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We decompose analysts’ earnings forecast error into predictable and unpredictable components, and investigate individual vis-à-vis institutional investors’ reactions to each of these components. We find that in the immediate post-earnings announcement window, only individuals under-react to the predictable component, while both individuals and institutions under-react to the unpredictable component. The price drift in this window is driven primarily by investors’ under-reaction to the unpredictable component. This drift remains highly significant in larger firms and intensifies in firms with complex financial reports, suggesting that it likely represents the slow and noisy process of price discovery. Around the next quarterly earnings announcement, only individuals under-react to the previous quarter’s predictable component, and this fixation drives the entire price drift in this window. This drift disappears in larger firms, and gets exacerbated in firms with greater forecast error autocorrelations, suggesting that it is likely attributable to incomplete processing of earnings information by individuals.

​​​​With permission of SAGE Publishing

The Relational Event Model (REM) solves a problem for organizational researchers who have access to sequences of time stamped interactions. It enables them to estimate statistical models without collapsing the data into cross-sectional panels, which removes timing and sequence information. However, there is little guidance in the extant literature regarding issues that may affect REM’s power, precision and accuracy: How many events or actors are needed? How large should the risk set be? How should statistics be scaled? To gain insights into these issues, we conduct a series of experiments using simulated sequences of relational events under different conditions and using different sampling and scaling strategies. We also provide an empirical example using email communications in a real-life context. Our results indicate that, in most cases, the power and precision levels of REMs are good, making it a strong explanatory model. However, REM suffers from issues of accuracy that can be severe in certain cases, making it a poor predictive model. We provide a set of practical recommendations to guide researcher’s use of REMs in organizational research.

With permission of SAGE Publishing

We study a canonical model of simultaneous price competition between firms that sell a homogeneous good to consumers who are characterized by the number of prices they are exogenously aware of. Our setting subsumes many employed in the literature over the last several decades. We show there is a unique equilibrium if and only if there exist some consumers who are aware of exactly two prices. The equilibrium we derive is in symmetric mixed strategies. Furthermore, when there are no consumers aware of exactly two prices, we show there is an uncountable-infinity of asymmetric equilibria in addition to the symmetric equilibrium. Our results show the paradigm generically produces a unique equilibrium. We also show that the commonly-sought symmetric equilibrium (which also nests the textbookBertrand pure strategy equilibrium as a special case) is robust to perturbations in consumer behaviour, while the asymmetric equilibria are not.

© 2021 The Editorial Board of The Journal of Industrial Economics and John Wiley & Sons Ltd.

In the final decision-making process for an application, companies want to avoid mistakes at all costs. This article explains two reasons why you should trust chance instead.

When it comes to hiring, blind luck can trump biased reasoning and generate a less-is-more effect.

Exceptional innovations are often attributed to brilliant strategies or leaders with superior foresight. But luck is in fact necessary for exceptional innovations in many ways.

This is a practitioner article based on a recently published academic paper on career shocks of executive. The article explains the phenomenon of a career shocks and offers strategies for dealing with them.