Exploring the Disposition Effect in Trading: A Qualitative Analysis of Emotional Influences
Bibliographic record
Abstract
Our study employs a qualitative and inductive methodology to examine the disposition effect in trading, capturing decision nuances and emotions that are often overlooked by quantitative tools. We ran a three-day simulation with eight management students, each managing a €100,000 virtual CAC 40 portfolio. To strengthen engagement, we introduced a non-monetary incentive in addition to hourly compensation. The experiment unfolded in a mildly declining CAC 40 environment, heightening worries about portfolio losses and fostering behavioral biases. After the simulation, we conducted semi-structured interviews and performed a structured thematic analysis, which was carried out by the three authors. The central theme is investors’ emotional reactions to fluctuations, which shape the disposition effect. On losses, participants reported stress, frustration, and resignation; they often held losing positions hoping for a rebound, delayed selling, and sometimes felt “abandonment” as losses deepened. On gains, fear of reversal and general risk aversion produced hesitation and rapid profit-taking, even for small gains. Across cases, emotions repeatedly conflicted with rational plans, steering decisions toward loss aversion and premature realization of gains.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".