This dissertation presents a collection of three essays on how investors process information, one of the most fundamental questions in finance. It employs advanced empirical techniques such as textual analysis and machine learning and spans various investor groups, including institutional investors, retail investors, and investors of emerging asset classes. The first essay “Does Partisanship Affect Mutual Fund Information Processing? Evidence from Textual Analysis on Earnings Calls” shows that partisan funds react stronger to information more consistent with their pre-existing beliefs. The second essay “Partisan Return Gap: The Polarized Stock Market in the Time of a Pandemic”, co-authored with Jinfei Sheng and Zheng Sun, explores how political beliefs affect asset prices during the COVID pandemic. The third essay “How Do Investors Value Technology in Cryptocurrency? Evidence from Textual Analysis”, co-authored with Jinfei Sheng and Yukun Liu, examines how investors evaluate and process information about new technologies within the cryptocurrency market. Overall, this dissertation offers valuable insights into how various factors shape investor information processing and asset prices.