References of "Management Science"
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See detailSpeculative Trading and Bubbles: Evidence from the Art Market
Penasse, Julien UL; Renneboog, Luc

in MANAGEMENT SCIENCE (2022), 68(7), 4755-5555

We argue that extrapolative expectations drive boom–bust cycles in the postwarart market. Price run-ups coincide with increases in demand fundamentals but are fol-lowed by predictable busts. Predictable ... [more ▼]

We argue that extrapolative expectations drive boom–bust cycles in the postwarart market. Price run-ups coincide with increases in demand fundamentals but are fol-lowed by predictable busts. Predictable changes account for about half of the variance offive-year price changes. High prices coincide with many attributes of speculative bubbles:trading volume, the share of short-term trades, the share of postwar art, and volatility areall higher during booms. In addition, short-term transactions underperform long-termtransactions. Survey evidence further confirms the link between beliefs, prices, and volumedynamics as in models in which extrapolative beliefs fuel speculative bubbles. [less ▲]

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See detailUnderstanding Alpha Decay
Penasse, Julien UL

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 ▲]

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See detailSpeculative Trading and Bubbles: Evidence from the Art Market
Penasse, Julien UL; Renneboog, Luc

in Management Science (2021)

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See detailAn Experimental Comparison of Two Exchange Economies: Long-Lived Asset Versus Short-Lived Asset
Neugebauer, Tibor UL; Carbone, Enrica; D. Hey, John

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

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See detailInnovation Search Strategy and Predictable Returns
Fitzgerald, Tristan; Balsmeier, Benjamin UL; Fleming, Lee et al

in Management Science (2021), 67(2), 1109-1137

Because of the intangible and highly uncertain nature of innovation, investors may have difficulty processing information associated with a firm’s innovation search strategy. Due to cognitive and strategic ... [more ▼]

Because of the intangible and highly uncertain nature of innovation, investors may have difficulty processing information associated with a firm’s innovation search strategy. Due to cognitive and strategic biases, investors are likely to pay more attention to unfamiliar explorative patents rather than incremental exploitative patents. We find that innovative firms focusing on exploitation rather than exploration tend to generate superior subsequent short-term operating performance. Analysts do not seem to detect this, as firms currently focused on exploitation tend to outperform the market’s near-term earnings expectations. The stock market also seems unable to accurately incorporate information about a firm’s innovation search strategy. We find that firms with exploitation strategies are undervalued relative to firms with exploration strategies and that this return differ-ential is incremental to standard risk and innovation-based pricing factors examined in the prior literature. This result suggests a more nuanced view on whether stock market pressure hampers innovation, and may have implications for optimal firm financing choices and corporate disclosure policy. [less ▲]

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See detailDistributionally Robust Mechanism Design
Kocyigit, Cagil UL; Iyengar, Garud; Kuhn, Daniel et al

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

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See detailBlockchain, Fractional Ownership, and the Future of Creative Work
Kräussl, Roman UL; Amy, Whitaker

in Management Science (2020)

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See detailEarly Disclosure of Invention and Reduced Duplication: An Empirical Test
Lück, Sonja; Balsmeier, Benjamin UL; Seliger, Florian et al

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 ▲]

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See detailHedge Funds and Stock Market Efficiency
Suominen, Matti UL; Kokkonen, Joni

in Management Science (2015)

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See detailNetwork-Independent Partner Selection and the Evolution of Innovation Networks
Baum, Joel; Cowan, Robin; Jonard, Nicolas UL

in Management Science (2010), 56

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See detailLoss Functions in Option Valuation: A Framework for Model Selection
Wolff, Christian UL; Bams, D.; Lehnert, Thorsten UL

in Management Science (2009), 55(5), 853-862

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See detailBilateral Collaboration and the Emergence of Networks
Jonard, Nicolas UL; Cowan, Robin; Zimmermann, Jean-Benoît

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 ▲]

Detailed reference viewed: 283 (8 UL)