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.
|Title of host publication||Behavior Computing|
|Subtitle of host publication||Modeling, Analysis, Mining and Decision|
|Publisher||Springer-Verlag London Ltd|
|Number of pages||21|
|Publication status||Published - Jan 1 2012|
ASJC Scopus subject areas
- Computer Science(all)