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Combining Alphas via Bounded Regression

1
Quantigic® Solutions LLC, 1127 High Ridge Road #135, Stamford, CT 06905, USA
2
Business School & School of Physics, Free University of Tbilisi, 240, David Agmashenebeli Alley, Tbilisi 0159, Georgia
Academic Editor: Jim Kyung-Soo Liew
Risks 2015, 3(4), 474-490; https://doi.org/10.3390/risks3040474
Received: 31 July 2015 / Accepted: 30 October 2015 / Published: 4 November 2015
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. View Full-Text
Keywords: hedge fund; alpha stream; alpha weights; portfolio turnover; investment allocation; weighted regression; diversification; bounds; optimization; factor models hedge fund; alpha stream; alpha weights; portfolio turnover; investment allocation; weighted regression; diversification; bounds; optimization; factor models
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MDPI and ACS Style

Kakushadze, Z. Combining Alphas via Bounded Regression. Risks 2015, 3, 474-490. https://doi.org/10.3390/risks3040474

AMA Style

Kakushadze Z. Combining Alphas via Bounded Regression. Risks. 2015; 3(4):474-490. https://doi.org/10.3390/risks3040474

Chicago/Turabian Style

Kakushadze, Zura. 2015. "Combining Alphas via Bounded Regression" Risks 3, no. 4: 474-490. https://doi.org/10.3390/risks3040474

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