The Performance of Market‐Timing Strategies of Italian Mutual Fund Investors
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Bibliographic record
Abstract
In this paper, we show that simple buy‐and‐hold strategies over‐perform market‐timing strategies effectively used by Italian investors in equity mutual funds. We estimate returns from market‐timing strategies using aggregate data on net flows for a large sample of equity mutual funds, available to Italian investors, that buy stocks in the following markets: Europe and the euro area, the United States and Emerging markets. In all cases, buy‐and‐hold over‐performs market‐timing with extra returns that go from 0.24 per cent per quarter (Europe and euro area) to 0.87 per cent per quarter (US market). These differences are not explained by differences in risk and risk exposure. Investors should re‐consider their investment strategies and choose cheaper, in terms of fees and simpler, in terms of portfolio allocation, passive strategies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it