Data and Code for: A Learning Model of Financial Instability

I develop an adaptive learning model where periods of stability lead to periods of instability, similar to Minsky's financial instability hypothesis. In tranquil times, investors increase their subjective risk-return assessments. They buy more stocks, driving up prices, and reinforcing the change in beliefs. Crashes arise endogenously as rapid booms increase perceptions of risk as well as returns. The model can help explain observed asset pricing phenomena. I also establish new results on instability and cycles in adaptive learning models. As long as agents put sufficient weight on new information, learning from asset returns leads to instability that drives endogenous booms and busts.<br>This is the data and code accompanying the paper.<br>