Behavioral Bias in Individual Investment Decisions: Is It a Common Phenomenon in Stock Markets?
Bibliographic record
Abstract
This paper aims to investigate the behavioral factors that influence individual investment decision making at a developing country stock market; the Sudanese Stock Exchange Market. The Study employs a cross-sectional survey design as well as analytical methods to collect the necessary data and establish the relationship between the study variables. Data is collected through a structured questionnaire from a sample of 203 individual investors and Correlation and Regression methods are used to conduct the analysis. The findings of the paper provide evidence that behavioral biases play a noticeable role in individual investment decision making process regardless of the degree of development of the stock market. The paper demonstrates that heuristic and market factors play a dominant role in the process of individual decision making in the Khartoum Stock Exchange. The factors that have a significant impact on individual investment decision making process include Representativeness, Overconfidence, Anchoring, Historical cost of stock, Customer preferences, Loss aversion, Mental accounting, Other investors’ trading volume, and Quick reaction to changes in other investors ‘decisions. Factors that have an insignificant impact include Availability bias, Change in stock prices, Regret aversion, and Other investors’ decisions and choices.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".