Investors’ Behavioural Biases and the Security Market: An Empirical Study of the Nigerian Security Market
Bibliographic record
Abstract
Behavioural biases describe a replicable pattern in perceptual distortion, inaccurate judgment, illogical interpretation, or what is broadly called irrationality. This paper adopts a primary data approach to investigate the effects of behavioural biases on security market performance in Nigeria. The objectives are in twofold: one, to examine the extent of behavioural biases among security market investors in Nigeria and, to examine the effects of behavioural biases on stock market performance in Nigeria. The paper employed questionnaire as instrument and the technique of correlation with Pearson Product Moment Coefficient to analyze a survey of 300 randomly selected investors in Nigeria security market. We find strong evidence that behavioural biases exists but not so dominant in the Nigeria security market because a weak negative relationship exists between behavioural biases and stock market performance in Nigeria. The paper recommends that individual investors in the market should engage the services of investment advisors which will reduce personal biases in the management of their portfolios.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".