Cashflow Timing vs. Discount-Rate Timing: An Examination of Mutual Fund Market-Timing Skills
Bibliographic record
Abstract
We measure the ability of professional investment managers in timing cashflow versus discount-rate news, the two components of market returns. We find that the average U.S. equity mutual fund exhibits cashflow-timing skills of 1.77%/year, but discount-rate timing of −0.87%/year; furthermore, cashflow-timing skills, but not discount-rate timing skills, strongly persist over future quarters. Our evidence indicates that misspecification of market-timing abilities accounts for the failure of prior research to locate talented timing funds. Importantly, we find that value funds outperform growth funds in timing cashflow news, which provides new evidence on the unique skills of value-oriented mutual funds. This paper was accepted by David Simchi-Levi, finance. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4693 .
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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".