Hybrid Corporate Performance Prediction Model Considering Technical Capability

Joonhyuck Lee, Gabjo Kim, Sangsung Park and Dongsik Jang
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Joonhyuck Lee: Department of Industrial Management Engineering, Korea University, Seoul 02841, Korea
Gabjo Kim: Korea Intellectual Property Strategy Agency, Seoul 06132, Korea
Sangsung Park: Graduate School of Management of Technology, Korea University, Seoul 02841, Korea
Dongsik Jang: Department of Industrial Management Engineering, Korea University, Seoul 02841, Korea

Sustainability, 2016, vol. 8, issue 7, 1-13

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.

Keywords: sustainable prediction model; corporate performance prediction; support vector machine; genetic algorithm; technical indicator (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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