Development of Bankruptcy Prediction Model for Latvian Companies (original) (raw)
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Actuality of Bankruptcy Prediction Models used in Decision Support System
International Journal of Computers Communications & Control, 2013
In the current conditions, the global economy is in a crisis situation. In terms of crisis management this article supports the Romanian companies. This article analyses some classical bankruptcy prediction models used in Decision Support Systems in order to validate or invalidate them in the actual Romanian economical conditions. It is essential to take the right decision at the right time, to help the company overcome an eventual moment of crisis, such as insolvency or even bankruptcy. Our study is based on the financial ratio of 60 Romanian companies, between 2005 and 2009. The firms are classified in two categories: bankrupted companies (B) and non-bankrupted companies (N-B).
Corporate Financial Distress Prediction of Slovak Companies : Z-Score Models vs . Alternatives
2016
In the recent paper "The portability of Altman’s Z-score model to predicting corporate financial distress of Slovak companies" published in 2016 in Technological and Economic Development of Economy, its authors claim that, under some assumptions, "Altman’s bankruptcy formula is portable into the Slovak economic conditions and useful for predicting financial difficulties". The main goal of our paper is to compare the ported Z-score prediction models from their paper, which are based on linear discriminant analysis, to prediction models based on other standard supervised classification methods, e.g. logistic regression, decision trees, random forests. In our comparison, we take into account accuracy as well as interpretability of the models. In order to assure comparability of results we use the same data set as it was utilized in the abovementioned paper.
2018
One of the issues helping make investment decisions is appropriate tools and models to evaluate financial situation 0f the organization. By means of these tools, investors can analyze financial situation of the organization and identify financial distress or an ideal condition, they become aware of making decisions to invest in appropriate conditions. The main objective of this study is to evaluate the power of using data mining models which are among new tools of prediction. This tool was used to predict the bankruptcy of companies listed in Tehran stock exchange and comparison the results with the Altman model as one of the prevalent methods of prediction the bankruptcy of a company. The research data includes information of all companies listed in Tehran stock exchange during the years 2013 to 2018 subjected to Title 141 of the law of trade and were bankrupt. Variables used in both models were five financial ratios. The data mining models on the average in the base year had a ...
Predicting Financial Distress for Romanian Companies
Technological and Economic Development of Economy
Using a moderately large number of financial ratios, we tried to build models for classifying the companies listed on the Bucharest Stock Exchange into low and high risk classes of financial distress. We considered four classification techniques: Support Vector Machines, Decision Trees, Bayesian logistic regression and Fisher linear classifier, out of which the first two proved to have the highest prediction accuracy. Classifiers were trained and tested on randomly drown samples and on four different databases built starting from the initial financial indicators. As the literature related to the topic on Romanian data is very scarce, our study, by using a variety of methods and combining feature selection and principal components analysis, brings a new approach to predicting financial distress for Romanian companies.
Decision tree based model of business failure prediction for Polish companies
Oeconomia Copernicana, 2019
Research background: The issue of predicting the financial situation of companies is a relatively young field of economic research. Its origin dates back to the 30's of the 20th century, but constant research in this area proves the currentness of this topic even today. The issue of predicting the financial situation of a company is up to date not only for the company itself, but also for all stakeholders. Purpose of the article: The main purpose of this study is to create new prediction models by using the method of decision trees, in achieving sufficient prediction power of the generated model with a large database of real data on Polish companies obtained from the Amadeus database. Methods: As a result of the development of artificial intelligence, new methods for predicting financial failure of the company have been introduced into financial prediction analysis. One of the most widely used data mining techniques in this field is the method of decision trees. In the paper, we...
The Bankruptcy Forecasting Model of Hungarian Enterprises
Advances in Economics and Business, 2018
The SME sector is really important for the Hungarian economy. In our analysis, we had a closer look at the publicly accessible version of Altman's Z-score bankruptcy forecast model for companies not quoted on the Stock Exchange together with the original and the modified, adjusted Springate bankruptcy prediction model. The adjusted Springate model regarded only 37% of the companies having gone bankrupt in real as insolvent, while the justified Altman Z-score model was able to identify only 46% of the stable ongoing firms. The variance analysis could not detect any correlations between the phenomenon of bankruptcy and financial types. By means of logistic regression, we managed to create a model that can forecast solvency for the examined enterprises with a probability of 78%. In the last part of our research, we were dealing with teaching artificial intelligence and creating decision trees based on neural network. Even by means of the first bankruptcy forecast model based on decision trees, a more efficient predicting system was gained than by using any other methods. We assume that only the decision tree made up by using artificial intelligence is efficient in forecasting bankruptcy of all the examined models.
IRJET- Machine Learning based Analysis of Industry Finances Subjected to Bankruptcy
IRJET, 2020
Bankruptcy prediction is the art of predicting bankruptcy and various measures of financial distress of public firms.The rationale for developing and predicting the financial distress of a company is to develop a predictive model used to forecast the financial condition of a company by combining several econometric variables of interest to the researcher. It is a vast area of finance and accounting research. The importance of the area is due in part to the relevance for creditors and investors in evaluating the likelihood that a firm may go bankrupt. The quantity of research is also a function of the availability of data and for that matter here the public data of five polish companies up to five years of financial records have been used to train the model.
A Comprehensive Review of Corporate Bankruptcy Prediction in Hungary
Journal of Risk and Financial Management 13(35), 2020
The article provides a comprehensive review regarding the theoretical approaches, methodologies and empirical researches of corporate bankruptcy prediction, laying emphasis on the 30-year development history of Hungarian empirical results. In ex-socialist countries corporate bankruptcy prediction became possible more than 20 years later compared to the western countries, however, based on the historical development of corporate bankruptcy prediction after the political system change it can be argued that it has already caught up to the level of international best practice. Throughout the development history of Hungarian bankruptcy prediction, it can be tracked how the initial, small, cross-sectional sample and classic methodology-based bankruptcy prediction has evolved to today's corporate rating systems meeting the requirements of the dynamic, through-the-cycle economic capital calculation models. Contemporary methodological development is characterized by the domination of artificial intelligence, data mining, machine learning, and hybrid modelling. On the basis of empirical results, the article draws several normative proposals how to assemble a bankruptcy prediction database and select the right classification method(s) to accomplish efficient corporate bankruptcy prediction.
A data mining approach to predict companies’ financial distress
International Journal of Financial Engineering
Financial distress and companies’ failure have always been a complicated and intriguing problem for businesses. Because of the unfavorable impacts of financial distress on companies and societies, accounting and finance researchers around the world are thinking of ways to anticipate corporate financial distress. Several models are provided in the literature for predicting financial distress. This research develops nonlinear decision tree and linear discriminant analysis models to predict financial distress of companies listed in Iranian Stock Exchange during 2010 to 2015. The drivers are firms’ financial ratios, intellectual capital and performance indicators. According to the results, intellectual capital and financial performance indices have no informational content in decision tree model. Comparing the result show that both models predict financial distress with 90.9% and 81.8% accuracy, respectively. Moreover, the difference between the accuracy of the models however is not mea...
Sustainability, 2020
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 repu...