Alternative Estimating Methodologies of the UK Industry Cost of Equity Capital: The Impact of 2007 Financial Crisis and Market Volatility
Bibliographic record
Abstract
We compare estimates of the UK industry cost of equity capital between the unconditional beta Arbitrage Pricing Model (APM), the conditional beta APM and the Capital Asset Pricing Model (CAPM). A statistically significant eight-factor APM leads to the best estimates of the UK industry cost of equity capital. During our full sample time period any of the APMs, unconditional APM or conditional APM, do a much better job than the CAPM. However at times of extreme market volatility during the 2007 financial crisis, the conditional APM is the best model with the least errors. During a financial crisis investors and market participants’ expectations are revised. Economic forces at play include: increased market uncertainty, increased investors’ risk aversion and capital scarcity. We find that the macroeconomic factors impeded in the Conditional APM that vary over time using the latest information in the market, incorporate the economic forces at play and capture the extreme market volatility. Our findings have direct implications in the financial markets for regulators, corporate financial decision makers, corporations and governments.
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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 | 0.005 | 0.040 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".