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Sensitivity analysis applied to tilting methodologies

Author

Listed:
  • Tom Chan

    (Senior Research Analyst, Research and Analytics, LSEG)

  • Julien Riposo

    (Digital Assets Quantitative Research, LSEG)

  • E. G. Klepfish

    (Manager, Research - Index Research & Design, EMEA, LSEG)

  • Andreas Schroeder

    (Head of EMEA Index Research & Design, LSEG)

Abstract

This paper addresses the challenge of constructing factor portfolios that accurately reflect asset characteristics while remaining robust to noise. We examine the variation of the portfolio weights produced on noisy versions of the original signal while targeting a fixed unit of signal exposure. It is proposed a framework to evaluate how well portfolio strategies handle noise by analyzing how portfolio weights vary with noisy versions of the original signal, while maintaining a consistent level of signal exposure. Assuming normal distribution for signal and noise, we analytically derive the distribution of portfolio weight deviations. These findings are supported by simulations using more realistic skewed, fat-tailed, and correlated signals. The framework is then applied to various common portfolio strategies and factors.

Suggested Citation

  • Tom Chan & Julien Riposo & E. G. Klepfish & Andreas Schroeder, 2025. "Sensitivity analysis applied to tilting methodologies," Journal of Asset Management, Palgrave Macmillan, vol. 26(2), pages 186-215, March.
    • Handle: RePEc:pal:assmgt:v:26:y:2025:i:2:d:10.1057_s41260-024-00388-7
    DOI: 10.1057/s41260-024-00388-7
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    References listed on IDEAS

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    1. Anil Bera & Sung Park, 2008. "Optimal Portfolio Diversification Using the Maximum Entropy Principle," Econometric Reviews, Taylor & Francis Journals, vol. 27(4-6), pages 484-512.
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