Does the Investor’s Trading Experience Reduce Susceptibility to Heuristic-Driven Biases? The Moderating Role of Personality Traits
Bibliographic record
Abstract
The aim of this study was to evaluate whether trading experience reduces exposure to heuristic-driven biases, namely availability bias, anchoring and adjustments bias, representativeness bias, and confirmation biases of individual investors operating in the Indian stock market, through the moderating role of the Big Five personality traits. To achieve these research objectives, primary data were collected through a structured questionnaire. The sample consisted of 408 individual investors trading on the Indian stock market, who were selected on a convenient basis. Confirmatory factor analysis and Cronbach’s alpha were used to measure the validity and reliability of the data. Further analysis was conducted using Pearson’s correlation and multiple regression. The results of this study prove that increased trading experience does not always reduce the susceptibility to heuristic biases. Increased trading experience reduces the susceptibility to availability, and anchoring and adjustment heuristics of individual investors operating on the Indian stock market. The present study has some relevant implications for investors, portfolio managers, financial advisors, and other interested persons in the stock market.
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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 | 0.002 | 0.014 |
| 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 | 0.001 |
| 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".