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Better Betas

Lisa R. Goldberg, Alex Papanicolaou, Alex Shkolnik and Simge Ulucam
The Journal of Portfolio Management November 2020, 47 (1) 119-136; DOI: https://doi.org/10.3905/jpm.2020.47.1.119
Lisa R. Goldberg
is a professor of the practice of economics and co-director of the Consortium for Data Analytics in Risk at the University of California in Berkeley, CA. She is also the director of research at Aperio Group in Sausalito, CA
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Alex Papanicolaou
is a chief data scientist at Intelligent Financial Machines in Palo Alto, CA, and is with the Consortium for Data Analytics in Risk, University of California in Berkeley, CA
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Alex Shkolnik
is an assistant professor in the Department of Statistics and Applied Probability at the University of California in Santa Barbara, CA, and is with the Consortium for Data Analytics in Risk, University of California in Berkeley, CA
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Simge Ulucam
is a quantitative researcher at Aperio Group in Sausalito, CA
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Abstract

The authors introduce the data-driven Goldberg, Papanicolaou, and Shkolnik (GPS) adjustment for estimated betas, which leads to material improvements in the accuracy of weights and risk forecasts of minimum variance portfolios. Like the widely used Blume 2/3 rule and Vasicek adjustment developed in the 1970s, the GPS adjustment for estimated betas shrinks raw beta estimates toward one. Unlike its antecedents, the GPS adjustment operates on the dominant factor of a sample covariance matrix, and this adjustment adapts dynamically to varying levels of beta dispersion. The authors illustrate the power of the GPS adjustment in a simulation that is calibrated to calm and stressed market regimes.

TOPICS: Factor-based models, portfolio construction, risk management, simulations

Key Findings

  • • Betas play a central role in determining optimized portfolios, and principal component analysis (PCA) is an effective tool to estimate betas.

  • • More accurate PCA betas can be achieved with the Goldberg, Papanicolaou, and Shkolnik (GPS) adjustment, which is analogous to standard beta shrinkage adjustments applied to betas obtained from time-series regression.

  • • Substantial improvements to the accuracy of minimum variance portfolio weights and risk forecasts may be realized when applying the GPS adjustment, which leads to better betas in any market regime.

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The Journal of Portfolio Management: 47 (1)
The Journal of Portfolio Management
Vol. 47, Issue 1
November 2020
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Better Betas
Lisa R. Goldberg, Alex Papanicolaou, Alex Shkolnik, Simge Ulucam
The Journal of Portfolio Management Oct 2020, 47 (1) 119-136; DOI: 10.3905/jpm.2020.47.1.119

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Better Betas
Lisa R. Goldberg, Alex Papanicolaou, Alex Shkolnik, Simge Ulucam
The Journal of Portfolio Management Oct 2020, 47 (1) 119-136; DOI: 10.3905/jpm.2020.47.1.119
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