Predicting the unpredictable: New experimental evidence on forecasting random walks

Te Bao, Brice Corgnet, Nobuyuki Hanaki, Yohanes Riyanto and Jiahua Zhu

Journal of Economic Dynamics and Control, 2023, vol. 146, issue C

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.

Keywords: Asset prices; Regime-switching; Price prediction; Experimental finance (search for similar items in EconPapers)
JEL-codes: C91 D84 D91 G41 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Working Paper: Predicting the unpredictable: New experimental evidence on forecasting random walks (2023)
Working Paper: Predicting the unpredictable: New experimental evidence on forecasting random walks (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:146:y:2023:i:c:s0165188922002743

DOI: 10.1016/j.jedc.2022.104571

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