To understand smart beta and factor investing, it is useful to understand its history and evolution. Half a century ago, people started using the Capital Asset Pricing Model (CAPM) to explain how sensitive an individual investment was to movements in the market. The CAPM was the original factor model, and there was only one factor: the market.
Variations on the Same Theme
Over the following decades, refinements were made to the original CAPM model to capture and quantify additional variables. Eugene Fama and Kenneth French determined that small cap stocks and stocks with a value tilt tended to outperform over time. Fama and French added both these factors to the original CAPM.
Dimensional Fund Advisors built their highly successful fund family upon these theories. Instead of paying active managers hefty salaries to research companies and assemble portfolios, DFA instead simply assigned “value” and “small” scores to stocks, sorted them from highest to lowest, and built their portfolios around those biases.
Once the concept of factor-based investing and cheap computing power became widely available 20 years ago, the floodgates opened. There was a surge in quantitative money managers, many using Barr Rosenberg’s Barra Risk Factor Analysis platform to construct portfolios. A whole new breed of “quants” spent their days trying to identify new explanatory factors or design optimization algorithms.
Smart Beta: All that Different?
This idea of factor-based investing eventually merged with the nascent exchange-traded fund industry to coalesce into the “smart beta” movement. The basic thesis behind smart beta is that indices based solely on market capitalization are lacking. The idea is systematic biases exist that would generate excess relative returns if these factors were over- or under-weighted relative to the cap-weighted market.
Every deviation from the original Capital Asset Pricing Model is some variation on this basic premise. Fama-French, BARRA, factor analysis, smart beta…it’s all variations on the same theme.