Investment Efficiency and Earnings Quality: European Evidence
Bibliographic record
Abstract
This study aims to analyze the relationship between earnings quality and investment efficiency in the European context, in order to understand whether higher earnings quality mitigates investment inefficiencies. To further understand the relationship between earnings quality and investment efficiency, the roles of cash and financial constraints are also analyzed. We use firm-year data based on unbalanced panel data, and control for country, year, and industry fixed effects using a sample composed of listed and unlisted European companies from 19 countries and 17 industries for the period 2010–2018. The results show a positive and significant relationship between earnings quality and investment efficiency. In both scenarios of investment inefficiency, overinvestment and underinvestment, the results suggest that a higher quality of reported earnings mitigates investment inefficiencies. The results also suggest that the negative relationship holds for cash-constrained and unconstrained firms, and that in firms that are financially unconstrained (higher levels of cash and lower levels of leverage) the combined effect with earnings quality is associated with a lower investment efficiency.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".