Learning from Liquidation Prices

Abstract

I develop a model of investor learning driven by mistaken inference from market prices. Investors have heterogeneous beliefs about the worst case return of a risky asset and take leverage to buy it. When the worst case becomes more likely, forced liquidations result in price crashes, which investors mistake for negative information about worst case returns. They therefore revise cash flow expectations downwards, henceforth requiring larger returns. The model predicts that crashes lead to persistent changes in future average returns and that larger crashes are followed by larger changes. To link the model to historical crashes, I consider two strategies associated with the Black Monday crash in 1987 and the Lehman Brothers bankruptcy in 2008. Hedged put options selling suffered severe losses around Black Monday, while arbitraging the difference in implied credit risk between the corporate bond and CDS markets was similarly negatively affected after the Lehman bankruptcy. The losses on these strategies in those crisis episodes were likely exacerbated by deleveraging, but the increased returns after the crashes have been remarkably persistent, consistent with the implications of my model.