The use of the accounting beta as an overall risk indicator for unlisted companies
Bibliographic record
Abstract
Purpose The purpose of this research is to verify whether or not the accounting beta, a recognized measure of overall risk in publicly traded companies, can be used with unlisted businesses. Design/methodology/approach The paper presents an empirical study using factorial and regression analysis to measure which components of the global risk of SMEs are linked to accounting beta. Findings The results show that accounting beta does not seem to constitute a global measure of SMEs' risk, being explained mostly by financial risk and not by commercial, technological, management and entrepreneurial risks components. Research limitations/implications Researchers will have to turn towards other models than accounting beta that include financial and nonfinancial dimensions of risk in order to obtain an adequate assessment of the overall SMEs' risk. Practical implications Risk is the element that determines access to external financing as well as the lending conditions. Results obtained in this research show that accounting data cannot be used to express overall risk of SMEs, because they are not global enough and are not good predictors of future situations. Originality/value This article presents limits inherent to financial data to properly measured global risk of SMEs.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".