Does the Income Statement Have Predictive Value
Bibliographic record
Abstract
This study investigates the predictive value of three key income statement line items: revenues, gross margin and net income (earnings). The income statement line items are tested to determine if they can predict a firm’s future performance in both the short-run and long-run across three broad measure: 1) a market-based measure (change in future stock price); 2) a cash-based measure (change in future cash flows); and 3) an accrual-based measure (change in return on assets). The study analyzes data from 5,244 firm-quarter observations using Standard & Poor’s 500 firms from 1998-2007. The results reveal that earnings are the most robust indicator of a firm’s future performance in the short-run and long-run, followed by gross margin and then revenues. Overall, this study suggests that the income statement does have predictive value, and that the predictive value increases as more cost information is presented. These results are significant for investors, boards of directors, and standard setters. In regards to standard setters, the results support the recognition, measurement and presentation standards for the income statement but suggest that further refinements to improve predictive ability are warranted.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".