Neural Network Simulation and the Prediction of Corporate Outcomes: Some Empirical Findings
AbstractNeural Networks (NN's) involve an innovative method of simulating and analysing complex and constantly changing systems of relationships. Originally developed to mimic the neural architecture and functioning of the human brain, NN techniques have recently been applied to many different business fields and have demonstrated a capability to solve complex problems. This paper investigates the use of NN techniques as a tool for the modelling and prediction of corporate bankruptcy and other corporate outcomes. The within and out-of-sample accuracy of trained NNs are compared with those of standard logit and multilogit techniques. The results of the study suggest that, from a pure predictive point of view, NN simulation produces a higher predictive accuracy and is more robust than conventional logit and multilogit models.
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Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal International Journal of the Economics of Business.
Volume (Year): 2 (1995)
Issue (Month): 1 ()
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Find related papers by JEL classification:
- JEL - Labor and Demographic Economics - - - - -
- cla - - - - - -
- G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
- G34 - Financial Economics - - Corporate Finance and Governance - - - Mergers; Acquisitions; Restructuring; Corporate Governance
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
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