We show how low-frequency boom and bust cycles in asset prices can emerge from Bayesian learning by investors. Investors rationally maximize infinite horizon utility but hold subjective priors about the asset return process that we allow to differ infinitesimally from the rational expectations prior. Bayesian updating of return beliefs then gives rise to self-reinforcing return optimism that results in an asset price boom. The boom endogenously comes to an end because return optimism causes investors to make optimistic plans about future consumption. The latter reduces the demand for assets that allow to intertemporally transfer resources. Once returns fall short of expectations, investors revise return expectations downward and set in motion a self-reinforcing price bust. In line with available survey data, the learning model predicts return optimism to comove positively with market valuation. In addition, the learning model replicates the low frequency behavior of the U.S. price dividend ratio over the period 1926-2006.
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