The Relationship Between Market And Accounting Determined Risk Measures: Reviewing And Updating The Beaver, Kettler, Scholes (1970) Study
Bibliographic record
Abstract
The association between market-determined risk measures and accounting-determined risk measures was originally explored in the 1970s by Beaver, Kettler, and Scholes (BKS). The results of the BKS (1970) study suggest that accounting information is usefulness in assessing firm specific risk. Since BKS, there have been few studies conducted to determine if these results still hold today. This cross-sectional study re-examines the relationship between market and accounting-determined risk measures. A total of 222 randomly selected publicly traded companies were examined to determine if there is a relationship between the accounting risk measures of dividend payout ratio, leverage, and earnings variability and the market risk measure of beta. The relationship is further analyzed by classifying the results based on the company’s size (market capitalization). Our study suggests that the original BKS (1970) results hold true in today’s market with some exceptions. These findings reiterate the importance of accounting policy choice and full disclosure in the financial statements, as accounting information proves to be a possible alternative to market risk information. This demonstrates that full disclosure is important to help capital markets determine a company’s risk profile.
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 | 0.023 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".