Case-based reasoning for predicting multiperiod financial performances of technology-based SMEs

Tae Hee Moon, So Young Sohn

Research output: Contribution to journalReview articlepeer-review

10 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)602-615
Number of pages14
JournalApplied Artificial Intelligence
Volume22
Issue number6
DOIs
Publication statusPublished - 2008 Jul

Bibliographical note

Funding Information:
Address correspondence to S. Y. Sohn, Department of Information and Industrial Engineering, Yonsei University, 134 Shinchon-dong, Seoul 120-749, South Korea. E-mail: sohns@yonsei.ac.kr. This work was supported by the Korea Research Foundation Grant (no. KRF-2007-357-D00281) funded by the Korean Government (MOEHRD).

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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