Research Interests

Corporate innovation, mergers and acquisitions, q theory of investment, behavioral finance

Working Papers

Using Patent Capital to Estimate Tobin's q

I construct a new proxy for Tobin's q that incorporates the replacement cost of patent capital. This proxy, which I call patent q, explains up to 62% more variation in investment than other proxies for q. Furthermore, investment is more sensitive to patent q than to other proxies for q. Although investment is predicted more accurately by, and is more sensitive to, patent q, controlling for patent q leads to relatively higher, not lower, cash flow coefficients. All results are stronger in subsamples with more patent capital. Overall, patent q strengthens the historically weak investment-q relation.

Small Innovators: No Risk, No Return (with Noah Stoffman and M. Deniz Yavuz)

We show that small innovators (i.e., small firms with recent patent grants) earn higher long-term returns. The higher returns are driven by risk, not underreaction to announcements of patent grants. Our results are consistent with small innovators being risky due to their reliance on external parties for commercializing their patents. That is, being small and innovative interacts with financial constraints to explain the higher returns. These interactions are more important in the presence of greater information asymmetry. The higher cost of equity among small innovators has implications for their investment, growth, and capital structure decisions.

Price-Path Convexity, Extrapolation, and Short-Horizon Return Predictability (with Zhi Da and Huseyin Gulen)

The curvature of intramonth stock price paths, which is distinct from cumulative return over the same period, contains significant additional return predictive power. In the cross section, stocks with the least convex price paths subsequently outperform stocks with the most convex price paths. This effect ranges from 1.34% to 1.53% per month and is not driven by small stocks, the bid-ask bounce, or other short-term return predictors. We find similar results in different time periods, in non-US G7 countries, and even at the aggregate market level. We argue that price-path convexity uniquely captures investor over-extrapolation of recent price changes. Therefore, our results provide broad evidence that extrapolative expectations are an important contributor to short-horizon return predictability.

Works in Progress

The Evolving Role of Innovation in Mergers and Acquisitions (with Logan Emery)

We conduct the most comprehensive examination to date of the role of innovation in mergers and acquisitions. We find that many previously-unstudied innovation characteristics are key determinants of transaction incidence. These characteristics include novel measures of technological overlap and patent value as well as proxies for innovation competition and patent scope. We find evidence that reducing information frictions, avoiding competition, and acquiring growth opportunities in the target's innovation space are all important motivations of M&A. Avoiding innovation competition is especially important for larger deals and has become even more so in recent decades. At the same time, acquirers are increasingly targeting firms innovating in a specific area of the acquirer's innovation space and that are direct technological antecedents of the acquirer. Overall, we document new innovation-related motivations of M&A and show that the importance of these motivations changes over time.