Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/253806 
Year of Publication: 
2020
Citation: 
[Journal:] Journal of Economics, Finance and Administrative Science [ISSN:] 2218-0648 [Volume:] 25 [Issue:] 50 [Publisher:] Emerald Publishing Limited [Place:] Bingley [Year:] 2020 [Pages:] 451-478
Publisher: 
Emerald Publishing Limited, Bingley
Abstract: 
Purpose - The current study aims to investigate the impacts of two behavioral biases, namely, loss aversion and overconfidence on the performance of US companies. First, the impact of loss aversion on the economic performance of companies was assessed. Second, the impact of overconfidence on market performance was discussed. Design/methodology/approach - This study used around 6,777 quarterly observations on the population of US-insured industrial and services companies over the 2006-2016 period. Ordinary least squares (OLS) regression in two panel data models were used to test the hypotheses formulated for the study. Findings - It was documented that the loss-aversion bias negatively affects the economic performance of companies and this is achieved for both sectors. In contrast, the findings suggest that overconfidence positively affects market performance of industrial firms but negatively affects market performance in service firms. Further robust evidence was found that overconfidence bias seems to be dominant, and hence, investors may tend to be more overconfident rather than more loss-averse. Originality/value - This research can be extended by focusing on the following question: What is the impact of the contradictory (positive and negative) effects of an investor's loss aversion and overconfidence on the US company performance in case of realization of a stock market crisis or stock market crash?
Subjects: 
Behavioural biases
Decision-making
Economic performance
Loss aversion
Market performance
Overconfidence
JEL: 
C12
D53
D81
D82
G11
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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