We develop evidence on the finite sample properties of the Generalized Method of Moments (GMM) in an asset pricing context. The models imply nonlinear, cross-equation restrictions on predictive regressions for security returns. We find that a two-stage GMM approach produces goodness-of-fit statistics that reject the restrictions too often. An iterated GMM approach has superior finite sample properties. The coefficient estimates are approximately unbiased in simpler models, but their asymptotic standard errors are understated. Simple adjustments for the standard errors are partially successful in correcting the bias. In more complex models the coefficients and their standard errors can be highly unreliable. The power of the tests to reject a single-premium model is higher against a two-premium, fixed-beta alternative than against a conditional Capital Asset Pricing Model with time-varying betas.

We investigate the financial recontracting of firms completing distressed exchanges and those reorganizing under Chapter 11. We find that recovery rates for creditors, on average, are higher in distressed exchanges than in Chapter 11 reorganizations, as are equity deviations from absolute priority. The difference in deviations potentially provides valuable information on the higher costs of formal reorganization. Also, cash is used more extensively to redeem creditors' claims in Chapter 11 than in distressed exchanges. The greater use of cash can be attributed to provisions of the Bankruptcy Code that permit conservation of cash and facilitate asset sales.