Deep Learning-Based Corporate Performance Prediction Model Considering Technical Capability

Joonhyuck Lee,Sangsung Park,Dongsik Jang

doi:10.3390/su9060899

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.

Highlights

Many studies have predicted the future performance of companies for the purpose of making investment decisions [1,2,3,4,5,6,7]. Most of these are based on the qualitative judgment of experts in related industries, who consider various financial and firm performance information [8,9]
We propose a deep neural network-based corporate performance prediction model that uses a company’s financial and technical indicators as predictors
There is a fine-tuning phase, which uses a backpropagation algorithm and a relatively up-to-date training data set. These data reflect the latest trends in the correlation between predictors and corporate performance in forecasting in order to improve the prediction accuracy of the network

Summary

Introduction

Many studies have predicted the future performance of companies for the purpose of making investment decisions [1,2,3,4,5,6,7]. Among the studies that predict a company’s corporate performance, there are few that construct a prediction model using patent data and a deep learning algorithm. We propose a deep neural network-based corporate performance prediction model that uses a company’s financial and technical indicators as predictors. There is a fine-tuning phase, which uses a backpropagation algorithm and a relatively up-to-date training data set. These data reflect the latest trends in the correlation between predictors and corporate performance in forecasting in order to improve the prediction accuracy of the network. The proposed model is expected to maintain sustainable prediction performance in a volatile business environment by fine-tuning the pre-trained model using the up-to-date data set

Related Studies

Deep Belief Networks

Predictors of the Proposed Prediction Model

Results