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Efficient Portfolio Selection in a Large Market

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  • Jiaqin Chen
  • Ming Yuan

Abstract

Recent empirical studies show that the estimated Markowitz mean–variance portfolios oftentimes perform rather poorly when there are more than several assets in the investment universe. In this article, we argue that such disappointing performance can be largely attributed to the estimation error incurred in sample mean–variance portfolios, and therefore could be improved by utilizing more efficient estimating strategies. In particular, we show that this "Markowitz optimization enigma" (Michaud, 1989) could be resolved by carefully balancing the tradeoff between the estimation error and systematic error through the so-called subspace mean–variance analysis. In addition to the consistent improvement observed on real and simulated data sets, we prove that, under an approximate factor model, it is possible to use this strategy to construct portfolio rules whose performance closely resemble that of theoretical mean–variance efficient portfolios in a large market.

Suggested Citation

  • Jiaqin Chen & Ming Yuan, 2016. "Efficient Portfolio Selection in a Large Market," Journal of Financial Econometrics, Oxford University Press, vol. 14(3), pages 496-524.
  • Handle: RePEc:oup:jfinec:v:14:y:2016:i:3:p:496-524.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbw003
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    References listed on IDEAS

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    1. Forni, Mario & Lippi, Marco, 2001. "The Generalized Dynamic Factor Model: Representation Theory," Econometric Theory, Cambridge University Press, vol. 17(6), pages 1113-1141, December.
    2. Gregory Connor & Lisa R. Goldberg & Robert A. Korajczyk, 2010. "Portfolio Risk Analysis," Economics Books, Princeton University Press, edition 1, number 9224.
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    Cited by:

    1. Lassance, Nathan, 2022. "Reconciling mean-variance portfolio theory with non-Gaussian returns," European Journal of Operational Research, Elsevier, vol. 297(2), pages 729-740.
    2. 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.
    3. Wang, Christina Dan & Chen, Zhao & Lian, Yimin & Chen, Min, 2022. "Asset selection based on high frequency Sharpe ratio," Journal of Econometrics, Elsevier, vol. 227(1), pages 168-188.
    4. N'Golo Kone, 2020. "A Multi-Period Portfolio Selection in a Large Financial Market," Working Paper 1439, Economics Department, Queen's University.
    5. N'Golo Kone, 2021. "Regularized Maximum Diversification Investment Strategy," Working Paper 1450, Economics Department, Queen's University.
    6. Stephen Boyd & Kasper Johansson & Ronald Kahn & Philipp Schiele & Thomas Schmelzer, 2024. "Markowitz Portfolio Construction at Seventy," Papers 2401.05080, arXiv.org.
    7. Lassance, Nathan & Vrins, Frédéric, 2021. "Portfolio selection with parsimonious higher comoments estimation," Journal of Banking & Finance, Elsevier, vol. 126(C).
    8. Petukhina, Alla & Klochkov, Yegor & Härdle, Wolfgang Karl & Zhivotovskiy, Nikita, 2024. "Robustifying Markowitz," Journal of Econometrics, Elsevier, vol. 239(2).
    9. Nisful Laila & Karina Ayu Saraswati & Himmatul Kholidah, 2019. "Efficient portfolio composition of Indonesian Islamic bank financing," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 7(1), pages 34-43, September.
    10. Elisabeth Leoff & Leonie Ruderer & Jörn Sass, 2022. "Signal-to-noise matrix and model reduction in continuous-time hidden Markov models," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 95(2), pages 327-359, April.
    11. N'Golo Kone, 2021. "Efficient mean-variance portfolio selection by double regularization," Working Paper 1453, Economics Department, Queen's University.
    12. Härdle, Wolfgang & Klochkov, Yegor & Petukhina, Alla & Zhivotovskiy, Nikita, 2021. "Robustifying Markowitz," IRTG 1792 Discussion Papers 2021-018, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    13. N’Golo Koné, 2020. "Regularized Maximum Diversification Investment Strategy," Econometrics, MDPI, vol. 9(1), pages 1-23, December.
    14. Han, Chulwoo, 2020. "A nonparametric approach to portfolio shrinkage," Journal of Banking & Finance, Elsevier, vol. 120(C).
    15. Mohammad Mehdi Hosseinzadeh & Sergio Ortobelli Lozza & Farhad Hosseinzadeh Lotfi & Vittorio Moriggia, 2023. "Portfolio optimization with asset preselection using data envelopment analysis," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(1), pages 287-310, March.
    16. Branger, Nicole & Lučivjanská, Katarína & Weissensteiner, Alex, 2019. "Optimal granularity for portfolio choice," Journal of Empirical Finance, Elsevier, vol. 50(C), pages 125-146.
    17. Wolfgang Karl Hardle & Yegor Klochkov & Alla Petukhina & Nikita Zhivotovskiy, 2022. "Robustifying Markowitz," Papers 2212.13996, arXiv.org.

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