Effects of Macroeconomic Variables on the Performance of Mutual Funds: Evidence from Bangladesh Financial Market
Bibliographic record
Abstract
Using multiple regression analysis in this research paper, this is examined that these macroeconomic variables (Money Supply-M2, Inflation Rates and Exchange Rates) have significant relationships with the performance of mutual funds (represented by monthly return based on NAV) in Bangladesh. According to this project paper, money supply M2 has negative relationship with the performance of mutual funds in the financial market of Bangladesh. Because, higher level of money supply in the market weaken the monetary value of taka which makes the market more vulnerable. And this vulnerable market leads a negative impact on the whole financial market as well as mutual fund industry. Interest rates have positive relationship with the performance of mutual funds in the financial market of Bangladesh because higher level of interest rate increases the tendency of savings in the ultimate consumers and they try to consume less and save more. This savings is going to be invested in the capital market as well as in mutual fund industry which leads the market to a better position for the fund managers. Inflation rates have negative relationship with the performance of mutual funds in the financial market of Bangladesh as the higher level of inflation make the price of commodities higher and the monetary price of the taka lower. This tendency of making the less value of money, most of the investors want to withdraw their investment from the market which leads a downturn in the financial market as well as in the mutual fund industry. At the end, exchange rates have positive relationship with the performance of mutual funds as the higher level of exchange rate makes the Bangladeshi taka more powerful in the international market.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.004 | 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".