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Proceedings ArticleDOI: 10.1109/ICISC.2018.8399072

Bankruptcy prediction using neural networks

01 Jan 2018-
Abstract: Bankruptcy prediction models are one of the most sought-after tools in financial decision making performed by pioneer financial institution. Evolution in computation technique have ushered an era where Artificial Intelligence (AI) and machine learning form the backbone of bankruptcy prediction models. However, use of more recent training algorithm like artificial neural network and random forest have proven to be more efficient over the traditional algorithms for a standard algorithm utilized in Bankruptcy Prediction Model. This paper proposes a prediction model utilizing Artificial Neural Network (ANN) and random forest as learning algorithm. A given Dataset will be utilised for analysis and result of analysis from traditional models will considered as a benchmark for comparison with the performance of the new prediction model. more

Topics: Bankruptcy prediction (70%), Artificial neural network (56%), Bankruptcy (53%) more

Open accessJournal ArticleDOI: 10.3390/SU12103954
12 May 2020-Sustainability
Abstract: Predicting the risk of financial distress of enterprises is an inseparable part of financial-economic analysis, helping investors and creditors reveal the performance stability of any enterprise. The acceptance of national conditions, proper use of financial predictors and statistical methods enable achieving relevant results and predicting the future development of enterprises as accurately as possible. The aim of the paper is to compare models developed by using three different methods (logistic regression, random forest and neural network models) in order to identify a model with the highest predictive accuracy of financial distress when it comes to industrial enterprises operating in the specific Slovak environment. The results indicate that all models demonstrated high discrimination accuracy and similar performance; neural network models yielded better results measured by all performance characteristics. The outputs of the comparison may contribute to the development of a reputable prediction model for industrial enterprises, which has not been developed yet in the country, which is one of the world’s largest car producers. more

20 Citations

Open accessJournal ArticleDOI: 10.3390/SU131810154
11 Sep 2021-Sustainability
Abstract: This study aims to provide a bibliometric analysis of business failure research, recognise the main existing research topics and establish future research challenges. The results, based on a sample of 588 articles, show that the number of published papers and citations has grown steadily, especially in the last 14 years. The most productive and relevant journals, countries, institutions and authors are presented using bibliometric performance indicators. In addition, through the graphical mapping of strategic diagrams, this study identifies the most significant research trends and proposes several directions for future research. The results of this research may be helpful for beginner researchers and experts in business failure, as they contribute to bringing clarity to this line of investigation. These results reveal all the aspects involved in business failure research, analysing its temporal and methodological characterisation, and the most prolific authors who have participated in its study (see, i.e., H. Li), leading journals (see, i.e., Expert Systems with Applications) or academic institutions that have headed the scientific analysis of this business phenomenon. Likewise, it has been possible to identify three main areas in which the research on business failure has been focused: business, management and accounting; economics, econometrics and finance; and social sciences. In addition, a complete, synthesised and organised summary of the various definitions, perspectives and research trends are presented. more

Topics: Business failure (57%), Entrepreneurship (54%)

5 Citations

Proceedings ArticleDOI: 10.1109/ICKII46306.2019.9042639
Tsung-Nan Chou1Institutions (1)
01 Jul 2019-
Abstract: For investors seeking solutions to optimize their portfolio of assets to generate profits and minimize losses, choosing a sophisticated model to evaluate the risk of corporate financial distress will be crucial to support their asset management and investment decisions. Both the machine learning and the newly developed deep learning techniques have been employed to construct bankruptcy prediction models for decades. However, applying the deep learning models might increase the predictive accuracy in exchange for losing model interpretability, because the structure and parameters of the model are not easy to provide accountability for investors. In this study, a hybrid approach integrating the decision tree with the deep neural network was proposed to provide a compromise solution for investors. The decision tree was adopted as the primary model to provide explainable ability, while the deep neural network was chosen to improve predictive accuracy. The decision fusion of two models was designed with the compensatory and non-compensatory approaches. The hybrid model was implemented by concatenating the deep neural network to the selected branches of decision tree that perform poor predictive accuracy during model training. The empirical results showed that the predictive accuracy of the deep neural network and the decision tree were 80% and 87% respectively, and the overall accuracy was improved to 91% by the hybrid model. more

Topics: Decision tree (61%), Deep learning (57%), Bankruptcy prediction (56%) more

3 Citations

Open accessJournal ArticleDOI: 10.1016/J.PROCS.2021.09.132
Pham Quoc Khang1, Klaudia Kaczmarczyk1, Piotr Tutak1, Paweł Golec1  +4 moreInstitutions (2)
Abstract: As a critical consideration in investment decisions, stock liquidity has significance for all stakeholders in the market. It also has implications for the stock market’s growth. Liquidity enables investors and issuers to meet their requirements regarding investment, financing or hedging, reducing investment costs and the cost of capital. The aim of this paper is to develop the machine learning models for liquidity prediction. The subject of research is the Vietnamese stock market, focusing on the recent years - from 2011 to 2019. Vietnamese stock market differs from developed markets and emerging markets. It is characterized by a limited number of transactions, which are also relatively small. The Multilayer Perceptron, Long-Short Term Memory and Linear Regression models have been developed. On the basis of the experimental results, it can be concluded that the LSTM model allows for prediction characterized by lowest value of MSE. The results of research can be used for developing the methods for decision support on stock markets. more

Topics: Market liquidity (68%), Stock market (66%), Investment decisions (57%) more

1 Citations

Journal ArticleDOI: 10.1007/S10479-021-04236-4
Abstract: The purpose of the paper is to propose two new procedures that deal with overfitting problem using neural techniques for variable selection and business failure prediction. The first procedure, called HVS-AUC, is based simultaneously on (i) the backward search, (ii) the HVS measure (Heuristic for Variable Selection), and (iii) the AUC criterion (Area Under Curve). The second procedure, called forward-AUC, is based on (i) the forward search and (ii) the AUC criterion. Using a sample of bankrupt and non-bankrupt firms in France, the implementation of the procedures using neural networks shows that the profitability, the repayment capacity, the taxation, and the importance of investment have a strong explanatory power in bankruptcy prediction. These procedures also provide more parsimonious and more efficient models compared to Linear Discriminant Analysis. more

Topics: Bankruptcy prediction (62%), Overfitting (54%), Linear discriminant analysis (54%) more


Journal ArticleDOI: 10.1111/J.1540-6261.1968.TB00843.X
01 Sep 1968-Journal of Finance
Abstract: ACADEMICIANS SEEM to be moving toward the elimination of ratio analysis as an analytical technique in assessing the performance of the business enterprise. Theorists downgrade arbitrary rules of thumb, such as company ratio comparisons, widely used by practitioners. Since attacks on the relevance of ratio analysis emanate from many esteemed members of the scholarly world, does this mean that ratio analysis is limited to the world of \"nuts and bolts\"? Or, has the significance of such an approach been unattractively garbed and therefore unfairly handicapped? Can we bridge the gap, rather than sever the link, between traditional ratio \"analysis\" and the more rigorous statistical techniques which have become popular among academicians in recent years? The purpose of this paper is to attempt an assessment of this issue-the quality of ratio analysis as an analytical technique. The prediction of corporate bankruptcy is used as an illustrative case.' Specifically, a set of financial and economic ratios will be investigated in a bankruptcy prediction context wherein a multiple discriminant statistical methodology is employed. The data used in the study are limited to manufacturing corporations. A brief review of the development of traditional ratio analysis as a technique for investigating corporate performance is presented in section I. In section II the shortcomings of this approach are discussed and multiple discriminant analysis is introduced with the emphasis centering on its compatibility with ratio analysis in a bankruptcy prediction context. The discriminant model is developed in section III, where an initial sample of sixty-six firms is utilized to establish a function which best discriminates between companies in two mutually exclusive groups: bankrupt and non-bankrupt firms. Section IV reviews empirical results obtained from the initial sample and several secondary samples, the latter being selected to examine the reliability of the discriminant more

Topics: Bankruptcy prediction (74%), Altman Z-score (63%), Financial ratio (59%) more

9,776 Citations

Proceedings ArticleDOI: 10.1109/IJCNN.1990.137710
Marcus D. Odom1, Ramesh Sharda1Institutions (1)
17 Jun 1990-
Abstract: A neural network model is developed for prediction of bankruptcy, and it is tested 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 more

721 Citations

Open accessJournal ArticleDOI: 10.1109/72.935101
Amir F. Atiya1Institutions (1)
Abstract: The prediction of corporate bankruptcies is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. This work presents two contributions. First we review the topic of bankruptcy prediction, with emphasis on neural-network (NN) models. Second, we develop an NN bankruptcy prediction model. Inspired by one of the traditional credit risk models developed by Merton (1974), we propose novel indicators for the NN system. We show that the use of these indicators in addition to traditional financial ratio indicators provides a significant improvement in the (out-of-sample) prediction accuracy (from 81.46% to 85.5% for a three-year-ahead forecast). more

Topics: Bankruptcy prediction (68%), Financial risk management (61%), Credit risk (57%) more

633 Citations

Abstract: This paper uses artificial neural networks (ANNs), multi-state ordered logit and nonparametric multiple discriminant analysis (NPDA) for predicting the three-state outcome of bankruptcy filing. The study compares the classification accuracy of these procedures. It differs from previous studies on predicting financial distress by focusing on the firm after the filing of bankruptcy using accounting data, market data, and court-related information. Following the filing and through court approval the bankruptcy is resolved as firms are either acquired by other firms, emerging as independent operating entities, or liquidated. Distinguishing this three-state outcome is more complex than discriminating between healthy and financially distressed firms. Models suggested in previous studies for predicting the two-group financial distress perform poorly for our three-state scenario. Therefore, we develop models which focus on characteristics relevant for the bankruptcy resolution. We use a sample of 237 publicly traded firms which have complete data. For the entire sample and estimation samples, ANNs provide significantly better three-state classification than logit and NPDA. However, for some holdout samples the differences in classification accuracies are statistically insignificant. © 1997 John Wiley & Sons, Ltd. more

55 Citations

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