Banking is a popular topic for empirical and methodological research that applies operational research (OR) and artificial intelligence (AI) methods. This article provides a comprehensive and structured bibliographic survey of OR- and AI-based research devoted to the banking industry over the...

The Bayes-Stein model provides a framework for remedying parameter uncertainty in the Markowitz mean-variance portfolio optimization. The classical version, however, suffers from estimation errors of model components and fails to consistently outperform the naive 1/N asset allocation rule. We...

We propose a novel measure of the ex-ante commodity downside-risk premium (CDP) for each commodity based on a term structure model of commodity futures. Our theory-based CDP, capturing forward-looking information in the futures markets, outperforms well-known characteristics in explaining the...

Bank failure prediction is a popular topic that requires highly accurate results. We contribute to the literature by determining whether models based on the crisis data are suitable for predicting bank failure during a stable period and which predictors can be held for long-term forecasting. In...

We propose a novel empirical strategy that enables the researcher to select buckets of volatility forecasting models with similar accuracy. Using our Multiple Hypothesis Testing framework with False Discovery Rate (FDR) approach, we are able to identify buckets of more accurate models relative...

Forecasting the mean returns vector and the covariance matrix is a key feature in implementing portfolio theory. The performance of the Bayes-Stein method for forecasting these parameters for use in the Markowitz model (with and without short sales) was compared with that of seven other...