Predicting the unpredictable: new experimental evidence on forecasting random walks

• Main Authors: Bao, Te, Corgnet, Brice, Hanaki, Nobuyuki, Riyanto, Yohanes E., Zhu, Jiahua
• Other Authors: School of Social Sciences
• Format: Article
• Language: English
• Published: 2023
• Subjects: Social sciences::Economic theory; Asset Prices; Price Prediction
• Online Access: https://hdl.handle.net/10356/172196
• Institution: Nanyang Technological University

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.