Performance Appraisal of Asset Management Companies in Bangladesh
Bibliographic record
Abstract
Mutual funds are a type of collective investment scheme where a large number of small investors pool their savings together and entrust it to an asset manager, who manages the capital to maximize returns in exchange for a management fee. While mutual funds and other collective investment schemes are popular in developed markets, with assets under management (AUM) to GDP ratio of 62% globally, they are yet to gain popularity in Bangladesh, where AUM-to-GDP ratio stands at only 0.53%. However, mutual funds and asset management companies have been growing at high rates, with 37 closed-end and 42 open-end funds now in operation, and there is enormous potential for growth in the mutual fund industry in Bangladesh. Since mutual funds are a new product in the Bangladeshi market, a detailed study was performed in order to distinguish skilled asset managers from unskilled asset managers. In this study, “skill” has been defined as the ability to beat the broad-market DSEX index on after-fee basis, with the underlying logic that managers - all of whom charge a management fee - should at least be able to beat a passive investment in the broad DSEX. For purposes of the study, the weekly NAV at market value was of 76 mutual funds managed by 16 asset management companies (AMCs) were collected. The weekly returns for the DSEX and each fund under consideration were calculated separately. Four well-known measures were used to rank each mutual fund utilizing the weekly returns. The measures were Jensen’s Alpha, the Sharpe Ratio, the Treynor Ratio and the Modigliani M2 Alpha ratio. For AMCs managing multiple funds, the measures were asset-weighted to calculate the measure for the AMC as a whole. Our findings illustrated that only 5 out of 16 AMCs managed to beat the DSEX index and earn an alpha over the benchmark. Our findings were in line with academic consensus which states that active management is a zero-sum game and that the majority of actively managed funds will underperform the index on an after-fee basis. Our recommendation is for AMCs to introduce passively-managed index funds which will at least keep up with the market return and minimize fees and trading 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".