Foundations of Financial Behavior and Adaptive Markets

Modern investment theory and practice is largely based on the Efficient Markets Hypothesis (EMH), which asserts that financial markets fully, accurately, and instantly incorporate all available information into market prices. However, underlying this idea is the assumption that market participants are rational beings. While this may be true most of the time, periods of booms, busts, and financial crises tell us that this isn’t always the case.

The Adaptive Markets Hypothesis (AMH) is an interdisciplinary approach to reconciling the EMH with human behavior. The AMH posits that the impact of evolutionary forces such as competition, mutation, reproduction, and natural selection on financial institutions and market participants determines the efficiency of markets and the waxing/waning of investment products, businesses, industries, and ultimately institutional and individual wealth. Further, it 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 market participants. In this light, the EMH isn’t wrong, it’s simply incomplete.

Current Research

  • Black’s Leverage Effect is Not Due to Leverage
    Jasmina Hasanhodzic and Andrew W. Lo
  • An Experimental Exploration of Loss Aversion and Probability Matching Behavior
    Alexander Huang, Ruixun Zhang, Katie Marlowe, and Andrew W. Lo
  • Artificial Intelligence Models of Investor Behavior
    Andrew W. Lo and Alexander Remorov
  • A Time and Frequency Domain Analysis of Contrarian Trading Profits
    Shomesh Chaudhuri and Andrew W. Lo
  • Is It Real, or Is It Randomized?: A Financial Turing Test
    Jasmina Hasanhodzic, Andrew W. Lo, and Emanuele Viola
  • Psychophysiological Analysis of Financial Traders
    Andrew Ang, Sourya Naraharisetti, David Hesketh, and Andrew W. Lo
  • Variety is the Spice of Life: Irrational Behavior as Adaptation to Stochastic Environments
    Ruixun Zhang, Thomas J. Brennan, and Andrew W. Lo