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Predict Stock Prices Using Supervised Learning Algorithms and Particle Swarm Optimization Algorithm

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  • Mohammad Javad Bazrkar

    (Shahid Bahonar University of Kerman)

  • Soodeh Hosseini

    (Shahid Bahonar University of Kerman)

Abstract

Forecasting the stock market has always been one of the challenges for stock market participants to make more profit. Among the problems of stock price forecasting, we can mention its dynamic nature, complexity and its dependence on factors such as the governing system of countries, emotions, economic conditions, inflation, and so on. In recent years, many studies have been conducted to predict the capital stock market using traditional techniques, machine learning algorithms and deep learning. The lower our forecast stock error, the More we can reduce investment risk and increase profitability. In this paper, we present a machine learning (ML) approach called support vector machine (SVM) that can be taught using existing data. SVM extracts knowledge between data and ultimately uses this knowledge to predict new stock data. We have also aimed to select the best SVM method parameters using the particle swarm optimization (PSO) algorithm to prevent over-fitting and improve forecast accuracy. Finally, we compare our proposed method (SVM-PSO) with several other methods, including support vector machine, artificial neural network (ANN) and LSTM. The results show that the proposed algorithm works better than other methods and in all cases, its forecast accuracy is above 90%.

Suggested Citation

  • Mohammad Javad Bazrkar & Soodeh Hosseini, 2023. "Predict Stock Prices Using Supervised Learning Algorithms and Particle Swarm Optimization Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 165-186, June.
    • Handle: RePEc:kap:compec:v:62:y:2023:i:1:d:10.1007_s10614-022-10273-3
    DOI: 10.1007/s10614-022-10273-3
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    References listed on IDEAS

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    1. Basak, Suryoday & Kar, Saibal & Saha, Snehanshu & Khaidem, Luckyson & Dey, Sudeepa Roy, 2019. "Predicting the direction of stock market prices using tree-based classifiers," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 552-567.
    2. Ayodele Ariyo Adebiyi & Aderemi Oluyinka Adewumi & Charles Korede Ayo, 2014. "Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-7, March.
    3. Sohrab Mokhtari & Kang K. Yen & Jin Liu, 2021. "Effectiveness of Artificial Intelligence in Stock Market Prediction based on Machine Learning," Papers 2107.01031, arXiv.org.
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    Cited by:

    1. Wanyi Deng & Xiaoxue Ma & Weiliang Qiao, 2024. "A Hybrid Intelligent Optimization Algorithm Based on a Learning Strategy," Mathematics, MDPI, vol. 12(16), pages 1-17, August.

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