Applying a difference-in-differences approach to explore variations in the timing of bank mergers in the U.S. over the last two decades, we document an increase in borrowers’ disclosure when their banks engage in mergers and acquisitions. The effect is stronger among borrowers more reliant on services from the merging banks and when mergers cause larger changes in banks’ monitoring and financing of borrowers. These findings suggest an information spillover effect from bank mergers to the public financial markets, and have implications for how changes in banking markets affect the availability of public disclosure in the stock markets.

Bayesian learning implies decreasing weights on prior beliefs and increasing weights on the accuracy of the analyst's past forecast record, as the number of forecast errors comprising her forecast record (its length) increases. Consistent with this model of investor learning, empirical tests show that investors’ reactions to forecast news are increasing in the product of the accuracy and length of analysts’ forecast records. Moreover, the Bayesian learning predicted by our model is more descriptive of investor reactions than is a static model which predicts that investors’ responses condition only on the prior accuracy of the analyst.

We posit that limited transparency of firms’ operations to outside investors increases demands on governance systems to alleviate moral hazard problems. We investigate how ownership concentration, directors’ and executive's incentives, and board structure vary with: (1) earnings timeliness, and (2) organizational complexity measured as geographic and/or product line diversification. We find that ownership concentration, directors’ and executives’ equity-based incentives, and outside directors’ reputations vary inversely with earnings timeliness, and that ownership concentration, and directors’ equity-based incentives increase with firm complexity. However, board size and the percentage of inside directors do not vary significantly with earnings timeliness or firm complexity.