We revisit several leading puzzles about the aggregate stock market by incorporating into a standard dividend discount model survey expectations of earnings of S&P 500 firms. Using survey expectations, while keeping discount rates constant, explains a significant part of “excess” stock price volatility, price-earnings ratio variation, and return predictability. The evidence is consistent with a mechanism in which good news about fundamentals leads to excessively optimistic forecasts of earnings, especially at long horizons, which inflate stock prices and lead to subsequent low returns. Relaxing rational expectations of fundamentals in a standard asset pricing model accounts for stock market anomalies in a parsimonious way.
We examine a new data set of laws and practices governing public procurement, as well as procurement outcomes, in 187 countries. We measure regulation as restrictions on discretion of the procuring agents. We find that laws and practices are highly correlated with each other across countries, better practices are correlated with better outcomes, but laws themselves are not correlated with outcomes. To shed light on this puzzle, we present a model of procurement in which both regulation and public sector capacity determine the efficiency of procurement. In the model, regulation is effective in countries with low public sector capacity, and detrimental in countries with high public sector capacity because it inhibits the socially optimal exercise of discretion. We find evidence broadly consistent with this prediction: regulation of procurement improves outcomes, but only in countries with low public sector capacity.
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.