Three mature dimensionality reduction techniques, including principal component analysis ( PCA), fuzzy robust principal component analysis ( FRPCA), and kernel-based principal component analysis ( KPCA) are applied to the whole data set to simplify and rearrange the original data structure. This paper presents a complete and efficient data mining process to forecast the daily direction of the S&P 500 Index ETF (SPY) return based on 60 financial and economic features. Among the few studies that focus on predicting daily stock market returns, the data mining procedures utilized are either incomplete or inefficient, especially when a large amount of features are involved. In financial markets, it is both important and challenging to forecast the daily direction of the stock market return.
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