Many classes of microstructure models, as well as intuition, suggest that it should be easier to trade when markets are more active. In the data, however, volume and liquidity seem unrelated over time. This paper offers an explanation for this fact based on a simple frictionless model in which liquidity reflects the average risk-bearing capacity of the economy and volume reflects the changing contribution of individuals to that average. Volume and liquidity are unrelated in the model, but volume is positively related to the variance of liquidity, or liquidity risk. Empirical evidence from the U.S. government bond and stock markets supports this new prediction.

[Strategic alliances are long-term contracts between legally distinct organizations that provide for sharing the costs and benefits of a mutually beneficial activity. In this paper, I develop and test a model that helps explain why firms sometimes prefer alliances over internally organized projects. I introduce managerial effort into a model of internal capital markets and show how strategic alliances help overcome incentive problems that arise when headquarters cannot pre-commit to particular capital allocations. The model generates a number of implications, which I test using a large sample of alliance transactions in conjunction with Compustat data.]

DuPont analysis, a common form of financial statement analysis, decomposes return on net operating assets into two multiplicative components: profit margin and asset turnover. These two accounting ratios measure different constructs and, accordingly, have different properties. Prior research has found that a change in asset turnover is positively related to future changes in earnings. This paper comprehensively explores the DuPont components and contributes to the literature along three dimensions. First, the paper contributes to the financial statement analysis literature and finds that the information in this accounting signal is in fact incremental to accounting signals studied in prior research in predicting future earnings. Second, it contributes to the literature on the stock market's use of accounting information by examining immediate and future equity return responses to these components by investors. Finally, it adds to the literature on analysts' processing of accounting information by again testing immediate and delayed response of analysts through contemporaneous forecast revisions as well as future forecast errors. Consistent across both groups of market participants, the results show that the information is useful as evidenced by associations between the DuPont components and stock returns as well as analyst forecast revisions. However, I find predictable future forecast errors and future abnormal returns indicating that the information processing does not appear to be complete. Taken together, the analysis indicates that the DuPont components represent an incremental and viable form of information about the operating characteristics of a firm.

We examine the accuracy and contribution of the Merton distance to default (DD) model, which is based on Merton's (1974) bond pricing model. We compare the model to a “naïve” alternative, which uses the functional form suggested by the Merton model but does not solve the model for an implied probability of default. We find that the naïve predictor performs slightly better in hazard models and in out-of-sample forecasts than both the Merton DD model and a reduced-form model that uses the same inputs. Several other forecasting variables are also important predictors, and fitted values from an expanded hazard model outperform Merton DD default probabilities out of sample. Implied default probabilities from credit default swaps and corporate bond yield spreads are only weakly correlated with Merton DD probabilities after adjusting for agency ratings and bond characteristics. We conclude that while the Merton DD model does not produce a sufficient statistic for the probability of default, its functional form is useful for forecasting defaults.

This paper re-evaluates evidence in Ayers and Freeman [Ayers, F., Freeman, R., 1997. Market assessment of industry and firm earnings information. Journal of Accounting and Economics 24, 205–218] suggesting that investors anticipate industry-wide components of earnings earlier than firm-specific components, and that post-earnings-announcement drift following annual earnings announcements is due primarily to firm-specific components of earnings. Our tests indicate that post-announcement drift is entirely attributable to coefficient bias due to measurement errors in the use of realized earnings changes as proxies for unexpected earnings. Also, coefficient differences in the market's anticipation of subsequent-year industry and firm-specific earnings become insignificant when we introduce suitable controls for non-linearity in the return/earnings relation.

Fund managers' bias toward geographically proximate securities is a well-researched phenomenon, yet the origins of managers' location choices have received little empirical scrutiny. This paper traces the employment and geographic heritage of 358 entrepreneurial fund managers and analyzes the determinants of where they locate their firms and stock selections. The evidence suggests that start-ups tend to be based close to the origins of their founders and in regions with more investment management firms, banking establishments, and large institutional money managers. New money managers show a strong local bias in their equity holdings, three times the levels previously documented for mutual funds. The propensity to invest closer to home correlates strongly with the presence of sub-advisory opportunities from institutional investors in the vicinity. While home bias levels between managers who relocate with their start-ups and the rest of the entrepreneurs are similar, preferences for stocks that were formally local persist.

Strategic alliances are long-term contracts between legally distinct organizations that provide for sharing the costs and benefits of a mutually beneficial activity. In this paper, I develop and test a model that helps explain why firms sometimes prefer alliances over internally organized projects. I introduce managerial effort into a model of internal capital markets and show how strategic alliances help overcome incentive problems that arise when headquarters cannot pre-commit to particular capital allocations. The model generates a number of implications, which I test using a large sample of alliance transactions in conjunction with Compustat data. , Oxford University Press.

[We have developed Bayesian Markov chain Monte Carlo (MCMC) methods for inferences of continuous-time models with stochastic volatility and infinite-activity Lévy jumps using discretely sampled data. Simulation studies show that (i) our methods provide accurate joint identification of diffusion, stochastic volatility, and Lévy jumps, and (ii) the affine jump-diffusion (AJD) models fail to adequately approximate the behavior of infinite-activity jumps. In particular, the AJD models fail to capture the "infinitely many" small Lévy jumps, which are too big for Brownian motion to model and too small for compound Poisson process to capture. Empirical studies show that infinite-activity Lévy jumps are essential for modeling the S&P 500 index returns.]

Restructuring electricity markets has enabled wholesalers to exercise market power. Using a common method to measure competition, several studies have found substantial inefficiencies. This method overstates actual welfare loss by ignoring production constraints that result in non-convex costs. I develop an alternative method that accounts for these constraints and apply it to the Pennsylvania, New Jersey, and Maryland market. For the summer following restructuring, the common method implies that market imperfections resulted in considerable welfare loss, with actual production costs exceeding the competitive model's estimates by 13%–21%. In contrast, my method finds that actual costs were only between 3% and 8% above the competitive levels.

Evidence on central banks’ twin objective, monetary and financial stability, is scarce. We suggest an integrated micro–macro approach with two core virtues. First, we measure financial stability directly at the bank level as the probability of distress. Second, we integrate a microeconomic hazard model for bank distress and a standard macroeconomic model. The advantage of this approach is to incorporate micro information, to allow for non-linearities and to permit general feedback effects between financial distress and the real economy. We base the analysis on German bank and macro data between 1995 and 2004. Our results confirm the existence of a trade-off between monetary and financial stability. An unexpected tightening of monetary policy increases the probability of distress. This effect disappears when neglecting microeffects and non-linearities, underlining their importance. Distress responses are largest for small cooperative banks, weak distress events, and at times when capitalization is low. An important policy implication is that the separation of financial supervision and monetary policy requires close collaboration among members in the European System of Central Banks and national bank supervisors.