Predicting the unpredictable: New experimental evidence on forecasting random walks

Te Bao, Brice Corgnet, Nobuyuki Hanaki, Yohanes Riyanto and Jiahua Zhu
Additional contact information
Jiahua Zhu: TJU - Tianjin University

Post-Print from HAL

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)
Date: 2023-01-01
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Published in Journal of Economic Dynamics and Control, 2023, 146, 32 p. ⟨10.1016/j.jedc.2022.104571⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Journal Article: Predicting the unpredictable: New experimental evidence on forecasting random walks (2023)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04376053

DOI: 10.1016/j.jedc.2022.104571

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().