Developing financial distress prediction models using cutting edge recursive partitioning techniques: a study of Australian mining performance  

Oleh:

Shah, Nikita

Jenis: Article from Proceeding
Dalam koleksi: SIBR-Thammasat 2014 Conference on Interdisciplinary Business & Economics Research June 5th- 7th, 2014 di Emerald Hotel Bangkok, page 1-38.
Topik: bankruptcy prediction; financial distress; insolvency; decision trees; survival analysis; artificial neural network
Fulltext: b14-086.pdf (680.89KB)

Isi artikel

The purpose of this paper is to analyze financial ratios of Australian Mining companies in order to specify and quantify the variables which are effective indicators and predictors of corporate distress. Using financial ratios, the paper explores the quantifiable characteristics of potential bankrupts using cutting edge Recursive Partitioning techniques like Discriminant Analysis, Decision Tree Method, Artificial Neural Network and Hybrid Method, and constructs financial distress prediction models. Australian mining industry is considered for the experiment data set and a sample of 351 healthy firms and 44 distressed firms are studied over a 12 month period from 2012 to 2013 as our experimental targets. The recursive partitioning, Decision and Hybrid Intelligence methods are found to have higher classification power and obtain higher accuracy than the other methods. It proves that this model for prediction of corporate financial crisis is a good solution and can also help investors to make the correct investment decisions.

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