Share price prediction of aerospace relevant companies with recurrent neural networks based on PCA

作者：

Highlights：

• Develop a two-stages model with PCA+RNN for share price prediction for aerospace.

• Study impact of fundamental & technical data on performance for two companies.

• Study the impact of RNN structures and training algorithms for two companies.

• Study the impact of PCA on the prediction performance of RNN for two companies.

摘要

•Develop a two-stages model with PCA+RNN for share price prediction for aerospace.•Study impact of fundamental & technical data on performance for two companies.•Study the impact of RNN structures and training algorithms for two companies.•Study the impact of PCA on the prediction performance of RNN for two companies.

论文关键词：Share price prediction,Principal component analysis,Recurrent neural networks,Fundamental analysis,Technical analysis,Aerospace Industry

论文评审过程：Received 6 December 2020, Revised 9 May 2021, Accepted 7 June 2021, Available online 12 June 2021, Version of Record 18 June 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115384