Abstract

http://ssrn.com/abstract=1977721
 
 

References (25)



 


 



Adaptive Markets and the New World Order


Andrew W. Lo


Massachusetts Institute of Technology (MIT) - Sloan School of Management; Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL); National Bureau of Economic Research (NBER)

December 30, 2011


Abstract:     
The traditional investment paradigm is based on several key assumptions including rational investors, stationary probability laws, and a positive linear relationship between risk and expected return with parameters that are constant over time and which can be accurately estimated. These assumptions were plausible during the “Great Modulation” — the seven decades spanning the mid-1930s to the mid-2000s in which equity markets exhibited relatively stable risk and expected returns — but have broken down during the past decade, implying temporary but significant violations of rational pricing relationships. This tension between rational and behavioral market conditions is captured by the Adaptive Markets Hypothesis (AMH), an evolutionary perspective on market dynamics in which intelligent but fallible investors learn from and adapt to changing environments. Under the AMH, markets are not always efficient, but they are highly competitive and adaptive, and can vary in their degree of efficiency as the economic environment and investor population change over time. The AMH has several new implications for financial analysis, including the possibility of negative risk premia, the transformation of alpha into beta, and the importance of macro factors and risk budgeting in asset-allocation policies.

Number of Pages in PDF File: 22

Keywords: Efficient Markets, Behavioral Finance, Asset Allocation, Investments

JEL Classification: G10, G11, G12, G14

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Date posted: January 1, 2012 ; Last revised: June 5, 2012

Suggested Citation

Lo, Andrew W., Adaptive Markets and the New World Order (December 30, 2011). Available at SSRN: http://ssrn.com/abstract=1977721 or http://dx.doi.org/10.2139/ssrn.1977721

Contact Information

Andrew W. Lo (Contact Author)
Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )
100 Main Street
E62-618
Cambridge, MA 02142
United States
617-253-0920 (Phone)
781 891-9783 (Fax)
HOME PAGE: http://web.mit.edu/alo/www
Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)
Stata Center
Cambridge, MA 02142
United States
National Bureau of Economic Research (NBER)
1050 Massachusetts Avenue
Cambridge, MA 02138
United States
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