Research Interests
Innovation economics, corporate investment, behavioral finance
Publications
Journal of Accounting and Economics, 2022, 74 (1), 101492
We find that small innovators (small firms with recent patent grants) earn higher future returns than small non-innovators. These returns are driven by risk, not underreaction due to information processing costs. Small innovators are riskier because of their reliance on external funding and strategic alliances.
Journal of Financial and Quantitative Analysis, 2022, 57 (8), 2899-2928
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 31% more variation in investment than the standard proxy for Q and up to 62% more variation in investment than total Q of Peters and Taylor (2017). Controlling for patent Q leads to larger, not smaller, cash flow coefficients.
Working Papers
We use patent thickets to study how patent-right uncertainty impacts target selection in mergers and acquisitions. This uncertainty can create additional costs that disrupt technological synergies. We find that firms are less likely to be acquired when their patents are in thickets that exacerbate these costs. Conversely, firms are more likely to be acquired when their patents create thickets that mitigate these costs. These relations strengthen with the extent to which acquirers and potential targets overlap technologically, which reduces information asymmetry and increases the value of potential synergies. We also find that when a firm occupies the same thicket as the acquirer, acquisition probability depends on each firm's ability to impose costs on the other. Overall, we conclude that patent-right uncertainty is an important determinant of target selection in mergers and acquisitions.
We document a strong negative relation between the curvature of stock price paths (i.e., price-path convexity) and future short-horizon returns at both the aggregate and firm levels. This relation obtains regardless of the cumulative return during the convexity estimation period. At the aggregate level, convexity is a better predictor of future returns than many commonly-used predictors. At the firm level, stocks with the least convex price paths subsequently outperform stocks with the most convex price paths by 0.84% to 1.07% per month. This effect is not explained by known return predictors, microstructure frictions, or illiquidity. Using survey-based expectations of short-horizon returns, we provide evidence that the negative relation between convexity and future returns is driven in part by overextrapolation of past short-horizon returns.
We find that house price momentum, defined as positive autocorrelation in aggregate house price changes, is stronger and house price change volatility is weaker when and where mortgage default risk at origination is higher. These facts appear widely, both geographically and temporally, and are difficult to reconcile with existing theories of house price dynamics. To explain these facts, we introduce a model in which lenders use valuations that incorporate information relatively slowly. In equilibrium, prices reflect the valuations used by lenders most when default risk is highest. Our model jointly explains the observed relations between default risk, momentum, and volatility.
Understanding differences in firms' ability to absorb external information is essential for identifying those best positioned to leverage knowledge spillovers and drive innovation. To advance this understanding, we introduce a novel measure of information absorption intensity based on firms' patent characteristics. Using the American Inventor's Protection Act of 1999 as an exogenous shock to spillovers, we establish a causal relation between absorption intensity and subsequent innovation output. This measure correlates with several fundamental factors identified in the literature as critical for developing the ability to absorb external information.