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A Progressive Resampling Algorithm for Finding Very Sparse Investment Portfolios

  • Marko Hassinen
  • Antti UkkonenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11054)

Abstract

The mean-variance framework by Markowitz is a classical approach to portfolio selection. Earlier work has shown that the basic Markowitz portfolios obtained by solving a quadratic program tend to have poor out-of-sample performance. These issues have been addressed by devising sparse variants of Markowitz portfolios in which the number of active positions is reduced either by applying a no-short-selling constraint or L1-regularisation. In this work we consider a combinatorial approach for finding sparse portfolios, which we call naive k-portfolios, that allocate available capital uniformly on a fixed number of k assets, and only take long positions. We present a novel randomised algorithm, progressive resampling, that efficiently finds such portfolios, and compare this with a number of well-known portfolio selection strategies using public stock price data. We find that naive k-portfolios can be a viable alternative to L1-regularisation when constructing sparse portfolios.

Keywords

Portfolio selection Randomized algorithms Sparse models 

Notes

Acknowledgements

This work has been supported by Academy of Finland (decisions 1308946 and 1314262).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of HelsinkiHelsinkiFinland

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