True Expense Ratio and True Alpha of Imperfect Diversification: Evidence from Stock Market in Bangladesh
Bibliographic record
Abstract
Actively managed funds try to outperform by deviating from passive benchmarks such as the S&P 500, leading to imperfect diversification and higher idiosyncratic volatility. The idiosyncratic volatility imposes an additional cost to the shareholders. In this study, using data of all the closed-end mutual funds listed with Dhaka Stock Exchange (DSE) from 2012 to 2019, I have attempted to quantify this higher idiosyncratic volatility as an additional expense on the portfolio and then estimate true expense ratio and true net alpha of the actively managed funds as a new measure for imperfect portfolio diversification. The study finds that mean volatility cost of the funds is 1.42% which is on an average around 89% of the explicit expense ratio and the findings that volatility costs are not strongly correlated with other performance measures such as Sharpe, Treynor or information ratios provides additional information about the fund performance. Moreover, when volatility cost is adjusted to traditional Jensen alpha measure to find a true net alpha of the funds, rankings of the funds significantly change and two alpha measures are not strongly positively correlated, suggesting new information about the fund performance.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".