Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis
Expert Systems with Applications: An International Journal
Data Mining techniques for the detection of fraudulent financial statements
Expert Systems with Applications: An International Journal
Pattern recognition in time series database: A case study on financial database
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
On sensitivity of case-based reasoning to optimal feature subsets in business failure prediction
Expert Systems with Applications: An International Journal
Data mining application on crash simulation data of occupant restraint system
Expert Systems with Applications: An International Journal
Detecting evolutionary financial statement fraud
Decision Support Systems
Evolutional RBFNs prediction systems generation in the applications of financial time series data
Expert Systems with Applications: An International Journal
Integrated expert system applied to the analysis of non-technical losses in power utilities
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Developing SFNN models to predict financial distress of construction companies
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Computers & Mathematics with Applications
Using ridge regression with genetic algorithm to enhance real estate appraisal forecasting
Expert Systems with Applications: An International Journal
Review: Data mining techniques and applications - A decade review from 2000 to 2011
Expert Systems with Applications: An International Journal
An overview of the use of neural networks for data mining tasks
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Information Sciences: an International Journal
International Journal of Intelligent Systems in Accounting and Finance Management
Measuring firm performance using financial ratios: A decision tree approach
Expert Systems with Applications: An International Journal
Hi-index | 12.07 |
The operating status of an enterprise is disclosed periodically in a financial statement. As a result, investors usually only get information about the financial distress a company may be in after the formal financial statement has been published. If company executives intentionally package financial statements with the purpose of hiding the actual status of the company, then investors will have even less chance of obtaining the real financial information. For example, a company can manipulate its current ratio by up to 200% so that its liquidity deficiency will not show up as a financial distress in the short run. To improve the accuracy of the financial distress prediction model, this paper adopted the operating rules of the Taiwan stock exchange corporation (TSEC) which were violated by those companies that were subsequently stopped and suspended, as the range of the analysis of this research. In addition, this paper also used financial ratios, other non-financial ratios, and factor analysis to extract adaptable variables. Moreover, the artificial neural network (ANN) and data mining (DM) techniques were used to construct the financial distress prediction model. The empirical experiment with a total of 37 ratios and 68 listed companies as the initial samples obtained a satisfactory result, which testifies for the feasibility and validity of our proposed methods for the financial distress prediction of listed companies. This paper makes four critical contributions: (1) The more factor analysis we used, the less accuracy we obtained by the ANN and DM approach. (2) The closer we get to the actual occurrence of financial distress, the higher the accuracy we obtain, with an 82.14% correct percentage for two seasons prior to the occurrence of financial distress. (3) Our empirical results show that factor analysis increases the error of classifying companies that are in a financial crisis as normal companies. (4) By developing a financial distress prediction model, the ANN approach obtains better prediction accuracy than the DM clustering approach. Therefore, this paper proposes that the artificial intelligent (AI) approach could be a more suitable methodology than traditional statistics for predicting the potential financial distress of a company.