Mood in foreign exchange trading: Cognitive processes and performance

ORGANIZATIONALBEHAVIOR

Organizational Behavior and Human Decision Processes 91 (2003) 322–338

www.elsevier.com/locate/obhdp

AND HUMANDECISION PROCESSES

Mood in foreign exchange trading: Cognitive processesand performanceq,qq

Kevin Au,a,* Forrest Chan,a Denis Wang,a and Ilan Vertinskyb

a Department of Management, KKL Building, Chinese University of Hong Kong, Shatin, Hong Kongb Centre for International Business Studies, University of British Columbia, Canada

Abstract

This paper examines the effects of mood on the behavior of traders or decision makers in financial markets. Based on a review of

the psychological theories and empirical findings which relate mood to decision making and information processing, we derive

hypotheses with respect to the impact of good, neutral, and bad moods on the behavior of traders. Two experiments (N ¼ 66 and 72)

were conducted on an Internet platform which simulated foreign exchange trading based on historical market data. The first ex-

periment manipulated mood using feedback and music whereas the second one asked subjects to read mood-laden statements. It was

found that traders in a good mood had an inferior trading performance (losing money) compared to those in a neutral or bad mood

(making profit). This is because traders in a good mood made less accurate decisions than those in neutral or bad moods. Those in a

bad mood were the most accurate in their decisions but behaved conservatively in their trading. Subjects in a good mood tended to

make less accurate decisions though they spent on average at least the same time as the other subjects on information processing and

decision-making. They also were over-confident taking unwarranted risks. The studies concluded with insights concerning the effects

of mood on choice of trading strategies.

� 2002 Elsevier Science (USA). All rights reserved.

Introduction

Financial markets are characterized by turbulence.Just like other decision-makers, traders in these markets

often display behaviors reflecting emotional responses to

market movements rather than the outcome of analysis

of fundamental economic trends and relationships

(Schwager, 1992). The role of affect in financial decision-

making has largely been ignored by financial theorists

who view market participants as rational expected-util-

qThe first and second authors contributed equally and substan-

tially to this paper and the order of their names is alphabetical. This

paper is partially based on the Master�s thesis of Forrest Chan while hestudied in the Department of International Business, Chinese Univer-

sity of Hong Kong. We thank Dennis Fan, Frederick Ho, Oana

Branzei, Tony Lempri�eere, Chun Hui, Charlene Zietsma, and two

anonymous reviewers for their comments on earlier drafts of this

paper, and Kelvin Wong and Ann Lam for their help in data

collection.qqThis paper was accepted under the editorship of Daniel R.

Ilgen.* Corresponding author. Fax: +852-2603-5473.

E-mail address: [email protected] (K. Au).

0749-5978/03/$ - see front matter � 2002 Elsevier Science (USA). All rights

doi:10.1016/S0749-5978(02)00510-1

ity-maximizers with stable preferences (Jegadeesh,

1995). Traders, on the other hand, recognize the role of

emotions in market behavior and the threats and op-portunities that emotional responses create for market

participants. Warren Buffet, one of this century�s mostsuccessful market participants, for example, observed

that when an investor ‘‘could insulate himself from the

emotional whirlwinds of the stock market, he had op-

portunity to exploit irrational behavior of other inves-

tors, who purchased stocks based on emotion, not logic’’

(Hagstrom, 1994, p. 48). The focus of financial practi-tioners� commentary on affect in the market is largely onstrong emotions that market participants display with

respect to market developments and on the impact of

emotions on market-related decisions. Thus, for exam-

ple, they tend to comment upon strong fears which lead

market participants to ‘‘panic’’ or euphoric exuberance

which results in market ‘‘bubbles.’’ There is evidence,

however, that affect may also play a subtler role inmolding market behaviors and determining the perfor-

mance of participants. Saunders (1993) and Lam and

Lam (1998), for example, have found that stock prices in

New York and Hong Kong, respectively, were related to

reserved.

mail to: [email protected]

K. Au et al. / Organizational Behavior and Human Decision Processes 91 (2003) 322–338 323

weather conditions which were in turn related to themood of investors. Investors tended to be more bullish

on sunny days than on cloudy days.

In this paper we study the consequences of mood in

molding behavior in financial markets and in deter-

mining performance of participants. Additionally, we

explore whether differences in performance are attrib-

utable to the ways participants process trade-related

information.

Mood, decision behavior, and performance in financial

markets

Mood is the subcategory of affect involving low in-

tensity feelings which are not directed at, or focused on,

a specific intentional object (Frijda, 1994; Morris, 1989).‘‘Mood is an undirected evaluative mental state which

temporarily predisposes a person to interpret and act

towards a variety of events in ways according with its

affective content’’ (Parkinson, Totterdell, Briner, &

Reynolds, 1996, pp. 9–10).

Mood has impact on a variety of aspects of decision

processes. It has an impact on search for and retrieval

and acquisition of information, the pattern of attentionpaid to various pieces of information, and the way they

are processed and interpreted (Bower, 1981; Wyer,

Clore, & Isbell, 1999). It may also have a direct impact

on valuation such as risk preferences (Mann, 1992). The

impact of mood on performance depends on context, in

particular the nature of the task. Different tasks impose

different demands on the decision processes (Kunda,

1991). The impact of mood on performance dependstherefore on whether specific changes that occur in de-

cision processes fit the specific demands imposed by the

task. For example, in a complex decision situation re-

quiring quick action, abilities to simplify and efficiently

process large quantities of information may be highly

valued even if they come at the expense of precision. In

some control tasks (e.g., accounting systems) when ac-

curacy is highly valued and time constraints are not tight,the tendency to simplify may damage performance while

quick efficient processing may not command a premium.

To develop the relationship of mood to performance,

we (1) articulate theories and examine findings con-

cerning mood on cognitive processes, (2) analyze the

specific demands upon decision processes that foreign

exchange markets impose, (3) reconcile the effect of

mood on risk-taking in light of the demands of theforeign exchange markets, and (4) assess the influences

of mood on trading performance.

Mood and cognitive processes

One of the most extensively researched topics about

the consequences of affect is the effect of mood on

memory (Bower, 1981; Weiss & Cropanzano, 1996).There is extensive research on the mood congruence

hypothesis. This hypothesis suggests that memories of a

certain emotional tone are triggered by moods of a

similar tone. Thus pleased people are more likely to

recall ‘‘happy’’ memories, memories which are consis-

tent with their mood. The evidence, however, indicates

asymmetry in impact of ‘‘good’’ and ‘‘bad’’ moods.

While pleasant moods facilitate recall of positive mate-rials and reduce the propensity to recall negative infor-

mation, unpleasant moods inhibit the recall of positive

materials but do not facilitate recall of negative infor-

mation (Weiss & Cropanzano, 1996).

Mood has also been shown to affect decision-making.

Isen (1987) and Mackie and Worth (1989) argued that

since people are more familiar with pleasant affective

experiences, pleasant mood is associated with more sit-uations than unpleasant mood and pleasant mood

leaves less capacity for other cognitive tasks. This would

imply that people in a pleasant mood are likely to take a

more heuristic/peripheral route in their information

processing and simplify their search procedures. They

are also likely to suffer from a variety of cognitive biases

that result from the use of heuristics, the simplification

of problem definitions, or immature acceptance of so-lutions based on heuristics (Schwartz & Clore, 1983;

Wyer et al., 1999). For instance, people with pleasant

mood were found to be influenced by peripheral cues

(Worth & Mackie, 1987), rely on stereotypes (Boden-

hausen, 1993), use fewer categories to classify objects

(Isen & Daubman, 1984), and display increased halo

among judgements on different dimensions (Sinclair,

1988). Isen and Means (1983) and Forgas (1989) foundthat subjects in pleasant moods took less time to make

decisions and reviewed information less. They were also

found to use broader categories and to form sweeping

global impressions in decision-making (Isen & Daub-

man, 1984).

There are, however, consequences of pleasant mood

that can improve performance in certain circumstances.

Murray, Sujan, Hirt, and Sujan (1990) found that sub-jects in a pleasant mood show more cognitive flexibility

in categorizing information relative to others. Isen,

Means, Patrick, and Nowicki (1982) showed that the

simplification tendency of subjects in pleasant moods is

related to an integrative organization of information.

This tendency enhances creative problem solving. Isen,

Rosenzweig, and Young (1991) have shown that the

tendency to simplify the processing of informationincreases efficiency in clinical problem solving.

People with unpleasant moods were found to process

information in more algorithmic ways compared to

people in pleasant moods (Isen et al., 1982). Those with

unpleasant moods also tended to process information in

more controlled and systematic ways with greater ac-

curacy and reduced halo and other biases (Sinclair,

324 K. Au et al. / Organizational Behavior and Human Decision Processes 91 (2003) 322–338

1988). Thus for example, people in unpleasant moodswere found to be more accurate in their judgements than

non-depressed subjects (Alloy & Abramson, 1979; Sin-

clair & Mark, 1992), more realistic about outcomes ir-

respective of their desirability, as well as more realistic

about their ability to control situations (Alloy &

Abramson, 1988). Likewise, people in unpleasant moods

were more influenced by central rather than peripheral

cues (Worth & Mackie, 1987) and were more immune toa variety of biases (Taylor & Brown, 1988).

Moods and risk-taking

Mood influences the evaluation of positiveness or

negativeness of events (Morris, 1989). Specifically, there

exists evidence that mood may have an impact on risk

preferences (Mann, 1992). Isen and Geva (1987) haveobserved that when risks are meaningful and the chance

of losing is high, a pleasant mood discourages risk-

taking. A pleasant mood encourages risk-taking when

the probability of loss is low or the stakes are considered

to be trivial. These results are surprising since they ap-

pear to contradict the findings that people in a pleasant

mood are more susceptible to illusions of control, more

optimistic and more confident. They also appear to beinconsistent with Forgas� (1989) findings that depressedpeople make more risk-avoiding choices compared to

non-depressed people. Mood preservation is suggested

to explain why people in a pleasant mood are more risk

averse when stakes and risks are high. People in a

pleasant mood do not want to risk having their positive

mood being altered and thus avoid doing risky tasks.

A contingent theory of risk-taking may reconcile thetwo conflicting predictions about risk preference. It

considers the magnitude of forces at play in a particular

context. People in a pleasant mood are expected to have

a higher propensity to take risks because they are more

confident about their ability to control, and they are

more optimistic and therefore use probability estimates

biased toward favorable outcomes. They are expected to

be conservative if they want to protect their pleasantmood. The balance depends on whether the situation

lends itself to probability estimation biases or whether

mood maintenance is the prime motive for behavior.

Thus, in well-defined situations where skills do not ap-

pear to be important and probabilities are given (the

typical risk-taking experiment using choice of gambles),

the priming effects of a pleasant mood will be minimal

while mood maintenance motives may dominate. Incontrast, in ambiguous situations where probabilities

are not given (e.g., the conditions in our market exper-

iment), illusion of control, optimism, and biases of

probability estimation may play a more forceful role in

molding risk behaviors. A pleasant mood will, in this

case, encourage risk-taking while an unpleasant mood

will lead to risk-avoiding behavior.

Mood and performance in foreign exchange markets

Our review of the literature indicated a variety of

impacts on different elements of the decision process.

These impacts may be beneficial or damaging to per-

formance depending on the particular demands that the

task environment imposes. Routine trading in foreign

exchange markets involves an understanding of complex

fundamental economic relationships. A trader must beable to relate changes in a variety of economic domains

(involving monetary, fiscal and trade policies and

trends) to fluctuations of exchange rates. This requires

analytical skills, careful consideration of a vast amount

of information, and accurate assessment of economic

and financial developments. Trading in foreign exchange

markets also requires assessment of the quality of pre-

dictions made, to ensure appropriate risk postures, thatis, placing large bets when the predictions involve less

uncertainty and lower bets when the situation is more

ambiguous. It appears that a pleasant mood, with its

propensity to simplify, to use heuristic rather than an-

alytic models of information processing, and to magnify

cognitive biases and illusions of control, would not en-

hance performance. Some of these effects may enhance

performance during high volatility ambiguous environ-ments where quick responses are needed and where the

increased complexity exceeds the power of cognitive

capacities and the grasp of tractable analytical models.

In such trading situations the tendency of people in an

unpleasant mood to procrastinate (rechecking informa-

tion and paying attention to irrelevant information) and

employ algorithmic modes of information processing,

and decision-making may result in low performance.

Hypotheses

Our study focuses on trading decisions in foreign

exchange markets. The basic hypothesis the study tests

relates mood and performance in such markets. On the

basis of the analysis above we postulate that

H1. Subjects in a pleasant mood will perform less wellthan those in neutral and unpleasant moods.

Subjects in an unpleasant mood are likely, according

to the literature, to be more accurate then neutral sub-

jects, but may display overly conservative posture

compared to neutral mood subjects. Since we have no

prior reason to judge which of those two contradicting

forces has a bigger impact, we present the following

opposing hypotheses.H2a: Subjects in an unpleasant mood will perform

better than those in a neutral mood because they are

more accurate in their predictions.

H2b: Subjects in an unpleasant mood will not per-

form as well as subjects in a neutral mood despite being

more accurate in their prediction because their overly

conservative position will constrain their gains.


To test these hypotheses we will establish the relativeperformance, the relative accuracy, and relative invest-

ment sizes of both groups. Finally, we will test several

hypotheses about information processing that found

support in non-trading contexts.

H3: A pleasant mood leads to quicker decision-

making.

H4: A pleasant mood increases the focus on relevant

information.H5: A pleasant mood leads to examination of fewer

information items.

We note that while, in general, people in an un-

pleasant mood may be more analytical, it is possible

that, given their propensity to be less confident, they

may rely on opinions of experts if the opinions are made

available to them.

Study 1

Bromiley and Curly (1992) criticized experimental

studies of risk decision-making as having low external

validity. They pointed out that (1) risk information in

these studies was presented to subjects in terms of pre-

cise probabilities; (2) experimental decision tasks wereunrealistic; and (3) subjects were aware that their deci-

sion tasks were fabricated and hypothetical. To raise the

external validity, our experiment employs a simulation

based on real financial data with an Internet trading

system similar to those used by traders in foreign ex-

change markets. Real market information was also

provided to subjects in the decision-making process.

The experiment was designed as a 3(mood: pleasant,neutral, and unpleasant)� 4(round: one to four) be-

tween-within factorial design. The mood factor was

between-subjects and the round factor was within-sub-

jects. We have tested the hypotheses on the basis of re-

sults of the first round, since success or failure in that

round could (and indeed did) contaminate moods gen-

erated through manipulation and since the inclusion of

data from subsequent rounds would introduce otherconfounding effects. We continued the experiment for

three additional rounds to gain perspective on the in-

teraction of trading performance and mood (Weiss &

Cropanzano, 1996).

The Internet-based trading simulation system

In the Internet trading system, subjects predicted therelative price movement of a currency pair (Deutsche

Mark and Swiss Franc) and made decisions to buy or

sell the currencies. Subjects were provided with news

items and information and charts about the price of the

currencies. The Internet system recorded important in-

formation regarding decision-making and performance.

The system had two web pages for each round: the

News and Charts page and the Placing Order page (see

Appendix for details). Subjects could click, in the News

and Charts page, on a news headline to view the news

content, or click on a chart to view the daily movement

of exchange rate in detail. All news headlines were

shown on the screen so that the order in which the in-

formation was displayed would not affect subjects� in-formation encoding and retrieval and confound results

with any priming effect (Payne, 1985). The charts de-

picted the daily closing prices in the previous three years.The amount of data in each chart and the chart layout

were kept constant to eliminate possible effects due to

chart presentation. The data presented simulates the

type of data which traders consult in making predictions

as to the likely changes in foreign currency rates of ex-

change. In order to trace what news a subject read and

the time spent, each piece of news could be viewed only

once. Subjects were told that they were free to read asmany news items as they wished before reaching their

decisions.

After the trading decision was made, subjects could

click on the Placing Order button to enter the Placing

Order page. In this page, subjects were given an initial

capital in Swiss Franc and could select to buy or sell

Deutsche Mark with contract size ranging from DM

100,000 to DM 1,000,000. The amount of initial capitaland trading amount were set up to ensure that all sub-

jects� account balances would be positive even if they

suffered the heaviest possible loss in all four rounds of

trading. In making an investment, subjects had to decide

if Mark would depreciate against Swiss Franc (i.e.,

DEM/CHF would fall), or Mark would appreciate

against Swiss Franc (i.e., DEM/CHF would rise). If they

predicted that DEM/CHF would fall further, theywould ‘‘Sell’’ DEM/CHF (i.e, convert their money from

Deutsch Mark to Swiss Franc); if DEM/CHF would

rise, they would ‘‘Buy’’ DEM/CHF (convert Swiss

Franc to Deutsch Mark). Just like real traders, subjects

had to take a position either to ‘‘sell’’ or to ‘‘buy.’’ They

then indicated their investment decision and investment

contract size in the Place Order web page.

We could decide the correctness of the trading deci-sions by evaluating the consequence of their ‘‘buy’’ or

‘‘sell’’ decisions against the historical movement of the

currency pair. For instance, the correct market decision

for the first round of the simulation was to buy Deutsche

Mark. Their decisions are correct if they purchase (long)

Deutsche Mark and this currency subsequently rises

against Swiss Franc, for they can profit by selling their

Mark at the higher price. Their decision is also correct ifthey sell (short) Mark and it subsequently falls against

Franc, for they can purchase cheap Mark back at the

lower price. Their decisions are wrong if they do

otherwise. Since subjects could profit regardless of the

price trend, the choice faced by them was not a choice

between action and inaction. Instead, they could buy

(long) or sell (short) in their desired amounts.


In the Place Order page, subjects could also choose touse cut-loss and/or take-profit orders. A cut-loss order

specified the lowest price threshold that triggered a sale

of held positions to minimize further losses. A take-

profit order specified an upper threshold price that trig-

gered sale of an asset to realize profits. These two orders

are available in real trading and incorporating them

renders additional reality for the study. After subjects

made their decisions, they were also required to brieflyexplain their rationale for their decision.

Before the actual trading simulation started, the

subjects participated in a trial run. It was designed to

familiarize them with the computer system and the tasks

and served as part of the manipulation of subjects�mood.

Selection of the currency pair

The currency pair selected was the cross-rate of the

Deutsch Mark and the Swiss Franc (DEM/CHF). We

had several reasons for choosing this pair. The curren-

cies are important in the market and therefore there are

sufficient news items affecting Mark/Franc movements.

Students in Hong Kong were not familiar with the

movements of European currencies and their priorknowledge did not introduce a bias to the experiment.

The decisions to ‘‘buy’’ and ‘‘sell’’ were cognitively

neutral–since buying one currency implied selling of the

other and vice versa, and these decisions did not involve

the local currency (Hong Kong dollar).

The starting date of each round of the simulation

used in the experiment was set to be a critical date in the

DEM/CHF movement, which means that the currencypair was moving around a turning point or an important

support or resistance level. Thus, subjects had to decide

whether the price would continue to follow the existing

trend, or would reverse it. The real time span between

rounds in the simulation was about two months and that

means subjects did not need to consider short-term

volatility.

Preparations of news items

Daily currency news items throughout the whole

trading period were downloaded from the Dow Jones

News Archive. For each round, nine news items were

selected. A typical news item described how the market

moved, what factors were affecting the market, e.g., the

development of European Monetary Union (EMU), andprovided comments and predictions from practitioners

such as economists of leading investment banks. A news

item was edited and shortened to no more than 200

words and contained a bullish, bearish, or neutral (ir-

relevant) message. The number of words in each cate-

gory was restricted to a similar number to avoid the

possible influence of the time spent in each category. The

items were pretested using undergraduates to ensure thatthe economic/financial context was comprehensible.

Manipulation of mood

Mood was induced by a combination of two meth-

ods: (a) positive or negative performance feedback

during the trial run prior to the main simulation task

(e.g., Isen & Baron, 1991) and (b) pleasant or unpleasantmusic or no music (neutral) during all rounds of trading

(e.g., Eich & Metcalfe, 1989; Gorn, Pham, & Sin, 2001).

The feedback provided to participants after the ‘‘free

trial’’ was not dependent on the decision made by the

subject. To induce a pleasant mood, positive feedback

was given to subjects in the trial run. Specifically, sub-

jects were told that they gained a profit. The amount of

profit was set to be 30% of investment size in order tomake the profit more realistic. To increase the vividness

of the feedback, the following phase was used: ‘‘Your

judgment has proven to be excellent! You just made SF

x00,000 in one trade!’’ Negative feedback was given to

subjects in the trial run to induce an unpleasant mood.

Subjects were told that they lost regardless of their de-

cision. The loss amount was also set to be 30% of in-

vestment size. They received the feedback ‘‘Lifesometimes is miserable! You just lost SF x00,000 in one

trade!’’ Not to induce any mood, the neutral groups

were told that ‘‘the free trial is over.’’ No other feedback

was given.

After the trial run, subjects were told that their results

were in fact randomly generated and real trading would

begin. In saying that the feedback is random, we as-

sumed that it could eliminate any effect of the feedbackon subjects� competence. The neutral group was given

no feedback and was told only that this is a practice run

to acquaint them with the system. Playing music from

Kenny G�s album Moment was used to reinforce a

pleasant mood. An experienced foreign currency trader

endorsed the music as ‘‘uplifting.’’ The music played to

reinforce an unpleasant mood was �The Rite of Spring,�an orchestral composition by Igor Stravinsky. Accord-ing to the album introduction, it was and still is a very

controversial music piece, which creates ‘‘permanent

deception through constantly weak stresses.’’ The piece

caused fighting in the audiences during its premiere in

1913 (Schmidt, 1991). It is controversial because of its

‘‘barbarous’’ rhythms and textual complexity. The

‘‘pleasant or unpleasant’’ music was played throughout

the experiment on a CD player with two amplifiedspeakers which produced good sound quality. No music

was played for the ‘‘neutral’’ experimental group.

Pretest of manipulation

Subjects. Twenty-eight female and sixteen male

business undergraduates participated in the pretest for


cash incentive. They were randomly assigned to thegroups of pleasant mood ðN ¼ 23Þ or unpleasant moodðN ¼ 21Þ. Their tasks were to complete the trial run andthe first round of the trading simulation.

Procedure. Before the pretest started, subjects were

told that there was another independent study of music,

and they would be required to fill out a questionnaire,

which was the Affect Grid from Russell, Weiss, and

Mendelson (1989). After finishing one round of tradingusing the Internet-based system, they were asked to

complete the same questionnaire as in Study 1, debriefed

and dismissed.

Findings. Results based on Affect Grid indicated that

mood manipulation was successful. There was a strong

between-group effect on the pleasant and unpleasant

mood groups, F ð1; 40Þ ¼ 40:24; p < :001, and a signifi-

cant treatment by period interaction effect, F ð2; 80Þ ¼5:86; p < :05. The period effect was insignificant

F ð2; 80Þ ¼ :10. The assumption of sphericity was upheldas the Mauchly sphericity test was insignificant. Exam-

ining the means showed that the pleasant and unpleas-

ant groups did not differ in mood (M ¼ 5:87 vs. 5.00).

The pleasant group was in a better mood than the un-

pleasant group after the manipulation (M ¼ 6:48 vs.

4.21) and after one round of trading (M ¼ 6:52 vs. 4.16).Based on the pretest results, the power for a three-group

ANOVA design was calculated. With a sample size of 20

for each of the three conditions, the power for a ¼ :05was over .71 even when the most conservative assump-

tions and parameter estimates were used. The sample

size for the main experiment was thus set to be a mini-

mum of 60.

Main experiment

Subjects. Thirty-two male and thirty-four female

finance or economics students of a large University in

Hong Kong were recruited. All students had knowledge

and experience with real or simulated financial trading.

Fifty-three percent of the participants reported experi-

ence in trading stocks, and 45.5% in currencies. No onehad previously been involved in the trading of the Mark

or Swiss Franc. Regarding their investment approach,

21.2% said that they used fundamental analysis, 3.0%

technical analysis, 24.2% both, and 9.1% econometrics.

Lastly, about 16% reported that they usually seek ‘‘tips’’

for investment. Their average investment experience was

5.3 months (SD ¼ 10:0 months). Participants received

HK$60 upon finishing the experiment. They were alsoinformed that the best three performers would be

granted prizes of HK$100–300.

Procedure. The study was run in small groups (fewer

than eight subjects) who were randomly assigned to the

pleasant, unpleasant, or neutral mood conditions. A

male research assistant briefed subjects about the in-

vestment tasks and told them that the study was com-

bined with another independent study on music. Afterfilling in background information and a mood mea-

surement questionnaire, the assistant started playing

music from a CD player. Participants then accessed the

trading system and completed the trial run. After the

trial run, subjects filled out another affect questionnaire

and a self-efficacy measurement instrument. The mood

of the participants was assessed after each round of real

trading as well. The participants were allowed to askquestions before they started real trading.

After the experiment, subjects filled out a question-

naire that evaluated the simulation and the music and

asked subjects to guess the true purpose of the simula-

tion. Five subjects identified the true nature of the study

relating investment behavior to emotions. Their data

were dropped from subsequent analysis although in-

cluding them or not did not affect the findings in anysignificant way. Subjects were paid, debriefed with a

written note, and dismissed.

Measurements. Two instruments were used to mea-

sure mood. The first was the Affect Grid from Russell

et al. (1989) and the second was a Likert-scale measure

by Baron and Bronfen (1994). The latter was used to

triangulate the measurement of mood. It measures affect

on 7-point scales (sad-happy, bad-good, negative-posi-tive, unpleasant-pleasant, tired-energetic, dull-alert,

sleepy-awake, and bored-interested) which formed two

factors, affect and arousal (a ¼ :86 and .71, respec-

tively). The two measurements of mood converged very

well ðr ¼ :69; p < :01Þ and demonstrated the same

testing results on mood. Only results of the Affect Grid

will be reported in the rest of the paper.

Performance was measured by the profit (loss) in thetrading, decision correctness, and investment size. The

time spent in reading each news item and the number of

items read were counted by the computer system and

grouped into confirming, irrelevant and disconfirming

categories. Confirming information was news consistent

with the subject�s decision, i.e., if a subject chose ‘‘buy-

ing the Franc,’’ all news items bullish on Swiss Franc

were classified as confirming and bearish news items onthe Franc were regarded as disconfirming. The data was

also processed to generate measures of time spent on

news items that reinforced the decision that the subject

took (confirming), were irrelevant to the decision (ir-

relevant), or contradicted the decision (disconfirming).

Decision time was measured by the time taken in the

Placing Order web page. However, information acqui-

sition and processing may be an interactive process. Inother words, people may read a news item to form a

tentative hypothesis, and adjust their hypothesis as they

read more news until the final decision is made. There-

fore, two other measures, (1) time spent in news reading

plus time spent in making the buy/sell decision and (2)

total time spent to finish a round, were also used in the

analysis. Results using these measures were similar, and


even showed greater significance, especially for the firstmeasure.

In addition, the uses of cut-loss order and uses of take-

profit order and the width of these orders reflect subjects�willingness to take risk. The cut-loss width and take-

profit width are the differences between the exchange rate

in the beginning of each round of the trading and sub-

jects� take-profit and cut-loss prices. They would set

narrow cut-loss and take-profit widths if they wereconservative. In effect, if they protected themselves from

large loss by setting a narrow cut-loss width, that would

leave them a sure loss even if the price came back up

after some initial dropping. On the other hand, they

could gain themselves a sure but small profit even if the

price moves just a little. But that would forgo the chance

to make a handsome profit if the price subsequently

increased dramatically.Control. Gender and trading experience were con-

trolled (Bromiley & Curly, 1992). More importantly,

self-efficacy in trading was also controlled. Giving po-

sitive or negative feedback in the trial run might have

affected the self-efficacy of subjects, and thus their per-

formance (Cervone, Kopp, Schaumann, & Scott, 1994).

A self-efficacy measure was therefore used to control for

this confounding effect, if there was any. A measurementwas adapted from Bandura (1984) and Silver, Mitchell,

and Gist (1995) to assess subjects� efficacy on making

profit. The subjects were asked about their confidence

about achieving a particular level of profit. They re-

ported their confidence on five items in a hierarchy of

increasing profit levels. Participants� self-efficacy score

was computed by summing their confidence levels.

Results and discussion

Manipulation check. The mood pleasure scores for the

unpleasant, neutral, and pleasant mood groups did not

show any differences before manipulation ðM ¼ 5:42;5:96; 6:00Þ; F ð2; 58Þ ¼ 1:00; p > :10. After the manipu-lation, the group subjected to pleasant mood manipu-

lation ðM ¼ 6:84Þ reported more positive mood than theneutral group ðM ¼ 6:41Þ and the group subjected to

unpleasant mood manipulation ðM ¼ 5:26Þ; F ð2; 58Þ ¼6:41; p < :01. A Dunnett test showed that the difference

between the groups in pleasant and neutral moods was

also significant at p < :05 level, suggesting that manip-

ulations were successful. The observed power at a ¼ :05was. 89, suggesting that the sample size was adequate.

The results of the additional rounds of the experi-ments beyond the one designed to test the hypotheses

showed erosion of the effects of the manipulation.

Though the results for the four rounds are generally

consistent with those of the first (experimental) run,

most of the difference between groups is explained by

differences in the first round where mood differences

between the groups were significant.

Effects of control variables. Correlation analysis be-tween profit in Round 1 with all control variables, i.e.,

experience, self-efficacy, and gender was performed.

Results were all insignificant (r ¼ :09; :13;�:18, respec-tively). The results were also insignificant when these

variables were correlated with total profit (r ¼:19; :02;�:09, respectively). More analyses showed that

the three control variables had negligible effects on

mood pleasantness and arousal (r ¼ :08–:14, p > :10).Results of the analyses including these variables were

similar to those that excluded them. Therefore, we re-

port here only the results of the analysis without these

covariates.

Trading performance in Round 1. As shown in Table 1,

the neutral mood group earned higher profit ðM ¼79; 136Þ than the group in an unpleasant mood

ðM ¼ 5; 642Þ, or the group in a pleasant moodðM ¼ �91; 430Þ. ANOVA showed significantmoodmain

effect, F ð2; 58Þ ¼ 5:67; p < :01 (see Table 2). Orthogonalcomparisons indicated that the significant result was due

to the difference between groups in a pleasant mood and a

neutral mood, tð58Þ ¼ 3:36; p < :001. Unpleasant-neu-tral mood groups� difference was insignificant, tð58Þ ¼1:43; p ¼ :16, n.s. These results support the hypothesisthat positive mood leads to inferior trading performance(H1).

To test H2a and H2b, profit was regressed on size

of investment, decision accuracy (dummy variable:

0–wrong, 1–correct), and their interaction. To elimi-

nate multicollinearity between the two variables and

their interaction variable, deviant scores were used to

estimate the model (Aiken & West, 1991). The model

explained profit well, R2 ¼ :71; F ð3; 57Þ ¼ 50:77; p <:001. Decision accuracy has a positive effect on per-

formance, ðb ¼ :65; t ¼ 9:41; p < :01Þ, whereas in-

vestment size has negative effect on performance,

ðb ¼ �:55; t ¼ �5:59; p < :01Þ. Furthermore, the in-

teraction effect is also significant ðb ¼ :78; t ¼ 7:93;p < :01Þ. As shown in Fig. 1, the implications of the

interaction effect are quite straightforward. If a deci-

sion was correct, increasing investment size wouldenhance performance. On the other hand, if a decision

was false, increasing investment size would incur a

heavy loss. Both the groups in neutral and pleasant

moods had larger average investment size than the

group in an unpleasant mood. As the subjects in a

pleasant mood were more prone to make wrong de-

cisions and place higher bets on their choices, they

occupied the bottom right of the graph. Neutralsubjects who made relatively more correct decisions

than the subjects in a pleasant mood and invested

larger sums than the ones in an unpleasant mood

occupied the upper right of the graph, indicating a

superior performance. Subjects in an unpleasant mood

made small investments and thus were concentrated

on the left. Because of their small investments, they

Table1

DescriptivestatisticsforRound1tradingasafunctionofmood

Variable

Study1

Study2

Unpleasant

Neutral

Pleasant

Unpleasant

Neutral

Pleasant

Perform

ance

5642(143,958)

79,136(170,061)

)91,430(175,232)

99,941(135,154)

15,225(121,930)

)7000(143,994)

Decisionaccuracy

Number

ofcorrectdecisions

13(68.42%)

14(63.64%)

6(30%)

18(75%)

13(54%)

13(54%)

Investm

entsize

242,105(130,451)

454,545(268,554)

425,000(307,580)

341,667(248,328)

270,833(139,811)

304,000(228,181)

Decisiontime

108.52(43.43)

179.59(106.49)

122.45(47.65)

92.96(41.52)

80.13(27.60)

112.67(70.92)

Confirm

ingnew

sNumber

ofitem

s1.47(1.02)

1.14(.94)

1.74(.99)

1.88(.95)

1.50(1.14)

1.80(.96)

Time(s)

75.47(65.54)

126.82(78.64)

75.84(63.88)

87.54(58.60)

70.88(72.00)

103.58(83.51)

Disconfirm

ingnew

sNumber

ofitem

s1.00(1.00)

1.09(.87)

1.63(1.01)

1.58(.97)

1.42(.24)

1.60(1.19)

Time(s)

91.74(82.22)

104.77(76.91)

65.63(62.63)

82.21(65.33)

76.58(76.21)

97.08(103.86)

Irrelevantnew

sNumber

ofitem

s1.11(.99)

.73(.77)

1.47(.90)

1.42(.93)

1.21(.98)

1.44(1.08)

Time(s)

32.74(26.86)

68.55(53.49)

36.63(43.54)

54.71(47.38)

44.88(48.88)

60.42(64.83)

Cut-loss

order

Number

ofusers

917

12

17

19

20

Width

.00(.00)

.01(.01)

.08(.22)

.05(.07)

.09(.08)

.06(.06)

Take-profitorder

Number

ofusers

911

10

12

16

17

Width

.01(.01)

.02(.01)

.03(.06)

.06(.06)

.14(.16)

.07(.05)

Note.Figuresin

parentheses

are

standard

deviations,exceptotherwisespecified.


earned only modest profits, despite making correctdecisions.

Analyzing the decision accuracy rates and investment

sizes confirm the observations (see Table 1). Sixty-eight

percent of the subjects in an unpleasant mood made

correct decisions (i.e., to buy). Their accuracy was sim-

ilar to the neutral group, of which 14 (about 64%) made

correct decisions. Both groups performed better than the

group in a pleasant mood, of which only 6 (30%) sub-jects made the right decision. A v2 test showed that suchdifferences in accuracy were significant, v2ð2Þ ¼ 7:05;p < :05.

Subjects in an unpleasant mood had made much

smaller investments ðM ¼ 242; 105Þ, about one-half ofthe average size of the investments made by the neutral

group ðM ¼ 454; 545Þ, or by the group in a pleas-

ant mood ðM ¼ 425; 000Þ; F ð2; 58Þ ¼ 4:20; p < :05. ANeuman–Keuls test was significant for the unpleasant-

neutral mood groups� difference, but it was not signifi-cant for the pleasant-neutral mood groups� difference.

Decision time. An ANOVA on the decision time

showed significant differences F ð2; 58Þ ¼ 5:46; p < :01.Planned comparisons (see Table 2) revealed that the

group in an unpleasant mood spent less time ðM ¼108:52Þ than the neutral group in reaching a decisionðM ¼ 179:59Þ. Similarly, the group in a pleasant mood

ðM ¼ 122:45Þ also spent less time than the neutral groupin reaching a decision. The results suggest that both

pleasant and unpleasant moods promoted the use of

selective information acquisition leading to a reduction

in information processing time and perhaps a reduction

in mental effort.

Information acquisition. As shown in Table 1, subjectsin a pleasant mood searched the largest number of news

items that disconfirmed their eventual decisions ðM ¼1:63Þ, more than the neutral mood subjects ðM ¼ 1:09Þand the subjects in an unpleasant mood ðM ¼ 1:00Þ.Planned comparisons indicated that the difference

between pleasant and neutral mood groups was mar-

ginally significant, whereas the difference between neu-

tral and unpleasant mood groups was not significant(see Table 2).

Time spent on news items that disconfirmed the

eventual decision shows a clearer pattern. Subjects in a

pleasant mood spent the least time in reading and ana-

lyzing disconfirming news (M ¼ 65:63 s), significantly

less time than the neutral ðM ¼ 104:77Þ and the un-

pleasant mood groups ðM ¼ 91:74Þ. That suggests thatsubjects in a pleasant mood tend to overlook informa-tion, which perhaps could shake their confidence and

disturb their mood.

Consistent with the results concerning disconfirming

news, subjects in a pleasant mood searched more news

items that confirmed their eventual decision ðM ¼ 1:74Þthan the neutral mood subjects ðM ¼ 1:14Þ. The subjectsin an unpleasant mood ðM ¼ 1:47Þ searched about the

Table 2

Summary of statistical test results (Study 1)

Variable Comparison groups Test type df Statistical values

Performance All three groups ANOVA 2,58 5.67��

Unpleasant vs. neutral Planned comparison 58 1.43

Pleasant vs. neutral Planned comparison 58 3.36��

Investment size All three groups ANOVA 2,58 4.20��

Unpleasant vs. neutral Planned comparison 58 2.72��

Pleasant vs. neutral Planned comparison 58 ).38Decision accuracy Done in the text Pearson v2 2 7.05��

Decision time Three groups ANOVA 2,58 5.46��


Pleasant vs. neutral Planned comparison 58 )2.51��

Total time to finish one round Three groups ANOVA 2,58 6.78��



Confirming news items Three groups ANOVA 2,57 1.93

Unpleasant vs. neutral Planned comparison 57 1.10

Pleasant vs. neutral Planned comparison 57 1.95�

Disconfirming news items Three groups ANOVA 2,57 2.45�

Unpleasant vs. neutral Planned comparison 57 .30

Pleasant vs. neutral Planned comparison 57 1.80�

Irrelevant news items Three groups ANOVA 2,57 3.62��

Unpleasant vs. neutral Planned comparison 57 )1.36Pleasant vs. neutral Planned comparison 57 2.69��

Confirming news time Three groups ANOVA 2,57 3.70��



Disconfirming news time Three groups ANOVA 2,57 1.44

Unpleasant vs. neutral Planned comparison 57 .55

Pleasant vs. neutral Planned comparison 57 )1.68�

Irrelevant news time Three groups ANOVA 2,57 4.28��



Total news items read Three groups ANOVA 2,57 4.07��

Unpleasant vs. neutral Planned comparison 57 ).93Pleasant vs. neutral Planned comparison 2,57 2.82��

Use of cut-loss order Three groups Pearson v2 2 3.97

Unpleasant vs. neutral Pearson v2 1 3.93��

Pleasant vs. neutral Pearson v2 1 .77

Use of limit profit order Three groups Pearson v2 2 1.45

Unpleasant vs. neutral Pearson v2 1 .96

Pleasant vs. neutral Pearson v2 1 .96

Note. Planned comparison analysis assumed equal variance.* p < :10.** p < :05.*** p < :01.


same number of news items as the neutral subjects.

Subjects in a pleasant mood spent less time on these

news items than the neutral subjects (M ¼ 75:84 vs.

126.82). The neutral subjects also spent more time on

these news items than the subjects in an unpleasant

mood ðM ¼ 75:47Þ.Subjects in a pleasant mood read two times more

irrelevant news items than neutral subjects (M ¼ 1:47 vs.0.73). Neutral subjects read similar numbers of irrele-

vant news items as the subjects in an unpleasant mood

ðM ¼ 1:11Þ. The subjects in a pleasant mood ðM ¼36:63Þ spent less time than neutral subjects ðM ¼ 68:55Þreading these messages. Similarly, the subjects in an

unpleasant mood spent less time on average than

the neutral subjects on irrelevant news items ðM ¼32:74Þ.

Use of contingent transactions (cut-loss and take-profit

orders). Intensive use of cut-loss orders could indicate a

conservative bias, while the use of stop-profit orders

may indicate a mood maintenance strategy. We expected

subjects in an unpleasant mood to be more conservative

and to use more cut-loss orders. Contrary to expecta-tions, only nine of these subjects used cut-loss orders in

round one, significantly less than the number of neutral

subjects, and subjects in a pleasant mood ðN ¼ 13Þ.Since these subjects displayed their conservative bias by

placing small bets, they did not need to further control

losses using cut-loss orders. We expected subjects in a

Fig. 1. Interaction between investment size and profit (Study 1).


pleasant mood to use more take-profit orders to ensure

mood maintenance. But there were no significant dif-

ferences, v2ð2Þ ¼ 1:45; p > :10. Furthermore, subjects

in a bad mood and subjects in a good mood chose

similar take-order and cut-loss widths as the neutral

subjects.

Results of Rounds 2–4. As indicated above, we used

the results of the first round as the experimental settingfor testing hypotheses to ensure that the manipulation

effects were not contaminated by ‘‘feedback’’ from the

first round. We conducted three additional rounds to

provide some information on the interactions of per-

formance and mood.

The results show that the mood differences narrowed

as successful neutral mood subjects became happier with

their positive performance, while subjects in a pleasantmood, who performed badly, lost their pleasant mood.

Subjects in an unpleasant mood, who succeeded, showed

the most persistent mood. It appears that an unpleasant

mood is stickier than other moods.

The results of mood-performance relationships are

similar to the round one results. As shown in Table 3,

the neutral group achieved the highest average cumu-

lative profits throughout the trading rounds, which wasfollowed by the group in an unpleasant mood. After

four rounds of trading, the average profit cumulated for

neutral subjects was $196,691, outperforming the group

in an unpleasant mood ðM ¼ 124; 379Þ by 58%! The

group in a pleasant mood suffered trading losses for the

four round simulation ðM ¼ �56; 120Þ!A 3(mood)� 4(round) ANOVA showed a strong

mood main effect, F ð2; 58Þ ¼ 3:40; p < :05, and round

main effect, F ð3; 174Þ ¼ 2:60; p ¼ :05, but no interac-tion effect, F ð6; 174Þ ¼ :29; p > :10. Mood therefore

significantly accounted for the differences in trading

performance. The existence of the round main effect

indicated that trading performance varied in each

round. The group in a pleasant mood suffered losses in

Round 1, and 4, in which there were market turning

points, while the group in an unpleasant mood earned

the largest profit in Round 4. This may be explained bysuggestions that pleasant mood promoted virtually au-

tomatic, habitual processing (Isen & Daubman, 1984;

Sinclair, 1988). Pleased subjects may have failed to de-

tect market reversal and increased their bet size, thus

accruing larger trading losses.

The results of the four rounds of experiments are

largely consistent with the results of the first round but

show that the interaction of performance and mood issignificant. They may also suggest that different moods

Table 3

Cumulative profit as a function of mood and round (Study 1)

Round Unpleasant Neutral Pleasant

1 5,642 79,136 )91,430��

(143,958) (170,061) (175,232)

2 28,084 22,618 11,210

(175,326) (153,982) (160,353)

3 16,610 53,563 30,980

(186,538) (240,588) (208,799)

4 74,042 41,372 )6,880(196,879) (204,175) (197,059)

Total 124,379 196,691 )56,120�

(347,137) (466,506) (416,328)

Note. Figures in parentheses are standard deviations.* p < :05.** p < :01.


may leave different imprints on behavior. These results

about the dynamic consequences of mood in trading

leave more questions than answers and indicate an im-

portant research direction for future studies (Weiss &

Cropanzano, 1996).The above findings confirmed some of our expecta-

tions and revealed interesting observations regarding

thought processes in currency trading. There are two

concerns that might be raised with respect to the ma-

nipulation. First, giving subjects false feedback related

to the experimental task as a way to induce mood could

have affected their confidence, as well as their assessment

of the heuristics they have used in the initial run. Thus,for example, those receiving feedback suggesting loss in

the trial run might have changed somewhat the methods

through which they analyzed the data and/or reached a

decision in subsequent runs. They could also have

formed an opinion about the risks associated with cur-

rency trading. The fact that we have not found any ef-

fects related to self-efficacy scores suggests that subjects

receiving negative feedback may have treated the feed-back as information about the particular situation in the

trial run and not as information about their abilities.

Thus, they have not lost confidence in their potential

performance. Interpreting the feedback as information

about the situation in currency markets may have af-

fected their subsequent actions. It is also possible that

they might have associated the bad mood with their

experiences in the trial run, reminding them about risksassociated with their decisions. In this case, mood served

as information in subsequent runs.

Second, there existed a chance that the subjects

could have discovered the true source of their mood.

In such case there is general evidence from mood

studies that this results in a reversal of what would

otherwise have been found (Wyer et al., 1999, p. 31).

Since we have not found any evidence of reversal it canbe assumed that this bias was not dominant in the

experiment. Nonetheless, to deal with these concerns

and to test the robustness of the results, we conducted

a second study in which a different manipulation of

mood was attempted.

Study 2

We used the same Internet-based currency trading

system and a similar group of students. We used the

unrelated-study paradigm (Sinclair, Mark, Enzle,

Borkovec, & Cumbleton, 1994), and altered the method

by which mood was manipulated. Mood was induced by

asking subjects to read mood-laden statements (Velten,

1968), so that subjects� performance expectation wouldnot be affected. Owing to this new procedure, we had to

change the experiment to only one round of trading. The

longer manipulation procedure took more time, and the

mood induced might subside over time, causing internal

validity problems (Isen & Gorgoglione, 1983). The

�round� factor was eliminated and the study became a

one-factor (3 mood conditions: pleasant, neutral, and

unpleasant) between-subjects design.

Subjects

Twenty male and fifty-two female students were re-

cruited from the same population pool as Study 1. All

students had some knowledge and experience with real

or simulated financial trading. Their investment experi-

ence and courses taken are similar to those in Study 1.Participants received compensation upon finishing the

experiment, and were informed that the best three

performers would be able to receive grand prizes.

Procedure

Subjects were recruited for an experimental session

composed of an attitude study and a trading perfor-mance study. They arrived in small groups of two to

thirteen. The experimenter introduced himself and ex-

plained that a draw would be used to decide which study


would be run first. In fact, the draw was bogus: the at-titude test was conducted first for all subjects. This

procedure helped minimize subjects� suspicion regarding

mood manipulation. It also had the advantage of

reducing experimental bias and local history effects

(Sinclair et al., 1994).

Manipulation of mood. In the second study subjects

were only manipulated using mood-laden statements (no

other manipulations were used in the second study).Induced moods lasts up to 35min, even after the inter-

vention of cognitive tasks (Sinclair et al., 1994). The

subjects, who were randomly assigned to the three ex-

perimental conditions, read either pleasant, neutral, or

unpleasant statements written on a stack of papers.

Considering time and appropriateness of statements, we

used only 55 statements instead of 60 in the original

procedure of Velten (1968). Statements making senseonly to Americans were dropped. The experimenter gave

subjects 15 s to read each statement and think about its

meaning. When they finished reading all the statements,

they incubated their mood for 2min following a set of

instructions. Subjects then filled out the items on mood.

The whole process lasted 20–25min.

After the manipulation, the experimenter proceeded

to explain how the Internet trading system worked. Thesubjects participated in a trial run and received the same

feedback statement that ‘‘The free trial is over.’’ The

experimenter asked subjects if they had any questions.

After he answered all the questions, he asked subjects to

fill out the self-efficacy questionnaire. Subjects then

started the trading. When they were done, they an-

swered several questions concerning the true purpose of

the study, and then were debriefed and dismissed.Measurements. Mood manipulation was assessed by

four semantic differentials from Sinclair et al. (1994):

happy-sad, good-bad, pleasant-unpleasant, and nega-

tive-positive. Trading decisions, investment sizes, and

other measures concerning the trading process were also

recorded in the Internet trading system as in Study 1 (see

Table 1).

Results and discussion

Manipulation check. The aggregated scores of the

four mood items ða ¼ :95Þ for the unpleasant, neutral,

and pleasant groups were different in accordance with

the experimental conditions. The pleasant mood group

ðM ¼ 5:21Þ reported more positive mood than the neu-tral group ðM ¼ 4:42Þ and the unpleasant mood group

(M ¼ 3:58), F ð2; 70Þ ¼ 16:30, p < :01. A Dunnett test

showed that the difference between the pleasant and

neutral groups was also significant at p < :05. The

manipulation was successful.

Effects of control variables. Correlation analysis be-

tween profit in round one with self-efficacy scores was

again insignificant ðr ¼ :04Þ, similar to the findings inStudy 1 and Cervone et al. (1994).

Trading performance. The negative mood group

earned higher profit ðM ¼ 99; 941Þ than the neutral

mood group ðM ¼ 15; 225Þ, and the pleasant mood

group ðM ¼ �7; 000Þ. ANOVA showed significant

mood main effect, F ð2; 69Þ ¼ 4:26; p < :01. Follow-upanalyses indicated that the difference between groups in

unpleasant mood and neutral mood is significant,tð69Þ ¼ 2:34; p < :05, and so is the difference between

unpleasant and pleasant mood, tð69Þ ¼ 3:15; p < :01.The difference between neutral and pleasant mood

groups was insignificant, t < 1.

Just as in Study 1, the unpleasant group exhibited

better accuracy (18 correct or 75%) than the neutral

group (13 correct or 54%) and the pleasant group (13

correct or 54%). Nevertheless, the differences among thegroups were not as large as that in Study 1. A likelihood

ratio test on the unpleasant and neutral groups showed

that such differences in accuracy were only marginally

significant, v2ð1Þ ¼ 2:30; p < :07. However, unlike

Study 1, we found that the unpleasant mood group

ðM ¼ 341; 666Þ invested no less than the neutral mood

ðM ¼ 270; 833Þ and pleasant mood groups ðM ¼304; 000Þ. In fact, the three groups invested more or lessthe same amount of money in this study, F < 1. Since

the unpleasant group did not bet overly conservatively,

as they did in Study 1, and continued to make the right

decision, they overtook the neutral group in investment

performance. We suggest a reason that may explain this

behavior (see below).

Decision time. An ANOVA on the decision time

showed marginally significant differences, F ð2; 70Þ ¼3:63; p < :05. Further analyses revealed that the pleas-

ant mood group spent more time ðM ¼ 112:67Þ than the

neutral group ðM ¼ 80:13Þ in reaching a decision. The

difference between the neutral group and the unpleasant

mood group ðM ¼ 92:96Þ is, however, not significant.

We must be cautious when interpreting these results

because the longer decision time in the pleasant mood

group is in fact due to two outliers, who spent a lot moretime (459s and 284s) than the average subject in this

group. After deleting these outliers, the mean is much

smaller ðM ¼ 97:59; SD ¼ 51:42Þ and the differences

between groups became insignificant, F ð2; 69Þ ¼ 1:13;p > :10. Nonetheless, it is interesting to note that the

pleasant mood group did not arrive at better decisions

though they used the same or more time to think about

their decision than the other groups.Information acquisition. Unlike Study 1, we found

that subjects in the three groups read similar numbers of

consistent, inconsistent, and irrelevant news items (see

Table 1). They also spent more or less the same amount

of time on reading these items.

Use of contingent transactions (cut-loss and take-profit

orders). Subjects in Study 2 used the cut-loss order (77%


vs. 56%) and take-profit order (62% vs. 44%) more thanthose in Study 1. This change also accompanied a be-

havioral change in the cut-loss and take-profit width.

Subjects in unpleasant and pleasant moods chose similar

widths to those of the neutral subjects in Study 1. Those

with unpleasant moods chose instead to reduce their bet

size to play it safe. In Study 2 the pleasant and un-

pleasant mood subjects had a narrower take-profit

width than the neutral subjects (.06 and .07 vs. 1.44,respectively), F ð2; 42Þ ¼ 2:67; p < :10. The pleasant andunpleasant mood subjects also seemed to have a nar-

rower cut-loss width than the neutral subjects (.05 and

.06 vs. .09, respectively), although the difference is not

significant, F ð2; 53Þ ¼ 1:51; p > :20. These results sug-

gest that these subjects were more inclined to protect a

low margin of profit while forfeiting the opportunity to

earn more. They also chose to lose an acceptableamount of money instead of waiting for the price to

come back up.

These results may explain the switch in trading

strategies. The most conservative strategy is to reduce

bet sizes. The alternative is to be somewhat less con-

servative and use cut-loss and take-profit orders to cap

the risks. It appears that in the first experiment the

feedback increased the conservative posture of those inunpleasant mood. In the second experiment they chose a

somewhat less conservative strategy. Instead of reducing

bet sizes they used cut-loss and take-profit orders. As a

result, they enhanced their performance, outperforming

the neutral group. The pleasant mood subjects exhibited

a tendency to maintain their mood by accepting lower

profit targets while avoiding major losses.

Unpleasant mood subjects in Study 2 were as accu-rate as they were in Study 1, but since they used the cut-

loss and take-profit points, they were willing to increase

their bet sizes as the neutral subjects. Their higher

conservatism was reflected in their choice of narrower

take-profit and cut-loss margins than those of the neu-

tral subjects. Subjects in good mood were again inac-

curate in their decisions. They made similar bets sizes as

the other groups but were the worst performers amongthe three groups. Despite their effort in reading news

and thinking about the decision, their trading perfor-

mance was still a loss and ranks the lowest among the

groups. Unlike Study 1, their patterns of processing

information were not dissimilar from the other groups.

General discussion

The results concerning the impact of mood on

judgement accuracy and performance were consistent

with the findings of other investigators and with our

hypotheses. Pleasant mood reduces accuracy of judg-

ments while increasing confidence in one�s judgments.

Being wrong but being confident is a sure prescription

for disappointing performance in Study 1. The accuracyof pleasant mood groups improved in Study 2 but the

groups continued to rank behind the other groups in

trading performance. Unpleasant mood promotes ac-

curate decision-making while increasing the traders�tendency to be conservative in their trading amounts.

These results are robust and were obtained in the two

studies. As a case in point, subjects in an unpleasant

mood failed to exploit fully their correct predictions inStudy 1. In Study 2, they gave up the overly conservative

method of using small bets, but set narrow take-profit

and cut-loss points. Although they still behaved con-

servatively that way, they could take advantage of their

accuracy in predicting market trends and obtain good

trading profits. As we have indicated previously traders

have the option of using alterative strategies to protect

themselves. In the second study they chose a somewhatless conservative strategy and thus could improve their

relative performance. The more conservative posture in

Study 1 may have been the result of the false negative

feedback they received in the free trial. Neutral mood

seems to lead to balanced decision-making where both

accuracy and appropriate attitude towards investment

amount result in a good trading profit. Neutral mood

subjects performed the best in Study 1 and narrowlyoutdid the unpleasant mood subjects in Study 2.

We also looked into findings in the literature about

the impacts of mood on different aspects of information

processing and judgment. We expected that subjects

with a pleasant mood would tend to review fewer in-

formation items and spend less time on each item. Su-

perficial information processing could have explained

inferior performance. However, the results in Study 1contradicted most of our expectations (and findings re-

ported by others). Subjects in pleasant and neutral

moods reviewed more information items. The only re-

sult that was consistent with the literature was that

pleasant mood seemed to induce a quicker processing of

each information item. While quick processing may in-

dicate efficiency, the results in terms of accuracy sug-

gested that it indicated rather superficial processing. InStudy 2 pleasant mood group subjects reviewed similar

amounts of information as the other two groups but

apparently spent more time in making the decision.

While there is some indication that their accuracy im-

proved, their performance remained the worst. It seems

that the good mood prevented them from using the in-

formation correctly and deriving unbiased conclusions

on the basis of that information. The sources of biasmay be increased confidence and perhaps illusion of

control.

The information processing and judgment modes of

people in an unpleasant mood seem to reflect the phe-

nomena of mood as information. A more pessimistic

outlook induces careful examination of information

pieces. As opposed to other studies, we have found that


neutral mood, at least in trading currencies, leads tosuperior performance compared to pleasant or un-

pleasant mood.

To conclude, this study adds support to the propo-

sitions that moods influence information processing,

accuracy in decision, and performance. The study also

corroborates a contingency theory of risk-taking that

was proposed in the paper to reconcile the conflicting

predictions found in the literature about the impact ofmood on risk preferences. The ambiguous currency

trading market, where probabilities are not given ex-

plicitly, allows the priming effects of a pleasant mood

(and the confidence it generates) to dominate. The

subjects in the ‘‘pleasant mood’’ group were confident

enough to acquire a lot of information and spend time

on thinking about them but without paying attention to

the usefulness of information inputs. They also tendedto ignore changes in their market environment. The

combination of bad predictions and high risk-taking is a

prescription for disastrous market performance. The

subjects with unpleasant mood appear to have less

confidence, which was reflected in the tendency to pro-

cess news in an intuitive way while taking small risks.

Taking smaller risks could, however, result in modest

returns. Neutral mood seems to inculcate a good com-

Fig. 2. ‘‘Explanation’’ webpage in t

bination of information processing strategies and a tol-erance of risk that leads to success in the complex

situation of currency trading. Researchers in finance and

economics may have plenty to learn from successful

practitioners who emphasize the importance of main-

taining a neutral mood in a turbulent markets

(Hagstrom, 1994; Schwager, 1992) and behavioral re-

searchers who found people in bad mood to be smarter

(e.g., Forgas & George, 2001; Sinclair, 1988).

Appendix A

In the experiment, subjects were required to simulate

foreign exchange traders to speculate based on real price

movement and news. Compared to real trading, the task

was simplified to let subjects analyse and predict, at

market turning points, the long-term (next 6 months)

movement of the currency pair Deutsch Mark and Swiss

Franc, starting from January 1996 (see Fig. 2).As the graph shows, subjects were ‘‘brought back’’ to

January 26, 1996. The Deutsch Mark (DEM) had

demonstrated a prolong period of depreciation against

Swiss Franc (CHF) and moved side-ways in a zigzag

manner in the past 3 months. Each subject then had to

he Internet trading platform.


decide if Mark would continue to depreciate againstSwiss Franc (i.e., DEM/CHF would fall), or Mark

would reverse the downward trend and appreciate

against Swiss Franc (i.e, DEM/CHF would rise). If he/

she predicted DEM/CHF would fall further, she would

‘‘Sell’’ DEM/CHF (i.e., convert her money from Deut-

sche Mark to Swiss Franc); if DEM/CHF would rise, he/

she would ‘‘Buy’’ DEM/CHF (convert Swiss Franc to

Deutsch Mark). Just like real traders, subjects had totake a position either to ‘‘sell’’ or to ‘‘buy’’ by placing

their order in the Take Order web page illustrated in the

next page (see Fig. 3).

Subjects have three tools to help them, as in the real

world, maximize profit, and reduce loss.

1. Investment size: It could vary from 100,000 Mark to

1,000,000 Mark, as subjects decided how much they

wanted to invest in the currency contract.2. Cut-loss order: If one placed a cut-loss order and

DEM/CHF moved against this prediction and

reached the cut-loss point, the cut-loss order would

be executed so that the loss would be limited to the

preset level without further losses. If the DEM/

CHF went in line with the prediction, the cut-loss

Fig. 3. ‘‘Take Order’’ webpage in t

order would not be executed. However, the cut-lossorder is a double-edged sword. If a subject, being

too conservative, set a cut-loss price too close to the

starting price, there was a chance that the cut-loss

price would be hit due to the short-term volatility

of the market. She or he would then suffer a small

loss, instead of making a profit, even though her or

his prediction of the long-term direction was correct.

On the other hand, the farther the cut-loss price wasfrom the starting price, the greater the potential loss.

In the experiment, subjects could decide on using it or

not.

3. Take-Profit Order: Similar to the concept of the

Cut-loss order, the take-profit order allowed traders

to secure their profit when DEM/CHF reached their

preset price. In this way, even though DEM/CHF

moved against the prediction after the take-profit or-der was executed the subject�s profit would not be af-

fected. You can imagine that if a subject, being overly

conservative, sets a take-profit price too close to the

starting price, he/she may only get a small profit even

though the actual profit potential may be much great-

er. Being too aggressive and setting the take-profit

he Internet trading platform.


price too high for the DEM/CHF, would mean thatthe take-profit point could not be executed. Subjects

could decide on using it or not.

Basically, the profit or loss is calculated by the fol-

lowing formula:

Profit ðlossÞ ¼ Decision� ðExecution Price

� Starting PriceÞ � Investment

� Leverage ratio

ðfixed at 20 in this simulationÞ:

The execution price would be adjusted if the subjects

chose to use cut-loss or take-profit order, but we do not

want to get into the calculation details here.

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Mood in foreign exchange trading: Cognitive processes and performance

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