Corporate credit rating analysis has attracted lots of research interests in the literature. Recent studies have shown that Artificial Intelligence (AI) methods achieved better performance than traditional statistical methods. This article introduces a relatively new machine learning technique, support vector machines (SVM), to the problem in attempt to provide a model with better explanatory power. We used backpropagation neural network (BNN) as a benchmark and obtained prediction accuracy around 80% for both BNN and SVM methods for the United States and Taiwan markets. However, only slight improvement of SVM was observed. Another direction of the research is to improve the interpretability of the AI-based models. We applied recent research results in neural network model interpretation and obtained relative importance of the input financial variables from the neural network models. Based on these results, we conducted a market comparative analysis on the differences of determining factors in the United States and Taiwan markets.

Experimental comparisons between statistical and machine learning methods appear with increasing frequency in the literature. However, there does not seem to be a consensus on how such a comparison is performed in a methodologically sound way. Especially the effect of testing multiple hypotheses on the probability of producing a "false alarm" is often ignored. We transfer multiple comparison procedures from the statistical literature to the type of study discussed in this paper. These testing procedures take the number of tests performed into account, thereby controlling the probability of generating "false alarms". The multiple comparison procedures selected are illustrated on well-known regression and classification data sets. 26.1 Introduction Recent interactions between the statistical and artificial intelligence communities (see e.g. [Han93, CO94]), have led to many studies that compare the performance of empirical statistical and machine learning methods on real-life data sets; ...

Ability to forecast market variables is critical to analysts, economists and investors. Among other uses, neural networks are gaining in popularity in forecasting market variables. They are used in various disciplines and issues to map complex relationships. We present a primer for using neural networks for forecasting market variables in general, and in particular, forecasting volatility of the S&P 500 Index futures prices. We compare volatility forecasts from neural networks with implied volatility from S&P 500 Index futures options using the Barone-Adesi and Whaley (BAW) model for pricing American options on futures. Forecasts from neural networks outperform implied volatility forecasts. Volatility forecasts from neural networks are not found to be significantly different from realized volatility. Implied volatility forecasts are found to be significantly different from realized volatility in two

This paper explores three of the most well known supervised neural network paradigms backpropagation, radial basis function and learning vector quantization for the task of rating U.S. corporate bonds. Using generally available historic data, bonds are assigned to ratings based on a classification scheme. The classification schemes investigated were a binary categorical assignment and an integer classification. Comparisons are made with logistic regression models on both the data set used to create the predictive models and on new data. Accepted to a special issue on Engineering Valuation and Computational Intelligence of Journal of Engineering Valuation and Cost Analysis September 1997 1 Corresponding author. 1 Alternative Neural Network Approaches to Corporate Bond Rating Ravipim Chaveesuk, Chat Srivaree-ratana and Alice E. Smith 2 Department of Industrial Engineering 1031 Benedum Hall University of Pittsburgh Pittsburgh, PA 15261 USA aesmith@engrng.pitt.edu A...