Risk Procyclicality and Dynamic Hedge Fund Strategies
Bibliographic record
Abstract
It is well-known that traditional financial institutions like banks follow procyclical risk strategies (Rajan 2005, 2009, Shin 2009, Jacques 2010) in the sense that they increase their leverage in economic expansions and reduce it in recessions, which leads to a procyclical behaviour for their betas and other risk and financial performance measures. But it is less known that the spectrum of the returns of many hedge fund strategies displays a high volatility at business cycle frequencies. In this paper, we study this unknown stylized fact resorting to two procedures: conditional modelling and Kalman filtering of Funds alphas and betas. We find that hedge fund betas are usually procyclical. Regarding the alpha, it is often high at the beginning of a market upside cycle but as the demand pressure stems from investors, it eventually fades away, which suggests that the alpha puzzle documented in the financial literature is questionable when cast in a dynamic setting.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".