Do Analysts’ Cash Flow Forecasts Improve Their Target Price Accuracy?
Bibliographic record
Abstract
ABSTRACT The literature on the usefulness of analysts’ cash flow forecasts is unsettled, with Call et al. ( ), Mohanram ( ), and Radhakrishnan and Wu ( ) providing evidence in favor of their usefulness, and Givoly et al. ( ), Bilinski ( ), and Ecker and Schipper ( ) questioning this. Target prices provide a good setting to test the usefulness of cash flow forecasts because they are an ultimate output of an analyst's valuation process to which cash flow forecasts are an input. Moreover, studying the effect of cash flow forecasts on target prices is more relevant for assessing their usefulness than is studying their effect on earnings‐forecast accuracy, as the accuracy of target prices requires a comparison with market prices, which are less subject to management influence than reported earnings. By improving an analyst's understanding of unexpected accruals and permanent earnings, a cash flow forecast can increase an analyst's target price accuracy and signal an analyst's superior forecasting ability. We examine whether, conditional on their earnings forecasts, analysts’ cash flow forecasts improve their target price accuracy. We find that when analysts issue cash flow forecasts, their target price accuracy increases. We also find that this accuracy increases with the accuracy of their cash flow forecasts. Finally, we find that this increased target price accuracy is greater for more challenging‐to‐value firms. Our study provides confirmatory evidence of the usefulness of analysts’ cash flow forecasts.
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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 | 0.010 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".