Defensive online portfolio selection

Fabio Stella () and Alfonso Ventura

International Journal of Financial Markets and Derivatives, 2011, vol. 2, issue 1/2, 88-105

Abstract: 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.

Keywords: constant rebalanced portfolio; CRP; online investment; portfolio selection; defensive forecasting; finite investment horizon; game theory; stock markets. (search for similar items in EconPapers)
Date: 2011
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Working Paper: Defensive online portfolio selection (2010)
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