Corporate innovation, q-theory of investment, behavioral finance
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, PI (physical plus intangible) q, explains up to 62% more variation in investment than other proxies for q. Furthermore, investment is more sensitive to PI q than to other proxies for q. Although investment is predicted more accurately by, and is more sensitive to, PI q, controlling for PI q leads to relatively higher, not lower, cash flow coefficients. All results are stronger in subsamples with more patent capital. Overall, using PI q strengthens the historically weak investment-q relation.
Revise and resubmit at Journal of Financial and Quantitative Analysis
Small Innovators: No Risk, No Return (with Noah Stoffman and M. Deniz Yavuz)
We find that small innovators earn higher returns than small non-innovators for up to five years. We find no such innovation premium among large firms. A battery of tests shows that this innovation premium among small firms is explained by risk. Our findings, which are based on a simple measure to identify innovative firms, are in contrast with previous papers that attribute higher returns to innovation to investor underreaction. We argue that an innovation premium exists among small firms, but not large firms, because small innovators focus more on risky product innovation and rely more on organization capital, which amplifies their systematic risk. In addition, small innovators contribute significantly to the size premium. Overall, 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 month, contains significant return predictive power. In the time series, price-path convexity negatively predicts one-month aggregate returns better than a host of other predictors both in and out of sample. 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 and non-US G7 countries. 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.