Model for Evaluation of Stock Values by Ensemble Model Using Deep Learning

• Published in: Transactions of the Japanese Society for Artificial Intelligence Vol. 33; no. 1; pp. A-H51_1 - 11
• Main Authors: Tamura, Koichiro, Uenoyama, Katsuya, Iitsuka, Shuhei, Matsuo, Yutaka
• Format: Journal Article
• Language: English; Japanese
• Published: Tokyo The Japanese Society for Artificial Intelligence 01.01.2018; Japan Science and Technology Agency
• Subjects: Accuracy; Classification; Deep learning; ensemble; enterprise value evaluation; Machine learning; Markets; Model accuracy; Regression analysis; stock value; summary of financial statements of company
• ISSN: 1346-0714; 1346-8030
• DOI: 10.1527/tjsai.A-H51

In order to encourage individual asset flow into the Japanese market through long-term investments, it is important to evaluate stock values of companies because stock prices of companies are determined not only by internal values, which are independent of other companies, but also by market fundamentalism. However, there are few studies conducted in this area in the machine learning community, while there are many studies about prediction of stock price trends. These studies use a single factor approach (such as textual or numerical) and focus on internal values only. We propose a model where we combine two major financial approaches to evaluate stock values: technical analysis and fundamental analysis. The technical analysis is conducted using Long-Short Term Memory and technical indexes as input data. On the other hand, the fundamental analysis is conducted transversely and relatively by creating a program which can retrieve data on financial statements of all listed companies in Japan and put them into a database. From the experiments, compared to single technical analysis proposed model’s accuracy in classification was 11.92% more accurate and the relative error of regression was 3.77% smaller on average. In addition, compared to single factor approaches the accuracy in classification was 6.16% more accurate and the relative error of regression was 3.22% smaller on average. The proposed model has the potential to be combined with other prediction methods, such as textual approaches or even traditional financial approaches, which would improve accuracy and increase practicality of this model.