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Comparing statistical methods and artificial neural networks in bankruptcy prediction
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Chu, Jung, Ph.D. (1997) Comparing statistical methods and artificial neural networks in bankruptcy prediction. PhD thesis, University of Warwick.
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Official URL: http://webcat.warwick.ac.uk/record=b1652923~S1
Abstract
The use of multivariate discriminant analysis (MDA) and logistic regression procedure (Logit) in
predicting business failure has been explored in numerous studies since 1960s. Recently, a newly
developed technique, artificial neural networks (ANNs), has attracted much attention and has been
applied to bankruptcy prediction area. At the same time, many papers attempted to compare the
predictive ability of these two distinct classes of discriminators in order to find a best failure prediction
method. However, most of their results, despite showing the superiority of ANNs, have been sharply
criticised either for the unfair comparison or for their specific data selection. There is a need to
undertake theory-based research to identify problem characteristics that predict when ANNs will
forecast better than statistical models; to identify which input variable characteristics predict when
ANNs will improve model estimation; and to identify when this advantage would give substantially
improved forecasting performance.
Motivated by the limited amount of research on investigating the relative effectiveness of traditional
methods as compared to the ANNs under a wide variety of modelling assumptions, one of the objectives
of this study is to compare their classification capacities on a theoretical basis, and to evaluate the
robustness on certain situations through the simulation study. The investigation is conducted on two
popular statistical techniques—the MDA and the Logit, as well as two different learning algorithms of
ANNs—the standard generalised delta rule (GDR) and the Projection approach (Proj). This can be
regarded as the horizontal assessments of bankruptcy prediction.
The other aim of this thesis is to evaluate the impacts of variations in failure prediction models through
the empirical study. These variations involve the issues we often encounter in the real world, such as
the different sizes of sample, a choice-based sampling bias, the sensitivity of optimal cutoff points to
misclassification costs of Type I and Type II errors, and the imbalance of the composition of failed to
nonfailed firms between training and testing data sets. This can be viewed as the vertical assessments of
bankruptcy prediction.
The simulation results indicate that the neural networks are indeed competitive approaches on
bankruptcy prediction. In particular, the Projection network, which was developed to overcome the
drawbacks that a commonly used GDR backpropagation algorithm often experiences, proves its
remarkable superiority not only quantitatively (i.e., lower overall accuracy), but also qualitatively
(lower Type I and Type II errors). The Projection network holds a promise for future elaboration.
Moreover, the outcomes of empirical experiments enhance our knowledge of some factors in
constructing a failure forecasting model. This knowledge is related to both traditional statistical tools
and modem neural networks and is essential for decision making.
Item Type: | Thesis (PhD) | ||||
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Subjects: | H Social Sciences > HG Finance Q Science > QA Mathematics |
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Library of Congress Subject Headings (LCSH): | Bankruptcy -- Mathematical models, Discriminant analysis, Regression analysis, Neural networks (Computer science) | ||||
Official Date: | 1997 | ||||
Dates: |
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Institution: | University of Warwick | ||||
Theses Department: | Warwick Business School | ||||
Thesis Type: | PhD | ||||
Publication Status: | Unpublished | ||||
Supervisor(s)/Advisor: | Steele, Anthony ; Hurrion, R. D. (Robert D.) | ||||
Extent: | xxv, 373 leaves | ||||
Language: | eng |
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