Dedi Rosadi, Ezra Putranda Setiawan, Matthias Templ, Peter Filzmoser
This study presents an improvement to the mean-variance portfolio optimization model, by considering both the integer transaction lots and a robust estimator of the covariance matrices. Four robust estimators were tested, namely the Minimum Covariance Determinant, the S, the MM, and the Orthogonalized Gnanadesikan–Kettenring estimator. These integer optimization problems were solved using genetic algorithms. We introduce the lot turnover measure, a modified portfolio turnover, and the Robust Sharpe Ratio as the measure of portfolio performance. Based on the simulation studies and the empirical results, this study shows that the robust estimators outperform the classical MLE when data contain outliers and when the lots have moderate sizes, e.g. 500 shares or less per lot. © 2020 The Authors
Department of Mathematics, Universitas Gadjah Mada, Indonesia; Department of Mathematics Education, Universitas Negeri Yogyakarta, Indonesia; Institute of Data Analysis and Process Design, Zurich University of Applied Sciences, Winterthur, Switzerland; Institute of Statistics and Mathematical Methods in Economics, TU, Wien, Austria
