When race is not directly observed, regulators and analysts commonly predict it using algorithms based on last name and address. In small business lending--where regulators assess fair lending law compliance using the Bayesian Improved Surname Geocoding (BISG) algorithm--we document large...

Proxies for race are commonly used in settings where race cannot be observed directly. In the context of small business lending, we examine the standard race prediction algorithm (BISG), which regulators use to assess compliance with fair lending laws. The algorithm relies on an individual’s...

Roll rates and net flow rates can be seen as the evolution of ageing of accounts receivable and Markov chains. They are accepted methodologies to model the behavior of non-performing consumer loans by buckets and to predict losses, but we find that quite often they are wrongly used as...

The parameter loss given default (LGD) of loans plays a crucial role for risk-based decision making of banks including risk-adjusted pricing. Depending on the quality of the estimation of LGDs, banks can gain significant competitive advantage. For bank loans, the estimation is usually based on...

This paper empirically investigates the causes of bank failures in Japan and Indonesia. Using logistic regression analysis of financial ratios, we explore the usefulness of domestic bank failure prediction models with a cross-country model that allows for cross-correlation of the error terms.Our...

This paper empirically investigates the causes of bank failures in Japan and Indonesia. Using logistic regression analysis of financial ratios, we explore the usefulness of domestic bank failure prediction models with a cross-country model that allows for cross-correlation of the error terms.Our...

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...

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...

We replicate three bank failure models (Martin (1977), Cole and White (2012), and DeYoung and Torna (2013)) and introduce a new predictive model along with several evaluation methods to compare their out-of-sample predictive accuracy. We find that the models are highly accurate individually, and...