Momentum, value, and size strategy returns: the explanatory power of global macroeconomic risks
Bibliographic record
Abstract
Abstract The work aims to empirically test whether the returns of Fama–French-Carhart (FFC) portfolio strategies can be explained by higher/lower sensitivity to the five macroeconomic factors considered by Chen-Ross-Roll (the industrial production growth index, the unexpected inflation, changes in inflation expectations, the yield curve, and the default spread) and to measure the premiums of the global macroeconomic risk factors. The work extends the definition of the three FFC strategies to equity, bond, and commodity asset classes and expands the sample of countries analyzed (26 countries from geographic areas around the world: Developed Asia, Canada, Continental Europe, Emerging Markets, Japan, the United Kingdom, and the United States). The results show that the returns of the three strategies are influenced by the overall macroeconomic factors. The result is a dataset of more than 43,000 monthly returns that can also be a useful reference for subsequent studies. The results show that the returns of the three strategies are influenced by the overall macroeconomic factors proposed by CRR and thus there are global premiums for the corresponding risks. The signs of the emerging relationships are also economically meaningful. Moreover, it is shown that macroeconomic factors could explain the observed positive returns of negatively correlated combinations of strategies, for example, of value-momentum and size-momentum combinations.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".