This dissertation contains three chapters that explore topics in empirical finance and political economy.

In Chapter 1, I study how the fundraising revenues of political campaigns affect the outcome of U.S. elections. First, I assemble a novel and granular dataset that provides a comprehensive picture of cash flows and voting intentions during U.S. congressional races. Then, I extract weekly shocks to the fundraising revenues of campaigns by using machine learning on the dataset. I find that the effect of revenues on the vote share decreases over the course of general elections. In races involving an incumbent, an additional $100,000 in challenger revenues increases her vote share by 1.48pp in the first half of the general election, but has no effect in the second half. Early cash infusions are more valuable than late cash infusions because they provide flexibility to respond to the opponent’s actions and mitigate current and future financing constraints.

In Chapter 2, I examine how strategic and financial considerations shape the spending behavior of political campaign committees. To discipline the empirical analysis, I derive a dynamic model of strategic investment under financing constraints. I test the predictions of the model using the revenue shocks constructed in Chapter 1. I find that a committee’s elasticity of advertising expenditures to the revenue shocks of its opponent is 8%, which is a third of a committee’s elasticity to its own shocks. Moreover, a committee that is relatively richer than its opponent reacts more aggressively to its opponent’s shocks, both in levels and as a fraction of cash reserves. This result suggests that the availability of internal financing can amplify the competitive aspect of political spending in electoral races.

In Chapter 3, I identify investor overreaction in a setting where information flows are not observable and learning pertains to multiple dimensions of an asset. Specifically, I measure how investors react to the information released during merger attempts and whether they form rational beliefs about the probability of deal completion. Using a model of distorted learning that generates testable implications, I find evidence of relative mispricing in the cross-section of merger targets. Empirically, a low price-implied probability of success underestimates the actual probability of success, and vice versa, suggesting that investors overreact to deal-specific information. The overreaction is unrelated to the unconditional merger premium and not driven by exposure to traditional risk factors.