We develop a model of neural networks to study the bankruptcy of U.S. banks. We provide a new model to predict bank defaults some time before the bankruptcy occurs, taking into account the specific features of the current financial crisis. Based on data from the Federal Deposit Insurance...

Students' financial difficulties can be considered as one of the most important barriers in postsecondary education. Researchers from various domains have attempted to understand the relationship between students' financial difficulties and persistence in postsecondary education. In this paper,...

Demand is growing for a better understanding of how assets are priced in countries outside of the U.S. While financial data are available for many firms world-wide, it is important to have a reliable and replicable method of constructing high-quality systematic risk factors from these data. This...

A major obstacle for research in international asset pricing and corporate finance has been a lack of reliable and publicly available data on international common risk factors and portfolios. To address this gap, we provide a step-by-step description of how appropriately screened data from...

Demand is growing for a better understanding of how assets are priced in countries outside of the U.S. While financial data are available for many firms world-wide, it is important to have a reliable and replicable method of constructing high-quality systematic risk factors from these data. This...

In recent years support vector regression (SVR), a novel neural network (NN) technique, has been successfully used for financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH method is proposed and is compared with a moving...