The recovery rate on defaulted corporate bonds has a time-varying distribution. We propose machine learning approaches for intertemporal analysis of U.S. corporate bonds' recovery rates with a large number of predictors. The most informative macroeconomic variables are selected from a broad...

Innovations in statistical technology have sparked concerns about distributional impacts across categories such as race and gender. Theoretically, as statistical technology improves, distributional consequences depend on how changes in functional forms interact with cross-category distributions...

We apply machine-learning techniques to predict drug approvals using drug-development and clinical-trial data from 2003 to 2015 involving several thousand drug-indication pairs with over 140 features across 15 disease groups. To deal with missing data, we use imputation methods that allow us to...

This article analyzes the manifold situations in which the efficient-market hypothesis (EMH) has influenced — or has failed to influence — federal securities regulation and state corporate law, and the prospective roles for the EMH in these contexts. In federal securities regulation, the EMH...

We study a class of backtests for forecast distributions in which the test statistic is a spectral transformation that weights exceedance events by a function of the modeled probability level. The choice of the kernel function makes explicit the user's priorities for model performance. The class...

The interest collected by the main borrowers is collected to pay back the principal borrowed from the depositary bank. In financial risk management, credit risk assessment is becoming a significant sector. For the credit risk assessment of client data sets, many credit risk analysis methods are...