THE ROLE OF EARNINGS VOLATILITY SOURCES IN FORECASTING
Bibliographic record
Abstract
Tan and Sidhu (2012) document that analysts’ forecasts fully incorporate information contained in earnings variability for firms with high income smoothing and for firms with low operating variability. We revisit their findings using other study’s approach. Sample in this study comprises of 295 Canadian firms and covers 2006-2011 period. Firstly, following Mishkin’s (1983) method of testing market efficiency, our findings confirm that investors recognize the earnings volatility effect on time series correlations of earnings in a post-earnings announcement drift context. Secondly, we examine whether the efficiency of investors’ forecasts with respect to earnings variability information is due to income smoothing and/or to volatility in operating activities. The empirical test document that income smoothing is a primary determinant of reported earnings volatility, while operating performance play a secondary role. The findings are consistent with the signal theory and the view that managers use income smoothing to convey information about a firm’s future earnings prospects.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".