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).

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 Bankruptcy Prediction And Its Effect On Stock Prices As Impact Of The COVID-19 Pandemic

2021 ◽  
Vol 25 ◽  
pp. 567-582
Author(s):  
Muhammad Ramadhani Kesuma ◽  
Felisitas Defung ◽  
Anisa Kusumawardani
Keyword(s):  
Prediction Model ◽  
Strong Evidence ◽  
Stock Prices ◽  
Stock Price ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Business Sector ◽  
The World ◽  
Hotel Sector

As COVID-19 pandemic hit the world since early 2020, one business sector in many countries that struggling to survive is tourism and its derivatives, such as restaurants and hotels.  This study aims to examine the accuracy of the Springate and Grover models in predicting bankruptcy, as well as the effect on stock prices of tourism, restaurant, and hotel sector in Indonesia. The results show that all sample tourism, restaurant, and hotel companies are bankrupt under the Springate model, whilst according to Grover's model the findings are varied during the study period. Furthermore, the Grover model is implied to be more accurate than the Springate model. The effect of both prediction models on stock price appears dissimilar. Springate's prediction model suggests a positive and significant effect on stock prices, whereas there is no strong evidence about the effect of Grover’s prediction model.

scholarly journals Analisis Model Prediksi Kebangkrutan Altman Z-Score. Springate dan Zmijewski untuk Menilai Kelangsungan Usaha PT Enseval Putera Megatrading Tbk

2018 ◽  
Vol 12 (2) ◽  
pp. 128-132
Author(s):  
Choirunnisa Nurahayu ◽  
Evi Yuniarti ◽  
Nurmala Nurmala
Keyword(s):  
Economic Development ◽  
Cash Flow ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Business Continuity ◽  
Z Score ◽  
Financial Condition ◽  
Analysis Technique ◽  
Not At Risk ◽  
A Company

The economic development of PT EPM Tbk in 2011 to 2016 did not improved, because in 2011 to 2016 the cash flow statement of PT EPM Tbk. decreased. This writing aims to determine the prediction of bankruptcy at PT EPM Tbk with the Altman Z-Score, Springate and Zmijewski Score models to assess the business continuity of PT EPM Tbk from 2011 to 2016. The analysis technique used is the Altman Z-Score bankruptcy prediction model, Springate and the Zmijewski Score for various types of companies. Based on the results of the analysis of the three bankruptcy prediction models Altman Z-Score, Springate Score and Zmijewski Score shows that the business continuity assessment of PT Enseval Putera Megatrading Tbk in 2011 to 2016 is a company in good financial condition or a healthy company and is not at risk The Z-Score is more than the Altman Z-Score, Springate Score and Zmijewski Score standards.Keywords: Altman Z-Score, Springate Score, Zmijewski Score and Business Continuity

scholarly journals Failure prediction of government funded start-up firms

2017 ◽  
Vol 14 (2) ◽  
pp. 296-306 ◽  
Author(s):  
Oliver Lukason ◽  
Kaspar Käsper
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Financial Development ◽  
Logistic Regression Analysis ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Financial Ratios ◽  
Development Patterns ◽  
Start Up

This study aims to create a prediction model that would forecast the bankruptcy of government funded start-up firms (GFSUs). Also, the financial development patterns of GFSUs are outlined. The dataset consists of 417 Estonian GFSUs, of which 75 have bankrupted before becoming five years old and 312 have survived for five years. Six financial ratios have been calculated for one (t+1) and two (t+2) years after firms have become active. Weighted logistic regression analysis is applied to create the bankruptcy prediction models and consecutive factor and cluster analyses are applied to outline the financial patterns. Bankruptcy prediction models obtain average classification accuracies, namely 63.8% for t+1 and 67.8% for t+2. The bankrupt firms are distinguished with a higher accuracy than the survived firms, with liquidity and equity ratios being the useful predictors of bankruptcy. Five financial patterns are detected for GFSUs, but bankrupt GFSUs do not follow any distinct patterns that would be characteristic only to them.

scholarly journals Comparative Analysis of Financial Sustainability Using the Altman Z-Score, Springate, Zmijewski and Grover Models for Companies Listed at Indonesia Stock Exchange Sub-Sector Telecommunication Period 2014 – 2019

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Samrony Eka Fauzi ◽  
◽  
Sudjono Sudjono ◽  
Ahmad Badawi Saluy
Keyword(s):  
Sampling Method ◽  
Prediction Models ◽  
Stock Exchange ◽  
Bankruptcy Prediction ◽  
Annual Reports ◽  
Financial Sustainability ◽  
Comparative Testing ◽  
Purposive Sampling ◽  
Financial Condition ◽  
Analysis Models

This study aims to compare the best bankruptcy prediction models between Altman, Springate, Zmijewski and Grover models against companies listed on the Indonesian stock exchange in the telecommunications sub-sector for the 2014-2019 period. The purposive sampling method is used to obtain a sample of companies with the following criteria: Companies listed on the Indonesian stock exchange, the telecommunications sub-sector, the company has conducted an IPO in 2010, the company is obedient in reporting annual reports from 2014 - 2019 and the company is free from delisting issues. There are 4 companies that meet the purposive sampling criteria, namely PT. Telkom TBK, PT. Indosat TBK. PT. XL Axiata TBK and PT. Smartfren TBK. The data used in this research is secondary panel data. The results showed that only PT. Telkom which is in a healthy financial condition. Meanwhile, PT. Indosat, PT. XL Axiata and PT. Smartfren is consistently in an unhealthy condition based on the analysis of the Altman and Springate models. The calculation of Zmijewski's model and Grover's model gave inconsistent results. Comparative testing of the four bankruptcy analysis models resulted in the Altman, Springate and Grover models recording accurate results but Altman modelling is the best because it is an accurate, consistent, and tested model both descriptively and statistically.

scholarly journals Asset Quality as a Predictor of Rural Bank Bankruptcy in Indonesia

2021 ◽  
Vol 4 (1) ◽  
pp. 44-45
Author(s):  
Hesti Budiwati ◽  
Ainun Jariah
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Classification Accuracy ◽  
Prediction Models ◽  
Predictor Variable ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Asset Quality ◽  
Productive Assets

The study aims to form a bankruptcy prediction model of rural bank in Indonesia at a time variation of 1 quarter (MP1), 2 quarters (MP2), 4 quarters (MP4), and 8 quarters (MP8) before bankruptcy. The quality of productive assets as a predictor variable consist of CEA, CEAEA, and NPL. The condition of rural bank bankrupt and non bankrupt as a dependent variable. The analytical method used is logistic regression followed by testing the model accuration. The population of this study is rural bank in Indonesia. The sample used was 241 rural banks that consist of 41 bankrupt rural banks and 200 non bankrupt rural banks. The data used are the quarterly financial statements of 2006 to 2019. The study result showed that of the four prediction models that successfully built, the 1 quarter (MP1) is the most feasible and accurate used as bankruptcy prediction model of rural banks in Indonesia that formed by CEAEA and NPL ratio. The MP1 has a classification accuracy of 93,8% at the level of modelling with cut off point of 0,29 and it has a classification accuracy of 83,93% at the level of validation with cut off point of 0,12. Based on those advantage, the MP1 was chosen as a model that able to predict the bankruptcy of rural bank in Indonesia.

scholarly journals Bankruptcy prediction model for private limited companies of Lithuania

Ekonomika ◽  
2016 ◽  
Vol 95 (1) ◽  
pp. 134-152 ◽  
Author(s):  
Gediminas Šlefendorfas
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Common Type ◽  
Primary Input ◽  
Primary Sample ◽  
Limited Company ◽  
Stepwise Method ◽  
Bankruptcy Probability ◽  
Multivariate Discriminant

The paper is mainly devoted to the bankruptcy prediction models and their ability to assess a bankruptcy probability for Lithuanian companies. The study showed that the most common type of companies in Lithuania is a private limited company, therefore, the main objective was to analyse such companies’ financial information and by using these results, create a new bankruptcy prediction model, which would allow to predict the bankruptcy probability as accurately as possible. 145 companies (73 already bankrupt and 72 still operating) were chosen as a primary sample and by using multivariate discriminant analysis stepwise method a linear function ZGS has been created. To achieve that, 156 different financial ratios were selected as a primary input data by using correlation calculation between bankruptcy and still operating companies and Mann – Whitney U test techniques. The results showed that 89% of companies were classified correctly, which states that the model is strong enough to predict bankruptcy probability for private limited companies operating in Lithuania in a sufficient accuracy.

Identifying impact of variables in deep learning models on bankruptcy prediction of construction contractors

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Youjin Jang ◽  
Inbae Jeong ◽  
Yong K. Cho
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Shapley Value ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Content Type ◽  
Macroeconomic Variables ◽  
Input Variables ◽  
The Impact

PurposeThe study seeks to identify the impact of variables in a deep learning-based bankruptcy prediction model, which has achieved superior performance to other prediction models but cannot easily interpret hidden processes.Design/methodology/approachThis study developed three LSTM-RNN–based models that predicted the probability of bankruptcy before 1, 2 and 3 years using financial, the construction market and macroeconomic variables as input variables. Then, the impacts of the input variables that affected prediction accuracy in each model were identified by using Shapley value and compared among the three models. This study also investigated the prediction accuracy using variants of input variables grouped sequentially by high-impact ranking.FindingsThe results showed that the prediction accuracies were largely impacted by “housing starts” in all models. As the prediction period increased, the effects of macroeconomic variables on prediction accuracy increased, whereas the impact of “return on assets” on prediction accuracy decreased. It also found that the “current ratio” and “debt ratio” significantly influenced the prediction accuracies in all models. Also, the results revealed that similar prediction accuracies could be achieved using only 8, 10, and 10 variables out of a total of 18 variables for the 1-, 2-, and 3-year prediction models, respectively.Originality/valueThis study provides a Shapley value-based approach to identify how each input variable in a deep-learning bankruptcy prediction model. The findings of this study can not only assist in obtaining better insights into the underlying concept of bankruptcy but also use to select variables by removing those identified as less significant.

scholarly journals Analisis Model Prediksi Kebangkrutan Altman Z-Score. Springate dan Zmijewski untuk Menilai Kelangsungan Usaha PT Enseval Putera Megatrading Tbk

2018 ◽  
Vol 12 (2) ◽  
pp. 128
Author(s):  
Choirunnisa Nurahayu ◽  
Evi Yuniarti ◽  
Nurmala Nurmala
Keyword(s):  
Economic Development ◽  
Cash Flow ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Business Continuity ◽  
Z Score ◽  
Financial Condition ◽  
Analysis Technique ◽  
Not At Risk ◽  
A Company

The economic development of PT EPM Tbk in 2011 to 2016 did not improved, because in 2011 to 2016 the cash flow statement of PT EPM Tbk. decreased. This writing aims to determine the prediction of bankruptcy at PT EPM Tbk with the Altman Z-Score, Springate and Zmijewski Score models to assess the business continuity of PT EPM Tbk from 2011 to 2016. The analysis technique used is the Altman Z-Score bankruptcy prediction model, Springate and the Zmijewski Score for various types of companies. Based on the results of the analysis of the three bankruptcy prediction models Altman Z-Score, Springate Score and Zmijewski Score shows that the business continuity assessment of PT Enseval Putera Megatrading Tbk in 2011 to 2016 is a company in good financial condition or a healthy company and is not at risk The Z-Score is more than the Altman Z-Score, Springate Score and Zmijewski Score standards.

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