Comparative Predictive Abilities of Earnings and Operating Cash Flows on Future Cash Flows: Empirical Evidence from Ghana
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Bibliographic record
Abstract
Cash flow prediction is an essential component of economic decision making, particularly in investment and credit evaluation. This paper examines the comparative predictive ability of earnings and operating cash flows variables on future operating cash flows within a developing economy’s setting. Ordinary Least Squares (OLS) method is used to develop regression models over the period of 2002 to 2012. Current operating cash flows, as proxy for future operating cash flows, are regressed on past one, two and three years of earnings and operating cash flows as predictors. Results from the regression analysis reveals earnings and operating cash flows are significant in predicting future operating cash flows but have different predictive powers with earnings providing a superior comparative predictive ability on future cash flows. The paper therefore concludes that earnings are a better predictor of future operating cash flows than historical operating cash flows itself.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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