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Constant rebalanced portfolios and side-information

Author

Listed:
  • E. Fagiuoli
  • F. Stella
  • A. Ventura

Abstract

In recent years much work has been carried out to design and analyse online investment strategies based on constant rebalanced portfolios. A constant rebalanced portfolio is a sequential investment strategy that maintains fixed through time, trading period by trading period, the wealth distribution among a set of assets. In this framework, Cover proposed the universal portfolio, which is competitive with the best constant rebalanced portfolio determined in hindsight, i.e. the constant rebalanced portfolio obtained by assuming perfect knowledge of future stock prices. However, the constant rebalanced portfolio is designed to deal with the portfolio selection problem in the case where no additional information concerning the stock market, is available. To overcome this limitation, Cover and Ordentlich proposed the state constant rebalanced portfolio, which is capable of appropriately exploiting the available side-information concerning the stock market. In this paper we study and analyse the topic introduced by Cover and Ordentlich and focus our attention on the interplay between constant rebalanced portfolios and side-information. We introduce a mathematical framework to deal with the constant rebalanced portfolio in the case where side-information, concerning the stock market, is available. The mathematical framework defines and analyses the mixture best constant rebalanced portfolio, which we propose as the investment benchmark to be considered in the case where side-information, concerning the stock market, is available. The mixture best constant rebalanced portfolio outperforms the best constant rebalanced portfolio by an exponential factor in terms of the achieved wealth and therefore offers an interesting opportunity for side-information specialized online investment algorithms. We describe a new online investment algorithm that exploits the definition of the mixture best constant rebalanced portfolio and the available side-information. The performance of the proposed online investment algorithm is investigated through a set of numerical experiments concerning four major stock market data sets, namely DJIA, S&P500, TSE and NYSE. The results emphasize the relevance of the proposed online investment strategy and underline the central role of the quality of the side-information in outperforming the best constant rebalanced portfolio.

Suggested Citation

  • E. Fagiuoli & F. Stella & A. Ventura, 2007. "Constant rebalanced portfolios and side-information," Quantitative Finance, Taylor & Francis Journals, vol. 7(2), pages 161-173.
    • Handle: RePEc:taf:quantf:v:7:y:2007:i:2:p:161-173
    DOI: 10.1080/14697680601157942
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    Citations

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    Cited by:

    1. Jin’an He & Shicheng Yin & Fangping Peng, 2024. "Weak aggregating specialist algorithm for online portfolio selection," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2405-2434, June.
    2. Xingyu Yang & Jin’an He & Hong Lin & Yong Zhang, 2020. "Boosting Exponential Gradient Strategy for Online Portfolio Selection: An Aggregating Experts’ Advice Method," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 231-251, January.
    3. Fabio Stella & Alfonso Ventura, 2011. "Defensive online portfolio selection," International Journal of Financial Markets and Derivatives, Inderscience Enterprises Ltd, vol. 2(1/2), pages 88-105.

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