Journal of Behavioral and Experimental Finance 37 (2023) 100722
Contents lists available at ScienceDirect
Journal of Behavioral and Experimental Finance
journal homepage: www.elsevier.com/locate/jbef
Review article
Emotions and stock market anomalies: A systematic review
∗
John W. Goodell a , , Satish Kumar b,c , Purnima Rao d , Shubhangi Verma d
a
College of Business, The University of Akron, USA
Department of Management Studies, Malaviya National Institute of Technology Jaiur, Rajasthan, India
Faculty of Business, Design and Arts, Swinburne University of Technology, Sarawak, Malaysia
d
Fortune Institute of International Business (FIIB), New Delhi, India
b
c
article
info
Article history:
Received 28 May 2022
Received in revised form 12 July 2022
Accepted 16 July 2022
Available online 22 July 2022
JEL classification:
D53
G21
G40
G41
N2
a b s t r a c t
While it is known that emotions affect financial markets, there has been little collective examination
of the literature in terms of identifying, for instance, which emotions, under what contexts, cause
particular market impacts. We systematically review articles on emotions and finance (1989–2020),
with particular emphasis on papers addressing emotions influencing stock-market anomalies, either
directly, or indirectly through first engendering behavioral patterns. We summarize literature linking
market anomalies to specific investor emotions, as well as identify directions for further research on
the emotional behavior of investors. We also identify research trends and designs, as well as data
collection and analysis techniques. Given the ongoing scholarly interest in emotions and markets,
results should be of great interest.
© 2022 Elsevier B.V. All rights reserved.
Keywords:
Systematic literature review
Behavioral finance
Emotional finance
Investor emotions
Stock market anomalies
Contents
1.
2.
3.
4.
5.
Introduction.........................................................................................................................................................................................................................
Data and methodology ......................................................................................................................................................................................................
Examining the corpus ........................................................................................................................................................................................................
3.1.
Distribution of articles ..........................................................................................................................................................................................
3.2.
Journal outlets ........................................................................................................................................................................................................
3.3.
Trends in research design.....................................................................................................................................................................................
3.4.
Theories used/discussed/tested ............................................................................................................................................................................
3.5.
Investor emotions and stock market anomalies ................................................................................................................................................
3.6.
Conceptual framework ..........................................................................................................................................................................................
Patterns of research and future opportunities................................................................................................................................................................
Conclusions..........................................................................................................................................................................................................................
References ...........................................................................................................................................................................................................................
1. Introduction
Traditional finance theory (Ross Stephen, 2005) assumes rational representative agents and perfect markets (Fama Eugene,
1970, 1995; Markowitz, 1952; Miller and Modigliani, 1961). These
∗ Corresponding author.
E-mail addresses: johngoo@uakron.edu (J.W. Goodell),
skumar.dms@mnit.ac.in (S. Kumar), purnima.rao@fiib.edu.in (P. Rao),
shubhangi.verma-fpm@fiib.edu.in (S. Verma).
https://doi.org/10.1016/j.jbef.2022.100722
2214-6350/© 2022 Elsevier B.V. All rights reserved.
1
3
4
4
5
5
6
6
9
9
9
11
economic models consider investor decision-making as resulting
in deliberative equilibriums reflecting considered costs and benefits of available options. However, such idealized models fail to
explain stock-market anomalies and patterns of investor behavior
(Chang, 2008; De Bondt and Thaler, 1985). The assumptions of
neoclassical finance have been challenged since the early 1980s
as not considering a variety of behavioral predilections such as
overconfidence, loss aversion, and present bias (Thaler, 1992;
Thaler et al., 1997).
J.W. Goodell, S. Kumar, P. Rao et al.
Journal of Behavioral and Experimental Finance 37 (2023) 100722
In contrast, as is well known, behavioral finance encompasses
non-pecuniary motivations and bounded rationality (Simon, 1955;
Conlisk, 1996; Statman, 1999; Shiller, 2003). Such investor behavior results in market anomalies (Nigam et al., 2018). Clearly,
understanding the emotional factors underlying the decisionmaking process has significant importance for investors. Behavioral finance examines the dynamics of investor behavior,
recognizing that investors are irrational and prone to human
errors (Statman, 1999). Many such errors have their origin in
bounded rationality (Conlisk, 1996; Epstein, 1994; Gilovich et al.,
2002). The focus of this review is more restrictive, being on
emotional influence (e.g., Shefrin, 2002; Nofsinger, 2005). While
it is long known that emotions impact markets, there has been
little comprehensive assessment of the literature, for instance in
terms of identifying which emotions cause what impacts.
Given the wide gamut possible behavioral biases, selecting a
financial model to describe markets through choosing particular individual-level behavioral aspects over others is not always
straight forward (Hirshleifer, 2015). Consequently, there is utility
to meaningfully aggregating differing biases into more collective
forms. For instance, regarding cultural finance, Goodell (2019)
suggests national cultures emerge long-term through economically optimizing selection and aggregation of individual behavioral biases. However, we also consider that social moods and
collective emotions also aggregate behavior, albeit more temporally.
The focus of this paper is to consider collective emotions. For
instance, does happiness lead to market volatility (Li et al. 2021)?
In this sense then, various diverse anomalies may have greater
associations with some collective emotions over others. This systematic review assesses the literature on emotional finance, as
leading to stock market anomalies. While behavioral finance considers that investors short-cut investment decisions with biases
and heuristics, such as prospect theory (Tversky and Kahneman,
1974), psychoanalytic theory (Fotaki et al., 2012), valence–arousal
theory (Feldman, 1995), and cognitive appraisal theory (Lerner
and Keltner, 2000), and disposition effects (Grinblatt and Han,
2005), literature on emotional finance considers the role of emotions and moods such as happiness, panic, anxiety, and irritation in influencing markets. In this study, we connect these two
threads by considering the literature that investigates emotions
catalyzing identified investor biases and bounded rationalities.
While emotions themselves cause cognitive errors, a focus on
emotions in finance extends beyond causing cognitive judgmental
errors (Hirshleifer, 2015) toward focusing on role of emotions
such as excitement (Lepori, 2015), euphoria (Lahav et al., 2016),
fear (Gurdgiev and O’Loughlin, 2020), panic (Shantha, 2019) or
anger (Chun et al., 2020) and shared emotions (Olson, 2006)
catalyzing or encouraging aggregate investment behavior (Andrikopoulos et al., 2021). Multiple studies have shown the effect
of positive and negative emotions influencing the investor decision making as these positive or negative emotions motivates or
demotivates them to take risk (Kuhnen and Knutson, 2011).
In addition to provoking errors, emotions impact a variety
of investor behavior such as predilection for risk-taking (Meloy,
2000). Research shows that investors respond to mood events,
with subsequent direct effects on financial market returns (Lahav
et al., 2016). Positive investor moods are expected to engender
overconfidence leading to accepting greater risk toward seeking
higher returns (Gavriilidis et al., 2020). Negative mood induces
opposite effects of lower confidence and less risk taking (Johnson and Tversky, 1983). Emotional influence from interpersonal
communications, social moods, is also identified to influence
investors (Nofsinger, 2005). Further, as stock market outcomes
are unpredictable, uncertainty motivates curiosity, with emotional responses of both a psychological and neurological nature
(Bechara and Damasio, 2005; Wolozin and Wolozin, 2007). We
consider that while investor psychology plays an essential role
in driving thinking processes (Mushinada and Veluri, 2019; Shefrin, 2002), emotional biases also shape decision making either
directly or as moderators of other factors (Jain et al., 2019).
Following prior reviews (Kumar and Goyal, 2015; Zahera and
Bansal, 2018; Valcanover et al., 2020; Shah et al., 2020) we
employ a systematic literature review (SLR) to establish the relationship between investor emotions and subsequent stock market
anomalies. Table 1 lists prior reviews either on behavioral finance, or on the financial impacts of social moods and exogenous
emotions-generating events. As can been seen in Table 1, reviews
have primarily been conducted in the preceding decade, rather
than earlier, indicating a trending topic of interest. The reviews
listed in Table 1 are generally exclusive to either a focus on
behavioral biases, social moods, sentiments, or calendar effects.
However, emotional aspects of investors in the investment decision making process and regarding stock market anomalies have
not been highlighted. Therefore, the present study provides a
comprehensive synthesis to the literature on emotional finance
and its impact on equity market investments.
Emotional finance encompasses a wide variety of investigative formats. For instance, experimental lab studies seek to understand investor decision-making behavior (Wuthisatian et al.,
2017). A review of this literature helps future researchers understand the influence of emotions and moods at the individuallevel decision-making process (Schwarz, 2000; Greifeneder et al.,
2011; Phelps et al., 2014; Lerner et al., 2015). Other studies
explore the effect of mood and emotions on investor behavior
(Dragouni et al., 2016; Harding and He, 2016; Wasiuzzaman and
Al-Musehel, 2018). Examples of mood generators used to understand the anomalies include the theatrical release of comedy
movies (Lepori, 2015); weather (Lucey and Dowling, 2005); religious celebrations (Abbes and Abdelhédi-Zouch, 2015); Islamic
calendar events (Munusamy, 2018); moon phases (Floros, 2008);
Pre-holiday effects (Bergsma and Jiang, 2016) and sports event
(Edmans et al., 2007).
Concerning the role of social interactions and moods, Gavriilidis et al. (2016), for instance, consider the influence of the Ramadan holiday on investor behavior. Such anomalies due to festival or holidays, or pre-holidays have been discussed for decades
and even referred to as mysteries by researchers (Lakonishok
and Smidt, 1984).[3] Various explanations for these anomalies
have been suggested, including a closing effect (Pettengill, 1989),
holiday euphoria (Lahav et al., 2016); investor short selling prior
to holidays (Ariel, 1990); as well as pre-holiday effects (Nofsinger,
2005; Lahav et al., 2016).
We identify the literature that seeks to examine emotions as
catalysts of trading behavior. Following a review of the literature
available on emotional finance and stock market anomalies, we
present a conceptual framework classifying the role of emotions
and other factors. Our conceptual framework argues that events
influence emotions which in turn influence investment decisions
leading to stock market anomalies. We build on past research
by consider a dynamic behavioral model to address plausible
emotional motivation for subsequent investment decisions. We
ask several Research questions (RQ) including 1) What are the
research trends in emotional finance regarding the distribution of
the literature across the regions and time? (RQ1); What are the
research designs, data collection methods, and data analysis techniques employed in emotional finance research? (RQ2); What are
the theoretical underpinnings and factors of emotional behavior
finance that lead to stock market anomalies? (RQ3); and What are
the gaps in the existing literature on emotional behavioral finance
and the avenues for future research? (RQ4).
2
J.W. Goodell, S. Kumar, P. Rao et al.
Journal of Behavioral and Experimental Finance 37 (2023) 100722
Table 1
Review articles on emotional or behavioral finance.
Article
Authors
Focus of review
Type of review
The Role of Feelings in Investor Decision-Making
Lucey and Dowling (2005)
Investor emotion
Literature review
Social mood and financial economics
Nofsinger (2005)
Social mood
Literature review
A literature review of social mood
Olson (2006)
Social mood
Literature review
A brief history and recent developments in
day-of-the-week effect literature
Philpot and Peterson (2011)
Calendar anomalies
Literature review
The efficient market hypothesis and calendar
anomalies: a literature review
Rossi (2015)
Calendar anomalies
Literature review
Behavioral biases in investment decision making
— a systematic literature review
Kumar and Goyal (2015)
Investor behavior
Systematic literature review
Do investors exhibit behavioral biases in investment
decision making? A systematic review
Zahera and Bansal (2018)
Investor behavior
Systematic literature review
Behavioral mediators of financial decision making
— a meta-analysis
Nigam et al. (2018)
Investor behavior
Meta-analysis literature review
A study of prominence for disposition effect: a
systematic review
Zahera and Bansal (2019)
Disposition effect
Systematic literature review
Behavioral Finance Experiments: A Recent Systematic
Literature Review
Valcanover et al. (2020)
Investor behavior
Systematic literature review
The Impact of the Behavioral Factors on Investment
Decision-Making: A Systemic Review on Financial
Institutions
Shah et al. (2020)
Investor behavior
Systematic literature review
This table review articles in emotional finance 1989–2020.
Table 2
Search protocol and database.
Keywords
‘Stock market anomaly’ AND ‘emotional
behavior of investor’ OR ‘investor emotions’
AND ‘Stock anomaly’ OR ‘investor behavior’
AND ‘stock market anomalies’ OR ‘emotional
biases of investor’ OR ‘behavioral biases’ AND
‘January effect’ OR ‘pre-holiday effect’ OR
‘investor behavior’ OR ‘investor irrationality’
AND ‘political stock market anomalies’ OR
‘disaster and stock market’
Databases
Refine
‘articles’
Refine
‘english’
Refine SCImago
Areas (AND/OR):
Accounting,
Finance; Economics
& Econometrics:
Business
Management &
Accounting
Refine
‘1989 to
2020’
Refine
AJG and ABS
Listed Journal
Eliminate
duplication
Read publications
by title, abstract
and keywords
Select
articles
148
EBSCO
125
112
98
86
54
0
54
Scopus
Google Scholar
172
330
165
227
138
153
94
123
68
72
23
41
45
31
Proquest
108
104
67
46
33
15
18
This table shows the textual corpus selection of the literature search through the systematic literature review process. It shares the inclusion and exclusion elements implemented while conducting the
literature review search.
2. Data and methodology
OR ‘behavioral biases’ AND ‘January effect’ OR ‘pre-holiday effect’
OR ‘investor behavior’ OR ‘investor irrationality’ AND ‘political
stock market anomalies’ OR ‘disaster’ AND ‘stock market’. Therefore, we conclude there is a need for a review. Preparation of the
review was done based upon this information and an extended
protocol was developed to determine the information need to
address specific objective of this study, using the criteria for
inclusion and exclusion in SLRs as identified by Tranfield et al.
(2003).
In Stage 2, we conduct the SLR. This stage involves conducting
the review in an objective and unbiased way by choosing and
selecting keywords that best describe the intent of the study. The
literature review is built by searching the online databases Scopus, Ebsco, Proquest, and Google Scholar to identify all relevant
papers published on emotional finance. The selection criteria restrict papers to those written in English and those published from
1989–2020. The year 1989 was chosen as the initial year of our
sample because the first study addressing the emotional aspects
of investors in investment decision-making was published in
1989. Journals considered are restricted to those included in the
Association of Business Schools Academic Journal Guide. Overall,
148 articles are identified.
Stage 3 involves the dissemination of knowledge. This stage
emphasizes analyzing the data. Following Callahan (2014), we focus on the four ‘W’s of what, why, where, and how as a structure
for this review. Our review also follows guidelines on what, why,
We employ a literature review methodology designed to
present an overview and unification of relevant sources. There
are several optional approaches, including meta-analytic, systematic, thematic, theoretical, historical, and bibliometric to conduct
literature reviews. The SLR method is adopted to ensure the production of reliable, scientific, and replicable research (Tranfield
et al., 2003). The SLR method is applied to analyze research conducted in emotional finance from 1989–2020. This research seeks
to describe literature that examines the relationship between the
emotional behavior of investors and stock market anomalies. SLR
is conducted based on search protocol to neutralize research bias
(Tranfield et al., 2003).
Our search protocol proceeds through planning, conducting,
and generation of knowledge via several stages. Table 2 shows
the textual corpus selection of the literature searched through
SLR. It shares the inclusion and exclusion elements. The SLR is
conducted in six stages: planning, conducting, dissemination of
knowledge, highlighting distribution, and classification of prior
studies, summarization, and discussing research gaps.
In Stage 1, we consider SLR planning. In this stage the need
for a review is identified. In this study, it is identified that there
are no publication using SLR methodology involving the keywords
‘stock market anomaly’ AND ‘emotional behavior of investor’ OR
‘investor emotions’ AND ‘stock anomaly’ OR ‘investor behavior’
AND ‘stock market anomalies’ OR ‘emotional biases of investor’
3
J.W. Goodell, S. Kumar, P. Rao et al.
Journal of Behavioral and Experimental Finance 37 (2023) 100722
Table 3
Research question and type of analysis.
Research question(s)
Type of analysis/
Units of analysis
RQ1. What are the research trends regarding the distribution of the
literature across the regions and time?
Descriptive analysis and
classification of literature
Year of publication, Region of the Study,
Journal of Publication
RQ2. What are the research designs, data collection methods, and
data analysis techniques employed?
Descriptive analysis and
classification of literature
Research Methodology employed in the
selected literature
RQ3. What are the theoretical underpinnings and factors that lead to
stock market anomalies?
Thematic analysis
Theories employed, emotions and
sentiments of an Investor, Stock market
anomalies
RQ4. What are the gaps in the existing literature and the avenues
for future research?
Descriptive and Thematic
analysis
Extraction of unexplored areas through
classification (as per RQ1, RQ2, and RQ3) of
literature under review
This table lists the research questions being addressed.
and how of advancing knowledge through literature reviews as
suggested by Lim et al. (2022).
Where and what research has been done? To address this question, we highlight the different contexts and the methods used in
prior studies on emotions playing a role in investment decisionmaking and their effect on stock market anomalies.
What do we know about emotional behavior and stock market
anomalies? Through the literature review, we present the general
picture of how scholars approach emotional finance and stock
market anomalies. We discuss the critical components of the
investor emotional behavior, along with what induces positive or
negative moods, and how these moods influence decision-making
in finance.
Why should people need to know more about emotional behavior
affecting stock markets? With the growing research interest in
behavioral finance, it is essential to study the anomalies that
are long identified by investors but yet unexplained. Study of
the influences on the investment-decision processes may well
the key to understanding and explaining the ’how’ and ‘why’ of
financial market anomalies (Taffler, 2017). This study adds to the
ongoing debate on role of bounded rationality in shaping financial
markets. Theoretical underpinning is required in considering the
impacts on markets of investor buying behavior (Duxbury et al.,
2020). This study proposes providing an inventory of all theories
in this context.
How can this review help fill the gaps in the literature and guide
future research? The study suggests future research by providing
insights on policy implications, and research gaps. This study will
help financial advisors, portfolio managers, individual investors,
market regulators, and policy makers to understand emotional
behavioral biases and their impacts on anomalies.
Stage 4 involves what research on emotion finance has been
done. To address this question, we highlight the different contexts
and the methods used in prior studies on emotions and investment decision-making. We highlight the distribution and classification of prior studies and their respective contexts. We also
describe the primary sources of publications, and their research
methodologies as well as the study types, and data-collection
methods.
Stage 5 summarizes what we know about emotional finance
and stock market anomalies. We present the general picture
of how scholars have approached emotional, finance and stock
market anomalies. We discuss the critical components of investor
emotional behavior concerning social interactions leading to positive or negative moods, and how this influences investment
decision-making.
Stage 6 discusses why people should need to know more about
emotional behavior affecting stock markets. We highlight known
investment anomalies that are left with unexplained reasoning,
our unidentified emotional catalysts.
Research on the association of emotions and investment
decision-making may well provide a coherent basis to understand
and explain financial-market anomalies. For instance,
Taffler (2018) shows how the investor psychodynamic perspective may explain the nature of financial activities Gavriilidis et al.
(2020) evidence a significant impact of investor mood on trading
conduct. Theoretical underpinning is required to describe the
emotional determinants affecting stock markets (Duxbury et al.,
2020). We propose providing an inventory of all theories used
regarding the role of emotions on the behavior of investors.
Toward this end, we offer a critical assessment of extant theories
and content analysis.
In Stage 7, we consider gaps in the literature toward guiding
future research. We suggest future research, as well as provide insights on policy implications. Consequently, this study will
help financial advisors, portfolio managers, individual investors,
market regulators, and policymakers understand the connection
between emotions and market anomalies. Finally, the present
review provides an important contribution toward advancing
knowledge on emotional finance following the what, why, and
how guide of Lim et al. (2022).
Our research design adopts the approach view of Lazarus and
Folkman (1984), that events, emotions, and actions are causally
connected. There are scenarios where personal relevance engenders emotions among investors and subsequently manifests as a
stock market effect or anomaly. Table 3 maps the research questions of this study with respective methods applied to seeking
answers, while Fig. 2 illustrates our concept of how behavioral
factors emerge from emotions.
3. Examining the corpus
The results of examining these 148 identified scholarly works
are presented in this section. Based on our systematic study of our
sample articles, we find investor emotions influencing decisioncontributing to stock market anomalies.
3.1. Distribution of articles
Figure 1 illustrates the distribution of articles over 1989–2020.
As shown in Fig. 1, there is an increasing number of articles published after 2005 on emotional explanations to market anomalies,
indicating the growing interest of researchers in understanding
investor emotions to explain the stock market anomalies. Table 4
shows the frequency of the region in which these studies have
been conducted. The classification shows a wide geographic author spread. We see that the highest number of studies have been
conducted in North America followed by Europe and Asia. As an
example, only 8% of studies have been conducted in the Indian
context.
4
J.W. Goodell, S. Kumar, P. Rao et al.
Journal of Behavioral and Experimental Finance 37 (2023) 100722
Fig. 1. Distribution of articles.
Fig. 2. Conceptual framework. [1] Certainly, culture finance research can overlap emotional finance research. For instance, Chui et al. (2010) evidence an association
of market momentum with individualism across countries. But their conceptual framework associates individualism with heightened feelings of optimism. [2] We
acknowledge that emotional finance research can readily overlap investor attention research, as shocks to investor attention are typically closely related to attention
events generating emotions. For example the attraction of investors toward dot.com stocks during dot.com bubble (Tuckett and Taffler, 2003); or large lottery prizes
(Hu et al., 2021). Similarly, the degree to which emotions impact financial market dynamics depends on aggregate investor emotional intelligence (Bucciol et al.,
2020), and also on how readily investors will act based on their emotions (Shen et al., 2017). [3] A pre-holiday effect is an anomaly that occurs due to abnormal
returns a day before a holiday (Pettengill, 1989; Oğuzsoy and Güven, 2004; Lahav et al., 2016). [4] This now is identified as extending to cryptocurrency markets
(Karaa et al., 2021). [5] There are interesting parallels to the theories of psychotropic voting in political science (Caplan; 2006; Caplan, 2007; Goodell and Bodey,
2012).
Table 4
Regional classification of the studies.
Region
Frequency
Percentage
North America
Africa
Asia Pacific
Europe
Middle East
Multiple regions
60
4
41
18
10
15
40.55%
2.70%
27.70%
12.16%
6.76%
10.13%
Total
148
100%
Banking and Finance has the highest number of articles written
on emotional aspects of financial stock market anomalies followed by Journal of Management, Journal of Economic Behavior &
Organization, and Journal of Economic Behavior & Organization for
those journals having more than one article on the emotional
behavioral finance in relation to stock market anomalies. Table 5
displays articles classified by ABDC and ABS rankings. Most of
these articles are from journals with either an ABDC rating of A*,
A, or B or are assigned an ABS ranking of 2 or above, implying that
investor emotions and stock market anomalies are appreciated by
the top tier journals.
This table represents the frequency and percentage of the article distribution
based on geographical region.
3.3. Trends in research design
3.2. Journal outlets
As analysis of the literature based on type of research study
and data helps to identify the focus of past research, we classify research on emotional finance into five categories: empirical
With respect to journal outlets for emotional finance, results
show that 148 articles are spread across 65 journals. Journal of
5
J.W. Goodell, S. Kumar, P. Rao et al.
Journal of Behavioral and Experimental Finance 37 (2023) 100722
Table 5
Rankings of corpus journals.
ABDC Ranking
Frequency
Cumulative Frequency
Percentage
Cumulative Percentage
A*
A
B
C
15
45
72
16
15
60
132
148
10%
30%
49%
11%
10%
41%
89%
100%
ABS Ranking
Frequency
Cumulative Frequency
Percentage
Cumulative Percentage
4*
4
3
2
1
4
6
31
46
61
4
10
41
87
148
3%
4%
21%
31%
41%
3%
7%
28%
59%
100%
This table shows the distribution of corpus articles by ABDC and ABS ranking.
(Cadsby and Ratner, 1992; Liano et al., 1992; Brailsford and Easton, 1993); modeling and analytical methodologies (Nigam et al.,
2018; Teng and Yang, 2018); conceptual (Taffler, 2018); and survey and review (Zahera and Bansal, 2019; Valcanover et al., 2020).
We include in the empirical category, studies based on observations or experiments (Duxbury et al., 2020). In this conceptual
research category, we include articles related to the development of models and theories. In the survey and review category,
we include studies that are related to surveys, fact gatherings,
as well as review papers and analytical research studies that
analyze previously available models or facts. Research articles
on stock market anomalies are empirical studies. Table 6 shows
that research articles on emotional finance are a mix of conceptual papers, empirical studies, and review papers. The most
used statistical technique used by authors is OLS regression, with
other econometric techniques including the Mann–Whitney U,
and Kruskal–Wallis testing, GARCH modeling, as well as Breusch–
Godfrey Lagrange multiplier and White heteroscedasticity testing.
Studies also involve mood proxies to understand the impact of
mood and emotions on the stock market returns. Out of 148
studies 103 studies use secondary data, while only 23 studies use
primary data.
2012). A vast array of literature notes the herding behavior of
investors (e.g., Hirshleifer and Hong Teoh, 2003; Spyrou, 2013).[4]
The disposition effect suggests investors sell assets that have
increased in value while keeping assets that have dropped in
value (Shefrin and Statman, 1985). Prominent theories used in
emotional finance research, along with seminal references are
listed in Table 7.
3.5. Investor emotions and stock market anomalies
Regarding papers highlighting the influence of emotions on investor sentiment, Parker (2019), notes that spontaneous
investment decisions are highly influenced by intuitions and
feelings resulting in irrational behavior by investors and causes
biases in decision making (Shefrin and Statman, 2009). Taffler
(2018) suggests the role of unconscious mental processes in
provoking feelings of excitement or anxiety generated through
investing in phantastic objects (objects that inspire individuals to
feel omnipotent). He illustrates the concept of emotional unconscious desire toward phantastic objects with the Berni Madoff’s
Ponzi scheme where investors found the scheme very satisfying
and wish fulfilling investment fantasy with no risk and high
annual returns (Eshraghi and Taffler, 2012). Duxbury et al. (2020)
highlights an emotion-based account that helps in explaining
the disposition effect in less experienced investors and how the
stock price might be influenced. They propose a theoretical representation where anticipatory emotions like the hope of earning
returns and fear of losing and anticipated emotions of elation of
realization of gains and disappointment of realization of losing
play an integral role in investor decision making. Bucciol et al.
(2020) highlight the positive effect of emotional intelligence in
explaining financial risk attitude, stressing the key components
were well-being, sociability, self-control, and emotionality.
Literature suggests that positive (negative) moods make investors more (less) willing to choose riskier choices (Deldin and
Levin, 1986). Lahav and Meer (2020) conduct an experimental
study on asset markets where the traders undergo a procedure of
mood induction prior to trade. Contradicting results from previous studies, they find that a positive mood reduces risk taking as
traders might desire to stay positive for a longer period. Emotions
play a key role in selecting the insurance policy especially casualty and life insurance which are affected by emotions aroused in
response to losses, health insurance due to impulsive emotions
and indemnity because of the fear of unknown (Brighetti et al.,
2014).
Olson (2006) posits that social mood is a summation of individual moods and investors tend to perceive reduced risk and
added gains when in a positive mood. This positive mood collectively turns into a social mood that leads to a perception
of reduced risk, with greater trust and greater expected gains.
Nofsinger (2005) posits that the impact of social interactions may
lead to an optimistic or pessimistic social mood, influencing the
3.4. Theories used/discussed/tested
Regarding thematic analysis, emotional influence on investor
behavior has been studied by various scholars (Taffler, 2018;
Duxbury et al., 2020; Hasso et al., 2020; Gavriilidis et al., 2020;
Pelster and Breitmayer, 2019; Duxbury, 2015), with these studies
evidencing that investor sentiments affect markets and create
anomalies (De Bondt and Thaler, 1985; Dimson, 1988; Wright and
Bower, 1992; Tetlock, 2007; Rahman, 2009; Teng and Yang, 2018;
Munusamy, 2018).
Research provides numerous theories regarding market
anomalies. For instance, prospect theory (Kahneman and Tversky,
1979a,b; Levy Jack, 1992; Barberis, 2013) suggests investors are
more risk-averse with respect to gains than to losses. Portfolio
theory shows the risk-averse nature of the investor and how
he maximizes his returns based on the market risk available.
Pericoli and Sbracia (2003) provide a theoretical framework for
the study of contagion. Conlisk (1996) and others suggest limits to
human rationality. The valence–arousal and cognitive-appraisal
approaches are two dominating emotion theories in psychology. According to the valence–arousal model, emotions can be
mapped out on a two-dimensional circular space constructed
by arousal in the vertical axis (mild to intense) and valence in
the horizontal axis (unpleasant to pleasant) (Feldman, 1995).
Cognitive appraisal theorists contend that emotions can be distinguished at a micro level, such as an individual’s appraisal
or mental response to a specific situation (Lerner and Keltner,
2000; Tiedens and Linton, 2001). Seasonal affective disorder is
seen impacting investors through seasonal mood changes (Marc,
6
J.W. Goodell, S. Kumar, P. Rao et al.
Journal of Behavioral and Experimental Finance 37 (2023) 100722
Table 6
Trends in research design.
Type of Paper
Type of Data
Data Collection Method and Prominent
Sources
Research Techniques
Example Citations
Empirical (n =
123)
Secondary Data
(n = 103)
Archival Research
Financial Database (E.g. Datastream,
Thomson Financials I/B/E/S database,
Nikkei NEEDS Financial Quest,
Bloomberg)
Stock Exchanges (e.g., Taiwan Stock
Exchange, Australian Stock Exchange,
Stock Exchange of Singapore Ltd)
Central Bank (e.g.,RBI)
Descriptive analysis (ANOVA,
T-tests, chi- square);
Regression Analysis; GARCH
regression models;
Experimentation; Cluster
Analysis; Panel; Kruskal–Wallis
(K–W) test; Mann–Whitney U
test
Cadsby and Ratner (1992);
Liano et al. (1992); Brailsford
and Easton (1993); Agrawal
and Tandon (1994); Keef and
Roush (2005); Wiley and
Zumpano (2009); Ashton et al.
(2011); Chuliá and Torró
(2011); Shen et al. (2017); Chiu
(2020); Duxbury et al. (2020)
Primary Data
(n = 20)
Questionnaire, Interviews, experiment
Regression, Factor analysis,
SEM
Liano and White (1994);
Meneu and Pardo (2004); Ding
et al. (2014); Duxbury (2015);
Harding and He (2016); Filiz
et al. (2019); Lahav and Meer
(2020); Rudiawarni et al.
(2020)
Modeling and
Analytical (n =
7)
Secondary (n =
7)
Archival data
Kronecker permutation; Wald
test; regression,
Afego (2015); Shu and Chang
(2015); Shen et al. (2017);
Nigam et al. (2018); Teng and
Yang (2018)
Conceptual and
General (n =
10)
NA
NA
Thematic analysis
Taffler (2017); Hasso et al.
(2020)
Literature
Survey and
Review (n =
12)
Secondary (n =
12)
Systematic, Meta-analysis
Zahera and Bansal (2019);
Valcanover et al. (2020)
This table shows the trends in the research design, displaying the statistical techniques used in past research. Research articles on emotional finance are conceptual
papers, empirical studies, and review papers. Data collection methods and type of data are identified.
caused by optimistic or pessimistic of CEOs (see also Huang et al.,
2019). Sports events as well induce emotions of excitement or
panic establishing links between game results and the stock market returns (Ashton et al., 2011). Disasters, of course, as events
catalyze emotions, with ensuing anxiety causing more pessimism
regarding future returns and impacting asset pricing (Kaplanski
and Levy, 2010).
Calendar dates and changes of seasons are also events, albeit regularly occurring at an annual frequency. Research evidences the impact of moods influenced by weather and markets
(Marc, 2012). For stock pricing, investors optimistically misattribute higher (lower) stock pricing when in a good (negative)
mood (Lucey and Dowling, 2005). E.M. (1993) evidences the
relationship between weather and returns, with returns being
significantly higher in times of increased sunshine. Brahmana
et al. (2012) support this relationship b a statistically significant
rely evidencing a relationship between weather and Irish equity
returns.
Calendar effects illustrate a market anomaly associated with
particular times of the calendar. Vasileiou (2018) evidence that
investor psychology influences financial trends which further
influence market performance. Calendar effects include the January effect, end-of-the-month effect, end-of-the-week effect, preholiday effects, lunar effects, Ramadan effect, and religious celebrations. Calendar effects are among anomalies that take place
due to unexplained and unexplored reasons. A pre-holiday effect
is a calendar anomaly where the stock market shows abnormal
higher returns on the day(s) before a holiday. Pettengill (1989)
documents higher returns on pre-holiday days versus trading
days after holidays.
While January holiday effects can be attributed to tax-loss
explanations, Ariel (1990) finds high mean return on trading days
before holidays. These effects could be attributable to activity
at the close of market and short sellers desire to close short.
decision makers in a society. He depicts the social mood cycle
and phases through a diagram showcasing the phases of mood in
a society due to social interaction starting from increasing mood
(optimism, happiness, generosity, inclusion, supportiveness and
hope), reaching a peak positive mood (overconfidence, euphoria,
excess, ambivalence, graciousness, trust), starting to declining
mood (pessimism, sadness, conservatism, exclusion, defensiveness and suspicion) and finally reaching the peak of negative
social mood (fear, depression, stinginess, segregation, antagonistic and mistrust). He concludes that the stock market reacts to the
high or low in the social mood with a high or row respectively.
Similarly, Prechter (1985, 1999) considers the association of
stock market cycles with societal emotions. The market uptrends
when society is calm, but peaks when society is energetic and
enthusiastic. The market downtrends when society is sad, insecure, inhibited, and fatigued and the market is at its lowest when
there is hostility, tense, and angry emotions in the society of the
investors.
Exogenous factors also catalyze the emotions-anomaly dynamic. For example, during ongoing terrorist attacks, it is observed that investors lower their trading activity, use less leverage, and engage in less short selling (Nigam et al., 2018). Such
changes in investor behavior can be attributed to increased risk
aversion and personal loss experiences. Corporate events such as
acquisitions, or board change announcements activate emotions
that shape cognitive bias. This is facilitated by asymmetric information. For instance, March and Shapira (1987) studies the
role played by a manager in broadcasting such announcements
to the investors. An optimistic manager may overestimate the
performance and iterate the same to the investors which leads
to overestimation of the growth and underestimation of the risk
(Hackbarth, 2004). Mitchell and Mulherin (1996) study the effect
of mergers on investment decision making with many mergers
leading to unexpected shocks due to creation of social mood
7
J.W. Goodell, S. Kumar, P. Rao et al.
Journal of Behavioral and Experimental Finance 37 (2023) 100722
Table 7
Theories used/discussed/tested.
Theory
Origin
Example Citations
Context
Psychoanalysis
Freud (1910)
Duxbury (2015); Nigam et al. (2018);
Taffler (2018)
Understanding of the investor mind sets out to
explore needs and desires to help drive their
investment decisions
Portfolio theory
Markowitz (1952)
Apergis et al. (2019)
The theory allows investors to allocate their
assets in a portfolio that minimizes risk and
maximized return
Bounded rational hypothesis
Simon (1957)
Ahmad et al. (2017)
The complexity that investors face while
making investment decision making
Mood maintenance model
Festinger (1957)
Harding and He (2016)
The theory explains how the mood of an
investor influences his financial risk tolerance.
A happy mood posits higher financial risk
tolerance.
Prospect theory
Kahneman and Tversky (1979a,b)
Wuthisatian et al. (2017); Ahmad
et al. (2017)
The investors perceive a higher value in
perceived gains when compared to the
perceived losses
Valence–arousal approach
Russell (1980)
(Feldman, 1995); Shen et al. (2017)
The theory explains the dimensional
valence–arousal defining emotional intensities
of trading.
Cognitive appraisal approach
Lazarus and Folkman (1984)
Tiedens and Linton (2001); Shen
et al. (2017)
Its asses the emotional situation wherein
investors evaluated the effect of an event
happening in the stock market will affect
them.
Seasonal affective disorder
Rosenthal et al. (1984)
Lin (2015)
A disorder occurring due to seasonal changes
causing sad mood or low energy during
winters.
Disposition effect
Shefrin and Statman (1985)
Zahera and Bansal (2019)
Hesitance of investors to realize the losses in
comparison to the gains. This hesitance helps
the investors in making a sound investment
decisions.
Fractal markets hypothesis
Peters (1994)
Gurdgiev and O’Loughlin (2020)
Through traditional quantitative methods, the
theory explains the investor behavior during
stock market anomalies.
Kirman herding model
Kirman (1993)
Ahmad et al. (2017); Shantha (2019);
Vidal-Tomás and Alfarano (2020)
Herd behavior is a social bias where investors
tend to follow what other investors are doing.
Adaptive market hypothesis
Lo (2005)
Gurdgiev and O’Loughlin (2020)
According to the theory, efficient market
hypothesis and investor behavior coexist that
reconciles market efficiency at the time of
disruptions.
Feelings-as-information theory
Schwarz (2012)
Lucey and Dowling (2005).
The theory states that emotions have
informational value. Positive emotions share
optimistic nature of the investor.
Financial contagion
Pericoli and Sbracia (2003)
Duxbury (2015); Nigam et al. (2018)
It is cascading effect of stock market anomaly
spread from a small portion to a larger system.
Neoclassical finance theory
William Stanley
Prechter and Parker (2007)
Investors unconscious need to predict stock
market prices to fulfill their wishful filling
fantasies.
This table lists the theories of employed in the corpus.
window dressing are argumentative explanations provided by
Agrawal and Tandon (1994) for the calendar anomalies. Other
factors stated by them in their paper are tax-induced trading, institutional characteristics, and market timing. Speculative trading
and arbitrage activity are plausible factors shared by Szakmary
and Kiefer (2004).
The January effect is known to be a seasonal increase in stock
prices throughout the month of January. This increased demand
for stocks is often preceded by a decrease in price during the
month of December, often attributed to tax-loss harvesting. It
is also suggested that many investors now time the January
effect so that it becomes priced into the market, nullifying it
all together. The month of Ramadan, with concomitant mental
attitudes of social empathy, peacefulness, and happiness among
Muslim devotees (Hassan and Kayser, 2019), is found to showcase
higher returns in Muslim dominated-countries (Wasiuzzaman
and Al-Musehel, 2018), with less volatility in the weekly market
return.
Similar studies over the past 30 years studying the holiday effect
anomaly evidence strong gains were found pre-holiday trading
days in comparison to post-holidays (Liano and White, 1994;
Agrawal and Tandon, 1994; Johnson and Cheng, 2002; Szakmary
and Kiefer, 2004; Oğuzsoy and Güven, 2004; Marrett and Worthington, 2009; Gama and Vieira, 2013; Yuan and Gupta, 2014;
Bergsma and Jiang, 2016; Wasiuzzaman and Al-Musehel, 2018;
Kudryavtsev, 2019; Chiu, 2020; Ziemba, 2020). Although, in contrast to these studies, (Liano, 1995) for the currency market shows
insignificant pre-holiday effects. Teng and Liu (2013) empirically
test the existence of pre-holiday effect in Taiwan using investor
emotions as proxies, finding that positive investor emotion plays
an important role in pre-holiday higher returns. Meneu and Pardo
(2004) evidence that the pre-holiday effect anomaly may occur
because small investors are reluctant to buy before holidays,
which further increases the trade size. Such dominance by the
increased average size may result in increased prices, further
leading to information-based trading. Inventory adjustment and
8
J.W. Goodell, S. Kumar, P. Rao et al.
Journal of Behavioral and Experimental Finance 37 (2023) 100722
4. Patterns of research and future opportunities
Events can also manifest inside the body through human
biorhythms or recurring cycles where decision-making is affected
by physical, emotional, and psychological cycles. Trifan (2019)
finds a significant impact of biorhythms, measured through seasonal affective disorder, on stock returns.
We observe increasing importance given to emotional finance
over our sample period. Indeed, we identify no substantial research for 1989–2000. Of additional note, for 2009–2011, the period of immediate reflection on the global financial crisis, we also
identify no substantial research. This is a surprising, given that
emotions can generally be considered as likely having important
roles in stock market crises crashes. (Oehler et al., 2018).
We find most emotional finance research has emerged from
North America, Europe, and Asia, with Australia and Africa particularly lacking in output. There is a need for a broader cross section
of author bases on emotional finance. Our analysis also identifies
a gap regarding statistical methods used in the existing literature,
where 78% is empirical research based on secondary data (70%)
and primary data (8%) as the sample collection method. Further,
we also find that extant studies suggest conceptual frameworks
primarily through the previous literature. Few of them contributing primary theoretical ideas. As secondary data is used in most
studies, regression analysis is the common statistical tool About
54% of the studies use regression, while statistical tools like
experimentation, cluster analysis, panel and factor analysis have
been much less used. Future studies may consider a wider tool
kit to evolve novel research. As a broad number of stock market anomalies, bubbles, and crashes have occurred throughout
history (Kindleberger, 1978), we encourage future researchers to
consider respective anomalies on a micro-level for each market
event and associated anomaly to better understand the role of
emotions in catalyzing such events.
3.6. Conceptual framework
We adopt a conceptual framework, illustrated in Fig. 2, which
draws on Lazarus and Folkman (1984), who posit that event
spur emotions that then influence behavior. Although Lazarus
and Folkman (1984) mainly focus on stress-inducing events that
then cause emotions, we consider a broad variety of positive
and negative mood-inducing events, including events closely related to what has been identified with causing investor attention,
such as large lottery prizes. Such attention-generating events in
our framework could be considered engendering, for instance,
jealousy for similar returns.
In the Lazarus and Folkman (1984) framework, a thought occurs before an emotional arousal. A person thinks of the situation
and experience before releasing any decision. Therefore, there is
event that occurs which develops the thought process and further
arouses emotions. Events trigger primary emotions, and then,
conditioned by thoughts, secondary emotions. Primary emotions
are the first emotions felt by the person as the consequence of
the event. Examples are joy, fear, sadness, shame, despair; disgust
(Ortony and Turner, 1990). These primary emotions, often based
on conscious and unconscious judgments, trigger secondary emotions like anger, hatred, euphoria, regret, jealousy, satisfaction,
love, optimism and so on.
Regarding positive emotions, excitement (Taffler, 2018), optimism (Balasuriya et al., 2010), euphoria, and joy are investigated
as examples of high-arousal emotions. The valence–arousal approach (Feldman, 1995) theoretically explains the effect of the
psychological feelings of an individual which affects the information processing bias. There is always hope in an investor’s
subconscious mind that the future price of the financial asset will
go up, which excites them to invest in financial assets. These wish
fulfilling ideas (Taffler, 2018) are powerful unknown building
blocks of conscious reflection. Understanding this behavior of an
investor may help in not only justifying the market bubbles but
also understanding the normal day-to-day trading activities.
Regarding negative emotions, fear, anxiety, pessimism, panic,
anger, and envy, such emotions are often felt in response to unanticipated and uncontrollable events (Nofsinger, 2005). These emotions create disappointment amongst investors. (Duxbury et al.,
2020) finds that disappointments and negative emotions affect
the decision-making process of less experienced investors more
than sophisticated investors.
Social mood refers to the cumulative investor sentiments influencing the financial markets (De Long et al., 1990). Social
mood has the emotional characteristics of influencing the investor sentiments from optimistic mood when financial market is
growing too pessimistic mood when financial market is declining
(Nofsinger, 2005). Social interaction plays an important role in influencing the investor mind. Pelster and Breitmayer (2019) finds
that the relationship between the peer interactions influence of
peers on the trading activities. Social interactions increase the
intensity of an individual to take risk and trade more even when
the trading environment is not favorable. Individuals with less
experience in trading may depend particularly on their peers
for information sharing and awareness (Chen et al., 2015). An
avenue for future research is to consider the role of emotions
in inducing herding behavior. Table 8 lists anomalies created by
events through emotional catalysts. (See Table 9.)
5. Conclusions
We conduct a systematic review of emotional finance, regarding this topic as a sub-area of behavioral finance that considers
emotions engendering market anomalies directly or by catalyzing
behaviorally motivated heuristics. By examining the literature on
this topic, we assess how investors respond to emotions, thereby
affecting financial market returns. We consequently gain insight
into how various social moods influence stock-market prices. We
conduct a systematic literature review of 148 published papers
on emotional finance and stock market anomalies. We do this by
offering a conceptual framework that describes the mechanisms
of how emotions lead to market anomalies.
In establishing our framework, we draw on Lazarus and Folkman (1984), who posit that events spur emotions that, in turn,
influence behavior. Events trigger primary emotions like anger,
joy, love, fear, and sadness followed by secondary emotions like
guilt, enthusiasm, frustration, shame, peace, optimism, confusion, happiness, and jealousy. Although Lazarus and Folkman
(1984) mainly focus on stress-inducing events that cause stresscoping emotional reactions, we consider a broad variety of positive and negative mood-inducing events, including political, corporate, and sports events, seasonality, calendar events, seasonal
biorhythms, disasters; as well as events closely related to what
has been identified with causing investor attention, such as large
lottery prizes. Such attention-generating events in our framework could be considered ultimately engendering, for instance,
jealousy for similar returns. We assess and summarize what has
so far been investigated. Overall, we survey literature on events
catalyzing emotions that lead to stock market anomalies.
We find that, while it has long been identified that emotions
impact financial markets, there has only more recently been studies that assess the specifics of particular emotions and particular
financial market impacts. Further there is much room for future
studies to expand in terms of geographic reach, newer and alternative contexts, more variety of econometric techniques, as well
as more developed conceptual frameworks. Given the importance
of this topic, this review will encourage further research on the
important area of emotional finance.
9
J.W. Goodell, S. Kumar, P. Rao et al.
Journal of Behavioral and Experimental Finance 37 (2023) 100722
Table 8
Anomalies and emotional catalysts.
Event
Emotion
Anomaly
Political Events
Fear
Abnormal Returns
Example
Mahmood et al. (2014); Nigam et al. (2018)
Corporate Events
Optimism
Overestimation of growth and underestimation
of risk
March and Shapira (1987); Mitchell and
Mulherin (1996); Hackbarth (2004)
Seasonality
Joy or Gloomy
Positive emotions lead to overestimation of
the prices and negative emotions lead to
underestimation of price
E.M. (1993); Lucey and Dowling (2005);
Brahmana et al. (2012)
Calendar effect
Euphoria, Joy,
Higher Returns
Teng and Liu (2013); Gavriilidis et al. (2016);
Qadan and Kliger (2016)
Biorhythms
Joy/Sadness
Overestimation/ Underestimation of Returns
Marc (2012); Trifan (2021)
Sports events
Excitement, Euphoria
Higher Returns, High Volatility
Ashton et al. (2008)
Disasters
Anxiety
Risk -Aversive, Lower Returns,
Underestimation of Price
Kaplanski and Levy (2010)
This table presents the anomalies created by the events triggered by investor emotion.
Table 9
Research suggestions.
Type
Research questions
References
Conceptualization
What is the scope of emotional behavior finance and how it can be defined
universally?
Shefrin (2002), Pompian (2006)
What constitutes of emotions?
Bechara and Damasio (2005)
What are stock market anomalies?
Wolozin and Wolozin (2007)
What are different events that affect the stock market?
Lucey and Dowling (2005), Floros (2008),
Bergsma and Jiang (2016)
What is the theoretical foundation for the linkage between finance and
human investor psychology?
Statman (1999), Gilovich et al. (2002)
How the theoretical approach used in psychology helps in defining investor
emotions?
Gavriilidis et al. (2016)
How can theoretical approaches used in other explain the various financial
behaviors?
Bouteska (2019)
Theories
Antecedents
Moderators
Consequences
Measures of EBF
Research methodology
What are the possible primary and secondary emotions of an investor?
Lahav et al. (2016), Chun et al. (2020)
Examine the relationship between emotions and investor decision-making.
Andrikopoulos et al. (2021)
How do events induce emotions in an investor?
Dragouni et al. (2016)
Examine the herd behavior conducted by investors during stock market
anomalies.
Nofsinger (2005)
Examine the emotions and stock market anomalies phenomenon before,
during, and after major externalities such as COVID-19, and the Great
Recession.
Lim (2021); Sahoo (2021); Rao et al. (2021)
Re-examine the moderating effect of geographical differences.
Saurabh and Dey (2020)
What is the moderating role of financial literacy in investor decision-making
influenced by investor behavior?
Hamurcu and Hamurcu (2020)
How do personality traits affect investor decision-making?
Kumari et al. (2019)
Whether there is an existence of a relationship between events and investor
emotions?
Lucey and Dowling (2005); Qadan and Kliger
(2016)
Whether emotional behavior of the investor affects the investment
decision-making of the investor?
Kaplanski and Levy (2010)
Examine the relationship between the events, emotions, investor
decision-making, and stock market anomalies
Brahmana et al. (2012)
Whether these emotions lead to stock market anomalies?
Mahmood et al. (2014)
How can the constructs be measured/which emotional behaviors?
Taffler (2018), Duxbury et al. (2020)
Develop scales to measure the investor’s emotional response while making
investment decisions.
Kishor (2020)
Extend the research by including more conceptual papers
Duxbury et al. (2020)
Add studies that are focused on primary sources of data collection.
Filiz et al. (2019), Lahav and Meer (2020)
Using not previously used methods of analysis such as SEM and event study
Kishor (2020); Kujawa and Ostrowska (2016)
Objective measures using neuroscience could be considered in analyzing the
emotional response of the subjects
Lim (2018a,b)
Nomological networks could also be established with a more powerful review
technique such as bibliometric analysis
Donthu et al. (2021); Mukherjee et al. (2022)
This table lists proposals for future research.
10
J.W. Goodell, S. Kumar, P. Rao et al.
Journal of Behavioral and Experimental Finance 37 (2023) 100722
References
Duxbury, D., Gärling, T., Gamble, A., Klass, V., 2020. How emotions influence
behavior in financial markets: A conceptual analysis and emotion-based
account of buy-sell preferences. Euro. J. Finance 26 (14), 1417–1438.
Edmans, A., Garcia, D., Norli, Ø., 2007. Sports sentiment and stock returns. J.
Finance 62 (4), 1967–1998.
E.M., Saunders Jr., 1993. Stock prices and wall street weather. Amer. Econ. Rev.
83 (5), 1337–1345.
Epstein, S., 1994. Integration of the cognitive and the psychodynamic
unconscious. Am. Psychol. 49 (8), 709.
Eshraghi, A., Taffler, R., 2012. Hedge funds and unconscious fantasy. Account.
Auditing Account. J..
Fama Eugene, F., 1970. Efficient capital markets: A review of theory and
empirical work. J. Finance 25 (2), 383–417.
Fama Eugene, F., 1995. Random walks in stock market prices. Financ. Anal. J. 51
(1), 75–80.
Feldman, L.A., 1995. Valence focus and arousal focus: individual differences in
the structure of affective experience. J. Personal. Soc. Psychol. 69 (1), 153.
Festinger, Leon, 1957. A Theory of Cognitive Dissonance, Vol. 2. Stanford
university press.
Filiz, I., Nahmer, T., Spiwoks, M., 2019. Herd behavior and mood: An experimental study on the forecasting of share prices. J. Behavioral Experimental
Finance 24, 100232.
Floros, C., 2008. The monthly and trading month effects in greek stock market
returns: 1996–2002. Manag. Finance 34, 453–464.
Fotaki, M., Long, S., Schwartz, H.S., 2012. What can psychoanalysis offer organisation studies today? Taking stock of current developments and thinking
about future directions. Org. Stud. 33 (9), 1105–1120.
Freud, S., 1910. The origin and development of psychoanalysis. Am. J. Psychology
21 (2), 181–218.
Gama, P.M., Vieira, E.F., 2013. Another look at the holiday effect. Appl. Financial
Econ. 23 (20), 1623–1633.
Gavriilidis, K., Kallinterakis, V., Öztürkkal, B., 2020. Does mood affect institutional
herding? J. Behav. Exp. Finance 26, 100290.
Gavriilidis, K., Kallinterakis, V., Tsalavoutas, I., 2016. Investor mood herding and
the ramadan effect. J. Econ. Behav. Org. 132, 23–38.
Gilovich, T., Griffin, D., Kahneman, D. (Eds.), 2002. Heuristics and Biases: The
Psychology of Intuitive Judgment. Cambridge University Press.
Goodell, J.W., 2019. Comparing normative institutionalism with intended
rationality in cultural-finance research. Int. Rev. Financial Anal. 62, 124–134.
Goodell, J.W., Bodey, R.A., 2012. Price-earnings changes during US presidential
election cycles: Voter uncertainty and other determinants. Public Choice 150
(3), 633–650.
Greifeneder, R., Bless, H., Pham, M.T., 2011. When do people rely on affective
and cognitive feelings in judgment? A review. Pers. Soc. Psychol. Rev. 15 (2),
107–141.
Grinblatt, M., Han, B., 2005. Prospect theory mental accounting, and momentum.
J. Financial Econ. 78 (2), 311–339.
Gurdgiev, C., O’Loughlin, D., 2020. Herding and anchoring in cryptocurrency
markets: Investor reaction to fear and uncertainty. J. Behav. Exp. Finance
25, 100271.
Hackbarth, D., 2004. Managerial traits and capital structure decisions. J. Financ.
Quant. Anal. 43 (4), 843–881.
Hamurcu, C., Hamurcu, H.D., 2020. Can financial literacy overconfidence
be predicted by narcissistic tendencies? Rev. Behavioral Finance 13 (4),
438–449.
Harding, N., He, W., 2016. Investor mood and the determinants of stock prices:
an experimental analysis. Account. Finance 56 (2), 445–478.
Hassan, M.H., Kayser, M.S., 2019. Ramadan effect on stock market return and
trade volume: Evidence from Dhaka Stock Exchange (DSE). Cogent Econ.
Finance 7 (1), 1605105.
Hasso, T., Pelster, M., Breitmayer, B., 2020. Terror attacks and individual investor
behavior: Evidence from the 2015–2017 European terror attacks. J. Behav.
Exp. Finance 28, 100397.
Hirshleifer, D., 2015. Behavioral finance. Ann. Rev. Financial Econ. 7, 133–159.
Hirshleifer, D., Hong Teoh, S., 2003. Herd behaviour and cascading in capital
markets: A review and synthesis. Eur. Financial Manag. 9 (1), 25–66.
Hu, Y., Li, X., John W. Goodell, J.W., Shen, D., 2021. Investor attention shocks and
stock co-movement: Substitution or reinforcement? Int. Rev. Financ. Anal. 73,
101617.
Huang, W., Goodell, J.W., Zhang, H., 2019. Pre-merger management in developing
markets: The role of earnings glamor. Int. Rev. Financ. Anal. 65, 101375.
Jain, J., Walia, N., Gupta, S., 2019. Evaluation of behavioral biases affecting
investment decision making of individual equity investors by fuzzy analytic
hierarchy process. Rev. Behav. Finance 12 (3), 297–314.
Johnson, J., Cheng, S.W., 2002. Holidays and trading and return patterns of
Australian SPI futures. J. Derivatives 9 (4), 56–67.
Johnson, E.J., Tversky, A., 1983. Affect generalisation, and the perception of risk.
J. Pers. Soc. Psychol. 45 (1), 20–31.
Kahneman, D., Tversky, A., 1979a. On the interpretation of intuitive probability:
A reply to Jonathan Cohen. Cognition 7 (4), 409–411.
Abbes, M.B., Abdelhédi-Zouch, M., 2015. Does hajj pilgrimage affect the islamic
investor sentiment? Res. Int. Bus. Finance 35, 138–152.
Afego, P.N., 2015. Market efficiency in developing African stock markets: what
do we know? J. Develop. Areas 49 (1), 243–266.
Agrawal, A., Tandon, K., 1994. Anomalies or illusions? Evidence from stock
markets in eighteen countries. J. Int. Money Finance 13 (1), 83–106.
Ahmad, Z., Ibrahim, H., Tuyon, J., 2017. Behavior of fund managers in Malaysian
investment management industry. Qualit. Res. Financ. Markets 9 (3),
205–239.
Andrikopoulos, P., Gebka, B., Kallinterakis, V., 2021. Regulatory mood-congruence
and herding: Evidence from cannabis stocks. J. Econ. Behav. Organ. 185,
842–864.
Apergis, N., Hayat, T., Saeed, T., 2019. The role of happiness in financial decisions:
Evidence from financial portfolio choice and five European countries. Atlantic
Econom. J. 47 (3), 343–360.
Ariel, R.A., 1990. High stock returns before holidays: Existence and evidence on
possible causes. J. Finance 45 (5), 1611–1626.
Ashton, J.K., Gerrard, B., Hudson, R., 2011. Do national soccer results really impact
on the stock market? Appl. Econ. 43 (26), 3709–3717.
Balasuriya, J., Muradoglu, G., Ayton, P., 2010. Optimism and portfolio choice.
Available at SSRN, 1568908.
Barberis, N.C., 2013. Thirty years of prospect theory in economics: A review and
assessment. J. Econ. Perspect. 27 (1), 173–196.
Bechara, A., Damasio, A.R., 2005. The somatic marker hypothesis: A neural theory
of economic decision. Games Econom. Behav. 52 (2), 336–372.
Bergsma, K., Jiang, D., 2016. Cultural new year holidays and stock returns around
the world. Financial Manag. 45 (1), 3–35.
Bouteska, A., 2019. The effect of investor sentiment on market reactions to
financial earnings restatements: Lessons from the United States. J. Behav.
Exp. Finance 24, 100241.
Brahmana, R.K., Hooy, C.W., Ahmad, Z., 2012. Weather investor irrationality and
day-of-the-week anomaly: Case of indonesia. J. Bioecon. 14 (2), 129–146.
Brailsford, T.J., Easton, S.A., 1993. New evidence on the impact of tax-loss selling
on the turn of the year effect. J. Bank. Financ. 17 (1), 131–144.
Brighetti, G., Lucarelli, C., Marinelli, N., 2014. Do emotions affect insurance
demand? Rev. Behav. Finance 6 (2), 136–154.
Bucciol, A., Guerrero, F., Papadovasilaki, D., 2020. Financial risk-taking and trait
emotional intelligence. Rev. Behav. Finance 13 (3), 259–275.
Cadsby, C.B., Ratner, M., 1992. Turn-of-month and pre-holiday effects on stock
returns: Some international evidence. J. Bank. Financ. 16 (3), 497–509.
Callahan, J.L., 2014. Writing literature reviews: A reprise and update. Hum.
Resour. Dev. Rev. 13 (3), 271–275.
Caplan, B., 2007. The Myth of the Rational Voter: Why Democracies Choose Bad
Policies. Princeton University Press, Princeton, NJ.
Chang, C.H., 2008. The impact of behavioral pitfalls on investors’ decisions: The
disposition effect in the Taiwanese warrant market. Soc. Behav. Pers. 36 (5),
617–634.
Chen, H., Shin, S.H., Sun, X., 2015. Return-enhancing strategies with international
ETFs: Exploiting the turn-of-the-month effect. Financial Serv. Rev. 24 (3), 271.
Chiu, C.N., 2020. Holiday effects on stock prices of the restaurant industry.
Current Issues Tourism 23 (9), 1109–1121.
Chui, A.C.W., Titman, S., Wei, K.C.J., 2010. Individualism and momentum around
the world. J. Finance 65 (1), 361–392.
Chuliá, H., Torró, H., 2011. Firm size and volatility analysis in the spanish stock
market. Euro. J. Finance 17 (8), 695–715.
Chun, J., Ahn, J., Kim, Y., Lee, S., 2020. Using deep learning to develop a stock
price prediction model based on individual investor emotions. J. Behav.
Finance 1–10.
Conlisk, J., 1996. Why bounded rationality? J. Econ. Lit. 34 (2), 669–700.
De Bondt, W.F., Thaler, R., 1985. Does the stock market overreact? J. Finance 40
(3), 793–805.
De Long, J.B., Shleifer, A., Summers, L.H., Waldmann, R.J., 1990. Positive feedback
investment strategies and destabilising rational speculation. J. Finance 45 (2),
379–395.
Deldin, P.J., Levin, I.P., 1986. The effect of mood induction in a risky decision
task. Bull. Psychon. Soc. 24, 4–6.
Dimson, E., 1988. Stock market anomalies. CUP Archive.
Ding, C.G., Wang, H.J., Lee, M.C., Hung, W.C., Lin, C.P., 2014. How does the change
in investor sentiment over time affect stock returns? Emerg. Markets Finance
Trade 50 (2), 144–158.
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., Lim, W.M., 2021. How to
conduct a bibliometric analysis: An overview and guidelines. J. Bus. Res.
133, 285–296.
Dragouni, M., Filis, G., Gavriilidis, K., Santamaria, D., 2016. Sentiment mood and
outbound tourism demand. Ann. Tourism Res. 60, 80–96.
Duxbury, D., 2015. Behavioral finance: insights from experiments I: theory and
financial markets. Rev. Behav. Finance 7 (1), 78–96.
11
J.W. Goodell, S. Kumar, P. Rao et al.
Journal of Behavioral and Experimental Finance 37 (2023) 100722
Mushinada, V.N.C., Veluri, V.S.S., 2019. Elucidating investors rationality and
behavioral biases in Indian stock market. Rev. Behav. Finance 11 (2),
201–219.
Nigam, R.M., Srivastava, S., Banwet, D.K., 2018. Behavioral mediators of financial
decision making–a state-of-art literature review. Rev. Behav. Finance 10.
Nofsinger, J.R., 2005. Social mood and financial economics. J. Behav. Finance 6
(3), 144–160.
Oehler, A., Wendt, S., Wedlich, F., Horn, M., 2018. Investor’s personality influences investment decisions: Experimental evidence on extraversion and
neuroticism. J. Behav. Finance 19 (1), 30–48.
Olson, K.R., 2006. A literature review of social mood. J. Behav. Finance 7 (4),
193–203.
Ortony, A., Turner, T.J., 1990. What’s basic about basic emotions? Psychol. Rev.
97 (3), 315.
Oğuzsoy, C.B., Güven, S., 2004. Holy days effect on Istanbul stock exchange. J.
Emerg. Mark. Finance 3 (1), 63–75.
Parker, T., 2019. Cognitive vs. emotional investing bias: What’s the difference?. https://www.investopedia.com/articles/investing/051613/behavioralbias-cognitive-vs-emotional-biasinvesting.asp.
Pelster, M., Breitmayer, B., 2019. Attracting attention from peers: Excitement in
social trading. J. Econ. Behav. Organ. 161, 158–179.
Pericoli, M., Sbracia, M., 2003. A primer on financial contagion. J. Econ. Surv. 17
(4), 571–608.
Peters, E.E., 1994. Fractal Market Analysis: Applying Chaos Theory to Investment
and Economics, Vol. 24. John Wiley & Sons.
Pettengill, G.N., 1989. Holiday closings and security returns. J. Financial Res. 12
(1), 57–67.
Phelps, E.A., Lempert, K.M., Sokol-Hessner, P., 2014. Emotion and decision
making: Multiple modulatory neural circuits. Annu. Rev. Neurosci. 37,
263–287.
Philpot, J., Peterson, C.A., 2011. A brief history and recent developments in
day-of-the-week effect literature. Manag. Finance 37 (9), 808–816.
Pompian, M.M., 2006. Behavioral Finance and Wealth Management – how to
Build Optimal Portfolios that Account for Investor Biases. John Wiley & Sons
Inc.
Prechter, R.R., 1985. Popular culture and the stock market, new classics library.
In: Prechter, R. (Ed.), Pioneering Studies in Socionomics. New Classics Library,
Gainesville, GA.
Prechter, R.R., 1999. The Wave Principle of Human Social Behavior and the New
Science of Socionomics. New Classics Library, Gainesville, GA.
Prechter, R.R., Parker, W.D., 2007. The financial/economic dichotomy in social
behavioral dynamics: the socionomic perspective. J. Behavioral Finance 8 (2),
84–108.
Qadan, M., Kliger, D., 2016. The short trading day anomaly. J. Empir. Financ. 38,
62–80.
Rahman, M.L., 2009. Stock market anomaly: Day of the week effect in Dhaka
stock exchange. Int. J. Bus. Manag. 4 (5), 193–206.
Rao, P., Goyal, N., Kumar, S., Hassan, M.K., Shahimi, S., 2021. Vulnerability of
financial markets in India: The contagious effect of COVID-19. Res. Int. Bus.
Finance 58, 101462.
Rosenthal, N.E., Sack, D.A., Gillin, J.C., Lewy, A.J., Goodwin, F.K., Davenport, Y.,
Wehr, T.A., 1984. Seasonal affective disorder: a description of the syndrome
and preliminary findings with light therapy. Archives General Psychiatry 41
(1), 72–80.
Ross Stephen, A., 2005. Neoclassical Finance. Princeton University Press,
Princeton, NJ.
Rossi, M., 2015. The efficient market hypothesis and calendar anomalies: A
literature review. Int. J. Manag. Financial Account. 7 (3–4), 285–296.
Rudiawarni, F.A., Narsa, I.M., Tjahjadi, B., 2020. Are emotions exacerbating the
recency bias? An experimental study. Int. J. Trade Glob. Mark. 13 (1), 61–70.
Russell, James A., 1980. A circumplex model of affect. J. Personality Social
Psychology 39 (6), 1161.
Sahoo, M., 2021. COVID 19 impact on stock market: Evidence from the Indian
stock market. J. Publ. Affairs 21 (4), e2621.
Saurabh, S., Dey, K., 2020. Unraveling the relationship between social moods and
the stock market: Evidence from the United Kingdom. J. Behav. Exp. Finance
26, 100300.
Schwarz, N., 2000. Emotion cognition, and decision making. Cogn. Emot. 14 (4),
433–440.
Schwarz, N., 2012. Feelings-as-information theory. In: Handbook of Theories of
Social Psychology, Vol. 1. pp. 289–308.
Shah, S.F., Alshurideh, M., Kurdi, B.Al., Salloum, S.A., 2020. The impact of the
behavioral factors on investment decision-making: A systemic review on
financial institutions. In: International Conference on Advanced Intelligent
Systems and Informatics. Springer, pp. 100–112.
Shantha, K.V.A., 2019. The evolution of herd behavior: Will herding disappear
over time? Stud. Econ. Finance 36 (3), 637–661.
Shefrin, H., 2002. Beyond Greed and Fear: Understanding Behavioral Finance and
the Psychology of Investing. Oxford University Press.
Shefrin, H., Statman, M., 1985. The disposition to sell winners too early and ride
losers too long: Theory and evidence. J. Finance 40 (3), 777–790.
Kahneman, D., Tversky, A., 1979b. Prospect theory: An analysis of decision under
risk. Econometrica 47 (2), 363–391.
Kaplanski, G., Levy, H., 2010. Sentiment and stock prices: The case of aviation
disasters. J. Financ. Econ. 95 (2), 174–201.
Karaa, R., Slim, S., Goodell, J.W., Goyal, A., Kallinterakis, V., 2021. Do investors
feedback trade in the Bitcoin—and why? Euro. J. Finance forthcoming.
Keef, S.P., Roush, M.L., 2005. Day-of-the-week effects in the pre-holiday returns
of the Standard & Poor’s 500 stock index. Appl. Financial Econ. 15 (2),
107–119.
Kindleberger, C., 1978. Manias, panics, and crashes. In: (Basic Books, New York).
Kirman, A., 1993. Ants, rationality, and recruitment. Quarter. J. Econom. 108 (1),
137–156.
Kishor, N., 2020. Development and validation of behavioral biases scale: a SEM
approach. Rev. Behavioral Finance 14 (2), 237–259.
Kudryavtsev, A., 2019. Holiday effect on large stock price changes. Ann. Econ.
Finance 20 (2), 633–660.
Kuhnen, C.M., Knutson, B., 2011. The influence of affect on beliefs preferences,
and financial decisions. J. Financial Quant. Anal. 46 (3), 605–626.
Kujawa, S., Ostrowska, E., 2016. Event study and application in behavioral finance. Zeszyty Naukowe Uniwersytetu Szczecińskiego Finanse Rynki
Finansowe Ubezpieczenia. 4.
Kumar, S., Goyal, N., 2015. Behavioural biases in investment decision making–a
systematic literature review. Qual. Res. Financial Mark. 7 (1), 88–108.
Lahav, Y., Meer, S., 2020. The effect of induced mood on traders’ preferences in
asset markets–experimental evidence. Rev. Behav. Finance.
Lahav, E., Shavit, T., Benzion, U., 2016. Can’t wait to celebrate: Holiday euphoria
impulsive behavior and time preference. J. Behav. Exp. Econ. 65, 128–134.
Lakonishok, J., Smidt, S., 1984. Volume and turn-of-the-year behavior. J. Financ.
Econ. 13 (3), 435–455.
Lazarus, R.S., Folkman, S., 1984. Stress, Appraisal, and Coping. Springer.
Lepori, G.M., 2015. Positive mood and investment decisions: Evidence from
comedy movie attendance in the US. Res. Int. Bus. Finance 34, 142–163.
Lerner, J.S., Keltner, D., 2000. Beyond valence: Toward a model of emotionspecific influences on judgment and choice. Cogn. Emot. 14 (4),
473–493.
Lerner, J.S., Li, Y., Valdesolo, P., Kassam, K.S., 2015. Emotion and decision making.
Annu. Rev. Psychol. 66, 799–823.
Levy Jack, S., 1992. An introduction to prospect theory. Political Psychol. 13 (2),
171–186.
Liano, K., 1995. A pre-holiday effect in the currency futures market: A note
international. Rev. Econ. Finance 4 (3), 299–304.
Liano, K., Marchand, P.H., Huang, G.C., 1992. The holiday effect in stock returns:
evidence from the OTC market. Rev. Financial Econ. 2 (1), 45–54.
Liano, K., White, L.R., 1994. Business cycles and the pre-holiday effect in stock
returns. Appl. Financial Econ. 4 (3), 171–174.
Lim, W.M., 2018a. Demystifying neuromarketing. J. Bus. Res. 91, 205–220.
Lim, W.M., 2018b. What will business-to-business marketers learn from neuromarketing? Insights for business marketing practice. J. Bus.-To-Bus. Mark. 25
(3), 251–259.
Lim, W.M., 2021. Conditional recipes for predicting impacts and prescribing
solutions for externalities: the case of COVID-19 and tourism. Tourism
Recreation Res. 46 (2), 314–318.
Lim, W.M., Kumar, S., Ali, F., 2022. Advancing knowledge through literature
reviews:‘what’ ‘why’, and ‘how to contribute’. Serv. Ind. J. 42 (7–8), 481–513.
Lo, A.W., 2005. Reconciling efficient markets with behavioral finance: the
adaptive markets hypothesis. J. Investment Consulting 7 (2), 21–44.
Lucey, B.M., Dowling, M., 2005. The role of feelings in investor decision-making.
J. Econ. Surv. 19 (2), 211–237.
Mahmood, S., Irfan, M., Iqbal, S., Kamran, M., Ijaz, A., 2014. Impact of political
events on stock market: Evidence from Pakistan. J. Asian Bus. Strateg. 4 (12),
163.
Marc, Joëts, 2012. Mood-misattribution effect on energy finance: A biorhythm
pproach. Int. Symp. Econ. Theory Econ. 22, 213–233.
March, J.G., Shapira, Z., 1987. Managerial perspectives on risk and risk taking.
Manage. Sci. 33 (11), 1404–1418.
Markowitz, H., 1952. Portfolio selection. J. Finance 7 (1), 77–91.
Marrett, G.J., Worthington, A.C., 2009. An empirical note on the holiday effect in
the Australian stock market 1996–2006. Appl. Econ. Lett. 16 (17), 1769–1772.
Meloy, M.G., 2000. Mood-driven distortion of product information. J. Consum.
Res. 27 (3), 345–359.
Meneu, V., Pardo, A., 2004. Pre-holiday effect large trades and small investor
behaviour. J. Empirical Finance 11 (2), 231–246.
Miller, M.H., Modigliani, F., 1961. Dividend policy growth, and the valuation of
shares. J. Bus. 34 (4), 411.
Mitchell, M.L., Mulherin, J.H., 1996. The impact of industry shocks on takeover
and restructuring activity. J. Financ. Econ. 41 (2), 193–229.
Mukherjee, D., Lim, W.M., Kumar, S., Donthu, N., 2022. Guidelines for advancing
theory and practice through bibliometric research. J. Bus. Res. 148, 101–115.
Munusamy, D., 2018. Islamic calendar and stock market behavior in India. Int.
J. Soc. Econ. 45 (11), 1550–1566.
12
J.W. Goodell, S. Kumar, P. Rao et al.
Journal of Behavioral and Experimental Finance 37 (2023) 100722
Trifan, R., 2019. Is the investor’s biorhythm affecting the stock market? In: Annu.
Conf. Finance Account.. Springer, pp. 71–82.
Tuckett, D.A., Taffler, R.J., 2003. The role of the unconscious in the dot. com
bubble: a psychoanalytic perspective1. Boom and Bust 150.
Tversky, A., Kahneman, D., 1974. Judgment under uncertainty: Heuristics and
biases. Science 1854157, 1124–1131.
Valcanover, V.M., Sonza, I.B., da Silva, W.V., 2020. Behavioral finance experiments: A recent systematic literature review. SAGE Open 10 (4),
2158244020969672.
Vasileiou, E., 2018. Is the turn of the month effect an ‘abnormal normality’?
Controversial findings, new patterns and. . . hidden signs (?). Res. Int. Bus.
Finance 44, 153–175.
Vidal-Tomás, D., Alfarano, S., 2020. An agent-based early warning indicator for
financial market instability. J. Econ. Interact. Coord. 15 (1), 49–87.
Wasiuzzaman, S., Al-Musehel, N.A., 2018. Mood religious experience and the
ramadan effect. Int. J. Emergi. Mark. 13 (1), 290–307.
Wiley, J.A., Zumpano, L.V., 2009. Institutional investment and the turn-of-themonth effect: evidence from REITs. J. Real Estate Finance Econ. 39 (2),
180–201.
Wolozin, H., Wolozin, B., 2007. The unconscious in economic decision-making:
Convergent voices. J. Soc.-Econ. 36 (6), 856–864.
Wright, W.F., Bower, G.H., 1992. Mood effects on subjective probability
assessment. Organ. Behav. Hum. Decis. Processes 52 (2), 276–291.
Wuthisatian, R., Guerrero, F., Sundali, J., 2017. Gain attraction in the presence of
social interactions. Rev. Behav. Finance 9 (2), 105–127.
Yuan, T., Gupta, R., 2014. Chinese lunar new year effect in Asian stock markets
1999–2012. Quart. Rev. Econ. Finance 54 (4), 529–537.
Zahera, S.A., Bansal, R., 2018. Do investors exhibit behavioral biases in
investment. Qual. Res. Financial Mark. 10.
Zahera, S.A., Bansal, R., 2019. A study of prominence for disposition effect: a
systematic review. Qual. Res. Financial Mark. 11 (1), 2–21.
Ziemba, W.T., 2020. Japanese security market regularities: Monthly turn-of-themonth and year, holiday and golden week effects. In: Handbook of Applied
Investment Research. pp. 187–214.
Shefrin, H., Statman, M., 2009. Striking Regulatory Irons While Hot. SCU Leavey
School of Business Research Paper, pp. 10–07.
Shen, J., Yu, J., Zhao, S., 2017. Investor sentiment and economic forces. J.
Monetary Econ. 86, 1–21.
Shiller, R.J., 2003. From efficient markets theory to behavioral finance. J. Econ.
Perspect. 17 (1), 83–104.
Shu, H.C., Chang, J.H., 2015. Investor sentiment and financial market volatility.
J. Behav. Finance 16 (3), 206–219.
Simon, H.A., 1955. A behavioral model of rational choice. Q. J. Econ. 69 (1),
99–118.
Simon, Herbert A., 1957. Models of man: social and rational; mathematical essays
on rational human behavior in society setting. Wiley, New York.
Spyrou, S., 2013. Herding in financial markets: a review of the literature. Rev.
Behav. Finance.
Statman, M., 1999. Behaviorial finance: Past battles and future engagements.
Financ. Anal. J. 55 (6), 18–27.
Szakmary, A.C., Kiefer, D.B., 2004. The disappearing january/turn of the year
effect: Evidence from stock index futures and cash markets. J. Futures Mark.
24 (8), 755–784.
Taffler, R., 2018. Emotional finance: investment and the unconscious. Euro. J.
Finance 24 (7–8), 630–653.
Teng, C.C., Liu, V.W., 2013. The pre-holiday effect and positive emotion in the
Taiwan stock market 1971–2011. Invest. Anal. J. 42 (77), 35–43.
Teng, C.C., Yang, J.J., 2018. Chinese lunar new year effect investor sentiment, and
market deregulation. Finance Res. Lett. 27, 175–184.
Tetlock, P.C., 2007. Giving content to investor sentiment: The role of media in
the stock market. J. Finance 62 (3), 1139–1168.
Thaler, Richard H., 1992. The Winner’s Curse: Paradoxes and Anomalies of
Economic Life. Princeton University Press, Princeton New Jersey.
Thaler, Richard H., Tversky, Amos, Kahneman, Daniel, Schwartz, Alan, 1997. The
effect of myopia and loss aversion on risk taking: an experimental test. Q.
J. Econ. 112, 647–661.
Tiedens, L.Z., Linton, S., 2001. Judgment under emotional certainty and uncertainty: the effects of specific emotions on information processing. J. Personal.
Soc. Psychol. 81 (6), 973.
Tranfield, D., Denyer, D., Smart, P., 2003. Towards a methodology for developing
evidence-informed management knowledge by means of systematic review.
Br. J. Manag. 14, 207–222.
13