Earnings Persistence Over The Macroeconomic Cycle: Evidence From Korea
Bibliographic record
Abstract
This paper examines whether the persistence of earnings components is affected by the macroeconomic cycle in Korea. To measure the macroeconomic cycle, we use the cycle variation value of Coincident Composite Index (CCI) data obtained from the Korea National Statistics Office. Results from a sample of 21,232 firm-quarter observations over the period 2002-2013 indicate that accruals (cash flows) are more persistent than cash flows (accruals) during expansions (recessions). Also, when going from an expansion to a recession, a decline in accruals persistence is greater than that in cash flows persistence. When total accruals are decomposed into non-discretionary and discretionary portions using the modified Jones model (Dechow et al., 1995), we find that non-discretionary accruals are most persistent than the other components during both expansions and recessions, and a decline in persistence is largest (smallest) for discretionary accruals (cash flows) when going from an expansion to a recession. Most of these results hold when we split the macroeconomic cycle into four phases including transitory periods. Taken together, we provide evidence on the differential effects of macroeconomic cycle on the persistence of individual earnings components in Korea. Our findings suggest that macroeconomic variables are needed to be considered in studies on earnings persistence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".