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Defensive online portfolio selection

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  • Stella, Fabio
  • Ventura, Alfonso

Abstract

The class of defensive online portfolio selection algorithms,designed for fi nite investment horizon, is introduced. The Game Constantly Rebalanced Portfolio and the Worst Case Game Constantly Rebalanced Portfolio, are presented and theoretically analyzed. 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 analyzed 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 emphasize the relevance of the proposed online investment algorithm which signi fi cantly outperformed the market index and the minimum variance Sharpe-Markowitz’s portfolio.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 33279.

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Date of creation: 2010
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Handle: RePEc:pra:mprapa:33279

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Keywords: on-line portfolio selection; universal portfolio; defensive strategy;

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  1. 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.
  2. Thomas M. Cover, 1991. "Universal Portfolios," Mathematical Finance, Wiley Blackwell, vol. 1(1), pages 1-29.
  3. Gaivoronski, A & Stella, F, 2000. "Nonstationary Optimization Approach for Finding Universal Portfolios," MPRA Paper 21913, University Library of Munich, Germany.
  4. 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.
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