Reducing algorithm aversion through experience

Ibrahim Filiz, Jan René Judek, Marco Lorenz and Markus Spiwoks

Journal of Behavioral and Experimental Finance, 2021, vol. 31, issue C

Abstract: In the context of an experiment, we examine the persistence of aversion towards algorithms in relation to learning processes. The subjects of the experiment are asked to make one share price forecast (rising or falling) in each of 40 rounds. A forecasting computer (algorithm) is available to them which has a success rate of 70%. Intuitive forecasts made by the subjects usually lead to a significantly poorer success rate. Feedback provided after each round of forecasts and a clear financial incentive lead to the subjects becoming better able to estimate their own forecasting abilities. At the same time, their aversion to algorithms also decreases significantly.

Keywords: Algorithm aversion; Overconfidence; Operating experience; Stock market forecasting; Behavioral finance; Experiments (search for similar items in EconPapers)
JEL-codes: D83 D84 D91 G17 G41 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:beexfi:v:31:y:2021:i:c:s221463502100068x

DOI: 10.1016/j.jbef.2021.100524

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