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Testing rebalancing strategies for stock-bond portfolios across different asset allocations

Author

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  • Hubert Dichtl
  • Wolfgang Drobetz
  • Martin Wambach

Abstract

We compare the risk-adjusted performance of stock-bond portfolios between rebalancing and buy-and-hold across different asset allocations by reporting statistical significance levels. Our investigation is based on a 30-year dataset and incorporates the financial markets of the United States, the United Kingdom and Germany. To draw useful recommendations to investment management, we implement a history-based simulation approach which enables us to mimic realistic market conditions. Even if the portfolio weight of stocks is very low, our empirical results show that a frequent rebalancing significantly enhances risk-adjusted portfolio performance for all analysed countries and all risk-adjusted performance measures.

Suggested Citation

  • Hubert Dichtl & Wolfgang Drobetz & Martin Wambach, 2016. "Testing rebalancing strategies for stock-bond portfolios across different asset allocations," Applied Economics, Taylor & Francis Journals, vol. 48(9), pages 772-788, February.
    • Handle: RePEc:taf:applec:v:48:y:2016:i:9:p:772-788
    DOI: 10.1080/00036846.2015.1088139
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    Cited by:

    1. Chendi Ni & Yuying Li & Peter A. Forsyth, 2023. "Neural Network Approach to Portfolio Optimization with Leverage Constraints:a Case Study on High Inflation Investment," Papers 2304.05297, arXiv.org, revised May 2023.
    2. Matthias Horn & Andreas Oehler, 2020. "Automated portfolio rebalancing: Automatic erosion of investment performance?," Journal of Asset Management, Palgrave Macmillan, vol. 21(6), pages 489-505, October.
    3. Kartikay Gupta & Niladri Chatterjee, 2021. "Stocks Recommendation from Large Datasets Using Important Company and Economic Indicators," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(4), pages 667-689, December.
    4. Nichanan Sakolvieng, 2024. "Optimizing Cryptocurrency Portfolios: A Comparative Study of Rebalancing Strategies," GATR Journals jfbr220, Global Academy of Training and Research (GATR) Enterprise.
    5. Jean-Baptiste Hasse & Christelle Lecourt & Souhila Siagh, 2023. "Institutional Stock-Bond Portfolios Rebalancing and Financial Stability," AMSE Working Papers 2322, Aix-Marseille School of Economics, France.
    6. Michael D. Mattei, 2018. "Enhanced Portfolio Performance Using a Momentum Approach to Annual Rebalancing," IJFS, MDPI, vol. 6(1), pages 1-9, February.
    7. Marc Chen & Mohammad Shirazi & Peter A. Forsyth & Yuying Li, 2023. "Machine Learning and Hamilton-Jacobi-Bellman Equation for Optimal Decumulation: a Comparison Study," Papers 2306.10582, arXiv.org.
    8. Forsyth, Peter A., 2022. "Short term decumulation strategies for underspending retirees," Insurance: Mathematics and Economics, Elsevier, vol. 102(C), pages 56-74.
    9. Martin Boďa & Mária Kanderová, 2020. "Performance of Six Sigma Rebalancing for Portfolios Mixing Polar Investment Styles," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 68(1), pages 139-155.
    10. Peter A. Forsyth, 2020. "A Stochastic Control Approach to Defined Contribution Plan Decumulation: "The Nastiest, Hardest Problem in Finance"," Papers 2008.06598, arXiv.org.
    11. Li, Yuying & Forsyth, Peter A., 2019. "A data-driven neural network approach to optimal asset allocation for target based defined contribution pension plans," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 189-204.
    12. Peter A. Forsyth & Kenneth R. Vetzal, 2019. "Defined Contribution Pension Plans: Who Has Seen the Risk?," JRFM, MDPI, vol. 12(2), pages 1-27, April.
    13. Peter A. Forsyth & Kenneth R. Vetzal & G. Westmacott, 2022. "Optimal performance of a tontine overlay subject to withdrawal constraints," Papers 2211.10509, arXiv.org.
    14. Forsyth, Peter A., 2020. "Optimal dynamic asset allocation for DC plan accumulation/decumulation: Ambition-CVAR," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 230-245.
    15. Pieter M. van Staden & Peter A. Forsyth & Yuying Li, 2023. "A parsimonious neural network approach to solve portfolio optimization problems without using dynamic programming," Papers 2303.08968, arXiv.org.
    16. van Staden, Pieter M. & Forsyth, Peter A. & Li, Yuying, 2024. "Across-time risk-aware strategies for outperforming a benchmark," European Journal of Operational Research, Elsevier, vol. 313(2), pages 776-800.

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