Introduction
Humankind’s search for rules to guide investment prosperity is at least as old as the first IPO in history when The Dutch East India Company went public in1602 on the Amsterdam Stock Exchange. In the modern era, the search for attributes associated with outperformance began in 1934 when Benjamin Graham published Security Analysis, a foundational text in value investing still relevant today. In it, Graham sorted stocks into value and speculative investments using financial ratios. He emphasized the importance of a cautious and rational approach to investing, writing that “investment is most intelligent when it is most businesslike.”
By that, he meant that investing, like running a business, should be a systematic effort to maximize the likelihood of earning a reasonable return and to minimize the probability of suffering a severe loss. “You are neither right nor wrong because the crowd disagrees with you,” Graham wrote. “You are right because your data and reasoning are right.” These sentiments underpin factor investing to this day.
Origins of Factor Investing (1950s–1960s)
Early financial theories, particularly Harry Markowitz’s Modern Portfolio Theory (MPT) in 1952, laid the groundwork for factor investing by introducing the concept of diversification. MPT suggested that portfolios could achieve an optimal risk-return trade-off by spreading investments across uncorrelated assets. MPT, however, did not dive into the specific factors driving returns of individual investments.
In the 1960s, the Capital Asset Pricing Model (CAPM), developed by William Sharpe, John Lintner, and Jack Treynor, advanced the conversation by introducing a single factor to explain stock returns: the market risk factor, represented by a stock’s beta. According to CAPM, the expected return of a stock is a function of its sensitivity to overall market movements. While CAPM was the beginning of a structural decomposition of stock returns, it was limited in its predictive power, prompting further research into other return drivers, or “factors.”
Emergence of Multi-Factor Models (1970s–1990s)
In the 1970s and 1980s, with the help of computers, researchers started exploring additional factors that could explain anomalies in stock returns. Among these, the size and value factors were perhaps most influential, culminating in the groundbreaking work of Eugene Fama and Kenneth French in 1992. The Fama-French Three-Factor Model posited that stock returns could be explained not just by exposure to the overall market (as in CAPM), but also by a stock’s exposure to two additional factors:
- Size (Small-Cap Premium): Historically, small-cap stocks had tended to outperform large-cap stocks, which Fama and French attributed to higher risk.
- Value (Book-to-Market Ratio): Stocks with high book-to-market ratios (i.e., value stocks) tended to outperform growth stocks, possibly due to behavioral biases or risk factors not accounted for by CAPM.
These findings spurred a shift in how academics and practitioners viewed stock returns, laying the groundwork for the development of multi-factor models and the broader concept of factor investing.
Expansion of Factors and Growth of Factor Investing (1990s–2000s)
Following the Fama-French Three-Factor Model, further research introduced additional factors that seemed to further explain patterns in asset returns:
- Momentum: This factor was first identified by Jegadeesh and Titman in 1993. It suggests stocks that have performed well in the past tend to continue performing well, and vice versa for poor performers.
- Profitability and Investment: In 2015, Fama and French expanded their original three-factor model to include profitability (the tendency of more profitable companies to generate higher returns) and investment (firms with more conservative investment policies tend to outperform more aggressive ones).
During this proliferation of empirical research, other factors such as low volatility and high quality (high earnings, strong balance sheets) were also identified as important predictors of stock performance.
This period also saw the rise of index funds and passive investing, which helped pave the way for factor-based strategies. Index providers, such as MSCI and FTSE Russell, began developing factor indices that tracked baskets of stocks with particular factor exposures (e.g., size, value, momentum). In addition, asset managers like Dimensional Fund Advisors helped proselytize small-cap value strategies. This allowed institutional and retail investors alike to gain systematic exposure to factors without having to manually select stocks.
Advent of Smart Beta (2010s–Present)
In the 2010s, factor investing entered the mainstream under the term “smart beta.” Traditional indices, such as the S&P 500, weight stocks by market capitalization, but smart beta indices aim to deliver better risk-adjusted returns by weighting stocks according to specific factors. The term “smart beta” refers to a set of rules that depart from cap-weighting in favor of factors like value, size, momentum, low volatility, and quality.
Smart beta strategies were initially marketed as an alternative to traditional active management as they allowed investors to capture systematic sources of outperformance in a cost-effective, rules-based manner. The first smart beta ETFs (Exchange-Traded Funds) quickly grew in popularity, offering liquidity, transparency, and the ability to invest based on specific factors.
As institutional investors, such as pension funds and endowments, began allocating more capital to factor-based strategies, the influence of factor investing continued to grow. It was no longer confined to academic circles but became an integral part of the investment toolkit.
Long-Short Implementation Today
Today, the ultimate expression of factor investing leads toward long-short implementation of factor concepts. Long-short models are optimal for accessing factor exposures because they allow investors to fully exploit both the positive and negative sides of factor performance. By going long securities with favorable factor characteristics and shorting those securities with unfavorable characteristics, these models effectively isolate and magnify targeted factors while neutralizing market beta. This approach enhances the precision of factor exposure, reduces unintended risks, and improves risk-adjusted returns. Additionally, long-short models provide better diversification and greater flexibility in managing factor tilts, resulting in more consistent and stable performance over time.
Conclusion
While factor investing has become increasingly sophisticated, the core principle remains the same: identifying and systematically exploiting underlying stock fundamentals that drive returns. Factor investing has also transformed the way investors think about portfolio construction. From its academic origins to its current widespread use in institutional and retail portfolios, factor investing has become a crucial component of modern financial theory and practice. As markets evolve, so too will the factors that drive them, ensuring the need for ongoing research and dynamic adaptation by practitioners.