Book chapter
Bankruptcy prediction using neural networks
Business Intelligence Techniques, pp 117-132
01 Jan 2004
Abstract
This study is an extension of prior studies that have used artificial neural networks to predict bankruptcy. The incremental contribution of this study is threefold. First, we use only financially stressed firms in our control sample. This sampling enables the models to more closely approximate the actual decision processes of auditors and other interested parties. Second, we develop a more parsimonious model using qualitative “bad news” variables that prior research indicates measure financial distress. Past research has focused on the “usefulness” of accounting numbers and therefore often ignored non-accounting variables that may contribute to the classification accuracy of the distress prediction models. In addition, rather than use multiple financial ratios, we include a single variable of financial distress using the Zmijewski distress score that incorporates ratios measuring profitability, liquidity, and solvency. Finally, we develop and test a genetic algorithm neural network model. We compare its predictive ability to that of a backpropagation neural network and a model using multiple discriminant analysis.
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Details
- Title
- Bankruptcy prediction using neural networks
- Creators
- Murugan Anandarajan - Drexel UniversityPicheng Lee - Pace UniversityAsokan Anandarajan - New Jersey Institute of Technology
- Publication Details
- Business Intelligence Techniques, pp 117-132
- Publisher
- Springer Berlin Heidelberg; Berlin, Heidelberg
- Resource Type
- Book chapter
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems); Bennett S. LeBow College of Business; Drexel University
- Other Identifier
- 991019551687604721