Analysis of Changes in Risk Preferences and Decision-Making of Individual Investors from the Perspective of Behavioral Finance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study explores the changes in risk preferences of individual investors in response to market fluctuations from the perspective of behavioral finance, and its impact on investment decisions. It was found that individual investors' risk preferences are influenced by various psychological factors including overconfidence, anchoring effect, and loss aversion tendencies. Additionally, market volatility and changes in economic environments significantly affect investors' willingness to accept risks. Through analyzing investment behaviors under different risk preferences, this paper reveals strategies for optimizing investment decision-making processes through education, emotional management, and policy-making. Moreover, the study points out that enhancing market information transparency and innovating investment tools can effectively guide individual investors towards more rational investment choices
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it