R&D indicators of a firm as predictors for predicting firm performance

Young Geun Shin, Sang Sung Parte, Dong Sik Jang

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

To more accurately predict firms' future performance, this study considers a set of influential variables that can affect firms' performance by using three methods based on the Bayesian technique. Then we verify the usefulness of the selected variables by using models based on Neural Networks and the Support Vector Machine technique. The results indicate that for more accurate predictions of firms' future performance, various indicators of R&D performance should be considered in conjunction with financial indicators. Thus, this study contributes to literatures by proposing a model that can better predict firms' future performance and reduce the risk associated with investment.

Original languageEnglish
Pages (from-to)577-596
Number of pages20
JournalInformation
Volume15
Issue number2
Publication statusPublished - 2012 Feb

Keywords

  • Bayesian technique
  • Firm performance
  • NNs
  • Patent
  • R&D
  • SVM

ASJC Scopus subject areas

  • Information Systems

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