A Review and Conceptual Analysis of Emotions in Financial Markets
Tommy Gärling and Amelie Gamble
Department of Psychology and
Center for Finance, School of Business, Economics, and Law,
University of Gothenburg
P.O. Box 500, SE-405 30 Göteborg, Sweden
Darren Duxbury
Newcastle University Business School,
Newcastle University,
5 Barrack Road, Newcastle upon Tyne, NE1 4SE, UK
Abstract
Emotions have played a negligible role in decision-making research until about two decades
ago. In financial economics a lagged interest in emotions is observed. In the paper we review
the relatively scant research on emotions in financial markets including studies of effects of
mood proxies on performance of financial markets, field studies of financial investors´
emotions, and laboratory tests of emotion influences on financial investors. Several shortcomings are identified. One is lack of clarity in defining different emotion constructs. Our
contribution is a classification of emotion-related phenomena drawing on basic emotion
research. We then follow up on this by demonstrating how emotions may explain buy and sell
decisions in asset markets. We argue that this is the level that needs to be examined although
it should not detract from the importance of analyses of market consequences. It is also
necessary to taking into account that investors comprise a heterogeneous group varying in
several characteristics that are likely moderators of emotion influences.
Key words: Emotion; Financial market; Investor; Measurement
Ever since the seminal formal analysis of decision making published by von Neumann and
Morgernstern (1947), emotions have played a negligible role in decision-making research.
About two decades ago this started to change (Peters, Västfjäll, Gärling, & Slovic, 2006;
Weber & Johnson, 2009), importantly due to influences from the neuropsychological research
by Damasio and collaborators (Damasio, 1994). In financial economics we currently observe
a lagged interest in emotions. We consider this to be a desirable development. As argued by
among others Forgas (1995), Loewenstein (1999), and Schwarz (2000), the influences of
emotions on judgment and decision making are straight-forward, ubiquitous, and not
necessarily irrational. Our stance elaborated on later is that emotions are input to the decisionmaking process engaging investors in asset markets moderated by their mandate, liquidity,
investment horizon, experience, and sophistication. All-in-all, the complex interaction of
emotion, cognition, and decision making at play in the financial markets make such a setting a
rich, but challenging environment in which to examine further the influence of emotions.
In this paper we first review the relatively scant research on emotions in financial markets.
Since this research tends to lack appropriate conceptualizations of emotions, in a following
section we present a classification of emotion-related phenomena drawing on basic emotion
research. A section then follows that presents an emotion account of buy and sell preferences
in asset markets.
Emotions in Financial Markets
Mood proxies and performance of financial markets
Although few would deny that emotions have some influence on judgment and decision
making in financial markets, the market impacts may not be substantial (Mehra & Sah, 2002).
With the aim of investigating this, several studies have empirically attempted to determine
effects of mood proxies, that is, factors that are known or believed to induce positive or
negative mood in people. In some early studies below-average returns in equity markets were
shown to correlate with (1) cloudy weather possibly leading to a negative mood (Saunders,
1993; Hishleifer & Shumway, 2003), (2) geomagnetic storms presumably causing depression
(Krivelyova & Robotti, 2003), (3) the full moon phase of the lunar cycle associated with
depressed mood (Dichev & Janes, 2003; Yuan, Zheng, & Zhu, 2006)1, and (4) lower
temperatures believed to increase aggressive risk-taking that results in above-average returns
(Cao & Wei, 2005). Below-average returns due to season have also been observed (Kamstra,
Kramer, & Levi, 2003), presumably due to shorter daylight hours (“winter blues”). Other
studies (Kamstra, Kramer, & Levi, 2000) have attempted to show negative effects of
disruption in sleep patterns dependent on daylight savings time changes. Two studies in the
UK (Dowling & Lucey, 2005, 2008) including several mood proxies related to weather and
bio-rhythm replicated some of the previous results. In reviewing these and other findings
published before 2005, Lucey and Dowling (2005) note that mood misattribution (Schwartz &
Strack, 1999) is a possible causal mechanism. A criticism is that only mood proxies have been
studied, with little or no attempt to measure investors’ moods directly. This leaves such
empirical studies open to critique that claims the reported results are explained by other
factors. For example, Jacobsen and Masquering (2008, 2009) suggest that the findings
reported in Kamstra et al. (2003) can be explained by a range of other factors related to the
seasons (they illustrate with ice cream consumption and airline travel), thus calling into
question their conclusion that changes in investors’ moods associated with the Seasonal
1
While Keef and Khaled (2011) confirm the findings in Dichev and Janes (2003) and Yuan et al .(2006), that
mean returns during the full moon phase are significantly lower than those during the new moon phase, they find
that the mean return on new moon days is higher than that observed on lunar control days, but the mean return
on full moon days is not lower than observed on such control days. Thus, they conclude that while there is
evidence of an enhanced new moon effect, there is little evidence of a depressed or negative full moon effect.
Affective Disorder directly influence stock market returns. The findings in Kamstra et al.
(2000, 2003) are also challenged by Gerlach (2010), who provides empirical evidence to
suggest that their results may be driven by seasonal patterns in market-related information and
not by changes in investor mood, while Gregory-Allen, Jacobsen, and Marquering (2010).
(2010), in a comprehensive study of daylight saving time changes, fail to find evidence to
support the hypothesis that investor mood changes induced by disrupted sleeping patterns
impact on stock returns. Furthermore, other measures of market performance than returns are
needed to increase understanding of the role of emotions. A notable exception is the study by
Symeonidis, Daskalakis, and Markellos (2010) examining the association between stock
market volatility and mood-proxies related to the weather conditions (cloudiness, temperature
and precipitation) and night-time length. They report that cloudiness and length of night-time
are inversely related to various measures of volatility.
Another emotion influence on prices in financial markets is investor sentiment (Baker &
Wurgler, 2007; DeLong, Shleifer, Summers, & Waldman, 1990) depending on widespread
moods in a society what Nofsinger (2005) and others refer to as “social moods”. Social
moods are the outcomes of interpersonal communication and would in general be adaptive.
Examples include optimism or hope about the future or pessimism about and fear for the
future. In particular investors’ buying and selling decisions in asset markets may easily be
influenced by such optimism or pessimism, as suggested by excessive price volatility (Shiller,
2003). Furthermore, speculative bubbles may be driven by high optimism (Shiller, 2002).
Similar to the caveats noted above, this research suffers from lack of measures of social
moods. A possible solution is demonstrated in a study of Bollen, Mao, and Zeng (2011) that
developed and cross-validated a measure of social mood based on classified recordings of
daily twitter feeds. A high degree of predictability of changes in stock prices was observed.
Another approach is exemplified by Bialkowski, Etebari, and Wisniewski (2012) who in 14
predominantly Muslim countries found that stock returns were higher and less volatile during
the month of Ramadan which is a significant social event when fasting is believed to have
positive mood effects.
Field studies of financial investors´ emotions
Other research has investigated emotion effects by directly targeting investors´ responses.
In semi-structured interviews of investors employed by four London investment banks,
Fenton-O´Creevy, Soane, Nicholson, and Willman (2011) found emotion and cognition to be
inextricably linked in the investors´ investment decisions. The question they asked was
therefore whether there are more or less effective strategies of managing emotions. The
answer was that high-performing investors regulated emotions better than low-performing
investors, in particular by avoiding being influenced by negative emotions. It was also found
that experience improved efficient emotion regulation. Another observation was that highperforming experienced investors were less influenced by (e.g.,weather-related) mood not
being relevant for their decisions.
In a pioneering study by Lo and Repin (2002), a broad battery of physiological emotion
markers was applied to monitor emotions in ten professional investors during their regular
trading activities. The measurements were correlated with real market data including several
types of price changes identified in advance by the investors themselves to being in need of
their attention. Emotion effects related to the price changes were observed compared to
controls, larger in less than in more experienced investors but still present in the latter. An
acknowledged problem is clearly to infer which role the emotions played for the trading
decisions. In a subsequent study by Lo, Repin, and Steenbarger (2005) asking trader trainees
to provide self-reports of how they felt after each trading day, modest correlations were found
between trading performance and emotion. In this study possible emotion effects on decisions
would be observed on subsequent trading days but any such effects were not reported.
Fenton- O´Creevy, Lins, Vohra, Richards, Davies, and Schaaf (2012) obtained
physiological emotion marker data from 28 professional traders who were market makers.
The results supported their previous interview data (Fenton-O´Creevy et al., 2011) in showing
that experienced traders were less affected. Consistent with the results of Lo and Repin
(2002), high price volatility had large effects. This effect was consistent with observed higher
cortisol levels related to fear responses observed by Coates and Herbert (2008).
The reported studies are steps towards an increased understanding of the role of emotion
effects in financial markets. Yet, the studies are only empirical demonstrations. Hypotheses
are needed to be derived from theory and tested.
Laboratory tests of emotion influences on financial investors
Some laboratory studies complement the research on effects of mood proxies in measuring
or inducing mood in participants who are asked to perform an investment task. A general
finding is that mood may have both positive and negative effects on investment performance.
Seo and Feldman Barret (2007) distinguished between feeling-as-bias-inducers implying that
feelings induce various biases in the decision-making process, for instance, that people in a
positive mood make judgments congruent with their mood (Isen, 2000), and feeling-asdecision-facilitators implying that mood improves decision making, for instance, that people
in a negative mood invest more effort in information processing (Schwartz, 2000). In an
empirical study, investment performance was related to feelings measured by means of
checklists of adjectives sampled from the affect grid (Russell, 1980, 2003; Yik, Russell, &
Steiger, 2011) to vary in both valence and activation (arousal). Participants were on each of
20 trading days asked to make simulated investment decisions and report their feelings,
broadly defined as mood or discrete emotions directed towards an object. Higher affective
reactivity was hypothesized and found to have positive effects on investment performance.
Implying again that experience is important, regulation of affect-influences mediated the
positive effect on performance. A similar procedure was used by Au, Chan, Wang, and
Vertinsky (2003) to study foreign exchange trading. An important difference was that positive
and negative moods were induced in different experimental groups. The results showed that in
a positive mood performance was worse and in a negative mood better than in a neutral mood.
Similar results were found by Kuhnen and Knutson (2011) in a task designed to measure
probability beliefs and risk-taking in repeated choices between a risky security and a risk-less
bond. Before each of 90 choices a picture was shown to induce a highly arousing positive, a
highly arousing negative or a neutral mood. Less suboptimal risk-taking was observed
following negative pictures compared to positive or neutral pictures.
Laboratory studies have also attempted to investigate investor sentiment. Bosman and Van
Winden (2010) designed an experiment to simulate influences of social moods, for instance,
fear due to international terrorism or political risks that may nourish aversion to risky
investments. In repeated trials participants either choose or did not choose a risky option.
After each choice they rated how they felt on scales defined by emotion words. In a “global
risk” condition subsequent to the base-line condition, participants were told that they with
some probability at the end would loose what they had earned. In this condition compared to
the base-line condition choices of the risky option were on average lower. It was also found
that amount invested was related to rated fear but that there was no difference between the
“global risk” and base-line conditions.
Social moods may also in a society or subgroups (e.g., investors) induce positive or
negative attitudes towards industrial sectors such as, for instance, information technology or
even specific companies listed on stock markets. Although attitudes have both informational
and affective determinants (Eagley & Chaiken, 1993), in several finance studies the focus has
been on affective determinants. Thus, an investment option associated with a positive or
negative affect-laden evaluation may when other information is unavailable or inaccessible
influence choice of the option. Slovic, Finucane, Peters, and MacGregor (2002) referred to
this as the affect heuristic. In a pioneering study (MacGregor, Slovic, Dreman, & Berry, 2000)
it was shown that positive affect images associated with industrial sectors influenced
simulated investments made by banking students that would have lead to worse outcomes. In
a similar study of a sample of business and economics undergraduates, Kempf, Merkle, and
Niessen-Ruenzi (2013) found that if attitudes towards 30 German companies rated on
adjective scales were classified as positive, returns on stocks in the companies were rated high
and risk low. In contrast, if the attitudes were classified as negative, returns were rated low
and risk high. Financial literacy reduced the difference, which is consistent with that returns
and risk are generally negatively instead of positively correlated. But also experts may
conceivably under some circumstances make the same mistake. Finucane, Alhakami, Slovic,
and Johnson (2000) found that a negative correlation between rated benefits and risk was less
negative under time pressure than under no time pressure.
A study attempting to show individual differences in response to positive and negative
price shocks were reported by Muehlfield, Weitzel, and van Wittelosstuijn (2013). Their
hypothesis was that investors would differ depending on the relative impact of two different
motivational systems (Gray, 1987), the Behavioral Approach System (BAS) and the
Behavioral Inhibition System (BIS). Investors high in BAS (as measured by the BIS/BAS
self-report scale developed by Carver and White, 1994) should emphasize the upside of price
movements, people high in BIS should in contrast emphasize the downside. Shocks were
expected to exaggerate these differences. An asset–market experiment employing
undergraduates found that irrespectively of shock, high BAS compared to high BIS
participants traded more actively, were more risk taking and, except when the shock was
negative, generated higher profits. Positive shocks “unfreezed” participants high in BIS who
started to trade more and take more risk.
In order to further increase the understanding of the role of emotions, still another
approach (see Dowling & Lucey, 2005; Gärling, 2011) would be to identify the role emotions
may play in observed anomalies in financial markets. One of the most well-documented and
robust anomalies is the disposition effect referring to the observation that winners are hold too
short and losers too long (Shefrin & Statman, 1985). In explaining the disposition effect,
Odean (1998), Shefrin and Statman (1985), and Weber and Camerer (1998) all draw on
prospect theory (Kahneman & Tversky, 1979; Tversky & Kahneman, 1992). A necessary
auxiliary hypothesis is that of realization utility (Barberis & Xiong, 2012), that is, that the
utility of gains and disutility of potential (paper) losses are derived from realizing the
outcome. Frydman et al. (2014) conducted a test of this hypothesis in an experimental market
with an unspecified group of 28 participants. For the average participant the results
conclusively demonstrated the disposition effect. Evidence in support of the realization utility
hypothesis was secured from brain-scanning (functional magnetic resonance imaging or
fMRI) data showing that at the moment of making a sell decision, neural activity in the brain
was as expected proportional to the capital gain. Similar results for losses were however not
found. It may also be asked whether the observed neural activity corresponds to emotions.
Related studies by Kuhnen and Knutson (2005) and Knutson, Wimmer, Kuhnen, and
Winkielman (2008) suggest this. Another unresolved question is whether the neural activity
corresponds to anticipated or experienced emotion. In the affect account of the disposition
effect proposed by Gärling, Blomman, and Carle (2014), only the former (as well as
anticipatory emotions, see next section) would have an influence on the sell decisions.
Summers and Duxbury (2012) similarly investigated the role of emotion in accounting for
the disposition effect. They did that in experiments examining whether an outcome
experienced in one period (at that point a potential gain or loss) and its associated selfreported emotional response had an impact on a sell or hold decision in the next period. No
responsibility for the decision to hold the risky asset in the first period lead to disappointment
due to a loss outcome and elation due to a gain outcome, while responsibility additionally lead
to regret due to the loss and rejoicing due to the gain outcome. It is concluded that regret is
necessary to drive investors to continue to holding losing shares, while elation is necessary to
cause investors to sell winning shares.
A Classification of Emotions
Evaluations versus emotions
An outcome of a choice is normally perceived to have an affective quality (e.g., good, bad
or neutral) (Russell, 2003). We refer to this as an evaluation of the choice outcome. An
emotion is a response to an impact of an object (e.g. the choice outcome) (Russell, 2003). An
evaluation as bad or good does not normally have an emotion impact. According to several
emotion theories (e.g., Carver & Scheier, 1990; Lazarus, 1991; Oatley, 2009), this will occur
if and only if the outcome has personal relevance, for instance if it is perceived to facilitate
attainment of a positive personal goal or prevent attainment of a negative personal goal. An
issue is to identify conditions that are personally relevant. One approach is to consider how
much an investor has at stake, for instance, as in Summers and Duxbury (2012), whether
being responsible for holding a risky asset. There are presumably several other conditions yet
to be identified.
Russell (2003) posits that core affects are elemental building blocks involved in all
emotion responses or states. More precisely, a core affect is a “neurophysiological state
consciously accessible as the simplest raw (nonreflective) feelings evident in moods and
emotions” (p. 148). Corroboration comes from brain-imaging research (e.g., WilsonMendenhall, Feldman Barret, & Barsalou, 2013). Core affects are always accessible, either
being neutral or having any other value in a dimensional system defined by the axis pleasuredispleasure and activation-deactivation. Several different methods to measure affect (selfreports, peripheral physiology, startle responses, EEG, neuro-imaging with fMRI or PET, face
expressions measured with EMG) support a dimensional description although all the methods
do not converge on the two dimensions of pleasure and activation (arousal) (Mauss &
Robinson, 2009). Approach-avoidance has been proposed to be a third dimension. Yet, this
dimension has been found to be a function of valence and activation (Mehrabian & Russell,
1974; Västfjäll, Gärling, & Kleiner, 2001). Another criticism (e.g. Lazarus, 1991) is that
emotions are discrete. A counter-argument is that discrete states may be conceptualized as
combinations of multiple dimensions. The two-dimensional system of pleasure and activation
is illustrated in Figure 1 (Russell, 1980, 2003; Yik et al., 2011), also showing discrete emotion
states located in the dimensional system.
Incidental versus integral emotions
An emotion impact of a choice outcome is an example of an integral emotion. Another
example is that a choice is influenced by an anticipated emotion associated with its outcome.
An unrelated emotion state such as mood affecting the choice is considered to be incidental.
The bulk of emotion research in financial markets (e.g., Lucey & Dowling, 2005) appears to
target incidental emotions or mood effects.
Because emotion and mood are not experienced to be very different, their distinction is less
clear to both lay people and researchers (Beedie, Terry, & Lane, 2005). Language is also a
fallible source for making or not making the distinction. The similarity of the experience of
emotion and mood is consistent with Russell´s (2003) claim that mood is a prolonged core
affect, also emphasizing that moods are less transient than emotions. Emotions are
furthermore frequently stronger, thus would more likely occupy the conscious focus with
mood residing in the background (Lazarus, 1991). Emotion impacts may trigger several
transient feeling, physiological, and behavioral changes. The conscious substrate is changes in
core affect resulting in discrete emotion states. Gärling et al. (2014) make the connection
between emotion and mood even stronger by proposing that emotion impacts result in
changes in mood (prolonged core affect) that linger after the transient changes caused by the
emotion impact have dissipated. A good-bad evaluation of a personally relevant outcome is
likely to have an emotion impact and thus also to some degree change mood in a positive or
negative direction. Note however that the mood effect is weaker such that only a very strong
negative emotion impact is likely to change the valence of current mood. Since people
generally are in a positive mood (Biswar-Diener, Vittersø, & Diener, 2005), a change from a
positive to a negative mood facing a negative emotion impact seems less likely than a change
in the reverse direction facing a positive emotion impact (Erber, Erber, & Poe, 2004).
Anticipatory versus anticipated emotions
Anticipatory emotions are experienced when thinking about what may happen in the
future. In the context of financial outcomes hope of earning and fear of losing would qualify.
Investor sentiment of optimism and pessimism is a similar phenomenon in financial markets
(Nofsinger, 2005).
In the neuropsychological research reviewed by Bechara, Damasio, and Damasio (2000),
the Iowa Gambling Task is used to investigate choices between decks of cards (sequences of
choice outcomes) that either are associated with a higher volatility such that if chosen the
probabilities of losing and gaining are both higher than if other decks associated with less
volatility are chosen. Anticipatory negative feelings as indexed by a physiological marker
such as the skin conductance response are more influenced by choices of the former than of
the latter decks. At a conscious level anticipatory fear may be experienced to a larger extent
than anticipatory hope when the volatility is higher. In reality the distinction between
anticipatory emotion and mood is not perfectly clear. An anticipatory emotion is associated
with a future unspecific event or series of event. In asset markets anticipatory emotions of
hope and fear may be associated with expectations of gaining or losing but not with earning or
losing any specific amounts. A mood is not associated with any specific future event or series
of events although still influenced by events in the past. Anticipatory emotions may be shaped
by mood, for instance a positive mood may strengthen a brighter outlook of the future, a
negative mood the reverse. Or the casual influence may go in the reverse direction. The
degree of influence in this direction may partly depend on individuals´ emotional
responsiveness (Rusting, 1998).
Another less clear distinction is that between anticipatory and anticipated emotions. In
contrast to the former, anticipated emotions are associated with specific choice outcomes
(Mellers, 2000), for instance the degree of anticipated elation that may vary with the amount
of gain or the degree of anticipated disappointment that may vary with the amount of loss.
Anticipatory and anticipated emotion may furthermore differ in quality, in our example hopefear versus elation-disappointment. Another distinction is that anticipatory emotions are felt,
whereas anticipated emotions are imagined. Both would still have conscious elements of core
affects (Västfjäll, Gärling, & Kleiner, 2004). A large body of research investigating affective
forecasting (e.g., Loewenstein & Schkade, 1999) has nevertheless demonstrated that
anticipated emotions tend to be underestimation of the corresponding experienced emotions
whether they are positive or negative.
Emotion Account of Buy and Sell Preferences
Influences of emotions in asset markets are documented by the studies we reviewed above.
At the same time we have noted that these studies have largely failed to specify the targeted
type of emotion. In the preceding section we made distinctions between different emotion
constructs that we draw on in the following, that is, the distinctions between incidental
(mood) and integral (anticipatory and anticipated) emotions and between anticipatory and
anticipated emotions. We also note that our focus is solely on emotions, that is, evaluations
that have personal relevance.
In our analysis presented in this section we propose that price movement in an asset market
is a primary covariate of emotion. As illustrated in Figure 2, we distinguish between (1)
changes in a (pleasant-displeasant) mood that, despite being influenced by circumstances
incidental to price movements, still may influence investor decision making (Isen, 2000;
Schwarz, 2000); (2) changes in anticipatory emotions of hope of earning and fear of losing
due to the price movements (Lopes, 1987; Nofsinger, 2005; Shefrin and Statman, 2000), and;
(3) changes in anticipated emotions of elation and disappointment (or pride and regret)
associated with decisions to realize gains and losses (Mellers, 2000). We further suggest that
whereas current mood may have a direct effect on choices, anticipatory emotions have effects
on choices through anticipated emotions. Anticipatory hope is strengthened by price increases
such that anticipated elation associated with buying or selling winners are triggered.
Conversely, decreasing prices strengthen anticipatory fear that triggers anticipated
disappointment associated with selling losers.
Our claim is that investors2 with anticipatory emotions of hope dominating anticipatory
emotions of fear are attracted to purchase stocks in upmarkets (Baker & Wurgler, 2007;
Kubinska et al., 2012). They may alternatively be attracted to purchase a particular popular
stock. This is illustrated in Figure 3 where we conjecture that some lag is needed for the hopefear balance to stabilize before preferences to buy are formed. A positive mood due to other
influences (a sunny weather, a windfall salary) may sometimes strengthen the hope-fear
balance such that preferences to buy increases.
More precisely, we propose that a preference to sell is formed when anticipated elation is
equally strong as anticipatory hope or anticipated disappointment is equally strong as
anticipatory fear. We assume that hope-fear varies along an axis oblique to the main axes in
the affect grid (see Figure 1; Yik et al., 2011), whereas elation-disappointment varies along an
axis at an angle to hope-fear. When moving from one end-point of the continua to the other
end-point, hope3 or elation decreases at the same time as fear or disappointment increases.
This is what we assume happens when prices change. We further assume the preference order
elation > hope > disappointment > fear (Västfjäll & Gärling, 2006). Elation is thus preferred
to hope and disappointment to fear when being of equal strength,
We further propose that a preference to sell winners is determined by the price reached
when anticipated elation associated with the outcome of a choice to sell is equally strong as
anticipatory hope. Volatility of prices may increase fear and thus decrease the hope-fear
balance such that the preference to sell is reached at a lower price. An asset price may still
start to decrease before this happens. A downward price trend then elicits anticipatory fear of
2
Investors are known to be a heterogeneous group differing in mandate, liquidity, investment horizon,
experience, and sophistication. Even though the consequences for rational investment behavior differ (e.g., Feng
& Seasholes, 2005), we see no compelling reason to not claim that all investors are influenced by emotions. Yet,
emotions determining preferences to buy or sell are only one input to a decision process resulting in a buy or sell
choice. The characteristics of this decision process are beyond our scope. In less experienced and sophisticated
investors with a short investment horizon and possibly limited liquidity, the decision process is likely to be shortcut such that emotions play a larger role (Finucane, Alhakami, Slovic, & Johnson, 2000). Note also that we use
the term preference to refer to what in consumer research would be called intention to buy (or sell). In addition to
the investor characteristics listed above, whether the preference is realized as a choice depends of course also on
the availability of other market actors willing to sell or buy.
3
In analogy with some neurophysiological research (LeDoux, 1996) hope may alternatively be conceived of as
reflecting cognitive control of emotion (fear). Why hope gives way for elation may conversely be conceived of
as loss of cognitive control of temptation. Then, the prediction that sell preferences are formed when hope and
elation is equally strong needs to be justified in some other way.
losing. We propose that preferences to sell are deferred until anticipated disappointment is
equally strong as anticipatory fear. Anticipatory fear may still for some time be balanced by
anticipatory hope of price reversion or high volatility of price decreases. Preferences for
selling may also be deferred because investors shield themselves from negative market
information (Karlsson, Loewenstein, & Seppi, 2009).
Conclusion
The review of emotion research in financial markets reveals a number of short-comings.
One is the lack of clarity in defining different emotion constructs. Our contribution is a
discussion of several useful distinctions. The most fundamental one, almost always
overlooked, is that between evaluation and emotion impact. It cannot thus be taken for
granted that investors respond emotionally unless they have some personal involvement. The
counter-argument is that gains or losses of money always have personal relevance. Yet, there
are other conditions (e.g. responsibility, reputation) which for at least some are more
personally relevant and therefore result in larger emotion impacts.
We then follow up our distinctions (i.e., between anticipatory and anticipated emotions) by
demonstrating how they may be used to explain buy and sell decisions in asset markets. We
argue that this is the level that needs to be examined although it should not detract from the
importance of analyses of market consequences. It is also necessary to take into account that
investors comprise a heterogeneous group varying in several characteristics that are likely
moderators of emotion influences.
Another observation is the over-reliance in finance research on physiological markers.
Even though their validity has improved, self-reports of emotion are still essential, at least to
provide converging evidence (Mauss & Robinson, 2009; Wilson-Mendenhall et al., 2013). It
is hard to otherwise claim that emotion is measured.
Ending on a positive note, we praise the use of asset-market experiments, which should
improve generalizability of the results. This method could and should also be used to test
changes to existing markets and actors in these markets, not only to simulate the existing
conditions.
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Activation
Fear
Elation
Displeasure
Pleasure
Disappointment
Hope
Deactivation
Figure 1
Anticipatory emotion:
Hope-fear
Mood:
Pleasantdispleasant
Buy-sell preference
Anticipated emotion:
Elation – disappointment
Figure 2
Balance
Downward price trend
Upward price trend
Anticipatory
hope – fear
Sell
Price change
Buy
Sell
Anticipated elation
disappointment
Figure 3