Knowledge Discovery Techniques for Predicting Country Investment Risk
Document Type
Article
Publication Date
9-2002
Keywords
data mining, knowledge discovery, country investing risk
Digital Object Identifier (DOI)
https://doi.org/10.1016/S0360-8352(02)00140-7
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.
Was this content written or created while at USF?
No
Citation / Publisher Attribution
Computers & Industrial Engineering, v. 43, issue 4, p. 787-800
Scholar Commons Citation
Becerra-Fernandez, Irma; Zanakis, Stelios H.; and Walczak, Steven, "Knowledge Discovery Techniques for Predicting Country Investment Risk" (2002). School of Information Faculty Publications. 199.
https://digitalcommons.usf.edu/si_facpub/199
