The Interaction of Market Risk and Idiosyncratic Risk on Equity Mutual Fund Returns
Bibliographic record
Abstract
This study examines the importance of idiosyncratic and systematic risks in explaining equity fund returns in Malaysia. The level of market and idiosyncratic risk in a mutual fund depends on what asset class it invests in. Equity type asset classes are exposed to both systematic and idiosyncratic risk but research generally suggest that only systematic risk is relevant in mutual fund selection since idiosyncratic risk can be reduced through fund diversification. This study attempts to expand the insights of the risk-return relationship by providing additional evidence on the direct and indirect effects of investment risk on equity mutual fund returns. Employing partial least squares structural equation modelling (PLS-SEM), we also explore if idiosyncratic risk moderates the relationship between market risk and mutual fund returns. A sample of 150 Malaysian domestic equity mutual funds comprising of large, mid & small-cap equity funds were selected from the Morningstar website. The results indicate that market risk does not influence mutual funds returns but idiosyncratic risk has a significant and positive effect. Idiosyncratic risk is proxied by fund characteristics comprising of size, age, expenses and fund manager ability. This study shows that fund size, age or expenses are not significant and only the fund alpha which measures fund manager ability is relevant in predicting fund returns. The study also finds that the fund alpha moderates the influence of market risk on returns by changing the nature of the relationship from positive to negative.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".