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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

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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.

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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

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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

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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

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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

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(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

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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.

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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

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