Reducing algorithm aversion through experience

• Published in: Journal of behavioral and experimental finance Vol. 31; p. 100524
• Main Authors: Filiz, Ibrahim, Judek, Jan René, Lorenz, Marco, Spiwoks, Markus
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2021
• Subjects: Algorithm aversion; Behavioral finance; Experiments; Operating experience; Overconfidence; Stock market forecasting; D84; Algorithm aversion; Operating experience; G41; Stock market forecasting; G17; D91; Experiments; D83; Behavioral finance; Overconfidence
• ISSN: 2214-6350; 2214-6350
• DOI: 10.1016/j.jbef.2021.100524

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. •Subjects overestimate their own competence, which can lead to rejection of algorithms.•Intuitive share price forecasts are clearly inferior to those of the algorithm.•Over time, subjects begin to use the algorithm more frequently.•Repeated tasks, constant feedback and financial incentives can reduce algorithm aversion.•A learning process can significantly weaken a tendency towards algorithm aversion.