Hybrid corporate performance prediction model considering technical capability

Joonhyuck Lee, Gabjo Kim, Sangsung Park, Dong Sik Jang

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Many studies have tried to predict corporate performance and stock prices to enhance investment profitability using qualitative approaches such as the Delphi method. However, developments in data processing technology and machine-learning algorithms have resulted in efforts to develop quantitative prediction models in various managerial subject areas. We propose a quantitative corporate performance prediction model that applies the support vector regression (SVR) algorithm to solve the problem of the overfitting of training data and can be applied to regression problems. The proposed model optimizes the SVR training parameters based on the training data, using the genetic algorithm to achieve sustainable predictability in changeable markets and managerial environments. Technology-intensive companies represent an increasing share of the total economy. The performance and stock prices of these companies are affected by their financial standing and their technological capabilities. Therefore, we apply both financial indicators and technical indicators to establish the proposed prediction model. Here, we use time series data, including financial, patent, and corporate performance information of 44 electronic and IT companies. Then, we predict the performance of these companies as an empirical verification of the prediction performance of the proposed model.

Original languageEnglish
Article number640
JournalSustainability (Switzerland)
Volume8
Issue number7
DOIs
Publication statusPublished - 2016 Jul 6

Keywords

  • Corporate performance prediction
  • Genetic algorithm
  • Support vector machine
  • Sustainable prediction model
  • Technical indicator

ASJC Scopus subject areas

  • Management, Monitoring, Policy and Law
  • Renewable Energy, Sustainability and the Environment
  • Geography, Planning and Development
  • Education

Fingerprint Dive into the research topics of 'Hybrid corporate performance prediction model considering technical capability'. Together they form a unique fingerprint.

  • Cite this