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Scoring and predicting risk preferences

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Ertek, 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)

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Official URL: http://dx.doi.org/10.1007/978-1-4471-2969-1_9

Abstract

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.

Item Type:Book Section / Chapter
Uncontrolled Keywords:behavior computing, risk scoring, risk preferences, risk-taking behavior, financial risk-taking
Subjects:T Technology > T Technology (General)
H Social Sciences > H Social Sciences (General)
H Social Sciences > HD Industries. Land use. Labor > HD0028 Management. Industrial Management
Q Science > QA Mathematics > QA075 Electronic computers. Computer science
H Social Sciences > HD Industries. Land use. Labor > HD0061 Risk Management
H Social Sciences > HD Industries. Land use. Labor > HD0030.2 Electronic data processing. Information technology
H Social Sciences > HA Statistics
ID Code:21065
Deposited By:Gürdal Ertek
Deposited On:01 Dec 2012 17:00
Last Modified:01 Aug 2019 09:54

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