Supporting ordinal four-state classification decisions using neural networks

Authors:
Anurag Agarwal;Jefferson T. Davis;Terry Ward
Affiliations:
Department of Decision and Information Sciences, Warrington College of Business Administration, University of Florida, Gainesville, FL 32611-7169, USA E-mail: aagarwal@ufl.edu;School of Accountancy, College of Business & Economics, Weber State University, Ogden, UT 84408-3803, USA E-mail: jtdavis@weber.edu;Department of Accounting, College of Business, Middle Tennessee State University, Murfeesbro, TN 37132, USA E-mail: tward@frank.mtsu.edu
Venue:
Information Technology and Management
Year:
2001

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Abstract

Many accounting and finance problems require ordinal multi-state classification decisions, (e.g., control risk, bond rating, financial distress, etc.), yet few decision support systems are available to aid decision makers in such tasks. In this study, we develop a Neural Network based decision support system (NN-DSS) to classify firms in four ordinal states of financial condition namely healthy, dividend reduction, debt default and bankrupt. The classification results of the NN-DSS model are compared with those of a Naïve model, a Multiple Discriminant Analysis (MDA) model, and an Ordinal Logistic Regression (OLGR) model. Four different evaluation criteria are used to compare the models, namely, simple classification accuracy, distance-weighted classification accuracy, expected cost of misclassification (ECM) and ranked probability score. Our study shows that NN-DSS models perform significantly better than the Naïve, MDA, and OLGR models on the ECM criteria, and provide better results than MDA and OLGR on other criteria, although not always significantly better. The effect of the proportion of firms of each state in the training set is also studied. A balanced training set leads to more uniform (less skewed) classification across all four states, whereas an unbalanced training set biases the classification results in favor of the state with the largest number of observations.