scholarly journals Bankruptcy prediction models as a source of assessment of the company’s financial situation

2018 ◽  
Vol 2 (2) ◽  
pp. 159
Author(s):  
Justyna Beata Barlik
Keyword(s):  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Financial Situation

scholarly journals Bankruptcy prediction in Visegrad group countries using multiple discriminant analysis

Equilibrium ◽  
2018 ◽  
Vol 13 (3) ◽  
pp. 569-593 ◽  
Author(s):  
Tomas Kliestik ◽  
Jaromir Vrbka ◽  
Zuzana Rowland
Keyword(s):  
Discriminant Analysis ◽  
Prediction Models ◽  
Value Added ◽  
Bankruptcy Prediction ◽  
Net Income ◽  
Multiple Discriminant Analysis ◽  
Financial Situation ◽  
The Future ◽  
A Company ◽  
Visegrad Group

Research background: The problem of bankruptcy prediction models has been a current issue for decades, especially in the era of strong competition in markets and a constantly growing number of crises. If a company wants to prosper and compete successfully in a market environment, it should carry out a regular financial analysis of its activities, evaluate successes and failures, and use the results to make strategic decisions about the future development of the business. Purpose of the article: The main aim of the paper is to develop a model to reveal the un-healthy development of the enterprises in V4 countries, which is done by the multiple discriminant analysis. Methods: To conduct the research, we use the Amadeus database providing necessary financial and statistical data of almost 450,000 enterprises, covering the year 2015 and 2016, operating in the countries of the Visegrad group. Realizing the multiple discriminant analysis, the most significant predictor and the best discriminants of the corporate prosperity are identified, as well as the prediction models for both individual V4 countries and complex Visegrad model. Findings & Value added: The results of the research reveal that the prediction models use the combination of same financial ratios to predict the future financial development of a company. However, the most significant predictors are current assets to current liabilities ratio, net income to total assets ratio, ratio of non-current liabilities and current liabilities to total assets, cash and cash equivalents to total assets ratio and return of equity. All developed models have more than 80 % classification ability, which indicates that models are formed in accordance with the economic and financial situation of the V4 countries. The research results are important for companies themselves, but also for their business partners, suppliers and creditors to eliminate financial and other corporate risks related to the un-healthy or unfavorable financial situation of the company.

scholarly journals Forecasting bankruptcy in the wood industry

2020 ◽  
Author(s):  
Tomasz Noga ◽  
Krzysztof Adamowicz
Keyword(s):  
Prediction Model ◽  
Final Stage ◽  
Management System ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Wood Industry ◽  
Financial Situation ◽  
Financial Condition ◽  
Enterprise Models ◽  
Wood Sector

AbstractThe assessment of a company’s financial condition is an effective tool, which supports the management system. Nowadays a number of models are available, most often multi-branch ones, which are able to predict the financial situation of an enterprise. Models solely intended for just one line of business are a rarity. As far as the wood sector is concerned, no homogenous model suited to the sector has been created. The article aims to present the final stage of research dealing with predicting bankruptcy in the wood sector. The bankruptcy prediction model presented in this paper, called the model for forecasting bankruptcy of wood enterprises (FMWE), has been developed specifically for the wood sector. The process of model construction was presented and the correctness of forecasts built with the use of FMWE was verified. The predictions were based on 1-, 2- or 3-year periods. Furthermore, the effectiveness of the FMWE projections was compared to the 10 most popular bankruptcy prediction models used in Poland. It was observed that in comparison with other prediction models, FMWE predictions for this particular industry indicate greater credibility, up to 90%, for 1-year and 2-year predictions.

scholarly journals Assessment applicability of selected models of multiple discriminant analyses to forecast financial situation of Polish wood sector enterprises

2017 ◽  
Vol 59 (1) ◽  
pp. 59-67 ◽  
Author(s):  
Krzysztof Adamowicz ◽  
Tomasz Noga
Keyword(s):  
Prediction Models ◽  
Difficult Problem ◽  
Bankruptcy Prediction ◽  
Net Income ◽  
Multiple Discriminant Analysis ◽  
Financial Situation ◽  
Industrial Enterprises ◽  
Discriminant Analyses ◽  
Wood Sector

Abstract In the last three decades forecasting bankruptcy of enterprises has been an important and difficult problem, used as an impulse for many research projects (Ribeiro et al. 2012). At present many methods of bankruptcy prediction are available. In view of the specific character of economic activity in individual sectors, specialised methods adapted to a given branch of industry are being used increasingly often. For this reason an important scientific problem is related with the indication of an appropriate model or group of models to prepare forecasts for a given branch of industry. Thus research has been conducted to select an appropriate model of Multiple Discriminant Analysis (MDA), best adapted to forecasting changes in the wood industry. This study analyses 10 prediction models popular in Poland. Effectiveness of the model proposed by Jagiełło, developed for all industrial enterprises, may be labelled accidental. That model is not adapted to predict financial changes in wood sector companies in Poland. The generally known Altman model showed the greatest effectiveness in the identification of enterprises at risk of bankruptcy. However, that model was burdened with one of the greatest errors in the classification of healthy enterprises as sick. The best effectiveness in the identification of enterprises not threatened with bankruptcy was found for forecasts prepared using the Prusak 2 model. However, forecasts based on those models were characterised by erroneous classification of sick companies as healthy. The model best fit to predict the financial situation of Polish wood sector companies was the Poznań model Pz = 3.562 · X1 + 1.588 · X2 + 4.288 · X3 + 6.719 · X4 - 2.368 where: X1 - net income / total assets; X2 - (current assets - stock) / current liabilities; X3 - fixed capital / total assets X4 - income from sales / sales revenue).

Bankruptcy Prediction Models: A Bet on Artificial Neural Networks

2012 ◽  
Vol 3 (2) ◽  
pp. 48-50
Author(s):  
Ana Isabel Velasco Fernández ◽  
◽  
Ricardo José Rejas Muslera ◽  
Juan Padilla Fernández-Vega ◽  
María Isabel Cepeda González
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Artificial Neural

scholarly journals Modelling bankruptcy prediction models in Slovak companies

2017 ◽  
Vol 39 ◽  
pp. 01013 ◽  
Author(s):  
Maria Kovacova ◽  
Jana Kliestikova
Keyword(s):  
Prediction Models ◽  
Bankruptcy Prediction

scholarly journals Family Businesses in Global Economy and Evaluating their Financial Health Using Bankruptcy Models

2021 ◽  
Vol 129 ◽  
pp. 03031
Author(s):  
Maria Truchlikova
Keyword(s):  
Financial Distress ◽  
Global Economy ◽  
Agricultural Sector ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Value Added ◽  
Business Environment ◽  
Bankruptcy Prediction ◽  
Family Businesses ◽  
Financial Health

Research background: Predicting and assessing financial health should be one of the most important activities for each business especially in context of turbulent business environment and global economy. The financial sustainability of family businesses has a direct and significant influence on the development and growth of the economy because they still represent the backbone of the economy and play an important role in national economies worldwide accounting. Purpose of the article: We used in this article the financial distress and bankruptcy prediction models for assessing financial status of family businesses in agricultural sector. The aim of the paper is to compare models developed by using three different methods to identify a model with the highest predictive accuracy of financial distress and assess financial health. Methods: The data was obtained from Finstat database. For assessing the financial health of selected family businesses bankruptcy models were used: Chrastinova’s CH-Index, Gurcik’s G-Index (defined for Slovak agricultural enterprises) and Altman Z-score. Findings & Value added: This article summarizes existing models and compares results of assessing financial health of family businesses using three different models.

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