Forecasting the Short-term Price Trend of Taiwan Stocks with Deep Neural Network

被引：4

作者：

Lee, Ming-Che ^{[1
]}

Liao, Jie-Shan ^{[1
]}

Yeh, Sheng-Cheng ^{[1
]}

Chang, Jia-Wei ^{[2
]}

机构：

[1] Ming Chuan Univ, Dept Comp & Commun Engn, 5 De Ming Rd, Gui Shan Dist 333, Taoyuan County, Taiwan

[2] Natl Taichung Univ Sci & Technol, Dept Comp Sci & Informat Engn, 129,Sec 3,Sanmin Rd, Taichung 40401, Taiwan

来源：

2020 11TH INTERNATIONAL CONFERENCE ON E-EDUCATION, E-BUSINESS, E-MANAGEMENT, AND E-LEARNING (IC4E 2020) | 2020年

关键词：

RNN; LSTM; Stock Price Prediction; Deep Neural Network;

D O I：

10.1145/3377571.3377608

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The relationship between technical indicators and prices in stock market has always been an important topic of concern for the academic and financial communities. Many literatures suggest that it is feasible to use technical analysis to estimate the future price of stocks. The use of machine learning to estimate stock prices has also gradually become mainstream in the financial market. This study aims to explore the feasibility 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. We use Long Short Term Memory (LSTM) to construct a deep network stock estimation model and conduct experiments on the Taiwan Stock Exchange's open data from 2019/01 to 2019/10. Experimental results show that LSTM model obtained an average of 75% accuracy on TWSE 0050 ETF.

引用

页码：296 / 299

页数：4

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