Introduction to non-linear optimization
Introduction to non-linear optimization
Neural Networks
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
Knowledge Acquisition From Multiple Experts: An Empirical Study
Management Science
Modeling the relationship between corporate strategy and wealth creation using neural networks
Computers and Operations Research - Neural networks in business
Mutual Information Theory for Adaptive Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Other than identifying whether a company may fail or not, explaining why a company may fail is essential. The most common way of explaining is to use a template like the standards used in commercial society. Because of the existence of heteroscedasticity, it is impossible to expect that there is only one standard within an industry. For instance, it is unrealistic to use one standard to evaluate performance of both a new-born company and a fifty-year old company. This paper presents a method of searching for templates using probabilistic neural networks. Each template represents a number of companies, which have similar financial performance and therefore similar financial outcomes. A comparison between a company and a template can explain how badly a company performs and what the problem is if its financial situation is not sound. The method has so far been applied to a data set of 2408 UK construction companies.