We develop a theory of market crashes based on differences of opinion among investors. Because of short-sales constraints, bearish investors do not initially participate in the market and their information is not revealed in prices. However, if other previously bullish investors bail out of the market, the originally bearish group may become the marginal "support buyers," and more will be learned about their signals. Thus accumulated hidden information comes out during market declines. The model explains a variety of stylized facts about crashes and also makes a distinctive new prediction—that returns will be more negatively skewed conditional on high trading volume.

This paper analyzes the linear regression model y = x + with a conditional median assumption Med( j z) = 0 where z is a vector of exogenous random variables. Added complication arise due to the censoring of the outcome y. We treat the censored model as a model with interval-observed outcomes thus obtaining an incomplete model with inequality restrictions on conditional median regressions. This allows us to use the estimator introduced by Manski and Tamer (2000) to analyze the information contained in these inequality restrictions. We give identication conditions in the absence of censoring and introduce a p N-consistent estimator based on the minimum distance method. We then give suÆcient conditions for global identication of with censored y and endogenous x. In the case of interval data on y and endogenous x, we provide a set-consistent estimator that is based on a modied minimum distance method. In the case where we have point identication, we show that the estimator is p N-normal and derive its asymptotic distribution with a feasible asymptotic variance. A Montecarlo analysis illustrates our estimator. We thank Bo Honore for comments and the Econometrics Research Program at Princeton for support.

We develop a theory of market crashes based on differences of opinion among investors. Because of short-sales constraints, bearish investors do not initially participate in the market and their information is not revealed in prices. However, if other previously bullish investors bail out of the market, the originally bearish group may become the marginal “support buyers,” and more will be learned about their signals. Thus accumulated hidden information comes out during market declines. The model explains a variety of stylized facts about crashes and also makes a distinctive new prediction—that returns will be more negatively skewed conditional on high trading volume.

This article proposes a new approach to evaluate contagion in financial markets. Our measure of contagion captures the coincidence of extreme return shocks across countries within a region and across regions. We characterize the extent of contagion, its economic significance, and its determinants using a multinomial logistic regression model. Applying our approach to daily returns of emerging markets during the 1990s, we find that contagion is predictable and depends on regional interest rates, exchange rate changes, and conditional stock return volatility. Evidence that contagion is stronger for extreme negative returns than for extreme positive returns is mixed.

Abstract Using a panel of Dutch listed firms this paper provides empirical evidence for the hypothesis that more risky firms are confronted with more severe capital market constraints than relatively less risky firms. The paper also contributes to the discussion on the usefulness of cash flow as a measure of financial constraints. We present a stochastic version of the Kaplan-Zingales (1997) model. We show that cash flow sensitivity can be used as a meaningful indicator of financing constraints if firms are classified by the degree of uncertainty they face and if the uncertainty originates from cost uncertainty. JEL classification codes: E22, G32.

This paper models firms’ choices between alternative means of presenting information, and the effects of different presentations on market prices when investors have limited attention and processing power. In a market equilibrium with partially attentive investors, we examine the effects of alternative: levels of discretion in pro forma earnings disclosure, methods of accounting for employee option compensation, and degrees of aggregation in reporting. We derive empirical implications relating pro forma adjustments, option compensation, the growth, persistence, and informativeness of earnings, short-run managerial incentives, and other firm characteristics to stock price reactions, misvaluation, long-run abnormal returns, and corporate decisions.

We examine security analysts' career concerns by relating their earnings forecasts to job separations. Relatively accurate forecasters are more likely to experience favorable career outcomes like moving up to a high‐status brokerage house. Controlling for accuracy, analysts who are optimistic relative to the consensus are more likely to experience favorable job separations. For analysts who cover stocks underwritten by their houses, job separations depend less on accuracy and more on optimism. Job separations were less sensitive to accuracy and more sensitive to optimism during the recent stock market mania. Brokerage houses apparently reward optimistic analysts who promote stocks.

This article proposes a new approach to evaluate contagion in financial markets. Our measure of contagion captures the coincidence of extreme return shocks across countries within a region and across regions. We characterize the extent of contagion, its economic significance, and its determinants using a multinomial logistic regression model. Applying our approach to daily returns of emerging markets during the 1990s, we find that contagion is predictable and depends on regional interest rates, exchange rate changes, and conditional stock return volatility. Evidence that contagion is stronger for extreme negative returns than for extreme positive returns is mixed. Copyright 2003, Oxford University Press.

It is often documented, based on autocorrelation, variance ratio, and power spectrum, that exchange rates approximately follow a martingale process. Because these data check serial uncorrelatedness rather than martingale difference, they may deliver misleading conclusions in favor of the martingale hypothesis when the test statistics are insignificant. In this paper, we explore whether there exists a gap between serial uncorrelatedness and martingale difference for exchange rate changes, and if so, whether nonlinear time series models admissible in the gap can outperform the martingale model in out-of-sample forecasts. Applying the generalized spectral tests of Hong to five major currencies, we find that the changes of exchange rates are often serially uncorrelated, but there exists strong nonlinearity in conditional mean, in addition to the well-known volatility clustering. To forecast the conditional mean, we consider the linear autoregressive, autoregressive polynomial, artificial neural network, and functional-coefficient models, as well as their combination. The functional coefficient model allows the autoregressive coefficients to depend on investment positions via a moving-average technical trading rule. We evaluate out-of-sample forecasts of these models relative to the martingale model, using four criteria—the mean squared forecast error, the mean absolute forecast error, the mean forecast trading return, and the mean correct forecast direction. White's reality check method is used to avoid data-snooping bias. It is found that suitable nonlinear models, particularly in combination, do have superior predictive ability over the martingale model for some currencies in terms of certain forecast evaluation criteria.