Bibliographic record
Abstract
The paper investigates the link between systematic risk and corporate business performance, represented mainly by the degree of operative and financial leverage. Although theoretical contributions link the value of the common stock to corporate performance, CAPM does not identify a satisfactory relation between the latter and ß, setting aside the relation to the corporate capital structure. A detailed analysis of CAPM highlights two relevant anomalies: short sales and R-squared low values explaining the fundamental relation between stock and stock market excess return. Using an alternative approach, we highlight how CAPM, on one side, can be an incomplete theory to explain the stock returns and, on the other side, that the portfolio risk could be equivalent to the underlying corporate businesses portfolio, filtered by the feedback effect of the stock market. The empirical evidence descending from the analysis of several portfolios with an increasing number of stocks belonging to the S&P 500 Index reveals that the optimisation process leads to progressively higher ß paired with a simultaneous R-squared deterioration; furthermore, ß appears subject to sudden oscillations. Overall, ß does not adequately represent the relation between stock risk and return. The integration of the joint performance of the stock market and corporate business in an MLR relation leads to a clear improvement in R-squared thanks to the surfacing of the correlation between these two explanatory variables, a condition entirely ignored by CAPM.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".