Bibliographic record
Abstract
This dissertation contains two essays in empirical finance. The first essay studies the mutual fund industry, and the second essay looks into the stock market. Both studies provide insights in the underlying mechanism of some asset return patterns identified from the data currently available.\n\nThe first essay investigates the sources of a recently identified performance pattern in mutual funds.\nSpecifically, actively managed mutual funds, in general, underperform a passive benchmark; however, some recent studies find they, in fact, outperform the benchmark in bad economic states.\nI examine whether a state dependent risk shifting behavior of mutual fund managers contributes to this performance difference across states, and find supportive evidence.\nAs shown in prior studies, the risk shifting behavior is motivated by a non-linear flow-performance relationship.\nUsing a piece-wise linear regression, I demonstrate that the non-linearity exists mainly in good states; whereas in bad states, the flow-performance relationship is close to linear. Thus, non-zero risk shifting incentives are only expected in good states.\nI empirically measure these incentives in good states, and show that managers do react to the ``gambling'' (i.e., positive) incentives. In addition, higher ``gambling'' incentives are found to be associated with lower fund performance.\n\nThe second essay, based on joint work with Hai Lu and Kevin Wang, examines how stock price shocks in the absence of public announcement of firm specific news affect future stock returns. We find that both large short term price drops and hikes are followed by negative abnormal returns over the subsequent twelve months. The pattern of asymmetric drifts, the return continuation for negative shocks versus the return reversal for positive shocks, is puzzling. We explore whether investor disagreement can explain the puzzle and find that the evidence is consistent with predictions of disagreement theory. Moreover, price shocks with public news disclosures are followed by weaker drifts, suggesting that reduction of information asymmetry from public disclosures mitigates disagreement-induced overpricing.
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.032 | 0.015 |
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".