Factor Models for Alpha Streams

TL;DR

This paper introduces a framework for creating factor models for alpha streams to address issues of singularity, stability, and tractability in large-scale alpha covariance matrices, drawing insights from stock factor models.

Contribution

It proposes a novel approach to construct factor models for alpha streams, including various risk factors and methods to improve covariance matrix stability and computational efficiency.

Findings

Framework effectively handles large alpha sets

Incorporates diverse risk factors including style, cluster, and principal components

Using underlying tradables as risk factors enhances model robustness

Abstract

We propose a framework for constructing factor models for alpha streams. Our motivation is threefold. 1) When the number of alphas is large, the sample covariance matrix is singular. 2) Its out-of-sample stability is challenging. 3) Optimization of investment allocation into alpha streams can be tractable for a factor model alpha covariance matrix. We discuss various risk factors for alphas such as: style risk factors; cluster risk factors based on alpha taxonomy; principal components; and also using the underlying tradables (stocks) as alpha risk factors, for which computing the factor loadings and factor covariance matrices does not involve any correlations with alphas, and their number is much larger than that of the relevant principal components. We draw insight from stock factor models, but also point out substantial differences.