Knowledge Discovery Techniques for Predicting Country Investment Risk

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data mining, knowledge discovery, country investing risk

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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.


This article was written before Steven Walczak was affiliated with the University of South Florida.

Was this content written or created while at USF?


Citation / Publisher Attribution

Computers & Industrial Engineering, v. 43, issue 4, p. 787-800