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Is It Real, or Is It Randomized?: A Financial Turing Test

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  • Jasmina Hasanhodzic
  • Andrew W. Lo
  • Emanuele Viola

Abstract

We construct a financial "Turing test" to determine whether human subjects can differentiate between actual vs. randomized financial returns. The experiment consists of an online video-game (http://arora.ccs.neu.edu) where players are challenged to distinguish actual financial market returns from random temporal permutations of those returns. We find overwhelming statistical evidence (p-values no greater than 0.5%) that subjects can consistently distinguish between the two types of time series, thereby refuting the widespread belief that financial markets "look random." A key feature of the experiment is that subjects are given immediate feedback regarding the validity of their choices, allowing them to learn and adapt. We suggest that such novel interfaces can harness human capabilities to process and extract information from financial data in ways that computers cannot.

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  • Jasmina Hasanhodzic & Andrew W. Lo & Emanuele Viola, 2010. "Is It Real, or Is It Randomized?: A Financial Turing Test," Papers 1002.4592, arXiv.org.
    • Handle: RePEc:arx:papers:1002.4592
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    References listed on IDEAS

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    1. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    2. Kroll, Yoram & Levy, Haim & Rapoport, Amnon, 1988. "Experimental tests of the mean-variance model for portfolio selection," Organizational Behavior and Human Decision Processes, Elsevier, vol. 42(3), pages 388-410, December.
    3. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    4. De Bondt, Werner P. M., 1993. "Betting on trends: Intuitive forecasts of financial risk and return," International Journal of Forecasting, Elsevier, vol. 9(3), pages 355-371, November.
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    Cited by:

    1. Élise PAYZAN LE NESTOUR, 2010. "Bayesian Learning in UnstableSettings: Experimental Evidence Based on the Bandit Problem," Swiss Finance Institute Research Paper Series 10-28, Swiss Finance Institute.

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