The Efficient Markets Hypothesis Does Not Hold When Securities Valuation Is Computationally Hard

Abstract

We study the Efficient Markets Hypothesis (EMH) in a setting where information heterogeneity emerges because securities valuation requires solving an NP-hard problem. We demonstrate experimentally that the quality of prices deteriorates substantially as computational complexity increases. Participants whose valuations are closer to true values earn more from trading. Participants improved their individual valuations by learning from market data, and their individual valuations on average were better than those reflected in market prices. These results are in sharp contrast with findings in experiments where correct valuation requires averaging of private information. They suggest that EMH only holds in very specific circumstances.

Keywords: Efficient Markets Hypothesis, Computational Complexity, Financial Markets, Grossman-Stiglitz Paradox, Hirshleifer Effect, Intellectual Discovery, Patents, Prediction Markets

JEL Classification: G14, C92, D82