Reconciling Efficient Markets with Behavioral Finance: The Adaptive Markets Hypothesis

28 Pages Posted: 5 Nov 2010

See all articles by Andrew W. Lo

Andrew W. Lo

Massachusetts Institute of Technology (MIT) - Laboratory for Financial Engineering; Santa Fe Institute

Multiple version iconThere are 2 versions of this paper

Date Written: 2005

Abstract

The battle between proponents of the Efficient Markets Hypothesis and champions of behavioral finance has never been more pitched, and little consensus exists as to which side is winning or the implications for investment management and consulting. In this article, I review the case for and against the Efficient Markets Hypothesis and describe a new framework - the Adaptive Markets Hypothesis - in which the traditional models of modern financial economics can coexist alongside behavioral models in an intellectually consistent manner. Based on evolutionary principles, the Adaptive Markets Hypothesis implies that the degree of market efficiency is related to environmental factors characterizing market ecology such as the number of competitors in the market, the magnitude of profit opportunities available, and the adaptability of the market participants. Many of the examples that behavioralists cite as violations of rationality that are inconsistent with market efficiency - loss aversion, overconfidence, overreaction, mental accounting, and other behavioral biases - are, in fact, consistent with an evolutionary model of individuals adapting to a changing environment via simple heuristics. Despite the qualitative nature of this new paradigm, I show that the Adaptive Markets Hypothesis yields a number of surprisingly concrete applications for both investment managers and consultants.

Keywords: Behavioral Finance, Market Efficiency, Efficient Markets Hypothesis, Adaptive Markets Hypothesis

JEL Classification: G10, G11, G14

Suggested Citation

Lo, Andrew W., Reconciling Efficient Markets with Behavioral Finance: The Adaptive Markets Hypothesis (2005). Journal of Investment Consulting, Vol. 7, No. 2, pp. 21-44, 2005, Available at SSRN: https://ssrn.com/abstract=1702447

Andrew W. Lo (Contact Author)

Massachusetts Institute of Technology (MIT) - Laboratory for Financial Engineering ( email )

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