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.
Ertek, G., Kaya, M., Kefeli, C., Onur, Ö., Uzer, K. (2012) “Scoring and predicting risk preferences” in Behavior Computing: Modeling, Analysis, Mining and Decision. Eds: Longbing Cao, Philip S. Yu. Springer.
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Scoring and Predicting Risk Preferences
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