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Combining Alphas via Bounded Regression

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  • Zura Kakushadze

    (Quantigic® Solutions LLC, 1127 High Ridge Road #135, Stamford, CT 06905, USA
    Business School & School of Physics, Free University of Tbilisi, 240, David Agmashenebeli Alley, Tbilisi 0159, Georgia)

Abstract

We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM) for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted) regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.

Suggested Citation

  • Zura Kakushadze, 2015. "Combining Alphas via Bounded Regression," Risks, MDPI, vol. 3(4), pages 1-17, November.
    • Handle: RePEc:gam:jrisks:v:3:y:2015:i:4:p:474-490:d:58313
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Zura Kakushadze & Willie Yu, 2017. "How to combine a billion alphas," Journal of Asset Management, Palgrave Macmillan, vol. 18(1), pages 64-80, January.
    2. Zura Kakushadze, 2015. "Heterotic Risk Models," Papers 1508.04883, arXiv.org, revised Jan 2016.
    3. Zura Kakushadze, 2020. "Quant Bust 2020," Papers 2006.05632, arXiv.org.

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