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Returns on Small Cap Growth Stocks, or the Lack Thereof: What Risk Factor Exposures Can Tell Us

This paper examines the historical performance of small cap growth stocks through the lens of the Fama-French three-factor and five-factor models and the use of quantitative measures, such as the Sharpe Ratio, seeking to shed light on why this investment style has historically given investors so little to cheer about. Through multi-factor regression analysis, we determine that the majority of small cap growth underperformance stems from the ‘growthiest’ of growth stocks in the index. We conclude with simulations that suggest that investors can enhance their US and international small cap growth risk-adjusted returns by employing strategies that seek to minimize or even eliminate exposure to the highest-growth quintile of the small cap growth universe.

In the early 1990s, Eugene Fama and Kenneth French analyzed the returns of all US equities over different independent time periods and identified three systematic sources of risk that explain over 90% of portfolio performance:1

1. Mkt (Market): This represents the exposure to the equity market. It is similar to beta but differs because of the presence of two additional factors:
2. SmB (Size): Small (market capitalization) minus Big represents the exposure to smaller market capitalization relative to the market. The greater the SmB factor exposure, the more the company behaves like a smaller company.
3. HmL (Value): High (book to price) minus Low represents the value/growth characteristics of the stock. The higher the book-to-price ratio of the company, the more value-oriented it is.

The ideas that were statistically and theoretically represented by this three-factor model, however, were not new ones. Investors have applied the principles of value investing for decades. Value investing was introduced by Benjamin Graham in 1928, further refined with the assistance of David Dodd, and made broadly famous by the so-called Sage of Omaha, Warren Buffett. Investors had also reaped the benefits of investing in small companies that had great potential to grow. What Fama and French’s three-factor model did was to quantify the risk-return relationship by attributing the performance that fund managers and individual investors produced to specific risk factors – as opposed to their stock-picking genius.

As successful as this model has been in explaining stock and portfolio returns, it still is not 100% accurate. By definition, models are imperfect because they are only approximations of actual phenomena. In practice, when returns are dissected and attributed to Fama and French’s three distinct factors, there is a component of the returns that can’t be explained by the factors.  In this paper, we will refer to this component as “alpha”2.

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