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A Hybrid Forecasting System Based on Comprehensive Feature Selection and Intelligent Optimization for Stock Price Index Forecasting

Author

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  • Xuecheng He

    (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
    School of Politics, Economics and International Relations, University of Reading, Whiteknights RG6 6UR, UK)

  • Jujie Wang

    (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

Abstract

Accurate forecasts of stock indexes can not only provide reference information for investors to formulate relevant strategies but also provide effective channels for the government to regulate the market. However, due to its volatility and complexity, predicting the stock price index has always been a challenging task. This paper proposes a hybrid forecasting system based on comprehensive feature selection and intelligent optimization for stock price index forecasting. First, a recursive feature elimination with a cross-validation (RFECV) algorithm is designed to filter variables that have a significant impact on the target data from multiple datasets. Then, the stack autoencoder (SAE) algorithm is constructed to compress the feature variables. At last, an enhanced least squares support vector machine (LSSVM) algorithm is established to obtain high-precision point prediction results, and the Gaussian process regression (GPR) algorithm is used to obtain reasonable interval prediction results. Taking the Shanghai Stock Exchange (SSE) as an example, the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the model were 6.989 and 0.158%, respectively. In addition, the prediction interval coverage probability (PICP) is 99.792%. Through experimental comparison, the model shows high prediction accuracy and generalization ability.

Suggested Citation

  • Xuecheng He & Jujie Wang, 2024. "A Hybrid Forecasting System Based on Comprehensive Feature Selection and Intelligent Optimization for Stock Price Index Forecasting," Mathematics, MDPI, vol. 12(23), pages 1-27, November.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:23:p:3778-:d:1533310
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    References listed on IDEAS

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    1. Tang, Ling & Zhang, Chengyuan & Li, Ling & Wang, Shouyang, 2020. "A multi-scale method for forecasting oil price with multi-factor search engine data," Applied Energy, Elsevier, vol. 257(C).
    2. Escobar-Anel, Marcos & Rastegari, Javad & Stentoft, Lars, 2021. "Option pricing with conditional GARCH models," European Journal of Operational Research, Elsevier, vol. 289(1), pages 350-363.
    3. Mojtaba Kadkhodazadeh & Saeed Farzin, 2021. "A Novel LSSVM Model Integrated with GBO Algorithm to Assessment of Water Quality Parameters," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 3939-3968, September.
    4. Kizilaslan, Recep & Freund, Steven & Iseri, Ali, 2016. "A data analytic approach to forecasting daily stock returns in an emerging marketAuthor-Name: Oztekin, Asil," European Journal of Operational Research, Elsevier, vol. 253(3), pages 697-710.
    5. Akshit Kurani & Pavan Doshi & Aarya Vakharia & Manan Shah, 2023. "A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting," Annals of Data Science, Springer, vol. 10(1), pages 183-208, February.
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