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2021 | OriginalPaper | Chapter

Taiwanese Stock Market Forecasting with a Shallow Long Short-Term Memory Architecture

Authors : Phuong Ha Dang Bui, Toan Bao Tran, Hai Thanh Nguyen

Published in: Context-Aware Systems and Applications, and Nature of Computation and Communication

Publisher: Springer International Publishing

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Abstract

The trading of stock in companies holds an important part in numerous economies. Stock Forecast which is popularly published in the public domain in the forms of newsletters, investment promotion organizations, public/private forums, and scientific forecast services is very necessary to contribute successes in financial for individuals or organizations. Leveraging advancements in machine learning, we propose an approach based on Long Short-Term Memory model and compare the performance to the classic machine learning such as Random Forest model and Support Vector Regression model when we do forecast tasks on Taiwanese stock market. The proposed method with deep learning algorithm shows better performance comparing to the classic machine learning in the tasks of forecasting the stock market in Taiwan.

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Metadata
Title
Taiwanese Stock Market Forecasting with a Shallow Long Short-Term Memory Architecture
Authors
Phuong Ha Dang Bui
Toan Bao Tran
Hai Thanh Nguyen
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-67101-3_16

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