A Study on the Effect of Geographic Diversification of Firms on Hedging Activity Using Derivatives
Bibliographic record
Abstract
This paper examines the effect of fund manager replacement on investment performances of mutual funds. In managerial labor market of mutual fund industries with information asymmetry about the type and action of a fund manager, separating compensation may not be achievable due to imperfect evaluation of performances of fund managers. This paper extends contract theory to model the situations where a mutual fund offers pooling compensation contract to a fund manager based on his reputation. Under these environments, the fund manager has an economic incentive to acquire private benefit by manipulating performances and then to turn over to other mutual fund. Fund manager’s replacement is an aspect of adverse selection in the managerial labor market of fund industries. That is, a fund manager with low ability can select and manipulate unsuccessful investment portfolio generating loss to fund while he turns over to hide himself in the reputation under pooling contract mechanism. The empirical analysis of this paper provides the significant evidence that, differently from those of mutual funds of which managers stay in the same mutual funds, the fund performances drop after the fund managers turn over to other mutual funds. These empirical evidences support the theoretical prediction that the fund managers have incentive to manipulate short-term performances to maintain reputation for acquiring favorable compensation contracts.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".