Scoring and Predicting Risk Preferences

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.

[1]  R. Thaler,et al.  A Survey of Behavioral Finance , 2002 .

[2]  R. V. Kulkarni,et al.  Expert system design for credit risk evaluation using neuro-fuzzy logic , 2012, Expert Syst. J. Knowl. Eng..

[3]  William P. Wagner,et al.  Selection of knowledge acquisition techniques based upon the problem domain characteristics of production and operations management expert systems , 2001, Expert Syst. J. Knowl. Eng..

[4]  Terje Aven,et al.  Risk management and governance , 2019, The Science of Risk Analysis.

[5]  Bertrand Clarke,et al.  Principles and Theory for Data Mining and Machine Learning , 2009 .

[6]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[7]  L. Thomas.  A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers , 2000 .

[8]  Gary Riley,et al.  Expert Systems: Principles and Programming , 2004 .

[9]  Chih-Fong Tsai,et al.  Using neural network ensembles for bankruptcy prediction and credit scoring , 2008, Expert Syst. Appl..

[10]  A. Patt,et al.  Making index insurance attractive to farmers , 2009 .

[11]  Doheon Lee,et al.  A Taxonomy of Dirty Data , 2004, Data Mining and Knowledge Discovery.

[12]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[13]  Roman Słowiński,et al.  Intelligent Decision Support , 1992, Theory and Decision Library.

[14]  Terje Aven,et al.  Risk Management and Governance: Concepts, Guidelines and Applications , 2010 .

[15]  謝楠楨.  An integrated data mining and behavioral scoring model for analyzing bank customers , 2004 .

[16]  Robert W. Faff,et al.  An empirical investigation of personal financial risk tolerance , 2004 .

[17]  X. T. Wang,et al.  Life history variables and risk-taking propensity , 2009 .

[18]  Sherman D. Hanna,et al.  Factors Related to Risk Tolerance , 1996 .

[19]  U. M. Feyyad.  Data mining and knowledge discovery: making sense out of data , 1996 .

[20]  Chen-Fu Chien,et al.  Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry , 2008, Expert Syst. Appl..

[21]  John E. Grable,et al.  Investor Risk Tolerance: Testing The Efficacy Of Demographics As Differentiating and Classifying Factors , 1998 .

[22]  Stefan Lessmann,et al.  Supervised Classification for Decision Support in Customer Relationship Management , 2008 .

[23]  Erik K. Antonsson,et al.  FITTING FUNCTIONS TO DATA IN HIGH DIMENSIONAL DESIGN SPACE , 1999 .

[24]  Feng-Chia Li,et al.  Combination of feature selection approaches with SVM in credit scoring , 2010, Expert Syst. Appl..

[25]  R. K. Dixon,et al.  Mitigation and Adaptation Strategies for Global Change , 1998 .

[26]  W. Ross Ashby,et al.  Principles of the Self-Organizing System , 1991 .

[27]  Jan Murlewski,et al.  Clustering algorithms for bank customer segmentation , 2005, 5th International Conference on Intelligent Systems Design and Applications (ISDA'05).

[28]  J. Galindo,et al.  Credit Risk Assessment Using Statistical and Machine Learning: Basic Methodology and Risk Modeling Applications , 2000 .

[29]  George M. Constantinides,et al.  Handbook of the Economics of Finance , 2013 .

[30]  Mu-Chen Chen,et al.  Credit scoring with a data mining approach based on support vector machines , 2007, Expert Syst. Appl..

[31]  Ingoo Han,et al.  Hybrid genetic algorithms and case‐based reasoning systems for customer classification , 2004, Expert Syst. J. Knowl. Eng..

[32]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[33]  Hian Chye Koh,et al.  A Two-step Method to Construct Credit Scoring Models with Data Mining Techniques , 2006 .

[34]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[35]  Morten I. Lau,et al.  Estimating Risk Attitudes in Denmark: A Field Experiment , 2005 .

[36]  Hee Seok Song,et al.  Detecting the change of customer behavior based on decision tree analysis , 2005, Expert Syst. J. Knowl. Eng..

[37]  Longbing Cao,et al.  In-depth behavior understanding and use: The behavior informatics approach , 2010, Inf. Sci..

[38]  Longbing Cao.  Behavior Informatics and Analytics: Let Behavior Talk , 2008, 2008 IEEE International Conference on Data Mining Workshops.

[39]  John E. Grable.  Financial Risk Tolerance and Additional Factors That Affect Risk Taking in Everyday Money Matters , 2000 .