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Predictive Model of Insolvency Risk for Australian Corporations
Baxter, R., Gawler, M. and Ang, R.
This paper describes the development of a predictive
model for corporate insolvency risk in Australia. The
model building methodology is empirical with out-ofsample
future year test sets. The regression method used
is logistic regression after pre-processing by quantisation
of interval (or numeric) attributes. We show that logistic
regression matches the performance of ensemble methods,
such as random forests and ada boost, provided that preprocessing
and variable selection is performed.
A distinctive feature of the insolvency risk model
described in this paper is its breadth; since we are using
income tax return data we are able to risk score one
million companies across all industries, all corporation
types (public, private) and all sizes, as measured either by
assets or number of employees. This is an application
paper that uses standard credit scoring methodology on a
new data source. The contribution is to demonstrate that
insolvency risk can be estimated using income tax return
data. The corporate insolvency prediction model is still in
development and so we welcome suggestions for
improvements on the current methodology. |
Cite as: Baxter, R., Gawler, M. and Ang, R. (2007). Predictive Model of Insolvency Risk for Australian Corporations. In Proc. Sixth Australasian Data Mining Conference (AusDM 2007), Gold Coast, Australia. CRPIT, 70. Christen, P., Kennedy, P. J., Li, J., Kolyshkina, I. and Williams, G. J., Eds. ACS. 21-28. |
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