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Exploring the Effectiveness of Deep Neural Networks with Technical Analysis Applied to Stock Market Prediction

Ming-Che Lee 1 , Jia-Wei Chang 2,* , Jason C. Hung 2 , and Bae-Ling Chen 3¹

1. Department of Computer and Communication Engineering
   Ming Chuan University, Taoyuan City, Taiwan
   leemc@mail.mcu.edu.tw
2. Department of Computer Science and Information Engineering
   National Taichung University of Science and Technology, Taichung City, Taiwan
   jiaweichang.gary@gmail.com, jhung@gm.nutc.edu.tw
3. College of Intelligence
   National Taichung University of Science and Technology, Taichung City, Taiwan
   chenbl@nutc.edu.tw

Abstract

The sustainable development of the national economy depends on the continuous growth and growth of the capital market, and the stock market is an important factor of the capital market. The growth of the stock market can generate a huge positive force for the country's economic strength, and the steady growth of the stock market also plays a pivotal role in the overall economic pulsation and is very helpful to the country's high economic development. There are different views on whether the technical analysis of the stock market is efficient. This study aims to explore the feasibility and efficiency of using deep network and technical analysis indicators to estimate short-term price movements of stocks. The subject of this study is TWSE 0050, which is the most traded ETF in Taiwan's stock exchange, and the experimental transaction range is 2017/01 ~ 2019 Q3. A four layer Long Short-Term Memory (LSTM) model was constructed. This research uses well-known technical indicators such as the KD, RSI, BIAS, Williams% R, and MACD, combined with the opening price, closing price, daily high and low prices, etc., to predict the trend of stock prices. The results show that the combination of technical indicators and the LSTM deep network model can achieve 83.6% accuracy in the three categories of rise, fall, and flatness.

Key words

deep neural network, long short-term memory, technical analysis, fintech

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS200301002L

Publication information

Volume 18, Issue 2 (April 2021)
Special Issue on Emerging Services in the Next-Generation Web: Human Meets Artificial Intelligence
Year of Publication: 2021
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

3, M. L. 1. ,. J. C. 2. ,. J. C. H. 2. ,. a. B. C.: Exploring the Effectiveness of Deep Neural Networks with Technical Analysis Applied to Stock Market Prediction. Computer Science and Information Systems, Vol. 18, No. 2, 401–418. (2021), https://doi.org/10.2298/CSIS200301002L