Management Quality, Financial and Investment Policies, and Asymmetric Information
Bibliographic record
Abstract
Abstract We develop measures of the management quality of firms and make use of a unique sample of hand-collected data to examine the relationship between the reputation and quality of a firm’s management and its financial and investment policies, a relationship that has so far received little attention in the literature. We hypothesize that better and more reputable managers are able to convey the intrinsic value of their firm more credibly to outsiders, thus reducing the information asymmetry facing their firm in the equity market. Given this, firms with better and more reputable managers will have more access to the equity market, so that we expect lower leverage ratios for these firms. In addition, they will have less need to signal using dividends, so that they will have lower dividend payout ratios. Further, since better managers are likely to select better projects (having a larger net present value (NPV) for any given scale) and to implement them more ably, higher management quality will also be associated with higher levels of investment. We present evidence consistent with the above hypotheses. Our direct tests of the relationship between management quality and asymmetric information also indicate that higher management quality leads to a reduction in the extent of information asymmetry facing a firm in the equity market.
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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 | 0.006 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".