Quantitative analyses on portfolios simulations : how complex should the quality stocks definition be ?

Abstract

This thesis aims at investigating the market anomaly quality as defined by Asness, Frazzini and Pedersen (2017) in their “Quality Minus Junk” factor. The undertaken study refines the quality stocks definition and its complexity. The concept of the quality anomaly has been for years arduous to portray, as its meaning is highly subjective and differs from one academician to another. Quality is occasionally not seen as a “pure anomaly” since it consists of an aggregation of numerous factors and ratios. This memoir is willing to enlighten this interpretation puzzle. The basic concepts of market theories and portfolio management are introduced and discussed, just like the evolution of pricing models. The most distinguished anomalies, other than quality, are acquainted as a preface for the quality concept debate. Hence, the QMJ factor (Asness, Frazzini, & Pedersen, 2017) is analyzed in its three components; profitability, growth and safety. A replica of its ratios is built using SAS software with the goal to simulate Fama/French styled long-short portfolios based on a CRSP/Compustat dataset. The computed portfolios are regressed on QMJ and analyzed using SAS Miner software, along with descriptive statistics, correlations, cumulated returns and Sharpe ratios. The results show that the growth component may be entirely dismissed without damaging the model. The safety factors greatly matter in the regressions and strengthen their roles into quality. Return on equity, return on assets and cash flows are profitability ratios that are significant in the definition as well. While the signals of gross profits are remarkably persistent and drove the quality performance in all empirical analyses. Hence, the source of quality is identified by these late six final ratios, cutting the complexity of the definition by more than two.