EconPapers    
Economics at your fingertips  
 

Predicting loss aversion behavior with machine-learning methods

Ömür Saltık (), Wasim ul Rehman (), Rıdvan Söyü (), Süleyman Değirmen () and Ahmet Şengönül ()
Additional contact information
Ömür Saltık: Konya Food and Agriculture University
Wasim ul Rehman: University of Punjab
Rıdvan Söyü: Toros University
Süleyman Değirmen: Konya Food and Agriculture University
Ahmet Şengönül: Sivas Cumhuriyet University

Palgrave Communications, 2023, vol. 10, issue 1, 1-14

Abstract: 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”.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1057/s41599-023-01620-2 Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-01620-2

Ordering information: This journal article can be ordered from
https://www.nature.com/palcomms/about

DOI: 10.1057/s41599-023-01620-2

Access Statistics for this article

More articles in Palgrave Communications from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-19
Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-01620-2