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

Tae Hee Moon, So Young Sohn

Research output: Contribution to journalReview article

9 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 1

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Case-based reasoning for predicting multiperiod financial performances of technology-based SMEs'. Together they form a unique fingerprint.

  • Cite this