Overconfidence and forecast accuracy

Bin Liu and Monica Tan

Studies in Economics and Finance, 2019, vol. 38, issue 3, 601-618

Abstract: Purpose - This paper aims to investigate how overconfidence bias affects financial market participants’ forecast accuracy based on the hard–easy effect concept of overconfidence research. Design/methodology/approach - The authors adopt an experimental method for behavioural finance studies. In the experiment, the authors measure and capture participants’ forecast accuracy as well as their individual confidence level. In particular, participants make incentive-compatible forecasts, that is, the elicited forecast determines the participants’ financial rewards in real monetary gain/loss. Findings - The results show that the hard–easy effect causes optimistic forecasts for hard-to-predict stocks, indicating that overconfident investors tend to make over-optimistic and less accurate price forecasts when making judgements on hard tasks. Consistent with the literature, the authors find evidence of a negative relationship between forecast accuracy and confidence level. The results also indicate that the overall relationship between overconfidence and forecast accuracy is driven by the price forecasts made for hard-to-predict stocks. Originality/value - As per the authors’ knowledge, this paper is one of the first studies that provides empirical evidence directly showing the hard–easy effect in the relation between overconfidence and forecast ability in an experimental setting. This study uses an experimental design that specifically measures the hard–easy effect in a stock market scenario using professional financial information and real monetary incentives, which have not been used in any previous studies of the hard–easy effect.

Keywords: Experimental finance; Overconfidence; Hard-easy effect; Stock price forecast accuracy (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eme:sefpps:sef-12-2017-0345

DOI: 10.1108/SEF-12-2017-0345

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