The Influence of Behavioural Finance Factors and the Moderating Effects of Contextual and Demographic Factors on Individual Investor’s Investment Performance
Bibliographic record
Abstract
Individual investors trading at the Colombo Stock Exchange (CSE), Sri Lanka, behave irrationally despite objective finance models available for them to refer in making rational decisions. Therefore this paper examines the irrationality by testing whether behavioural finance factors (BF), stock broker’s recommendations (SBR) as a contextual factor, and individual investor’s existing knowledge of the stock market (EK) as a demographic factor affect individual investor’s investment performance (IP). Heuristic behaviour, prospect behaviour and market factors were conceptualised as independent variables of the study whereas SBR and EK act as moderators on the relationship between BF and IP. Data of 221 individual investors of CSE during first half of 2019 were analyzed using structural models to draw empirical evidence to test hypotheses of the study. Results of the study reveal that market information and past stock trends as market factors have a significant bearing on investment decision making, which ultimately affect IP, while the aggregate effect of BF upholds a significant impact on IP. The results expose some novel findings such as: investors receive inferior financial returns when imitating other investors’ trading behaviour whilst trading on SBR; receive lower returns once trading on market factors whilst resuming SBR; and receive mediocre returns when EK is affirmative whilst following other investors’ decisions; and suffer losses when trading on market factors whilst exploiting EK. The findings imply that the stock brokers should not merely consider the output of objective finance models, but market wide herding, market manipulations, market factors and EK in investment recommendations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".