Impact of Behavioral Factors in Making Investment Decisions and Performance: Study on Investors of National Stock Exchange
Bibliographic record
Abstract
Market anomalies and irrational behavior caused investors changes in the stock market, and this has led to an investigation into the impact of various behavioral biases and factors affecting decision-making for individual investors. The purpose of this study was to find out the effect of the four factors, heuristic, prospect, market, herding on decisions of investors at NSE. Data are collected from the questionnaire on the basis of a likert scale. To determine the reliability of the questionnaire, the Cronbach alpha factor, which was 0.728, was used. EFA and multiple regression tests have been applied. Cronbach-alpha was used to check the interal consistency of the element. Cronbach alpha emphasized to each factor: Heuristic, Prospect, Market, Herding, Investment performance and Investors decisions that consistency at an acceptable level. The result of the analysis is that the four variables have greatly influenced the investment decision and return on investment. All behavioral variables have a significant impact on the decision-making process of investors, which led to the acceptance of all assumptions regarding the level of influence of behavioral factors in decision making for individual investors.
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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 | 0.006 |
| 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".