Stock-market trading is increasingly dominated by sophisticated professionals, as opposed to individual investors. Will this trend ultimately lead to greater market efficiency? I consider two complicating factors. The first is crowding—the fact that, for a wide range of “unanchored” strategies, an arbitrageur cannot know how many of his peers are simultaneously entering the same trade. The second is leverage—when an arbitrageur chooses a privately optimal leverage ratio, he may create a fire-sale externality that raises the likelihood of a severe crash. In some cases, capital regulation may be helpful in dealing with the latter problem.

ABSTRACT Stock‐market trading is increasingly dominated by sophisticated professionals, as opposed to individual investors. Will this trend ultimately lead to greater market efficiency? I consider two complicating factors. The first is crowding—the fact that, for a wide range of “unanchored” strategies, an arbitrageur cannot know how many of his peers are simultaneously entering the same trade. The second is leverage—when an arbitrageur chooses a privately optimal leverage ratio, he may create a fire‐sale externality that raises the likelihood of a severe crash. In some cases, capital regulation may be helpful in dealing with the latter problem.

This paper helps close the gap between theory and empirical evidence in the literature on asymmetric employer learning. If an employer's private learning is reflected in a worker's wage and one employer's private information is transmitted to the next when the worker makes a job-to-job transition, then asymmetric employer learning will appear in wage regressions as learning over an employment spell. Extending previous work that assumes all learning takes place publicly, this paper develops wage regressions that test for both asymmetric employer learning and public learning. The empirical results, including tests of alternative explanations, are consistent with asymmetric employer learning's having at least as much of an effect on wages during an employment spell as does public learning. The model developed in this paper illustrates how the story suggested by the empirical work might unfold. It shows that outside firms can profitably compete with a better-informed employer through bidding wars, even when the worker is equally productive in all firms. Furthermore, this competition results in different wages for workers with the same publicly observable characteristics, a result that previous models of asymmetric learning have not produced.

I identify three option exercise strategies executives engage in, including (i) exercising with cash and immediately selling the shares, (ii) exercising with cash and holding the shares, and (iii) delivering some shares to the company to cover the exercise costs and holding the remaining shares. Stock price patterns suggest executives manipulate option exercises. They use private information to increase the profitability of all three strategies, and likely backdated some exercise dates in the pre-Sarbanes-Oxley period to enhance the profitability of the latter two strategies, where the executive's company is the only counterparty. Backdating is associated with reporting of internal control weaknesses.

We examine the repair of one party's trust in another via repairing trustworthiness (Mayer, Davis, & Schoorman, 1995). Based on Weiner's (1986) causal attribution theory, we posit that causal attributions (i.e., locus of causality, controllability, and stability) for the cause of a negative outcome in a trusting relationship explain when trustworthiness is in need of repair and how trustworthiness may be repaired by the trustee's efforts. We also discuss the role of specific emotional reactions of the trustor in this process.

One hesitates to write history as it happens, or to draw policy lessons from current events. The conference took place in May 2008 - after the government-assisted takeover of Bear Stearns but before a capital market downturn fueled a system-wide liquidity crisis, with successive insolvencies at IndyMac, Fannie Mae, Freddie Mac, Lehman, AIG, WaMu, and, as I write, Citigroup. But it would be odd to comment on capital market regulation without mentioning the events of the last three months. I am first to acknowledge that anything I might have written in May would not have foreseen the crisis or linked capital market regulation to financial institutions, which in the US have been conventionally treated as discrete in discourse and institutions (e.g., U.S. Treasury 2008; Leuz and Wysocki 2008).

In striving to learn about an information technology innovation, organizations draw on knowledge resources available in the community of diverse interests that convenes around that innovation. But even as such organizations learn about the innovation, so too does the larger community. Community learning takes place as its members reflect upon their learning and contribute their experiences, observations, and insights to the community’s on-going discourse on the innovation. Community learning and organizational learning thus build upon one another in a reciprocal cycle over time, as the stock of interpretations, adoption rationales, implementation strategies, and utilization patterns is expanded and refined. We advance an overall model of this learning cycle, drawing on two community-level theories (management fashion and organizing vision), both of which complement the dominant emphases of the literature on IT innovation and learning. Relative to this cycle, we then empirically examine, in particular, the dependence of community learning on organizational learning. Sampling the public discourse on enterprise resource planning (ERP) over a 14-year period, we explore how different kinds of organizational actors can play different roles, at different times, in contributing different types of knowledge to an innovation’s public discourse. The evidence suggests that research analysts and technology vendors took leadership early on in articulating the “know-what” (interpretation) and “know-why” (rationales) for ERP, while later on adopters came to dominate the discourse as its focus shifted to the “know-how” (strategies and capabilities). We conclude by identifying opportunities for further inquiry on and strategic management of community learning and its interactions with organizational learning.

ABSTRACT The acceleration of the U.S. productivity growth in the late 1990s suggests a significant advance in technological innovation, making the perceived probability of entering a “new economy” ever increasing. Based on macroeconomic data, we identify a Bayesian investor's belief evolution when facing a possible structural break in the economy. We show that such belief evolution plays a significant role in explaining both the stock market boom and crash during 1998 to 2001. We conclude that a rational investor's uncertainty about the future of the U.S. economy provides an alternative explanation for the late 1990s stock market “bubble.”