Some market crashes occur because of significant imbalances in demand and supply. Conventional models fail to explain the large magnitudes of price declines. We propose a unified structural framework for explaining crashes, based on the insights of market microstructure invariance. A proper adjustment for differences in business time across markets leads to predictions which are different from conventional wisdom and consistent with observed price changes during the 1987 market crash and the 2008 sales by Société Générale. Somewhat larger-than-predicted price drops during 1987 and 2010 flash crashes may have been exacerbated by too rapid selling. Somewhat smaller-than-predicted price decline during the 1929 crash may be due to slower selling and perhaps better resiliency of less integrated markets.

We estimate the stock’s likelihood of extreme returns by measuring the extent to which the stock’s trades are correlated with market-wide and industry-wide trades during normal times, referred to as herding. We find that stocks whose trades herd most with aggregate-level trades experience most negative (positive) returns during market crashes (booms). While herding generates extreme returns in both sides, investors appear to demand compensation for the possibility of extreme low returns. This is the case even when we control for standard asset pricing variables and other tail risk proxies.

In this article we develop a model to analyze patent-protected R&D investment projects when there is (imperfect) competition in the development and marketing of the resulting product. The competitive interactions that occur substantially complicate the solution of the problem since the decision maker has to take into account not only the factors that affect her/his own decisions, but also the factors that affect the decisions of the other investors. The real options framework utilized to deal with investments under uncertainty is extended to incorporate the game theoretic concepts required to deal with these interactions. Implementation of the model shows that competition in R&D, in general, not only increases production and reduces prices, but also shortens the time of developing the product and increases the probability of a successful development. These benefits to society are countered by increased total investment costs in R&D and lower aggregate value of the R&D investment projects.

To create value and reduce agency costs, firms adopt available organizational structures that match their attributes. This paper studies the characteristics of firms that choose to become master limited partnerships (MLPs). The MLP sample is dominated by firms in low-growth industries that have highly focused operations and superior profitability compared to their industry peers. After becoming an MLP, sample firms reduce capital expenditures and increase cash distributions, taking advantage of their focus, profitability, and status as non-taxable entities. A subsample of MLPs subsequently change back to corporate form. After becoming corporations, these firms reverse course by cutting cash distributions and increasing capital spending. This cycle demonstrates how firms restructure to adopt organizational forms that best fit their needs.

We construct a simple measure of the aggregate illiquidity of hedge fund portfolios, based on the cross-sectional average first-order autocorrelation coefficient of hedge fund returns, and show that it has strong and robust in- and out-of-sample forecasting power for 72 portfolios of international equities, U.S. corporate bonds, and currencies over the 1994 to 2013 period. The forecasting ability of hedge fund illiquidity for asset returns is in most cases greater than, and provides independent information relative to, well-known predictive variables. We rationalize these findings using a simple equilibrium model, in which hedge funds provide liquidity in asset markets.

This paper examines the role of economic factors in determining retirement behavior using a unique new data archive on more than 8,700 workers covered by 10 different pension plans. We build on our earlier work by estimating several different retirement models including both linear and discrete choice formulations. This framework provides new insights into how and why retirement ages differ across firms. We conclude that older workers' income opportunities differ depending on their pension rules, which in turn have a powerful influence on their retirement patterns. In addition, the models indicate that older workers' tastes for income are not uniform, either across individuals or across firms. Finally, we show that retirement age differences are due in part to differences in worker preferences and in part to differences in income opportunities. There appears to be some evidence of worker sorting across pension plans.