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Sectoral herding behavior in the aftermarket of Malaysian IPOs
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DOI: 10.1080/13691066.2014.921100
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Sectoral herding behavior in the
aftermarket of Malaysian IPOs
a
b
Pegah Dehghani & Ros Zam Zam Sapian
a
Graduate School of Business, National University of Malaysia,
43600 UKM Bangi, Selangor, Malaysia
b
Faculty of Economics and Management, National University of
Malaysia, 43600, UKM Bangi, Selangor, Malaysia
Published online: 16 Jul 2014.
To cite this article: Pegah Dehghani & Ros Zam Zam Sapian (2014) Sectoral herding behavior in
the aftermarket of Malaysian IPOs, Venture Capital: An International Journal of Entrepreneurial
Finance, 16:3, 227-246, DOI: 10.1080/13691066.2014.921100
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Venture Capital, 2014
Vol. 16, No. 3, 227–246, http://dx.doi.org/10.1080/13691066.2014.921100
Sectoral herding behavior in the aftermarket of Malaysian IPOs
Downloaded by [Universiti Kebangsaan Malaysia], [Ros Zam Zam Sapian] at 18:12 21 July 2014
Pegah Dehghania1 and Ros Zam Zam Sapianb*
a
Graduate School of Business, National University of Malaysia, 43600 UKM Bangi, Selangor,
Malaysia; bFaculty of Economics and Management, National University of Malaysia, 43600 UKM
Bangi, Selangor, Malaysia
(Received 26 November 2013; final version received 18 April 2014)
The Malaysian initial public offering (IPO) market is characterized by substantial
uncertainties due to limited disclosure of information, ‘fixed-price pricing mechanism’,
and cognitive biases of Asian investors. Together, these characteristics might induce
investors to engage in herding behavior in the aftermarket of an IPO. This study investigates
investors’ herding behavior in the IPO aftermarket from 2001 to 2011 using Christie and
Huang’s [Christie, W. G., and R. D. Huang. 1995. “Following the Pied Piper: Do Individual
Returns Herd Around the Market?” Financial Analysts Journal 51 (4): 31–37] method.
The findings of this study show that for non-private placements, a negative and insignificant
b1 coefficient, as an indication of herding, is reported for Technology sector. The herding
behavior that is only constrained to technological firms during down market may be due to
the risky nature of the new issues in the down market, rather than the uninformed
characteristic of the individual investors. The findings of this study also show that for the
private placement category, negative and insignificant coefficients of b1 and b2 are
reported for Consumer Product and Technology sectors, respectively. Since the negative
coefficients are not limited to the down market, with risky and uncertain shares, the results
could be an indication of the herding of informed investors in the two mentioned sectors.
Keywords: behavioral finance; herding; behavioral (cognitive) decision theory; IPO
aftermarket; private placement; non-private placement; cross-sectional dispersion of
return; Bursa Malaysia
Introduction
Companies raise capital through the issuance and sale of new shares in the initial public
offering (IPO) market. Most of the past studies on IPOs are motivated by the two
anomalies inherent in this market: the initial underpricing and the long-run
underperformance. Underpricing refers to the positive difference between the offer
price and the market price on the first day of listing. The long-run returns are generally
called the cumulative return or buy-and-hold returns, one year or more after the date of
listing. Empirical support on the long-run performance is inconclusive with the majority
of the developed stock markets reporting underperformance, whilst their developing
counterparts reporting overperformance. Prior literature provides an extensive review
about underpricing and long-run underperformance anomalies of IPOs, for instance by
Ritter (2003) and more recently by Yong (2007b) for Asian markets.
With regard to the Malaysian IPO market, abundant studies have been carried out
that relate some unique or specific features of IPO with underpricing anomaly. Among
*Corresponding author. Email: zamzam@ukm.my
q 2014 Taylor & Francis
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P. Dehghani and R.Z.Z. Sapian
these are firm size (Yong 1996), underwriter’s reputation (Jelic, Saadouni, and Briston
2001), oversubscription rate (Yong 1996; Yong and Isa 2003), offering size (Yong,
Yatim, and Sapian 2002), main shareholders ownership (Abdullah and Taufil Mohd
2004), firm’s age (Abdullah and Taufil Mohd 2004), owners’ participation and lockup
provision (Wan-Hussin 2005), government public policy and regulatory intervention
(Prasad, Vozikis, and Ariff 2006), regulation (Mohd 2007), proportion of IPO shares
allocated to Bumiputra investors (How et al. 2007), disposition effect and flippers
(Chong 2009), flipping activity (Abdul Rahim and Yong 2010), Shari’ah-compliant
(Abdul Rahim and Yong 2010), board structure (Yatim 2011), earnings management
(Ahmad-Zaluki, Campbell, and Goodacre 2011), and intellectual capital disclosures in
IPO prospectuses (Rashid et al. 2012). In addition, there are some studies that have
applied the models used in the developed markets such as bandwagon effect, lawsuit
avoidance, prospect theory, signaling hypothesis, speculative bubble hypothesis, and
Winner’s curse to explain underpricing in the Malaysian IPO market. This includes
studies by Annuar and Shamsher (1998), Chong (2009), Yong (2011b, 2013).
Earlier IPO literature in Malaysia reveals that very few studies investigate the investors’
behavior in the immediate IPO aftermarket. For instance, Yong (2011a) studied the immediate
behavior of the Malaysian IPOs, but concentrated on the first day of the trading only. Yong
(2013) investigated the immediate IPO aftermarket for 20 trading days and analyzed the
behavior of the investors across different listing boards only. Yong (2011b) studied the
investor’s imitating behaviors, concentrating on the relation between these behaviors and
underpricing. Accordingly, the present study fills these gaps and expands the previous
literature by investigating whether the imitating behavior of the informed (private placement)
and uninformed (non-private placement) investors exists in the aftermarket of Malaysian
IPOs across different sectors from the second day until 30 days of trading. The past studies
such as Yong (2011b) and Yong (2011a) reveal that research into the private placement type of
IPO in Malaysia has only concentrated on its relations with underpricing. Theoretically and
empirically, the past literature such as Miller (1977), Baron (1982), Ritter (1984), Beatty and
Ritter (1986), Rock (1986), Miller and Reilly (1987), Megginson and Weiss (1991), Houge
et al. (2001), and Lowry and Schwert (2002) supports the existence of a greater level of
uncertainty in IPO stocks in comparison with other stocks. In addition to this general
uncertainty regarding the IPO market, specific characteristics in the Malaysian IPO market
could make it more uncertain. These include ‘fixed pricing’ method instead of book-building
and limited disclosure of information. In this study, a ‘fixed-price’ pricing mechanism refers
to a situation where the IPO price is fixed in advance between the promoter and the
underwriter before being sold to the investors. In this case, the public investors do not
participate in setting of the IPO price, thus widening the problem of asymmetric information
faced by them.
Due to the lack of historical records such as prices and volume of new issues, IPO
investors will not be able to compute expected returns to gauge investment risk of an
IPO investment. Thus, to estimate the risk and return relationship, they have to rely on
public statements and filings of IPO companies or on information acquired during the
book-building auction phase of the offering instead. However, in Malaysia, the dominant
pricing method for IPOs is ‘fixed priced.’ Yong (2013) mentions that a higher level of
uncertainty is expected for the Malaysian IPO market due to fixed-priced pricing
mechanism rather than book-building or auction pricing mechanisms. In addition,
Benveniste and Spindt (1989), Biais, Bossaerts, and Rochet (2002), Derrien and
Womack (2003), and Chahine (2007) mention that book-building and auction types of
IPO provide an opportunity to the investors to put forward bids and thus reveal their
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assessment of the IPO value. Thus, for these types of issuance methods, the offer price
reflects the investors’ opinion about the true value of the new issues, which is established
during the bidding process. Contrary to book-building and auction methods, the ‘fixedpriced’ pricing mechanism, in which the price has been set before the allocation, lowers
the investors’ chance to place bids; therefore, the offer price does not reflect the
investors’ opinions or valuations of the new issues. Consequently, more asymmetric
information exists among investors. Due to information asymmetry, there exists a
prevailing uncertainty regarding the future return of the IPO and the performance of the
new firms.
The previous literature such as Antoniou et al. (1997) and Gelos and Wei (2002)
mentions that a deficiency in corporate disclosure and information quality could also
lead to uncertainty in the market. Disclosure is more important in the IPO market
compared with other financial markets because of the lack of historical data and ‘fixed
pricing’ method. According to Campos, Newell, and Wilson (2002), the Malaysian
equity market has a limited degree of disclosure, thus creating illiquidity in the market.
Due to an uncertain environment, limited information and the cognitive biases of
Asian investors, investors tend to follow the actions of their peers in trading activities
(Kallinterakis and Kratunova 2007), and these kinds of actions can constitute herding
behavior. Kremer (2010) confirms that herding and uncertainty or availability of
information is interrelated. He indicates that this possibility is greater for an emerging
market due to imperfect regulatory frameworks particularly in terms of market
transparency. In addition, a herding trading pattern has some negative consequences in
the financial market. For instance, it tends to dilute the quality of information of stock
prices, aggravate stock price volatility, and destabilize capital markets. These
phenomena may lead or contribute to bubbles and crashes (Shiller 1989; Scharfstein
and Stein 1990; Topol 1991; Orléan 1995; Morris and Shin 1999; Persaud 2000;
Hirshleifer and Hong Teoh 2003; Hwang and Salmon 2004). Moreover, in investigating
the causes of the 1997 financial crisis in Malaysia, Jomo (1998) reports that the Asian
financial crisis was partly due to the herding behavior of the investors. The Malaysian
IPO market is herd potential due to these characteristics, which would lead to negative
consequences. However, no study has yet investigated whether the uncertainty faced by
the IPO investors (informed/uninformed) and IPO shares would result in herding, which
inhibits some preventive measures to be taken.
The past literature suggests that the majority of the explanations on anomalies are
based on the assumption that the market is efficient and rational; therefore, normative
models are used to explain the behavior of new issues. For example, Annuar and
Shamsher (1998) apply signaling process in explaining the underpricing of the IPO
market in Malaysia. However, the inefficiency of the Malaysian market has been
empirically demonstrated in the previous literature (Dawson 1987; Yong 1991; Ismail,
Abidin, and Zainuddin 1993; Isa and Ahmad 1996; Yong, Yatim, and Sapian 1999;
Leong, Vos, and Tourani-Rad 2000; Mat-Nor, Lai, and Hussin 2002; Lai, Guru, and Nor
2003; Abdullah and Taufil Mohd 2004; Cheng, Chan, and Mak 2005; Husni 2005;
Ahmad-Zaluki, Campbell, and Goodacre 2007). Therefore, it would be more proper to
examine such a market using behavioral theories. For instance, based on behavioral
(cognitive) decision theory, generally, human judgment and choice do not support
optimal decision models. Not all human behaviors are cost/benefit efficient and they are
different from those dictated by normative models. This is due to the biases that occur
during the decision-making process. Herding is such a behavior that could be considered
as an irrational behavior. This is because during herding, individual investors ignore
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P. Dehghani and R.Z.Z. Sapian
their own information and follow the market consensus. This phenomenon could lead to
some negative consequences such as incorrect and poor decision-making made by the
whole populations due to the behavior patterns that are correlated across individuals
(Bikhchandani, Hirshleifer, and Welch 1992; Devenow and Welch 1996). In addition, as
prices are influenced by the group decisions, they tend to depart from their equilibrium
levels and investors are forced to transact at inefficient prices (Christie and Huang 1995).
The herding behavior of the investors has been investigated in the equity market of
many countries: Christie and Huang (1995) on US markets, Chang, Cheng, and Khorana
(2000) on international shares, Gleason, Mathur, and Peterson (2004) on the Exchange
Traded Funds, Demirer and Kutan (2006) and Tan et al. (2008) on the Chinese markets,
Chiang and Zheng (2010) on developed equity markets (except the USA) and Asian
Market, Demirer, Kutan, and Chen (2010) on Taiwanese stock market, Bhaduri and
Mahapatra (2013) on Indian stock market, Ge˛bka and Wohar (2013) on 32 international
markets categorized as emerging, developing, and developed markets, Hsieh (2013) on
Taiwanese stock market, Kremer and Nautz (2013) on German stock market, and Yao,
Ma, and He (2014) on Chinese A and B stock markets.
Likewise, the study of herding behavior on the Malaysian equity market has been carried
out by Lai and Lau (2004). They examined the existence of herd behavior among market
traders of the Kuala Lumpur Stock Exchange (KLSE) for two sub-sample periods, bullish
and bearish, and in different sectors. They investigate the herding behavior using monthly
prices of all stocks listed on the Main Board of the KLSE from January 1992 to December
2001 using the Christie and Huang (1995) method. However, with regard to the Malaysian
equity market, only a few studies have investigated the price behavior of the new issues
based on behavioral theories, including, for example, disposition effect (Chong 2009),
bandwagon effect (Yong 2011b), and speculative bubbles (Yong 2013). Nonetheless, a
study of herding behavior has not been explored before in the Malaysian IPO market. Thus,
this study is the first of its kind that focuses on the behavior of uninformed and informed
investors during short-term immediate aftermarket of the Malaysian IPOs, based on
behavioral theories such as herding and bounded rationality.
The rest of this paper is organized as follows: ‘Literature review’ section discusses
related literature. ‘Methodology and sampling’ section describes the data and
methodology employed in this study. ‘Results and discussions’ section presents the
results and finally ‘Conclusions’ section provides the conclusions of this study.
Literature review
In the late 1990s, the resurgence of behavioral finance led to increasing interest among
researchers to examine the aftermarket trading behavior of investors, especially in the
developed markets such as the US stock market. The main reason that prompted the
resurgence of behavioral finance was the failure of the efficient market hypothesis to
rationalize a number of anomalies as well as investors’ behavior on asset prices
valuation. Behavioral finance relies on the commonly accepted belief that an investor’s
behavior is not only influenced by how well informed he or she is but also by other
psychological attributes or factors. Golberg and Nitzsch (2001), for example, mention
that asset price and its movement reflect the behavior of market participants with regard
to information interpretation and form of ideas or opinions after the interpretation. This
means that the knowledge or information about an IPO will affect an investor’s
behavior, and the overall investors’ behavior will in turn affect the IPO market
performance.
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Herding is an example of an anomaly in the area of behavioral finance. There are
different definitions and models of herding proposed in the literature, and there is no
commonly accepted meaning and method of measurement for this behavior. However, the
literature distinguishes between irrational and rational herding. Irrational herding behavior
is likened to a scenario of collective actions of individuals in uncertain conditions. Rational
herding behavior is likened to a situation where the investors ignore voluntarily their own
analyses and follow manager who possesses a source of more reliable information
(Bikhchandani and Sharma 2001). Irrational herding moves the asset prices beyond the
fundamental (Puckett and Yan forthcoming), whereas the rational herding has the potential
to generate efficient prices and seize information in the asset prices more quickly. Most of
the theoretical financial literature has focused on rational herding behavior. Rational
herding could be due to different conditions and could have informational, reputational, and
compensational reasons. Bikhchandani and Sharma (2001) classify the rational herding into
three categories that are herding based on information, reputation, and compensation. The
examples of some theoretical models are as follows.
Herding based on information is attributed to the works of Banerjee (1992) and
Bikhchandani, Hirshleifer, and Welch (1992). Banerjee (1992) defines a herd as
‘everybody doing what everyone else is doing, even when their private information
suggests doing something else.’ He analyzes a sequential decision model where each
rational institutional decision-maker observes the decisions made by earlier decisionmakers. The model by Bikhchandani, Hirshleifer, and Welch (1992), which is based on
informational cascades, examines the behavior of rational institutional investors. Their
model explains not only conformity but also rapid and short-lived fluctuations such as
fads, fashions, booms, and crashes. They apply the concept of ‘perfect Bayesian
equilibrium’ in explaining the investor’s herding behavior.
Herding based on reputation and compensation describes the idea that the investors and
more exactly the institutions who are subject to damage their reputation by acting differently
from the crowd ignore their own information and herd. The model introduced by Scharfstein
and Stein (1990) can be an example of herding behavior as the result of reputational
concerns, ‘sharing-the-blame,’ unattractive outside opportunities, and dependence of the
compensation on absolute rather than relative ability assessment. Scharfstein and Stein
(1990) mention that managers simply imitate the behavior of other investment managers
and ignore substantive private information, in certain circumstances. Their model is more
appropriate to be used to the example of corporate investment rather than to the stock
market, because it is assumed that the investments under consideration are available in
perfectly elastic supply at a given price, which allows explicit avoidance of considering the
feedback from investment demand to prices, thereby simplifying the analysis considerably.
Due to its characteristic as non-quantifiable behavior, herding cannot be precisely
quantified but can only be inferred by examining related measurable parameters. There
are two distinguished categories to measure herding according to the nature of the
defined data. The first model focuses on the trading activities of the individual investors.
Herding is said to have occurred when the individuals purposely imitate the trading
behavior of other investors over a period. Thus, to measure herding, data on the trading
activities as well as the investment portfolio of the investors are collected. Lakonishok,
Shleifer, and Vishny (1992) and Wermers (1999) provide examples of such measures
that are known as the LSV measure and the portfolio-change measure (PCM),
respectively. The PCM is designed to capture both the direction and the intensity of
investors’ trading. These measures are designed to estimate the intention of herding
either by institutional or individual investors to buy (or to sell) simultaneously of any
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P. Dehghani and R.Z.Z. Sapian
particular securities (Grinblatt, Titman, and Wermers 1995; Oehler 1998; Wermers
1999; Voronkova and Bohl 2005; Wylie 2005; Walter and Weber 2006; Puckett and Yan
2007). In addition, these methods also intend to assess the strong changes of the
securities fractions held by institutional investors (Nofsinger and Sias 1999; Sias 2004;
Kim and Nofsinger 2005; Dasgupta et al. 2011).
The presence of herding behavior is also being studied using other models, and it is
identified by exploiting the information contained in the cross-sectional stock price
movements. The methodology that investigates herding behavior by estimating the
dispersion of returns of all the shares by the cross-sectional standard (or absolute) deviations
of the returns includes Christie and Huang (1995), Chang, Cheng, and Khorana (2000),
Gleason, Mathur, and Peterson (2004), Demirer and Kutan (2006), and Tan et al. (2008).
This study investigates herding behavior of the investors in the aftermarket of
Malaysian IPOs. The IPO immediate aftermarket is characterized by high initial returns
as documented by several studies (Dawson 1987; Ismail, Abidin, and Zainuddin 1993;
Mohamad, Nassir, and Ariff 1994; Yong 2011a); therefore, the immediate aftermarket
can be considered as stressful period. Consequently, applying the Christie and Huang
(1995) method is appropriate to investigate herding in this context as they define and
measure herding during market stress or large price movement. They suggest that during
the periods of unusual market movement, individuals are more likely to suppress their
own belief in favor of the market consensus; therefore, security returns would not stray
far from the market and herds form. In this case, herding can be measured through
dispersion, which is defined as the cross-sectional standard deviation (CSSD) of return.
Dispersion measures the average proximity of individual return to the mean.
In this study, the terms of herding and fads are used interchangeably. The definition
of the Goetzman (1995) for fad is very similar to the Christie and Huang’s (1995)
definition of herding. According to Goetzman (1995), a fad occurs when stock prices are
apparently moving together to a greater extent than normal. He mentions that during a
fad, the cross-sectional variation would be expected to be low. He also claims that this
condition is most likely to occur with investor mass pessimism, such as during a panic or
crash. In addition, based on the definition of Bikhchandani, Hirshleifer, and Welch
(1992), fads is a drastic and seemingly unpredictable swing in mass behavior without
obvious external stimulus. The simultaneous occurrence of such a shift for a large
number of individuals remains, however, unexplained as mentioned by Bikhchandani,
Hirshleifer, and Welch (1992). This void could be filled by a theory of herding and a fad
could be interpreted as the result of herding. Thus again, the two concepts, herding and
fads, may be more closely connected in reality than suggested by their theoretical
distinctiveness.
This study uses bounded rationality as a general theory that supports investors’
irrational behavior and explains the decision-making of the investors. This theory which
is based on rather a realistic situation is developed by Simon (1957) in direct response to
the narrow view of decision-making offered by the economic versions of rational choice
theory (RCT). The term ‘RCT’ is often used interchangeably with ‘public choice
theory,’ ‘neoclassicism,’ ‘expected utility theory’ (Zey 1998), ‘rational actor theory’
(Zey 1998; Monroe 2001), and ‘utilitarianism’ (Zafirovski 1999). Derived from
economics, RCT is a normative theory that provides an explanation of purposeful human
action. The key to understand RTC lies in the concept of ‘optimization.’ Optimization
occurs when actors make decisions and take actions after assessing all of the costs and
benefits of each alternative with the objective of maximizing utility (James 1990). When
the choice of action matches with the optimal choice, it is deemed to be rational.
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Devenow and Welch (1996) mention that irrational herding behavior reflects a scenario
whereby individuals perform collective actions in uncertain conditions. The investors prefer
such a behavior to reduce the uncertainty and assure their needs to feel in confidentiality.
Keynes (1936) describes irrational type of herding as ‘animal spirits’ or an inherent
tendency of human behavior. Thus, one could similarly suspect such instincts to be present
in humans without explaining the true motivation for this behavior. Such an explanation of
irrational herding as used by Keynes seems to fit the fad hypothesis much better than the
herding hypothesis. Keynes writes, for example, as follow:
A conventional valuation which is established as the outcome of the mass psychology of a
large number of ignorant individuals is liable to change violently as the result of a sudden
fluctuation of opinion due to factors which do not really make much difference to the
prospective yield. (1936, 154)
Furthermore, Andersson (2009) indicates that the view of herding as irrational centers on
investor psychology where the investors follow each other without rational analysis.
Some regularities of human behavior observed in psychological experiments actually
support the hypothesis of such an inherent herding instinct in humans. Particularly
interesting in this respect is an experiment conducted by Asch (1952). He had subjects to
compare the lengths of different line segments. The lengths of the segments were
sufficiently different to make such comparison relatively straightforward. However,
Asch was able to show that the outcome of this comparison depends crucially on the
social context. If the subjects are alone in making their decision, they derive the correct
size ranking. However, if other people (who are part of the experiment) are present and
the subjects can observe their deliberately incorrect comparisons prior to their own
decision, the result of the subject’s comparison is much more likely to be wrong and
closer to the group judgment. Therefore, there seems to be a preference for conformity
that directs human behavior. Sherif (1937) conducts an experiment similar to Asch’s.
However, in contrast to Asch’s experiment, subjects do not seem to be aware of the
influence of the group in the experimental set-up of Sherif. Thus, the conformity effect
apparently goes beyond a conscious peer pressure effect. Consequently, it is expected
that investors imitate each other’s investment decisions in the IPO market, as this
context is characterized by substantial uncertainty and ambiguity due to the lack of the
historical data and ‘fix-priced’ pricing method in Malaysia.
Gleitman (1981) also mentions that the need for social comparison is especially large
in situations that are ambiguous or frightening. Thus, irrational or behavioral herding
may be especially relevant in economic decision-making, which is characterized by
substantial uncertainties and ambiguities. In addition, during the periods of high stress,
the individual’s capacity to make rational decisions is reduced (Holsti 1979). This
behavior is opposite to the assumption of RCT as a normative theory that provides an
explanation of purposeful human action. Investors do not assess all of the costs and
benefits of each alternative with the objective of maximizing utility especially when they
are during high period of stress. However, Simon’s (1957) bounded rationality theory
can support it as he suggests that individual decision aims at providing satisfactory rather
than optimal decisions, and in such an environment, investors are more satisfied to
reduce their stress by following each other rather than maximizing their utility.
Based on the earlier discussions, there are few studies that have analyzed the
Malaysian IPO market using behavioral theories and herding behavior has not yet been
tested in the aftermarket of Malaysian IPOs. Therefore, this study would shed light and
expand knowledge on this area, especially in the context of developing or emerging
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P. Dehghani and R.Z.Z. Sapian
capital market. Specifically, this could be the first study that concentrates on herding
behavior of private placement and non-private placement investors, in different
industries in the aftermarket of Malaysian IPOs from 2001 to 2011 using the Christie and
Huang (1995) method. The cross-sectional analysis is conducted based on the Bursa
Malaysia sectoral classification as there is a tendency for a group to act as a herd if it is
sufficiently homogeneous (Bikhchandani and Sharma 2001). In addition, Hirshleifer and
Hong Teoh (2003) find that overpricing is observed in the US technology stocks in the
1990s. This finding suggests the possibility of herding in certain sectors such as
technology due to their uncertain and risky natures. Thus, there is high possibility that
herd formation will be observed at the point of investments in a group of stocks such as
firm’s stocks categorized by industry or sector.
The division of the data by the proportion of the IPOs subscribed by the institutional/
informed investors (private placement investors) as opposed to the proportion of IPOs
subscribed by the individual/not well-informed investors (non-private placement
investors) allows testing of herding behavior. The private placement IPOs consist of
institutional investors who are well informed. These investors are widely regarded as
being sophisticated (Michaely and Shaw 1994; Badrinath, Kale, and Noe 1995; Cohen,
Gompers, and Vuolteenaho 2002; Nagel 2005). Chung, Firth, and Kim (2002) and Bos
and Donker (2004) indicate that one of the characteristics of institutional investors is that
they are more experienced and knowledgeable in processing information. Empirically,
the past literature such as De Long et al. (1990), Shleifer and Summers (1990), Banerjee
(1992), and Hirshleifer, Subrahmanyam, and Titman (1994) mentions that more
experienced investors are those who are unlikely to behave irrationally or demonstrate
cognitive biases. Institutional investors are expected not to show faddish behavior in
their investment decision, as they would rely on their information for decision-making.
Contrary to institutional investors, individuals are considered naı̈ve investors. Thus,
their trading behaviors are often regarded as irrational and tend to form market
anomalies. They are disadvantaged in information and are more prone to overreact
toward new events occurring in the market, and thus individual investors are less rational
among all investors (Chemmanur et al. 2010). According to Lee, Shleifer, and Thaler
(1991), certain characteristics of individual investors such as ignorance, being
uninformed, and trading based on sentiment are general features in the herding
literature. In addition, according to Shiller (1984) and De Long et al. (1990), fad and
fashion, rather than fundamentals, play important roles in investments decision-making
of individual investors as limited source of information is available to them.
Methodology and sampling
Herding is a non-quantifiable behavior. It cannot be quantified directly but can only be
inferred by studying related measurable parameters. The models to measure herding are
developed according to how researchers define herding. Empirically, there are several
methods to measure herding. The first one concentrates on the trading activities of the
individual investors. Herding is said to have occurred when the individuals deliberately
imitate the behavior of other investors in equity trading over a period of time. Thus, to
measure herding, data are collected on the trading activities as well as the changes in
investment portfolio of investors such as the LSV measure introduced by Lakonishok,
Shleifer, and Vishny (1992). Several applications of this methodology include Wermers
(1999), Carpenter and Wang (2007), Uchida and Nakagawa (2007), and Kremer and
Nautz (2013). In this method, they use an adjusted ratio of net buyers in a security over
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the sum of net buyers and net sellers and examine the probability distribution of this
ratio in order to make inferences on herding. This method is designed to capture both
investors’ direction and intensity of trading.
In the second method that is designed by Christie and Huang (1995), price data are
utilized and cross-sectional behavior of returns within a group of securities is examined
to assess the formation of herds among investors. The examples of studies that have
applied this method include Gleason, Lee, and Mathur (2003), Lin and Swanson (2003),
Gleason, Mathur, and Peterson (2004), Demirer and Kutan (2006), Tan et al. (2008),
Zhou and Lai (2009), Chiang and Zheng (2010), Demirer, Kutan, and Chen (2010), and
Yao, Ma, and He (2014). Chang, Cheng, and Khorana (2000) developed another model
to capture herding behavior similar to Christie and Huang (1995), except that the return
dispersion used in their model is based on cross-sectional absolute standard deviation
(CSAD) of returns instead of CSSD as employed by the latter researchers. The
difference between CSAD and CSSD is in terms of the relation between return
dispersion and market return whereby CSAD and CSSD assume the relationships to be
nonlinear and linear, respectively. CSAD is more suitable method to employ to capture
herding behavior especially during periods of market stress if one expects that there is
nonlinear association between return dispersion and market return.
Hwang and Salmon (2004) develop a different methodology in which instead of
return, cross-sectional variability of factor sensitivity is used to measure herding
behavior. In their model, herding behavior is measured based on the relative dispersion
of the betas for all assets in the market. The advantage of this method is that herding
behavior could be examined not only during extreme market conditions but also during
normal times. Thus, a detailed analysis on the evolution of herding can be performed
over time. In a later study, Bhaduri and Mahapatra (2013) propose a new model to detect
herding behavior that is known as cross-sectional absolute mean – median difference
(CSMMD) model. CSMMD is an extension of a model developed by Chang, Cheng, and
Khorana (2000) and is built with reference to the rational asset-pricing models. Rational
asset-pricing models predict that the relation between return dispersion and market
return is linear and in increasing function. However, the rational asset-pricing models
will no longer be valid if the market participants instead of depending on their prior
beliefs follow the behavior of the aggregate market in their equity trading especially
during extreme average market movement.
Among the aforementioned methods, this study employs the Christie and Huang
(1995) method to investigate herding across different sectors during 30 days in the
aftermarket of Malaysian IPOs. The previous literature shows that this method was used
to investigate herding in the equity market in Malaysia (Lai and Lau 2004). It is
appropriate to apply this method for the IPO market as well because the mentioned
method examines herding behavior of the investors during the period of market stress,
and IPO immediate aftermarket also has such a characteristic due to the high initial
returns (Ritter 2003), which is an inherent characteristic of this market. Another
contribution to the methodology is to capture herding behavior for the short-term
immediate aftermarket (30 days). Herding behavior should be investigated in the short
run because the feedback from rational investors offsets the signals provided to the
market by the herders (Gleason, Mathur, and Peterson 2004) and also because the
literature supports that the Malaysian IPO market stabilizes immediately (Yong 2013).
Finally, this is the first study that investigates herding of uninformed and informed
investors during the stressful immediate IPO aftermarket based on a different
categorization of the IPO market according to different sectors. Yong (2013) investigates
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236
P. Dehghani and R.Z.Z. Sapian
the immediate IPO aftermarket for 20 trading days and concentrates on the short-term
aftermarket behavior of the IPOs, based on the listing board categorization only.
Theoretically, at the time of herding, equity return dispersions concentrate around
aggregate returns of the market, with the assumption that individuals suppress their own
beliefs and decisions on investment activities are based solely on the collective actions
of the market, such that security returns would not deviate far from the overall market
return. Accordingly, Christie and Huang (1995) focus on the dispersion that is the
average proximity of individual stock return to the average return to come up with
inferences on herding in a market. For this purpose, they first define return dispersion as
the CSSD of security returns within a portfolio. Consistent with Christie and Huang
(1995), the CSSD of return is measured with some modifications for the IPO market as
Equation (1):
sP
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
n
2
j¼1 ðr jt 2 rt Þ
CSSDt þ
;
n21
ð1Þ
where n is the number of the IPO firms in the portfolio, r jt is the observed return on IPO
j for day t, and rt is the cross-sectional average of the n IPO returns in the portfolio for
day t.
Accordingly, CSSD is examined to determine whether it is significantly different
from average during both period of upper and lower extremes market movements or
market stress by using Equation (2). Christie and Huang (1995) indicate that the
tendency to mimic the behavior of others appears to be stronger during periods
characterized by unusual market trends and phases of high volatility; therefore, these
situations are characterized by high uncertainty. Accordingly, they suggest the values of
CSSD calculated in Equation (1) during the periods of extreme market movements or
high volatility is carried out using the following linear regression:
CSSDt ¼ a þ bL DLt þ bU DU
t þ 1t ;
ð2Þ
where DLt ¼ 1 if the return on the aggregate IPO portfolio on day t lies in the lower tail
of the IPO return distribution and zero otherwise and DU
t ¼ 1 if the return on the
aggregate IPO portfolio on day t lies in the upper tail of the return distribution and zero
otherwise.
The a coefficient denotes the average dispersion of the sample excluding the regions
covered by the two dummy variables. The dummy variables in Equation (2) seek to
capture differences in return dispersions during the periods of extreme market
movements. As a herd formation indicates conformity with market consensus, the
presence of negative and statistically significant bL (for declining market) and bU (for
rising market) coefficients would indicate herd formation by market participants.
Equation (2) is estimated using the criterion to define extreme market movements. In
this study, the herding behavior during market stress is tested using 10% criterion, in other
words the heteroscedastisity consistent p-values are reported based on 10% criterions
during extreme market movement. The 10% criterion restricts DLt ; and DU
t to 10% of the
lower tail and 10% of the upper tail of the IPO portfolio return distribution. In other words,
the b1 coefficient indicates how much the dispersion changes, when the IPO portfolio return
is in the bottom 10% of the IPO portfolio return distribution, which is also known as a lower
market stress and the b2 coefficient indicates how much the dispersion changes in an upper
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IPO portfolio stress context. The 10% criterion is adopted due to an arbitrary definition of an
extreme market return (Christie and Huang 1995).
In this study, the investors’ herding behavior in the short-term IPO aftermarket is
investigated during a 30-day period. For 30 days to select the 10% criterion, the average
aggregate cross-sectional IPO return of this period is arranged in ascending order and 10%
from the lowest returns and highest returns (including 3 days of the lowest IPO returns and
3 days of the highest IPO return) are considered to carry the IPO portfolio stress, and the
value of 1 is assigned to the dummies of the mentioned percentages. IPO return is calculated
from the daily closing prices of new issues using Equation (3) as follow:
r jt ¼
Pjt 2 Pjt21
;
Pjt21
ð3Þ
where Pjt is the closing IPO price for day t and Pjt21 is the closing IPO price for day t 2 1.
The return calculation begins from the second day of the listing as it is based on the
closing price of day t minus the closing price of the previous day (i.e., day 1 of the
listing). For some IPOs, the closing prices of the first or the first few days are not
available, as a result the first day for which the closing price is available is considered as
the first trading day of those IPOs and the return calculation is started from that day.
This method seems to be appropriate to catch herding behavior at the time of new issues
because the IPO immediate aftermarket is characterized by high initial returns (Dawson
1987; Ismail, Abidin, and Zainuddin 1993; Mohamad, Nassir, and Ariff 1994; Yong
2011a). High initial return is considered as period of large price swings. In addition,
there is a lack of historical information regarding the new issues, which can create
uncertainty and lead to market stress during IPO. Based on the theoretical and empirical
discussions, it is hypothesized that herding takes place in non-private placement IPOs
(implying by negative coefficients of DLt ; and DU
t ) and no herding takes place in private
placement IPOs (implying by positive coefficients of DLt ; and DU
t ).
Between January 2001 and December 2011, Bursa Malaysia statistics report 440 new
listings. Out of 440 shares, 55 are labeled as dead and 5 as suspended. Thirteen IPOs that
are categorized as real estate investment trusts (REITS), and eight IPOs that are
categorized as right, special, restricted, bonus, and closed-end fund issues, are also
excluded from this study. IPOs issued under the REITS category are excluded due to the
different formats of presentation for their financial statements. As the occurrence of
book-built issues is rare in Malaysia and also because the focus of this study is on fixed
pricing of IPOs, five issues that apply book-built pricing method are also excluded from
this study. The closing prices for seven companies were not available in Datastream.
Eight companies have zero returns in 10 consecutive days, so they are also excluded
from the sample. These companies are SKB Shutters Corporation Berhad, NTPM
holding Berhad, Oriented Media Group Berhad, Guan Chong Berhad, MQ Technology
Berhad, 1 Utopia Berhad, Uzma Berhad, and Sunzen Biotech Berhad.
Accordingly, the number of companies with available closing prices narrows down
to 339, of which 176 and 163 are categorized as private placement and non-private
placement IPOs, respectively. This study also excludes the rare type of IPOs, for
example restricted offer for sale, restricted public issue, offer for sale to eligible
employees, restricted offer for sale to Bumiputra investors, special and restricted issue
to Bumiputra investors, tender offer and special issue. The reason to exclude these
companies with uncommon types of offer is due to the fact that the number of
companies with these issues is very few, leading to less meaningful outcomes as
suggested in Abdul Rahim and Yong (2010) and Yong (2007a).
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P. Dehghani and R.Z.Z. Sapian
Results and discussions
In this study, several sectors are excluded for analysis. The excluded sectors under private
placement category are Construction (three IPOs), Plantation (three IPOs), Properties (11
IPOs), and Finance (four IPOs). Meanwhile, for non-private placement category, the sectors
that are excluded for analysis are Construction (five IPOs), Plantation (two IPOs), and
Properties (two IPOs). These sectors with limited number of IPOs might not result in a
meaningful analysis. Consequently, in this study, only Consumer, Industrial, Technology,
and Trading and Services sectors are included in the analysis.
Table 1 reports the sectoral descriptive statistics both for non-private placement and
private placement of IPOs. The statistics are 30 days average level of dispersion, its
associated standard deviation, and the number of firms for each category. Across
different sectors for the non-private placement of IPOs, the level of dispersion ranges
from 0.0354 for Trading and Services to 0.0649 for Industrial. Meanwhile, for private
placement of IPOs, the range is from 0.0313 for Consumer to 0.0532 for Trading and
Services. The lowest average return dispersion and associated standard deviation belongs
to Consumer sector for private placement IPOs category, and the second lowest in the
case of non-private placement of IPOs. These statistics reflect the stable nature of this
sector. Lai and Lau (2004) confirm these results. They report the lowest average and
standard deviation of dispersion for the Consumer sector in their entire samples and by
sector over a period of 10 years from January 1992 to December 2001.
Table 2 presents regression estimates across different sectors over a 30-day period.
The heteroscedastisity consistent p-values reported are based on 10% criterions during
extreme market movement. The b1 coefficients indicate by how much return dispersion
changes when the return of the market lies in the bottom 10% of the return distribution
of the market, which is also known as lower market stress. Meanwhile, the
b2 coefficients indicate by how much the return dispersion changes during upper market
stress. As there is an arbitrary definition for an extreme market return (Christie and
Huang 1995), this study adopts 10% criterion for the analysis.
In non-private placement and private placement IPOs, heteroscedasticity regression
results are reported for the transformed dependent variable, except for Consumer and
Technology sectors. For these two sectors, coefficients’ heteroscedasticity consistent pvalue was reported for untransformed data, as there was no heteroscedasticity problem
and as a result no data transformation was conducted. For Industrial and Trading and
Services sectors, the dependent variable reaches the desirable level of normality using
the inverse values of the variable. Inverse transformation (1/x) makes very small
numbers very large and very large numbers very small. This transformation allows
reversing the order of the scores. Thus, in order for the result interpretation not to be
Table 1.
Summary statistics.
Consumer
Industrial
Technology
Trading and service
Ave return
dispersion
non-PP
SD of
dispersion
non-PP
Number
of firms
non-PP
Ave return
dispersion
PP
SD of
dispersion
PP
Number
of firms
PP
0.0382
0.0649
0.0459
0.0354
0.0188
0.0771
0.0433
0.0219
37
57
21
26
0.0313
0.0469
0.0500
0.0532
0.0116
0.0302
0.0184
0.0469
19
44
58
45
Note: PP, private placement; SD, standard deviation; Ave, average.
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affected after inversing, the inversed value is multiplied by 2 1, and then a constant (1.0)
is added to bring the minimum value back above 1.0. As a result, the ordering of the
values will be identical to the original data (Osborne 2002).
Based on the results in Panel A of Table 2, during a down market, not well-informed
investors act as significantly rational in investing in the Consumer sector as indicated by
a positive and significant b1 coefficient of 0.0340 (0.002). During an up market, they
remain rational, but not significant as indicated by positive and insignificant b2
coefficient of 0.0034 (0.739). In the industrial sector, investors are significantly rational
during down and upward movement of the market as shown by positive and significant
b1 and b2 coefficients of 10.3369 (0.027) and 25.0599 (0.000), respectively. Technology
sector’s not well-informed investors tend to herd and follow the market based on the
negative b1 coefficient of 2 0.0131 (0.635) during down market movement. During an
up market, a positive though not significant coefficient of 0.0019 (0.946), for not wellinformed investors, asserts their reluctance to follow the herd in investing in the
Technology sector. Not well-informed investors in the Trading and Services sector tend
to be rational during down market conditions as an insignificant positive b1 coefficient
of 10.3043 (0.181) indicates. During an up market, not well-informed investors investing
in the Trading and Services sector act rationally and significantly shown by positive and
significant b2 coefficient of 20.9433 (0.010).
The result of the sectoral analysis for not well-informed investors indicates that they
show rational behavior during down market in Consumer, Industrial, and Trading and
Services sectors. Their rational behavior is significant regarding the Consumer and
Industrial sectors as significant positive b1 coefficients indicate. This result is in contrast
with the suggestion of Bikhchandani and Sharma (2001) where a group is more likely to
Table 2. Non-private placement and private placement daily dispersions (herding) during market
stress categorized by sectors.
10% Criterion heteroscedastisity
consistent p-value untransformed
a
b1
Panel A: non-private placement 30 days
Consumer
0.0345
0.0340
p-Value
0.000
0.002***
Industrial
p-Value
Technology
0.0470 2 0.0131
p-Value
0.000
0.635
Trading and Services
p-Value
Panel B: private placement 30 days
Consumer
0.0317 2 0.0041
p-Value
0.000
0.583
Industrial
p-Value
Technology
0.0470
0.0341
p-Value
0.000
0.001***
Trading and Services
p-Value
b2
0.0034
0.739
0.0019
0.946
0.0005
0.947
2 0.0039
0.686
10% Criterion heteroscedastisity
consistent p-value transformed
A
b1
b2
2 27.7835
0.000
10.3369
0.027**
25.0599
0.000***
2 36.8834
0.000
10.3043
0.181
20.9433
0.010***
2 27.5345
0.000
5.2298
0.349
14.0103
0.017**
2 25.3810
0.000
2.5774
0.583
17.4477
0.001***
Notes: b1 denotes dispersion in the down market; b2 denotes dispersion in the up market; ***Significant at 1%
level, **Significant at 5% level, and *Significant at 10% level.
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P. Dehghani and R.Z.Z. Sapian
herd if it is sufficiently homogeneous. During down market conditions, not wellinformed investors only show a tendency to follow the market in investing in the
Technology sector based on an insignificant negative b1 coefficient, which confirms the
finding that the possibility of occurring of herding in some sectors such as technology, as
the nature of the companies listed in the Technology sector, which are small sized and
risky ones, causes them to be more uncertain and leads investors to an imitating
behavior. However, during upward movement of the market, this irrationality also fades
away which is in line with the findings of Christie and Huang (1995) and Demirer and
Kutan (2006) that herding is more expected during market downturn rather than upturn.
The herding behavior reported in the Technology sector is consistent with the findings
by Lai and Lau (2004) who report a negative b1 coefficient in 10 sectors during the
period of market downturn of the Malaysian stock market, both based on 5% and 10%
significant levels. They also find no evidence of herding during market upturn.
According to the results given in Panel B of Table 2, Consumer sector’s wellinformed investors tend to herd and follow the market based on the negative, but nonsignificant b1 coefficient of 0.0041 (0.583) during down market movement. During up
market, Consumer sector’s investors tend to be rational, though not significant as shown
by b2 coefficient of 0.0005 (0.947). The reported b1 coefficient of 5.2298 (0.349) during
down market movement for well-informed investors in the Industrial sector shows the
rational tendency of these investors. During an up market, investors in the Industrial
sector act rationally and significantly at 5% level as shown by b2 coefficient of 14.0103
(0.017). During a down market, well-informed investors act as significantly rational in
investing in the Technology sector as indicated by a positive and significant b1
coefficient of 0.0341 (0.001). Surprisingly, well-informed investors tend to herd in the
Technology sector during an up market as indicated by a negative, but not significant b2
coefficient of 2 0.0039 (0.686). Well-informed investors in the Trading and Services
sector tend to be rational during down market conditions as an insignificant positive b1
coefficient of 2.5774 (0.583) indicates. During an up market, well-informed investors
investing in the Trading and Services sector act rationally and significantly as shown by
b2 coefficient of 17.4477 (0.001).
In summary, for the private placement category, well-informed investors in the
Industrial, Technology, and Trading and Services sectors show the tendency to act
rationally during a down market as shown by the positive b1 coefficients. However, the
investors’ rational behavior is only remarkable for the Technology sector as indicated by
significant positive b1 coefficients. On the other hand, well-informed investors in the
Consumer sector show a tendency to follow the market as shown by an insignificant
negative b1 coefficient. During upward market movements, the well-informed investors in
Industrial and Trading and Services sectors behave rationally. The investors who buy IPO
stock in the Consumer sector also behave rationally as the market condition improves during
upward market and investors are less likely to herd (Christie and Huang 1995; Demirer and
Kutan 2006). However, the rational behavior of the Technology sector investors turns to
irrational during an up market, as a negative b2 coefficient indicates.
Conclusions
This study examines the short-term herding behaviors of not well-informed (non-private
placement) and informed (private placement) investors in the IPO immediate aftermarket
during the periods of market stress, or exaggerated price movements. The findings of this
study reveal that the not well-informed investors exhibit rational behavior during market
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downturns for Consumer, Industrial, and Trading and Services sectors. However, the not
well-informed investors in the Technology sector tend to follow the market movement
during down market conditions. This scenario signifies the possibility of herding in some
sectors such as technology, as the nature of the companies listed in the Technology sector
of Bursa Malaysia are smaller in size, which makes them to be considered as risky ones by
the investors.
The results of this study reveal that the well-informed investors have the tendency to
demonstrate herding behavior particularly for Consumer and Technology sectors during
down and up markets, respectively. It can be implied that these investors have the
tendency to follow the market because the negative coefficient is not limited to the
downward market and to risky and uncertain shares. The previous literature also
suggests that institutional investors often follow other institutional investors (Grinblatt
and Hwang 1989; Nofsinger and Sias 1999; Wermers 1999; Wylie 2005; Walter and
Weber 2006; Agudo, Sarto, and Vicente 2008; Andreu, Ortiz, and Sarto 2009), and such
a trend is particularly apparent in the emerging markets (Lobao and Serra, forthcoming;
Voronkova and Bohl 2005; Tan et al. 2008). As the IPO market is volatile and risky in
comparison with seasoned equity markets, this situation can lead to herding behavior of
the institutional investors. Furthermore, Shiller and Pound (1989) state that for volatile
stocks, institutional investors emphasize the advice of other professionals on their buy
and sell decisions. There is a common phenomenon whereby an investor’s behavior
influences other investors’ behavior. In this scenario, investors may forego their own
rational analysis. They tend to adopt behavior that is similar to the group.
Note
1.
Email: pegahdehghani@yahoo.com
References
Abdul Rahim, R., and O. Yong. 2010. “Initial Returns of Malaysian IPOs and Shari’a-Compliant
Status.” Journal of Islamic Accounting and Business Research 1 (1): 60 – 74.
Abdullah, N. A. H., and K. N. Taufil Mohd. 2004. “Factors Influencing the Underpricing of Initial
Public Offerings in an Emerging Market: Malaysian Evidence.” IIUM Journal of Economics
and Management 12 (2): 1 – 23.
Agudo, L. F., J. Sarto, and L. Vicente. 2008. “Herding Behaviour in Spanish Equity Funds.”
Applied Economics Letters 15 (7): 573– 576.
Ahmad-Zaluki, N. A., K. Campbell, and A. Goodacre. 2007. “The Long Run Share Price
Performance of Malaysian Initial Public Offerings (IPOs).” Journal of Business Finance and
Accounting 34 (1 – 2): 78 – 110.
Ahmad-Zaluki, N. A., K. Campbell, and A. Goodacre. 2011. “Earnings Management in Malaysian
IPOs: The East Asian Crisis, Ownership Control, and Post-IPO Performance.” The
International Journal of Accounting 46 (2): 111– 137.
Andersson, M. 2009. Social Influence in Stock Markets. Sweden: University of Gothenburg,
Department of Psychology.
Andreu, L., C. Ortiz, and J. L. Sarto. 2009. “Herding Behaviour in Strategic Asset Allocations:
New Approaches on Quantitative and Intertemporal Imitation.” Applied Financial Economics
19 (20): 1649– 1659.
Annuar, M. N., and M. Shamsher. 1998. “The Performance and Signaling Process of Initial Public Offers
in Malaysia: 1980–1996.” Pertanika Journal of Social Sciences and Humanities 6 (2): 71–79.
Antoniou, A., N. Ergul, P. Holmes, and R. Priestley. 1997. “Technical Analysis, Trading Volume
and Market Efficiency: Evidence from an Emerging Market.” Applied Financial Economics
7 (4): 361– 365.
Asch, S. E. 1952. Social Psychology. Englewood Cliffs, NJ: Prentice-Hall.
Downloaded by [Universiti Kebangsaan Malaysia], [Ros Zam Zam Sapian] at 18:12 21 July 2014
242
P. Dehghani and R.Z.Z. Sapian
Badrinath, S. G., J. R. Kale, and T. H. Noe. 1995. “Of Shepherds, Sheep, and the CrossAutocorrelations in Equity Returns.” Review of Financial Studies 8 (2): 401–430.
Banerjee, A. V. 1992. “A Simple Model of Herd Behavior.” The Quarterly Journal of Economics
107 (3): 797– 817.
Baron, D. P. 1982. “A Model of the Demand for Investment Banking Advising and Distribution
Services for New Issues.” Journal of Finance 37 (4): 955– 976.
Beatty, R. P., and J. R. Ritter. 1986. “Investment Banking, Reputation, and the Underpricing of
Initial Public Offerings.” Journal of Financial Economics 15 (1– 2): 213– 232.
Benveniste, L. M., and P. A. Spindt. 1989. “How Investment Bankers Determine the Offer Price
and Allocation of New Issues.” Journal of Financial Economics 24 (2): 343– 361.
Bhaduri, S. N., and S. D. Mahapatra. 2013. “Applying an Alternative Test of Herding Behavior: A
Case Study of the Indian Stock Market.” Journal of Asian Economics 25: 43 – 52.
Biais, B., P. Bossaerts, and J. C. Rochet. 2002. “An Optimal IPO Mechanism.” The Review of
Economic Studies 69 (1): 117– 146.
Bikhchandani, S., D. Hirshleifer, and I. Welch. 1992. “A Theory of Fads, Fashion, Custom, and
Cultural Change as Informational Cascades.” Journal of Political Economy 100 (5):
992– 1026.
Bikhchandani, S., and S. Sharma. 2001. “Herd Behavior in Financial Markets.” IMF Staff Papers
47 (3): 279– 310.
Bos, A., and H. Donker. 2004. “Monitoring Accounting Changes: Empirical Evidence from the
Netherlands.” Corporate Governance: An International Review 12 (1): 60 – 73.
Campos, C. E., R. E. Newell, and G. Wilson. 2002. “Corporate Governance Develops in Emerging
Markets.” McKinsey on Finance: 15 – 18.
Carpenter, A., and J. Wang. 2007. “Herding and the Information Content of Trades in the
Australian Dollar Market.” Pacific-Basin Finance Journal 15 (2): 173– 194.
Chahine, S. 2007. “Investor Interest, Trading Volume, and the Choice of IPO Mechanism in
France.” International Review of Financial Analysis 16 (2): 116– 135.
Chang, E. C., J. W. Cheng, and A. Khorana. 2000. “An Examination of Herd Behavior in Equity
Markets: An International Perspective.” Journal of Banking and Finance 24 (10): 1651 –1679.
Chemmanur, T. J., B. D. Jordan, M. H. Liu, and Q. Wu. 2010. “Anti-Takeover Provisions in
Corporate Spin-Offs.” Journal of Banking and Finance 34 (4): 813– 824.
Cheng, L. T. W., K. C. Chan, and B. S. C. Mak. 2005. “Strategic Share Allocation and
Underpricings of IPOs in Hong Kong.” International Business Review 14 (1): 41 – 59.
Chiang, T. C., and D. Zheng. 2010. “An Empirical Analysis of Herd Behavior in Global Stock
Markets.” Journal of Banking and Finance 34 (8): 1911– 1921.
Chong, F. 2009. “Disposition Effect and Flippers in the Bursa Malaysia.” The Journal of
Behavioral Finance 10 (3): 152– 157.
Christie, W. G., and R. D. Huang. 1995. “Following the Pied Piper: Do Individual Returns Herd
Around the Market?” Financial Analysts Journal 51 (4): 31 – 37.
Chung, R., M. Firth, and J. B. Kim. 2002. “Institutional Monitoring and Opportunistic Earnings
Management.” Journal of Corporate Finance 8 (1): 29 – 48.
Cohen, R. B., P. A. Gompers, and T. Vuolteenaho. 2002. “Who Underreacts to Cash-Flow News?
Evidence from Trading Between Individuals and Institutions.” Journal of Financial Economics
66 (2): 409– 462.
Dasgupta, A., A. Prat, and M. Verardo. 2011. “The Price Impact of Institutional Herding.” Review
of Financial Studies 24 (3): 892– 925.
Dawson, S. M. 1987. “Secondary Stock Market Performance of Initial Public Offers, Hong Kong,
Singapore and Malaysia: 1978– 1984.” Journal of Business Finance and Accounting 14 (1):
65 – 76.
De Long, J. B., A. Shleifer, L. H. Summers, and R. J. Waldmann. 1990. “Noise Trader Risk in
Financial Markets.” Journal of Political Economy 98 (4): 703– 738.
Demirer, R., and A. M. Kutan. 2006. “Does Herding Behavior Exist in Chinese Stock Markets?”
Journal of International Financial Markets, Institutions and Money 16 (2): 123– 142.
Demirer, R., A. M. Kutan, and C. D. Chen. 2010. “Do Investors Herd in Emerging Stock
Markets?: Evidence from the Taiwanese Market.” Journal of Economic Behavior and
Organization 76 (2): 283– 295.
Derrien, F., and K. L. Womack. 2003. “Auctions Vs. Bookbuilding and the Control of
Underpricing in Hot IPO Markets.” Review of Financial Studies 16 (1): 31 – 61.
Downloaded by [Universiti Kebangsaan Malaysia], [Ros Zam Zam Sapian] at 18:12 21 July 2014
Venture Capital
243
Devenow, A., and I. Welch. 1996. “Rational Herding in Financial Economics.” European
Economic Review 40 (3– 5): 603– 615.
Ge˛bka, B., and M. E. Wohar. 2013. “International Herding: Does It Differ Across Sectors?”
Journal of International Financial Markets, Institutions and Money 23: 55 – 84.
Gelos, R. G., and S. J. Wei. 2002. “Transparency and International Investor Behavior.” NBER
Working Paper Series (9260), National Bureau of Economic Research, Cambridge, MA. http://
www.nber.org/papers/w9260
Gleason, K. C., C. I. Lee, and I. Mathur. 2003. “Herding Behavior in European Futures Markets.”
Finance Letters 1: 5 –8.
Gleason, K. C., I. Mathur, and M. A. Peterson. 2004. “Analysis of Intraday Herding Behavior
Among the Sector ETFs.” Journal of Empirical Finance 11 (5): 681– 694.
Gleitman, H. 1981. Psychology. New York: Norton and Company.
Goetzman, W. 1995. “Discussion: On Fads, Crashes and Asymmetric Information.” In AngloAmerican Finance Systems: Institutions and Markets in the 20th Century, edited by Richard
Sylla and Mike Bordo, 323– 328. New York: Irwin Publishers.
Golberg, J., and R. Nitzsch. 2001. Behavioral Finance. London: John Wiley and Sons.
Grinblatt, M., and C. Y. Hwang. 1989. “Signalling and the Pricing of New Issues.” Journal of
Finance 44 (2): 393– 420.
Grinblatt, M., S. Titman, and R. Wermers. 1995. “Momentum Investment Strategies, Portfolio
Performance, and Herding: A Study of Mutual Fund Behavior.” The American Economic
Review 85 (5): 1088 –1105.
Hirshleifer, D., and S. Hong Teoh. 2003. “Herd Behaviour and Cascading in Capital Markets:
A Review and Synthesis.” European Financial Management 9 (1): 25 – 66.
Hirshleifer, D., A. Subrahmanyam, and S. Titman. 1994. “Security Analysis and Trading Patterns
When Some Investors Receive Information Before Others.” Journal of Finance 49 (5):
1665– 1698.
Holsti, O. R. 1979. “The Three-Headed Eagle: The United States and System Change.”
International Studies Quarterly 23 (3): 339– 359.
Houge, T., T. Loughran, X. S. Yan, and G. Suchanek. 2001. “Divergence of Opinion, Uncertainty,
and the Quality of Initial Public Offerings.” Financial Management 30 (4): 5 – 23.
How, J., R. Jelic, B. Saadouni, and P. Verhoeven. 2007. “Share Allocations and Performance of
KLSE Second Board IPOs.” Pacific-Basin Finance Journal 15 (3): 292– 314.
Hsieh, S. F. 2013. “Individual and Institutional Herding and the Impact on Stock Returns: Evidence
from Taiwan Stock Market.” International Review of Financial Analysis 29: 175–188.
Husni, T. 2005. “Price Randomness, Contrarians and Momentum Strategies: A Study of Return
Predictability in the Malaysian Stock Exchange.” Thesis doctoral dissertation, Universiti Sains
Malaysia.
Hwang, S., and M. Salmon. 2004. “Market Stress and Herding.” Journal of Empirical Finance
11 (4): 585– 616.
Isa, M., and R. Ahmad. 1996. “Performance of New Issues on the Malaysia Stock Market.”
Malaysia Journal of Economic Studies 13 (2): 53 – 66.
Ismail, K., F. Z. Abidin, and N. Zainuddin. 1993. “Performance of New Stock Issues on the
KLSE.” Capital Markets Review 1 (1): 81 – 95.
James, C. 1990. Foundations of Social Theory. Cambridge, MA: Belknap.
Jelic, R., B. Saadouni, and R. Briston. 2001. “Performance of Malaysian IPOs: Underwriters
Reputation and Management Earnings Forecasts.” Pacific-Basin Finance Journal 9 (5): 457–486.
Jomo, K. S. 1998. “Financial Liberalization, Crises, and Malaysian Policy Responses.” World
Development 26 (8): 1563– 1574.
Kallinterakis, V., and T. Kratunova. 2007. “Does Thin Trading Impact Upon the Measurement of
Herding? Evidence from Bulgaria.” Ekonomia 10 (1): 42 – 65
Keynes, J. M. 1936. The General Theory of Employment Interest and Money, The Collected
Writings of John Maynard Keynes. Vol. VII. London: Macmillan.
Kim, K. A., and J. R. Nofsinger. 2005. “Institutional Herding, Business Groups, and Economic
Regimes: Evidence from Japan.” The Journal of Business 78 (1): 213– 242.
Kremer, S. 2010. “Herding of Institutional Traders.” SFB 649 Discussion Paper 2010-025,
Humboldt University, Berlin, Germany.
Kremer, S., and D. Nautz. 2013. “Short-Term Herding of Institutional Traders: New Evidence
from the German Stock Market.” European Financial Management 19 (4): 730– 746.
Downloaded by [Universiti Kebangsaan Malaysia], [Ros Zam Zam Sapian] at 18:12 21 July 2014
244
P. Dehghani and R.Z.Z. Sapian
Lai, M. M., B. K. Guru, and F. M. Nor. 2003. “Do Malaysian Investors Overreact.” Journal of
American Academy of Business 2 (2): 602– 609.
Lai, M., and S. Lau. 2004. “Herd Behavior and Market Stress: The Case of Malaysia.” Academy of
Accounting and Financial Studies Journal 8 (3): 85 – 102.
Lakonishok, J., A. Shleifer, and R. W. Vishny. 1992. “The Impact of Institutional Trading on
Stock Prices.” Journal of Financial Economics 32 (1): 23 –43.
Lee, C. M. C., A. Shleifer, and R. H. Thaler. 1991. “Investor Sentiment and the Closed-End Fund
Puzzle.” The Journal of Finance 46 (1): 75 – 109.
Leong, K., E. Vos, and A. Tourani-Rad. 2000. “Malaysia IPOs Performance Pre- and Post Asian
Crisis.” Paper presented at the ABN-AMRO International IPO Conference, Amsterdam.
Lin, A. Y., and P. E. Swanson. 2003. “The Behavior and Performance of Foreign Investors in Emerging
Equity Markets: Evidence from Taiwan.” International Review of Finance 4 (3–4): 189–210.
Lobao, J., and A. P. Serra. 2002. “Herding Behavior: Evidence from Portuguese Mutual Funds.”
Unpublished Working Paper. Instituto de Estudos Financieros e Fiscais, Portugal.
Lowry, M., and G. W. Schwert. 2002. “IPO Market Cycles: Bubbles or Sequential Learning?” The
Journal of Finance 57 (3): 1171– 1200.
Mat-Nor, F., M. M. Lai, and A. M. Hussin. 2002. “Price Randomness, Fundamental Factors, and
Stock Market Contrarian Strategy: Further Evidence on Malaysian Stock Market.” Proceedings
of the 4th Malaysian Finance Association, Penang, Malaysia.
Megginson, W. L., and K. A. Weiss. 1991. “Venture Capitalist Certification in Initial Public
Offerings.” Journal of Finance 46 (3): 879– 903.
Michaely, R., and W. H. Shaw. 1994. “The Pricing of Initial Public Offerings: Tests of AdverseSelection and Signaling Theories.” Review of Financial Studies 7 (2): 279– 319.
Miller, E. M. 1977. “Risk, Uncertainty, and Divergence of Opinion.” The Journal of Finance
32 (4): 1151– 1168.
Miller, R. E., and F. K. Reilly. 1987. “An Examination of Mispricing, Returns, and Uncertainty for
Initial Public Offerings.” Financial Management 16 (2): 33 – 38.
Mohamad, S., A. Nassir, and M. Ariff. 1994. “Analysis of Underpricing in the Malaysian New
Issues Market During 1975– 1990: Are New Issues Excessively Underpriced.” Capital
Markets Review 2 (2): 17 – 27.
Mohd, K. N. T. 2007. “Regulations and Underpricing of IPOs.” Capital Markets Review 15 (1 – 2):
1 – 27.
Monroe, K. R. 2001. “Paradigm Shift: From Rational Choice to Perspective.” International
Political Science Review 22 (2): 151– 172.
Morris, S., and H. S. Shin. 1999. “Risk Management with Interdependent Choice.” Oxford Review
of Economic Policy 15 (3): 52 – 62.
Nagel, S. 2005. “Short Sales, Institutional Investors and the Cross-Section of Stock Returns.”
Journal of Financial Economics 78 (2): 277–309.
Nofsinger, J. R., and W. Sias. 1999. “Herding and Feedback Trading by Institutional and
Individual Investors.” The Journal of Finance 54 (6): 2263– 2295.
Oehler, A. 1998. “Do Mutual Funds Specializing in German Stocks Herd?” Finanzmarkt und
Portfolio Management 4 (1998): 452– 465.
Orléan, A. 1995. “Bayesian Interactions and Collective Dynamics of Opinion: Herd Behavior and
Mimetic Contagion.” Journal of Economic Behavior and Organization 28 (2): 257– 274.
Osborne, J. 2002. “Notes on the Use of Data Transformations.” Practical Assessment, Research
and Evaluation 8 (6): 1 – 8.
Persaud, A. 2000. “Sending the Herd Off the Cliff Edge: The Disturbing Interaction Between
Herding and Market-Sensitive Risk Management Practices.” The Journal of Risk Finance
2 (1): 59 – 65.
Prasad, D., G. S. Vozikis, and M. Ariff. 2006. “Government Public Policy, Regulatory
Intervention, and Their Impact on IPO Under-Pricing: The Case of Malaysian IPOs.” Journal
of Small Business Management 44 (1): 81 – 98.
Puckett, A., and X. Yan. 2007. “The Determinants and Impact of Short-Term Institutional
Herding.” Mimeo. http://rssrn.com/abstract=972254
Rashid, A. A., M. K. Ibrahim, R. Othman, and K. F. See. 2012. “IC Disclosures in IPO
Prospectuses: Evidence from Malaysia.” Journal of Intellectual Capital 13 (1): 57 – 80.
Ritter, J. R. 1984. “The “Hot Issue” Market of 1980.” Journal of Business 57 (2): 215– 240.
Downloaded by [Universiti Kebangsaan Malaysia], [Ros Zam Zam Sapian] at 18:12 21 July 2014
Venture Capital
245
Ritter, J. R. 2003. “Investment Banking and Securities Issuance.” Handbook of the Economics of
Finance 1: 255– 306.
Rock, K. 1986. “Why New Issues Are Underpriced.” Journal of Financial Economics 15 (1 – 2):
187– 212.
Scharfstein, D. S., and J. C. Stein. 1990. “Herd Behavior and Investment.” The American
Economic Review 80 (3): 465–479.
Sherif, M. 1937. “An Experimental Approach to the Study of Attitudes.” Sociometry 1 (1–2): 90–98.
Shiller, R. J. 1984. “Stock Prices and Social Dynamics.” Brookings Papers on Economic Activity
15 (2): 457– 510.
Shiller, R. J. 1989. Market Volatility. Cambridge: MIT Press.
Shiller, R. J., and J. Pound. 1989. “Survey Evidence on Diffusion of Interest and Information
Among Investors.” Journal of Economic Behavior and Organization 12 (1): 47 – 66.
Shleifer, A., and L. H. Summers. 1990. “The Noise Trader Approach to Finance.” The Journal of
Economic Perspectives 4 (2): 19 – 33.
Sias, R. W. 2004. “Institutional Herding.” Review of Financial Studies 17 (1): 165– 206.
Simon, H. A. 1957. Models of Man; Social and Rational. New York: Wiley.
Tan, L., T. C. Chiang, J. R. Mason, and E. Nelling. 2008. “Herding Behavior in Chinese Stock
Markets: An Examination of A and B Shares.” Pacific-Basin Finance Journal 16 (1): 61 – 77.
Topol, R. 1991. “Bubbles and Volatility of Stock Prices: Effect of Mimetic Contagion.” The
Economic Journal 101 (407): 786– 800.
Uchida, H., and R. Nakagawa. 2007. “Herd Behavior in the Japanese Loan Market: Evidence from
Bank Panel Data.” Journal of Financial Intermediation 16 (4): 555– 583.
Voronkova, S., and M. T. Bohl. 2005. “Institutional Traders’ Behavior in an Emerging Stock
Market: Empirical Evidence on Polish Pension Fund Investors.” Journal of Business Finance
and Accounting 32 (7 – 8): 1537– 1560.
Walter, A., and F. M. Weber. 2006. “Herding in the German Mutual Fund Industry.” European
Financial Management 12 (3): 375– 406.
Wan-Hussin, W. 2005. “The Effect of Owner’s Participation and Share Lock-Up on IPO
Underpricing in Malaysia.” Asian Academy of Management Journal 10 (1): 19 – 36.
Wermers, R. 1999. “Mutual Fund Herding and the Impact on Stock Prices.” The Journal of
Finance 54 (2): 581– 622.
Wylie, S. 2005. “Fund Manager Herding: A Test of the Accuracy of Empirical Results Using UK
Data.” The Journal of Business 78 (1): 381 –403.
Yao, J., C. Ma, and W. P. He. 2014. “Investor Herding Behaviour of Chinese Stock Market.”
International Review of Economics and Finance 29: 12 – 29.
Yatim, P. 2011. “Underpricing and Board Structures: An Investigation of Malaysian Initial Public
Offerings (IPOs).” Asian Academy of Management Journal of Accounting and Finance 7 (1): 73–93.
Yong, O. 1991. “Performance of New Issues of Securities in Malaysia.” Malaysian Accountant: 3–6.
Yong, O. 1996. “Size of the Firm, Over-Subscription Ratio and Performance of IPOs.” Malaysian
Management Review 31 (2): 28 – 39.
Yong, O. 2007a. “Investor Demand, Size Effect and Performance of Malaysian Initial Public
Offerings: Evidence from Post-1997 Financial Crisis.” Jurnal Pengurusan 26: 25 – 47.
Yong, O. 2007b. “A Review of IPO Research in Asia: What’s Next?” Pacific-Basin Finance
Journal 15 (3): 253– 275.
Yong, O. 2011a. “Investor Demand, Size Effect and the Immediate Post-Listing Behavior of
Malaysian IPOs.” Universiti Tun Abdul Razak E-Journal 7 (2): 23 – 32.
Yong, O. 2011b. “Winner’s Curse and Bandwagon Effect in Malaysian IPOs: Evidence from
2001– 2009.” Jurnal Pengurusan 32: 21 – 26.
Yong, O. 2013. “When Do the Aftermarket IPO Prices Stabilize? Evidence from Malaysian Fixed
Price IPOs.” International Review of Business Research Papers 9 (4): 77– 90.
Yong, O., and Z. Isa. 2003. “Initial Performance of New Issues of Shares in Malaysia.” Applied
Economics 35 (8): 919– 930.
Yong, O., P. Yatim, and R. Z. Sapian. 1999. “Significance of Board of Listing and Type of Issue
on the Under-Pricing and After-Market Performance of Malaysian IPOs.” Capital Markets
Review 7 (1– 2): 47 – 74.
Yong, O., Yatim, and Z. Sapian. 2002. “Size of Offer, Over Subscription Ratio and Performance of
Malaysian IPOs.” Malaysian Management Journal 6 (1 – 2): 35 – 51.
246
P. Dehghani and R.Z.Z. Sapian
Downloaded by [Universiti Kebangsaan Malaysia], [Ros Zam Zam Sapian] at 18:12 21 July 2014
Zafirovski, M. 1999. “What Is Really Rational Choice? Beyond the Utilitarian Concept of
Rationality.” Current Sociology 47 (1): 47 – 113.
Zey, M. 1998. Rational Choice Theory and Organizational Theory: A Critique. Thousand Oaks,
CA: SAGE Publications.
Zhou, R. T., and R. N. Lai. 2009. “Herding and Information Based Trading.” Journal of Empirical
Finance 16 (3): 388– 393.
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