In this work we propose a new methodology to compare different stochastic discount factor (SDF) proxies based on relevant market information. The starting point is the work of Fama and French, which evidenced that the asset returns of the U.S. economy could be explained by relative factors linked to characteristics of the firms. In this sense, we construct a Monte Carlo simulation to generate a set of returns perfectly compatible with the Fama and French factors and, then, investigate the performance of different SDF proxies. We use some goodness-of-fit statistics and the Hansen Jagannathan distance as a formal criterion to compare asset-pricing models. An empirical application of our setup is also provided, revealing that the novel non-parametric estimator proposed by Araujo et al. (2006) exhibit, in general, the best performance among several traditional SDF proxies.