By testing the interaction between product market competition and investment in R&D on stock returns, the author tackles two asset pricing puzzles. R&D projects are inherently risky and more likely to fail in the presence of more competition. Thus, a strong, positive, and interactive R&D–return relationship in competitive industries is consistent with a risk-based framework.
The author examines the impact of R&D investment and competition on aggregate measures of firm performance and adds to the literature with her explanation for a strong positive association between R&D and future stock returns. She finds a robust empirical relationship between R&D intensity and stock returns—but only in competitive industries. This finding suggests that the risk derived from product market competition has important asset pricing implications and potentially drives a large portion of the positive R&D–return relationship.
How Is This Research Useful to Practitioners?
The recent popularity of smart beta equity strategies has led to a resurgence of interest in factor-based approaches, many of which include the relationship between stock prices and the book value of a firm’s assets. The market value of a firm’s shares ultimately reflects the value of all its net assets—including intangible assets. When most assets are physical (e.g., plant and equipment), the link between asset values and stock prices is comparatively straightforward, but the widespread growth of science- and knowledge-based industries raises the importance of whether stock market values reflect intangible capital. R&D is of particular interest because firms are required by US accounting standards to disclose their R&D expenditures in their financial statements, unlike other intangible asset–related spending. If investors disregard a firm’s intangible assets during their assessment of the book-to-market ratio, they may misstate the systematic risk of an R&D-intensive firm.
The author explains the relationship between stock returns and R&D activities by empirically documenting variations across levels of competition and degree of R&D intensity. R&D stock returns covary, and firm exposure to a systematic R&D risk factor predicts future returns, even after including other asset pricing controls. The author incorporates the latest factor models (i.e., the Fama–French five-factor model and the Hou, Xue, and Zhang Q-model) along with traditional neoclassical approaches. These new models are able to capture many anomalies in the cross section but not the R&D-to-market anomaly; a complete explanation for the R&D anomaly is still needed.
Many factors proposed as a potential explanation of the ubiquitous book-to-market effect (i.e., business risk, information asymmetry, financial constraints, and financial distress risk) represent common risk characteristics that combine to drive the systematic covariation in R&D stock return but have not been captured adequately by existing pricing models. The persistence of the anomaly implies that this factor is unidentified because it has yet to be incorporated into asset pricing models.
How Did the Author Conduct This Research?
To study the interaction effect between these two variables, the author derives a partial equilibrium model for a single multistage R&D venture. In this mathematical model, competition risk is idiosyncratic and thus does not demand a risk premium itself, but the firm is riskier than the underlying stochastic cash flows because the R&D projects could be suspended at any time. The author then examines actual monthly stock returns and accounting information from CRSP and finds results consistent with the model’s predictions.
Using a conventional double-sorting approach, the author observes the results from firms with a range of R&D intensity and level of market competition. R&D intensity is measured by R&D expenditure scaled by market equity, and competition is measured using the Herfindahl–Hirschman Index. The double-sorting approach reveals that firms in competitive industries earn higher returns than firms in concentrated industries only among R&D-intensive firms. The author computes excess returns using a number of factor models, which indicate that premium results remain unchanged after controlling for beta, size, and a range of breakpoints. The author also runs a number of subsamples to test alternative mechanisms (e.g., to explore whether financial constraint is an R&D-related characteristic and to examine the effect of innovative ability).
R&D investment is often followed by above-average stock returns that asset pricing models struggle to explain. There are two competing explanations for the future returns. The first suggests that investors underestimate the long-term impact of the R&D investment. This explanation is plausible if short-sighted investors are focusing on earnings per share because R&D produces an immediate expense that lowers earnings.
An alternative notion is that R&D is risky and thus the returns to R&D investment are compensation for risk borne by the equity investors (e.g., a drug that does not pass its US Food and Drug Administration trials or a phone that is never sold but for which a satellite is launched). Under this school of thought, firms with large amounts of R&D will experience greater earnings volatility, which will be reflected in the firm’s discount rate. The author presents evidence in support of a risk-based explanation—that there is a relationship between earnings volatility and expected stock returns—but it seems to be at odds with the smart beta view that low-volatility companies with more consistent earnings and cash flow streams outperform.