Advances in Economics, Management and Political Sciences

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Proceedings of the 7th International Conference on Economic Management and Green Development

Series Vol. 40 , 10 November 2023


Open Access | Article

Analysis and Time-series Forecasting of Corporate Stock Price

Zhuoling Lyu * 1
1 Zhejiang University

* Author to whom correspondence should be addressed.

Advances in Economics, Management and Political Sciences, Vol. 40, 14-21
Published 10 November 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Zhuoling Lyu. Analysis and Time-series Forecasting of Corporate Stock Price. AEMPS (2023) Vol. 40: 14-21. DOI: 10.54254/2754-1169/40/20231982.

Abstract

Fluctuations in the stock market represent the changes in the national economy objectively, stock price prediction predicts the future trend of stocks using the past data, which has been widely focused on. Some machine learning algorithms, such as linear fitting and sequence mining, are used to predict the stock market. However, linear fitting faces the problem of overfitting and black relationships with historical data, while sequence mining is short in efficiency and lacks dynamic adaptations. State-of-the-art methods using attention mechanism in neural networks have shown exceptional performance targeting sequential prediction and classification. In this paper, we propose a combined LSTM-CNN attention model to explore the role of attention mechanism in Long Short-Term Memory (LSTM) network-based stock price movement prediction. Experimental results show that our LSTM-CNN-attention model can provide an accurate prediction and reliable trial on the stock price prediction, and the attention mechanism significantly improves the model performance in the stock market.

Keywords

stock, time-series forecast, machine learning, attention mechanism

References

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 7th International Conference on Economic Management and Green Development
ISBN (Print)
978-1-83558-101-8
ISBN (Online)
978-1-83558-102-5
Published Date
10 November 2023
Series
Advances in Economics, Management and Political Sciences
ISSN (Print)
2754-1169
ISSN (Online)
2754-1177
DOI
10.54254/2754-1169/40/20231982
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated