You Learn When It Hurts: Evidence in the Mutual Fund Industry
Bibliographic record
Abstract
This paper aims to contribute to the lack of research on the learning process of mutual fund markets. The empirical design is focused on the ability of the Spanish equity mutual fund industry to learn from its important errors. The choice of this industry is justified by both its relevance in the European mutual fund markets and some specific characteristics, such as the concentration and the banking control of the industry, which may affect the learning process. Our main objectives are to identify important trading errors in mutual fund management by applying three independent filters based on the relative importance of each decision, and then testing the evolution of these errors both at the industry level and at the fund family level. We apply the dynamic model of generalized method of moments (GMM), and we find an overall significant decrease in the percentage of important trading errors over time, thereby providing evidence of the global learning process of the industry. In addition, we find that a large number of fund families drive this evidence. Finally, we obtain that the family size and its dependence on financial groups do not seem to play significant roles in explaining the learning process. Therefore, we conclude that fund managers have incentives to learn from their important trading errors, in order to avoid them in future decisions, due to their serious negative consequences on fund performance, regardless of the characteristics of the families to which they belong.
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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 | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".