Sequential Learning, Asset Allocation, and Bitcoin Returns

82 Pages Posted: 2 Aug 2021 Last revised: 11 Nov 2021

See all articles by James Yae

James Yae

University of Houston - C. T. Bauer College of Business

George Zhe Tian

University of Houston - C. T. Bauer College of Business

Date Written: July 23, 2021

Abstract

For optimal asset allocation, mean-variance investors must learn about the joint dynamics of new and existing asset classes, not only their profitability. Bitcoin's digital gold narrative provides a unique laboratory to test this hypothesis. We find that a decrease in investors' estimate on correlation between Bitcoin and the US stock markets strongly predicts higher Bitcoin returns the next day. The same empirical pattern universally appears in out-of-sample predictions, global equity markets, and other cryptocurrencies. Our stylized model and empirical proxy for Bitcoin demand explain the predictability pattern in light of asset allocation practices and investors' learning on time-varying correlation.

Keywords: Bitcoin, Uncertainty, Learning, Time-Varying Correlation, Return Predictability

JEL Classification: G12, G15, D83

Suggested Citation

Yae, James and Tian, George Zhe, Sequential Learning, Asset Allocation, and Bitcoin Returns (July 23, 2021). Available at SSRN: https://ssrn.com/abstract=3896611 or http://dx.doi.org/10.2139/ssrn.3896611

James Yae (Contact Author)

University of Houston - C. T. Bauer College of Business ( email )

Houston, TX 77204-6021
United States

George Zhe Tian

University of Houston - C. T. Bauer College of Business ( email )

Houston, TX 77204-6021
United States

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