Natural Language Processing in Finance and Empirical Macroeconomics

The three essays collected in this Ph.D. thesis apply Natural Language Processing (NLP) tools to asset pricing and household finance. The first essay identifies the triggers of flight-to-safety episodes from financial news and highlights the importance of political, geopolitical, and macroeconomic risks. The second essay shows how the inflation expectation gap between households and experts widens with narrative disagreement between general and specialized newspapers about the demand or supply origins of inflation. The third essay demonstrates how the temporal references of newspaper articles can add predictive power toward future excess stock returns above and beyond their tone and topics.