Deep Learning-Based Corporate Performance Prediction Model Considering Technical Capability

doi:10.3390/su9060899

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Deep Learning-Based Corporate Performance Prediction Model Considering Technical Capability

2017-05-26

Joonhyuck Lee

Dong‐Sik Jang

Sangsung Park *

DOI：10.3390/su9060899

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Abstract

En 中文

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.

Keywords:

prediction model

corporate performance prediction

deep learning

deep belief network

technical indicator

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