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Optimal Portfolio Choice with Unknown Benchmark Efficiency

Author

Listed:
  • Raymond Kan

    (Rotman School of Management, University of Toronto, Toronto, Ontario M5S 3E6, Canada)

  • Xiaolu Wang

    (Ivy College of Business, Iowa State University, Ames, Iowa 50011)

Abstract

When a benchmark model is inefficient, including test assets in addition to the benchmark portfolios can improve the performance of the optimal portfolio. In reality, the efficiency of a benchmark model relative to the test assets is ex ante unknown; moreover, the optimal portfolio is constructed based on estimated parameters. Therefore, whether and how to include the test assets becomes a critical question faced by real world investors. For such a setting, we propose a combining portfolio strategy, optimally balancing the value of including test assets and the effect of estimation errors. The proposed combining strategy can work together with some existing estimation risk reduction strategies. In both empirical data sets and simulations, we show that our proposed combining strategy performs well.

Suggested Citation

  • Raymond Kan & Xiaolu Wang, 2024. "Optimal Portfolio Choice with Unknown Benchmark Efficiency," Management Science, INFORMS, vol. 70(9), pages 6117-6138, September.
    • Handle: RePEc:inm:ormnsc:v:70:y:2024:i:9:p:6117-6138
    DOI: 10.1287/mnsc.2021.01767
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    Citations

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    Cited by:

    1. Yonghe Lu & Yanrong Yang & Terry Zhang, 2024. "Double Descent in Portfolio Optimization: Dance between Theoretical Sharpe Ratio and Estimation Accuracy," Papers 2411.18830, arXiv.org.
    2. Taras Bodnar & Nikolaus Hautsch & Yarema Okhrin & Nestor Parolya, 2024. "Consistent Estimation of the High-Dimensional Efficient Frontier," Papers 2409.15103, arXiv.org.

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