A Study of Financial Distress Prediction based on Discernibility Matrix and ANN
Xin-Zhong Bao, Xiu-Zhuan Meng, Hong-Yu Fu
Available Online June 2014.
- https://doi.org/10.2991/msmi-14.2014.66How to use a DOI?
- Rough set, Artificial neural network, Financial distress prediction.
- Financial distress prediction is a significant issue that concerns stakeholders of enterprises. Due to the limitation of the samples available, most studies of financial prediction generally catagorize the financial situations of companies by whether they are bankrupt or specially treated in the stock market. In addition, the variables for prediction are determined mainly by subjective judgments. In order to overcome the influence of these limitations, this paper establishes a reduced variable system with sufficient information by rough set discernibility matrix method, and forms a progressive classification of financial situations by hierarchical clustering. These classifications are transformed as the target value of artificial neural network output layer to enhance the interpretation ability of the whole network. Combined with the inputs determined by Rough Set reduction, a neural network model is established to predict the financial distress, especially the turning point of financial degeneration.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Xin-Zhong Bao AU - Xiu-Zhuan Meng AU - Hong-Yu Fu PY - 2014/06 DA - 2014/06 TI - A Study of Financial Distress Prediction based on Discernibility Matrix and ANN BT - 2014 International Conference on Management Science and Management Innovation (MSMI 2014) PB - Atlantis Press SP - 361 EP - 365 SN - 2352-5428 UR - https://doi.org/10.2991/msmi-14.2014.66 DO - https://doi.org/10.2991/msmi-14.2014.66 ID - Bao2014/06 ER -