Semi-Supervised Machine Learning Method for Predicting Observed Individual Risk Preference Using Gallup Data

Author:

Ahmed Faroque12ORCID,Shamsuddin Mrittika3,Sultana Tanzila4,Shamsuddin Rittika5ORCID

Affiliation:

1. Graduate School of Economics and Management, Ural Federal University, Lenin Ave., 51, Yekaterinburg 620075, Russia

2. Bangladesh Institute of Governance and Management, E-33, Sher-E-Bangla Nagar, Dhaka 1207, Bangladesh

3. Department of Economics, Dalhousie University, 6299 South St., Halifax, NS B3H 4R2, Canada

4. Department of Economics, College of Business Administration, Southern Illinois University, Carbondale, IL 62901, USA

5. Department of Computer Science, Oklahoma State University, Stillwater, OK 74078, USA

Abstract

Risk and uncertainty play a vital role in almost every significant economic decision, and an individual’s propensity to make riskier decisions also depends on various circumstances. This article aims to investigate the effects of social and economic covariates on an individual’s willingness to take general risks and extends the scope of existing works by using quantitative measures of risk-taking from the GPS and Gallup datasets (in addition to the qualitative measures used in the literature). Based on the available observed risk-taking data for one year, this article proposes a semi-supervised machine learning-based approach that can efficiently predict the observed risk index for those countries/individuals for years when the observed risk-taking index was not collected. We find that linear models are insufficient to capture certain patterns among risk-taking factors, and non-linear models, such as random forest regression, can obtain better root mean squared values than those reported in past literature. In addition to finding factors that agree with past studies, we also find that subjective well-being influences risk-taking behavior.

Publisher

MDPI AG

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