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Scoring and predicting risk preferencesErtek, Gürdal and Kaya, Murat and Kefeli, Cemre and Onur, Özge and Uzer, Kerem (2012) Scoring and predicting risk preferences. In: Cao, Longbing and Yu, Philip S., (eds.) Behavior Computing: Modeling, Analysis, Mining and Decision. Springer, Berlin, pp. 143-163. ISBN 978-1-4471-2968-4 (Print) 978-1-4471-2969-1 (Online) This is the latest version of this item.
Official URL: http://dx.doi.org/10.1007/978-1-4471-2969-1_9 AbstractThis 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.
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