research-article

Combining LSTM and CNN methods and fundamental analysis for stock price trend prediction

Authors:

Zahra Nourbakhsh,

Narges HabibiAuthors Info & Claims

Multimedia Tools and Applications, Volume 82, Issue 12

Pages 17769 - 17799

https://doi.org/10.1007/s11042-022-13963-0

Published: 12 October 2022 Publication History

Abstract

Stock market trend prediction has always been a major challenge for investors. In this paper, the combination of Convolutional Neural Network and long short-term memory methods, as well as fundamental analysis components such as P/E ratio, profitability and the number of company transactions have been used to increase the performance and reduce the model error in stock price trend prediction. To evaluate the model, the parameters of evaluating mean absolute error and mean absolute percentage error in four groups of financial, petroleum, basic metals and non-metallic minerals were employed, the results of which indicated an increase in the performance and a reduction in error. According to the results, in the financial group, we obtained 0.49 for the mean absolute percentage error and 4.30 the for mean absolute error. In petroleum group, mean absolute percentage error is 0.33 and mean absolute error equals 3.64. In basic metals group, mean absolute percentage error is 0.29 and mean absolute error equals 2.39. Finally, in non-metallic minerals group, we achieved 0.73 for mean absolute percentage error and 6.16 for mean absolute error. The values obtained in the proposed method show the effect of the model on the performance and prediction of error.

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Cited By

Guo ZLiu HZhou W(2024)Compare the Accuracy of the LSTM Algorithm and the Linear Regression Algorithm in Predicting Stock PricesProceedings of the 2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence10.1145/3675417.3675583(994-999)Online publication date: 19-Jan-2024
https://dl.acm.org/doi/10.1145/3675417.3675583
Sakib MMustajab S(2024)Enhanced Multi-variate Time Series Prediction Through Statistical-Deep Learning Integration: The VAR-Stacked LSTM ModelSN Computer Science10.1007/s42979-024-02950-x5:5Online publication date: 23-May-2024
https://dl.acm.org/doi/10.1007/s42979-024-02950-x
Ren SWang XZhou XZhou Y(2023)A novel hybrid model for stock price forecasting integrating Encoder Forest and InformerExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121080234:COnline publication date: 30-Dec-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.121080

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Information & Contributors

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Published In

cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 82, Issue 12

May 2023

1535 pages

ISSN:1380-7501

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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Kluwer Academic Publishers

United States

Publication History

Published: 12 October 2022

Accepted: 13 September 2022

Revision received: 11 January 2022

Received: 22 August 2021

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Cited By

Guo ZLiu HZhou W(2024)Compare the Accuracy of the LSTM Algorithm and the Linear Regression Algorithm in Predicting Stock PricesProceedings of the 2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence10.1145/3675417.3675583(994-999)Online publication date: 19-Jan-2024
https://dl.acm.org/doi/10.1145/3675417.3675583
Sakib MMustajab S(2024)Enhanced Multi-variate Time Series Prediction Through Statistical-Deep Learning Integration: The VAR-Stacked LSTM ModelSN Computer Science10.1007/s42979-024-02950-x5:5Online publication date: 23-May-2024
https://dl.acm.org/doi/10.1007/s42979-024-02950-x
Ren SWang XZhou XZhou Y(2023)A novel hybrid model for stock price forecasting integrating Encoder Forest and InformerExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121080234:COnline publication date: 30-Dec-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.121080

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