Corporate Bankruptcy Prediction Using the Principal Components Method (original) (raw)
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A neural network model for bankruptcy prediction
1990 IJCNN International Joint …, 1990
One interesting area for the use of neural networks is in event prediction. This study develops a neural network model for prediction of bankruptcy and tests it using financial data from various companies. The same set of data is analyzed using a more traditional method of bankruptcy prediction, multivariate discriminant analysis. A comparison of the predictive abilities of both the neural network and the discriminant analysis method is presented. The results show that neural networks might be applicable to this problem.
Quality & Quantity, 2015
Our study aims to present an optimisation method for the forecasting of bankruptcy. To this end, we elaborate and optimise an artificial neural network (ANN) which, based on the situation of real companies in Eastern Europe, can forecast bankruptcy state. After describing the network structure, the performance is evaluated. Using specific statistical methods, a statistical network optimisation is performed. The conclusion is that ANNs are extremely productive in predicting firm bankruptcy, with the forecast accuracy being higher than the accuracy obtained by traditional methods. The results are applicable at an international level, though the target group of this study contains mainly Eastern European Small Manufacturing Enterprises.
Application of Neural Networks to Business Bankruptcy Analysis in Thailand
International Journal of Computational Intelligence Research, 2007
The recent East Asian economic crisis is a lesson one can learn from the absence of effective early warning systems. To serve as a sound early warning signal, the accuracy of a failure prediction model is as important as its robustness over time. This study analyses financial and ownership variables using principal component analysis. It can reduce huge number of financial data of the business bankruptcy prediction problem. Using neural networks for bankruptcy forecasting, the obtained features are fed into neural networks as the input data. Our experiments examine the predictive performance of three neural networks: Learning Vector Quantization, Probabilistic Neural Network, and Feedforward network with backpropagation learning. All these approaches are applied to data sets of 41 Thai financial institutions for the period 1993-2003.
Bankruptcy prediction using neural networks
Decision Support Systems, 1994
Prediction of firm bankruptcies have been extensively studied in accounting, as all stakeholders in a firm have a vested interest in monitoring its financial performance. This paper presents an exploratory study which compares the predictive capabilities for firm bankruptcy of neural networks and classical multivariate discriminant analysis. The predictive accuracy of the two techniques is presented within a comprehensive, statistically sound framework, indicating the value added to the forecasting problem by each technique. The study indicates that neural networks perform significantly better than discriminant analysis at predicting firm bankruptcies. Implications of our results for the accounting professional, neural networks researcher and decision support system builders are highlighted.
Bankruptcy Risk Prediction Models Based on Artificial Neural Networks
2017
The purpose of this research is to study the ability of artificial neural networks to forecast the companies' risk of financial distress. We predicted the bankruptcy risk using the associated financial ratios (overall liquidity ratio and the overall solvency ratio) and two artificial neural network models based on the backpropagation algorithm. The proposed models were implemented and tested using the PyBrain software and have been applied to 55 companies listed on the Bucharest Stock Exchange during 2010-2014. After a total of 19,944 iterations for the learning stage, the two algorithms converged and the errors obtained during the tests reached the fixed target. The empirical results showed that the artificial neural network models are efficient and reliable in detecting the risk of bankruptcy. The artificial neural networks are very useful in economic analysis when the complexity of data makes it difficult to implement functions that proper describe the link between economic variables. The use of the neural networks method for predicting the risk of bankruptcy is less common in Romania. This study intends to fill this gap in the literature and we believe it could be of interest not only for the companies listed on the stock exchange, but also for investors, shareholders and banks.
The Bankruptcy Prediction by Neural Networks and Logistic Regression
International Journal of Academic Research in Accounting Finance and Management Sciences, 2013
Today, the intensity of industry competition has led many companies going bankrupt and pulling out of race. The early warning against the possibility of bankruptcy enables the managers and investors to take pre-emptive actions when it is necessary. The bankruptcy prediction models reveal the latent problems in financial structures like a warning bell and provide timely feedback to managers and investors as well as other people who benefit from this. The bankruptcy of manufacturing companies in Tehran Stock Exchange Market has been predicted in this study using artificial neural network in this respect. It has been also used the logistic regression to do compare with neural network as well. All information which has been used here is related to time periods from 2001 to 2011 and the bankrupt groups have been selected on the basis of Article 141 of the Commercial Code of Iran. In the years before bankruptcy, the financial management has the chance to predict the probability of bankruptcy by using this model and take necessary actions in this regard since the results derived from the neural network predictions are very consistent with reality. Moreover, this model is more accurate than that of logistic regression in prediction process.
Ekonomichny visnik DVNZ UDKhTU, 2020
The article is devoted to the substantiation of the choice of financial indicators for discriminant and neural network models for diagnosing the financial condition of innovative active enterprises and determining the probability of their bankruptcy, as well as the construction of these models based on a study of the financial condition of 36 enterprises. The modern imperative of the successful development of the domestic economy is its transition to the rails of innovative development. This process is impossible without competent distribution of financial resources by business entities. In this regard, especially important is the question regarding the development of new approaches and methods for the assessment of readiness of enterprises for implementation of innovation activities due to which investors or, indeed, the state itself will be able to determine the amount of financial resources which is necessary for the development and implementation of new technologies, products or services. It is shown the importance of researching the financial condition of Ukrainian enterprises that are engaged in innovations, since their innovative activity is almost entirely financed by own means. With the aid of Deductor analytical platform, a discriminant model for assessing the financial situation and the probability of bankruptcy for innovative enterprises was built. The neural network model, which together with the analysis «if-then» gives an adequate forecast of the financial state of enterprises engaged in innovation activity, was substantiated and built. Five financial ratios (X1, X2, X3, X4 and X5) are selected and calculated for the analysis of the financial condition of 36 enterprises. For all the studied enterprises (both bankrupt and those against which bankruptcy proceedings were not initiated), the satisfactory forecast was for 30 out of 36 enterprises (83.33%), unsatisfactory for 2 enterprises (5.56%), in the gray zone there were 4 enterprises (11.11%). It is shown that the built neural network model provides forecasts of the financial condition of enterprises and the probability of their bankruptcy at a level significantly higher than discriminant models. The neural network model takes into account the specifics of domestic economic activity of enterprises, because it is built on the basis of financial data of Ukrainian enterprises.
Neural network performance on the bankruptcy classification problem
Computers & Industrial Engineering, 1993
Due to the recent changes in the world economy and as more firms, large and small, seem to fail now than ever, the bankruptcy classification problem is of increasing importance. Unfortunately, there are no easy-to-use and accurate tools to help make bankruptcy classification decisions. In this study, artificial neural network (ANN) technology is used to predict the going concern of firms based on financial ratios of 300 companies. The results indicate that ANN is as accurate or more accurate as a multiple regression model in predicting bankruptcy in addition to being easier to use and readily adapting to the changing environment.
A “User Friendly” Bankruptcy Prediction Model Using Neural Networks
Accounting and Finance Research, 2014
Belgium has faced an important number of corporate bankruptcies during the last decade. The aim of this paper is to develop a model that predicts bankruptcy using three financial ratios that are simple and easily available, even for small businesses. We used a sample of 3,728 Belgian Small and Medium Enterprises (SME's) including 1,864 businesses having been declared bankrupt between 2002 and 2012 and conducted a neural network analysis. Our results indicate that the neural network methodology based on three financial ratios that are simple and easily available as explanatory variables shows a good classification rate of more or less 80 percent. Results of this study may be of interest for financial institutions and for academics.