Knowledge that Transforms

We analyze the disclosure practices of companies as a function of their interaction with U.S. markets for a group of 794 firms from 24 countries in the Asia‐Pacific and Europe. Our analysis uses the Transparency and Disclosure scores developed recently by Standard & Poor's. These scores rate the disclosure of companies from around the world using U.S. disclosure practices as an implicit benchmark. Results show a positive association between these disclosure scores and a variety of market interaction measures, including U.S. listing, U.S. investment flows, exports to, and operations in the United States. Trade with the United States at the country level, however, has an insignificant relationship with the disclosure scores. Our empirical analysis controls for the previously documented association between disclosure and firm size, performance, and country legal origin. Our results are broadly consistent with the hypothesis that cross‐border economic interactions are associated with similarities in disclosure and governance practices.

Managers often explain their earnings forecasts by linking forecasted performance to their internal actions and the actions of parties external to the firm. These attributions potentially aid investors in the interpretation of management forecasts by confirming known relationships between attributions and profitability or by identifying additional causes that investors should consider when forecasting earnings. We investigate why managers choose to provide attributions with their forecasts and whether the attributions are related to security price reactions to management earnings forecasts. Using a sample of 951 management earnings forecasts issued from 1993 to 1996, we find that attributions are more likely for larger firms, less likely for firms in regulated industries, less likely for forecasts issued over longer horizons, more likely for bad news forecasts, and more likely for forecasts that are maximum type. Furthermore, attributions are associated with greater absolute price reactions to management forecasts, more negative price reactions to management forecasts (forecast news held constant), and a greater price reaction per dollar of unexpected earnings. Our findings hold after control for the aforementioned determinants of attributions and after control for other firm‐ and forecast‐specific variables that are often associated with security prices.

We introduce a family of generalized-method-of-moments estimators of the parameters of a continuous-time Markov process observed at random time intervals. The results include strong consistency, asymptotic normality, and a characterization of standard errors. Sampling is at an arrival intensity that is allowed to depend on the underlying Markov process and on the parameter vector to be estimated. We focus on financial applications, including tick-based sampling, allowing for jump diffusions, regime-switching diffusions, and reflected diffusions.

A speaker wishes to persuade a listener to accept a certain request. The conditions under which the request is justified, from the listener's point of view, depend on the values of two aspects. The values of the aspects are known only to the speaker and the listener can check the value of at most one. A mechanism specifies a set of messages that the speaker can send and a rule that determines the listener's response, namely, which aspect he checks and whether he accepts or rejects the speaker's request. We study mechanisms that maximize the probability that the listener accepts the request when it is justified and rejects the request when it is unjustified, given that the speaker maximizes the probability that his request is accepted. We show that a simple optimal mechanism exists and can be found by solving a linear programming problem in which the set of constraints is derived from what we call the L-principle.

This paper investigates a generalized method of moments (GMM) approach to the estimation of autoregressive roots near unity with panel data and incidental deterministic trends. Such models arise in empirical econometric studies of firm size and in dynamic panel data modeling with weak instruments. The two moment conditions in the GMM approach are obtained by constructing bias corrections to the score functions under OLS and GLS detrending, respectively. It is shown that the moment condition under GLS detrending corresponds to taking the projected score on the Bhattacharya basis, linking the approach to recent work on projected score methods for models with infinite numbers of nuisance parameters (Waterman and Lindsay (1998)). Assuming that the localizing parameter takes a nonpositive value, we establish consistency of the GMM estimator and find its limiting distribution. A notable new finding is that the GMM estimator has convergence rate , slower than , when the true localizing parameter is zero (i.e., when there is a panel unit root) and the deterministic trends in the panel are linear. These results, which rely on boundary point asymptotics, point to the continued difficulty of distinguishing unit roots from local alternatives, even when there is an infinity of additional data.

A new class of autocorrelation robust test statistics is introduced. The class of tests generalizes the Kiefer, Vogelsang, and Bunzel (2000) test in a manner analogous to Anderson and Darling's (1952) generalization of the Cramér–von Mises goodness of fit test. In a Gaussian location model, the error in rejection probability of the new tests is found to be O(T-1logT), where T denotes the sample size.

This paper analyzes models of securities markets with a single strategic informed trader and competitive market makers. In one version, uninformed trades arrive as a Brownian motion and market makers see only the order imbalance, as in Kyle (1985). In the other version, uninformed trades arrive as a Poisson process and market makers see individual trades. This is similar to the Glosten–Milgrom (1985) model, except that we allow the informed trader to optimize his times of trading. We show there is an equilibrium in the Glosten–Milgrom-type model in which the informed trader plays a mixed strategy (a point process with stochastic intensity). In this equilibrium, informed and uninformed trades arrive probabilistically, as Glosten and Milgrom assume. We study a sequence of such markets in which uninformed trades become smaller and arrive more frequently, approximating a Brownian motion. We show that the equilibria of the Glosten–Milgrom model converge to the equilibrium of the Kyle model.

This paper considers learning rules for environments in which little prior and feedback information is available to the decision maker. Two properties of such learning rules are studied: absolute expediency and monotonicity. Both require that some aspect of the decision maker's performance improves from the current period to the next. The paper provides some necessary, and some sufficient conditions for these properties. It turns out that there is a large variety of learning rules that have the properties. However, all learning rules that have these properties are related to the replicator dynamics of evolutionary game theory. For the case in which there are only two actions, it is shown that one of the absolutely expedient learning rules dominates all others.

This paper develops a new methodology that makes use of the factor structure of large dimensional panels to understand the nature of nonstationarity in the data. We refer to it as PANIC-Panel Analysis of Nonstationarity in Idiosyncratic and Common components. PANIC can detect whether the nonstationarity in a series is pervasive, or variable-specific, or both. It can determine the number of independent stochastic trends driving the common factors. PANIC also permits valid pooling of individual statistics and thus panel tests can be constructed. A distinctive feature of PANIC is that it tests the unobserved components of the data instead of the observed series. The key to PANIC is consistent estimation of the space spanned by the unobserved common factors and the idiosyncratic errors without knowing a priori whether these are stationary or integrated processes. We provide a rigorous theory for estimation and inference and show that the tests have good finite sample properties. Copyright The Econometric Society 2004.

<br>We consider bilateral matching problems where each person views those on the other side of the market as either acceptable or unacceptable: an acceptable mate is preferred to remaining single, and the latter to an unacceptable mate; all acceptable mates are welfare-wise identical.</br> <br>Using randomization, many efficient and fair matching methods define strategyproof revelation mechanisms. Randomly selecting a priority ordering of the participants is a simple example. Equalizing as much as possible the probability of getting an acceptable mate across all participants stands out for its normative and incentives properties: the profile of probabilities is Lorenz dominant, and the revelation mechanism is groupstrategyproof for each side of the market.</br>