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Sustainability 2017, 9(6), 899; doi:10.3390/su9060899

Deep Learning-Based Corporate Performance Prediction Model Considering Technical Capability

1
Department of Industrial Management Engineering, Korea University, Seoul 02841, Korea
2
Graduate School of Management of Technology, Korea University, Seoul 02841, Korea
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Author to whom correspondence should be addressed.
Academic Editor: Giuseppe Ioppolo
Received: 15 May 2017 / Revised: 23 May 2017 / Accepted: 24 May 2017 / Published: 26 May 2017
(This article belongs to the Section Economic, Business and Management Aspects of Sustainability)
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Abstract

Many studies have predicted the future performance of companies for the purpose of making investment decisions. Most of these are based on the qualitative judgments of experts in related industries, who consider various financial and firm performance information. With recent developments in data processing technology, studies have started to use machine learning techniques to predict corporate performance. For example, deep neural network-based prediction models are again attracting attention, and are now widely used in constructing prediction and classification models. In this study, we propose a deep neural network-based corporate performance prediction model that uses a company’s financial and patent indicators as predictors. The proposed model includes an unsupervised learning phase and a fine-tuning phase. The learning phase uses a restricted Boltzmann machine. The fine-tuning phase uses a backpropagation algorithm and a relatively up-to-date training data set that reflects the latest trends in the relationship between predictors and corporate performance. View Full-Text
Keywords: prediction model; corporate performance prediction; deep learning; deep belief network; technical indicator prediction model; corporate performance prediction; deep learning; deep belief network; technical indicator
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Lee, J.; Jang, D.; Park, S. Deep Learning-Based Corporate Performance Prediction Model Considering Technical Capability. Sustainability 2017, 9, 899.

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