Comparative Performance of Mutual Funds and Hedge Funds: Riding the COVID-19 Pandemic
Bibliographic record
Abstract
We present an analysis of the performance and flow of U.S. mutual funds (includingequity funds, taxable, and tax-exempt fixed-income funds) and hedge funds during and after the COVID19 pandemic. We find that during the COVID-19 period, equity funds experienced inflow, while fixed-income mutual funds (tax-exempt) experienced outflow. Additionally, although fixedincome mutual funds (taxable and tax-exempt) underperform the passive benchmarks right after the pandemic (the post COVID-19 vaccine period), equity funds outperform the benchmarks during this period. Similar analysis on hedge funds shows that investors increase their investments into these funds; thus, hedge funds experienced inflow during the post COVID-19 vaccine period, and they outperformed the benchmarks during the COVID-19 period. Moreover, the five-factor Fama-French analysis results confirm that mutual funds outperform the market with a significant positive alpha during and after the pandemic. Overall, investors holding equity funds incur the least losses due to the COVID-19 pandemic among other investors.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".