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536. Mean–trend risk portfolio selection with non-dominated sorting asset preselection

Invited abstract in session MD-51: Portfolio risk management, stream Risk management in finance.

Monday, 14:30-16:00
Room: M5 (building: 101)

Authors (first author is the speaker)

1. David Neděla
Finance, VSB-TU Ostrava
2. Sergio Ortobelli Lozza
University of Bergamo
3. Tomáš Tichý
Department of Finance, Faculty of Economics, VSB-Technical University Ostrava

Abstract

By monitoring the financial markets, we can discover a huge number of potential assets to be invested in. It is very complicated and time-consuming to analyze all assets and then use all of them in optimization according to the preferred portfolio strategy. For this reason, it is potentially useful for portfolio managers to focus on a specific sample of assets identified by the filtering process. This paper proposes an efficient approach for asset preselection based on multidimensional non-dominated sorting of selected asset statistics. In contrast to previous research, we designed an innovative application on statistics obtained from approximated return series using nonparametric regression and principal component analysis (PCA). Additionally, we study its impact on mean–variance and recently proposed complex mean–trend risk large-scale portfolio selection strategies. In particular, this process examines the efficient frontier of portfolios on the basis of differing return and risk perspectives. In the empirical part applied to the US stock market data, we present an ex-post and ex-ante analysis with results for 40 portfolio strategies. The results obtained strongly indicate that for most risk-averse investors, the mean–trend risk strategies with the preselection outperform the same strategies without the preselection, as well as the mean–variance strategies.

Keywords

Status: accepted

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