Does Information Asymmetry Affect Firm Disclosure? Evidence from Mergers and Acquisitions of Financial Institutions
Bibliographic record
Abstract
We use a quasi-exogenous shock to information asymmetry among shareholders to evaluate the effect of information asymmetry on corporate disclosure. In the post-Regulation Fair Disclosure (FD) period, the merger between a shareholder and a lender of the same firm provides a shock to the information asymmetry among equity investors, because Regulation FD applies to shareholders but not lenders. After the merger, the shareholder gains access to the firm-specific private information held by the lender, which produces an asymmetry in the information held by shareholders. We first provide evidence that information asymmetry among shareholders indeed increases after the shareholder–lender mergers. We then use a difference-in-differences research design to show that after shareholder–lender merger transactions, firms issue more quarterly forecasts (including earnings, sales, capital expenditure, earnings before interest, taxes, amortization (EBITDA), and gross margin), and the quarterly earnings forecasts are more precise. This study provides direct empirical evidence that information asymmetry among shareholders affects corporate disclosure. Firms can address increased information asymmetry by providing more disclosures, fostering a more equitable information environment. Additionally, policymakers might consider these results when evaluating the implications of Regulation FD, particularly in the context of mergers and acquisitions (M&A) of financial institutions where a shareholder gains access to private information held by a debt holder.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".