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.

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 Does The Development Context Affect Bankruptcy Prediction Models’ General Accuracy? A Comparative Analysis Of Four Multivariate Discriminant Models In The Italian Context

2016 ◽  
Vol 12 (10) ◽  
pp. 445 ◽  
Author(s):  
Salvatore Madonna ◽  
Greta Cestari ◽  
Francesca Callegari
Keyword(s):  
Prediction Models ◽  
Predictive Accuracy ◽  
Bankruptcy Prediction ◽  
Development Context ◽  
Wide Scale ◽  
Discriminant Models ◽  
Score Model ◽  
The One ◽  
Italian Context ◽  
Multivariate Discriminant

This research starts from the work by Madonna and Cestari (2015) that aimed at assessing the usability of three bankruptcy prediction models applied in contexts other than the ones of their elaboration, in order to evaluate their generalizability and the possibility to apply them in wide-scale investigations. We took the cue from that study to assess the usability of four bankruptcy prediction models, when applied to a sample with characteristics other than the ones related to their elaboration. We aimed at verifying the predictive accuracy and the discriminant capacity of the four models, basing on the assumption that the performances displayed by bankruptcy prediction models are usually better when they are applied in contexts similar to the one of their elaboration. Given this premise, we hypothesized that Italian models should perform better than the American one. In order to verify this hypothesis, we tested the four multivariate discriminant models twice: the predictive accuracy was tested applying the models on a sample of firms gone bankrupt within 2012 and 2014; the discriminant capacity on a sample equally composed by bankrupt and operating firms. Both samples were composed by firms located in Italy and operating in recent years. Hence the sample provided and the context of application were different from the ones of the models‘ elaboration. The results show that even if the Italian models were elaborated basing on contexts more similar to the one of the present application, the best performance is reached by the American Altman’s Z‘-Score model.

scholarly journals Bankruptcy prediction model for listed companies in Greece

2021 ◽  
Vol 18 (2) ◽  
pp. 166-180
Author(s):  
Evangelos Sfakianakis
Keyword(s):  
Discriminant Analysis ◽  
Prediction Model ◽  
Economic Value ◽  
Value Added ◽  
Bankruptcy Prediction ◽  
Manufacturing Firms ◽  
Financial Health ◽  
Policy Makers ◽  
Risk Managers ◽  
Multivariate Discriminant

This paper deals with the ever-increasing issue of bankruptcy prediction in distressed economies. Specifically, the aim of this study is to create a model by establishing a new set of predictor variables, which achieves significant discrimination among listed manufacturing firms in Greece, by using multivariate discriminant analysis (MDA). An equally balanced matched sample of 28 Greek-listed manufacturing firms was used in this study covering the distressed period from 2008 to 2015 (including all firms that went bankrupt between 2008–2015). It is found that the quick ratio, cash flow interest coverage, and economic value added (EVA) divided by total assets are significant for predicting bankruptcy in Greece. The discriminant analysis (DA) model comprised the aforementioned variables and correctly classified 96.43% of grouped cases 1 year before bankruptcy. The adjusted DA prediction model for two and three years before bankruptcy used the same variables and correctly classified 92.86% and 89.29% of grouped cases, respectively. Consequently, this mix of financial ratios achieved strong classification accuracy even three years before bankruptcy, captivating an overall picture of a firm’s financial health and providing a powerful tool for decision making to investors and risk managers in the banking section and economic policy makers.

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 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.

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 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 The Accuracy of the Altman, Ohlson, Springate and Zmejewski Models in Bankruptcy Predicting Trade Sector Companies in Indonesia

2020 ◽  
Vol 3 (1) ◽  
pp. 334-347 ◽  
Author(s):  
Sri Elviani ◽  
Ramadona Simbolon ◽  
Zenni Riana ◽  
Farida Khairani ◽  
Sri Puspa Dewi ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Prediction Models ◽  
Binary Logistic Regression ◽  
Bankruptcy Prediction ◽  
Accurate Model ◽  
Analysis Technique ◽  
Trade Sector ◽  
Altman Model

Bankruptcy prediction models continue to develop both in terms of forms, models, formulas, and analysis systems. Various bankruptcy prediction studies currently conducted aim to find the most appropriate and accurate bankruptcy prediction model to be used in predicting bankruptcy. This study aims to determine the most appropriate and accurate model in predicting the bankruptcy of 53 trade sector companies in Indonesia. The analysis technique used in this study is binary logistic regression. The results of this study prove that the most appropriate and accurate model in predicting bankruptcy of trade sector companies in Indonesia is the Springate model and the Altman model

scholarly journals Logit and Probit application for the prediction of bankruptcy in Slovak companies

Equilibrium ◽  
2017 ◽  
Vol 12 (4) ◽  
pp. 775-791 ◽  
Author(s):  
Maria Kovacova ◽  
Tomas Kliestik
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Statistical Methods ◽  
Prediction Models ◽  
Slovak Republic ◽  
Relevant Literature ◽  
Value Added ◽  
Bankruptcy Prediction ◽  
Data Set ◽  
Prediction Ability

Research background: Prediction of bankruptcy is an issue of interest of various researchers and practitioners since the first study dedicated to this topic was published in 1932. Finding the suitable bankruptcy prediction model is the task for economists and analysts from all over the world. forecasting model using. Despite a large number of various models, which have been created by using different methods with the aim to achieve the best results, it is still challenging to predict bankruptcy risk, as corporations have become more global and more complex. Purpose of the article: The aim of the presented study is to construct, via an empirical study of relevant literature and application of suitable chosen mathematical statistical methods, models for bankruptcy prediction of Slovak companies and provide the comparison of overall prediction ability of the two developed models. Methods: The research was conducted on the data set of Slovak corporations covering the period of the year 2015, and two mathematical statistical methods were applied. The methods are logit and probit, which are both symmetric binary choice models, also known as conditional probability models. On the other hand, these methods show some significant differences in process of model formation, as well as in achieved results. Findings & Value added: Given the fact that mostly discriminant analysis and logistic regression are used for the construction of bankruptcy prediction models, we have focused our attention on the development bankruptcy prediction model in the Slovak Republic via logistic regression and probit. The results of the study suggest that the model based on a logit functions slightly outperforms the classification accuracy of probit model. Differences were obtained also in the detection of the most significant predictors of bankruptcy prediction in these types of models constructed in Slovak companies.

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