Equity returns and the business cycle: The role of supply and demand shocks
Bibliographic record
Abstract
The equity premium in the UK appears to have risen significantly since the start of the financial crisis and the associated extended recession. This paper examines the relationship between the business cycle and equity market returns to see how robust this association is. Several classifications of UK business cycle quarters are examined and related to the returns from an investment strategy which buys the market one or more quarters after a business cycle quarter and holds it for one year. Official business cycle dating methods as well as identified structural macroeconomic shocks are examined. The findings are that there is clear evidence for counter-cyclicality in excess returns. Returns are significantly higher in the year following a recession rather than an expansion quarter. There is also a significant difference in the pattern of returns if the downturn in the quarter is the result of a supply or demand shock. Negative supply shocks are found to have an especially large and significant counter cyclical impact on returns. This paper analyses a long period of UK data for determining realised returns using revised data as well as expected returns using a shorter dataset of real-time data. The paper finds similar results for the two datasets suggesting that realsied and expected returns may not be so different from one another. The paper also assesses the ability of the models to forecast outside of their sample period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".