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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 SandP500 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 cantlyoutperformed the market index and the minimum variance Sharpe-Markowitz’s portfolio.



Item Type: MPRA Paper -

Original Title: Defensive online portfolio selection-

Language: English-

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

Subjects: D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D81 - Criteria for Decision-Making under Risk and UncertaintyC - Mathematical and Quantitative Methods > C0 - GeneralD - Microeconomics > D9 - Intertemporal Choice > D90 - GeneralC - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic Analysis-





Autor: Stella, Fabio

Fuente: https://mpra.ub.uni-muenchen.de/33279/







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