Risk Factors and Stock Price Performance of U.S. Sectors: A Quintile Approach

Abstract

The present paper examines the ability of factor pricing models to explain the returns of U.S. stock market sectors. Using monthly data for ten U.S. sectors, from October 1989 to December 2020, classified according to the Global Industry Classification Standards (GICS), we find that asset pricing characteristics vary by industry, however, there are distinct patterns in terms of risk factor loadings and their respective significance depending on whether industries are classified as cyclical or defensive. This suggests that within industries’ classification sectors might be, at least at some level, homogenous. Our analysis also reveals that four sectors exhibit an off-pattern behavior, namely Finance, Information Technology, Consumer Staples and Energy. The time period consists of our analysis is quite diverse and includes periods of booming markets, but also extreme recession periods. Thus, we employ quantile regressions to investigate the validity of the models under extreme conditions. Our basic conclusions do not seem to be affected by fat-tails and conditional on the quantile the best performing model may vary, in some sectors.