An analysis of investing in U.S. equities with the application of quantitative factor portfolios

Abstract

This study aimed to examine the potential of applying factors of the modern asset pricing models to automated long-term portfolio management in the U.S. context. Specifically, the factors of Fama and French’s Three-factor model and Five-factor models were used. The analysis was performed on a period from 2003 to 2018 based on firm-specific secondary data. The primary goal was to discover whether the factor models could be used by a retail investor to implement portfolios that could outperform the market on a risk-adjusted basis. The secondary data included corporate fundamentals provided by Morningstar and pricing data sourced from the Quantopian database. The usage of the Quantopian software enabled performing simulation and sensitivity analyses that provided a series of descriptive statistics about the robustness of the factor-based strategies and strength of the factors’ predictive qualities. Additionally, the cost simulation analysis revealed the impacts of the portfolio size and the invested capital on the performance of the strategies. Those methods served to test the asserted hypotheses and to answer the research questions. The empirical findings suggested that the portfolios based on a combination of factors tended to outperform single-factor portfolios on a risk-adjusted basis. In their turn, the single-factor portfolios achieved a higher risk-adjusted return than the S&P 500, RSP (equal-weight S&P 500) and Russell 3000. The analysis also showed significant variability in sensitivity to factors between the sectors of the U.S. economy. Likewise, stocks in different sectors demonstrated diverse factor sensitivity patterns during the three sub-periods: pre-crisis, crisis, and post-crisis. Furthermore, the results revealed that - given sufficient capital - it should be possible for a retail investor to outperform the market using the factor portfolios.