Abstract In the vast landscape of financial markets, identifying potential investment assets such as stocks can be overwhelming and time-consuming. For portfolio managers, focusing on a specific selection of stocks through an effective filtering process can streamline this task. This paper introduces an efficient stock preselection method using multidimensional non-dominated sorting of selected return statistics. Unlike previous research, our approach leverages statistics derived from approximated return series through nonparametric regression and principal component analysis (PCA). We further explore the impact of this preselection on mean-variance and the newly proposed mean-trend risk large-scale portfolio selection strategies. By examining the efficient frontier of portfolios from various return and risk perspectives, our empirical analysis on US stock market data provides both ex-post and ex-ante results for 40 portfolio strategies. The findings suggest that for most risk-averse investors, mean-trend risk strategies with preselection significantly outperform both the same strategies without preselection and traditional mean-variance strategies.
Neděla et al. (Wed,) studied this question.
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