Sector-Based Factor Models for Asset Returns

TL;DR

This paper introduces a sector-based factor model for asset returns that improves interpretability by explicitly incorporating sector information, demonstrating more coherent factor components across sectors compared to traditional models.

Contribution

The study develops a novel sector-based factor model with an EM algorithm, enhancing interpretability of factors in stock return analysis over 15 years of data.

Findings

Most sector factors have the same sign, indicating coordinated movement within sectors.

Classic factor models show mixed positive and negative components, less interpretable.

Sector-based models produce more intuitive and sector-coherent factor components.

Abstract

Factor analysis is a statistical technique employed to evaluate how observed variables correlate through common factors and unique variables. While it is often used to analyze price movement in the unstable stock market, it does not always yield easily interpretable results. In this study, we develop improved factor models by explicitly incorporating sector information on our studied stocks. We add eleven sectors of stocks as defined by the IBES, represented by respective sector-specific factors, to non-specific market factors to revise the factor model. We then develop an expectation maximization (EM) algorithm to compute our revised model with 15 years' worth of S&P 500 stocks' daily close prices. Our results in most sectors show that nearly all of these factor components have the same sign, consistent with the intuitive idea that stocks in the same sector tend to rise and fall in coordination over time. Results obtained by the classic factor model, in contrast, had a homogeneous blend of positive and negative components. We conclude that results produced by our sector-based factor model are more interpretable than those produced by the classic non-sector-based model for at least some stock sectors.