Using fear, greed and machine learning for optimizing global portfolios: A Black-Litterman approach

Ronil Barua and Anil K. Sharma

Finance Research Letters, 2023, vol. 58, issue PC

Abstract: We introduce a new dimension in constructing relative investor views for the Black-Litterman model by incorporating fear/greed technical indicator predictions as a proxy for investor sentiment in the portfolio construction process. We apply a hybrid CEEMDAN-GRU deep learning model to forecast this indicator and the XGBoost ensemble learning algorithm to forecast returns for ten country ETFs and create relative views for the Black-Litterman model. These models beat several benchmark forecasting models. Our empirical results show that the proposed approach outperforms the Markowitz, minimum-variance, equally-weighted and risk-parity strategies along with four other Black-Litterman approaches from the literature for six investment periods.

Keywords: Black-Litterman; Investor sentiment; Financial forecasting; CEEMDAN; GRU; XGBoost (search for similar items in EconPapers)
JEL-codes: C45 C53 G11 G41 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:58:y:2023:i:pc:s1544612323008875

DOI: 10.1016/j.frl.2023.104515

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