Stock price prediction using decision tree classifier and LSTM network

Research Article
Open access

Stock price prediction using decision tree classifier and LSTM network

Hongyi Xu 1*
  • 1 Beijing Jiaotong University    
  • *corresponding author 21281057@bjtu.edu.cn
Published on 7 February 2024 | https://doi.org/10.54254/2755-2721/37/20230512
ACE Vol.37
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-299-2
ISBN (Online): 978-1-83558-300-5

Abstract

Nowadays, stock price prediction has become a popular research topic, many researchers try to predict stock prices in various ways. However, there are many different tools, but not all of them have good performance, so it is necessary for researchers to evaluate and compare different tools. In this paper, to achieve the goal of predicting stock price precisely, the main approach chosen is building deep learning models and use them to make predictions. Two methods, decision tree and long short-term memory (LSTM) neural network, are used in this study. In the model using the decision tree classifier, the daily state of the stock is divided into two types: the rise and fall of the stock price. The task of the model is to make predictions about daily stock prices and classify them. The other model uses the LSTM network, which is used to make accurate closing price predictions. In the end, the performance of the two models is assessed for further work.

Keywords:

Stock Price, Machine Learning, Decision Tree, LSTM Network

Xu,H. (2024). Stock price prediction using decision tree classifier and LSTM network. Applied and Computational Engineering,37,222-229.
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References

[1]. Khan W, Ghazanfar M A, Azam M A, Karami A, Alyoubi K H and Alfakeeh A S 2022 Stock market prediction using machine learning classifiers and social media, news J. Ambient Intell Human Comput 3433–3456(2022)013

[2]. Lu W, Li J, Wang J 2021 A CNN-BiLSTM-AM method for stock price prediction Neural Comput & Applic 4741–4753(2021)033

[3]. Ampomah E K, Qin Z and Nyame G 2020 Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement Information 11(6)(2020)332

[4]. Beyaz E, Tekiner F, Zeng X-j and Keane J 2018 IEEE 20th Int. Conf. on High Performance Computing and Communications; IEEE 16th Int. Conf. on Smart City; IEEE 4th Int. Conf. on Data Science and Systems (HPCC/SmartCity/DSS) (Exeter, UK) p 1607-1613

[5]. Patil P, Wu C-S M, Potika K and Orang M 2020 Proc. of 3rd Int. Conf. on Software Engineering and Information Management (ICSIM '20) (Sydney, NSW, Australia) p 85–92

[6]. Luo Y-X and Ji Y 2022 Int. Conf. on Machine Learning and Cybernetics (ICMLC) (Japan) p 43-48

[7]. Althelaya K A, El-Alfy E S M and Mohammed S 2018 9th Int. Conf. on information and communication systems (ICICS) (Irbid, Jordan) p 151-156

[8]. Karim R, Alam M K and Hossain M R 2021 1st Int. Conf. on Emerging Smart Technologies and Applications (eSmarTA) (Sana'a, Yemen) p 1-6

[9]. Mehtab S, Sen J and Dutta A 2020 Machine Learning and Metaheuristics Algorithms, and Applications: 2nd Symp.(SoMMA 2020) (Chennai, India) p 88-106.

[10]. Singh D and Singh B 2020 Investigating the impact of data normalization on classification performance Applied Soft Computing 97(2020)105524

[11]. Garbin C, Zhu X and Marques O 2020 Dropout vs. batch normalization: an empirical study of their impact to deep learning Multimedia Tools and Applications 12777-12815(2020)079

Cite this article

Xu,H. (2024). Stock price prediction using decision tree classifier and LSTM network. Applied and Computational Engineering,37,222-229.

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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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-299-2(Print) / 978-1-83558-300-5(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.37
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Khan W, Ghazanfar M A, Azam M A, Karami A, Alyoubi K H and Alfakeeh A S 2022 Stock market prediction using machine learning classifiers and social media, news J. Ambient Intell Human Comput 3433–3456(2022)013

[2]. Lu W, Li J, Wang J 2021 A CNN-BiLSTM-AM method for stock price prediction Neural Comput & Applic 4741–4753(2021)033

[3]. Ampomah E K, Qin Z and Nyame G 2020 Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement Information 11(6)(2020)332

[4]. Beyaz E, Tekiner F, Zeng X-j and Keane J 2018 IEEE 20th Int. Conf. on High Performance Computing and Communications; IEEE 16th Int. Conf. on Smart City; IEEE 4th Int. Conf. on Data Science and Systems (HPCC/SmartCity/DSS) (Exeter, UK) p 1607-1613

[5]. Patil P, Wu C-S M, Potika K and Orang M 2020 Proc. of 3rd Int. Conf. on Software Engineering and Information Management (ICSIM '20) (Sydney, NSW, Australia) p 85–92

[6]. Luo Y-X and Ji Y 2022 Int. Conf. on Machine Learning and Cybernetics (ICMLC) (Japan) p 43-48

[7]. Althelaya K A, El-Alfy E S M and Mohammed S 2018 9th Int. Conf. on information and communication systems (ICICS) (Irbid, Jordan) p 151-156

[8]. Karim R, Alam M K and Hossain M R 2021 1st Int. Conf. on Emerging Smart Technologies and Applications (eSmarTA) (Sana'a, Yemen) p 1-6

[9]. Mehtab S, Sen J and Dutta A 2020 Machine Learning and Metaheuristics Algorithms, and Applications: 2nd Symp.(SoMMA 2020) (Chennai, India) p 88-106.

[10]. Singh D and Singh B 2020 Investigating the impact of data normalization on classification performance Applied Soft Computing 97(2020)105524

[11]. Garbin C, Zhu X and Marques O 2020 Dropout vs. batch normalization: an empirical study of their impact to deep learning Multimedia Tools and Applications 12777-12815(2020)079

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