Stock index futures price prediction using feature selection and deep learning

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.

Automated summary

Stock index futures is a useful tool for stock investors to manage risk. This paper proposes a hybrid model combining AdaBoost feature selection and deep learning for predicting stock index futures prices. The results show that the model outperforms other popular prediction models such as random forest, multi-layer perception, gated recurrent unit, deep belief network and stacked denoising autoencoder.