Knowledge discovery techniques for predicting country investment risk

Authors:
Irma Becerra-Fernandez;Stelios H. Zanakis;Steven Walczak
Affiliations:
Florida International University, Decision Sciences and Information Systems Department, College of Business Administration, Miami, FL;Florida International University, Decision Sciences and Information Systems Department, College of Business Administration, Miami, FL;University of Colorado at Denver, College of Business, Denver, CO
Venue:
Computers and Industrial Engineering
Year:
2002

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Abstract

This paper presents the insights gained from applying knowledge discovery in databases (KDD) processes for the purpose of developing intelligent models, used to classify a country's investing risk based on a variety of factors. Inferential data mining techniques, like C5.0, as well as intelligent learning techniques, like neural networks, were applied to a dataset of 52 countries. The dataset included 27 variables (economic, stock market performance/risk and regulatory efficiencies) on 52 countries, whose investing risk category was assessed in a Wall Street Journal survey of international experts. The results of applying KDD techniques to the dataset are promising, and successfully classified most countries as compared to the experts' classifications. Implementation details, results, and future plans are also presented.