95 percent and 99 percent levels.
Herding was proven if dummy variable coefficients were negative and
statistically significant.
The cross-sectional Standard deviation (CSSD) approach is sensitive
to outliers. For avoiding the problem, Chang et al. (2000) used the
cross-sectional absolute deviation to measure herding. He proposed that
individual securities return dispersal would reduce when herding
behaviour occurs. CSAD is the average of the aggregate difference
between the expected return of individual securities and market return.
According to the Rational Asset Pricing Model, the relationship between
market return and CSAD has been positive. Chang et al., (1999), propose
that this relationship should be negative and non-linear when herding
behaviour occurs because the absolute market return value increases,
whereas the CSAD decreases, or increase at a decreasing rate. Therefore,
a non-linear regression, which consists of a component of a nonlinear
market return, captures the non-linear relationship between
[CSAD.sub.t] = 1/N [N.summation over (i=1)] [absolute value of
[R.sub.i,t] - [R.sub.m,t]]
[CSAD.sub.t] = a + [y.sub.1] [absolute value of [R.sub.m,t]] +
[y.sub.2][R.sup.2.sub.m,t] + [[epsilon].sub.t]
individual security returns and market return.
Return dispersion, CSAD, can be measured by following equation:
where,
[absolute value of [R.sub.mt]] represents the market return,
[Y.sub.1] is the coefficient of [absolute value of [R.sub.mt]]
[R.sup.2.sub.m,t] is the square of [absolute value of [R.sub.mt]]
[Y.sup.2] is the coefficient of [R.sup.2.sub.m,t]
[R.sub.i,t] is the individual stock return of firm i at time t.
Lamda 2 is the coefficient of herding behaviour, if it comes as
significantly negative; it will indicate the presence of herding
behaviour.
Data Analysis
This section tries to analyze the behaviour of Indian stock market,
i.e. whether Indian stock market exhibits herding behaviour or not as
the literature existing in India provides mixed results. The study
employed Chang et al. (2000) methodology on the time period April 1,
2006-March 31, 2013 and April 1, 2008-March 31 2013. The results shown
in Table III .
Table III applies Chang et al. (2000) methodology to measure
herding effect. Positive and significant coefficient of [beta]2
indicates absence of herding behaviour during the time period 2006-2013.
Also during 2008-13, positive and significant squared coefficient
validates the same, i.e., absence of herding behaviour. The relationship
between market return and equity return dispersion is positive and
linear because individual securities have different reaction to the
market return to reflect the different belief held by investors in the
rational market.
Findings and Conclusions
Herding behaviour is generally characterized by mimicking the
actions of other investors, which constitute the market consensus.
Multiple researches on this behaviour have been conducted, validating
the existence or absence of it in specific stock markets, thereby
investigating the effect of the direction of the market movement and
extreme market returns on herd behaviour respectively. Review of
literature has shown that investors tend to herd more intensively during
either an upward movement or a downward movement of the market.
Empirical work done in our research also violates the presence of
herding behaviour. Till now in the studies, the approach of the
researchers are essentially restricted to a single market analysis, and
no attempt is made to detect the interaction of herding behaviour across
national borders. Future research scope could be to separate the herding
behaviour between individual and institutional investors as not much
literature is available to support the same. The above mentioned
findings triggered the motivation to investigate the relationships with
herd behaviour in a specific stock market especially emerging stock
markets like India, thereby, combining the approaches of multiple
researches. This may be because of two major reasons. Firstly, Indian
stock market is considered to be one of the emerging markets (being part
of the 'BRICs' economies), expecting to be a major economic
power around the year of 2050. This makes it interesting to test whether
indeed herd behaviour is to be more profound in such an emerging market.
Secondly, the effect of herding behaviour in India is not thoroughly
investigated. Therefore, the contribution of the research done on Indian
stock market is of academic relevance, since it provides more insight
into the effect of herd behaviour on asset prices in the Indian stock
market, while also investigating the several influences on the extent of
herding.
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Harsh Purohit
Dean, WISDOM, School of Management Studies, Banasthali Vidyapith,
Rajasthan.
Vibha Dua Satija
Assistant Professor, Institute of Marketing and Management, Delhi.
Sakshi Saxena
Research Scholar, WISDOM, School of Management Studies, Banasthali
Vidyapith, Rajasthan.
Table I
A Review of Extant Studies on Herding Across the World
S.No. Author (Year) Findings
Studies which supports herding behaviour
1. Chiang et The study examined investor herding
al. (2011) behaviour for ten Pacific-Basin markets:
Australia, China, Hong Kong, Indonesia,
Japan, Malaysia, South Korea, Singapore,
Thailand, and Taiwan. The results showed
that herding is positively related to
stock returns, but negatively related to
market volatility. Herding estimates
across markets are positively correlated,
signifying co-movement of investor
behaviour in the region.
2. Kapusuzoglu The authors studied the existence of
(2011) herding behaviour in Istanbul stock
exchange (ISE) National 100 index. The
existence of herding behaviour was
examined in terms of two models as rising
and falling days and asymmetrical and
nonlinear relationships were put forward.
The result shows that that increasing
index return rate in rising days of
markets increased cross sectional
volatility significantly and these
findings were supported by other
findings, which were obtained for falling
days in markets.
3. Caporale et The study investigated the presence of
al. (2008) herding behaviour in the Athens stock
market under extraordinary market
conditions during the period between 1998
and 2007 and identified the presence of
herding behaviour in this market.
4. Chiang et This research investigated the presence
al. (2010) of herding behaviour in 18 countries in
the global market during the period
between 1988 and 2009. Their research
demonstrated that herding behaviour
exists in advanced stock markets except
for the US and that stock return
dispersions in the US had a significant
role in explaining the herding behaviour
in non-US markets.
5. Tan et This study examines herding behaviour in
al. (2008) dual-listed Chinese A-share and B-share
stocks. The findings states that evidence
of herding within both the Shanghai and
Shenzhen A-share markets that are
dominated by domestic individual
investors, and also within both B-share
markets, in which foreign institutional
investors are the main participants.
Herding occurs in both rising and
falling market conditions.
6. Demirer et The study finds that the linear model
al. (2010) based on the cross-sectional standard
deviation (CSSD) testing methodology
yields no significant evidence of
herding. However, the non-linear model
proposed by Chang et al. (2000)and the
state space based models of Hwang and
Salmon (2004) lead to consistent results
indicating strong evidence of herd
formation in all sectors. It also states
that the herding effect is more prominent
during periods of market losses.
7. Sumit Agarwal The author examined herding behaviour of
et al. (2011) domestic and foreign investors in the
Indonesian stock market. The result
documented that both domestic and foreign
investors from a particular brokerage
firm tend to herd. The foreign investors
exhibit a greater propensity to herd
than domestic investors.
8. Hwang et The author developed a measure to test
al. (2001) herding in US, UK, and South Korean stock
markets. They evaluated the direction
towards which the market may be herding.
Their measure took into account the
fundamentals of the firms and influence
of time series volatility. With this
they could differentiate intentional
herding from spurious herding.
9. Phansatan et This paper examines the trading behaviour
al. (2011) and decomposes the trading performance of
foreign, individual and institutional
investors as well as proprietary traders
in a dynamic emerging stock market, the
Stock Exchange of Thailand. Foreign
investors follow a positive feedback,
momentum strategy and are good short
term market timers but have poor security
selection performance in poor markets,
thus suggesting that they have a macro
(market timing) but not a micro (security
selection) informational advantage
relative to local investors.
Studies which do not support herding begaviour
10 Khoshsirat et This study examined existence of herd
al. (2011) formation in Tehran stock exchange at
aggregate market level as well as within
9 major industries during an eight-year
period from April 10, 2001 through
July 11, 2009. The primary findings show
that there is no empirical evidence of
herd formation in the whole market as
well as within industries except for
two ones: Automobile and Minerals.
11. Hachicha et Authors of the study proposed a new herd
al. (2008) measure which is based on the
cross-sectional dispersion of beta to
detect the prevalence of herding
phenomenon towards the market. This
measure was applied to Tunisian market
which states that the new herd measure
applied provided better results than
those obtained by the cross-sectional
stock price's models developed by
Christie and Huang (1995), Chang, Cheng
and Khorana (2000) and Hwang and
Salmon (2001, 2004).
12. Demirer et Authors conducted a study covering six
al. (2007) geographical regions and the period
between 1998 and 2004, in which they
empirically tested herding behaviour.
They examined the movements of returns
in African, Asian, Eastern-Western-Central
European, Central Asian, and Latin
American markets according to S&P 500 and
MSCI indices and oil prices. They failed
to find any evidence of herding behaviour
in all of the markets, except for Asian
and Middle Eastern markets.
13. Demirer et This study investigated the Chinese
al. (2006) market, and find no evidence of herding,
suggesting that participants in the
Chinese stock market make investment
choices rationally.
14. Naujoks et The study attempts to examine the herding
al. (2009) (or anti-herding) behaviour of German
analysts regarding earnings forecasts.
The findings states that German analysts
anti-herd, that is, they systematically
issue earnings forecasts that are further
away from the consensus forecast than
their private information indicates.
Anti-herding is more severe in cases of
higher competition among analysts.
Table II
Studies done on Indian Stock Market
S. No. Author (Year) Findings
1. Prosad et The study explored the herding effect
al. (2012) in India. The presence of herding
linear has been tested using
regression model and linear regression
using quadratic functional form. The
finding states that Indian markets are
efficient as no severe herding has been
reported. However when presence of
herding was tested for periods of
market stress, it prevailed in
bull phase.
2. Lao et The author examined herding behaviour
al. (2011) in the Chinese and Indian stock markets
by employing the Cross Sectional
Absolute Deviation (CSAD) approach
proposed by Tan, Chiang, Mason and
Nelling (2008) to measure herding
behaviour. The result shows that
herding behaviour exists in both the
Chinese and Indian stock markets
depending on some market conditions.
In the Chinese market, herding behaviour
is greater when the market is falling
and the trading volume is high. On the
other hand, in India the study finds
herding behaviour during the up market.
3. Lakshman et The study observed that the presence
al. (2011) of market wide herding in Indian stock
markets is not very severe. They found
that FII's do not significantly impact
herding, however Mutual Funds increase
herding. They also found that Nifty
returns have no impact on herding.
They documented that herding was on a
rising trend from 2003 - 2005, however
post 2006 herding started to decline.
They suggested that periods of market
crisis can help return markets to
equilibrium, and that herding can be
more apparent before market stress,
rather than during it.
4. GokhanSonaer The author examined whether herding by
(2011) actively managed equity funds affects
their performance. First the effect of
herding on stock returns is re-examined
and evidence is found that, during the
herding quarter, stocks bought
intensely by herds outperform stocks
sold intensely by herds.
5. Sanjay Sehgal This study empirically evaluate if
and Neeta Foreign Institutional Investors (FIIs)
Tripathi (2009) adopt positive feedback and herding
strategies in the Indian environment
and found that FIIs exhibit return
chasing behaviour when they use monthly
data. However, they do not seem to be
working on the positive feedback
strategy when they used daily files.
Table III
Total Market Regression Results
[CSAD.sub.t] = [varies] + [Y.sub.1] [absolute value of
[R.sup.2.sub.m,t]] |+ [Y.sub.2] [R.sup.2.sub.m,t] + [e.sub.t]
Daily (April 1, Daily (Apri 11,
2006 - March 2008 - March
31, 2013) 31, 2013)
[alpha] 1.406148 (a) 1.431228 (a)
t-statistics 58.56050 44.49213
[[beta].sub.1] -0.017887 -0.027856
t-statistics -1.368138 -1.502962
[[beta].sub.2] 0.031395 (a) 0.029027 (a)
t-statistics 13.19403 9.577954
Adjusted [R.sup.2] 0.090816 0.067891
F statistic 87.70172 46.04897
Residual tests
ARCH test
F value 423.0196 (a) 456.4097 (a)
Obs*[R.sup.2] 340.4522 (a) 333.7919 (a)
Durbin-Watson stat 1.782541 2.164542
(a) Mean significant at a level of 1%.
