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Risk-adjusted exponential gradient strategies for online portfolio selection

Author

Listed:
  • Jin’an He

    (Bauhinia Institute)

  • Fangping Peng

    (Sun Yat-sen University)

  • Xiuying Xie

    (Guangdong University of Technology)

Abstract

This paper concerns online portfolio selection problem whose main feature is with no any statistical assumption on future asset prices. Since online portfolio selection aims to maximize the cumulative wealth, most existing online portfolio strategies do not consider risk factors into the model. To enrich the research on online portfolio selection, we introduce the risk factors into the model and propose two novel risk-adjusted online portfolio strategies. More specifically, we first choose several exponential gradient ( $$\text {EG}(\eta )$$ EG ( η ) ) with different values of parameter $$\eta $$ η to build an expert pool. Later, we construct two risk methods to measure performance of each expert. Finally, we calculate the portfolio by the weighted average over all expert advice. We present theoretical and experimental results respectively to analyze the performance of the proposed strategies. Theoretical results show that the proposed strategies not only track the expert with the lowest risk, but also are universal, i.e., they exhibit the same asymptotic average logarithmic growth rate as best constant rebalanced portfolio (BCRP) determined in hindsight. We conduct extensive experiments by using daily stock data collected from the American and Chinese stock markets. Experimental results show the proposed strategies outperform existing online portfolio in terms of the return and risk metrics in most cases.

Suggested Citation

  • Jin’an He & Fangping Peng & Xiuying Xie, 2024. "Risk-adjusted exponential gradient strategies for online portfolio selection," Journal of Combinatorial Optimization, Springer, vol. 48(1), pages 1-25, August.
    • Handle: RePEc:spr:jcomop:v:48:y:2024:i:1:d:10.1007_s10878-024-01187-x
    DOI: 10.1007/s10878-024-01187-x
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    References listed on IDEAS

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    1. Hwang, Inchang & Xu, Simon & In, Francis, 2018. "Naive versus optimal diversification: Tail risk and performance," European Journal of Operational Research, Elsevier, vol. 265(1), pages 372-388.
    2. Esther Mohr & Robert Dochow, 2017. "Risk management strategies for finding universal portfolios," Annals of Operations Research, Springer, vol. 256(1), pages 129-147, September.
    3. Sergio Albeverio & LanJun Lao & XueLei Zhao, 2001. "On-line portfolio selection strategy with prediction in the presence of transaction costs," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 54(1), pages 133-161, October.
    4. Alexei Gaivoronski & Fabio Stella, 2000. "Stochastic Nonstationary Optimization for Finding Universal Portfolios," Annals of Operations Research, Springer, vol. 100(1), pages 165-188, December.
    5. Yong Zhang & Hong Lin & Lina Zheng & Xingyu Yang, 2022. "Adaptive online portfolio strategy based on exponential gradient updates," Journal of Combinatorial Optimization, Springer, vol. 43(3), pages 672-696, April.
    6. Alexander, Gordon J. & Baptista, Alexandre M., 2006. "Portfolio selection with a drawdown constraint," Journal of Banking & Finance, Elsevier, vol. 30(11), pages 3171-3189, November.
    7. Elad Hazan & Satyen Kale, 2015. "An Online Portfolio Selection Algorithm With Regret Logarithmic In Price Variation," Mathematical Finance, Wiley Blackwell, vol. 25(2), pages 288-310, April.
    8. Ha, Youngmin & Zhang, Hai, 2020. "Algorithmic trading for online portfolio selection under limited market liquidity," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1033-1051.
    9. Pengyu Wei, 2018. "Risk management with weighted VaR," Mathematical Finance, Wiley Blackwell, vol. 28(4), pages 1020-1060, October.
    10. Bin Li & Jialei Wang & Dingjiang Huang & Steven C. H. Hoi, 2018. "Transaction cost optimization for online portfolio selection," Quantitative Finance, Taylor & Francis Journals, vol. 18(8), pages 1411-1424, August.
    11. Patrick O'Sullivan & David Edelman, 2015. "Adaptive universal portfolios," The European Journal of Finance, Taylor & Francis Journals, vol. 21(4), pages 337-351, March.
    12. 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.
    13. Yong Zhang & Xingyu Yang, 2017. "Online Portfolio Selection Strategy Based on Combining Experts’ Advice," Computational Economics, Springer;Society for Computational Economics, vol. 50(1), pages 141-159, June.
    14. Sanford J. Grossman & Zhongquan Zhou, 1993. "Optimal Investment Strategies For Controlling Drawdowns," Mathematical Finance, Wiley Blackwell, vol. 3(3), pages 241-276, July.
    15. Paolo Guasoni & Marko Hans Weber, 2020. "Nonlinear price impact and portfolio choice," Mathematical Finance, Wiley Blackwell, vol. 30(2), pages 341-376, April.
    16. Jin'an He & Xingyu Yang & Hong Lin & Yong Zhang, 2020. "Adaptive online successive constant rebalanced portfolio based on moving window," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 34(1), pages 107-123.
    17. J. D. M. Yamim & C. C. H. Borges & R. F. Neto, 2023. "Portfolio Optimization Via Online Gradient Descent and Risk Control," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 361-381, June.
    18. Thomas M. Cover, 1991. "Universal Portfolios," Mathematical Finance, Wiley Blackwell, vol. 1(1), pages 1-29, January.
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