caa a22 4500 606232974 CHVBK 20210128101229.0 cr unu---uuuuu 210128e20150701xx s 000 0 eng 10.1007/s10287-014-0227-5 doi (NATIONALLICENCE)springer-10.1007/s10287-014-0227-5 Constructing optimal sparse portfolios using regularization methods [Elektronische Daten] [B. Fastrich, S. Paterlini, P. Winker] Mean-variance portfolios have been criticized because of unsatisfying out-of-sample performance and the presence of extreme and unstable asset weights, especially when the number of securities is large. The bad performance is caused by estimation errors in inputs parameters, that is the covariance matrix and the expected return vector. Recent studies show that imposing a penalty on the 1-norm of the asset weights vector (i.e. $$\ell _{1}$$ ℓ 1 -regularization) not only regularizes the problem, thereby improving the out-of-sample performance, but also allows to automatically select a subset of assets to invest in. However, $$\ell _{1}$$ ℓ 1 -regularization might lead to the construction of biased solutions. We propose a new, simple type of penalty that explicitly considers financial information and then we consider several alternative penalties, that allow to improve on the $$\ell _{1}$$ ℓ 1 -regularization approach. By using U.S.-stock market data, we show empirically that the proposed penalties can lead to the construction of portfolios with an out-of-sample performance superior to several state-of-art benchmarks, especially in high dimensional problems. Springer-Verlag Berlin Heidelberg, 2014 Minimum variance portfolio nationallicence Statistical regularization nationallicence Lasso nationallicence Non-convex penalties nationallicence Fastrich B. Department of Economics, University of Giessen, Licher Strasse 64, 35394, Giessen, Germany aut Paterlini S. Department of Finance and Accounting, European Business School, Gustav-Stresemann-Ring 3, 65189, Wiesbaden, Germany aut Winker P. Department of Economics, University of Giessen, Licher Strasse 64, 35394, Giessen, Germany aut Computational Management Science Springer Berlin Heidelberg 12/3(2015-07-01), 417-434 1619-697X 12:3<417 2015 12 10287 https://doi.org/10.1007/s10287-014-0227-5 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10287-014-0227-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Fastrich B. Department of Economics, University of Giessen, Licher Strasse 64, 35394, Giessen, Germany aut NATIONALLICENCE 700 1- Paterlini S. Department of Finance and Accounting, European Business School, Gustav-Stresemann-Ring 3, 65189, Wiesbaden, Germany aut NATIONALLICENCE 700 1- Winker P. Department of Economics, University of Giessen, Licher Strasse 64, 35394, Giessen, Germany aut NATIONALLICENCE 773 0- Computational Management Science Springer Berlin Heidelberg 12/3(2015-07-01), 417-434 1619-697X 12:3<417 2015 12 10287