Stock index futures price prediction using feature selection and deep learning

Wan-Lin Yan

The North American Journal of Economics and Finance, 2023, vol. 64, issue C

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.

Keywords: Stock index futures price prediction; Long short-term memory; AdaBoost algorithm; Feature selection; Technical analysis (search for similar items in EconPapers)
JEL-codes: C53 C55 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:64:y:2023:i:c:s1062940822002029

DOI: 10.1016/j.najef.2022.101867

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