Financial Anomalies in Asset Allocation: Risk Mitigation with Cross-Sectional Equity Strategies
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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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".