Mutual Fund Transaction Costs and Their Effect on Funds Performance
Bibliographic record
Abstract
Objective: Iranian mutual funds’ average turnover rate stood at 330% in the years from 2001 to 2007. Such a high rate could lead to excessive trading costs for investors without necessarily bringing in high returns. The goal of this paper is to estimate transaction costs and examine their effect on the performance of the funds. Methods: In addition to the explicit costs of trading such as transaction taxes and commissions, there are other, implicit, components such as bid-ask spread and price impact which are harder to compute and less recognized by investors. This paper used high frequency (tick-by-tick) trade and limit order book data, as well as the hand-collected quarterly holding and trading data for a sample of Iranian equity mutual funds to estimate the aforementioned trading costs. Then, the effect of trading costs on the risk-adjusted fund returns was examined using the four-factor alpha as our proxy. Results: Funds understudy spend on average 11% of their AUM annually on trading costs which is a considerable amount in comparison to their return (15.6%), during the sample period. The present study found no significant effect of trading costs on the funds’ performance in the sample. However, during the subperiod of 2013-2014, a negative and significant relationship was discovered between them. Conclusion: As the obtained results of the present study proved, more trading activities of the Iranian fund managers’ do not lead to higher returns; accordingly a high trading volume generates only enough excess return to offset the associated transactions costs.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".