We construct an index of long term expected earnings growth for S&P500 firms and show that it has remarkable power to jointly predict errors in these expectations and stock returns, in both the aggregate market and the cross section. The evidence supports a mechanism whereby good news cause investors to become too optimistic about earnings growth, for the market as a whole but especially for specific firms. This leads to inflated stock prices and, as beliefs are systematically disappointed, to subsequent low returns in the aggregate market and for specific firms in the cross section. Overreaction of measured long-term expectations helps resolve major asset pricing puzzles without time series or cross-sectional variation in required returns.
Recent empirical work has revived the Minsky hypothesis of boom-bust credit cycles driven by uctuations in investor optimism. To quantitatively assess this hypothesis, we incorporate diagnostic expectations into an otherwise standard business cycle model with heterogeneous firms and risky debt. Diagnostic expectations are a psychologically founded, forward-looking model of belief formation that captures over-reaction to news. We calibrate the diagnosticity parameter using microdata on the forecast errors of managers of listed firms in the US. The model generates countercyclical credit spreads and default rates, while the rational expectations version generates the opposite pattern. Diagnostic expectations also offer a good fit of three patterns that have been empirically documented: systematic reversals of credit spreads, systematic reversals of aggregate investment, and predictability of future bond returns. Crucially, diagnostic expectations also generate a strong fragility or sensitivity to small bad news after steady expansions. The rational expectations version of the model can account for the rst pattern but not the others. Diagnostic expectations offer a parsimonious account of major credit cycles facts, underscoring the promise of realistic expectation formation for applied business cycle modeling.