Updated August 2018
A factor is a characteristic that has been shown to contribute to market outperformance.
Research into factors was largely driven by academics trying to figure out why certain stocks tended to generate higher returns than theories about efficient markets would have predicted.
For example, widely accepted factors include size (the observation that small companies tend to beat large firms over time); value (cheap companies beat expensive ones); yield (high- yielding stocks do better than low-yielding ones); and momentum (stocks that go up just keep on going up). To be clear, these factors will not always beat the market over any given time period – but looking at historical data over the long run, they have generated superior returns in many different global markets.
“Smart beta” is one trend in the investment industry that tries to exploit both the rapid growth in computing power and the growing sense of disillusionment with active fund managers. Smart-beta exchange-traded funds (ETFs) promise to use computer algorithms to construct and invest in indices based on various factors.
So you might invest in a momentum ETF that continually rebalances into momentum stocks, or a value ETF that does the same for stocks viewed as cheap on certain measures.
One problem with the race to find new factors for smart beta to exploit is the risk of data mining – if you look hard enough at historical data, you can find apparently meaningful patterns that are in fact simply statistical flukes.
A 2017 paper by Kewei Hou and Lu Zhang of Ohio State University, and Chen Xue of the University of Cincinnati, found that the vast majority of 447 anomalies found by equity- market researchers were precisely that. However, it’s fair to say that the best- established factors (such as those above) are widely accepted as true.