Bibliographic record
Abstract
This paper is an extension of the work of Lawson and Schwartz (2018) which analyzes the risk-adjusted performance of hedge funds by employing a collection of four, five, seven, and eight-factor models. The purpose is to evaluate how well the top and bottom performing subset of hedge fund strategies have profited on known asset pricing anomalies during two unique time periods, 1994 to 2000 and 2001 to 2008. The bifurcation of the data into two distinct periods allows for a deeper exploration of the potential time-varying significance of estimated factor arbitrage. Our empirical testing suggests that both the top and bottom performing funds did utilize the asset growth anomaly to generate abnormal profits. Top performers tended to invest with a long emphasis on low asset growth, value firms while the bottom-five performing hedge fund strategies tested positive for a predilection towards going long small firms with low asset growth characteristics. Arguably, these outcomes probably align with the nature of the investment philosophy of each fund strategy. Interestingly, however, the time-varying significance of estimated coefficients for the value and returns momentum factors between the two distinct timeframes suggests either intentional or unintentional rotation between the use of available pricing anomalies and risk premiums.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".