Defensive online portfolio selection

Fabio Stella () and Alfonso Ventura

MPRA Paper from University Library of Munich, Germany

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.

Keywords: on-line portfolio selection; universal portfolio; defensive strategy (search for similar items in EconPapers)
JEL-codes: C0 C61 D81 D90 (search for similar items in EconPapers)
Date: 2010
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Journal Article: Defensive online portfolio selection (2011)
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