When understanding how individual assets, asset
classes, or even whole portfolios behave, a useful analytical tool for an analyst is factor analysis. A factor is a variable or characteristic, with which individual asset returns are correlated.
The most common factors historically have been size, value, momentum, and quality. These factors are positively related to long-term return premiums and are often referred to as rewarded factors. Then you have unrewarded factors, such as idiosyncratic risks, because of their ability to be diversified away by holding a diversified portfolio of companies.
One of the most well-known factor-based models is the Fama-French three-factor model, which expanded on the Capital Asset Pricing Model (CAPM). Here, they added the size and value factors to the market risk factor. This model is considered one of the foundations in understanding risk in equity markets.
So far, we have just discussed style factors, but this can be extended to macroeconomic metrics like interest rates, inflation, or GDP. We can also use statistical analysis to derive principal component or latent factors.
Factors analysis supports a wide range of critical functions, from portfolio construction, risk management, and performance attribution, whilst being popular for quantitative strategies. However, factors can provide drawbacks, as seen in factor crowding, where popular factors can become overvalued – a recent trend we have been seeing in global markets with momentum.
Momentum has been the hallmark of the recent rally in equity markets, partially supported by the rise of passive investing. Whilst investors have bet on momentum in recent years, it is important to remember that nothing lasts forever, as a mean reversion could spur heightened market volatility if momentum is to fall out of favour.
