An Analysis of Behavioral Biases in Investment Decision-Making
Bibliographic record
Abstract
Individual investor’s behavior is extensively influenced by various biases that highlighted in the growing discipline of behavior finance. Therefore, this study is also one of another effort to assess the impact of behavioral biases in investment decision-making in National Stock Exchange. A questionnaire is designed and through survey responses collected from 243 investors. The present research has applied inferential statistics and descriptive statistics. In the existing study, four behavioral biases have been reviewed namely, overconfidence, anchoring, disposition effect and herding behavior. The results show that overconfidence and herding bias have significant positive impact on investment decision. Overall results conclude that individual investors have limited knowledge and more prone towards making psychological errors. The findings of the study also indicate the existence of these four behavioral biases on individual investment decisions. This study will be helpful to financial intermediaries to advice their clients. Further, study can be elaborated to study other behavioral biases on investment decisions.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".