Factor-based models are often criticized for data mining. One way to address that charge is with “out-of-sample” testing over longer time frames. But that takes time. New research provides an alternative out-of-sample test – using emerging-market bonds.

I will explain that test, but first let’s review the context of how those factor-driven models have evolved.

Academic research into asset pricing now covers more than 600 factors that could add explanatory power to the cross-section of returns. From that “zoo” of factors, how can investors determine which exhibits are worthy of investment? In our book, “Your Complete Guide to Factor-Based Investing,” Andrew Berkin and I established the following criteria. For a factor to be considered, it must meet all of the following tests. To start, it must provide incremental explanatory power to portfolio returns and have delivered a premium (higher returns). Additionally, the factor must be:

  • Persistent – It holds across long periods of time and different economic regimes.
  • Pervasive – It holds across countries, regions, sectors and even asset classes.
  • Robust – It holds for various definitions (for example, there is a value premium, whether it is measured by price-to-book, earnings, cash flow or sales).
  • Investable – It holds up not just on paper but also after considering actual implementation issues, such as trading costs.
  • Intuitive – There are logical risk-based or behavioral-based explanations for its premium and why it should continue to exist.