The usefulness of operating cash flow and accrual components in improving the predictive ability of earnings: a re-examination and extension
Why this work is in the frame
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Bibliographic record
Abstract
We examine whether the components of accruals and operating cash flows improve the predictive ability of earnings for forecasting future cash flows. Unlike most previous studies, we avoid data estimation errors and sample self-selection bias because we exploit data from Australia where reporting of actual cash flow components had been mandatory since 1992. We show that accrual components and operating cash flow components together are more useful than (i) earnings, (ii) operating cash flows and total accruals and (iii) the combination of operating cash flows with accrual components in forecasting future cash flows. These results are robust to several contextual factors, including the length of the operating cash cycle, industry membership, firm profitability and firm size.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it