استفاده از شبکه های عصبی و تکنیک های داده کاوی برای مدل پیش بینی بحران مالی

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.

In Taiwan, domestic and foreign capital markets have developed rapidly in recent years, gradually giving people the idea of making a financial investment. There are various financial investment objects, such as stocks, futures, options, bond funds etc., and investment stock is the most widely accepted in society. However, capital markets are volatile, and most investors only know that a company is in financial trouble after the financial statement of the company has been made public. Therefore, forecasting corporate financial distress plays an increasingly important role in today’s society since it has a significant impact on lending decisions and the profitability of financial institutions. The ability to make accurate bankruptcy predictions are of critical importance to various professionals, such as bank loan officers, creditors, stockholders, bondholders, financial analysts, governmental officials, as well as the general public, as it provides them with timely warnings (Ko & Lin, 2006). Financial failure occurs when a firm suffers chronic and serious losses or when the firm becomes insolvent with liabilities that are disproportionate to its assets (Hua, Wang, Xu, Zhang, & Liang, 2007). Common causes and symptoms of financial failure include lack of financial knowledge, failure to set capital plans, poor debt management, inadequate protection against unforeseen events and difficulties in adhering to proper operating discipline in the financial market. The common assumption underlying bankruptcy prediction is that a firm’s financial statements appropriately reflect above characteristics. Several classification techniques have been suggested to predict financial distress using ratios and data originating from these financial statements, e.g., univariate approaches (Beaver, 1966), multivariate approaches, linear multiple discriminant approaches (MDA) (Altman, 1968 and Altman et al., 1977), multiple regression (Meyer & Pifer, 1970), logistic regression (Dimitras, Zanakis, & Zopounidis, 1996), factor analysis (Blum, 1974), and stepwise (Laitinen & Laitinen, 2000). However, strict assumptions of traditional statistics such as linearity, normality, independence among predictor variables and pre-existing functional form relating to the criterion variable and the predictor variable limit their application in the real world (Hua et al., 2007). With radical changes taking place in corporate finance and the global economic environment, critical financial ratios can change dynamically (John & Robert, 2001). This means that it is both important as well as necessary to develop an evolutionary approach for coping with future dynamic financial environments. Therefore, this paper proposes a model of financial distress prediction integrating artificial neural network (ANN) and data mining (DM) techniques. The main objectives of this paper are to (1) adopt ANN and DM techniques to construct a financial distress prediction model, (2) use financial and non-financial ratios to enhance the accuracy of the financial distress prediction model, (3) employ a traditional statistical method (factor analysis) to compare the degree of accuracy with that of the artificial intelligent (AI) approach, and (4) to expand this model so that it will work within a financial distress prediction system to provide information to investors as well as investment monitoring organizations. The data for our experiment were collected from the Taiwan stock exchange corporation (TSEC) database. The rest of this paper is organized as follows. A literature review of related studies is provided in Section 2. Section 3 describes our proposed approach and the functionalities of each process. Section 4 presents the process for selecting suitable indicators by factor analysis. To prove the prediction performance of our approach, we carried out several experiments which are described in Section 5. In Section 6, we compared our results with the ANN, and DM approaches. Finally, in Section 7 we draw our conclusions about financial distress forecasting and discuss future work.

This research aimed at the financial and the non-financial ratios in the financial statement, and used the BPN and the clustering model to compare the performance of the financial distress predictions, in order to find a better early-warning method. This research took 34 companies that were facing a financial crisis, and matched them with 34 normal companies of the similar industry. In addition, we adopted the necessary dataset from the TSEC database and sampled them into the past 2, 4, 6, 8 seasons prior to the financial crisis occurrence. This data was then used to carry out a statistical factor analysis, with each ratio variable being generated going into BPN and clustering methods in order to make a comparison. After the experiments, we summarized four critical contributions. First, the more time we used factor analysis, the less accurate the results for the BPN and clustering approaches. In our experiments, we found that when we applied all of the 37 variables with non-factor analysis into the BPN and clustering models, we could obtain a better prediction performance except for the past 8 seasons in the BPN model and for the past 2 seasons in the clustering model. Second, the closer we get to the time of the actual financial distress, the more accurate the prediction will be. For example, the accuracy rate with the non-factor analysis for 2 seasons before the financial distress occurs is 82.14% in BPN, while it is only 60% over 8 seasons. The results are similar for the clustering model, where the accuracy rate with non-factor analysis for 2 and 8 seasons before the occurrence of financial distress are 73.81% and 61.21%, respectively. Third, most investors are concerned with the Type II error rate and avoid investing in these companies. Our empirical results show that factor analysis increases the error forecasts of classifying companies with a potential financial crisis as a normal company. Moreover, we also found that the average rate of the Type II error in the clustering model is higher than in the BPN model. Therefore, the prediction performance for the clustering approach is more aggressively influenced than the BPN model. Finally, the BPN approach obtains a better prediction accuracy than the DM clustering approach in developing a financial distress prediction model, with the exception that the accuracy rate (non-factor analysis) for the past 8 seasons model and the accuracy rate (2nd factor analysis) for the past 6 seasons is lower with the BPN model. In future research, additional artificial intelligence techniques, such as other neural network models, classification mining, genetic algorithms, and others, could also be applied. And certainly, researchers could expand the system so as to deal with more financial datasets.