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.
Bibliographical noteFunding 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: firstname.lastname@example.org. 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