Analyzing the financial distress of Chinese public companies using probabilistic neural networks and multivariate discriminate analysis
Many studies have applied backpropagation feedforward neural networks (BPNNs) as an alternative to multivariate discriminant analysis (MDA) in attempts to predict business distress using relatively small data sets. Although these studies have generally reported the superiority of BPNNs vs. MDA, they seem to ignore the fact that the former suffers from overfitting if the data set is too small compared to the free parameters of the network. We thus suggest an alternative approach that involves use of a probabilistic neural network (PNN). From our study of financially distressed Chinese public companies, we found that both the PNN and MDA algorithms provide good classifications. Relative to MDA, however, the PNN method provides better prediction, and, at the same time, does not require multivariate normality of the data. Our results appear to offer an improvement from those of earlier efforts that employ MDA, BPNN, and other models. In particular, PNN was here able to predict company distress with greater than 87.5% short-term accuracy, and 81.3% medium-term accuracy.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
When requesting a correction, please mention this item's handle: RePEc:eee:soceps:v:42:y:2008:i:3:p:206-220. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.