Innovation economics, mergers and acquisitions, corporate investment, behavioral finance
Journal of Accounting and Economics, 2022, 74 (1), 101492
Journal of Financial and Quantitative Analysis, 2022, 57 (8), 2899-2928
Patent-Right Uncertainty and Mergers and Acquisitions (with Logan Emery)
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 exacerbating these costs. Conversely, firms are more likely to be acquired when their patents create thickets mitigating these costs. When a firm occupies the same thicket as the acquirer, acquisition probability depends on each firm's ability to impose costs on the other. Consistent with patent-right uncertainty disrupting synergies, we find that targets' thickets are associated with post-acquisition profitability.
Extrapolation Bias and Short-Horizon Return Predictability (with Huseyin Gulen)
We use survey data on expectations of short-horizon returns to show that extrapolative expectations play a significant role in short-horizon stock return predictability. We develop a proxy, which we call convexity, for the extrapolative component of expectations and show that convexity is negatively associated with future aggregate-level and firm-level returns over periods spanning one week through one month. Our extrapolation proxy allows us to test predictions of extrapolation theories both at the aggregate and firm levels and over long time series. Our results suggest that investor extrapolation of near-term weekly returns generates short horizon predictability, and the results are not driven by firm size, the bid-ask bounce, illiquidity, or other short-term return predictors. Our results support extrapolation-based theories and suggest that short-horizon predictability is better understood when we consider the possibility of return extrapolation over short horizons.
Disagreement in Collateral Valuation (with Jordan Martel)
We present a model of secured lending in which borrowers and lenders agree to disagree about collateral values. Lenders' beliefs distort equilibrium prices of collateralized assets, and the extent to which lenders' beliefs distort prices is mediated by borrower riskiness. Specifically, prices are more reflective of lenders' beliefs when borrowers are riskier and more reflective of borrowers' beliefs when borrowers are safer. Disagreement in a dynamic setting can generate positive return autocorrelation that strengthens with borrower riskiness. We use data on U.S. residential mortgages to test the model's main predictions, for which we find strong empirical support.