Three of the authors previously developed a model to predict the duration of Chapter 11 bankruptcy and the payoff to shareholders (Partington et al ., 2001). This work augments that study using a much larger sample to reestimate the model and assess its stability. It also provides an opportunity for out-of-sample testing of predictive accuracy. The resulting models are based on Cox's proportional hazards model and the current article points to the need to test two important assumptions underlying the model. First, that the hazards are proportional and, second, that censoring is independent of the event studied. Using the extended data set, all the previously significant accounting variables drop out of the model and only two covariates of the original model remain significant. These are the market wide credit spread and the market capitalization of the firm, both measured immediately prior to the firm's entry to Chapter 11. Receiver operating characteristic curves are then used to assess the predictive accuracy of the original and extended models. The results show that Lachenbruch tests can provide a misleading indication of predictive ability out of sample. Using the Lachenbruch method of in-sample testing, both models show predictive power, but in a true out-of-sample test they fail dismally. The lessons of this work are relevant to better predicting the gains and losses likely to accrue to shareholders of companies in Chapter 11 bankruptcy and in similar administrative arrangements in other jurisdictions.

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This study investigates the extent to which broker anonymity in an electronic central limit order book impairs the ability of the market to detect informed trading in the lead up to takeover announcements. Our research represents the first study in this area to analyse the effects of broker anonymity in the context of significant information asymmetry, where one would expect anonymity to be of greatest importance. This article, therefore, extends prior research which only investigates the effects of broker anonymity averaged across all types of information environments. The results of this study indicate that informed traders are less detected, and therefore better off when broker identifiers are concealed. This finding has important policy implications for exchange officials deciding whether or not to reveal broker identifiers surrounding trades, especially considering that almost all prior research suggests that broker anonymity is correlated with improved liquidity in the form of lower bid-ask spreads.

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