Factor-Based Investing in Market Cycles: Fama–French Five-Factor Model of Market Interest Rate and Market Sentiment
Bibliographic record
Abstract
This study explores risk–reward patterns in the US stock market and establishes optimal factor-based investing using the Fama–French five-factor model through market cycles constructed by Shiller’s interest rates and Baker–Wurgler’s sentiments. Our emerging evidence confirms that the high-interest rate, high-sentiment cycle generates higher excess returns, and the low-interest rate, low-sentiment cycle generates lower excess returns, which supports the hypothesis that the market cycles as investment horizons have an asymmetric effect on stock returns. Furthermore, the size factor outperforms in the low-interest rate, low-sentiment cycle, whilst the value factor outperforms in the high-interest rate, high-sentiment cycle. Using the asymmetric GARCH model, the asymmetric leverage effect of interest rates and sentiments on five-factor returns is empirically demonstrated with explanatory power of five-factor characteristics. Unlike previous studies, our findings also imply that high- and low-sentiment cycles asymmetrically affect the value factor, and the value premium does not disappear over time, highlighting the role of the market cycles in five-factor returns.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".