A Time-series Financial Distress Model and Its Application---Taking Taiwan Listed Company as An Example

• Main Authors: Chia-Pang Chan, 詹家榜
• Other Authors: Ching-Hsue Cheng
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/sa62c9

博士 === 國立雲林科技大學 === 資訊管理系 === 107 === Financial distress prediction is an important and challenging issue in the financial field. Now, many methods have been proposed to forecast company bankruptcy and financial crisis, and many studies show that artificial intelligence is better than traditional statistical methods in prediction capacity. Financial statements are quarterly reports; hence the attributes of a financial crisis are quarterly time-series data, and financial crisis data have the properties of the imbalance class and are non-stationary. This study uses machine learning techniques to build two time-series financial distress model. In the first model proposed a gene expression programming model for predicting the financial distress of companies. In the second model proposes a MetaCost to add cost-sensitive classification in the training of base classifiers. The proposed model has the following advantages: (1) utilize the MetaCost algorithm to handle the imbalance class; (2) the proposed model is a time series model and is calculated in quarters.; (3) employ attribute selection to find the core attributes and reduce data dimension; (4) the results of the study can be used as a reference for investors and decision makers. The results show that the proposed GEP and Metacost methods are better than the listed classifiers. The prediction results of the artificial intelligence GEP method proposed in this study have relative accuracy, and the advantages of Type I error and Type II error. MetaCost method raises a little sensitivity, it lifts to identify the companies’ financial health when the companies are actually healthy; and type II errors are reduced by 21.6%, it denotes that the proposed method can raise the correct classification of financial distress and define the attribute range value to provide financial distress reference the results can early enable the investors to detect the level of financial distress of a corporation.