Bridging personality and behavior in financial trading: A qualitative perspective

1. Introduction

Behavioral finance has renewed the understanding of financial decision-making by demonstrating that investors could depart from the predictions of perfect rationality. Since the foundational work on heuristics and prospect theory (Tversky & Kahneman, 1974; Kahneman & Tversky, 1979), research has documented patterns such as loss aversion and reference dependence, the disposition effect (Shefrin & Statman, 1985), overconfidence and excessive trading (Odean, 1999; Barber & Odean, 2001), and escalation of commitment (Brockner, 1992). These regularities are often framed as cognitive “errors.” A large body of work suggests that they are deeply linked to emotional reactions and self-regulatory processes under uncertainty and performance pressure (Statman, 2014; Statman, 2019).

A complementary stream highlights that emotions constitute a central dimension of trading experience. Lo and Repin (2002) showed that even experienced traders exhibit strong emotional responses to market fluctuations. Similarly, the capacity to regulate affects distinguishes individuals who can execute a strategy consistently from those whose emotions destabilize discipline and result in reactive decisions (Fenton-O’Creevy et al., 2011; Fenton-O’Creevy et al., 2012). These findings call into question the assumption of a fully rational trader and instead depict decision-making as being shaped by complex emotional dynamics (Finet et al., 2025a, b). Accordingly, affective signals can either distort or support judgment depending on regulation and context (Statman, 2014).

In parallel, personality psychology provides a dispositional lens on financial behavior. The Big Five (OCEAN) model captures stable individual differences in openness, conscientiousness, extraversion, agreeableness, and neuroticism (Costa & McCrae, 1999; John et al., 2008). Prior work links conscientiousness to planning, self-control, and cautious behavior, whereas neuroticism can be associated with anxiety and impulsiveness, traits that shape responses to gains, losses, and uncertainty (Durand et al., 2008; Brown & Taylor, 2014). Evidence also associates extraversion (and, in some contexts, neuroticism) with stimulation seeking and risk-related tendencies (Lauriola & Levin, 2001; Oehler et al., 2018). Recent studies suggest that personality interacts with behavioral biases and risk tolerance, influencing how biases are expressed across individuals (Akhtar et al., 2018; Sarwar et al., 2020; Singh et al., 2023).

Despite these developments, literature suffers from several shortcomings. First, research linking behavioral finance and personality often remains fragmented: biases are generally studied without considering how personality traits may modulate them, while research on personality is frequently limited to correlations between traits and attitudes toward risk without analyzing trading behavior (Akhtar et al., 2018). Second, most empirical studies rely on quantitative methods. These approaches may overlook the lived dimension of decision-making, including tensions between rational intentions and emotional reactions, and post-hoc rationalizations (Edmondson & McManus, 2007; Saldaña, 2014). Third, conventional views of trading styles tend to oppose rational investors and emotional traders, or compulsive speculators and disciplined investors, underestimating intermediate styles that may better reflect real-world behavior under pressure (Baker & Nofsinger, 2002; Pompian, 2006; Statman, 2004; Statman, 2014).

Given these limitations, our study combines the contributions of behavioral finance and personality psychology by linking personality traits to trading styles and decision processes. We adopt an exploratory qualitative approach based on narratives of students involved in a stock market simulation to examine the subjective dimension of financial decision-making. Our research question is: How do personality traits and emotional regulation patterns influence decision-making styles during simulated trading situations? Methodologically, we follow qualitative recommendations for theory development and transferability by focusing on configurations rather than testing causal relations (Edmondson & McManus, 2007; Guba & Lincoln, 1994; Yin, 2018; Larkin et al., 2019). Empirically, eight second-year management students participated in a three-day stock market simulation using the ABC Bourse platform (French companies included in CAC40 index; €100,000 virtual capital), with performance rankings making social comparison more prominent (Nofsinger, 2005; Lerner & Tetlock, 1999). Participants completed a Big Five inventory before the simulation and were interviewed just after it. Transcripts were analyzed thematically through an iterative coding process (John et al., 2008; Saldaña, 2014). In addition, we considered participants’ daily number of transactions (buy and sell orders) over the three days as a behavioral indicator of trading intensity to complement and triangulate the qualitative profiles.

While many studies analyze relationships between personality traits and single variables (Akhtar et al., 2018; Sarwar et al., 2020; Singh et al., 2023), without translating them into decision-making patterns, our work proposes to connect personality profiles to trading styles through a typology of decision-making registers. Specifically, we identify four decision registers (rational-analytical, intuitive-emotional, social, and more marginally compulsive or defensive/stoic) while showing that participants most often exhibit hybrid profiles combining dominant and complementary registers. Emotions appear less as irrational responses than as adaptive signals regulated (rather than suppressed) even among analytical profiles, aligning with evidence on emotion regulation in trading (Lo & Repin, 2002; Fenton-O’Creevy et al., 2011; Fenton-O’Creevy et al., 2012). By linking traits, emotions, trading behavior, and context, our study contributes an integrative typology to behavioral finance and offers practical implications for simulation-based training and investor education (Markowitz, 1952; Kahneman, 2011; Statman, 2014).

The remainder of the paper is organized as follows. Section 2 reviews the literature on behavioral biases, emotions, and personality in financial decision-making. Section 3 details the research design and qualitative methodology. Section 4 presents the findings and the resulting typology of decision styles. Section 5 discusses theoretical and practical implications. Section 6 concludes, and Sections 7 and 8 outline limitations and avenues for future research.

2. State of the art

2.1. Types of trading behavior

Investor typologies seem to present some limitations. Many of them are based on general psychological classifications (Pompian, 2006; Baker & Nofsinger, 2002), describing investors through stable traits such as conservatism, confidence, conformity, or independence. Others are based on investment styles (Barberis & Huang, 2001; Statman, 2004), identifying cautious, aggressive, or speculative profiles. These approaches classify individuals without considering the dynamics of financial behavior. Pompian's typology (2006) distinguishes four investor profiles (Preservers, Followers, Independents, and Accumulators) based on psychological traits and risk tolerance. However, this classification does not consider the influence of context and emotional regulation. Barberis and Huang (2001) developed a typology based on risk appetite and time horizon, leading to conservative, cautious, and aggressive styles. However, by focusing on risk tolerance, it overlooks emotional and social dimensions. Finally, Statman (2004) links investment styles to individuals' behavioral preferences. However, this model does not consider the potentially contradictory nature of behavior.

According to these limitations, we built a typology including four decision-making profiles: rational-analytical, intuitive-emotional, compulsive, and stoic. Each of these categories was based on financial literature. The rational-analytical refers to the classical economic agent model and the optimization principle of efficient portfolio theory (Markowitz, 1952). The intuitive-emotional category is derived from decision psychology: Kahneman (2011) distinguishes between System 1, which is fast and intuitive, and System 2, which is slow and analytical. The compulsive register is based on disposition effect (Shefrin & Statman, 1985), excessive trading (Odean, 1999), and escalation in commitment (Brockner, 1992). Finally, the stoic can be linked to work on emotional regulation (Fenton-O'Creevy et al., 2011) to preserve the investor's identity consistency (Langevoort, 2004). This typology provides a structure for understanding the range of decision-making styles, with each register supported by prior research. It offers a common language for connecting personality traits with decision-making styles, distinguishing investors who use analytical control, those who rely on intuition and emotion, those who develop self-perpetuating decision habits, and those who regulate their emotions. Finally, this typology remains open to incorporate hybrid profiles and contextual effects and is therefore a useful tool both for conducting deductive work in the future.

2.2. The OCEAN model for understanding trading behavior

The Big Five model, or OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism), provides a framework for studying individual differences in personality psychology (Costa & McCrae, 1999; John et al., 2008; Borghans et al., 2008; Durand et al., 2008), unlike approaches such as the MBTI, which have been criticized for their lack of scientific validity (Pittenger, 2005), or alternative models such as HEXACO (Ashton & Lee, 2007), the Big Five offers a relevant model for analyzing financial behavior. Personality traits can generate cognitive biases. For example, high levels of neuroticism increase sensitivity to losses and support risk aversion, reinforcing disposition bias and escalation in commitment (Shefrin & Statman, 1985; Mayfield et al., 2008). Conversely, high conscientiousness, by improving planning and self-control, reduces the propensity for excessive trading (Durand et al., 2013). High extraversion is often accompanied by increased risk-taking, which drives compulsive behavior (Oehler et al., 2018). Openness can stimulate the search for new opportunities, with greater sensitivity to weak signals and intuition, linking it to emotional trading (Kumar & Goyal, 2016). Finally, agreeableness, by increasing receptivity to external influences, can result in emotional contagion (Corgnet et al., 2018).

2.3. OCEAN and its links with the selected typology

Openness refers to an individual's attitude toward novelty, imagination, and complexity. A high score characterizes curious and creative people (John et al., 2008). Conversely, a low score indicates a tendency toward routine. In the context of trading, high Openness can encourage innovative strategies and may result in dispersion, while low Openness may translate into a more traditional style (Durand et al., 2008).

Conscientiousness reflects the degree of self-control. High Conscientiousness shapes rational-analytical profiles, supporting discipline (Brown & Taylor, 2014).

Extraversion is reflected in high energy levels and a search for stimulation. A high score is associated with individuals who are dynamic and highly stimulation-seeking. Oehler et al. (2018) have established a link between high Extraversion and a propensity for risk-taking, associated with emotional trading. In contrast, Introversion involves a tendency to renew energy through withdrawal and reflection.

Agreeableness reflects interpersonal patterns. A high score indicates empathetic individuals, while a low one suggests skeptical individuals. In trading, high agreeableness could make a trader vulnerable to external influences. Conversely, a low level can induce stoicism by enabling traders to take objective decisions without being influenced by relational considerations (Durand et al., 2008).

Finally, a high level of Neuroticism is indicative of pronounced sensitivity to anxiety and stress. High Neuroticism can predict compulsive behavior (impulsivity and difficulty accepting losses, Oehler et al. (2018; Durand et al. (2008). Conversely, low neuroticism corresponds to a stoic attitude (Fenton-O'Creevy et al., 2011).

The value of this test lies in its ability to link personality traits to the four selected decision-making registers. Each decision-making register can be associated with traits from the OCEAN model (Table 1).

Decision-Making Style Associated OCEAN Traits Key Characteristics
Rational-Analytical High conscientiousness, low neuroticism Discipline and planning; reliance on data and indicators; effort to exclude emotions; ability to maintain a coherent strategy.
Intuitive-Emotional High extraversion, high openness, moderate to high neuroticism Quick decisions guided by instinct; strong reactivity to market signals; marked emotional sensitivity; search for stimulation; valuing intuition as a key resource.
Compulsive High neuroticism, high extraversion, low conscientiousness Difficulty disengaging from losses; repetitive and impulsive behaviors; overtrading; high emotional intensity; tendency toward escalation in commitment.
Stoic Low agreeableness, moderate to high conscientiousness, low neuroticism Detachment from gains and losses; effective emotional regulation; ex-post rationalization; long-term vision; insensitivity to external pressures.
Table 1.Correspondence between the selected trading typology and personality traits. Note: O = Openness; C = Conscientiousness; E = Extraversion; A = Agreeableness; N = Neuroticism (OCEAN Model). Each decision-making style corresponds to the dominant combination of traits from the Big Five framework.

3. Methodology

3.1. General methodological perspective

Our research follows an interpretative approach aimed at capturing how individuals construct meaning in relation to their financial decisions. Financial behaviors are understood through narratives. This exploratory posture is consistent with aligning the degree of theoretical maturity with the selected empirical approach: when prior work needs revision, qualitative studies based on a limited number of observations are more suited to proposing provisional configurations than to statistically testing hypotheses. (Edmondson & McManus, 2007). From this perspective, we use a small-sample design to examine the alignment between decision-making registers and personality traits, identify regularities and deviations, and assess the consistency of the profiles. This approach aligns with qualitative research guidelines that underscore the importance of individual comparisons for strengthening constructs and their interrelations prior to undertaking large-scale confirmatory assessments. (Saldaña, 2014). This methodological choice is also consistent with recommendations to complement the quantitative approaches (Fenton-O'Creevy et al., 2012; Statman, 2019). It is in line with economic sociology (Bourdieu, 2016) and phenomenological psychology (Varela, 1996), which emphasize the importance of lived experiences and subjective representations in financial practices. This inductive orientation is supplemented with data from the OCEAN test, which provides a quantitative perspective. It provides a psychometric framework for subjective narratives and emotional dynamics and brings two complementary approaches: analysis of discourse resulting from lived experiences and quantitative measures of personality traits.

3.2. Methodological protocol

The methodological approach comprises five steps, from participant selection through to the construction of new registers in the typology.

3.2.1. Selection and sociodemographic characteristics of participants

The experiment was conducted with a sample of eight students enrolled in Management Sciences at the University of Mons (Belgium). The sample size (eight participants, including seven men and one woman) and the duration of the simulation were determined both by financial constraints (the students were paid) and by the intensive nature of the qualitative analysis. Although the use of student samples is sometimes criticized because of potential differences with the behavior of professional traders (Harrison & List, 2004), this choice remains widely validated in experimental finance research (Fréchette, 2011). Calls for applications were sent in November 2024 through the institutional channels to the student community. The conditions for participation required prior successful completion of two financial courses – Introduction to Financial Reality and Stock Markets – to provide a minimum of knowledge. Candidates were required to submit a letter of motivation explaining their interest beyond compensation. Nine applications were received, and the final selection of eight students was made by the three authors based on the applicants' letters. None of the participants had significant trading experience, which meant that their “raw” behavior (similar to novice investors) was analyzed. Using a student population also offers a methodological advantage in terms of sample homogeneity: it limits the influence of external variables such as age, professional experience, or investment habits (Harrison & List, 2004). The sample consisted of individuals aged 19 to 25. The overrepresentation of men reflects a general trend in trading (Barber & Odean, 2001). Men are more likely to take risks and overtrade, while women prefer a more cautious and diversified approach (Croson & Gneezy, 2009). This observation may carry emotional consequences, as it can encourage competition and intensify impulsive behavior.

It should also be noted that a small sample size has several advantages from a qualitative perspective. It facilitates longitudinal analysis of individual trajectories (Yin, 2018) and helps to build trust with participants. A sample of eight participants does not provide a statistically representative sample of novice traders' behavior, but our objective was transferability rather than generalization (Guba & Lincoln, 1994). By documenting profiles, personality traits, and the context of the study, other researchers can assess the relevance of the results in comparable situations. Finally, this size is consistent with qualitative research standards (Larkin et al., 2019).

3.2.2. Organization and experimental design

Participants took part in a stock market simulation using the ABC Bourse platform. Each participant was provided with a virtual capital of €100,000, which they could invest exclusively in shares of companies listed on the CAC40 index. This restriction was designed to avoid the use of heterogeneous financial products and to ensure that the behaviors reflect participants’ decision-making style rather than product characteristics.

The simulation took place over three days (January 27-29, 2025). There were no restrictions on the volume or number of transactions, giving students complete freedom in managing their portfolio. To simulate the social and competitive pressure of financial markets, individual performances were displayed. It was made to intensify the emotional and social dynamics between participants. The experiment was conducted in the context of a slight downward trend (see Table 2). This climate can be explained by several pieces of information, including the announcement of DeepSeek's arrival in the Artificial Intelligence sector and the publication by LVMH of results that fell short of expectations.

Index 01.27.2025 01.28.2025 01.29.2025 Change
CAC40 -0.0003 -0.00012 -0.0032 -0.0036
Table 2.Change in the Index over the Three Days of the Experiment . Note: The table reports daily variations of the CAC40 index during the simulation period. Negative values indicate market declines; the data contextualize the trading environment.

This gap between the objective degree of price movements and their subjective interpretation illustrates a well-documented mechanism in behavioral finance: market perceptions are shaped as much by lived experience as by market data. Under these conditions, some biases may have been activated. For example, disposition effect (Shefrin & Statman, 1985) may have been strengthened, with participants closing winning positions prematurely and keeping losing stocks in the expectation of a rebound.

3.2.3. Data collection

Three sources were selected:

Personality traits were measured using the Big Five (OCEAN) test. Prior to the simulation, participants completed a standardized questionnaire, which was used to produce individual scores for the five dimensions.

The narratives were taken from semi-structured interviews conducted immediately after the stock market simulation. The interview guide covered (see Appendix A.2.): emotional responses (stress, excitement, frustration), decision-making strategies (rational analysis, intuition), reactions to gains and losses, and post justifications for the decisions. All interviews were recorded with the participants' consent and fully transcribed.

Trading activity indicator (number of transactions): to strengthen our results and to complement self-reported narratives with behavioral trading style, we also considered each participant’s daily number of transactions (buy and sell orders) placed during the simulation. This indicator provides a proxy for trading intensity and helps us to assess the convergence between interview-based decision registers and observable trading activity (see Appendix A.3.)

3.2.4. Thematic analysis of narratives

The fourth stage of the methodological protocol consists of a thematic analysis of the qualitative data collected during the interviews (see Table 3).

Participant Duration Number of Words Number of Pages
I.1. 42 min 4466 10
I.2. 42 min 6827 12
I.3. 59 min 7922 14
I.4. 36 min 5946 11
I.5. 43 min 7492 12
I.6. 36 min 6124 11
I.7. 42 min 5949 12
I.8. 33 min 5577 10
Total 333 min 50303 92
Mean 42 min 6288 11.5
Max 59 min 7922 14
Min 33 min 4466 10
Standard Deviation 8 1102 1.3
Table 3.Descriptive Statistics for Semi-Structured Interviews. Note: Duration indicates the length of each interview in minutes; number of words and pages refer to the fully transcribed material used for thematic coding.

After being transcribed, discourses were subjected to triple coding. First, open coding was performed, extracting units of meaning derived from the participants' comments (e.g., “I refreshed the page in the hope that” or “I absolutely did not want to show any emotion”). These segments were associated with temporary codes to identify the attitudes, strategies, and emotions expressed. Second, axial coding was carried out to group these units into broader categories. For example, narratives describing the inability to close a position were assigned to the “compulsive” category, while those expressing a desire for rationalization were brought under “analytical orientation.” Similarly, passages referring to excessive confidence after a win were included in “overconfidence”, and passages relating to relativization were included in “stoicism”. Finally, we used selective coding to identify the decision-making styles (rational-analytical, intuitive-emotional, compulsive, stoic) and to derive new profiles. This step helps reconstruct a new typology, which was tied to personality traits (see step 5). The analysis process was conducted iteratively. Data and categories were reviewed repeatedly to refine the thematic analysis and limit interpretation bias. The three authors also cross-checked their readings. This process translated individual narratives into an analytical structure.

3.2.5. Consolidation of the typology by linking it to OCEAN scores

The fifth step involved relating the decision-making categories to the OCEAN personality scores. Core traits were attributed to each profile to examine their alignment with the OCEAN personality dimensions. This comparison revealed several mismatches: some participants displayed personality traits in tension with the decision-making register, which opened avenues for identifying hybrid positions.

For this analysis, we consider the OCEAN test scores (see Appendix A.1.). To ensure a common basis for comparison, the scores for each OCEAN dimension were rescaled and interpreted using three levels: low, medium, and high.

Openness and Agreeableness: 10 items (maximum score 50),

Conscientiousness: 9 items (maximum score 45),

Extraversion and Neuroticism: 8 items (maximum score 40).

The scores (low, medium, high) were expressed as a proportion of the maximum possible value for each dimension. For each trait, the categorizations were calculated as a percentage of the maximum score:

Low < 40% of the maximum score,

Medium 40–70% of the maximum score,

High > 70% of the maximum score.

4. Results

4.1. Presentation by Participant

4.1.1. Participant I.1.

First, his approach is rational and analytical. He bases his decisions on economic information, publications, and technical analysis:

“It was based on the news and a little bit on technical analysis.”

His strategy evolves on a daily basis:

“We had to adapt day by day.”

“Some high-level information will never come to us.”

At the same time, his discourse has an intuitive and emotional dimension. His mood fluctuates with the market:

“If it went up, we were happy; if it went down, we were more cautious.” “

He also relies on intuition:

”Three things: the news, indicators, and intuition.“

These emotions sometimes lead to compulsive behavior, especially when he tries to correct the situation under stress:

”I sold some shares at a loss to buy others... in the end, it was pointless."

The ranking reinforces his impulsiveness:

“I feel bad, and I want to get back on track, so I'm taking more risks.”

Finally, some passages have a more stoic tone. He puts some losses into perspective:

“An unexpected loss hurts less.”

“There are people better than me.”

“You have to take a step back, otherwise you lose yourself.”

Analysis of the participant's profile according to the Big Five model reveals a personality with a high level of openness (29) and agreeableness (31) and a low score for neuroticism (24). His openness refers to a propensity to consider new ideas. It supports adaptability and a high tolerance for uncertainty. Agreeableness makes him receptive to the opinions of others. This student's personality scores also reveal a low level of neuroticism, which could indicate an ability to regulate emotions (Costa & McCrae, 1992). This participant shows a degree of emotional stability, which allows him to protect himself from market uncertainty (Lauriola & Levin, 2001; Hidayah & Kustina, 2020).

Analysis of his decision-making style and Big Five profile shows a strong consistency between the rational-analytical register and his personality. His high level of openness (29), combined with intellectual curiosity, imagination, and tolerance for uncertainty, is reflected in his trading approach, which relies on information gathering and rapid adaptation. It supports the integration of intuition, which was expressed by combining “news, indicators, and intuition.” His high level of agreeableness (31) is consistent with his sensitivity to the collective atmosphere, which moderates his decisions without dominating them. Finally, given his low neuroticism (24), he manages to put things into perspective and keep a degree of emotional control, which helps him stay focused on an analytical approach. These qualitative registers are reflected in his simulation behavior: participant I.1 placed 47 orders over the three days (21 on Day 1, 18 on Day 2, and 8 on Day 3), with activity largely driven by buy orders (32 buys vs. 15 sells), suggesting an initially proactive, opportunity-oriented engagement rather than frequent revisions. The decline in trading on Day 3 is consistent with his reported capacity to “take a step back”: after early phases where stress and ranking pressure may result in rapid adjustments, he progressively reduces interventions, reflecting a shift toward a more analytical and stoic posture in which emotions are acknowledged and increasingly kept under control.

4.1.2. Participant I.2.

Decisions are based on rational analysis, while dealing with a personal impulsiveness that he tries to control, and intuition is used as a complement.

He adopts a cautious, consistent, and information-based approach. Portfolio diversification, searching for reliable data, and the fact that he rejects technical tools he does not understand are indicative of a strong analytical orientation.

“I don't want to use something that I don't fundamentally understand.”

He also refers to his experience, particularly a case where impulsiveness led to a significant loss, which now leads him to prefer caution and reflection.

“The other experience... I was impulsive... I lost $6,000 in one day.”

Nevertheless, his intuition remains influential in his choices when it comes to perceiving opportunities or trusting his instincts.

“I changed a few things based on my feeling.”

His compulsive nature is reflected in a tendency to act quickly, sometimes too quickly, which he explicitly recognizes and tries to control.

“My basic personality is to go for it.”

Finally, his attitude is defined by a strong emotional detachment. Losses are put into perspective, and rankings or results do not change his behavior or emotional state. This stoic posture reinforces the stability of his strategy.

“It’s not my money… it doesn’t matter if I lose. “

Thus, his style is characterized by a strong rational basis, supported by a detached attitude, where intuition plays a secondary role, and impulsiveness is contained by discipline.

“I found that consistency is good, staying consistent.”

Analysis of the Big Five scores reveals a personality defined by high conscientiousness (44). This trait translates into a methodical approach oriented toward careful risk assessment. However, his openness score (42) has a moderating effect, allowing for a certain level of uncertainty in decision-making and encouraging the exploration of alternative investment opportunities. With moderately high scores in extraversion (38) and agreeableness (38), the participant appears sociable without being dependent on the opinions of others. Finally, his level of neuroticism (20) highlights an emotional stability to manage stress.

The participant's rational-analytical decision-making profile is consistent with his personality. Both analyses describe an individual whose decision-making is structured by reason and control, leaving little space for emotions. First, the dominant decision-making register, which is rational and analytical, can be explained by the very high level of conscientiousness. Second, his openness score reinforces this result. It explains the presence of intuition in his decisions. In addition, the participant's low neuroticism score supports emotional style, but he tries to prevent them from interfering with his strategy. Finally, his levels of extraversion and agreeableness suggest that his decisions remain relatively independent of the other participants.

This profile is reflected in his simulation behavior: participant I.2 placed 43 orders over the three days (28 on Day 1, then 8 on Day 2, and 7 on Day 3), with activity mostly driven by buy orders (30 buys vs. 13 sells), consistent with an initial phase of opportunity scouting and portfolio construction, followed by corrective moves. The sharp contraction after Day 1 supports his emphasis on discipline and consistency: rather than maintaining a high-frequency pattern throughout the experiment, his behavior stabilizes quickly, suggesting that any initial action-oriented impulse was contained early and replaced by a more cautious approach aligned with his preference for understanding information before acting.

4.1.3. Participant I.3.

This participant can be classified as emotionally-intuitive: his decisions are strongly influenced by ego, fear, and frustration:

“I'm young, I prefer high returns, something very speculative that generates a lot of excitement.”

This desire for performance is accompanied by overconfidence but also a need for recognition:

"I was in first place... I took even more risks out of ego.“

This participant develops plans and recognizes that he does not always follow them (“I don’t necessarily stick to my plan”). Rationality functions more as a benchmark than as a driving principle.

Finally, the social register plays a role through comparison, which intensifies emotions:

“I wanted to beat Mr. Y.”

“If the other person is losing, I feel reassured.”

These elements show a need for recognition, common among novice traders. The social dimension reinforces ego and emotions more than reflection.

His profile is characterized by a very high level of agreeableness (41), which indicates an empathetic personality. It helps to explain the need for recognition, not in a competitive perspective, but in the search for validation. We observe a high openness score (39), which generally translates into intellectual curiosity. Openness reinforces the inclination towards risk and “highly speculative situations that generate a lot of emotion,” while potentially destabilizing the initial investment plans. His level of conscientiousness (29) shows that he can build strategies but not necessarily respect them. The extraversion score (27) reflects a thoughtful personality. However, the risks taken reflect more an ego-driven personal challenge. Finally, low neuroticism (27) indicates a high degree of emotional stability: he can manage uncertainty, even if his decisions are influenced by pleasure, fear and regret.

The relationship between intuitive-emotional decision-making style and personality traits remains consistent, but there are nevertheless some differences. The participant’s personality is defined by high agreeableness, high openness, and emotional stability. His decisions are largely driven by emotion, ego, and the search for performance. This gap can be attributed to competitive design. In other words, it is not his personality but the design that elicits defensive emotions and intuitive decision-making: while his personality profile points toward a propensity for reflective decision-making, the market context leads him toward emotion-based choices.

Consistent with this emotionally driven and ego-sensitive posture, participant I.3 executed 23 transactions over the three days, with activity heavily concentrated at the beginning of the simulation (17 on Day 1, then 4 on Day 2 and 2 on Day 3) and an overall dominance of buy orders (17 buys vs. 6 sells), suggesting an early, opportunity-seeking positioning phase rather than a sustained pattern of frequent revisions. The day-by-day structure further nuances this reading: Day 1 is largely buy-oriented, whereas Day 2 becomes sell-heavy before activity nearly stops on Day 3, which is consistent with a decision process shaped by speculative impulses and social comparison : high initial engagement followed by a sharp reduction in trading activity as uncertainty, disappointment, or performance pressure accumulates, rather than continuous analytical rebalancing.

4.1.4. Participant I.4.

The decision-making style is analytical-rational, emphasizing logic, indicators, and control over choices:

“I prefer to rely on indicators, which are not 100% reliable, but which I have some control over.”

“I did my best to be as rational as possible, even though I'm not a robot yet, of course.”

This is indicative of a desire to stay away from information he considers biased:

“The media manipulates things... I prefer indicators, they're more reliable.”

He associates rationality with discipline, self-control, and emotional detachment:

”If we all thought like robots, we would inevitably get better returns."

However, his desire to be rational is influenced by emotions. Emotions shape his decisions, especially after losses or gains:

“After a loss, I had the desire to recover my loss as a priority... to wipe out my loss and withdraw as soon as I was in profit.”

“After a gain, I felt really happy and satisfied... it increases confidence and therefore increases the chances that I will invest.”

Finally, the presence of other participants also plays a role; it increases competitive pressure:

“On the first day, I had to invest quickly, otherwise the others would get ahead.”

"X was behind me... he said, ‘Look at this stock.’ At that moment, I was more inclined to check it out. "

Thus, the participant's decision-making process is based on a rational register and on logic, indicators, and the search for control, but it is nevertheless influenced by emotions. Social dimensions also contribute, to a lesser extent, to influencing his choices.

This rational-analytical decision-making style, marked by hesitation between rationality and emotions, is reflected in his personality. His very high level of agreeableness (40) reveals an empathetic personality. He seems attentive to social dynamics, which may explain his sensitivity to other participants. In addition, his high openness (37) highlights intellectual curiosity and a positive attitude towards new things. This personality trait is associated with a need for improvement and a desire to rationalize his choices. His conscientiousness score (34) reinforces his orientation towards rationality and control, while leaving space for adaptation. His average extraversion (24) favors analysis and discretion. Finally, his very low level of neuroticism (9) shows great emotional stability. It reinforces his desire for control, even though emotions may temporarily affect his decisions.

The cross-analysis shows consistency between the participant's decision-making style and his personality traits. His rational decision-making style is supported by a high level of conscientiousness and a very low degree of emotional reactivity. His need for control is consistent with a personality that does not easily give in to panic.

The participant's high openness also highlights his ability to learn from failures. His high agreeableness explains the social dimension in his decision-making. He remains attentive to the collective context and adopts a cooperative rather than competitive style. However, there is a contradiction between his desire to make rational choices and some decision-making influenced by emotions (the need to recover from a loss).

Consistent with this control-oriented stance under competitive pressure, participant I.4 executed 75 transactions over the three days (49 on Day 1, 14 on Day 2, and 12 on Day 3), making him the most active trader and indicating a strong early “implementation” phase consistent with his statement that he “had to invest quickly” to avoid falling behind. His activity is also predominantly buy-driven (54 buys vs. 21 sells), which suggests an initial strategy focused on rapid position-building rather than defensive liquidation. The contraction after Day 1 supports the tension described in his interview between a desire for rational control and episodic emotional responses: after the initial competitive rush, the reduction in trades implies fewer reactive interventions, aligning with his preference for indicators and discipline, while still allowing for occasional corrective “recovery” moves when losses or stress temporarily heighten emotional pressure.

4.1.5. Participant I.5.

Analysis of this semi-structured interview highlights intuitive-emotional decision-making, followed by social influence. She makes decisions based on her personal feelings, relying on familiar reference points and immediate information rather than on structured analysis.

“Always listen to your intuition... What I've learned is to always listen to my intuition.”

“I knew about L'Oréal, LVMH... I'm a woman, these are things I aspire to have in the future.”

This intuitive approach is strengthened by strong emotional dynamics. Emotions do not appear to guide behavior immediately; they arise later and shape levels of confidence, motivation, or discouragement:

“Disappointed, inside, sometimes I felt a little angry, I had hope...”

“After a loss: it's a disaster... a little disappointed, regret.”

Social comparison also plays a role, sometimes providing encouragement but at other times proving disconcerting:

“I based my investment on what my peers did.”

“The rankings had an influence... it made us push ourselves a little harder.”

Analysis of her personality shows strong awareness (39), high agreeableness (41), and emotional stability. She appears organized, receptive to others, and prefers observation and reflection before acting. Her openness (29) is moderate: she is sufficiently curious to adapt, while avoiding precipitate decisions.

Finally, this participant’s personality and emotional decision-making patterns display a high degree of alignment. Her emotional stability does not prevent her from developing emotions. Her agreeableness highlights the importance of other people's opinions. Her low level of extraversion (25) encourages her to stay away when social influences become excessive. Finally, her moderate openness supports an intuition that is reinforced by experience rather than by impulsiveness.

Consistent with this intuition-based but socially shaped pattern, participant I.5 executed 55 transactions over the three days (18 on Day 1, 21 on Day 2, and 16 on Day 3), indicating a relatively high level of engagement rather than a single initial allocation followed by inactivity.

Her activity remains stable across the simulation and is almost evenly split between buys (29) and sells (26), which suggests ongoing re-positioning (monitoring, revising, and reacting to intermediate outcomes) rather than a specific plan-and-hold approach. This dynamic is consistent with the interview evidence: intuition guides initial choices (familiar reference points), while emotions (confidence, discouragement) and social comparison act as recurrent drivers for changes across days, producing a trading style that evolves through successive small decisions rather than a single strategic commitment.

4.1.6. Participant I.6.

The participant shows an analytical-rational style. He places great importance on analysis and avoids impulsive behavior. His decision-making is defined by risk aversion and an ability to stay quiet under stress, which allows him to remain emotionally stable even in uncertain situations. From a social perspective, his agreeableness level encourages a cooperative decision-making style. His low extraversion level indicates a preference for personal reflection, reinforcing an independent and analytical decision-making style. This rational preference is reflected in several statements:

“I prefer to be dependent on my own decisions.”

“I tried to diversify… into completely different sectors.”

His attitude under pressure is reflected in the way he describes his approach to market movements.

“When it was heavily bearish… I felt a bit powerless… but I might as well keep what I already had initially.”

These decision-making characteristics are consistent with his personality traits. His high conscientiousness score (34) reflects an organized, rigorous, and persevering personality, with a strong attachment to reliability and planning. He avoids impulsive behavior and relies on thoughtful approaches, reinforcing his rational decision-maker profile. This alignment is evident when he explains the importance of avoiding unnecessary trades due to transaction fees:

“The transaction fees… made it more appealing to stay and maintain one's position rather than to switch.”

His agreeableness (31) confirms empathetic and prosocial orientation: he is empathetic and other-oriented, concerned with fairness and maintaining cooperative relationships. Moderate openness (25) indicates a capacity to consider new ideas, coupled with a continued attachment to stability. His low neuroticism (16) illustrates strong emotional regulation: he remains calm when confronted with stress, which leads to controlled reactions. Finally, his level of extraversion (15) reinforces the image of a thoughtful and independent individual.

The link between personality and the rational decision-making style appears to be highly consistent. His high level of conscientiousness provides the basis of his decision-making process: it directly supports cautious choices and rational approaches. Agreeableness adds a human dimension, resulting in a cooperative decision-making style that is concerned with collective cooperation. Moderate openness allows new alternatives to be integrated without becoming excessively innovative. His neuroticism reinforces the emotional consistency observed in his decisions, while low extraversion supports an introspective, autonomous decision-making style that is not influenced by social pressure. In short, his personality and the rational and cautious decision-making style are closely aligned, providing the profile of a thoughtful, reliable, and socially minded decision-maker.

This cautious posture is clearly reflected in his simulation activity: participant I.6 placed only 17 orders over the three days (15 on Day 1, then just 2 on Day 2 and 0 on Day 3), making him the least active trader in the sample and indicating a strong preference for limited intervention once positions are established. Moreover, the structure of his trades (12 buys versus 5 sells) suggests an initial allocation phase followed by a deliberate “hold” strategy, consistent with his emphasis on diversification, cost awareness, and the idea that switching positions is not worthwhile given transaction costs. Finally, the fact that his activity falls to zero trades on the final day provides a behavioral indicator of emotional regulation and risk aversion: rather than engaging in late corrective moves under uncertainty, he appears to preserve his initial strategy, aligning with his interview narrative of staying calm in bearish conditions and remaining independent from social pressure.

4.1.7. Participant I.7.

His decision-making style is cognitive, with an emphasis on rationality and self-control. He belongs to the rational-analytical category. He expresses his desire to use a logical and structured approach, relying on specific indicators and excluding any kind of emotion:

“using [his] logic.”

“leaving no space for doubt.”

“I focused on news and indicators, such as the RSI, the Nasdaq, and the CAC 40.”

This rationality initially provides a stabilizing framework when confronted with uncertainty, but is gradually challenged by emotions associated with losses and stress:

“Not at first, but as time went on, I felt like it was my money.”

“I was really disgusted at the end.”

Over time, a kind of distancing develops, reflecting an emotional adjustment to frustration.

“At the end, I saw that it was in the red… it just made me laugh.”

This tension between reason and emotion points to an internal tension that the participant seeks to manage by emphasizing the importance of rationality.

“Being emotional means not being rational. You must be rational.”

Social interaction provides analytical support: the participant listens to others but rejects passive imitation.

“I listened to the others, how they worked.”

“I didn't want to follow them like a sheep but take their advice to build my own opinion.”

The relationship with the experience can be described as a gradual process of taking distance, shaped by mistakes and the lessons learned from them :

“I wanted to think long term over three days... I saw that it wasn't logical.”

“If I had the mindset I have now, I would react differently.”

This rational decision-making style is in line with the participant’s personality traits. His level of agreeableness (41) is reflected in a preference for collaboration, which reinforces his social skills. His openness (40) supports his capacity to consider alternatives before deciding. His moderate extraversion (31) allows him to interact with others without seeking a dominant position, while his conscientiousness (30) indicates adaptability, sometimes at the expense of discipline. Finally, his low neuroticism (30) enables him to handle stress and remain emotionally detached when facing uncertainty.

The link between his rational and cognitive decision-making and his personality thus appears consistent. The cognitive register is supported by intellectual openness and emotional stability, which facilitate controlled decision-making. Moderate emotions correspond to a low level of neuroticism: emotions exist but remain under control. Selective use of social skills reflects high agreeableness, combined with independent judgment. Finally, the growing influence of experience can be related to his cognitive flexibility and moderate self-awareness, which facilitate learning through adjustments. The main tension lies between the desire for rational control and the inconsistent execution of decisions, associated with a limited degree of discipline. However, the overall profile remains coherent: rational, able to evolve toward a more integrative decision-making process in which reason, emotion, and experience coexist.

This evolution is reflected in his simulation activity: participant I.7 executed 26 transactions in total (13 on Day 1, 12 on Day 2, and only 1 on Day 3), showing control-oriented engagement during the first two days followed by a near-complete withdrawal from trading as the simulation progressed. Moreover, the structure of his order (16 buys versus 10 sells) points to progressive position-building combined with selective revisions, consistent with his reliance on indicators and “selective” peer input to form an independent judgment rather than repeatedly changing positions. The reduction to a single trade on Day 3 aligns with the distancing described in the interview (from stress and frustration toward detachment): once the emotional cost of losses became clear, rational self-control appears to translate behaviorally into fewer corrective moves.

4.1.8. Participant I.8.

Analysis of this semi-structured interview highlights the predominance of emotion and intuition in the participant's decision-making. His choices are determined by fear, doubt, hope, or regret. From the beginning of the interview, he expresses feelings of incompetence and doubt about the market:

“I quickly felt inadequate.”

“I kept telling myself: I'm useless, I'm useless.”

These narratives reflect a strong emotional involvement despite the “non-real” situation. This emotional response is accompanied by a feeling of losing control, a pattern frequently observed among novice investors when they find it difficult to interpret events. The participant alternates between hope and resignation when confronted with losses :

“On the third day, I said to myself: it's over, it's done.”

The social dimension also plays a role in the emotions expressed. He feels judged by others, which reinforces insecurity and influences his decisions:

“The rankings put pressure on me. I saw the others doing well, and I thought I was useless.”

“I felt like I was being watched.”

“I watched the others. I tried to copy them.”

These comments underline the importance of social comparison and the need for validation, which are central dimensions of the social role (Baker & Nofsinger, 2002). The participant’s attitude reflects imitative behavior, driven by fear of judgment and a desire to belong to the group.

In terms of personality, this participant displays a high level of openness (42) and neuroticism (37), combined with low extraversion (16), agreeableness (29), and moderate conscientiousness (27). His openness reflects intellectual curiosity and a tendency to use a rational strategy (Baer et al., 2008; Dollinger, 2011), but this is often neutralized by his high level of neuroticism, which generates anxiety, doubts, and an increased perception of risk (Lauriola & Levin, 2001; Byrne et al., 2015). In addition, his low level of extraversion explains his withdrawal and his sensitivity to the judgment of others, which is consistent with the social comparison observed in his discourse. His moderate agreeableness and conscientiousness support an empathetic and flexible style but also lead to hesitation (Lerner & Tetlock, 1999).

This participant's intuitive-emotional profile and personality traits seem to converge. His high level of neuroticism is consistent with the predominance of emotional registers: fear of making mistakes, stress when confronted with losses, and challenge in keeping a rational posture. His level of extraversion and the need for social validation explain the influence of others' opinions. This paradox between personal withdrawal and social sensitivity is often associated with introverted profiles displaying high neuroticism (Sokolowska & Pohorille, 2000). Conversely, high openness suggests a capacity for rational thinking but is overridden by emotional reactions. This participant represents the profile of an introspective, curious decision-maker who is emotionally fragile.

This emotional vulnerability and sensitivity to social evaluation are also reflected in his simulation activity: participant I.8 executed 36 transactions over the three days (11 on Day 1, 9 on Day 2, and 16 on Day 3), showing a significant increase in trading activity on the final day rather than the gradual disengagement typically associated with more controlled profiles. Moreover, the day-by-day structure of his orders, Day 1 dominated by buys (10 buys vs. 1 sell), then a sell-heavy Day 2 (3 vs. 6), followed by balanced and intensive adjustments on Day 3 (8 vs. 8), suggests a volatile decision process characterized by successive corrective attempts to recover from losses and reduce discomfort. This aligns with his narrative of doubt, fear of judgment, and imitative coping: as pressure accumulates, trading becomes shaped by peer comparison and late corrective moves.

Our results indicate that all participants could be placed into the typology. However, it also appears that all profiles are systematically hybrid, combining a core register and a complementary one. The complementary register comes from the initial selected typology or emerges from the analysis of interviews (e.g., social influence). For example, the same individual may adopt a rational style based on indicators while at the same time displaying strong emotional sensitivity during loss phases or appear independent while occasionally being influenced by others. These variations can be understood as the outcome of processes through which personality traits, experimental context, financial issues, and social interactions are interconnected and co-constructed. The selected typology offers a structuring framework for interpretation, but the identification of hybrid profiles enhances the understanding of the diversity of decision-making styles.

This hybridity is also evident in the simulation’s behavioral indicators: across the three days, participants placed 322 orders (198 buys and 124 sells), with activity strongly concentrated on Day 1 (172 transactions) before declining on Day 2 (88) and Day 3 (62), a pattern consistent with an initial phase of rapid engagement (often exploratory) followed by more selective, experience-based adjustment. In addition, trading intensity shows substantial dispersion across individuals (17 to 75 transactions), indicating that, even within the same experimental constraints, participants alternate between phases of action and restraint. This variability reinforces the interpretation of a dominant register complemented by secondary registers that become more prominent as emotions, uncertainty, and social pressure evolve.

Participant Dominant Decision-Making Profile Complementary Decision-Making Profile Personality Traits Key Features
I.1. Rational-Analytical Intuitive-Emotional O (29/50) ; C (25/45) ; E (28/40) ; A (31/50) / N (24/40) Emotions present but regulated; low neuroticism, emotions used as a guide rather than as a disruptor; emergence of an analytical style.
I.2. Rational-Analytical Intuitive-Emotional O (42/50) ; C (44/45) ; E (38/40) ; A (38/50) ; N (20/40) High conscientiousness, analytical discipline; low neuroticism, stability; intuition supported by high cognitive openness.
I.3. Intuitive-Emotional Social O (39/50) ; C (29/45) ; E (27/40) ; A (41/50) N (27/40) Strong emotional basis, social influence reinforced by agreeableness; impulsivity activated by the competitive context.
I.4. Rational-Analytical Intuitive-Emotional O (37/50) ; C (34/45) ; E (24/40) ; A (40/50) ; N (9/40) Low neuroticism; dominant rational profile; emotions only play a temporary role.
I.5. Intuitive-Emotional Rational-Analytical O (29/50) ; C (39/45) ; E (25/40) ; A (41/50) ; N (17/40) Intuition present but stabilized; emotions present but controlled.
I.6. Rational-Analytical Social O (25/50) ; C (34/45) ; E (15/40) ; A (31/50) ; N (16/40) Emotional stability; dominant analytical profile; social influence.
I.7. Rational-Analytical Intuitive-Emotional O (40/50) ; C (30/45) ; E (31/40) ; A (41/50) ; N (30/40) Dominant rational logic but high neuroticism; emotions emerge in situations of frustration; tension between logic and emotions.
I.8. Intuitive-Emotional Social O (42/50) ; C (27/45); E (16/40) ; A(29/50) ; N (37/40) High neuroticism, strong emotional reactivity; low extraversion, social sensitivity; openness allows for analytical potential, but it does not play a dominant role.
Table 4.Overview table of decision-making styles and relationship to personality traits. Note: O, C, E, A, N refer to OCEAN personality dimensions. The table summarizes each participant’s dominant and complementary decision-making registers derived from interviews and cross-referenced with personality scores.

5. Discussion

The purpose of our research was to compare the personality traits of the OCEAN model with decision-making styles in trading. Our results partially confirm existing literature and highlight hybrid styles resulting from tensions between personality, emotions, and rationality.

5.1. Hybrid profiles and tensions between rationality and emotion

Our findings highlight hybrid profiles, where several decision-making registers coexist within the same individual. Rationality can coexist with intuition or with emotional sensitivity that arises during periods of frustration or loss. Emotional stability does not prevent impulsive decision-making motivated by ego or competition. These hybrid configurations suggest that typologies based on the opposition between “rational investors” and “emotional traders” are too restrictive. Our results show a simultaneous coexistence of decision-making logic, activated according to context, stress level, and social comparison (Statman, 2014). Personality traits function as predispositions that interact with situational factors. For example, high openness can support analytical processing while facilitating intuitive insights; high agreeableness can result in cooperation but also increase vulnerability to social influence. These tensions highlight the dynamic nature of decision-making styles.

5.2. The role of social and competitive context in shaping trading styles

The social and competitive context of the design plays a key role in activating and modulating the profiles. The role of ranking and the influence of others appear to amplify emotions and encourage riskier or mimetic behavior. For some participants, the visibility of performance led to comparison, anxiety, or a need to prove their competence, resulting in more impulsive decisions. For others, social pressure has become a motivational force or a means for reflective learning, encouraging them to analyze peers’ strategies and integrate them into their reasoning.

These results add nuance to studies that view social comparison primarily as a source of bias (Baker & Nofsinger, 2002). In our data, social influence can reinforce compulsive dynamics (overtrading, refusal to accept losses) and support learning through observation and dialogue. The social and competitive environment acts as a catalyst that intensifies existing tendencies associated with personality traits and can temporarily shift decision-making registers.

5.3. Theoretical and practical implications

On a theoretical level, our research confirms that personality traits do not entirely determine trading styles and work as a predisposition modulated by emotion, context, and experience. Our research proposes an integrated typology linking OCEAN and decision-making styles and leaves space for individual contradictions. This perspective invites future work to consider trading behavior as the result of configurations of traits, emotional regulation strategies, and social positioning, rather than as the expression of a single and stable profile. From a practical perspective, these results highlight the importance of training investors in metacognition, emotional management, and self-awareness. Simulation platforms could incorporate customized feedback, for example, by helping participants identify their dominant decision-making registers, the situations in which they switch to a different register, and the role played by social comparison.

6. Conclusion

Our study investigated how personality traits, emotional processes, and social influences interact to shape decision-making in a trading context. By using the OCEAN model and observing investors’ behavior in an experimental simulation, we proposed a typology of decision-making registers (rational-analytical, intuitive-emotional, social, and more marginally compulsive or defensive) and we highlighted that, in practice, these registers rarely operate independently. Decision-making appears less as the expression of a single stable profile than as a configuration in which personality, emotions, and context interact. Each style may be viewed as a dominant orientation that is constantly reshaped by evolving market conditions, social signals, and past experiences. In this perspective, considering trading styles through behavioral indicators (e.g., the number of transactions) can support qualitative findings by providing an objective indicator of trading intensity that helps triangulate the identified decision-making registers.

A central contribution lies in showing the hybrid nature of decision-making profiles. The same individual may rely on indicators and structured reasoning while simultaneously being highly sensitive to losses, rankings, or the presence of others. Personality traits help to understand these tensions (high conscientiousness resulting in rational control, high neuroticism reinforcing emotional vulnerability, openness facilitating both analysis and intuition), but they do not fully determine them. The experimental context, the individual performance, and social comparison play a decisive role in activating some registers rather than others. The typology thus offers a framework for articulating personality traits and decision-making styles, while leaving space for tensions, transitions, and contradictions. This perspective invites a shift toward a more process-oriented view of financial behavior.

Methodologically, the qualitative and exploratory design enabled the identification of these nuances by linking behaviors to narrative accounts and personality scores. The limited number of participants and the virtual money restrict the generalization of the results. However, these constraints are consistent with the objective of refining concepts and proposing provisional configurations rather than statistically testing hypotheses. The value of this work lies in the ability to follow how individuals move from one register to another depending on the situation, revealing how emotions, personality, and context interact at key decision points (entering, maintaining, or closing a position).

Our findings support a more integrative approach to behavioral finance. Personality models such as OCEAN provide a useful structure, but they need to be combined with detailed analyses of emotional regulation strategies and social positioning to fully understand decision-making processes. Hybrid profiles suggest that financial decision-making should be considered as a configuration of traits, emotional tendencies, and social interaction rather than as the manifestation of a single dominant style. It opens opportunities for developing intermediate positions (such as “rational and socially sensitive” or “emotionally exposed and learning-oriented” investors) that may better reflect real-world heterogeneity.

7. Limitations

Our research has several limitations that must be considered when interpreting the results. First, the sample size is small, and its composition is homogeneous: management students sharing the same level of knowledge, similar academic socialization, and limited investment experience. This sampling choice limits the generalizability of the findings to other investor profiles, such as experienced individual investors, asset management professionals, or financial advisors, whose decision environments, constraints, and routines may differ substantially. Accordingly, the purpose of this study is not statistical generalization but exploratory theory-building: to identify configurations and mechanisms linking personality traits, emotional processes, and decision styles in a controlled and socially trading setting. To support an analytical perspective, we emphasize cross-case comparison, and we provide detailed descriptions of the context and participants to facilitate transferability. Future research should replicate and extend the findings using larger and more heterogeneous samples (e.g., retail investors with varying experience, professional traders, and advisors) and should test whether the typology remains stable across different market contexts (e.g., longer simulation periods, varying levels of volatility, and alternative incentive structures).

Second, the empirical framework is based on a short-term trading simulation built on a declining market configuration and a set of rules. This framework does not replicate the characteristics of a real financial environment, such as multiple investment horizons, accumulated experience, variable time pressure, and personal financial issues. The absence of financial exposure and the design may also influence the level of commitment, perception of risk, and tolerance for losses. In this perspective, some behaviors are closely tied to the experimental design.

Third, the use of an OCEAN personality test, given at a specific moment and based on self-report, involves limitations linked to social desirability bias, contextual influences, and the perceived stability of traits. They do not reflect trait dynamics or their interactions with emotional states throughout the experience: the perceived negative stock market climate may have influenced participants’ personality profiles. Similarly, qualitative data is derived from semi-structured interviews conducted after the experience, based on participants' discourses, justifications, and retrospective reconstructions. These materials are useful for accessing the meaning that individuals give to their decisions, but they cannot be assimilated into measurements of cognitive or emotional processes in real time. There may therefore be a gap between the experience being lived and its narration, particularly when decisions are perceived as “irrational” or “uncomfortable”.

Fourth, qualitative interpretation involves a degree of subjectivity. The categories built, the links between personality traits and trading behaviors, as well as the typology of profiles, reflect theoretical and analytical choices. Even with procedures to strengthen the analysis, other teams might choose slightly different classifications or interpretations.

Finally, the experimental design incorporates specific mechanisms (performance ranking, peer comparison, investment rules) that may influence behaviors. They could limit the external scope of the results: the selected design may accentuate some biases (overreaction due to competition, mimetism, overtrading for quick recovery) while underestimating other dimensions where social pressure is lower, regulatory constraints are stronger, or financial issues are significant. In this perspective, the typology should be read in conjunction with the framework: it highlights how personality and emotions are displayed in a given decision-making design, without pretending to cover the full range of financial contexts.

8. Avenues for Future Research

Our study offers several avenues for future research. One direction would be to extend the study to larger and more heterogeneous samples. The participation of experienced individual investors, portfolio management professionals, or financial analysts would be an opportunity to test the typology's reliability in contexts where financial issues and performance pressure differ compared to our simulation.

A second approach is to use a longitudinal perspective. This would involve tracking the same individuals over a longer period, through several sessions. This would offer the opportunity to explore the impact of market shocks (episodes of high volatility, crises, euphoric phases) on the emotional dynamics of different profiles.

Third, future research could combine qualitative and quantitative approaches and incorporate other behavioral indicators (timing of entries and exits, whether to hold on to losing positions), cognitive reflection or risk tolerance tests, or physiological measurements (electrodermal responses, heartbeat), and process tracking tools (screen recordings, eye tracking). Such devices would reinforce data triangulation, link traits and behaviors more directly, and generate testable hypotheses.

Fourth, it would be interesting to explore the role of control, incentive, and information mechanisms as central variables, rather than just contextual elements. Further experimental studies could manipulate the type of feedback, the visibility of performance, the structure of rewards, and the presence or absence of risk management rules to identify configurations that would reduce or amplify behavioral biases according to personality profiles. It would extend the implications of our research to the design of educational programs, risk control systems, and investment decision-making models.

Finally, a last avenue involves testing the typology as a diagnostic tool for decision-making styles. Future research could examine how this framework can be used to design customized training programs, support mechanisms for novice investors, or raise awareness of behavioral biases according to individual profiles. Similarly, its articulation with risk management, recruitment, or responsibility assignment practices could be explored, while paying attention to ethical issues and the need for caution when using psychological categories for managerial control purposes.

Author Contributions: Conceptualization: Alain Finet, Kevin Kristoforidis, Julie Laznicka; Methodology: Alain Finet, Kevin Kristoforidis, Julie Laznicka; Software: Alain Finet, Kevin Kristoforidis, Julie Laznicka; Validation: Alain Finet, Kevin Kristoforidis, Julie Laznicka; Formal analysis: Alain Finet, Kevin Kristoforidis, Julie Laznicka; Investigation: Alain Finet, Kevin Kristoforidis, Julie Laznicka; Resources: Alain Finet, Kevin Kristoforidis, Julie Laznicka; Data curation: Alain Finet, Kevin Kristoforidis, Julie Laznicka; Writing—original draft preparation: Alain Finet, Kevin Kristoforidis, Julie Laznicka; Writing—review and editing: Alain Finet, Kevin Kristoforidis, Julie Laznicka; Visualization: Alain Finet, Kevin Kristoforidis, Julie Laznicka; Supervision: Alain Finet, Kevin Kristoforidis, Julie Laznicka; Project administration: Alain Finet; Funding acquisition: Alain Finet. All authors have read and agreed to the published version of the manuscript.

Funding: This research was funded by the Walloon-Brussels Federation (Belgium) through a Concerted Research Action (Grant Number: ARC-25/29 UMONS5).

Conflicts of Interest: The authors declare no conflict of interest.

AI Use Statement:The authors use DeepL and Gemini (Google) for grammar and language refinement. All content was carefully reviewed and verified by the authors.

Appendices

Appendix A.1. Explanation of How Personality Scores Were Computed.

Participants' personalities were assessed using the Big Five Inventory (John et al., 2008). This questionnaire consists of 45 items presented in the format of short sentences designed to assess the five major personality factors: extraversion, agreeableness, conscientiousness, neuroticism, and openness. For each statement, participants were asked to position their answers on a five-point Likert scale, with 1 equating to “strongly disagree” and 5 equating to “strongly agree.” Each participant's personality score was calculated as the average of the items included in each dimension. Extraversion comprises eight items (16, 1, 6R, 11, 21R, 26, 31R, and 36). Agreeableness comprises ten items (2R, 7, 12R, 17, 22, 27R, 32, 37R, 42, and 45R). Conscientiousness comprises nine items (3, 8R, 13, 18R, 23R, 28, 33, 38, 43R). Neuroticism comprises eight items (4, 9R, 14, 19, 24R, 29, 34R, and 39). Finally, openness includes the following items: 5, 10, 15, 20, 25, 30, 35R, 40, 41R, and 44. Each of the five factors represents the average of the sum of all items, reversing the items with an “R.”

Appendix A.2. Guide for Semi-Structured Interviews.

Availability Bias

  1. Can you tell me about your research on the companies you wanted to invest in?
  2. What kind of information did you look for?
  3. What type of information did you prioritize?
  4. How has information accessibility impacted your operations?

Overconfidence

  1. How do you rate your trading skills?
  2. How did you feel after a successful series of moves?
  3. How has this influenced your trading behavior?
  4. Do you think you underestimated the risks at times?

Anchoring Bias

  1. When you decided to sell a stock, how did the price you bought it at influence it?
  2. How have past price levels influenced your decisions?
  3. Why did the initial purchase value prevent you from adapting to new information?

Herd Behavior

  1. What was the main influence on your choosing one action over another?
  2. How have general trends influenced your decisions?
  3. How did you react to market movements in situations of high activity?

Prospect Theory

  1. What would you do if you had a winning stock or a losing stock in your portfolio?
  2. What were your motivations for selling winning positions, even though they could still bring you additional profits in the future?
  3. What were your motivations for maintaining a losing position?

General Emotions

  1. In your opinion, what role did emotions play in this experiment?

Emotions Changes

  1. After a session where you made several decisions that were unsuccessful, how did you react emotionally, and how did this influence the next session?
  2. Have you noticed changes in your emotions or behaviors when you have several successive losses?
  3. Do you feel like your emotions have changed the way you've structured your strategy over time?

Impact of Emotions on Decision-Making

  1. Before placing an order, what emotions did you usually feel?
  2. Can you describe a situation where your emotions directly influenced your decision-making, whether in a losing or winning situation?
  3. Have there been times when, despite feeling stressed or anxious, you were able to make a successful decision?
  4. Do you feel like your emotions have changed the way you've structured your strategy over time?

Reactions to Gains or Losses

  1. How did you react to loss?
  2. Have the losses affected your behavior or decisions?
  3. How did you react to a gain?
  4. Did you react more impulsively afterward?

Emotion Management

  1. How did you handle the pressure of making decisions quickly?
  2. Did the fact that there were breaks between each session influence your emotions?

Appendix A.3.

Total D1 Buy Sell Total D2 Buy Sell Total D3 Buy Sell Total Buy Sell
I.1. 21 12 9 18 15 3 8 5 3 47 32 15
I.2. 28 17 11 8 4 4 7 3 4 43 24 19
I.3. 17 15 2 4 1 3 2 1 1 23 17 6
I.4. 49 28 21 14 10 4 12 9 3 75 47 28
I.5. 18 12 6 21 10 11 16 7 9 55 29 26
I.6. 15 11 4 2 1 1 0 0 0 17 12 5
I.7. 13 9 4 12 7 3 1 0 1 26 16 10
I.8. 11 10 1 9 3 6 16 8 8 36 21 15
Total 172 114 58 88 51 35 62 33 29 322 198 124
Mean 21,5 14,25 7,25 11 6,37 4,38 7,75 4,12 3,62 40,25 24,75 15,5
Table 5.General trading orientation over the three days of the experiment. Note: Total D1–D3 indicates the number of buy and sell transactions executed by each participant per day during the simulation. This data provides a behavioral indicator of trading intensity.

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