What Information in Financial Statements Could Be Used to Predict the Risk of Equity Investment?
Bibliographic record
Abstract
Theoretically, accounting earnings could be used to estimate the intrinsic value of equity. If accounting earnings could be predicted accurately, then, so could be the value of equity, thereby, creating much less risk in equity investment. However, earnings surprises are common, and therefore so is the risk in equity investment. To quantify the risk in the investment implied from accounting earnings, I propose to use financial statements to construct abnormal sales growth rates (ABG) and abnormal changes in profit margins (ABPM) to measure the uncertainty embedded in the accounting earnings. I measure ABG (ABPM) as the difference between the current value of sales growth rate (profit margin) and its benchmark, a weighted value of the three preceding years’ sales growth rate (profit margin). Then, I quantify whether and to what extent the news of ABG and ABPM are material enough to change the expected earnings (proxied by analysts’ forecasted earnings revisions [FREV] and predicted unexpected earnings [UE], and future stock returns [SAR]). Fama–MacBeth regression results show that, together, solely ABPM and ABG could explain 8.2% (2.3%) (5.4%) of the variation of FREV (UE) (SAR). The risk-predictability of ABPM and ABG is robust to the presence of abnormal growth in net operating assets and accruals quality, which, suggested by previous literature, might influence unexpected earnings. Further contingent analyses indicate that the capital market reacts more strongly to the bad news embedded in the ABPM/ABG (with negative signs) than the good news in ABPM/ABG (with positive signs).
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".