Understanding Alpha Decay

in Management Science (2022), 68(5), 3966-3973

I study the importance of alpha decay for the measurement of realized and conditional expected returns in asset pricing studies. Alpha decay refers to the reduction in abnormal expected returns (relative ... [more ▼]

I study the importance of alpha decay for the measurement of realized and conditional expected returns in asset pricing studies. Alpha decay refers to the reduction in abnormal expected returns (relative to an asset pricing model) in response to an anomaly becoming widely known among market participants. As decreases in alpha are associated (ceteris paribus) with positive realized returns, the econometrician may misinterpret these repricing returns as evidence that the anomaly will persist in the future. Because alpha decay is generally a nonstationary phenomenon, asset pricing tests that impose stationarity may lead to biased inference. I illustrate the importance of alpha decay using the most commonly studied anomalies in the asset pricing literature and find that the measured alpha differs from the true alpha by about 1.4% per year. I provide a simple formula to correct for this bias and show how to incorporate alpha decay tests into the standard asset pricing toolkit. [less ▲]

An Experimental Comparison of Two Exchange Economies: Long-Lived Asset Versus Short-Lived Asset

in Management Science (2021), 67(11), 6629-7289-

Distributionally Robust Mechanism Design

in Management Science (2020), 66(1), 159--189

Early Disclosure of Invention and Reduced Duplication: An Empirical Test

in Management Science (2020)

Much work on innovation strategy assumes or theorizes that competition in innovation elicits duplication of research and that disclosure decreases such duplication. We validate this empirically using the ... [more ▼]

Much work on innovation strategy assumes or theorizes that competition in innovation elicits duplication of research and that disclosure decreases such duplication. We validate this empirically using the American Inventors Protection Act (AIPA), three complementary identification strategies, and a new measure of blocked future patent applications. We show that AIPA—intended to reduce duplication, through default disclosure of patent applications 18 months after filing—reduced duplication in the U.S. and European patent systems. The blocking measure provides a clear and micro measure of technological competition that can be aggregated to facilitate the empirical investigation of innovation, firm strategy, and the positive and negative externalities of patenting. [less ▲]

Network-Independent Partner Selection and the Evolution of Innovation Networks

in Management Science (2010), 56

Bilateral Collaboration and the Emergence of Networks

in Management Science (2007), 53(7), 1051-1067

In this paper, we model the formation of innovation networks as they emerge from bilateral decisions. In contrast to much of the literature, here firms only consider knowledge production, and not network ... [more ▼]

In this paper, we model the formation of innovation networks as they emerge from bilateral decisions. In contrast to much of the literature, here firms only consider knowledge production, and not network issues, when deciding on partners. Thus, we focus attention on the effects of the knowledge and information regime on network formation. The effectiveness of a bilateral collaboration is determined by cognitive, relational, and structural embeddedness. Innovation results from the recombination of knowledge held by the partners to the collaboration, and its success is determined in part by the extent to which firms’ knowledge complement each other. Previous collaborations (relational embeddedness) increase the probability of a successful collaboration, as does information gained from common third parties (structural embeddedness). Repeated alliance formation creates a network. Two features are central to the innovation process: how firms pool their knowledge resources, and how firms derive information about potential partners. When innovation is decomposable into separate subtasks, networks tend to be dense; when structural embeddedness is important, networks become cliquish. For some regions in this parameter space, small worlds emerge. Key Words: networks; innovation; knowledge; collaborative R&D; embeddedness [less ▲]