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Defensive online portfolio selection

  • Fabio Stella
  • Alfonso Ventura

The class of defensive online portfolio selection algorithms, designed for finite investment horizon, is introduced. The game constantly rebalanced portfolio and the worst case game constantly rebalanced portfolio, are presented and theoretically analysed. The analysis exploits the rich set of mathematical tools available by means of the connection between universal portfolios and the game-theoretic framework. The empirical performance of the worst case game constantly rebalanced portfolio algorithm is analysed through numerical experiments concerning the FTSE 100, Nikkei 225, Nasdaq 100 and S&P500 stock markets for the time interval, from January 2007 to December 2009, which includes the credit crunch crisis from September 2008 to March 2009. The results emphasise the relevance of the proposed online investment algorithm which significantly outperformed the market index and the minimum variance Sharpe-Markowitz's portfolio.

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Article provided by Inderscience Enterprises Ltd in its journal Int. J. of Financial Markets and Derivatives.

Volume (Year): 2 (2011)
Issue (Month): 1/2 ()
Pages: 88-105

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Handle: RePEc:ids:ijfmkd:v:2:y:2011:i:1/2:p:88-105
Contact details of provider: Web page: http://www.inderscience.com/browse/index.php?journalID==307

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  1. Gaivoronski, A & Stella, F, 2000. "Nonstationary Optimization Approach for Finding Universal Portfolios," MPRA Paper 21913, University Library of Munich, Germany.
  2. Gaivoronski, Alexei A. & Stella, Fabio, 2003. "On-line portfolio selection using stochastic programming," Journal of Economic Dynamics and Control, Elsevier, vol. 27(6), pages 1013-1043, April.
  3. Igor V. Evstigneev & Klaus Rainer Schenk-Hoppé, . "From Rags to Riches: On Constant Proportions Investment Strategies," IEW - Working Papers 089, Institute for Empirical Research in Economics - University of Zurich.
  4. Thomas M. Cover, 1991. "Universal Portfolios," Mathematical Finance, Wiley Blackwell, vol. 1(1), pages 1-29.
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