This dissertation explores different issues in corporate finance. The first two essays explore how innovation, disclosure, and technological progress impact the ability of managers to learn from their investors through stock prices. The third essay analyzes the lending and portfolio reallocation behavior of financial institutions especially banks when they are faced with an increase in political uncertainty. In Chapter 1, "Trade Secrets, Proprietary Information, and Managerial Learning,'' I study changes in managerial learning from stock prices after an increase in the protection of trade secrets allows managers to withhold proprietary information and substitute it with increased disclosure of nonproprietary information. Using the staggered U.S. state-level adoption of the Uniform Trade Secrets Act, I show that this trade-off leads to a decline in the sensitivity of firm investment to Tobin's Q. Consistent with the managerial learning hypothesis, further analysis shows that this decline is concentrated in firms with low financing constraints and more sophisticated investors. Using theoretical motivation from literature and machine learning techniques, I identify industries with a positive "treatment'' effect, but a different relative value of proprietary and nonproprietary information to provide novel evidence that the type of disclosure plays an additional role beyond its level. Disclosure of information that acts as a substitute (complement) to the information aggregation activities leads to reduced (increased) managerial learning. In Chapter 2 (joint with Kevin Aretz and Gaurav Kankanhalli), "Technological Progress, Managerial Learning, and the Investment-to-Stock Price Sensitivity,'' motivated by a real-options framework in which managers learn about the unobservable characteristics of new production technologies from their recently installed assets and their stock price, we show that the corporate investment-to-stock price sensitivity rises with the time since a firm last acquired new capacity. Notably, managers learn less from the stock price when they have better information, investors have worse information, or when alternative outside information sources exist. We shed light on the nature of information managers extract from markets by showing that firms with outdated capital learn more from the stock price when exogenously exposed to accelerated innovation. In Chapter 3 (joint with Pradeep Muthukrishnan), "Political Uncertainty and Geographical Reallocation of Bank Syndicated Lending,'' we study how banks exposed to uncertainty shocks geographically reallocate their syndicated lending portfolios. We employ a series of unprecedented events that lead to political uncertainty shocks in European countries and show that shock-exposed banks diversify their loan portfolios and enter new geographies. We show that aggregate bank lending remains constant, but with geographical reallocation of lending portfolios away from affected countries. We find that banks increase lending strictness in shock-affected countries, but are more lenient when entering new geographies, highlighting the importance of geographical proximity in monitoring. Banks choose borrowers in foreign markets based on their ex-ante performance indicators, prefer smaller and high-growth borrowers, and lend to firms with existing access to debt capital markets. Our results are novel in showing that uncertainty shocks have a compositional effect on banks' loan portfolios in interconnected markets --- depressing credit in shock countries while expanding credit access in other markets.