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Dynamic Black Litterman portfolios with views derived via CNN-BiLSTM predictions

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  • Barua, Ronil
  • Sharma, Anil K.

Abstract

We use daily price and technical indicators' data for the ten MSCI Asia Pacific sector indices for the past 20 years and find that our hybrid multivariate Convolutional Neural Network - Bidirectional Long Short-Term Memory (CNN-BiLSTM) deep learning model gives reasonably better predictions when predicting index closing prices out-of-sample than using either CNN or BiLSTM alone. After utilizing these predictions as investor views inside the Black-Litterman model with time variation in the conditional distribution of returns, we find that the portfolios generated outperform all benchmark model portfolios by a considerable margin in terms of financial efficiency and diversification.

Suggested Citation

  • Barua, Ronil & Sharma, Anil K., 2022. "Dynamic Black Litterman portfolios with views derived via CNN-BiLSTM predictions," Finance Research Letters, Elsevier, vol. 49(C).
    • Handle: RePEc:eee:finlet:v:49:y:2022:i:c:s154461232200335x
    DOI: 10.1016/j.frl.2022.103111
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    Cited by:

    1. Yuqin Sun & Yungao Wu & Gejirifu De, 2023. "A Novel Black-Litterman Model with Time-Varying Covariance for Optimal Asset Allocation of Pension Funds," Mathematics, MDPI, vol. 11(6), pages 1-21, March.
    2. Chi-Lin Li & Chung-Han Hsieh, 2023. "On Adaptive Portfolio Management with Dynamic Black-Litterman Approach," Papers 2307.03391, arXiv.org, revised Nov 2023.

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    More about this item

    Keywords

    Deep learning; Stock prediction; Portfolio optimization; Black-Litterman;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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