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Predicting loss aversion behavior with machine-learning methods
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AI
DOI：10.1057/s41599-023-01620-2.png)
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Abstract
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This paper proposes to forecast an important cognitive phenomenon called the Loss Aversion Bias via Hybrid Machine Learning Models. One of the unique aspects of this study is using the reaction time (milliseconds), psychological factors (self-confidence scale, Beck's hopelessness scale, loss-aversion), and personality traits (financial literacy scales, socio-demographic features) as features in classification and regression methods. We found that Random Forest was superior to other algorithms, and when the positive spread ratio (between gain and loss) converged to default loss aversion level, decision-makers minimize their decision duration while gambling, we named this phenomenon as irresistible impulse of gambling.
Keywords:
PROSPECT-THEORY
DECISION TREE
NEURAL BASIS
REGRESSION
CONSISTENCY
CLASSIFIER
INVESTMENT
FORESIGHT
CHOICE
POLICY
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3.6
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6.6K
Citations:
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