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Analyzing the interrelations among investors’ behavioral biases using an integrated DANP method

Authors :
Nasser Safaie
Amir Sadighi
Majid Mirzaee Ghazani
Source :
Decision Science Letters, Vol 13, Iss 1, Pp 119-134 (2024)
Publication Year :
2024
Publisher :
Growing Science, 2024.

Abstract

This research investigates the relationships between investors’ behavioral biases and compares their relative importance. For this purpose, a survey is conducted, and analytical methods are used. The sample for this study has been 512 individual investors of the Tehran Stock Exchange who completed an online questionnaire. The respondents replied about their behavior in different situations to analyze the prevalence of asymmetric discounting, mental accounting, shifting risk preference, loss aversion, regret aversion, overconfidence, proxy decision making, ambiguity aversion bias, anchoring, and herd behavior as significant fields of behavioral biases in their investment decisions. The data is analyzed using two different analytical techniques. A model based on structural equations is designed and tested to analyze the relations between these fields. Another integrated method, the DEMATEL-based analytic network process, is also used to prioritize and rank these behavioral biases. Finally, the results are compared and confirmed by each other. Analyzing the results proves the existence of 19 positive and statistically significant relations between these fields. Thus, an increase or decrease in the intensity of a particular field of behavioral biases in one’s decisions significantly affects the intensity of other fields. The present study finds that shifting risk preference, anchoring, loss aversion, and regret aversion are the most important fields of behavioral biases based on their prevalence among investors and their correlations with other biases.

Details

Language :
English
ISSN :
19295804 and 19295812
Volume :
13
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Decision Science Letters
Publication Type :
Academic Journal
Accession number :
edsdoj.083fcd57cd71418d95e70ee4757abd12
Document Type :
article
Full Text :
https://doi.org/10.5267/j.dsl.2023.11.003