دانلود مقاله ISI انگلیسی شماره 1410
عنوان فارسی مقاله

پیش بینی آشفتگی مالی شرکت ها در یکپارچه سازی طبقه بندی درخت تصمیم گیری و رگرسیون لجستیک

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
1410 2011 12 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Predicting corporate financial distress based on integration of decision tree classification and logistic regression
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 38, Issue 9, September 2011, Pages 11261–11272

کلمات کلیدی
آشفتگی مالی - هوشمند مصنوعی - طبقه بندی درخت تصمیم گیری - رگرسیون لجستیک -
پیش نمایش مقاله
پیش نمایش مقاله پیش بینی آشفتگی مالی شرکت ها در یکپارچه سازی طبقه بندی درخت تصمیم گیری و رگرسیون لجستیک

چکیده انگلیسی

Lately, stock and derivative securities markets continuously and rapidly evolve in the world. As quick market developments, enterprise operating status will be disclosed periodically on financial statement. Unfortunately, if executives of firms intentionally dress financial statements up, it will not be observed any financial distress possibility in the short or long run. Recently, there were occurred many financial crises in the international marketing, such as Enron, Kmart, Global Crossing, WorldCom and Lehman Brothers events. How these financial events affect world’s business, especially for the financial service industry or investors has been public’s concern. To improve the accuracy of the financial distress prediction model, this paper referred to the operating rules of the Taiwan Stock Exchange Corporation (TSEC) and collected 100 listed companies as the initial samples. Moreover, the empirical experiment with a total of 37 ratios which composed of financial and other non-financial ratios and used principle component analysis (PCA) to extract suitable variables. The decision tree (DT) classification methods (C5.0, CART, and CHAID) and logistic regression (LR) techniques were used to implement the financial distress prediction model. Finally, the experiments acquired a satisfying result, which testifies for the possibility and validity of our proposed methods for the financial distress prediction of listed companies. This paper makes four critical contributions: (1) the more PCA we used, the less accuracy we obtained by the DT classification approach. However, the LR approach has no significant impact with PCA; (2) the closer we get to the actual occurrence of financial distress, the higher the accuracy we obtain in DT classification approach, with an 97.01% correct percentage for 2 seasons prior to the occurrence of financial distress; (3) our empirical results show that PCA increases the error of classifying companies that are in a financial crisis as normal companies; and (4) the DT classification approach obtains better prediction accuracy than the LR approach in short run (less one year). On the contrary, the LR approach gets better prediction accuracy in long run (above one and half year). 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 short run.

مقدمه انگلیسی

Recently, one of the most attractive business news is a series of financial crisis events related to the public companies. Some of these companies are famous and also at high stock prices, originally (e.g. Enron Corp., Kmart Corp., WorldCom Corp., Lehman Brothers Bank, etc.). In consequence of the financial crisis, it is always too late for many creditors to withdraw their loans, as well as for investors to sell their own stocks, futures, or options. Therefore, corporate bankruptcy is a very important economic phenomenon and also affects the economy of every country. In Taiwan, domestic and foreign capital markets have developed rapidly in recent years, gradually giving people the idea of making a financial investment. Nevertheless, Procomp Corp. and Cdbank Corp. bankruptcy events have also caused tremendous disorder in the financial market and related industries are also affected by these economic shocks in Taiwan. The number of bankruptcy firms is important for the economy of a country and it can be viewed as an indictor of the development and robustness of the economy (Zopounidis & Dimitras, 1998). The high individual, economic, and social costs encountered in corporate failures or bankruptcies have spurred searches for better understanding and prediction capability (McKee & Lensberg, 2002). 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. A common methodology to bankruptcy prediction is to summarize the literature to search a large set of potential predictive financial and/or non-financial variables and then reduce a set of not significant variables, through traditional mathematical analysis that will predict bankruptcy (Lensberg, Eilifsen, & McKee, 2006). Many traditional classification techniques have been presented to predict financial distress using ratios, 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, Wang, Xu, Zhang, & Liang, 2007). Therefore, this paper proposes a model of financial distress prediction comparing decision tree (DT) classification and logistic regression (LR) techniques. The main objectives of this paper are to (1) adopt DT and LR 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 (principle component analysis, PCA) 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 techniques is provided in Section 2. We describe our proposed approach and its capabilities of each step in Section 3. Section 4 presents the process for choosing appropriate variables by PCA. In Section 5, we analyzed the prediction performance of our approach and fulfilled several experiments. Moreover, we compared our results with the DT, and LR approaches in Section 6. Finally, we inference our conclusions and discuss future research in Section 7.

نتیجه گیری انگلیسی

This research aimed at the financial and the non-financial ratios in the financial statement, and used the DT and the LR models to compare the performance of the financial distress predictions, in order to find a better early-warning method. This research took 50 companies that were facing a financial crisis, and matched them with 50 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 DT and LR methods in order to make a comparison. After the experiments, we summarized four critical contributions. First, the more time we used PCA, the less accurate the results for the DT and LR approaches. In our experiments, we found that when we applied all of the 37 variables with non-factor analysis into the DT and LR models, we could obtain a better prediction performance except only for the past 6 seasons in the CHAID model. Second, the closer we get to the time of the actual financial distress, the more accurate the prediction will be in DT models. For example, the accuracy rate with the non-factor analysis for 2 seasons before the financial distress occurs is 97.01% in C5.0, while it is only 88.80% over 8 seasons. However, the results are not similar for the LR model, where the accuracy rate with non-factor analysis for 2 and 8 seasons before the occurrence of financial distress are 85.07% and 91.70%, 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 LR model is higher than in the DT model. Therefore, the prediction performance for the LR approach is more aggressively influenced than the DT model. Finally, the DT approach obtains a better prediction accuracy than the LR approach in developing a financial distress prediction model, with the exception that the accuracy rate (non-factor and 1st factor analysis) for the past 6 and 8 season model is lower with the LR model. Therefore, the DT approach is suitable for financial distress prediction in short run. Otherwise, the LR approach is appropriately for long run prediction for financial distress. In future research, additional macroeconomic index and technical indicator could be considered as input variables and expands the explanation capability for the proposed models. Moreover, additional artificial intelligence techniques, such as neural network models, genetic algorithms, and others, could also be applied. And certainly, researchers could expand the system so as to deal with more financial datasets.

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