Deep Learning-Based Corporate Performance Prediction Model Considering Technical Capability
By Joonhyuck Lee, Dong-Sik Jang, Sangsung Park. 2017
49 citations recorded by the source corpus.
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.
Subfields: Accounting, Artificial Intelligence, Management Science and Operations Research
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Citations in the corpus
- Deep Learning for Financial Applications : A Survey (2020, methodology): “[202] Corporate Performance Prediction 22 pharmaceutical companies data in US stock market 2000-2015 11 financial and 4 patent indicator RBM, DBN RMSE, profit -”