The Influence of Earnings Quality and Liquidity on the Cost of Equity
Bibliographic record
Abstract
This study uses sample companies listed in Taiwan Stock Exchange and GreTai Securities Market during 2000 to 2011 to investigate the influence of earnings equality and liquidity on the cost of equity. We define discretionary accruals with three measures and real earnings management with three measures as indicators of earnings quality; trading volume, individual stock liquidity and market liquidity as liquidity measures and individual stock and market liquidity risk as liquidity risk measures. Panel data is suggested for this analysis. Firms manipulating discretionary accruals increase in the cost of equity, but ones operating real earnings management decrease in it when considering that the earnings quality and liquidity directly impact on it. The cost of equity is indirectly influenced by earnings quality and liquidity through information asymmetry measured by bid-ask spreads. The results show that no matter firms engaging in discretionary accruals or real earnings can decrease the cost of equity under higher levels of information asymmetry. The higher the trading volume or the individual stock liquidity risk, the lower the cost of equity when information asymmetry is low.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".