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The strong performance of large-cap equities (versus small caps) over the last decade has left many investors questioning the potential benefits of explicitly allocating to small-cap equities in portfolios. One potential benefit of owning small caps that many investors may not be aware of is how the relative risk of small-cap equities and large-cap equities changes by investment horizon.
In this piece, I demonstrate that small-cap equities become increasingly attractive for investors with longer investment horizons. However, this effect is often ignored in common portfolio optimization routines, such as mean variance optimization (MVO), which typically assume returns are random across time (i.e., follow a random walk).
Overall, this analysis provides strong evidence that small-cap stocks should be actively used in well-diversified portfolios, especially for investors with longer investment horizons.
The small-cap effect
The small-cap effect is relatively well documented in the academic literature, beginning perhaps most notably with research1 by Eugene Fama and Kenneth French which introduced the three-factor model. In their research, they found that a model including factors focused on the market capitalization of a company (size or SMB) and the value effect (HML) better describe the cross-sectional variation in stock returns than a model using a market factor (i.e., beta) alone. The research noted the historical outperformance of small-cap (and value) stocks has had a significant impact on building portfolios as well as how funds are benchmarked more generally (e.g., this research led to the creation of the Morningstar Style Box).
The exhibit below provides evidence of the small-cap effect and includes rolling five-year annualized performance of the small-cap factor from December 1930 to December 2023 using the Ibbotson SBBI data, obtained from Morningstar Direct.
While small caps outperformed large caps by 2.3 percent over the historical five-year periods, there have been notable variations over time. For example, there have been multiple periods where small caps either outperformed or underperformed large cap equities by approximately 20 percent (annualized) over a five-year period, which would translate into a cumulative return difference exceeding 100 percent.
This suggests that while the small-cap effect has been relatively robust historically, there can be prolonged periods of out- and underperformance. For example, small-cap equities have notably underperformed large caps over the last decade or so, resulting in many investors questioning the potential benefits of including them in a portfolio.
Investment horizon and optimal portfolios
Portfolio optimization routines often assume returns are random in nature. Although this is often a simplifying assumption, the actual historical serial correlations that exist can have important implications on optimal portfolios.
I cover this topic somewhat extensively in some recent research2 published through the CFA Institute titled “Investment Horizon, Serial Correlation, and Better (Retirement) Portfolios” written with Jeremy Stempien. In the research, we provided several examples of how efficient portfolios can vary notably by investment horizon when using actual historical time series versus simply assuming returns are random.
One test in the analysis, which I replicated for this piece, focused on how the optimal allocation to small caps changes by investment horizon. The analysis used historical actual rolling returns for US large-cap and US small-cap stocks, as well as inflation, where data is from the Ibbotson SBBI series.
The optimizations used either actual annual historical return series (e.g., rolling returns for some period, such as five years) or re-centered annual historical returns. With recentering, the historical returns were adjusted (by first estimating z-values) so that the average annual return and standard deviations of the time series were consistent with some other estimate (e.g., a forward-looking return). Re-centering ensures the historical serial correlations (i.e., relationships over time) are maintained, but allows for the average return to be consistent with expected returns (e.g., reducing the small cap premium).
The specific values for the analysis were based on PGIM Quantitative Solutions Q1 2024 Capital Market Assumptions (CMAs), where the expected arithmetic returns for large caps and small caps are 8.94 percent and 7.65 percent, respectively, and standard deviations are 19.81 percent and 15.35 percent, respectively.
The optimal allocation to small caps are included in the exhibit below. Note, if returns were truly random over time (i.e., there was no serial correlation) there would be no change in the optimal allocation by investment horizon. This effect was demonstrated in the research through techniques such as bootstrapping, a resampling method where you randomly redraw returns from a given time series, so the mean and risk levels are maintained, but any kind of serial correlations are broken.
The analysis demonstrates that optimal allocations to small caps can vary notably depending on whether using actual historical returns versus a forward-looking estimate, as well as by investment duration. Optimal allocations are notably higher using the actual historical returns, which is not surprising given the research on the small cap premium.
Interestingly, the forward-looking benefits of owning small caps depends notably on whether the analysis considers serial correlation. Using only one-year returns, which is common practice in optimization techniques like MVO that ignore serial correlation, the allocations to small-cap and large-cap stocks are roughly equal. However, over longer time periods, the allocations to small-cap stocks increase considerably, completely crowding out large caps at investment horizons exceeding eight years.
Whichever set of returns (pure historical or adjusted to include CMAs) and time horizon are used, though, the optimal allocations are notably higher than implied by their pure market capitalization weight. For example, the total market capitalization of the stocks in the Russell 2000 ($2.5 trillion) are only roughly five percent of the total market capitalization of the Russell 3000 ($50.6 trillion) as of May 2024.
Conclusions
While large-cap stocks have generated relatively attractive returns lately, it’s important to stay diversified and focus on the long-term. This piece explored the potential benefits of owning small cap equities, and noted that historically small-cap stocks have been increasingly attractive for investors with longer investment horizons, regardless of whether using pure historical returns or returns adjusted to reflect forward looking expectations. Therefore, investors should “stay the course” when it comes to building portfolios and ensure small caps are actively considered during the portfolio construction process!
David Blanchett, PhD, CFA, CFP®, is managing director, portfolio manager and head of retirement research at PGIM. PGIM is the global investment management business of Prudential Financial, Inc. He is also an adjunct professor of wealth management at The American College of Financial Services and a research fellow for the Retirement Income Institute.
1 “The Cross-Section of Expected Stock Returns” by Eugene Fama and Kenneth French. 1992. Journal of Finance. Access here: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1540-6261.1992.tb04398.x
2 Access the piece here: https://rpc.cfainstitute.org/research/foundation/2024/investment-horizon-serial-correlation-better-portfolios
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