4_ behavioural finance
TRANSCRIPT
-
8/18/2019 4_ Behavioural Finance
1/12
Review
Neoclassical nance, behavioral nance and noise traders: A review and
assessment of the literature☆
Vikash Ramiah a,⁎, Xiaoming Xu b, Imad A. Moosa c
a School of Commerce, University of South Australia, 37-44 North Terrace, Adelaide, South Australia, 5000, Australiab Beijing Technology and Business University, Lab Center of Business and Law, Liang-Xiang-Gao-Jiao-Yuan-Qu, Fang-Shan Dist, Beijing, P.R. China, 102488c School of Economics, Finance and Marketing, RMIT University, 445 Swanston Street, Melbourne, Victoria, 3000, Australia
a b s t r a c ta r t i c l e i n f o
Article history:Received 4 February 2015
Received in revised form 4 May 2015
Accepted 31 May 2015
Available online 4 June 2015
JEL classi cation:
G1
G11
Keywords:
Behavioral nance
EMH
Noise trader risk
Market anomalies
While mainstream neoclassical nance ignores therole played by noise traders, a signicant amount of empiricalevidence is available to show that noise traders are active market participants and that their participation gives
rise to market anomalies. Unlike neoclassical nance, behavioral nance allows for market inef ciency on the
grounds that market participants are subject to common human errors that arise from heuristics and biases. In
this paper we review theliteratureon thebehavior of noisetraders andanalyze theconsequences of their presence
in the market, starting with a distinction between neoclassical nance and behavioral nance. We identify the
market anomalies that provide evidence for the tendency of markets to trade at irrational levels, demonstrate
how noise trading is related to some market fundamentals, and describe the models used to quantify noise trader
risk.
© 2015 Elsevier Inc. All rights reserved.
Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
2. Neoclassical nance versus behavioral nance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
3. Market anomalies and evidence for irrational behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
3.1. Momentum prot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
3.2. Contrarian prot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
3.3. Overreaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
3.4. Underreaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
3.5. Information pricing errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
3.6. Technical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4. Noise trading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
5. Noise trading and fundamentals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
5.1. Volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
5.2. Earnings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
5.3. Firm size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
5.4. Leverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
5.5. Capital expenditure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
5.6. Sales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
6. Quantifying noise trader risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
7. Conclusions and future remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
International Review of Financial Analysis 41 (2015) 89–100
☆ We would liketo thank theeditorof this journal andan anonymousrefereefor useful comments. We are grateful to Afaf Moosa fordrawing Fig.1 andPetkoKalevfor hishelp with
the revision of the paper.
⁎ Corresponding author at: UNISA.
http://dx.doi.org/10.1016/j.irfa.2015.05.021
1057-5219/© 2015 Elsevier Inc. All rights reserved.
Contents lists available at ScienceDirect
International Review of Financial Analysis
http://dx.doi.org/10.1016/j.irfa.2015.05.021http://dx.doi.org/10.1016/j.irfa.2015.05.021http://dx.doi.org/10.1016/j.irfa.2015.05.021http://dx.doi.org/10.1016/j.irfa.2015.05.021http://www.sciencedirect.com/science/journal/10575219http://www.sciencedirect.com/science/journal/10575219http://dx.doi.org/10.1016/j.irfa.2015.05.021http://dx.doi.org/10.1016/j.irfa.2015.05.021http://crossmark.crossref.org/dialog/?doi=10.1016/j.irfa.2015.05.021&domain=pdf
-
8/18/2019 4_ Behavioural Finance
2/12
1. Introduction
A “noise trader” is a term that is used to describe a market participant
who makes investment decisions without the use of nance fundamen-
tals, exhibits poor market timing, follows trends and tends to overreact
or underreact to good and bad news. For instance, Black (1986) describes
noise traders as investors who do not trade on the basis of information
while Bender, Osler, and Simon (2013) nd evidence indicating that
noise traders use technical analysis in the form of the “
head-and-shoulders” chart pattern. Lee, Shleifer, and Thaler (1991) demonstrate
that noise traders are active and that they do inuence market prices.
In terms of rationality, traders may be classied into information users
(rational or information traders) and irrational (noise) traders.
Yet mainstream neoclassical nance does not recognize noise
traders, ignoring them on the grounds that their role is trivial. The
main pillar of neoclassical nance, the ef cient market hypothesis
(EMH), postulates thatnancial asset prices reect all available informa-
tion because market participants are rational processors of information.
Prior to the 1980s not much attention was paid to noise traders and
other forms of irrational behavior, but the observation of market
anomalies changed that as the proponents of behavioral nance posed
a challenge to the EMH. A signicant amount of empirical evidence is
available to show that noise traders are involved in liquidity trading
(Dow & Gorton, 1993; Foster & Viswanathan, 1990, 1993; Pagano &
Roell, 1996), hedging (Dow & Gorton, 1994) and speculation (De Long,
Shleifer, Summers, & Waldman, 1990). The behavioral nance school
of thought allows for market inef ciency on the grounds that market
participants are subject to common human errors that arise from
heuristics and biases.
In this paper we review the literature on the behavior of noise
traders and analyze the consequences of their presence in the market.
We start with a distinction between the mainstream neoclassical
nance and the behavioral nance schools of thought. This is followed
by a description of various market anomalies that provide evidence for
the tendency of markets to trade at irrational levels. We then move on
to a discussion of noise trading and noise trader risk, followed by an
examination of how noise trading is related to some market fundamen-
tals. Next we present the theoretical models used to quantify noise traderrisk and the related empirical evidence before we nish with some
concluding remarks and suggestions for future research.
2. Neoclassical nance versus behavioral nance
Haugen (1999) describes the evolution of nance as a separate
discipline by identifying three schools of thought: old nance, modern
nance and new nance. The old nance school focused on nancial
statement analysis and the nature of nancial claims. Modern nance
focuses on asset pricing and valuation based on rational economic
behavior. Under this paradigm, the market is always ef cient, and devi-
ations from fundamental values are expected to be short-lived as they
are eliminated by arbitrage. In the 1980s several papers challenged
the modern nance doctrine, leading to the emergence of the newnance school of thought in the 1990s. The new nance doctrine deals
with inef cient markets, primarily by adopting behavioral models. In
this paper we distinguish between neoclassicalnance(modernnance),
as the mainstream discipline, and behavioral nance (new nance) as
the unorthodox discipline. Recently we witnessed the emergence of
“quantitative behavioural nance” as a discipline (see, for example,
Duran & Caginalp, 2007).
Statman (1999) identies the pillars of neoclassical nance (which
he calls “standard nance”) as being “the arbitrage principles of Miller
and Modigliani, the portfolio principles of Markowitz, the capital asset
pricing theory of Sharpe, Lintner, and Black,and the option-pricing theory
of Black, Scholes, andMerton”. He describes the discipline as “compelling”
because “it uses a minimum of tools to build a unied theory intended to
answer all the questions of nance”. The neoclassical nance era started
in the early 1950s when Markowitz (1952) introduced portfolio optimi-
zation theory. That was followed by Modigliani and Miller (1958, 1963)
who put forward the capital structure irrelevance theorem. Sharpe
(1964) and Lintner (1965) developed asset pricing models, including
the CAPM whereas Fama (1965, 1970) set out the conditions for various
forms of market ef ciency and put forward the ef cient market
hypothesis. In the 1970s Black and Scholes (1973) pioneered option-
pricing theory. In the 1990s, Fama and French (1993, 1996) created a
“
thriving industry”
out of their three-factor model, and since then noless than 50 factors have been tried in various modications of the
three-factor model (Subrahmanyam, 2010).
In short, neoclassical nance tells us the following: (i) the market
value of an asset should be aligned with its fundamental value;
(ii) nancial markets react quickly to new information; (iii) prices
follow a random walk process resulting from the random arrival of infor-
mation; and (iv) no investor can consistently earn abnormal return in ex-
cess of what is consistent with risk. While the contribution of neoclassical
nance is unquestionable, the doctrine has failed to provide valid expla-
nations for the persistence of market anomalies. Furthermore, the main
pillars of neoclassical nance (the ef cient market hypothesis and
CAPM) have come under severe criticism since the global nancial crisis
(for a survey of the views for and against, see Moosa, 2013; Moosa &
Table 1
Timeline of research evolution in neoclassical nance.
Author(s) Issue (s) Findings/Conclu sions
Markowitz
(1952)
Portfolio
selection
The rst stage of portfolio selection
involves the formation of relevant beliefs
on the basis of observation. The second
stage starts with the relevant beliefs and
ends with the selection of a portfolio.
Modigliani and
Miller (1958)
Capital structure Laying the foundations of a theory of the
valuation of rms in a world of
uncertainty.
Modigliani and
Miller (1963)
Capital structure A modied model that still shows
quantitatively large differences from the
traditional model.
Sharpe (1964) Asset pricing In equilibrium there is a simple linear
relation between the expected return andthe standard deviation of return for
ef cient combinations of risky assets.
Lintner (1965) Asset pricing Establishing conditions under which
stocks are held long (short) in optimal
portfolios even when risk premia are
negative (positive).
Fama (1965) Ef cient market
hypothesis
Stock prices follow a random walk process
such that the actual price of a security at any
pointin time is a good estimate of its intrinsic
value.
Fama (1970) Ef cient market
hypothesis
Evidence in support of the EMH is
extensive while contradictory evidence is
sparse.
Black and Scholes
(1973)
Option pricing The development, for the rst time, of a
model that gives a theoretical estimate of
the price of a European-style option.
Jensen andMeckling (1976) Capital structure The agency cost theory states that anoptimal capital structure is determined by
minimizing the costs arising from conict
between the parties involved.
Myers and Majluf
(1984)
Capital Str ucture The pecking order theory of capital structure
rejects the idea of a well-dened target debt
ratio.
Fama and French
(1993)
Asset pricing Identication of three s tock-market
factors: an overall market factor and
factors related to rm size and
book-to-market equity.
Fama and French
(1996)
Asset pricing Except for the continuation of short-term
returns, the anomalies largely disappear in
a three-factor model. The results are
consistent with rational ICAPM or APT
asset pricing.
Subrahmanyam
(2010)
CAPM and
extensions
Identication of some 50 variables that have
been used in extensions of the CAPM.
90 V. Ramiah et al. / International Review of Financial Analysis 41 (2015) 89–100
-
8/18/2019 4_ Behavioural Finance
3/12
Ramiah, 2015). Table 1 presents a timeline for the evolution of research
on neoclassical nance.
Behavioral nance can bedenedas the application of psychology to
explain market anomalies. The focus on interpersonal behavior and the
role of social forces in governing behavior is known as social psycholo-
gy. According to Statman (1999), “people are rational in standard
[neoclassical]nance; they are normal in behavioralnance”. Behavior-
al nance models allow for the possibility that market participants can
make mistakes in their valuations (cognitive errors). Research in behav-ioral nance covers a variety of topics such as representativeness bias,
overcondence, self-serving bias, gambler's fallacy, hindsight, panic,
herding behavior,status quo,survivorship bias, money illusion, lossaver-
sion, attachment, disposition effect, recovery, familiarity, illusion of con-
trol, home bias, conservatism and even narcissism. In many respects the
assumptions underlying behavioral nance models are similar to those
used to construct traditional models, but the following differences are
observed: (i) investors do not simply look at mean-variance congura-
tions to make investment decisions as they may be inuenced by other
non-statistical characteristics such as taste, preference and otherpsycho-
logical factors; (ii) investors may perceive trends even though no
obvious pattern is present; (iii) imperfect information exists in the
presence of trader heterogeneity; (iv) different investors tend to have
different investment opportunities, depending on taste, while herding
behavior may result in a common taste; and (v) the market is not
necessarily in equilibrium, and while arbitrage opportunities exist they
may be subject to market sentiment.
Table 2 presents a timeline for the evolution of research in behavioral
nance.One of the earliest contributions was made by Selden(1912) who
suggested, long before the emergence of behavioralnance as a discipline
or school of thought, that stock price movements depended crucially on
the mental attitude of market participants. It was, however, Tversky and
Kahneman (1973, 1974, 1981) who made the most signicant contribu-
tions to the eld, including the development of the heuristics of availabil-
ity, representativeness, anchoring and framing. Their most important
contribution, however, was the development of prospect theory (Tversky
and Kahneman, 1979), which Thaler (1980) advocated as an alternative
descriptive theory. Shiller (1981) was the rst to describe the ef cient
market hypothesis (the backbone of neoclassical nance) asan “academicmodel that bears little to reality”. Signicant contributions have been
made about the expected utility theory (Yaari, 1987), status quo bias
(Samuelson & Zechauser, 1988), loss aversion (Kahneman, Knetsch, &
Thaler, 1990), the equity premium puzzle (Benartzi & Thaler, 1995), and
the disposition effect (Odean, 1998a). Needless to say, this list of impor-
tant contributions is not exhaustive.
3. Market anomalies and evidence for irrational behavior
In this section we demonstrate that the EMH does not necessarily
hold at all times, giving rise to irrational behavior. Various market
anomalies are described to demonstrate that the behavior of market
participants can be inconsistent with asset pricing models such as the
CAPM, the Fama-French (1993, 1996) three-factor model and theCarhart (1997) four-factor model. Neoclassical nance theories fail to
provide adequate explanation as to why anomalous behavior persists
while behavioral nance theories provide psychological explanations
for observed market phenomena. Fig. 1 is a schematic representation
of how biases (heuristics) lead to observed market anomalies. In the
remainder of this section we describe some market anomalies—a
summary of the relevant ndings are reported in Table 3.
3.1. Momentum pro t
Both individual investors and institutional investors are exposed to
the challenge of asset allocation. Brinson, Hood, and Beebower (1986)
and Vora and McGinnis (2000) discuss the complexity for an individual,
even at the most basic level, of portfolio selection. At present there is an
ongoing debate on the protability of the high-frequency tactical
asset allocation strategy known as momentum trading (also known
as return continuation). Jegadeesh and Titman (1993) and Lee and
Table 2
Timeline of research evolution in behavioral nance.
Author(s) Issue(s) Findings/Conclusions
Selden (1912) Psychology of the stock
market
Movements of stock prices aredependent to a considerable degree on
the mental attitude of market
participants.
Festinger, Riecken, and
Schachter (1956)
Social
psychology
A state of cognitive dissonance arises
when two simultaneously held
cognitions are inconsistent. Because
the experience of dissonance is
unpleasant, the person will strive to
reduce it by changing beliefs.
Pratt (1964) Utility and
risk
A consideration of utility functions,
risk aversion and risk as a proportion
of total assets.
Tversky and Kahneman
(1973)
Judgmental
heuristics
Development of the availability
heuristic postulating that a person
evaluates the frequency of classes or
the probability of events by
availability.
Tversky and Kahneman
(1974)
Judgmental
heuristics
Three heuristics are employed to
make judgment under uncertainty:
representativeness, availability and
anchoring.
Kahneman and Tversky
(1979)
Prospect
theory
People underweight outcomes that
are merely probable in comparison
with outcomes that are obtained with
certainty.
Thaler (1980) Prospect
theory
Advocating the use of prospect theory
as an alternative descriptive theory.
Tversky and Kahneman
(1981)
Judgmental
heuristics
Introduction of the concept of framing.
Shiller (1981) Ef cient
market
hypothesis
The ef cient markets model is at best
an “academic” model and does not
describe observed movements in
nancial prices.
De Bondt and Thaler
(1985)
Market
inef ciency
People overreact systematically to
dramatic news events, which resultsin substantial weak-form
inef ciencies in the stock market.
Yaari (1987)) Expected
utility theory
Modication to expected utility theory
to obtains the “dual theory of choice
under risk”.
Samuelson and Zechauser
(1988)
Status quo
bias
Decision making experiments conrm
the presence of status quo bias.
Kahneman et al. (1990) Loss aversion Loss aversion and the endowment
effect persist even in market settings
with opportunities to learn.
Shefrin and Statman
(1994)
Noise trading There is a heterogeneous capital
market where noise traders tend to
distort certain principles of nance.
The behavioral ef cient market
hypothesis is presented.
Benartzi and Thaler
(1995)
Equity
premiumpuzzle
The puzzle is explained in terms of
behavioral concepts: loss aversioncombined with a prudent tendency to
monitor wealth frequently.
Odean (1998a) Disposition
effect
Investors have a tendency to sell
wining investments too soon and hold
losing investments for too long.
Holt and Laury (2002) Risk aversion A simple lottery choice experiment
shows differences in risk aversion
between behavior under hypothetical
and real incentives.
Harrison and Rutstrom
(2009)
Prospect
theory
Expected utility theory and prospect
theory can be reconciled by using a
mixture model.
Frydman, Barberis,
Camerer, Bossaerts, and
Rangel (2014)
Realization
utility
Activity in two areas of the brain,
which are important for economic
decision making, exhibit activity
consistent with the predictions of
realization utility.
91V. Ramiah et al. / International Review of Financial Analysis 41 (2015) 89 –100
-
8/18/2019 4_ Behavioural Finance
4/12
Swaminathan (2000) argue that traders can take advantage of momen-
tum strategies by buying well-performing stocks while simultaneously
short-selling poor-performing stocks. This nding is not conned to
the U.S. market: Rouwenhorst (1998) observes momentum prots in
12 European countries, Rouwenhorst (1999) documents momentum
prots in emerging markets, Chan, Hameed, and Tong (2000) reveal
further evidence using 23 stock market indices, Hameed and Kusnadi
(2002) show similar behavior in Asian markets, and Connolly and
Stivers (2003) present evidence for the British and Japanese markets.
Another vein of the literature is concerned with explaining why this
market anomaly persists. For this purpose, researchers have used assetpricing models, behavioral nance, macroeconomic factors, seasonality
and a restricted set of nance variables. Applying the three-factor
model, Fama and French (1998) fail to establish any relationship
between abnormal prots and three systematic risk factors. Behavioral
nance specialists, on the other hand, seek to explain observed momen-
tum prot with behavioral phenomena such as expectation extrapo-
lation (De Long et al., 1990), conservatism in expectations (Barberis,
Shleifer, & Vishny, 1998), biased self-attribution (Daniel, Hirshleifer,
& Subrahmanyam, 1998), disposition effect (Grinblatt & Han, 2005),
and selective information conditioning (Hong, Lim, & Stein, 2000).
Menkhoff and Schmidt (2005) describe momentum traders as investors
who seek to prot from trend analyses whereas Chordia and
Shivakumar (2002) nd that momentum strategies perform well
when macroeconomic conditions are good and that momentum prot
Fig. 1. Anomalies and biases.
Table 3
Evidence on market anomalies.
Anomaly Findings/Conclusions
Momentum prot Momentum prot persists across various stock markets,
which provides a challenge to the ef cient market hypothesis.
While some explanations have been put forward for why
momentum prot arises, little work has been done to explain
momentum prot in terms of noise trader risk.Contrarian prot Contrarian prot is produced by naïve investors who pay
attention to recent information only. Extensive literature
supports the presence of contrarian prot in markets around
the world.Overreaction Overreaction occurs when traders either overweight present
information or underweight past information. The literature
detects overreaction and explains why it arises. Noise trading
does not appear to explain overreaction.Underreaction Interaction between information traders and noise traders
leads to underreaction. The literature suggests that investors
do not fully incorporate earning announcements into asset
pricing. Underreaction may subsequently lead to overreaction
and momentum prot.
Information
pricing errors
Pricing errors are caused by overcondence and
self-attribution bias. The evidence shows that trading
volume is higher following periods of high returns as
investment success leads to a higher degree of
overcondence.Technical analysis Technical analysis is used to detect “illusory correlation”.
Trading volume is 60 per cent higher following the
emergence of head-and-shoulders patterns.
92 V. Ramiah et al. / International Review of Financial Analysis 41 (2015) 89–100
-
8/18/2019 4_ Behavioural Finance
5/12
depends on trading volume, earnings and the size of the underlying
rm.
The following lessons can be drawn from the literature:
(i) momentum prot persists across various stock markets, (ii) the
evidence provides a challenge to the ef cient market hypothesis,
(iii) the rst wave of studies tend to detect momentum prots across
various markets while the second wave focus on explaining why
momentum prot arises, (iv) momentum studies have attracted
increasing interest from
nance academics, and (v) little work hasbeen done to explain momentum prot in terms of noise trader risk.
3.2. Contrarian pro t
Lo and MacKinlay (1990) dene a contrarian investment strategy as
one that exploits negative serial dependence in asset returns by buying
poorly-performing stocks and short-selling well-performing stocks. De
Bondt and Thaler (1985) were pioneers in suggesting the notion of
contrarian prot. They challenge the ef cient market hypothesis by
arguing that contrarian prot is produced by naïve investors who tend
to pay more attention to recent information and less attention to prior
information, resulting in overreaction. As in the case of the momentum
anomaly, the protability of contrarian strategies is supported by exten-
sive literature. Several studies document contrarian prot in European
markets, including Brouwer, Van Der Put, and Veld (1997)—covering
France, Germany, Netherlands and the U.K.—Mun, Vasconcellos, and
Kish (1999) who examine markets in France and Germany; Forner
and Marhuenda (2003) who study the Spanish stock market; Novak
and Hamberg (2005) who investigate the Swedish market; and
Antoniou, Galariotis, and Spyrou (2005) who conduct similar work
on the Greek market. In the Asia Pacic region, Chin, Prevost, and
Gottesman (2002) nd contrarian prot in New Zealand; Yoshio,
Hideaki-Kiyoshi, and Toshifumi (2004) an d Chou, Wei, and Chung
(2007) document contrarian behavior in Japan; Hameed and Ting
(2000) examine the Malaysian stock market; Lo and Coggins (2006)
and Ramiah, Mugwagwa, and Naughton (2011b) report contrarian
prot in Australia.
Numerous papers document the presence of contrarian prot inthe
Chinese market. For instance, Kang, Liu, and Ni (2002) nd statisticallysignicant short-term contrarian prot in China while Otchere and
Chan (2003), Fung (1999) and Ramiah, Cheng, Orriols, Naughton, and
Hallahan (2011a) report contrarian prot in the Hong Kong market.
By following the methodology of De Bondt and Thaler (1985), Otchere
and Chan (2003) detect a small but signicant degree of overreaction
prior to the advent of the Asian nancial crisis. They argue that price
reversals are more pronounced for winners than for losers, an observa-
tion that they attribute to cultural differences. Ramiah et al. (2011a)
investigate the possibility of generating contrarian prot from stocks
that are cross-listed in Hong Kong, Mainland China, Australia, U.K.,
U.S., Singapore and Europe. They document contrarian prot as high
as 8.01 per cent per month for dually-listed stocks. The literature
strongly supports the proposition that contrarian trading behavior is
present in the Chinese stock market.
3.3. Overreaction
Research in experimental psychology suggests that overreaction
occurs when traders assign too much weight to present information
or too little weight to past information. De Bondt and Thaler (1985)
present the leading empirical study of the overreaction hypothesis,
providing evidence that challenges the ef cient market hypothesis.
Subsequent papers, such as Chopra, Lakonishok, and Ritter (1992),
reinforce the ndings of De Bondt and Thaler in terms of asymmetry
in overreaction, suggesting that individuals tend to overreact more
than institutions as individuals predominantly hold small-rm stocks
whereas institutional traders hold large-rm stocks. Further evidence
in support of the overreaction hypothesis has been produced by
Lakonishok, Shleifer, and Vishny (1994), Dreman and Berry (1995),
Lobe and Rieks (2011) and by Farag (2014).
As is the case with other anomalies, the literature is about detecting
overreaction and explaining why it arises. Odean (1998b) and Graham,
Harvey, and Huang (2009) use psychological factors to point out that
overcondent investors tend to overrate their own beliefs, which in
turn leads to excessive trading. Barberis et al. (1998) develop a model
to examine the role of both overreaction and underreaction and use
the Tversky and Kahneman (1974)
nding of representativeness biasto explain overreaction. Chen, Rui, and Wang (2005) show that Chinese
investors have a tendency to overreact to good news and underreact to
bad news in a bullish market. Ramiah and Davidson (2007) introduce
the information-adjusted noise model to explain how noise traders
overreact to news arrival. They show that there is a relatively low
level of overreaction to news arrival in the Australian market. The
literature indicates that while noise trading does not appear to explain
overreaction, strong evidence indicates that it explains underreaction.
3.4. Underreaction
Ramiah and Davidson (2007) show that interaction between
information traders and noise traders leads to underreaction in the
Australian stock market. They study interaction between the two
categories of traders over the period 2000–2002 where they consider
the arrival of 12,273 information items pertaining to 46 stocks. They
test market ef ciency on a daily basis by determining whether
underreaction, information pricing error (IPE) or overreaction prevails,
breaking down these different effects into positive and negative. Their
ndings show that the ef cient market hypothesis holds in 40 per
cent of the cases. They conclude that noise traders appear to be present
in the market in 60 per cent of the cases, classied into ve per cent
overreaction, 25 per cent underreaction and around 35 per cent of IPE.
Underreaction to rm-specic information is not a new phenomenon
as there is some literatureon howinvestors react to accounting andnan-
cial information. A signicant portion of the literature suggests thatinves-
tors do not fully incorporate earnings announcements into the pricing of
assets—examples of these studies are Balland Brown (1968), Bernard and
Thomas (1989), Bartov (1992), Narayanamoorthy (2006) and, morerecently, You and Zhang (2011). Another recent study by Fischer
(2012), which explores underreaction in certain sectors, nds that
while traders underreact to earnings news (captured by post-earnings
announcement drift), they overreact to product news in the form of
subsequent stock price reversals. It is worth noting that certain
studies—such as You and Zhang (2011) and Bernard (1992)—detect
both overreaction and underreaction.
Earlier papers, such as Bernard (1992), highlight the presence of
underreaction, which in turn can cause overreaction, implying that
traders tend to underreact to initial earnings announcements and over-
react subsequently. Studies carried out by Freeman and Tse (1989),
Bernard and Thomas (1990), Wiggins (1991), Mendenhall (1991), and
by Abarbanell and Bernard (1992) suggest that post-announcement
drift occurs because asset prices fail to reect current levels of earnings,which means that subsequent earnings announcements come as a
surprise to market participants. This framework involves a naïve expec-
tation model where prices are predictable, implicitly implying that fore-
casting errors can be either positively or negatively autocorrelated.
Bernard (1992) is intrigued by the existence of a naïve expectation
approach, as he wonders why such a trend/autocorrelation in the lags
does not disappear. He argues that there may be some other kind of
systematic risk factors (including noise trader risk) that prevent reversals.
The noise trader risk argument is also supported by Andreassen (1987)
who suggests that certain systematic psychological forces can inuence
price behavior.
Support forthe underreaction hypothesis is found by Cutler, Poterba,
and Summers (1991) who examine autocorrelation in various indexes
for different horizons and report positive autocorrelation in excess
93V. Ramiah et al. / International Review of Financial Analysis 41 (2015) 89 –100
-
8/18/2019 4_ Behavioural Finance
6/12
returns for holding periods of 1 to 12 months. Evidence of autocorrelation
is used to support the underreaction hypothesis, implying a delayed
reaction as pricesadjustslowlyto newinformation (hencethe emergence
of trends in returns). Bernard and Thomas (1990) observe positive
autocorrelation in earnings in the rst three quarters and a change into
negative autocorrelation in the fourth quarter—such outcomeis perceived
as a mean-reverting process. Jegadeesh and Titman (1993) provide
further evidence as they detect autocorrelation over a 6-month horizon
where such evidence is linked to momentum pro
t. The underreactionhypothesis is used to explain momentum prot on the grounds that
slow adjustment to new information leads to return continuation,
which means that winners continue to be winners and losers continue
to lose.
Following Edwards (1968), Barberis et al. (1998) use conservatism
bias to explain underreaction. Within the cognitive psychology literature,
conservatism is a bias that may occur when human beings process
information such that there is a tendency to rely more on previous
knowledge/information and less on new information. When applied to
the stock market, conservatism may make traders adjust slowly to new
information. Therecentliteratureshows thatinvestors tend to underreact
to announcements about earnings, dividends, stock splits and others. In
their experiment, Barberis et al. nd that individuals have a tendency to
update their posteriors in the right direction but by a smaller magnitude
than what is required where the right direction and magnitude are
provided by a Bayesian framework.
3.5. Information pricing errors
The implication of the ef cient market hypothesis is that informa-
tion traders are sophisticated market participants who end up making
the right investment decisions. Recent evidence, however, challenges
this proposition. For instance, Ramiah and Davidson (2007) detect in-
formation pricing errors whereby information traders end up becoming
noise traders. A signicant portion of the literature demonstrates that
professionals tend to make mistakes while another part of the literature
explains why they make mistakes.
Cordell, Smith, and Terry (2011) argue that the dual burden phe-
nomenon explains why professionals make errors. This phenomenonrefers to the instance where people with less experience believe that
they know more with greater certainty than people who have more
experience. Cordell et al. compare two groups of nancial planners:
the rst group has earned one certication whereas the second group
has more than one qualication and specialized skills. They report that
therst group (with less knowledge) tends to be more condent, giving
riseto the“dual burden phenomenon”. Grif n andTversky (1992)show
that when predictability is low, nancial analysts might even be more
overcondent than the novices because they put too much faith into
the models and theories in which they believe.
Overcondence as a phenomenon in the work place has been docu-
mented by Frank (1935) who reported that traders were overcondent
about their ability and that overcondence increased with the personal
importance of the task. Abreu and Mendes (2012) investigate the rela-tionship between investors' overcondence and trading frequency,
demonstrating that both overcondence and non-overcondence in
information results in more trading. They contribute to the litera-
ture by showing that overcondent investors trade less frequently
when they collect information via family and friends whereas non-
overcondent investors trade more frequently when they use
specialized sources of information. Their ndings are consistent
with the behavioral nance literature in that overcondence boosts
trading volume. For example, Statman, Thorley, and Vorkink (2006)
provide evidence indicating that trading volume is higher following
periods of high returns as investment success leads to a higher degree
of overcondence. De Bondt and Thaler (1995) argue that overcon-
dence is an important behavioral factor that explains the trading puzzle
whereas Odean (1998b) argues that a high level of trading volume,
volatility and irrational prices are consequences of overcondence.
Barber and Odean (2001) suggest that men are more overcondent
than women—consequently, men tend to trade more than women.
Ahmed and Duellman (2013) show that overcondent managers tend
to overestimate future returns on their rms' investments and that
they have a tendency to delay loss recognition.
Self-attribution bias is another behavioral bias that explains why
professionals make sub-optimal decisions—in this case because people
tend to have unrealistically positive views about themselves (Taylor &Brown, 1988). The behavioral nance literature shows that the effect
of this bias is similar to that of overcondence bias whereby traders
tend to trade excessively (Deaves, Lüders, & Luo, 2009; Glaser & Weber,
2007; Graham et al., 2009). Like overcondence, self-attribution makes
investors trade below the optimal trading point, leading to excessive
trading and mistakes.
3.6. Technical analysis
The ef cient market hypothesis dictates that investors cannot earn
abnormal returns consistently when they trade on the basis of historical
data. Technical analysts use charts to discern patterns that help them
make their investment decisions—one of these patterns is the head-
and-shoulders formation. Bender et al. (2013) use the head-and-
shoulders pattern to identify “illusory correlation” in nancial markets.
They provide evidence indicating that technical analysis is alive and
well, reporting that trading volume is over 60 per cent higher than nor-
mal around the time when head-and-shoulders patterns are observed.
However, they provide evidence indicating that trading on these signals
is not protable, suggesting that this technique is an “illusion”. They
explain that their ndings about head-and-shoulders trading t
Black's (1986) description of those who trade on noise as if it was
information. They conclude that, in aggregate, technical analysis
contributes signicantly to noise trading.
Campbell, Lo, and MacKinlay (1997) refer to technicalanalysisas the
“black sheep of the academic nance community.” Nevertheless, when
we look at the growing literature about momentum and contrarian
prot, we cansee that technicalanalysisis becoming a matter of interest
for many nance academics. Kavajecz and Odders-White (2004) reportthat most investment banks and tradingrms employ traders who rely
on technical analysis—the fact that these institutions are willing to
invest in technical analysis implies that some benets are associated
with this technique. We gather from the studies of Park and Irwin
(2007) and Billingsley and Chance (1996) that about 60 per cent of
commoditytrading advisors andbetween 30 and40 percent of currency
traders use technical analysis as a major tool in the decision making
process. Sturm (2013) discusses the issue of whether market ef ciency
and technical analysis can co-exist and argues that the presence of
noise traders leads to deviations from fundamentals.
4. Noise trading
Trading usually takes place when market agents assign differentvalues to a particular asset. Following Black (1986) and Shefrin and
Statman (1994), two categories of traders are present in the market: in-
formation (sophisticated) traders and noise traders. Shefrin and
Statman (1994) argue that information tradersact on thebasisof funda-
mental information and process information rationally. The term “noise
traders” appears frequently in popular nancial websites—in other
words, it has become a household expression. In Table 4 we present
some denitions of noise traders taken from some popular websites as
well as some formal denitions taken from academic articles.
A number of studies have shown that trading on information
is protable, including Easley, Hvidkjaer, and O’Hara (2002);
Vachadze (2001); Blair, Poon, and Taylor (2001); Gervais, Kaniel,
and Mingelgrin (2001); Pritamani and Singal (2001); Chen, Mohan,
and Steiner (1999); Atkins and Basu (1995); Berry and Howe
94 V. Ramiah et al. / International Review of Financial Analysis 41 (2015) 89–100
-
8/18/2019 4_ Behavioural Finance
7/12
(1994); and Penman (1987). Unlike other behavioral nance segments,
the noise trading literature is relatively thin although it has been growing
rapidlysince 2000. Noise traders havebeen identiedas a major sourceof
volatility, giving rise to what may be called “noise trader risk”. Black
(1986) provides a denition for noise traders but fails to develop a
model that captures noise trading effects.
Lee et al. (1991) attempt to capture the behavior of noise traders by
studying closed-end funds where typically a large pool of small inves-
tors conducts business. Closed-end funds, which are listed on major
stock exchanges, invest almost exclusively in the securities of other
publicly traded companies. Theprice of a closed-end fund tends to differ
from net asset value, which is referred to as the “closed-end fund
puzzle”. Lee et al. suggest a behavioral nance explanation in terms of
“differential clienteles” whereby individual investors prefer mutual
funds while institutional investors choose individual stocks to replicate
the portfolios. If small investors trade more on the basis of noise, then
the closed-end funds become more risky, which explains the discount
compared to the replicated portfolio. One implicit assumption underly-
ing this argument is that small investors trading on noise alter system-atic risk, which triggers a discussion of the risk associated with noise
trading—that is, noise trader risk. De Long et al. (1990) argue that the
discount could be regarded as a measure of sentiment in the market,
indicating that when noise traders are excessively bullish, the discount
should decline—the reverse is expected when noise traders are bearish.
Bodurtha, Kim, and Lee (1995) nd that changes in country-fund
discounts reect therisk associated with the sentimentof U.S. investors.
Brown (1999) shows that unusual levels of individual investor senti-
ment are associated with greater volatility of closed-end funds. Muller
and Pfnuer (2013) examine the net asset value spread in real estate in-
vestment trusts (REITs) and postulate that the price of REITs may reect
noise traders' sentiment.
Brown (1999) supports the hypothesis that irrational investors
acting on noisy signals could cause systematic risk while Odean(1998b) shows that volatility goes up with theintensity of noise trading.
Nguyen and Daigler (2006) nd that uninformed traders cause exces-
sive variability in trading volume when they face return or volatility
shocks. De Long et al. (1990) argue that under certain conditions,
noise traders may earn more than rational traders—nonetheless, this
may not be due to the skills of rational investors but most likely because
they assume greater risk exposure. Furthermore, they show that some
“sophisticated users” (informed traders) convert into noise traders as
it pays to do so.
Following Shleifer and Summers (1990) and De Long et al. (1990),
noise trader risk has to be evaluated in addition to basic market volatil-
ity. Several models have been developed to measure the volatility
caused by noise traders at the start of the third millennium. Lee, Jiang,
and Indro (2002) use the Investors' Intelligence of New Rochelle as a
sentiment proxy and a series of independent advisory services rated
by the editor of Investors' Intelligence. They estimate a GARCH model
to evaluate the impact of sentiment on return and volatility to demon-
strate that changes in sentiment are negatively correlated with condi-
tional volatility, implying that volatility goes up when investors
become more bearish, and vice versa. However, Verma and Verma
(2006) suggest that volatility is more affected by bullish sentiment by
using the sentiment index of the American Association of Individual
Investors (AAII) and an EGARCH model to check for asymmetric effects.
Wang, Li, and Lin(2009), on the other hand, employ other models (such
as GJR-GARCH, EGB2 and SWARCH models) to explore the effects of
investor sentiment on the Taiwan Futures Exchange. Using an
EGARCH model, Uygur and Taş (2014) investigate the proposition that
earnings shocks have more inuence on conditional volatility in high
sentiment periods in the U.S., Japan, Hong Kong, U.K., France, Germany
and Turkey. They nd that earnings shocks have more inuence on
conditional volatility when sentiment is high.
Low (2004) arguesthat noise traders caninate assetpricevolatility,
particularly during market downturns, which createsa debate on asym-metric volatility. Avramov, Chordia, and Goyal (2006) argue that asym-
metric volatility is governed by the trading dynamics of informed and
uninformed traders although they do not have a direct measure of in-
formed and uninformed trades. They assume that selling activity on
negative-return days is dominated by uninformed (noise) traders and
that selling activity on positive-return days is dominated by informed
traders. Kittiakarasakun, Tse, and Wang (2012) conrm the ndings of
Avramov et al. (2006) by using the traderidentication of the computer
trade reconstruction data set, which distinguishes between informed
and uninformed trades. Likewise, Baklaci, Olgun, and Can (2011) show
that noise traders contribute signicantly to volatility in spreads and
that the volatility impact is short lived.
Currently there is an on-going debate on whether or not market
sentiment reects the behavior of noise traders, but there is no generalconsensus on this issue. A number of researchers refer to market senti-
ment effects as noise trading. In their explanation of theclosed-end fund
puzzle, Lee et al. (1991) argue that the risk factor caused by small inves-
tors may account for the difference between the net asset value and the
price of the fund, suggesting that this can be used as evidence that noise
traderrisk is priced. Using the Michigan consumer condence index asa
proxy for investor sentiment, Lemmon and Portniaguina (2006) show
that consumer condence explains time variation in equity portfolio
returns—this proposition is supported by other studies such as Baker
and Wurgler (2006) and Qiu and Welch (2004).
Baker and Wurgler (2007) estimate a different sentiment index by
averaging six widely accepted proxies: trading volume, dividend
premium, closed-end fund discount, the number and rst-day returns
on IPOs, and the equity share in new issues. Each proxy is regressed
Table 4
Popular and formal denitions of noise traders.
Author Denition
Invesopedia (http://www.investopedia.com/terms/n/noisetrader.asp) Investors who make decisions regarding buy and sell trades without the use of fundamental data.
These investors generally have poor timing, follow trends, and overreact to good and bad news.
Wikipedia (http://en.wikipedia.org/wiki/Noise_trader) A noise trader is a trader whose decisions to buy, sell or hold are irrational and erratic.
Financial Dictionary
(http:// nancial-dictionary.thefreedictionary.com/Noise+Trader+Risk)
A noise trader is an investor who makes decisions on feelings, such as fear or greed, rather than
fundamental or technical changes to a security.
Financial Dictionary
(http:// nancial-dictionary.thefreedictionary.com/Noise+Trader)
A trader that makes investment decisions based on perceived market movements rather than a
security's fundamentals. A noise trader buys when everyone else seems to be buying and sellswhen everyone else seems to be selling.
Investwords (http://www.investorwords.com/11717/noise_trader.html) Investors who make their trading decisions without using any fundamental data. Typically, they
have poor timing and aremuchmore aptto overreact to good or badnewsabouttheirinvestments.
Personal Finance (http://www.pfhub.com/noise-trader/ ) A noise trader as an investor who bases investment decisions on trends prevailing in the market
rather than fundamental factors and information.
Bloomeld, O’Hara, and Saar (2009) Noise traders do not possess fundamental information and have no exogenous reasons to trade.
De Long (2005) Noise traders trade on bad information or no information at all.
Tetlock (2006) Noise traders are agents who have hedging motives or irrational reasons to trade.
Osler (1998) Noise trading is not rationally based on the arrival of new information about asset values.
95V. Ramiah et al. / International Review of Financial Analysis 41 (2015) 89 –100
http://www.investopedia.com/terms/n/noisetrader.asphttp://en.wikipedia.org/wiki/Noise_traderhttp://financial-dictionary.thefreedictionary.com/Noise+Trader+Riskhttp://financial-dictionary.thefreedictionary.com/Noise+Trader+Riskhttp://financial-dictionary.thefreedictionary.com/Noise+Trader+Riskhttp://financial-dictionary.thefreedictionary.com/Noise+Traderhttp://financial-dictionary.thefreedictionary.com/Noise+Traderhttp://financial-dictionary.thefreedictionary.com/Noise+Traderhttp://www.investorwords.com/11717/noise_trader.htmlhttp://www.pfhub.com/noise-trader/http://www.pfhub.com/noise-trader/http://www.investorwords.com/11717/noise_trader.htmlhttp://financial-dictionary.thefreedictionary.com/Noise+Traderhttp://financial-dictionary.thefreedictionary.com/Noise+Trader+Riskhttp://en.wikipedia.org/wiki/Noise_traderhttp://www.investopedia.com/terms/n/noisetrader.asp
-
8/18/2019 4_ Behavioural Finance
8/12
on macroeconomic variables such as industrial production, growth
in employment, and a recession indicator to lter out the effects of
macroeconomic news. Their results show that in periods of low (high)
sentiment, speculative stocks have greater (lower) future returns on
average than bond-like stocks.
Brown and Cliff (2005) also explore the relationship between inves-
tor sentimentand returnand report that previous returns areimportant
determinants of sentiment indexes. Aase, Bjuland, and Øksendal (2012)
show that noise tradersdo not lose on average while informed investorsmake zero expected prot. Davidson and Ramiah (2010) use two differ-
ent proxies for noise trading, the change in behavioral error and the
residual behavioral error after controlling for rm-specic information.
They nd evidence that these two proxies are related to return and con-
tend that the absence of a relationship between noise trader behavior
and return implies that the market is behaviorally inef cient. They
also identify a positive relationship as “systematic noise effect” and a
negative relationship as “cash noise effect”.
5. Noise trading and fundamentals
Ramiah and Davidson (2007) argue that using a sentiment index to
capture the behavior of noise traders is not suf cient as various factors
(such as rm-specic information, portfolio rebalancing and liquidity)
affect trading behavior. To that end, they control for the arrival of
rm-specic information in their measure of noise trader risk. The
literature on how rm-specic information affects noise trading is
rather thin. In the accounting literature, on the other hand, Chau,
Dosmukhambetova, and Kallinterakis (2013) study the relationship
between International Financial Reporting Standards (IFRS) and noise
trading. They report that the adoption of IFRS has enhanced the stability
and informational ef ciency of capital markets by promoting
information-based trading, which has the effect of reducing the impact
of noise traders. In the remainder of this section we discuss some of the
relevant factors while Table 5 reports a summary of the relevant
ndings.
5.1. Volume
Kyle (1985) argues that in continuous auction equilibrium the
quantity traded by noise traders follows a Brownian motion process.
This observation implies that an ex ante doubling of the quantities
traded by noise traders induces insiders and market makers to double
the quantities they trade without exerting any effect on prices, leading
to the doubling of prots for insiders. Campbell, Grossman, and Wang
(1993)nd thattrading volume and stock return autocorrelations are in-
versely related, suggesting that rational, risk-averse market participants
have a tendency to accommodate the buying and selling pressures of
uniformed investors or noise traders. Odean (1998b) provides evidence
suggesting that overcondence boosts trading volume and volatility,
leading to underreaction. Song, Tan, and Wu (2005) nd that the
relationship between volatility and volume on the Chinese stock market
is driven mainly by the number of trades.
Furthermore, Groenewold, Tang, and Wu (2003) observe a contem-
poraneous V-shaped relationship between stock returns and market
turnover in the Shanghai, Shenzhen and Hong Kong stock markets.Dennis and Mayhew (2002) investigate the relative importance of
factors such as leverage ratio, volume andrm sizeto explain volatility,
nding evidence for positive correlation between sizeand volume. They
also nd that the problem of multicollinearity cannot be ignored in
studies involving various fundamentals. There is rich literature on
trading volume, but this literature does not address adequately the
issue of how volume and noise trading are related.
5.2. Earnings
In an early piece of research, Ball and Brown (1968) develop the link
between earnings announcements, expectations and stock prices. Other
studies—such as Rendleman, Jones, and Latane (1982) and Easton and
Harris (1991)—useearningsas an explanatory variable forstockreturns.
Copeland, Dolgoff, and Moel (2004) support prior studies in that they
nd signicant results when a cross-section of market-adjusted stock
returns is regressed on changes in analyst expectation of short-term
and long-term earnings. Uygur and Taş (2014) show that bad news
(negative earnings shocks) cause more volatility than good news
(positive earnings shocks). Like trading volume, the literature on
earnings is extensive, but there is almost no literature on how earnings
affect noise trader risk, and vice versa.
5.3. Firm size
Fama andFrench (1993,1996) positthata three-factor model largely
captures average returns on U.S. stock market portfolios—this observa-
tion is conrmed by Chui and Wei (1998). Drew, Naughton, and
Veeraraghavan (2003) extend this literature by showing a relationshipbetween rm size, book-to-market equity and average stock returns
for several Asian markets. Furthermore, they show that small and
growth rms generate superior returns as compared with those of big
and value rms. The rst study of Fama and French triggered a debate
amongnanceacademics over the three factors. Unfortunately, the liter-
ature hardly discusses the issue of whether noise traders prevail in the
market for large, small, value or growth stocks.
5.4. Leverage
If we start with the classic work of Modigliani and Miller (1963) and
Miller (1977), we nd that a tax shield on interest payments on debt
places a premium on the value of a rm. However Miller's subsequent
incorporation of personal tax effects greatly reduces the tax advantagesof debt. Modigliani (1982) contributed to this literature by suggesting
that an optimal capital structure may involve a trade-off between tax
shelters on debt, ination, and personal tax effects. Few years later,
Myers and Majluf (1984) presented the pecking order theory to explain
the tendency to rely on internal funds andthe preference fordebt rather
than equity. Myers (1977, 1984) and Flannery (1986), inter alia, focus
on long-termnancial management. Bowman (1980) shows empirical-
ly that marketvaluemeasurementof owners' equityis important forthe
assessment of the effect of nancial leverage on risk. He nds that the
market value of debt does not appear to be important, which can be
attributed to noise. Ryan (1997) nds that systematic risk is positively
associated with nancial leverage. The ndings of Barkham and Ward
(1999) imply that property stocks are likely to provide return that can
differmarkedly from thereturn on theunderlyingassets over a relatively
Table 5
Evidence on the role of rm-specic information.
Indicator Findings/Conclusions
Tradingvolume The literature does not address adequately the issue of howvolume and noise trading are related. Trading volume and return
autocorrelations are inversely related, giving rise to the tendency
of informed traders to accommodate the market pressure created
by noise traders.
Earnings Very little in the literature on how earnings affect noise trader
risk, and vice versa.
Firm size Despite the importance of rm size in the Fama-French model,
the literature hardly deals with the issue of whether noise traders
are found more (or less) in the markets for large or small stocks.
L ever age A ltho ugh t he nance literature deals with leverage extensively,
particularly in theories of capital structure, the literature is silent
on how the behavior of noise traders is inuenced by leverage.
Capital
expenditure
The literature does not examine the relation between capital
expenditure and the behavior of noise traders.
Sales While it is intuitive that negative sales announcements should be
expected to reduce protability, the literature does not examine
the issue of how sales affect the behavior of noise traders.
96 V. Ramiah et al. / International Review of Financial Analysis 41 (2015) 89–100
-
8/18/2019 4_ Behavioural Finance
9/12
long period of time. Although leverage is a major component of the
nance literature, the relationship between leverage and noise trading
is rarely, if at all, examined.
5.5. Capital expenditure
Yang-Tzong, Alt, and Gordon (1993) contend that cost-cutting exer-
cises in inef cient capital expenditure tend to have a positive effect on
the market value of
rms. On the other hand, Copeland et al. (2004)fail to nd a statistically signicant relationship between the return on
shareholders' equity and capital expenditure. Surprisingly the literature
on capital expenditure is relatively thin compared to other factors such
as debt and rm size. We do not nd any consideration of the relation-
ship between noise trading and capital expenditure.
5.6. Sales
There is no literature to date on how noise trader risk is related to
sales. Therationale foraddingthis variable canbe found in a preliminary
study conducted by Ramiah et al. (2011b) that considers sales
announcements by loser rms. They assume that traders acting on fun-
damentals incorporate a rm's sales gures into their asset allocation
decisions. Intuitively, any negative sales announcement would be
expected to reduce protability expectations for rm.
6. Quantifying noise trader risk
Several studies have been conducted to quantify noise, including
Sias, Starks, and Tinic (2001), De Long et al. (1990), Osler (1998), Lee
et al. (2002), Verma and Verma (2006), Ramiah and Davidson (2007)
and Hu and Wang (2013). Others incorporate noise trading in their
models, including Blume and Easley (1994), Barberis et al. (1998),
Daniel et al. (1998) and Shefrin and Statman (1994). Sias et al. (2001)
use closed-end funds as their testing grounds, which represents a limi-
tation in the sense that other categories of listed companies are
overlooked. Osler (1998) identies noise traders in the U.S. market
using the“head-and-shoulder” chart pattern—again, thismodel is limited
to trading based on technical analysis. Lee et al. (2002), Verma andVerma (2006) and Hu and Wang (2013) fail to control for the effects of
rm-specic factors. The rest of this section focuses on Ramiah and
Davidson (2007) and Davidson and Ramiah (2010) as a continuation of
the work of De Long et al. (1990) and Shefrin and Statman (1994).
The model proposed by De Long et al. (1990) can be represented by
two equations:
λst ¼ r þt pt þ1− 1 þ r ð Þ pt
2γ t σ 2 pt þ1
ð1Þ
λnt ¼ r þt pt þ1− 1 þ r ð Þ pt
2γ t σ 2 pt þ1
þ ρt 2γ t σ 2 pt þ1
ð2Þ
where λt s is the demand for risky assets by informed traders, λt
n is the
demand for risky assets by noise traders, r is a xed dividend, pt is the
stock price at time t , γ is the coef cient of absolute risk aversion and
t σ 2
pt þ1is the one-period variance of pt + 1 at time t . It is assumed that
informed investors at time t perceive correctly the distribution of
returns from holding risk assets. Noise traders, on the other hand,
misperceives the expected price of a risky asset by an independently
and identically distributed normal random variable, ρt ~:N ρ;σ 2 p
,
where ρ⁎ is a measure of the average “bullishness” of noise traders and
σ p2 is the variance of noise traders' misperception of the expected return
per unit of the risky asset or some element of noise trader risk. It is
assumed that noise traders maximize their expected utility, given
next-period dividend, the one-period variance of pt + 1, and their false
belief that the distribution of next-period price has a mean ρt above its
true value. The implication of the work of De Long et al. is that an
element of noise trader risk, which neoclassicalnance fails to consider,
leads to a misspecied CAPM.
To that end, Shefrin and Statman (1994) develop a behavioral asset
pricing model (BAPM), which allows for heterogeneous traders and
produces behavioral beta, consisting of the traditional beta and noise
trader risk. BAPM is similar to the traditional CAPM, except that that
the market portfolio is proxied by a sentiment index. The CAPM isrepresented by
~r it −~r ft ¼ α i þ β C i ~r mt −~r ft
þ ~ε it ð3Þ
where~r it is the return on asset i at time t ,~r ft is therisk- free rate at time t ,
~r mt is the market return at time t , ~ε it is the error term,α i is the intercept
of the regression equation and β iC is the CAPM beta. Eq. (3) can be
re-written by replacing the CAPM beta with the behavioral beta ( β iB)
or and the noise element ( η i). Hence
~r it −~r ft ¼ α i þ β Bi þ η i
~r mt −~r ft
þ ~ε it ð4Þ
where the noise element ( η i), which is referred to as behavioral error
(BE ), can be expressed as the difference between the CAPM beta and
the BAPM beta. This gives
BE i ¼ η i ¼ β C i − β
Bi ð5Þ
The BAPM of Shefrin and Statman (1994) is used to estimate the
behavioral beta from the following equation
~r it −~r ft ¼ α i þ β B
~r Bmt −~r ft
h i þ ~ε it ð6Þ
where ~r Bmt is the return on the behavioral market portfolio, which is
represented by a sentiment index made up of the “preferred stocks” of
small investors.
The methodology used to calculate BE involves the assumption that
indices would changeonly when there is a divergence in opinioncausedby irrational traders but fails to allow for new information arrival. To
extract rm-specic information from BE , Ramiah and Davidson
(2007) employ the information-adjusted noise model, which is written
as
ΔBE it ¼ α þ γ INFOit þ ε it ð7Þ
where INFO is a dummy variable that takes thevalue of one when there
is a news announcement and zero otherwise. α is the mean change in
the behavioral error caused by noise traders, γ is a measure of the con-
tribution of information traders to the behavioral error, and μ = α + γ
reects the net change in the behavioral error following the interaction
between noise and information traders—hence μ is a measure of noise
trader risk. This model can be used to detect overreaction, underreaction
and IPE.Davidson and Ramiah (2010) go one step further to nd out if the
proxies for noise trader risk (BE and μ ) are related to the return on
the underlying asset. For this purpose they use the following equations
in which return is a function of noise trader risk:
~r it ¼ ϕ1;i þ ϕ2;iΔBE t −1 þ ~ε it ð8Þ
~r it ¼ φ 1;i þ φ 2;i μ t −1 þ ~ε it ð9Þ
When Ramiah and Davidson (2007) apply their model to the
Australian market, they nd the market to be ef cient 37 per cent of
the times and observe IPE (33 per cent), underreaction (24 per cent)
and overreaction (6 per cent) as market inef ciency. Xu, Ramiah,
Moosa, and Davidson (in press) apply the model to the Chinese
97V. Ramiah et al. / International Review of Financial Analysis 41 (2015) 89 –100
-
8/18/2019 4_ Behavioural Finance
10/12
market and report pronounced market inef ciency with overreaction
(40 per cent), underreaction (18 per cent) and IPE (42 per cent). Both
of these studies show that noise trader risk is priced to a certain degree.
7. Conclusions and future remarks
This paper has outlined some major market anomalies that may
indicate irrational trading. One possible cause of these anomalies is
noise trader risk, which was not a very well-documented area prior totheyear 2000. Since then, we have seen a number of papers attempting
to quantify the impact of noise trading on nancial prices.
According to the literature, there are reasons to believe that traders
do not operate rationally. Whilemainstreamneoclassicalnancetrivial-
izes the effects of noise trading, the growing interest in the area of
behavioral nance has triggered a wave of studies to acknowledge and
explain noise trading. In particular, we have seen a growing number
of papers in the area of quantitative behavioral nance, of which we
attempted a selective survey in this paper.
The lessons that we have learnt from this review is that markets are
not always ef cient as indicated by the presence of market anomalies,
which can be explained in terms of noise trader risk. Although it has
been highlighted on numerous occasions, to this date no attempt has
been made to explain a market anomaly by using noise trader risk,with the exception of the closed-end fund puzzle. Several models have
been proposed to quantify noise trader risk, but the application of
some of these models is too restrictive while others are misspecied
(in terms of not controlling for rm-specic information).
So, where do we go from here? Numerous research avenues exist in
the eld of behavioral nance in general and the specic topic of noise
trading. A number of biases have not been investigated to the same
extent as representativeness, loss aversion, overreaction and conserva-
tism. Hence more research needs to be conducted, inter alia, on panic,
recovery and status quo. A recent trend that has opened extensive
opportunities for research in behavioral nance is the use of neurosci-
ence to analyze nancial decision making (for example, Bossaerts,
2009). As far as noise trading is concerned, we have already stated
that further research is required into how noise trading is related to
fundamentals andrm-specic information—in particular identication
of the fundamental factors that noise traders react to. Other areas of
future research about noise trading include a comparative study of
noisy markets and the inclusion of a noise trader risk factor in a multi-
factor asset pricing model. Last, but not least, high-frequency trading
is attracting signicant attention (for example, Moosa, in press). It
would be interesting to investigate if and how high-frequency trading
leads to noise trading activity.
References
Aase, K. K., Bjuland, T., & Øksendal, B. (2012). Partially informed noise traders.Mathematics and Financial Economics, 6 , 93–104.
Abarbanell, J. S., & Bernard, V. L. (1992). Test of analysts overreaction/underreaction toearnings information as an explanation for anomalous stock price behavior. Journal
of Finance, 47 , 1181–1227.Abreu, M., & Mendes, V. (2012). Information, overcondence and trading: Do the sources
of information matter?’. Journal of Economic Psychology, 33, 868–881.Ahmed, A. S., & Duellman, S. (2013). Managerial overcondence and accounting conserva-
tism. Journal of Accounting Research, 51, 1–30.Andreassen, P. (1987).On the social psychology of the stock market:Aggregate attributional
effects and the regressiveness of prediction. Journal of Personality and Social Psychology,53, 490–496.
Antoniou, A., Galariotis, E. C., & Spyrou, S. I. (2005). Contrarian prots and the overreactionhypothesis: The case of the Athens Stock Exchange. European Financial Management ,11, 71–98.
Atkins, A. B., & Basu, S. (1995). ‘The effect of after-hours announcements on the intradayU-shaped volume pattern. Journal of Business Finance and Accounting , 22, 789–809.
Avramov, D., Chordia, T., & Goyal, A. (2006). The impact of trades on daily volatility.Review of Financial Studies, 19, 1241–1277.
Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. Journal of Finance, 61, 1645–1680.
Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21, 129–151.
Baklaci, H. F.,Olgun,O., & Can, E. (2011).Noise traders: A newapproachto understand thephantom of stock markets’. Applied Economics Letters, 18, 1035–1041.
Ball, R., & Brown, P. (1968). An empirical investigation of accounting income numbers. Journal of Accounting Research, 6 , 159–178.
Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overcondence, and com-mon stock investment. Quarterly Journal of Economics, 116 , 261–292.
Barberis, N., Shleifer, A., & Vishny, R. (1998). A model of investor sentiment. Journal of Financial Economics, 49, 307–343.
Barkham, R. J., & Ward, C. W. R. (1999). Investor sentiment and noise traders: Discount tonet asset value inl isted property companies in the UK. Journal of Real Estate Research,18, 291–312.
Bartov, E. (1992). Patterns in unexpected earnings as an explanation for post-earningsannouncement drift. Accounting Review, 67 , 610–622.
Benartzi, S., & Thaler, R. H. (1995). Myopic loss aversion and the equity premium puzzle.Quarterly Journal of Economics, 110, 73–92.
Bender, J. C., Osler, C. L., & Simon, D. (2013). Noise trading and illusory correlations in U.S.equity markets. Review of Finance, 17 , 625–652.
Bernard, V. (1992). Stock price reactions to earnings announcements. In R. Thaler (Ed.), Advances in Behavioral Finance (pp. 303–340). New York: Russell Sage Foundation.
Bernard, V., & Thomas, J. (1989). Post-earnings announcement drift: Delayed priceresponse or risk premium? Journal of Accounting Research, 27 , 1–36 (Supplement).
Bernard, V., & Thomas, J. (1990). Evidence that stock prices do not fully reect the implica-tions of current earnings. Journal of Accounting and Economics, 13, 305–341.
Berry, T. D., & Howe, K. M. (1994). Public information arrival. Journal of Finance, 49,1331–1346.
Billingsley, R. S., & Chance,D. M. (1996). Benets and limitations of diversication amongcommodity trading advisors. Journal of Portfolio Management , 23, 65–80.
Black, F. (1986). Noise. Journal of Finance, 41, 529–543.Black, F., & Scholes, M. (1973). The Pricing of options and corporate liabilities. Journal of
Political Economy, 81, 637–665.Blair, B. J., Poon, S., & Taylor, S. J. (2001). Modeling S&P 100 volatility: The information
content of stock returns. Journal of Banking & Finance, 25, 1665–1679.Bloomeld, R.,O’Hara, M.,& Saar, G. (2009).Hownoise tradingaffects markets: An exper-
imental analysis. Review of Financial Studies, 22, 2275–2302.Blume, L., & Easley,D. (1994).Marketstatistics andtechnical analysis: Therole of volume.
Journal of Finance, 49, 153–182.Bodurtha, J., Kim, D., & Lee, C. (1995). Closed-end country funds and U.S. market sentiment.
Review of Financial Studies, 3, 879–918.Bossaerts, P. (2009). What Decusion Neuroscince teaches nancial decision making.
Annual Review of Financial Economics, 1, 383–404.Bowman, R. (1980). The importance of a market-value measurement of debt in assessing
leverage. Journal of Accounting Research, 18, 242–254.Brinson, G. P., Hood, R. L., & Beebower, G. L. (1986). Determinants of portfolio performance.
Financial Analysts Journal, 42, 39–44.Brouwer, I., Van Der Put, J., & Veld, C. (1997). Contrarian INVESTMENT STRATEGIES IN A
EUROPEAN CONTEXT. Journal of Business Finance and Accounting , 24, 1353–1366.Brown,G. W. (1999).Volatility, sentiment and noise traders. Financial Analysts Journal, 55,
82–90.
Brown, G. W., & Cliff, M. (2005). Investor sentiment and asset valuation. Journal of Business, 35, 405–440.Campbell, J.Y., Grossman, S. J., & Wang, J. (1993). Tradingvolume andserialcorrelation in
stock returns. Quarterly Journal of Economics, 108, 905–939.Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The econometrics of nancial markets.
Princeton: Princeton University Press.Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52,
57–82.Chan, K., Hameed, A., & Tong, W. (2000). Protability of momentum strategies in the
international equity markets. Journal of Financial and Quantitative Analysis, 35 ,153–172.
Chau, F., Dosmukhambetova, G. B., & Kallinterakis, V. (2013). International nancialreporting standards and noise trading evidence from central and eastern Europeancountries. Journal of Applied Accounting Research, 14, 37–53.
Chen, C. R., Mohan, N. J., & Steiner, T. L. (1999). Discount rate changes, stock marketreturns, volatility, and trading volume: evidence from intraday data and implicationsfor market ef ciency. Journal of Banking & Finance, 23, 897–924.
Chen, G., Rui, O. M., & Wang, S. S. (2005). The effectiveness of price limits and stockcharacteristics: Evidence from the Shanghai and Shenzhen Stock Exchanges. Review
of Quantitative Finance and Accounting , 25, 159–182.Chin, J. Y. F., Prevost, A. K., & Gottesman, A. A. (2002). Contrarian investing in a small
capitalization market: Evidence from New Zealand. Financial Review, 37 , 421–446.Chopra, N., Lakonishok, J., & Ritter, J. R. (1992). Measuring abnormal performance: Do
stocks overreact? Journal of Financial Economics, 31, 235–268.Chordia, T., & Shivakumar, L. (2002). Momentum, business cycle, and time-varying
expected returns. Journal of Finance, 57 , 985–1019.Chou, P. K. C., Wei, J., & Chung, H. (2007). Sources of contrarian prots in the Japanese
market. Journal of Empirical Finance, 14, 261–286.Chui, A. C. W., & Wei, K. C. J. (1998). Book-to-market, rm size, and the turn-of-the-year
effect: Evidence from Pacic-Basin emerging markets. Paci c Basin Journal of Finance,6 , 275–293.
Connolly,R., & Stivers, C. (2003). Momentum and reversals in equity-indexreturns duringperiods of abnormal turnover and return dispersion. Journal of Finance, 58,1521–1556.
Copeland, T., Dolgoff, A., & Moel, A. (2004). The role of expectations in explaining thecross-section of returns. Review of Accounting Studies, 9, 149–188.
Cordell, D.M., Smith, R., & Terry, A. (2011).Overcondence in nancial planners. FinancialServices Review, 20, 253–263.
98 V. Ramiah et al. / International Review of Financial Analysis 41 (2015) 89–100
http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0005http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0005http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0005http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0005http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0005http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0005http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0005http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0010http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0010http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0010http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0010http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0010http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0010http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0010http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0010http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0010http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0015http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0015http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0015http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0015http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0015http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0015http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0015http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0015http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0015http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0015http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0015http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0015http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0020http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0020http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0020http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0020http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0020http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0020http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0020http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0020http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0020http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0020http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0025http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0025http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0025http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0025http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0025http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0025http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0025http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0025http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0030http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0030http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0030http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0030http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0030http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0030http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0030http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0030http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0030http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0030http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0035http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0035http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0035http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0035http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0035http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0035http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0035http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0035http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0035http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0040http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0040http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0040http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0040http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0040http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0040http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0040http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0045http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0045http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0045http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0045http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0045http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0045http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0045http://refhub.elsevier.com/S1057-5219(15)00103-9/rf0050http://refhub.elsevier.c