Research Article
Taiwanese Stock Market Forecasting with a Shallow Long Short-Term Memory Architecture
@INPROCEEDINGS{10.1007/978-3-030-67101-3_16, author={Phuong Ha Dang Bui and Toan Bao Tran and Hai Thanh Nguyen}, title={Taiwanese Stock Market Forecasting with a Shallow Long Short-Term Memory Architecture}, proceedings={Context-Aware Systems and Applications, and Nature of Computation and Communication. 9th EAI International Conference, ICCASA 2020, and 6th EAI International Conference, ICTCC 2020, Thai Nguyen, Vietnam, November 26--27, 2020, Proceedings}, proceedings_a={ICCASA \& ICTCC}, year={2021}, month={1}, keywords={Trading of stock Machine learning Forecast tasks}, doi={10.1007/978-3-030-67101-3_16} }- Phuong Ha Dang Bui
Toan Bao Tran
Hai Thanh Nguyen
Year: 2021
Taiwanese Stock Market Forecasting with a Shallow Long Short-Term Memory Architecture
ICCASA & ICTCC
Springer
DOI: 10.1007/978-3-030-67101-3_16
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.
