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Predicting the unpredictable : New experimental evidence on forecasting random walks

Author

Listed:
  • Te Bao

    (NTU - Nanyang Technological University [Singapour])

  • Brice Corgnet

    (EM - EMLyon Business School)

  • Nobuyuki Hanaki

    (UOsaka - The University of Osaka)

  • Yohanes E. Riyanto

    (NTU - Nanyang Technological University [Singapour])

  • Jiahua Zhu

    (TJU - Tianjin University)

Abstract

We investigate how individuals use measures of apparent predictability from price charts to predict future market prices. Subjects in our experiment predict both random walk times series, as in the seminal work by Bloomfield and Hales (2002) (BH), and stock price time series. We successfully replicate the experimental findings in BH that subjects are less trend-chasing when there are more reversals in random walk times series. We do not find evidence that subjects overreact less to the trend when there are more reversals in the stock price prediction task. Our subjects also appear to use other variables such as autocorrelation coefficient, amplitude and volatility as measures of predictability. However, as random walk theory predicts, relying on apparent patterns in past data does not improve their prediction accuracy.

Suggested Citation

  • Te Bao & Brice Corgnet & Nobuyuki Hanaki & Yohanes E. Riyanto & Jiahua Zhu, 2023. "Predicting the unpredictable : New experimental evidence on forecasting random walks," Post-Print hal-04376053, HAL.
    • Handle: RePEc:hal:journl:hal-04376053
    DOI: 10.1016/j.jedc.2022.104571
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    Cited by:

    1. Nobuyuki Hanaki & Bolin Mao & Tiffany Tsz Kwan Tse & Wenxin Zhou, 2024. "Valuing Algorithms Over Experts: Evidence from a Stock Price Forecasting Experiment," ISER Discussion Paper 1268r, Institute of Social and Economic Research, The University of Osaka, revised Jul 2025.
    2. Tse, Tiffany Tsz Kwan & Hanaki, Nobuyuki & Mao, Bolin, 2024. "Beware the performance of an algorithm before relying on it: Evidence from a stock price forecasting experiment," Journal of Economic Psychology, Elsevier, vol. 102(C).

    More about this item

    Keywords

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    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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