Towards Automation of Short-Term Financial Distress Detection: A Real-World Case Study

2021 ◽  
pp. 1-35
Author(s):  
Kristina Sutiene ◽  
Kestutis Luksys ◽  
Kristina Kundeliene
Keyword(s):  
Real World ◽  
Financial Distress ◽  
Bankruptcy Prediction ◽  
Financial Health ◽  
Real World Application ◽  
Depth Analysis ◽  
Recent Developments ◽  
Big Data Technologies ◽  
Potential Applications ◽  
A Company

The bankruptcy prediction research domain continues to evolve with the main aim of developing a model suitable for real-world application in order to detect early stages of financial distress of a company. The recent developments in computing, combined with the potential applications of big data technologies and artificial intelligence solutions have already made possible the integration of timely and recent information about business activities in order to monitor the financial health of companies. Therefore, this paper focuses on the predictions made a few months prior to the potential default of a company with the aim of identifying the determinants that signal about the insolvency. The experiments include in-depth analysis of model performances using different dataset configurations.

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.

scholarly journals Feature Selection for Bankruptcy Prediction

2012 ◽  
pp. 158-178
Author(s):  
A. Gaspar-Cunha ◽  
F. Mendes ◽  
J. Duarte ◽  
A. Vieira ◽  
B. Ribeiro ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Feature Selection ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Decision Makers ◽  
Support Vector ◽  
Financial Health ◽  
Vector Machines ◽  
Feature Selection Approach ◽  
A Company

In this work a Multi-Objective Evolutionary Algorithm (MOEA) was applied for feature selection in the problem of bankruptcy prediction. This algorithm maximizes the accuracy of the classifier while keeping the number of features low. A two-objective problem, that is minimization of the number of features and accuracy maximization, was fully analyzed using the Logistic Regression (LR) and Support Vector Machines (SVM) classifiers. Simultaneously, the parameters required by both classifiers were also optimized, and the validity of the methodology proposed was tested using a database containing financial statements of 1200 medium sized private French companies. Based on extensive tests, it is shown that MOEA is an efficient feature selection approach. Best results were obtained when both the accuracy and the classifiers parameters are optimized. The proposed method can provide useful information for decision makers in characterizing the financial health of a company.

Feature Selection for Bankruptcy Prediction

2010 ◽  
Vol 1 (2) ◽  
pp. 71-91 ◽  
Author(s):  
A. Gaspar-Cunha ◽  
F. Mendes ◽  
J. Duarte ◽  
A. Vieira ◽  
B. Ribeiro ◽  
...  
Keyword(s):  
Feature Selection ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Decision Makers ◽  
Support Vector ◽  
Financial Health ◽  
Vector Machines ◽  
Selection For ◽  
Feature Selection Approach ◽  
A Company

In this work a Multi-Objective Evolutionary Algorithm (MOEA) was applied for feature selection in the problem of bankruptcy prediction. This algorithm maximizes the accuracy of the classifier while keeping the number of features low. A two-objective problem, that is minimization of the number of features and accuracy maximization, was fully analyzed using the Logistic Regression (LR) and Support Vector Machines (SVM) classifiers. Simultaneously, the parameters required by both classifiers were also optimized, and the validity of the methodology proposed was tested using a database containing financial statements of 1200 medium sized private French companies. Based on extensive tests, it is shown that MOEA is an efficient feature selection approach. Best results were obtained when both the accuracy and the classifiers parameters are optimized. The proposed method can provide useful information for decision makers in characterizing the financial health of a company.

scholarly journals Bridging the gap between laboratory and application in photocatalytic water purification

2019 ◽  
Vol 9 (3) ◽  
pp. 533-545 ◽  
Author(s):  
Gylen Odling ◽  
Neil Robertson
Keyword(s):  
Water Treatment ◽  
Water Purification ◽  
Real World ◽  
Real World Application ◽  
Recent Developments ◽  
Number Of Publications

Despite a large number of publications in the field, photocatalytic water treatment is still somewhat disconnected from real world application and we highlight recent developments to address this.

Returns to Scale as an Established Scaling Indicator: Always a Good Advisor?

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Andreas Dellnitz ◽  
Wilhelm Rödder
Keyword(s):  
Data Envelopment Analysis ◽  
Real World ◽  
Returns To Scale ◽  
Increasing Returns ◽  
Data Envelopment ◽  
Productivity Improvement ◽  
Increasing Returns To Scale ◽  
Numerical Examples ◽  
Real World Application ◽  
A Company

AbstractIn data envelopment analysis (DEA), returns to scale (RTS) are a widely accepted instrument for a company to reveal its activity scaling potentials. In the case of increasing returns to scale (IRS), a company learns that upsizing activities improves its productivity. For decreasing returns to scale (DRS), the instrument likewise should depict a downsizing force, again for improving productivity. Unfortunately, here the classical RTS concept shows misbehavior. Under certain circumstances, it is the wrong indicator for scaling activities and even hides respective productivity improvement potentials. In this paper, we study this phenomenon, using the DEA concept, and illustrate it via little numerical examples and a real-world application consisting of 37 Brazilian banks.

scholarly journals New approaches for the financial distress classification in agribusiness

2013 ◽  
Vol 61 (4) ◽  
pp. 1177-1182 ◽  
Author(s):  
Jan Vavřina ◽  
David Hampel ◽  
Jitka Janová
Keyword(s):  
Financial Distress ◽  
Bankruptcy Prediction ◽  
Set Covering ◽  
Data Envelopment ◽  
Financial Health ◽  
It Industry ◽  
Data Set ◽  
New Approaches ◽  
Recent Financial Crisis ◽  
Visegrad Group

After the recent financial crisis the need for unchallenged tools evaluating the financial health of enterprises has even arisen. Apart from well-known techniques such as Z-score and logit models, a new approaches were suggested, namely the data envelopment analysis (DEA) reformulation for bankruptcy prediction and production function-based economic performance evaluation (PFEP). Being recently suggested, these techniques have not yet been validated for common use in financial sector, although as for DEA approach some introductory studies are available for manufacturing and IT industry. In this contribution we focus on the thorough validation calculations that evaluate these techniques for the specific agribusiness industry. To keep the data as homogeneous as possible we limit the choice of agribusiness companies onto the area of the countries of Visegrad Group. The extensive data set covering several hundreds of enterprises were collected employing the database Amadeus of Bureau van Dijk. We present the validation results for each of the four mentioned methods, outline the strengths and weaknesses of each approach and discuss the valid suggestions for the effective detection of financial problems in the specific branch of agribusiness.

scholarly journals Bankruptcy Practice in Countries of Visegrad Four

2017 ◽  
Vol 14 (1) ◽  
pp. 108-118
Author(s):  
Maria Misankova ◽  
Katarina Zvarikova ◽  
Jana Kliestikova
Keyword(s):  
Financial Distress ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Major Influence ◽  
Simple Task ◽  
Data Set ◽  
Prediction Ability ◽  
Score Model ◽  
A Company ◽  
Prediction Capability

Abstract Numerous economists and analysts from all over the world have been trying to find an appropriate method to assess company health and to predict its eventual financial distress for many years. No economy is a small isolated subject, and the bankruptcy of a company can cause through its stakeholders′ significant impact on the sustainable economic development. Otherwise, companies are very complicated entities, and it is not a simple task to estimate company’s future development. Together with the best-known Z-Score model of bankruptcy prediction developed by Altman, numerous models worldwide that are based on different methodologies have been developed. We assume that individual state’s economy has major influence on the final form of these models as well as there are several common characteristics between Slovak economy and economy of countries of Visegrad Four. Therefore, we applied chosen bankruptcy prediction models developed in countries of Visegrad Four on the set of Slovak companies and validated their prediction ability in specific condition of the Slovak economy. On the basis of the provided calculations, we compared gained results with the prediction capability of other popular prediction models also applied on the data set of Slovak companies. Through this, we pointed out the importance of the development of unique bankruptcy prediction model, which will be constructed in the specific condition of individual countries, and highlighted the weak forecasting ability of foreign models.

scholarly journals Financial Distress and Bankruptcy Prediction: an Empirical Analysis of the Manufacturing Industry in Albania

2020 ◽  
Vol 17 ◽  
Keyword(s):  
Empirical Analysis ◽  
Financial Distress ◽  
Manufacturing Industry ◽  
Predictive Accuracy ◽  
Bankruptcy Prediction ◽  
Financial Position ◽  
Reduce Risk ◽  
Score Model ◽  
The Empirical Analysis ◽  
A Company

Bankruptcy is the conclusive affirmation of the inability of a company to support and endure current operations given its current financial position and debt obligations. If bankruptcy could be expected with affordable precision ahead of time, managers and investors of companies may have the possibility to take action to secure their companies, reduce risk and loss of business and perhaps even avoid bankruptcy itself. The aim of this paper is to test the suitability and predictive accuracy of the Altman Z-Score model in the Albanian manufacturing industry. After performing the empirical analysis, the conclusion is that this model clearly fails to effectively predict financial distress and bankruptcy and it isn’t reliable in our case. Lastly, a logistic regression model is proposed, which is more adequate for the Albanian context.

scholarly journals ANALISIS FINANCIAL DISTRESS DENGAN MENGGUNAKAN BANKRUPTCY PREDICTION MODELS PADA EMITEN ROKOK PERIODE 2014-2018

2021 ◽  
Vol 4 (1) ◽  
pp. 16-27
Author(s):  
Ani Wahyuningsih ◽  
Hartono Hartono ◽  
Rini Armin
Keyword(s):  
Early Warning ◽  
Financial Distress ◽  
Prediction Models ◽  
Stock Exchange ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Correct Prediction ◽  
Financial Health ◽  
Z Score ◽  
Financial Difficulties

ABSTRACT Financial Distress is a condition of financial difficulties where if this happens to the company foa along period of time, the company is in the initial stages before bankruptcy. Bankruptcy is a state of being or a situation in which company failed to or not able to meet obligations because firm experienced lack of. If the company goes bankrupt there will be many parties who are harmed. Therefore it is necessary to conduct financial distress analysis for early warning. The research aims to determine the financial health of the cigarette sub-sector companies by analyzing financial distress using three bankruptcy prediction models with Altman Z-Score, Springate, Grover and to determine which of these three models has the highest level of accuracy. The data used in this research is the company’s financial statements published on the Indonesia Stock Exchange website. The population in this research is the cigarette sub-sector companies listed on the Indonesia Stock Exchange in the 2014-2018 period. Based on the result of research shows that in the calculation Altman and Springate models, PT. Bentoel International Investama in the category of the company experiencing symptoms of bankruptcy. While in the Grover model calculation, all companies fall into category healthy companies. Of the three models that have the highest level of accuracy are Altman and Springate models by one hundred percent. This shows that Altman and Springate models have the correct prediction of the company correctly.

scholarly journals Assessment of financial health of Slovak family businesses using models predicting financial distress

2021 ◽  
Vol 115 ◽  
pp. 02010
Author(s):  
Mária Trúchliková
Keyword(s):  
Risk Management ◽  
Family Business ◽  
Financial Distress ◽  
Financial Statements ◽  
Family Businesses ◽  
Financial Viability ◽  
Financial Health ◽  
Financial Problems ◽  
Different Types ◽  
A Company

The financial health of a company can be seen as the ability to maintain a balance against changing conditions in the environment and companies should pay more attention to the financial viability and risk management. There many models for predicting of financial problems of the companies, especially Altman, Ohlson or Zmijewski are the most cited ones. The main objective of the article is the review and assessment of the level of financial health of Slovak family business in selected industries. The data was obtained from Finstat database and financial statements from 2017, 2018 and 2019 were analysed. For assessing the financial health of selected family businesses 3 models predicting financial distress were used: Kralicek Quick Test, Taffler model and Virág-Hajdu model. The results show how many family businesses are facing to the financial problems using different types of predicting models.

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