Using merger announcements and applying methods from computational linguistics we find strong evidence that stock prices underreact to information in financial media. A one standard deviation increase in the media-implied probability of merger completion increases the subsequent 12-day return of a long-short merger strategy by 1.2 percentage points. Filtering out the 28% of announced deals with the lowest media-implied completion probability increases the annualized alpha from merger arbitrage by 9.3 percentage points. Our results are particularly pronounced when high-yield spreads are large and on days when only few merger deals are announced.

How does lending-market competitiveness shape new firms’ financing? Using a unique U.S. representative panel of new firms, we document that in more concentrated local lending markets: (a) new firms are less likely to access credit; (b) new firms have lower leverage; and (c) the best-performing firms are more severely affected by reduced debt financing. We develop a contingent-claims model with monopolistically competitive banks that rationalizes these facts and shows how credit-market conditions determine loan fees and concentration. Our findings highlight banks’ market power as a channel through which the financial sector influences firms’ development and, hence, economic growth.

The crisis is not yet over; the housing market continues to deteriorate and there are spill-overs into other markets. Growth is declining, with potentially self re-enforcing mechanisms between financial markets and the real economy coming into play. A local problem became a global crisis because of poor risk management, lack of transparency and excessive leverage. Not only does the capital base need re-building, but also incentive schemes need reconsideration.

We examine the empirical implications of learning under ambiguity for the cross-section of stock returns. We introduce a theoretically-motivated ambiguity measure and find that ambiguity is priced in the cross-section of average stock returns. Ambiguity is not subsumed by state variables known to predict stock returns, nor by value, size, and momentum factors. In R-squared comparative tests, a model that takes ambiguity into account performs better than empirical implementations of the Bayesian learning model, the intertemporal CAPM, and the four-factor model of Fama and French (1993) and Carhart (1997).

We develop a model of the dynamic interaction between CEO overconfidence and dividend policy. The model shows that an overconfident CEO views external financing as costly and hence builds financial slack for future investment needs by lowering the current dividend payout. Consistent with the main prediction, we find that the level of dividend payout is about one-sixth lower in firms managed by CEOs who are more likely to be overconfident. We document that this reduction in dividends associated with CEO overconfidence is greater in firms with lower growth opportunities and lower cash flow. We also show that the magnitude of the positive market reaction to a dividend-increase announcement is higher for firms with greater uncertainty about CEO overconfidence.