Seasonal asset allocation: evidence from mutual fund flows’, working paper
Bibliographic record
Abstract
This paper documents a strong seasonality in flows between mutual funds that invest in different asset classes. While some of this seasonality is related to other influences, we find a strong correlation between investment flows (and exchanges) and the onset and recovery from seasonal affective disorder (SAD), consistent with the seasonally varying risk-aversion hypothesis of Kamstra, Kramer and Levi (2003). Specifically, our paper shows that substantial money moves from U.S. equity to U.S. government money market mutual funds in the fall, then back to equity funds in the spring, controlling for the influence of past performance, advertising, and capital gains overhang on fund flows and exchanges. While prior evidence regarding the influence of SAD relies on seasonal patterns in the returns on asset classes, our paper provides the first direct trade-related evidence. Further, we find a stronger seasonality in Canadian fund flows, consistent with its more northerly location and higher incidence of SAD, and a reverse seasonality in flows in Australian funds, consistent with the southern hemisphere seasons being offset by six months relative to the
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".