Investor Emotions and Cognitive Biases in a Bearish Market Simulation: A Qualitative Study
Bibliographic record
Abstract
Our paper investigates how emotions and cognitive biases shape small investors’ decisions in a bearish market or are perceived as such. Using semi-structured interviews and a focus group, we analyze the behavior of eight management science students engaged in a three-day trading simulation with virtual portfolios. Our findings show that emotions are active forces influencing judgment. Fear, often escalating into anxiety, was pervasive in response to losses and uncertainty, while frustration and powerlessness frequently led to decision paralysis. Early successes sometimes generated happiness and pride but also resulted in overconfidence and excessive risk-taking. These emotional dynamics contributed to the emergence of cognitive biases such as loss aversion, anchoring, confirmation bias, overconfidence, familiarity bias and herd behavior. Emotions often acted as precursors to biases, which then translated into specific decisions—such as holding losing positions, impulsive “revenge” trades or persisting with unsuitable financial strategies. In some cases, strong emotions bypassed cognitive biases and directly drove behavior. Social comparison through portfolio rankings also moderated responses, offering both comfort and additional pressure. By applying a qualitative perspective—not commonly used in behavioral finance—our study highlights the dynamic chain of emotions → biases → decisions and the role of social context. While limited by sample size and the short simulation period, this research provides empirical insights into how psychological mechanisms shape investment behavior under stress, offering avenues for future quantitative studies.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".