Combining Alphas via Bounded Regression

Risks

Zura Kakushadze¹ 

[1] Quantigic^® Solutions LLC, 1127 High Ridge Road #135, Stamford, CT 06905, USA; E-Mail:

关键词: hedge fund;    alpha stream;    alpha weights;    portfolio turnover;    investment allocation;    weighted regression;    diversification;    bounds;    optimization;    factor models;   

DOI  :  10.3390/risks3040474

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.