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This paper tests the hypothesis that irrational market misvaluation a#ects firms' takeover behavior. We employ two contemporaneous proxies for market misvaluation, pre-takeover book/price ratios and pre-takeover ratios of residual income model value to price. Misvaluation of bidders and targets influences the means of payment chosen, the mode of acquisition, the premia paid, target hostility to the o#er, the likelihood of o#er success, and bidder and target announcement period stock returns. The evidence is broadly supportive of the misvaluation hypothesis

This paper explores the determinants of corporate failure and the pricing of financially distressed stocks whose failure probability, estimated from a dynamic logit model using accounting and market variables, is high. Since 1981, financially distressed stocks have delivered anomalously low returns. They have lower returns but much higher standard deviations, market betas, and loadings on value and small-cap risk factors than stocks with low failure risk. These patterns are more pronounced for stocks with possible informational or arbitrage-related frictions. They are inconsistent with the conjecture that value and size e¤ects are compensation for the risk of financial distress.

We provide maximum likelihood estimators of term structures of conditional probabilities of bankruptcy over relatively long time horizons, incorporating the dynamics of firm-specific and macroeconomic covariates. We find evidence in the U.S. industrial machinery and instruments sector, based on over 28,000 firm-quarters of data spanning 1971 to 2001, of significant dependence of the level and shape of the term structure of conditional future bankruptcy probabilities on a firm's distance to default (a volatility-adjusted measure of leverage) and on U.S. personal income growth, among other covariates. Variation in a firm's distance to default has a greater relative e#ect on the term structure of future failure hazard rates than does a comparatively sized change in U.S. personal income growth, especially at dates more than a year into the future.

This paper shows that the probability of extreme default losses on portfolios of U.S. corporate debt is much greater than would be estimated under the standard assumption that default correlation arises only from exposure to observable risk factors. At the high confidence levels at which bank loan portfolio and CDO default losses are typically measured for economic-capital and rating purposes, our empirical results indicate that conventionally based estimates are downward biased by a full order of magnitude on test portfolios. Our estimates are based on U.S. public non-financial firms existing between 1979 and 2004. We find strong evidence for the presence of common latent factors, even when controlling for observable factors that provide the most accurate available model of firm-by-firm default probabilities. ∗ We are grateful for financial support from Moody’s Corporation and Morgan Stanley, and for research assistance from Sabri Oncu and Vineet Bhagwat. We are also grateful for remarks from Torben Andersen, André Lucas, Richard Cantor, Stav Gaon, Tyler Shumway, and especially Michael Johannes. This revision is much improved because of suggestions by a referee, an associate editor, and Campbell Harvey. We are thankful to Moodys and to Ed Altman for generous assistance with data. Duffie is at The Graduate School of Business, Stanford University. Eckner and Horel are at Merrill Lynch. Saita is at Lehman

and the Workshop on Capital Structure Arbitrage at the University of Evry. We are particularly grateful for the comments and suggestions of Jun Liu, the Editor We conduct an analysis of the risk and return characteristics of a number of widelyused fixed income arbitrage strategies. We find that the strategies requiring more “intellectual capital ” to implement tend to produce significant alphas after controlling for bond and equity market risk factors. These positive alphas remain significant even after taking into account typical hedge fund fees. In contrast with other hedge fund strategies, many of the fixed income arbitrage strategies produce positively skewed returns. These results suggest that there may be more economic substance to fixed income arbitrage than simply “picking up nickels in front of a steamroller.

One critical difficulty in implementing Merton’s (1974) credit risk model is that the underlying asset valuecannotbedirectlyobserved. Themodelrequirestheunobservedassetvalueandtheunknown volatility parameter as inputs. The estimation problem is further complicated by the fact that typical data samples are for the survived firms. This paper applies the maximum likelihood principle to develop an estimation procedure and study its properties. The maximum likelihood estimator for the mean and volatility parameters, asset value, credit spread and default probability are derived for Merton’s model. To our knowledge, this paper is the first to address the survivorship issue as well as the first to apply the maximum likelihood method to credit risk assessment in a portfolio context. A Monte Carlo study is conducted to examine the performance of this maximum likelihood method. An application to real data is also presented.

In this paper we revisit the theoretical relation between financial leverage and stock returns in a dynamic world where both the corporate investment and finance decisions are endogenous. We find that the link between leverage and stock returns is more complex than the static textbook examples suggest and will usually depend on the investment opportunities available to the firm. In the presence of financial market imperfections leverage and investment are generally correlated so that highly levered firms are also mature firms with relatively more (safe) book assets and fewer (risky) growth opportunities. We use a quantitative version of our model to generate empirical predictions concerning the empirical relationship between leverage and returns. We test these implications in actual data and find support for them.

Moody’s KMV method is a popular commercial implementation of the structural credit risk model pioneered by Merton (1974). It is an algorithm for estimating the unobserved asset value and the unknown parameters required for implementing such a model. This estimation method has found its way to the recent academic literature, but it has not yet been formally analyzed to assess its statistical properties. This paper fills this gap and shows that, in the context of Merton’s model, the KMV estimates are identical to maximum likelihood estimates (MLE) developed in Duan (1994). Unlike the MLE method, however, the KMV algorithm is silent about the distributional properties of the estimates and thus ill-suited for statistical inference. The KMV algorithm also cannot generate estimates for capital-structure specific parameters. In contrast, the MLE approach is flexible and can be readily applied to different structural credit risk models.

data are acquired from CreditTrade. This research is partially supported by a grant from