Scoring and Predicting Risk Preferences

Gurdal Ertek, Murat Kaya, Cemre Kefeli, onur Onur, Kerem Uzer

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationBehavior Computing
Subtitle of host publicationModeling, Analysis, Mining and Decision
PublisherSpringer-Verlag London Ltd
Pages143-163
Number of pages21
ISBN (Electronic)9781447129691
ISBN (Print)9781447129684
DOIs
Publication statusPublished - Jan 1 2012
Externally publishedYes

ASJC Scopus subject areas

  • General Computer Science

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