Forex Traders psychology. A Behavioral Finance research paper

Research Paper (undergraduate), 2016

18 Pages, Grade: 90%

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Introduction.. 4

Literature Review.. 5

Methods.. 6

Description of target population and sampling design.. 6

Recruitment.. 7

Trading Emotions Stability Measurement.. 7

Measuring Trading Stress Measurement.. 8

Variable definitions.. 9

Data Analysis.. 10

Results.. 12

Trading Emotional stability versus trading behavior.. 12

Trading Emotions Stability Index Validation.. 13

Mood versus Trading Behaviors.. 14

Stress versus Trading Return.. 14

Discussion and findings.. 15

Recommendations.. 16

Bibliography.. 17

Background: Psychological factors like emotion, mood, stress and personality interfere with trading behaviors, stable emotions leads to good trades. The research study how stable emotions, mood and stress affect trading behavior. Methods: Traders were randomly selected over 2 questionnaires (N=50) each in 39 different countries, Trading emotion stability Index TESI and trading Stress index TSI where used to measure both Emotions and stress and compare them with different factors within sample. Results: traders with low TESI are more confidence and more risk takers, traders with good mood reflect stable and confident traders, there is significant correlation between stress and traders rate of return. Conclusion: The more stable trading emotion and the better mood they have the more confidence decision traders make and the more risk they take, decreasing trading stress can play role in increasing a traders return. Over 40 countries around the world, forex traders faces same difficulties, same challenges and emotional biases. What make one traders different of others is his personality and psychology

Keywords: Forex, Trading, Psychology, Emotions, Stress, Mood, Cognition.

“They are not only hurt your account, but they also cause a mental and emotional suffering. No other profession test you psychology as does trading ” (Dayton, 2015)


Today world tend to focus more on psychological factors that contribute to market and world economy, in other words they tend to focus on what called behavioral finance or behavioral economy. However, behavioral finance is a subtopic of the broader subject of behavioral economics (Burton & Shah, 2013). There has been an assumption in finance claims that efficiency is the heart in every financial modeling strategy. However, efficient market hypothesis (EMH) was developed in 1965 and remained under debated for 40 years, the debate issue is whether the market is fully efficient according to that hypothesis (Tuyon & Ahmad, 2016). Recent critics of Efficient Market Hypothesis argue that investors are generally irrational, exhibiting a number of predictable and financially ruinous biases such as overconfidence (Fishcoff and Slovic, as cited in Andrew, Dmitry, & Brett, 2015). Neoclassical theory as named by Szyszka (2013) assumes that large majority of decision makers behave rationally and they know how to interpret incoming information while on the other hand, when irrational behavioral occurred, people take corrective action immediately, hence neoclassical theory largely ignored in real life. The rationality of financial market has been one of the most hotly contested issue in the history of modern financial economics (Andrew et al., 2005).

This study is intended to focus on currencies (FOREX [1]) trader’s psychological factors, Mood and Stress that would play role in trading patterns, profit and loss and risk factors. There are a plenty of studies state the relationships between trading and emotions or psychology in general, but most of them are focusing on equity trading or stocks trading. However, few are focusing on FOREX trading psychology. according to Mr. Labwam, a financial analyst and FOREX trader at the national bank [2], FOREX trading consider to be the most complex trading amongst all the financial markets and the amount of efficiency in this market is very low comparing to stock market, emotions always play the major role in that kind of trading. Since the FOREX market consider the largest and most accessible financial market in the world (Stammers, n.d) also considered being the most efficient market in the world. Many studies conducted on trading psychology. As mentioned above, psychology in finance is a subtitle for behavioral finance. Behavioral finance studies the application of psychology to finance, with a focus on individual level cognitive biases (Hirshleifer, 2014). Behavioral finance is nascent field developed over the past 40 years, examines observed financial behavior and involved in application of psychology to finance. Individual investors’ behavioral and biases is consider to a subtitle in behavioral finance (Perren et al., 2015). Many researches connected behavioral finance with irrational behavioral or acts. Chaudhary (2013) reveals that behavioral finance is a new field that seeks to combine behavioral and cognitive psychological theory with economic and finance to provide explanations for why people make irrational financial decisions. According to Dr. Van Tharp as cited in Using APA (2013) trading process is breaking into three categories that affect traders: trading strategies (10%), money management (30%) and psychology (60%). Most traders lose their money when attempt FOREX trading (Fuller, n.d.). A group of scientists believe that when it comes to money, people start act irrationally (Clark, 2010) .

Literature Review

Charles and Kasilingam (2015) stated that emotions and mood are so related, but mood consider being less intense. Mood and emotions could be positive (pleasant) or negative (unpleasant). According to Charles and Kasilingam (2015) emotions are feelings about particular that arise from cognitive appraisals of circumstances, while moods are more generalized non-specific state. However, mood are not affected by a personal believe while emotion do. An overall conclusion of this study reveals that investor’s emotions matured over a period on their investment life cycle. The study suggested future research should focused on other psychological factors like mood, heuristics and investors’ personality because they excluded from this research (Charles & Kasilingam, 2015). Ward (2015) explains how emotions like stress and pressure can affect trading performance; Emotions, mood, thoughts, believes, perception, attention, mental short cuts, energy levels, the environment you are in, past and recent performance and your hormones. Also to outsider factors that could play part in trading decision like changing in market conditions, regularity changes, institutional resections. In his book, Ward (2015, P.7) has translated this psychological term into behaviorally of trading and being aware of your own thoughts, emotions, physical sensations and other acts. Dayton, (2015) brings psychology and trading together is commonly stating some psychological trading behaviors with the study of (Ward, 2015). In that regard, Ward and Dayton (2015) are describing these behaviors in trading that mostly make traders lose his money; first: taken profit too early or cut winning trades even the target profit wasn’t hit. Second, when the market is suddenly start moving, traders taking unplanned traders or taking trades that not of their strategy. Third, taking too much risk by trading in very big position size even more than your money management can handle. Fourth, revenge trading, or chasing loses trade by becoming more aggressive after losing money. However, Dayton (2015) added even more trading behaviors that most traders do; first, traders failed to pull the trigger on perfectly good trade because of fear of loss. Second, for losing trades, the trader is hoping only for return to break even. Third, adding to a losing position in the hope that market will turn around, or what called “averaging”. Fourth, stopping trading or reduce position after loss. According to Dayton (2015), emotions can be a significant challenge for traders, (cognitive biases) and emotions can cause loses. Fear and its various forms, stress, boredom and emotional hijacking are discussed and explained in a way how they affect traders’ account. O'Creevy et al., (2010) reported that emotions and their regulations play a central in trader’s decision making. The study tests wheatear emotions play a significant role in decision making in financial trading. It is also asking whether traders themselves are aware or understand of emotions’ effects on their decision- making performance. Emotions can bias decision making as example of these emotions are fear and anger which have opposite effects on risk perception. A laboratory study of decision making under risk found that low levels of emotions experience led to higher levels of performance through greater risk (O'Creevy et al., 2010). Andrew et al., (2005) investigate several possible factors links between psychology factors and trading performance using daily emotional survey over a five weeks period. The study found that traders whose emotional reaction to monetary gains and losses was more intense on both positive and negative sides exhibit significantly worse trading performance. The study shows that there is a small negative correlation with self-reported personality and actual trading performance. Older subjected tend to perform worst and woman tend to trade less than men, however, daily performance is highly positively correlated with pleasant and highly negatively with unpleasant (Andrew et al., 2015). The study recommended that traders should keep their emotions stable and stay open to new experiences.


The expected study duration is from 10 April to 27 May of the year 2016. The study based on collection of secondary data. The study will take place world widely involves sample of dealers around the world using online survey tool. Two questionnaires will be used in this study. As stated before, both questionnaires will be published online targeting the practiced Forex traders around the world using of the best social trading platforms [3]. Each of the two questionnaire (S1 and S2) will be used for specific purpose as follows:

S1 (Sample Group 1): designed for measuring traders Emotions and mood and some trading behaviors using 3 case scenarios.

S1 (Sample Group 2): designed for measuring Stress Using stress Index. It also asses each object rate of return in addition to other trading behavior.

Description of target population and sampling design

The study conducted a random sample. Total sample size (n = 100) for both questionnaires. As of S1 and S2 (n=50) sample size each. As mentioned all data were randomly distributed using online platforms. The data were collected of 39 countries. Most of them where in England and USA (13.1% and 10.1 % respectively). However, 14.1% of the data were collected from unknown countries (see chart 1). Online data collection tool was kwiksurvey [4].

[Figure is omitted from this preview]

Chart 1 data collection diversity


As mentioned above, data were collected using two questionnaires. S1 was designed to measure trading behavior through creating 3 separated trading scenarios; Profitable Scenario, loss Scenario and confusing scenario. We could measure objects or traders behaviors and some trading psychology through these three scenarios. S1 also assesses trading emotions stability through directly asking each object 4 questions. Simple Trading emotions stability Index (STEI) is creating by adding up the weight of these 4 questions. It also asks object directly of Mood affect their trading performance. The other questionnaire S2 where designed to measure stress stability and trading returned through the object trading life it also describe some trading behaviors.

Trading Emotions Stability Measurement

Four questions were used to assess emotion traders’ emotions stability. These four questions was collected from literature reviews and the object answers for each is given a weight to come up with Trading Emotion Stability Index (TESI). The questions asked for each object to measure TESI are; A- if the object (i.e. trader) become more aggressive after losing money, B-if traders stop trading after losing money, C- if traders’ strategy affected by other traders’ opinion and D- of they adding to their loosing position. However, object answers for each of the 4 questions will be limited to: No, never (which given a value of 0), Yes, Some Times (Which given a value of 1) and Yes, Usually (which given a value of 2). TESI total score is given by adding the sum of the answers of the questions (see equation 1)

[Formula is omitted from this preview]

Total score of TESI is then assigned to a specific interval to determine each the ordinal level of TESI as the following (see table 1 below).

[Table is omitted from this preview]

Table 1

Measuring Trading Stress Measurement

We used 20- question trading stress test that first used by (Farrell, 2012) by asking traders 20 questions, answers for each question will be either Yes or No ( scored as 0 and 1 respectively). According to (Farrell, 2012) if the summation of “ Yeses” exceed 6, then then at least right now day trading and market timing are probably too stressful and risky for traders and making traders prone to errors. However, our study uses the same trading stress measurement tool but in form of index on which summation of answers ( yes or no) will be given a value of 0 to 8 and assigned to Trading Stress Index ( TSI) as the following equation:

[Formula is omitted from this preview]

Score of 20 will be assigned to ordinal scale of TSI as follows:

[Table is omitted from this preview]

Table 2

Variable definitions

For emotions sample questionnaire (S1) that studied the relationship between emotions and some major trading behaviors, we used the following variables:

Scenario 1 (pendent variable): this was defined as profitable scenario in which objects are exposed to profitable position and the price moved on their favor but it will continue giving more profit according to their solid market analysis. Therefore, they have to choose between five options. However, each option is consider to be objective respond behavior of traders to this scenario see table (3).

[Table is omitted from this preview]

Table 3

Scenario 2 (dependent Variable): in this scenario object were experienced losing position on which the priced moved against them. They had to choose one of four choices that most likely represent their decision as table (4) shows.

[Table is omitted from this preview]

Table 4

Scenario 3 (dependent variables): this confusing scenario on which objects are exposed to profitable position but the price suddenly moved against them to the breakeven point. Object have to choose one of 3 options as table (5) shows.

[Table is omitted from this preview]

Table 5

Emotion Self-Estimation effect (Independent Variable): on which objects are asked if emotions are affect their trading performance (0 = Strongly Not Agree, 1 = Not Agree, 2 = Agree, 3 = strongly not agree).

Trading Emotions Stability Index TESI (Independent Variable): which is combination of 4 questions (0= Constant Trading Emotions, 1= Low Trading Emotions, 2= High Trading Emotions, 3= Very High Trading Emotions).

Mood self-Estimation (Independent Variable): on which objects are required to state if the mood affect their trading performance (0 = Strongly Not Agree, 1 = Not Agree, 2 = Agree, 3 = strongly not agree).

For emotions sample questionnaire (S2) that studied the relationship between Stress and some major trading behaviors and trade return, we used the following variables:

Trading Stress Index TSI (Independent Variable): on which 20- questions are added up to create TSI (0= trading is not risky and stressful, 1= trading is risky and stressful, 3= trading is highly risky and stressful).

If object are making profit or loss (Dependent variable): object are required to answer if they made profit or loss since they start trading (0 = loss, 1= Profit).

Rate of Return (Dependent Variables): on which object are required to state their rate of return on their initial investment since they start trading (0 = Negative Return, 1= 0-5%, 2 = 6-10%, 3 = 11-15%, 4 = 16- 20%, 5 = over 20 %).

Data Analysis

Data set of both questionnaires (N=100) were analyzed with SPSS 20 (Statistical Package for the Social Science). Microsoft excel were used to for calculating Emotions and Stress Indexes (TESI and TSI) using Vlookup function (=Vlookup (score summation, table array, column index, approximate match) in order to assign each index score to its appropriate level. We used Cross Tabulation adding chi-square to compare each scenario object behavior with TESI. Pearson Correlation was used to determine if TESI comparable with self-emotion estimation. Pearson, spearman and chi-square correlation were added to analyze correlation between mood and each scenario (see table 6). Crosstabs with chi-square and Pearson Correlating is used also to compare relationship between Trading Stress Index (TSI) and Profit or loss or trading return (see table 7)

[Tables are omitted from this preview]

Table 6, Table 7


As for first sample group S1 which design to measure emotion stability and link it to trading behaviors, 2 missing values for all variables except for the second scenario variable which has 3 missing values. No missing value for the second sample group S2 (see table 6-7). Starting with S1, most of the sample (42%) choose to place both stop loss and take profits orders (Both Fear and Greed are equal (Confident)) in the first scenario (profitable scenario), while (27%) choose to close their position at current price and book their 70 pips profits (Pleased Emotion Exceeded Fear). Moving to the second scenario on which objected where exposed to loss position, most of sample (24%) choose place stop loss order which explained as Confident ( Less Fear and Greed). As for the third scenario most of the sample (25%) choose to place stop loss orders at somewhere and take profit at 1.465 which explained as (Confident (low Fear and Low Greed)). However most of the sample (50%) are emotionally stable from trading point of view and mostly agreed that emotion and mood affects their trading performance. shifting to stress, most of S1 sample (62%) recorded high stress emotions while the minority (30%) succeeded the stress test.

Trading Emotional stability versus trading behavior

Crosstabs results shows that the more stable emotions in the Trading emotional stability index (TESI), the more the decision was more stable and rational. Traders with constant trading emotions (Zero TESI) was more confident and their fear and greed were equal so they are willing to place rational stop loss and take profit at the intended point.

In the second scenario, where the object are expose to loss case, the more confident traders who choose the best decision where the traders whom have more constant emotion. TESI shows that traders where more confidence and choose to the best option when their emotions where more stable (see table chart 2). This applied also within the third scenario as well. However, people with low TESI tend to be more risk takers and sometimes irrationals (see chart 3).

[Figures are omitted from this preview]

Chart 2, Chart 3

Trading Emotions Stability Index Validation

To validate TESI, we find significant medium correlation between TESI score and what people answer effect of emotion on their performance. Table (8) shows Pearson correlation between TESI and emotional self-rating of 0.492 with significant value (P<.01). Which means TESI can be validate to the whole test.

[Table is omitted from this preview]

Table 8

Mood versus Trading Behaviors

[Figure is omitted from this preview

Chart 4

In line with results in chart (4), the more traders realize there that mood affect their trading performance, the more they become more confident and the more they become emotionally

Stable. However, Mood is also correlated with TESI; Results shows that trading emotion stability Index and Mood effect realization are significantly correlated (P<0.01) by 0.474 See table (9)

[Table is omitted from this preview]

Table 9

Stress versus Trading Return

Pearson correlation between TSI and Rate of Return shows a negative significant relationship between Stress Index in trading and rate of return (P<0.01) See table (10). Traders whom make more profits end to have less trading stress and trading is not risky and stressful for most of subjects who make profits.

[Table is omitted from this preview]

Table 10

Discussion and findings

In line with results above, our study shows that the more trading emotion stability the more likely they make a stable and confidence decision in their trades. However, the more they were emotionality stable the more they became more risky in their trades and the more they behave irrationally. On the other hand, being irrational and risky does not mean always bad thing. In trading levels of emotions experience led to higher levels of performance through greater risk (O'Creevy et al., 2010). However, irrational behavior means sometimes to go against the majority which led to price moves in your favor. No matter whether trader are having a smart trading system, if emotions start to interfere their trading, like felling upset, frightened or greedy their battle will be lost (Elder, 2015).

The more be aware of their mood fluctuations the more likely they made good decisions and the more they became confident and rational in their trading decision. On the other hand stressed within stress stability measure, the more stable stress the more they become they made profit. Stress stability index and rate of their return in their initial investment is positively correlated.

Over 40 countries around the world, forex traders faces same difficulties, same challenges and emotional biases. What make one traders different of others is his personality and psychology. When traders are more aware of their emotion cognition or biases, they become more confident about their decision.


Forex traders should be aware of their emotions, mood and stress cognition in order to be able to overcome their fear and greed and make best decisions. The study highly recommend forex traders to do stress test, results of the test can work as guide if they have to keep going with trading and if their trading habits are risky. It’s better of all forex traders to control their mood when they trading in the market.

Future studies should be focused on 14 emotional swings on orders to study in details who physiology affects trading. However, future studies should use observation for some period of time to study traders’ psychology. Doing observational study might be more accurate than asking traders about their emotions because its studies emotions within its natural environment. Future studies should also focus on personality of traders.


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[1] FOREX or FX : Foreign Exchange referrers mostly for currency trading

[2] The National Bank: Palestine, Ramallah



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Forex Traders psychology. A Behavioral Finance research paper
Birzeit University  (Business and economics)
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ISBN (Book)
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Behavioral Finance, Finance, Forex Trading, Forex Traders physcology
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Qais Sbaih (Author), 2016, Forex Traders psychology. A Behavioral Finance research paper, Munich, GRIN Verlag,


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