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Stock index futures price prediction using feature selection and deep learning

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  • Yan, Wan-Lin

Abstract

Stock index futures allows stock investors to manage different kinds of risk. This paper combines the AdaBoost feature selection and deep learning model for predicting stock index futures prices. In particular, a hybrid model is proposed in which the sklearn wrapped AdaBoost regressor is used for feature selection and the two-layer long short-term memory-based predictor is constructed. Performance metrics consistently show that the proposed model outperforms other popular prediction models such as random forest, multi-layer perception, gated recurrent unit, deep belief network and stacked denoising autoencoder.

Suggested Citation

  • Yan, Wan-Lin, 2023. "Stock index futures price prediction using feature selection and deep learning," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    • Handle: RePEc:eee:ecofin:v:64:y:2023:i:c:s1062940822002029
    DOI: 10.1016/j.najef.2022.101867
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    Cited by:

    1. Sun-Feel Yang & So-Won Choi & Eul-Bum Lee, 2023. "A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices," Energies, MDPI, vol. 16(11), pages 1-39, May.

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    More about this item

    Keywords

    Stock index futures price prediction; Long short-term memory; AdaBoost algorithm; Feature selection; Technical analysis;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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