The Research and Development Factor

Since the development of the CAPM, which explains about two-thirds of the variation of returns among diversified portfolios, academic research has attempted to find models that increase the explanatory power of the cross-section of stock returns. Models are not like cameras that provide an exact replica of the world. If models were perfectly accurate, they would be laws, like we have in physics. Instead, models are engines that advance our understanding of how markets work, and prices are set.

As new research findings were published, we moved from the single-factor CAPM (market beta) to the three-factor Fama-French model (adding size and value), to the Carhart four-factor model (adding momentum), to Lu Zhang’s q-factor model (beta, size, investment, profitability), to the Fama-French five-factor (adding value to the q-factor model) and six-factor models (adding back value and momentum to the q-factor model). There have also been versions that use different metrics for profitability and value, and Stambaugh and Yuan’s mispricing (anomaly)-based model. The empirical evidence demonstrates that stocks with high research and development (R&D) expenses have delivered a premium regardless of the model usedindicates. Intangible assets such as R&D are becoming increasingly important as the economy has moved from primarily manufacturing to service and knowledge.

For example, using a measure of R&D intensity (R&D relative to market value), Woon Sau Leung, Khelifa Mazouz, and Kevin Evans, authors of the 2020 study “The R&D Anomaly: Risk or Mispricing?,” found a statistically economically significant monotonic increase in average returns from 0.67% for Portfolio 1 (lowest decile) to 2.23% for Portfolio 10 (highest decile). They also found that the premium persisted after adjusting for size, value, and momentum effects. In addition, the zero-cost spread portfolio (Portfolio 10-1) yielded a Carhart four-factor alpha of 1.35% and a Fama French five-factor alpha of 1.52% per month, both significant at the 1% level—the R&D anomaly cannot be explained by existing pricing, including the relatively recent investment and profitability factors.

Leung, Mazouz, and Evans also found:

“The R&D premium correlates positively with innovations to the aggregate dividend yield, and negatively with shocks to the default spread and risk-free rate, demonstrating the sensitivity of R&D stocks to variables that predict future business conditions. Moreover, the loadings on these three state variable innovations are significantly priced in the cross-section of R&D stock returns and even drive out the size and book-to-market equity factors. These results demonstrate that the R&D premium represents a significant and incremental reward for bearing intertemporal risk.”

Their findings of risk-based explanations for the R&D effect are consistent with those of Jangwook Lee and Jiyoon Lee, authors of the March 2020 study “Mispricing or Risk Premium? An Explanation of the R&D-to-Market Anomaly.” They are also consistent with the risk-based theoretical prediction of Jonathan Berk, Richard Green, and Vasant Naik, authors of the 2004 study “Valuation and Return Dynamics of New Ventures,” who explained:

“The firm learns about the potential profitability of the project throughout its life, but that research and development effort itself is only resolved through additional investment by the firm.”  

The result is that the risks associated with the ultimate cash flows have a systematic component, while the purely technical risks are idiosyncratic.

In an out-of-sample test, Kewei Hou, Po-Hsuan Hsu, Shiheng Wang, Akiko Watanabe, and Yan Xu, authors of the study “Corporate R&D and Stock Returns: International Evidence” published in the June 2022 issue of the Journal of Financial and Quantitative Analysis, examined the cross-sectional return predictability of R&D in international equity markets. Their findings were consistent with prior research. For example, in global sorts the top quintile portfolio outperformed the bottom one by 1.024% (0.537%) per month in equal-weighted (value-weighted) returns. The finding that the equal-weighted spread was more significant than the value-weighted spread suggests that substantially higher subsequent returns for more intensive R&D investments are more pronounced among smaller firms. They also found that the R&D effect could not be explained by common equity factors used in asset pricing models (including market beta, size, value, and momentum).

New Research

Li Cai, Ricky Cooper, and Di He, authors of the study R&D Premium: The Intangible Side of Value” published in the August 2023 issue of The Journal of Investing, investigated the relationship between R&D investment and excess returns. Their data sample spanned the period July 1978-June 2018 and included all common stocks traded on NYSE, AMEX, and Nasdaq, excluding those in the utility and financial industries. They chose 1978 because R&D expense reporting was standardized in 1974, and their R&D variable was calculated using current and previous four years’ R&D expenditures:

Both RDC (R&D capital) and RDE (R&D expenditures) represent a 20% periodic amortization of productivity of dollar spending. They selected stocks with ratios higher than the median in each year to construct R&D-intensive portfolios (RDI) using five highly correlated measures of R&D intensity. Stocks with ratios lower than the median R&D intensity measures and stocks that did not report R&D were put into an “otherwise” portfolio. Here is a summary of their key findings:

• A median company invested about 5% of its revenue into R&D in more recent years, compared to only 1% 40 years ago.
• The RDI portfolio provided a monthly average return of 1.74% versus a return of 1.10% for the otherwise portfolio. The difference of 0.64 percentage point was statistically significant at the 5% confidence level. The geometric returns were 19.17% versus 11.65%, respectively. The RDI portfolio’s Sharpe ratio was 0.68 versus 0.48 for the otherwise portfolio.
• The RDI portfolio had a book equity-to-market equity ratio of 0.63 versus 0.76 for the otherwise portfolio. Relative to the three-, four-, and five-factor models, the RDI portfolio had negative loadings on the value factor (HML) of -0.20, -0.29, and -0.20, all statistically significant at the 1% confidence level. 
• Relative to the three-, four-, and five factor models, the RDI portfolio generated monthly alphas of 0.52%, 0.71%, and 0.81%—all statistically significant at the 1% confidence level.
• The alphas for the RDI portfolio were persistent across all three models and all four decades, though they were strongest in the decade surrounding the “tech bubble” (July 1998-June 2008). However, the alphas were no longer significant in the last decade (July 2008-June 2018).
• In the five-factor model, the RDI portfolio had a -0.73 loading on the profitability factor, statistically significant at the 1% confidence level.
• The 10 largest drawdowns for the RDI portfolio ranged from -19% to -60% (6/30/07-2/28/09). In nine of the 10 cases, the drawdowns were greater than experienced by the S&P 500, with the largest differential being 19 percentage points.
• The R&D portfolio, which is a growth strategy, provided a natural hedge for a value strategy (which tends to buy firms with inexpensive tangible assets).
• A simple 50/50 mix of R&D portfolio and value portfolio generated strong and stable performance—the two strategies are complementary—resulting in a much better Sharpe ratio than value alone. Over five-year periods, the Sharpe ratio of RDI averaged 0.69 and had 12 negative observations versus 0.71 for the value portfolio with 38 negative cases. In contrast, Sharpe ratios of the simple mix strategy were negative in only four observations, and the average ratio was 1.26. Using a risk-parity strategy, the results were even better—while the average return fell, volatility fell even further, producing a Sharpe ratio of 1.46.

The results were even stronger on double-sorted portfolios—stocks with high RDI and low valuations using top quintiles. Sorting first on RDI and then value resulted in an annualized return of 24.5% and a Sharpe ratio of 0.95. Sorting first on value and then RDI resulted in an annualized return of 17.3% and a Sharpe ratio of 1.12. The monthly five-factor alphas were 1.01% and 0.84%, respectively. 

Their conclusions led Cai, Cooper, and He to conclude:

“Our results indicate that a value investor can harvest R&D premium without taking on extra risks. Adding value strategy on top of R&D-intensive portfolio cuts volatility, but also potentially advances investors’ returns.” They added: “This strategy retains low rebalancing costs because R&D and value are both sticky.”

Investor Takeaways

Supported by the findings of a significant positive relationship between R&D expenditures and future stock returns and the risk-based explanations for the R&D effect, the empirical research suggests a fundamentally important role for intellectual capital, specifically R&D, in asset pricing—the higher returns to high R&D stocks represent compensation for heightened systematic risk not captured in standard asset pricing models. Thus, we can conclude that the empirical evidence demonstrates that the R&D premium remains an anomaly in all models. The other takeaway is that the increasing role of intangibles is highlighted by the fact that R&D expenditures increased from 1% of company expenditures in 1975 to 7.5% in 2018 and that in 2015, services’ share of GDP stood at 74% in high-income countries and just under 69% globally.

Larry Swedroe is head of financial and economic research for Buckingham Wealth Partners, collectively Buckingham Strategic Wealth, LLC and Buckingham Strategic Partners, LLC.

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