Take Bitcoin into your portfolio: a novel ensemble portfolio optimization framework for broad commodity assets

Yuze Li (), Shangrong Jiang (), Yunjie Wei () and Shouyang Wang ()
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Yuze Li: University of Chinese Academy of Sciences
Shangrong Jiang: University of Chinese Academy of Sciences
Yunjie Wei: Chinese Academy of Sciences
Shouyang Wang: University of Chinese Academy of Sciences

Financial Innovation, 2021, vol. 7, issue 1, 1-26

Abstract: Abstract The emergence and growing popularity of Bitcoins have attracted the attention of the financial world. However, few empirical studies have considered the inclusion of the newly emerged commodity asset in the global commodity market. It is of great importance for investors and policymakers to take advantage of this asset and its potential benefits by incorporating it as a part of the broad commodity trading portfolio. In this study, we propose a novel ensemble portfolio optimization (NEPO) framework utilized for broad commodity assets, which integrates a hybrid variational mode decomposition-bidirectional long short-term memory deep learning model for future returns forecast and a reinforcement learning-based model for optimizing the asset weight allocation. Our empirical results indicate that the NEPO framework could effectively improve the prediction accuracy and trend prediction ability across various commodity assets from different sectors. In addition, it could effectively incorporate Bitcoins into the asset pool and achieve better financial performance compared to traditional asset allocation strategies, commodity funds, and indices.

Keywords: Portfolio optimization; Bitcoin; Deep learning; Reinforcement learning; Variational mode decomposition (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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DOI: 10.1186/s40854-021-00281-x

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