Experience Is Wealth: Does Work Experience in Other Professions Matter to Mutual Fund Managers’ Portfolio Decisions?
Bibliographic record
Abstract
We document that, among stocks held in mutual fund portfolios, fund managers tend to invest more heavily in firms for which they have prior professional experience in the form of working for sell-side analysts, audit firms, and underwriters in capacities where they could learn about companies that may belong to their portfolios when they later become fund managers. Cross-sectional results reveal that the impact of this professional experience intensifies when the work experience is less distant and lasts for longer, when the firm suffers more severe agency problems and worse information asymmetry, and when the fund manager has more power. Additionally, we observe that fund managers trade on stocks of firms for which they have relevant professional experience prior to upcoming earnings news, and this trading activity leads to superior returns to their funds. Finally, we document empirical patterns consistent with both knowledge acquisition and professional connections from prior work experience contributing to our evidence.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".