Financial Anomalies in Asset Allocation: Risk Mitigation with Cross-Sectional Equity Strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is a myriad of financial anomalies in the cross-section of equity returns. They have been widely studied in the literature, which gives investors a large choice in terms of investment styles. In this article, the authors show a perhaps unappreciated quality of financial anomalies: They exhibit strong countercyclical behavior. Specifically, some anomalies (e.g., profitability and investment) perform particularly well when traditional portfolios (e.g., 60/40 or risk parity portfolios) exhibit prolonged periods of negative drawdowns and during National Bureau of Economic Research (NBER) recessions. With the exception of momentum strategies, the authors do not find evidence that financial anomalies are inflation hedging. Last, the authors examine whether financial anomalies lead to better portfolio performance. The results show that combining anomalies based on their style and then adding them to a traditional portfolio leads to higher Sharpe ratios overall, while also limiting portfolio losses during recessions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it