Since the development of the CAPM, academic research has attempted to find models that increase the explanatory power of the cross-section of stock returns. 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. Regardless of the model used, an anomaly for all models is that the empirical evidence demonstrates that stocks with high research and development (R&D) expenses have delivered a premium.
There are economically significant increases in average returns to portfolios sorted on R&D expenditures.
The R&D effect is not concentrated in either smaller or larger firms.
The R&D anomaly cannot be explained by existing asset pricing models, including the relatively recent investment and profitability factors.
R&D-intensive firms are associated with higher future operating performance, return volatility, and default likelihood—the R&D effect is closely related to risk-bearing. This conflicts with a mispricing (behavioral) story: Investors underappreciate the value of R&D, resulting in low prices and high future (realized) returns.
Sunil Wahal and Amit Goyal contribute to the literature with their January 2023 study, “R&D, Expected Profitability, and Expected Returns,” in which they examined the asset pricing implications of corporate R&D expenditures. Their data sample covered stocks on the NYSE, AMEX, and Nasdaq exchange and the period July 1975 (after the issuance of SFAS2, which requires firms to expense R&D in the year the expenditure is incurred) through December 2021. Here is a summary of their key findings:
Adjusting for industry, high R&D firms had positive loadings on a cash-based operating profitability factor and zero alphas.
Current R&D expenditures did not forecast asset growth.
Current R&D expenditures forecasted future firm-level profitability at least three years and sometimes ten years into the future, establishing the channel by which R&D should show up in asset prices—expectations of future profitability. The evidence that R&D forecasted future profitability was even stronger in large stocks.
The original versions of both the five-factor and q pricing models were unable to price for R&D expenditures, principally because their profitability factors did not account for accruals. However, using cash-based operating profitability “cleans up the models and eviscerates pricing errors.”
It was unnecessary to capitalize R&D to reflect intangible investment in book values so long as expected profitability was explicitly recognized as a determinant of expected returns.
Large firms accounted for about 90 percent of market capitalization and about 80 percent of aggregate R&D.
Table 5: Amended Five-Factor Model Regressions for R&D Sorted Portfolios We sort stocks at the end of each June based on their R&D to book equity ratio in Panel A and their R&D to market equity ratio in Panel B as in Table 3. R&D is calculated as described in the text. We industry adjust R&D/B and R&D/M using 2-digit SIC codes before sorting. Stocks with R&D/B = 0 and R&D/M = 0 and are placed in one group and the remaining stocks are sorted into quartiles based on breakpoints from NYSE stocks only. These quartiles are labeled Low, Q2, Q3, and High. The portfolios are value-weighted and rebalanced once every year at the end of June. The amended five-factor model replaces the RMW factor of Fama and French (2015) with RMWCP, the cash-based operating profitability factor of Ball, Gerakos, Linnainmaa, and Nikolaev (2016)). All alphas are reported in annualized percent. t-statistics are reported in parenthesis below alphas/loadings. The sample includes all stocks with positive sales at portfolio formation. The sample period is 1975 to 2021.
Their findings led Wahal and Goyal to conclude: “There is robust evidence that R&D fulfils its primary purpose of generating future profits, and does so over long horizons. In contrast, regressions of future investment on current R&D show no meaningful relation, regardless of the scaling variable (book or market equity) or horizon.”
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 of 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.
Unfortunately, the ability to interpret financial statements has been complicated by the dramatic shift from tangible investments (such as plant and equipment) to intangible investments (such as R&D, advertising and expenses related to human capital). The increasing role of intangibles is highlighted by the fact that that R&D expenditures increased from 1 percent of company expenditures in 1975 to 7.5 percent in 2018 and that in 2015, services’ share of GDP stood at 74 percent in high-income countries and just under 69 percent globally.
The research has found that the increasing importance of intangibles, at least for industries with high concentrations of them, is playing an important role in the cross-section of returns. Thus, the role of intangibles should be addressed in portfolio construction because not accounting for them affects not just value metrics but other measures (e.g., profitability) that often scale by book value or total assets, both of which are affected by intangibles. Wahal and Goyal specifically recommend using cash profitability as a factor. A solution recommended and used by some investment management firms is to capitalize the investments on the income statement and amortize them over their useful lives. This means treating an intangible investment the same as a tangible investment. However, that raises questions such as: Which income statement items are appropriately considered investments? And what is a proper useful life for those assets?
In terms of the intensity of intangible assets, there is significant variance across industries. This is important for investors who rely predominately on multiples for valuation and for portfolio construction. Adjusting for intangibles can result in a large operating profit margin expansion for companies that are intangible-intensive, and insignificant changes for ones that are tangible-intensive. This suggests caution in comparing earnings or multiples across industries and over time.
Academics and fund managers have been trying to address the issues related to intangibles not being on the balance sheet through various methods. One method is to use alternatives to price-to-book (P/B) as the value metric, such as price-to-earnings (P/E), price-to-cash flow (P/CF) and enterprise value-to-earnings before interest, taxes, depreciation and amortization (EV/EBITDA). Many fund families (such as Alpha Architect, AQR, BlackRock, Bridgeway and Research Affiliates) use multiple value metrics (such as P/E, P/CF, P/S and EBITDA/EV), some of which indirectly provide exposure to the profitability factor. Another alternative is to add other factors into the definition of the eligible universe. For example, since 2013 Dimensional has included a sort for profitability in their value funds. A third alternative is to add back to book value an estimate of the value of intangible R&D and organizational expenses. A fourth way to address the issue is to apply what some call “contextual” stock selection, using different metrics or different weightings of those metrics depending on the intangible intensity. For example, if book value is not well specified for industries with high intangibles, it may be less effective in those industries than in industries with low intangibles.
At any rate, at least for most practitioners, the exclusive use of the traditional high-minus-low (HML) factor to build a value portfolio is no longer standard practice. None of the fund families my firm, Buckingham Strategic Wealth, uses in its portfolios exclusively use HML to construct its value funds.
Larry Swedroe is head of financial and economic research for Buckingham Wealth Partners. For informational and educational purposes only and should not be construed as specific investment, accounting, legal, or tax advice. Certain information is based upon third-party data and may become outdated or otherwise superseded without notice. Third-party information is deemed to be reliable, but its accuracy and completeness cannot be guaranteed. By clicking on any of the links above, you acknowledge that they are solely for your convenience, and do not necessarily imply any affiliations, sponsorships, endorsements or representations whatsoever by us regarding third-party websites. We are not responsible for the content, availability or privacy policies of these sites, and shall not be responsible or liable for any information, opinions, advice, products or services available on or through them. The opinions expressed by featured authors are their own and may not accurately reflect those of Buckingham Strategic Wealth® or Buckingham Strategic Partners®, collectively Buckingham Wealth Partners. Neither the Securities and Exchange Commission (SEC) nor any other federal or state agency have approved, determined the accuracy, or confirmed the adequacy of this article. LSR-22-457
As Chief Research Officer for Buckingham Strategic Wealth and Buckingham Strategic Partners, Larry Swedroe spends his time, talent and energy educating investors on the benefits of evidence-based investing with enthusiasm few can match. Larry was among the first authors to publish a book that explained the science of investing in layman’s terms, “The Only Guide to a Winning Investment Strategy You’ll Ever Need.” He has since authored seven more books: “What Wall Street Doesn’t Want You to Know” (2001), “Rational Investing in Irrational Times” (2002), “The Successful Investor Today” (2003), “Wise Investing Made Simple” (2007), “Wise Investing Made Simpler” (2010), “The Quest for Alpha” (2011) and “Think, Act, and Invest Like Warren Buffett” (2012). He has also co-authored eight books about investing. His latest work, “Your Complete Guide to a Successful and Secure Retirement was co-authored with Kevin Grogan and published in January 2019. In his role as chief research officer and as a member of Buckingham’s Investment Policy Committee, Larry, who joined the firm in 1996, regularly reviews the findings published in dozens of peer-reviewed financial journals, evaluates the outcomes and uses the result to inform the organization’s formal investment strategy recommendations. He has had his own articles published in the Journal of Accountancy, Journal of Investing, AAII Journal, Personal Financial Planning Monthly, Journal of Indexing, and The Journal of Portfolio Management. Larry’s dedication to helping others has made him a sought-after national speaker. He has made appearances on national television shows airing on NBC, CNBC, CNN, and Bloomberg Personal Finance. Larry is a prolific writer and contributes regularly to multiple outlets, including Advisor Perspective, Evidence Based Investing, and Alpha Architect. Before joining Buckingham Wealth Partners, Larry was vice chairman of Prudential Home Mortgage. He has held positions at Citicorp as senior vice president and regional treasurer, responsible for treasury, foreign exchange and investment banking activities, including risk management strategies. Larry holds an MBA in finance and investment from New York University and a bachelor’s degree in finance from Baruch College in New York.
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