Forecasting daily stock market return using dimensionality reduction

作者：

Highlights：

• A data mining procedure to forecast daily stock market return is proposed.

• The raw data includes 60 financial and economic features over a 10-year period.

• Combining ANNs with PCA gives slightly higher classification accuracy.

• Combining ANNs with PCA provides significantly higher risk-adjusted profits.

摘要

•A data mining procedure to forecast daily stock market return is proposed.•The raw data includes 60 financial and economic features over a 10-year period.•Combining ANNs with PCA gives slightly higher classification accuracy.•Combining ANNs with PCA provides significantly higher risk-adjusted profits.

论文关键词：Daily stock return forecasting,Principal component analysis (PCA),Fuzzy robust principal component analysis (FRPCA),Kernel-based principal component analysis (KPCA),Artificial neural networks (ANNs),Trading strategies

论文评审过程：Received 24 July 2016, Revised 6 September 2016, Accepted 16 September 2016, Available online 21 September 2016, Version of Record 29 September 2016.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.09.027