Comparing the effectiveness of deep feedforward neural networks and shallow architectures for predicting stock price indices

作者：

Highlights：

• Deep neural networks (cf. shallower architectures) better predict stock indices.

• A rectifier linear (cf. tanh) activation function better predicts stock indices.

• Relative predictive accuracy of deep neural networks peaks with increasing data.

摘要

•Deep neural networks (cf. shallower architectures) better predict stock indices.•A rectifier linear (cf. tanh) activation function better predicts stock indices.•Relative predictive accuracy of deep neural networks peaks with increasing data.

论文关键词：Financial time series forecasting,Deep feedforward neural network,Market efficiency,Machine learning

论文评审过程：Received 7 March 2019, Revised 18 July 2019, Accepted 19 July 2019, Available online 23 July 2019, Version of Record 7 August 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.112828