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On Adaptive Portfolio Management with Dynamic Black-Litterman Approach

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  • Chi-Lin Li
  • Chung-Han Hsieh

Abstract

This paper introduces a unified framework for adaptive portfolio management, integrating dynamic Black-Litterman (BL) optimization with the general factor model, Elastic Net regression, and mean-variance portfolio optimization, which allows us to generate investors' views and mitigate potential estimation errors systematically. Specifically, we propose an innovative dynamic sliding window algorithm to respond to the constantly changing market conditions. This algorithm allows for the flexible adjustment of the window size based on market volatility, generating robust estimates for factor modeling, time-varying BL estimations, and optimal portfolio weights. Through extensive empirical studies using the top 100 capitalized assets in the S&P 500 index, accounting for turnover transaction costs, we demonstrate that this combined approach leads to computational advantages and promising trading performances.

Suggested Citation

  • Chi-Lin Li & Chung-Han Hsieh, 2023. "On Adaptive Portfolio Management with Dynamic Black-Litterman Approach," Papers 2307.03391, arXiv.org, revised Nov 2023.
    • Handle: RePEc:arx:papers:2307.03391
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    References listed on IDEAS

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    1. Barua, Ronil & Sharma, Anil K., 2022. "Dynamic Black Litterman portfolios with views derived via CNN-BiLSTM predictions," Finance Research Letters, Elsevier, vol. 49(C).
    2. Guiso, Luigi & Sapienza, Paola & Zingales, Luigi, 2018. "Time varying risk aversion," Journal of Financial Economics, Elsevier, vol. 128(3), pages 403-421.
    3. Ang, Andrew, 2014. "Asset Management: A Systematic Approach to Factor Investing," OUP Catalogue, Oxford University Press, number 9780199959327.
    4. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    5. Michael J. Best & Robert R. Grauer, 1991. "Sensitivity Analysis for Mean-Variance Portfolio Problems," Management Science, INFORMS, vol. 37(8), pages 980-989, August.
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