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New Evidence on Optimal Asset Allocation

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  • Gerald R. Jensen
  • Jeffrey M. Mercer

Abstract

Brocato and Steed (1998) showed that portfolio rebalancing based on NBER business cycle turning points substantially improves in‐sample Markowitz efficiency. In a similar vein, we investigate potential improvements from rebalancing based on turning points in the monetary cycle. We find that the monetary cycle has greater influence than the business cycle on the variance/covariance structure of multiple asset classes. Furthermore, we find substantial improvements in in‐sample efficiency beyond a buy‐and‐hold strategy and the business‐cycle approach. Importantly, our indicator of monetary cycle turning points has a practical advantage over NBER business cycle turning points, in that it relies only on ex ante information. In out‐of‐sample tests, we continue to find superior portfolio performance after transactions costs using the monetary cycle to time portfolio rebalancing.

Suggested Citation

  • Gerald R. Jensen & Jeffrey M. Mercer, 2003. "New Evidence on Optimal Asset Allocation," The Financial Review, Eastern Finance Association, vol. 38(3), pages 435-454, August.
    • Handle: RePEc:bla:finrev:v:38:y:2003:i:3:p:435-454
    DOI: 10.1111/1540-6288.00054
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    2. Samuel Kyle Jones & Joe Bert Stine, 2010. "Expected utility and the non-normal returns of common portfolio rebalancing strategies," Journal of Asset Management, Palgrave Macmillan, vol. 10(6), pages 406-419, February.
    3. Wolff, Dominik & Bessler, Wolfgang & Opfer, Heiko, 2012. "Multi-Asset Portfolio Optimization and Out-of-Sample Performance: An Evaluation of Black-Litterman, Mean Variance and Naïve Diversification Approaches," VfS Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62020, Verein für Socialpolitik / German Economic Association.
    4. Apostolos Thomadakis, 2012. "Contagion or Flight-to-Quality Phenomena in Stock and Bond Returns," School of Economics Discussion Papers 0612, School of Economics, University of Surrey.
    5. Riza Demirer & Konstantinos Gkillas & Christos Kountzakis & Amaryllis Mavragani, 2020. "Risk Appetite and Jumps in Realized Correlation," Mathematics, MDPI, vol. 8(12), pages 1-11, December.
    6. Jammazi, Rania & Ferrer, Román & Jareño, Francisco & Hammoudeh, Shawkat M., 2017. "Main driving factors of the interest rate-stock market Granger causality," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 260-280.
    7. Narayan, S. & Le, T.-H. & Sriananthakumar, S., 2018. "The influence of terrorism risk on stock market integration: Evidence from eight OECD countries," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 247-259.
    8. Chin Man Chui & Jian Yang, 2012. "Extreme Correlation of Stock and Bond Futures Markets: International Evidence," The Financial Review, Eastern Finance Association, vol. 47(3), pages 565-587, August.
    9. Poshakwale, Sunil S. & Mandal, Anandadeep, 2016. "What drives asymmetric dependence structure of asset return comovements?," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 312-330.
    10. Demirovic, Amer & Guermat, Cherif & Tucker, Jon, 2017. "The relationship between equity and bond returns: An empirical investigation," Journal of Financial Markets, Elsevier, vol. 35(C), pages 47-64.
    11. Bessler, Wolfgang & Wolff, Dominik, 2015. "Do commodities add value in multi-asset portfolios? An out-of-sample analysis for different investment strategies," Journal of Banking & Finance, Elsevier, vol. 60(C), pages 1-20.
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    13. Yang, Jian & Zhou, Yinggang & Wang, Zijun, 2009. "The stock-bond correlation and macroeconomic conditions: One and a half centuries of evidence," Journal of Banking & Finance, Elsevier, vol. 33(4), pages 670-680, April.

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