Economic Cycles and Stock Return Volatility: Evidence from the Past Two Decades
Bibliographic record
Abstract
We use EGARCH-M models to examine the co-cyclical nature of stock returns in relation to economic cycles, focusing on three key variables, namely stock return volatility, risk premium and information asymmetry. We incorporate a wider and systematic alley of major global economic events since 1990s to the end of 2011. The main objective is to provide a corroborative evidence of the cyclicality nature of stock return volatility in the global context, and to present a consolidated volatility alley in association with major economic events. The overall conclusion is that increases in stock returns during good economic conditions tend to be associated with increases in risk premium, but decreases in overall risk and the impact of bad news (information asymmetry), and increase or decrease in volatility persistent. It is the vice versa during bad times. This conclusion emphasizes findings from previous studies, while providing new intuitions for stimulating more debate on the nature of contradictions from previous studies. Also, our findings have significant implications for investors and decision-makers at corporate, national, and international levels.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".