Forecasting business profitability by using classification techniques: A comparative analysis based on a Spanish case |
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| Institution: | 1. School of Production Engineering and Management, Technical University of Crete, University Campus, 73100 Crete, Greece;2. Audencia Business School, 8 Route de la Joneliere, 44312, Nantes, France;1. School of Management, University of Bath, Wessex House, Bath BA2 7AY, United Kingdom;2. University of Glasgow Business School, University of Glasgow, Gilbert Scott Building, Glasgow G12 8QQ, United Kingdom;3. Essex Business School, University of Essex, SO4 3SQ, United Kingdom;1. School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang 212003, China;2. Lancaster Centre for Forecasting, Lancaster University, Lancaster LA1 4YX, UK;3. Kent Business School, University of Kent, Kent ME4 4AG, UK;1. Department of Industrial Systems Engineering and Product Design, Ghent University, Technologiepark 903, Zwijnaarde 9052, Belgium;2. Department of Management Science, Lancaster University Management School, Lancaster LA1 4YX, UK;3. Solventure NV, Sluisweg 1, Gent 9000, Belgium;4. Flanders Make |
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| Abstract: | A comparative study of the performance of a number of classificatory devices, both parametric (LDA and Logit) and non-parametric (perceptron neural nets and fuzzy-rule-based classifiers) is conducted, and a Monte Carlo simulation-based approach is used in order to measure the average effects of sample size variations on the predictive performance of each classifier. The paper uses as a benchmark the problem of forecasting the level of profitability of Spanish commercial and industrial companies upon the basis of a set of financial ratios. This case illustrates well a distinctive feature of many financial prediction problems, namely that of being characterized by a high dimension feature space as well as a low degree of separability. Response surfaces are estimated in order to summarize the results. A higher performance of model-free classifiers is generally observed, even for fairly moderate sample sizes. |
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