CASE-BASED REASONING FOR PREDICTING MULTIPERIOD FINANCIAL PERFORMANCES OF TECHNOLOGY-BASED SMEs

Authors:
Tae Hee Moon;So Young Sohn
Affiliations:
Centre for Technology Management (CTM), Institute for Manufacturing (IFM), University of Cambridge, UK;Centre for Technology Management (CTM), Institute for Manufacturing (IFM), University of Cambridge, UK
Venue:
Applied Artificial Intelligence
Year:
2008

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Abstract

Recently, various types of technology funds became available to support the programs for technology development and commercialization of SMEs (Small and Medium Enterprise) in Korea. However, the potential financial performances have not been sufficiently considered at the selection stage of fund recipient SMEs whereas the default risk has been a major concern. This article proposes a Case Based Reasoning (CBR) system with Genetic Algorithm (GA) for predicting the Exponentially Weighted Moving Average (EWMA) of multiperiod financial performances of technology-oriented SMEs. It is expected that the proposed model can be applied to a wide range of technology investment-related decision-making procedures.