Combining Alphas via Bounded Regression

TL;DR

This paper introduces a bounded regression algorithm for combining alpha streams that addresses issues of diversification and skewness, especially when sample covariance matrices are unreliable due to limited data.

Contribution

It provides an explicit algorithm and source code for bounded regression, improving alpha combination and portfolio construction under data constraints.

Findings

Bounded regression improves diversification of alpha weights.

The method effectively handles limited historical data for covariance estimation.

Applicable to stock and asset portfolio construction.

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.