Forecasting daily stock market return using dimensionality reduction
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- 作者：Xiao Zhonga ; xz4y4@mst.edu" class="auth_mail" title="E-mail the corresponding author ; David Enkeb ; enke@mst.edu" class="auth_mail" title="E-mail the corresponding author
- 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
- 刊名：Expert Systems with Applications
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：67
- 期：Complete
- 页码：126-139
- 全文大小：727 K
文摘
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A data mining procedure to forecast daily stock market return is proposed.
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The raw data includes 60 financial and economic features over a 10-year period.
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Combining ANNs with PCA gives slightly higher classification accuracy.
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Combining ANNs with PCA provides significantly higher risk-adjusted profits.