Do Mutual Fund Managers Time Market Sentiment?
Bibliographic record
Abstract
This paper examines whether fund managers can adjust the exposure of portfolio to time market sentiment, thus expanding the new dimension of the study of mutual fund managers’ timing ability. Using the data of Chinese open-end equity funds from January 2010 to December 2019, based on the CICSI sentiment index developed by Yi and Mao (2009), we find strong evidence that Chinese mutual fund managers have sentiment ability during the sample period. In addition, the funds with positive sentiment timing ability outperforms those without such by 2.20% per year, and the longer the fund survives, the more likely for it to have sentiment timing ability. Our findings remain robust even after controlling the impact of bull and bear market on China’s A-share market in 2015, market timing, volatility timing and liquidity timing, and after using three new sentiment indicators to verify the finding, three indicators being the net buying amount of northward capital, the net buying amount of financing, and the net ratio of limit up.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".