Research on the Stock Price Prediction Using Machine Learning

Research Article
Open access

Research on the Stock Price Prediction Using Machine Learning

Yang Shi 1*
  • 1 Case Western Reserve University    
  • *corresponding author yxs879@case.edu
Published on 13 September 2023 | https://doi.org/10.54254/2754-1169/22/20230307
AEMPS Vol.22
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-915371-87-4
ISBN (Online): 978-1-915371-88-1

Abstract

Stock price prediction is a complex and challenging problem that has attracted the attention of investors and researchers for decades. In recent years, machine learning algorithms have become powerful tools for predicting stock prices. This paper first introduces four popular machine learning algorithms used for stock price prediction which are linear regression, support vector machines, artificial neural networks and long short-term memory. In addition, applications and potential challenges of stock price prediction using machine learning are examined. Overall, this paper provides a comprehensive overview of ML-based models for stock price prediction and highlights the potential benefits and limitations of these models for financial researchers and artificial intelligence developers.

Keywords:

stock price prediction, machine learning algorithms, linear regression, long short-term memory, model performance evaluation

Shi,Y. (2023). Research on the Stock Price Prediction Using Machine Learning. Advances in Economics, Management and Political Sciences,22,174-179.
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References

[1]. Nti, IK. Adekoya, AF., and Weyori, BA. (2020) A systematic review of fundamental and technical analysis of stock market predictions. Artificial Intelligence Review, 53: 3007-3057.

[2]. Obthong, M. Tantisantiwong, N. Jeamwatthanachai, W., and Wills, G. (2020) A survey on machine learning for stock price prediction: algorithms and technique. 2nd International Conference on Finance, Economics, Management and IT Business, 63-71.

[3]. Umer, M. Awais, M. and Muzammul, M. (2019) Stock market prediction using machine learning (ML) algorithms. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 8(4): 97-116.

[4]. Chen, J. (2023) Analysis of Bitcoin Price Prediction Using Machine Learning. Journal of Risk and Financial Management, 16(1): 51.

[5]. Panwar, B., Dhuriya, G., Johri, P., Yadav, S. S., and Gaur, N. (2021) Stock market prediction using linear regression and svm. In 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 629-631.

[6]. Emioma, C. C. and Edeki, S. O. (2021) Stock price prediction using machine learning on least-squares linear regression basis. In Journal of Physics: Conference Series, 1734 (1): 012058.

[7]. Cory, Mitchell (Investopedia), 2021. Understanding an OHLC Chart and How to Interpret It. www.investopedia.com/terms/o/ohlcchart.asp#:~:text=An%20OHLC%20chart%20shows%20the,structure%20is%20called%20a%20bar.

[8]. Yang, J. (2023) Support Vector Machine-based Stock Prediction Analysis. Highlights in Business, Economics and Management, 3: 12-18.

[9]. Kumar, M. and Thenmozhi, M. (2006) Forecasting stock index movement: A comparison of support vector machines and random forest. In Indian institute of capital markets 9th capital markets conference paper.

[10]. Di Persio, L. and Honchar, O. (2016) Artificial neural networks architectures for stock price prediction: Comparisons and applications. International journal of circuits, systems and signal processing, 10(2016): 403-413.

[11]. Shahvaroughi Farahani, M., and Razavi Hajiagha, S. H. (2021) Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models. Soft computing, 25(13): 8483-8513.

[12]. Taghizadeh Firouzjaee, J. and Khaliliyan, P. (2022) Considering Interpretability of the LSTM Architecture for Oil Stocks Prices Prediction. Available at SSRN 4178888.

[13]. Karim, M. E., Foysal, M., and Das, S. (2022) Stock Price Prediction Using Bi-LSTM and GRU-Based Hybrid Deep Learning Approach. In Proceedings of Third Doctoral Symposium on Computational Intelligence: DoSCI 2022: 701-711.

[14]. Diqi, M. (2022) StockTM: Accurate Stock Price Prediction Model Using LSTM. International Journal of Informatics and Computation, 4(1): 1-10.

Cite this article

Shi,Y. (2023). Research on the Stock Price Prediction Using Machine Learning. Advances in Economics, Management and Political Sciences,22,174-179.

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 Management Research and Economic Development

ISBN：978-1-915371-87-4(Print) / 978-1-915371-88-1(Online)
Editor：Canh Thien Dang, Javier Cifuentes-Faura
Conference website: https://2023.icmred.org/
Conference date: 28 April 2023
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.22
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Nti, IK. Adekoya, AF., and Weyori, BA. (2020) A systematic review of fundamental and technical analysis of stock market predictions. Artificial Intelligence Review, 53: 3007-3057.

[2]. Obthong, M. Tantisantiwong, N. Jeamwatthanachai, W., and Wills, G. (2020) A survey on machine learning for stock price prediction: algorithms and technique. 2nd International Conference on Finance, Economics, Management and IT Business, 63-71.

[3]. Umer, M. Awais, M. and Muzammul, M. (2019) Stock market prediction using machine learning (ML) algorithms. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 8(4): 97-116.

[4]. Chen, J. (2023) Analysis of Bitcoin Price Prediction Using Machine Learning. Journal of Risk and Financial Management, 16(1): 51.

[5]. Panwar, B., Dhuriya, G., Johri, P., Yadav, S. S., and Gaur, N. (2021) Stock market prediction using linear regression and svm. In 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 629-631.

[6]. Emioma, C. C. and Edeki, S. O. (2021) Stock price prediction using machine learning on least-squares linear regression basis. In Journal of Physics: Conference Series, 1734 (1): 012058.

[7]. Cory, Mitchell (Investopedia), 2021. Understanding an OHLC Chart and How to Interpret It. www.investopedia.com/terms/o/ohlcchart.asp#:~:text=An%20OHLC%20chart%20shows%20the,structure%20is%20called%20a%20bar.

[8]. Yang, J. (2023) Support Vector Machine-based Stock Prediction Analysis. Highlights in Business, Economics and Management, 3: 12-18.

[9]. Kumar, M. and Thenmozhi, M. (2006) Forecasting stock index movement: A comparison of support vector machines and random forest. In Indian institute of capital markets 9th capital markets conference paper.

[10]. Di Persio, L. and Honchar, O. (2016) Artificial neural networks architectures for stock price prediction: Comparisons and applications. International journal of circuits, systems and signal processing, 10(2016): 403-413.

[11]. Shahvaroughi Farahani, M., and Razavi Hajiagha, S. H. (2021) Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models. Soft computing, 25(13): 8483-8513.

[12]. Taghizadeh Firouzjaee, J. and Khaliliyan, P. (2022) Considering Interpretability of the LSTM Architecture for Oil Stocks Prices Prediction. Available at SSRN 4178888.

[13]. Karim, M. E., Foysal, M., and Das, S. (2022) Stock Price Prediction Using Bi-LSTM and GRU-Based Hybrid Deep Learning Approach. In Proceedings of Third Doctoral Symposium on Computational Intelligence: DoSCI 2022: 701-711.

[14]. Diqi, M. (2022) StockTM: Accurate Stock Price Prediction Model Using LSTM. International Journal of Informatics and Computation, 4(1): 1-10.

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