Motivated by the public debate regarding corporate responsibility, we construct a memory-based model of decision-making to illustrate how corporate and political communication can impact policy preferences. We test the predictions of our model in a new large-scale survey of U.S. citizens on...

Although corporate finance theory suggests how adverse shocks influence shareholder preferences toward corporate risk-taking and executive compensation, few researchers explore this relationship empirically. We construct a firm-year measure of unexpected shocks to environmental regulatory...

This paper studies institutional investors' decision-making using novel data from a major proxy advisor. We highlight the significant role of customized proxy advice in shaping shareholders' voting decisions. About 80% of funds receive customized advice, and custom recommendations differ...

This paper investigates how shocks to expected cash flows influence CEO incentive compensation. Exploiting changes in compliance with environmental regulations as shocks to expected future cash flows, we find that adverse shocks typically prompt corporate boards to recalibrate CEO compensation...

This study analyzes information production and trading behavior of banks with lending relationships. We combine trade-by-trade supervisory data and credit-registry data to examine banks' proprietary trading in borrower stocks around a large number of corporate events. We find that relationship...

For many firms, the acquisition process begins with the development of an acquisition plan that is communicated to investors. We construct a comprehensive sample of acquisition plans to provide novel perspectives on the acquisition process and find that acquisition plans are informative to...

We revisit the relationship between firm competition and real efficiency in a novel setting with informational feedback from financial markets. Although intensified competition can decrease market concentration in production, it reduces the value of proprietary information (e.g., market...

We measure investors' short- and long-term stock-return expectations using both options and survey data. These expectations at different horizons reveal what investors think their own short-term expectations will be in the future, or forward return expectations. While contemporaneous short-term...

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance--in terms of SDF Sharpe ratio and test asset pricing errors--is improving in model parameterization (or "complexity"). Our empirical findings verify the...

This paper studies the predictability of ultra high-frequency stock returns and durations to relevant price, volume and transactions events, using machine learning methods. We find that, contrary to low frequency and long horizon returns, where predictability is rare and inconsistent,...