Risk Attitudes and Personality Traits Among Investors in Funds
Bibliographic record
Abstract
How do an investor’s thoughts and feelings influence their behavior? Financial institutions must assess the risk attitudes of investors to ensure investors are being recommended appropriate financial products. This study is a further examination into whether risk attitudes are correlated with personality traits and to determine the risk attitudes of investors from different backgrounds. The risk attitudes of investors were examined according to the Big Five personality traits. Investor personality traits were linked to their investment decisions and risk attitudes. Differences in risk attitudes between investors from different backgrounds were also explored. A questionnaire survey was administered. Investors with fund investment experience were recruited. Correlations were observed between the Big Five personality traits and risk attitudes. Extroversion, agreeableness, conscientiousness, and openness to new experiences were positively correlated with risk attitudes, and neuroticism was inversely correlated with risk attitudes. These results indicated direct relationships between the Big Five personality traits and risk attitudes. This study also revealed significant differences in risk preferences between gender, marital status, discretionary budget, fund investment experience, and risk profile. The study results provide a broader reference for establishing investment risk profile charts that integrate personality traits into behavioral finance models in financial practices.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".