Financial Reporting Discretion, Managerial Myopia, and Investment Efficiency
Bibliographic record
Abstract
ABSTRACT We explicitly model financial reporting discretion and earnings management in an investment setting where managers have incentives to behave myopically. We show that when managers are sufficiently, but not excessively, myopic, granting them some discretion over the mandatory financial reports can lead to better investment decisions. This finding contrasts with the conventional argument that financial reporting discretion facilitates earnings management and exacerbates managerial myopia, leading to inefficient investments. Costly earnings management, while offering managers some ex post protection against bad luck by decreasing the incidence of low financial reports, reduces the expected net benefit of high financial reports ex ante. Consequently, managers with negative private information find it too costly to mimic those with positive private information, facilitating separation of managers through efficient investment. Thus, curbing managerial myopia by removing or overly restricting earnings management may have the unintended consequence of impairing investment efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".