Essays in Finance, Technology, and Behavior

Abstract

This dissertation consists of three essays that study how patterns in human behavior inform models of finance and technology. The first essay develops methods to measure economic representations from language, and uses these representations to study how the market values firms. It finds that firms can be misvalued when they are misperceived, including during waves of investor attention to new technologies. The second essay, co-authored with Keyon Vafa, builds a framework to evaluate lookahead bias in pretrained language models, and finds evidence of the bias in applications to finance and political economy. It describes how to prevent lookahead bias by using time-indexed language models in forecasting analyses. The third essay, co-authored with Johnny Tang, analyzes how partisanship relates to economic beliefs, and finds partisan differences in optimism, attention to economic topics, and interpretation of economic shocks. It discusses how these differences in stated beliefs can help to interpret partisan differences in economic decisions.