Predicting loss aversion behavior with machine-learning methods

Abstract

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”.

Endowment Effect, Information Asymmetry, and Real Estate Market Decisions: Willingness to Pay and Willingness to Accept Disparities

“…Despite the well-established paradigms, the cause of the WTA -WTP gap remains debatable as it is mainly influenced by what characterizes the market in which the endowment effect is being tested, the framing of experiments (Plott & Zeiler, 2011), and the nature of commodities. Thaler (1980) said that the endowment effect exists because equal amounts of gains and losses do not yield the same emotional impact, as losses are felt more acutely than gains (Saltık et al, 2023). According to Song and Ahn (2019), the endowment effect is also traced to a situation where individuals prefer their current state as compared to the available alternatives, particularly when the disutility of giving up an object is greater than the utility associated with keeping it.…”