Remember Moneyball, the American sports drama movie that was nominated for multiple awards in 2011?
The movie was based on a book with the same name that was written by financial journalist and author Michael Lewis in 2003. The story was about the 2002-season of the Oakland Athletics baseball team and its general manager (GM), Billy Beane. The Oakland Athletics wasn’t the club with the largest fan-base or recall value and Billy Beane’s career as GM had just started a few years earlier.
Oakland Athletics managed something incredible in 2002. They went on a 20-match winning streak, breaking the league record for the most wins on the trot, and eventually won the division they were playing in. More impressively, they did this at less than half the budget of some of their storied rivals such as the New York Yankees. What Beane did was astounding, but how he did it was even more radical.
Because of his tight budget, he was on the lookout for players that could win him matches cheaply. He started by applying rigorous quantitative analysis to historical data to identify the key player-attributes that contributed the most to wins. To his surprise, he found that none of these attributes were looked at by the traditional scouts who were tasked with selecting players in other teams. As a result, most of the players that scored highly on the factors he was looking for, were not very expensive.
Due to Oakland’s success, their ‘Moneyball’ approach has become popular among all other baseball teams. In fact, ‘Moneyball’ seems to have transcended baseball and found its way into other games such as cricket, where players are now also valued for their ability to hit sixes rather than just scoring the most runs or having the highest batting average.
But long before Moneyball became a part of sporting vocabulary, economists and academicians had been applying similar principles in investing. Their objective was to identify quantitative characteristics of stocks that contributed to their market performance. These characteristics were called ‘Factors’.
Evolution of Factors
Successful investors have historically used quantitative descriptors to guide their investment decision-making. But the prevailing belief was that all the returns generated by stocks were attributable only to stock-specific parameters, and hence an investor could only generate returns by carefully identifying the ‘right’ stocks. The entire portion of returns was believed to be ‘alpha’. However, the first breakthrough in the recognition of factors came with the development of the ‘Capital Asset Pricing Model’ in the 1960s.
The model proposed that returns of stocks depended on their sensitivity to the market, or the ‘market factor’, and only additional returns could be called ‘alpha’. This model was incomplete, but it provided a framework for the discovery of more factors.
Over the next three decades researchers identified that small cap stocks, undervalued stocks, high momentum stocks, stocks that were more profitable and stocks that exhibited low price volatility all exhibited superior performance over their benchmarks over long time frames. The ‘alpha’ remaining, once all these factors were accounted for, was a small fraction of what it was initially believed to be. These research findings were so powerful that they could even explain the ‘alpha’ of eminent investors such as Warren Buffett.
From Factors to Factor Investing
‘Factor Investing’ is a strategy that seeks to take advantage of these findings by building investment portfolios that invest in factors in a rules-based manner without any subjective decision taken based on human judgment. It involves three steps:
-Using a fundamental descriptor to express a factor. For e.g., Price to Book (P/B) as a descriptor of the ‘value’ factor, to identify companies undervalued in relation to their book-value.
-Defining rules to include or exclude companies by using these fundamental descriptors, to create a portfolio. For e.g., a value portfolio could be formed by selecting the 30 percentile companies with the lowest P/B ratio.
-Applying these rules on an investment universe at a pre-defined periodicity (annually, quarterly, etc.)
Not everything is a Factor
The ability to enhance returns over the benchmark is a necessary but not sufficient condition for an indicator to be considered a factor.
Researcher and author Larry Swedroe came up with a checklist of important criteria to be met before any indicator can be called a factor:
Persistent: The return enhancing properties of the indicator must hold true over extended periods of time, spanning various market and economic cycles.
Pervasive: The indicator must demonstrate consistent results across various geographies.
Robust: The indicator must not be susceptible to small changes in definition of fundamental descriptor used to express the factor.
Investable: The indicator must continue to demonstrate outperformance even after transaction and market impact costs have been considered.
Intuitive: There must be a logical explanation for why the indicator has been able to exhibit the observed outperformance.
Factor Investing strategies have found widespread adoption among institutions, HNIs, as well as retail investors in the developed world. ~USD 1.9 trillion was managed using factor strategies globally in 2017, rising from ~USD 650 billion in 2011, growing at ~20% annually. Blackrock further estimates these strategies to reach ~USD 3.4 trillion by 2022. However, in India, awareness of factor investing strategies has been limited thus far, and factor-based funds are just starting to come up. As investors look for different ways to enhance their returns, these strategies might offer an interesting alternative. However, allocation to these strategies must be done only after understanding the risks inherent in them.
(The writer is Head of Research, Passive Funds, Motilal Oswal AMC)