This study presents a methodology to determine risk scores of individuals, for a given financial risk preference survey. To this end, we use a regression- based iterative algorithm to determine the weights for survey questions in the scoring process. Next, we generate classification models to classify individuals into risk-averse and risk-seeking categories, using a subset of survey questions. We illustrate the methodology through a sample survey with 656 respondents. We find that the demographic (indirect) questions can be almost as successful as risk-related (direct) questions in predicting risk preference classes of respondents. Using a decision-tree based classification model, we discuss how one can generate actionable business rules based on the findings.
CITATION STYLE
Ertek, G., Kaya, M., Kefeli, C., Onur, onur, & Uzer, K. (2012). Scoring and Predicting Risk Preferences. In Behavior Computing: Modeling, Analysis, Mining and Decision (pp. 143–163). Springer-Verlag London Ltd. https://doi.org/10.1007/978-1-4471-2969-1_9
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