Do Verified Earnings Reports Increase Investment?*
Bibliographic record
Abstract
ABSTRACT A common view is that verified earnings reports encourage investment through improved transparency. We lack direct evidence on this foundational proposition because researchers cannot observe counterfactuals in which a manager either (i) must remain silent about performance or (ii) can make any statement about performance they desire, even a bald‐faced lie. We experimentally manipulate whether a manager can provide information to an investor by voluntarily disclosing a verified earnings report, communicating freely via unverifiable cheap talk, or both. Our experiment involves repeated interactions between an uninformed investor with funds that, if invested, generate uncertain gains, and a trustee‐manager who observes and then divides gains after they are realized. We hypothesize and find that (i) the provision of a verified earnings report leads to higher investment compared with a world in which reporting is not possible and (ii) the provision of a verified earnings report leads to more accurate cheap talk communication than when earnings reports are unavailable. Contrary to our prediction, we find that investment when both earnings reports and cheap talk are possible is statistically indistinguishable from investment when only cheap talk communication is available. Further tests reveal that a lack of verified earnings reports leads managers to sustain a partner's investment by providing high returns to the investor while also limiting (but not completely eliminating) deceptive communication and profit‐taking. Our main conclusion is that verified earnings reports promote investment on a stand‐alone basis by improving transparency, but the effect of greater transparency from earnings reports on investment is more nuanced when earnings reports can influence the disclosure of unverifiable information. The main implication of our evidence is that the greater transparency of management behavior with verified earnings reports is not unambiguously positive because making behavior more transparent can lead managers to change their behavior.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".