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).

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.

Bankruptcy Prediction Using Quarterly Financial Statement Data

1992 ◽  
Vol 7 (3) ◽  
pp. 269-285 ◽  
Author(s):  
Jane Baldwin ◽  
G. William Glezen
Keyword(s):  
Classification Accuracy ◽  
Prediction Models ◽  
Statistical Significance ◽  
Bankruptcy Prediction ◽  
Securities And Exchange Commission ◽  
Fiscal Year ◽  
Annual Data ◽  
Multiple Discriminant Analysis ◽  
Quarterly Data ◽  
Annual Period

The purposes of this study were to assess the usefulness of quarterly data for predicting bankruptcy and to determine if the earlier prediction by quarterly bankruptcy models can be obtained without the sacrifice of accuracy achieved by annual bankruptcy models. A sample of 40 public firms entering bankruptcy from 1977 to 1983 was matched on the basis of fiscal year, industry, and asset size with 40 nonbankrupt firms. Quarterly financial data were obtained from the firms' 10-Q reports filed with the Securities and Exchange Commission (SEC), whereas annual data were obtained from the 10-K reports. Multiple discriminant analysis was used to derive quarterly bankruptcy prediction models for each of the three quarters before and after the last annual period preceding bankruptcy and for the last annual period preceding bankruptcy. Twenty-four financial ratios that were identified in previous studies as being useful for bankruptcy prediction were selected as the independent variables in the stepwise discriminant process. The classification accuracy, using alternative assumptions regarding prior probability of bankruptcy and cost of misclassification and the statistical significance of the quarterly models for each of the six quarters tested, indicated that quarterly data are useful for predicting bankruptcy. There was no statistical evidence to suggest that the classification accuracy of the annual model was superior to that of the quarterly model. This finding suggests that more timely bankruptcy predictions can be provided to investors, creditors, and auditors by quarterly models without the loss of accuracy provided by annual models.

Corporate bankruptcy prediction models applied to emerging economies

2007 ◽  
Vol 17 (4) ◽  
pp. 295-311 ◽  
Author(s):  
Ariel R. Sandin ◽  
Marcela Porporato
Keyword(s):  
Information Content ◽  
Emerging Economies ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Data Availability ◽  
Business Failure ◽  
Multiple Discriminant Analysis ◽  
Public Companies ◽  
Content Type ◽  
Profitability Ratios

PurposeThe paper's aim is to test the usefulness of ratio analysis to predict bankruptcy in a period of stability of an emerging economy, such as the case of Argentina in the 1990s.Design/methodology/approachFinancial profiles of 22 bankrupt and healthy companies are examined and a model is built using the multiple discriminant analysis technique, thus providing comparability with previous studies.FindingsThe set of models tested in this paper show that the financial data of Argentine companies in the 1990s do have information content, but the model to use depends on the preferences of the decision maker. Comparing models it is observed a common use of solvency ratios in terms of total assets and profitability ratios in terms of sales.Research limitations/implicationsData availability constitutes the primary limitation of this and similar studies, here is reflected in the sample size: 11 healthy and 11 bankrupt.Practical implicationsThe model can be used to assist investors, creditors, and regulators in Argentina and other emerging economies to predict business failure. The Z ′‐score model of Altman can be used for public companies in emerging economies because it pays attention to solvency indicators, but in rapid changing environment, profitability ratios should also be considered.Originality/valueThe incremental information content of profitability and solvency in predicting bankruptcy is examined and a simple and reliable failure prediction model for large Argentinean firms is developed. Also this paper offers a classification method that is publicly available to all investors and creditors interested in Argentinean companies.

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

Improving management as a way to increase labour productivity

Upravlenie ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 24-30
Author(s):  
A. O. Ivanov
Keyword(s):  
Productivity Growth ◽  
Labour Productivity ◽  
Production Costs ◽  
Managerial Practices ◽  
Industrial Enterprises ◽  
Labour Productivity Growth ◽  
Period 2 ◽  
Main Factors ◽  
The Moment

The article gives an overview, performs analysis and classification of successful managerial practices applied at Russian industrial enterprises in the framework of the national project “Labour productivity and employment support”. The paper emphasizes the main factors of labour productivity growth as follows: investment policy, growth of human capital, and efficient use of managerial capital of enterprise. In order to determine the need of enterprises to increase labour productivity, the author proposes four universal criteria that signal the existing inefficiency even before the loss of competitiveness: 1) the dynamics of labour productivity in the company is not positive during a given period; 2) the company is behind competitors by labour productivity indicator; 3) the company is behind competitors by labour productivity growth rates indicator for a certain period; 4) unit production costs rise. These criteria allow you to take into account the situation both within the enterprise and in comparison with other enterprises. Each criteria can be considered separately or in combination with the others, applied to enterprises of different industries, specialization, and scale. Criteria indicate the direction of development in which the company is experiencing difficulties at the moment, or may experience them in the future.

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

CLASSIFICATION OF PRODUCTION RISKS OF AN INDUSTRIAL ENTERPRISE

2021 ◽  
pp. 26-30
Author(s):  
V. F. Bezjazychnyi ◽  
E. V. Kiselev ◽  
V. A. Troshkin
Keyword(s):  
Risk Management ◽  
Management System ◽  
Industrial Enterprise ◽  
Management Systems ◽  
Enterprise Management ◽  
Industrial Enterprises ◽  
Main Production ◽  
Enterprise Management System ◽  
Production Risks

Improvement of modern management systems for industrial enterprises involves the active use of risk management methods that are accompanied by their activities. Issues of production risks and their place in the General enterprise management system are discussed in the article. There is a classification of the main production risks and possible risk management methods applicable in the activities of an industrial enterprise.

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