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The Journal of Financial Data Science

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On the Black–Litterman Model: Learning to Do Better

Ren-Raw Chen, Shih-Kuo Yeh and Xiaohu Zhang
The Journal of Financial Data Science Summer 2022, jfds.2022.1.096; DOI: https://doi.org/10.3905/jfds.2022.1.096
Ren-Raw Chen
is a professor at the Gabelli School of Business at Fordham University in New York, NY
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Shih-Kuo Yeh
is a professor of finance at National Chung Hsing University in Taichung, Taiwan
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Xiaohu Zhang
is a staff member at Bank of Nova Scotia in Ontario, Canada
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Abstract

In this article, the authors study the performance of the Black–Litterman model (BLM) and compare it to the traditional mean–variance theory (MVT) of Markowitz (1952) and Sharpe (1964). They begin with the standard Bayesian learning on which the BLM is based (but the existing literature does not follow). Then, they perform a series of tests of the BLM using machine learning tools and view specifications consistent with the existing literature. Their empirical evidence (which uses 30 years of monthly data from January 1991 till December 2020) suggests that the BLM is highly sensitive to the specification of the view.

Given that the view is arbitrary (even though in our study, they are rule based), it is quite a challenge to use the BLM in an actual situation. A great amount of caution must be exercised in specifying the view and its corresponding required return. This validates the previous result that BLM specification of views is very important and there is no consistent manner how one can specify a winning portfolio.

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The Journal of Financial Data Science: 4 (2)
The Journal of Financial Data Science
Vol. 4, Issue 2
Spring 2022
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On the Black–Litterman Model: Learning to Do Better
Ren-Raw Chen, Shih-Kuo Yeh, Xiaohu Zhang
The Journal of Financial Data Science Jun 2022, jfds.2022.1.096; DOI: 10.3905/jfds.2022.1.096

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On the Black–Litterman Model: Learning to Do Better
Ren-Raw Chen, Shih-Kuo Yeh, Xiaohu Zhang
The Journal of Financial Data Science Jun 2022, jfds.2022.1.096; DOI: 10.3905/jfds.2022.1.096
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