research-article

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

Authors:

Sheng-Cheng Yeh,

Jia-Wei ChangAuthors Info & Claims

IC4E '20: Proceedings of the 2020 11th International Conference on E-Education, E-Business, E-Management, and E-Learning

Pages 296 - 299

https://doi.org/10.1145/3377571.3377608

Published: 03 May 2020 Publication History

Abstract

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.

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Cited By

Rahmani ARezazadeh BHaghparast MChang WTing S(2023)Applications of Artificial Intelligence in the Economy, Including Applications in Stock Trading, Market Analysis, and Risk ManagementIEEE Access10.1109/ACCESS.2023.330003611(80769-80793)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3300036
Wu JTang XHsu C(2022)A prediction model of stock market trading actions using generative adversarial network and piecewise linear representation approachesSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07716-227:12(8209-8222)Online publication date: 9-Dec-2022
https://dl.acm.org/doi/10.1007/s00500-022-07716-2
Kapoor K(2022)DeceptionTime: Predicting the Movement of Shares Using Momentum IndicatorsProceedings of International Conference on Computational Intelligence, Data Science and Cloud Computing10.1007/978-981-19-1657-1_12(139-153)Online publication date: 18-Aug-2022
https://doi.org/10.1007/978-981-19-1657-1_12

Index Terms

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

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IC4E '20: Proceedings of the 2020 11th International Conference on E-Education, E-Business, E-Management, and E-Learning

January 2020

441 pages

ISBN:9781450372947

DOI:10.1145/3377571

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Ritsumeikan University: Ritsumeikan University

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Association for Computing Machinery

New York, NY, United States

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Published: 03 May 2020

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IC4E 2020

IC4E 2020: 2020 the 11th International Conference on E-Education, E-Business, E-Management, and E-Learning

January 10 - 12, 2020

Osaka, Japan

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Cited By

Rahmani ARezazadeh BHaghparast MChang WTing S(2023)Applications of Artificial Intelligence in the Economy, Including Applications in Stock Trading, Market Analysis, and Risk ManagementIEEE Access10.1109/ACCESS.2023.330003611(80769-80793)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3300036
Wu JTang XHsu C(2022)A prediction model of stock market trading actions using generative adversarial network and piecewise linear representation approachesSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07716-227:12(8209-8222)Online publication date: 9-Dec-2022
https://dl.acm.org/doi/10.1007/s00500-022-07716-2
Kapoor K(2022)DeceptionTime: Predicting the Movement of Shares Using Momentum IndicatorsProceedings of International Conference on Computational Intelligence, Data Science and Cloud Computing10.1007/978-981-19-1657-1_12(139-153)Online publication date: 18-Aug-2022
https://doi.org/10.1007/978-981-19-1657-1_12

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