Determinants of the Time Series of Earnings and Implications for Earnings Quality*
Bibliographic record
Abstract
Abstract This paper examines whether differences in accrual accounting methods across balance sheet accounts influence the time‐series process of earnings. We define earnings quality as the responsiveness of earnings to shifts in permanent earnings and predict that responsiveness will increase in a firm's use of variable rate debt, where accruals move directly with shifts in interest rates. We also predict that responsiveness will decrease in a firm's investment in property plant and equipment because depreciation is largely predetermined and does not respond to shifts in opportunity costs. To test these hypotheses, we regress earnings on lagged earnings and a proxy for permanent earnings (that is, the implied dividend annuity in lagged equity value). Within the context of an adjustment cost model, this regression captures the responsiveness of earnings by the coefficient on lagged price and by one minus the coefficient on lagged earnings. Consistent with this framework, we find the unconstrained estimated coefficients on these two variables to be negatively correlated. Furthermore, consistent with our hypotheses, we find that the coefficient on lagged earnings (lagged price) is positively (negatively) associated with the relative magnitude and life of fixed assets on the balance sheet and negatively (positively) associated with the relative magnitude of variable rate debt on the balance sheet.
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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 | 0.028 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".