Since the development of the first asset pricing model, the capital asset pricing model (CAPM), academic research has attempted to find models that increase the explanatory power of the cross-section of stock returns. We moved from the single-factor CAPM (market beta), to the three-factor Fama-French model (adding size and value), to the Carhart four-factor model (adding momentum), to Lu Zhang’s q-factor model (beta, size, investment, profitability), to the Fama-French five-factor (adding value to the q-factor model) and six-factor models (adding back value and momentum to the q-factor model). There have also been versions that use different metrics for profitability and value, and Stambaugh and Yuan’s mispricing (anomaly)-based model.

The fact that we have seen keen competition to improve existing models should not surprise us. One reason is that, by definition, all models are flawed. If they were perfect representations of the way the world worked, we would have laws (not models), like we have in physics. Though models are flawed, that doesn’t mean they’re absent of value.

Think about it this way: Financial models aren’t cameras that provide us with a perfect picture of the way financial markets work. Instead, they are engines that advance our understanding of how markets operate and how prices are set – which is why we continue to see efforts to improve on existing models.

Matthias Hanauer contributes to the literature on asset pricing models with his March 2020 study, “A Comparison of Global Factor Models.” Hanauer compared commonly employed factor models across 50 non-U.S. developed and emerging market countries by ranking them based on their Sharpe ratios. His addition of international markets is a major contribution, as out-of-sample tests reduce the risks of sample-specific outcomes and data mining. His sample covers the period July 1990 through October 2018.