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Factor tracking: A new smart beta strategy that outperforms naïve diversification

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  • Jiang, Chonghui
  • Du, Jiangze
  • An, Yunbi
  • Zhang, Jinqing

Abstract

This paper proposes a factor tracking strategy that minimizes the variance of the difference in returns between a target portfolio and return-driving factors. We establish and solve single- and multi-factor tracking models, and analyze the properties of the optimal portfolios. We find that the optimal factor tracking portfolios consist of factor mimicking portfolios and the minimum-variance portfolio determined in the traditional mean-variance model. Moreover, we derive the condition under which the multi-factor tracking portfolio is a linear convex combination of the corresponding single-factor tracking portfolios. Using various datasets from the US market, we show that the factor tracking portfolios outperform the equally-weighted portfolio, measured by both risk and Sharpe ratio. The superior performance of the factor tracking portfolios are attributed to these portfolios’ better upside participation and downside protection.

Suggested Citation

  • Jiang, Chonghui & Du, Jiangze & An, Yunbi & Zhang, Jinqing, 2021. "Factor tracking: A new smart beta strategy that outperforms naïve diversification," Economic Modelling, Elsevier, vol. 96(C), pages 396-408.
    • Handle: RePEc:eee:ecmode:v:96:y:2021:i:c:p:396-408
    DOI: 10.1016/j.econmod.2020.03.023
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    2. Mehmet Balcilar & Riza Demirer & Festus V. Bekun, 2021. "Flexible Time-Varying Betas in a Novel Mixture Innovation Factor Model with Latent Threshold," Mathematics, MDPI, vol. 9(8), pages 1-20, April.
    3. Riccardo Lucchetti & Mihaela Nicolau & Giulio Palomba & Luca Riccetti, 2022. "Reconciling TEV and VaR in Active Portfolio Management: A New Frontier," Working Papers 461, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.

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