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

Ronil Barua and Anil K. Sharma

Finance Research Letters, 2022, vol. 49, issue C

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.

Keywords: Deep learning; Stock prediction; Portfolio optimization; Black-Litterman (search for similar items in EconPapers)
JEL-codes: C45 G11 G17 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:49:y:2022:i:c:s154461232200335x

DOI: 10.1016/j.frl.2022.103111

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