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Predicting loss aversion behavior with machine-learning methods

Economics

Predicting loss aversion behavior with machine-learning methods

Ö. Saltık, W. U. Rehman, et al.

Explore the intriguing world of forecasting loss aversion bias with innovative hybrid machine learning models! This research, conducted by Ömür Saltık, Wasim ul Rehman, Rıdvan Söyü, Süleyman Değirmen, and Ahmet Şengönül, unveils fascinating interactions between psychological factors and decision-making processes, highlighting a newly identified phenomenon in gambling behavior.

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Playback language: English
Abstract
This paper proposes forecasting loss aversion bias using hybrid machine learning models. It uniquely incorporates reaction time, psychological factors (self-confidence, hopelessness, loss aversion), and personality traits (financial literacy, socio-demographics) as features. Random Forest emerged as the superior algorithm. A new phenomenon, "irresistible impulse of gambling," was identified where decision-makers minimize decision duration as the positive spread ratio converges to the default loss aversion level.
Publisher
HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS
Published On
Apr 27, 2023
Authors
Ömür Saltık, Wasim ul Rehman, Rıdvan Söyü, Süleyman Değirmen, Ahmet Şengönül
Tags
loss aversion
hybrid machine learning
reaction time
gambling behavior
psychological factors
financial literacy
decision making

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