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Zeitschriftenartikel zum Thema „Business Bankruptcy Prediction“

Autor: Grafiati

Veröffentlicht am 7. Juni 2025

Zuletzt aktualisiert am 2. August 2025

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1

Syed Nor, Sharifah Heryati, Shafinar Ismail, and Yap Bee Wah. "Personal Bankruptcy Prediction Using Logistic Regression Model." Information Management and Business Review 16, no. 3S(I)a (2024): 366–78. http://dx.doi.org/10.22610/imbr.v16i3s(i)a.4139.

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According to the Insolvency Department of Malaysia, as of December 2023, 233,483 Malaysians are currently involved in bankruptcy cases due to their defaults on hire purchase loans, credit card loans, personal loans, housing loans, and business loans. This is indeed a critical issue because the growing number of personal bankruptcy cases will hurt the Malaysian economy as well as society. From an individual's economic perspective, bankruptcy minimizes their chances of getting a job. Apart from that, their accounts will be frozen, they will lose control of their properties and assets, and they w

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Syed Nor, Sharifah Heryati, Shafinar Ismail, and Bee Wah Yap. "Personal bankruptcy prediction using decision tree model." Journal of Economics, Finance and Administrative Science 24, no. 47 (2019): 157–70. http://dx.doi.org/10.1108/jefas-08-2018-0076.

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Purpose Personal bankruptcy is on the rise in Malaysia. The Insolvency Department of Malaysia reported that personal bankruptcy has increased since 2007, and the total accumulated personal bankruptcy cases stood at 131,282 in 2014. This is indeed an alarming issue because the increasing number of personal bankruptcy cases will have a negative impact on the Malaysian economy, as well as on the society. From the aspect of individual’s personal economy, bankruptcy minimizes their chances of securing a job. Apart from that, their account will be frozen, lost control on their assets and properties

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Lukason, Oliver, and Kaspar Käsper. "Failure prediction of government funded start-up firms." Investment Management and Financial Innovations 14, no. 2 (2017): 296–306. http://dx.doi.org/10.21511/imfi.14(2-2).2017.01.

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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

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Lawrence, Judy Ramage, Surapol Pongsatat, and Howard Lawrence. "The Use Of Ohlson's O-Score For Bankruptcy Prediction In Thailand." Journal of Applied Business Research (JABR) 31, no. 6 (2015): 2069. http://dx.doi.org/10.19030/jabr.v31i6.9468.

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Business failure is a major concern to all parties involved and can create high costs and heavy losses. If bankruptcy can be predicted with reasonable accuracy ahead of time, firms can better protect their businesses and can take action to minimize risk and loss of business, and perhaps even prevent the bankruptcy itself. Bankruptcy prediction in thailand is important because business in thailand has historically operated on a system of trust where one person doing business trusts the other to perform as agreed upon in written and oral contracts. The threat of bankruptcy tends to diminish that

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Iwan, Mohamad. "BANKRUPTCY PREDICTION MODEL WITH ZETAc OPTIMAL CUT-OFF SCORE TO CORRECT TYPE I ERRORS." Gadjah Mada International Journal of Business 7, no. 1 (2005): 41. http://dx.doi.org/10.22146/gamaijb.5563.

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This research examines financial ratios that distinguish between bankrupt and non-bankrupt companies and make use of those distinguishing ratios to build a one-year prior to bankruptcy prediction model. This research also calculates how many times the type I error is more costly compared to the type II error. The costs of type I and type II errors (cost of misclassification errors) in conjunction to the calculation of prior probabilities of bankruptcy and non-bankruptcy are used in the calculation of the ZETAc optimal cut-off score. The bankruptcy prediction result using ZETAc optimal cut-off

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Vukčević, Milica, Milan Lakićević, Boban Melović, Tamara Backović, and Branislav Dudić. "Modern models for predicting bankruptcy to detect early signals of business failure: Evidence from Montenegro." PLOS ONE 19, no. 5 (2024): e0303793. http://dx.doi.org/10.1371/journal.pone.0303793.

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This paper explores predicting early signals of business failure using modern models for bankruptcy prediction. It reviews how continuous operations enhance market value, strengthening competitiveness and reputation among stakeholders. The study involves medium and large companies in the Montenegrin market from 2015 to 2020, comprising 30 bankrupt and 70 financially stable firms. Logistic regression is also employed to create a logit model for early detection of bankruptcy signals in companies. This research establishes the empirical validity of modern models in predicting business failure in

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Bogdan, Siniša, Luka Šikić, and Suzana Bareša. "Predicting bankruptcy based on the full population of Croatian companies." Ekonomski pregled 72, no. 5 (2021): 643–69. http://dx.doi.org/10.32910/ep.72.5.1.

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This paper analyses the bankruptcy prediction based on the population of companies representative of the total business sector in Croatia. The representativity of the sample is achieved through the propensity score matching of the full population of bankrupt and similar non-bankrupt companies. The robust estimation of bankruptcy prediction is carried out through the multiple discriminant analysis (MDA) and logistic regression (logit). The results indicate high classification accuracy of both models, but more favourable performance of the logit estimation. Overall accuracy of the MDA model was

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Del Castillo García, Agustín, and Sergio Manuel Fernández Miguélez. "Predictive potential of the bankruptcy global models in the tourism industry." Tourism & Management Studies 17, no. 4 (2021): 23–31. http://dx.doi.org/10.18089/tms.2021.170402.

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The globalisation process and the recent economic crises have increased the development of models to identify the factors related to business bankruptcy. The tourism industry is not immune to this concern, and in the previous literature, bankruptcy prediction models are generally focused on hotels or restaurants. However, there are no experiences of global models for tourism companies. This study develops a global bankruptcy prediction model capable of predicting any activities carried out in the tourism industry with high precision. To this end, a sample of 406 Spanish companies that have dev

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Aruldoss, Martin, Miranda Lakshmi Travis, and V. Prasanna Venkatesan. "A reference model for business intelligence to predict bankruptcy." Journal of Enterprise Information Management 28, no. 2 (2015): 186–217. http://dx.doi.org/10.1108/jeim-09-2013-0069.

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Purpose – Bankruptcy is a financial failure of a business or an organization. Different kinds of bankruptcy prediction techniques are proposed to predict it. But, they are restricted as techniques in predicting the bankruptcy and not addressing the associated activities like acquiring the suitable data and delivering the results to the user after processing it. This situation demands to look for a comprehensive solution for predicting bankruptcy with intelligence. The paper aims to discuss these issues. Design/methodology/approach – To model Business Intelligence (BI) solution for BP the conce

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Lindsay, David H., and Annhenrie Campbell. "A Chaos Approach To Bankruptcy Prediction." Journal of Applied Business Research (JABR) 12, no. 4 (2011): 1. http://dx.doi.org/10.19030/jabr.v12i4.5779.

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<span>Chaotic systems, although deterministic and predictable over short horizons, appear to be random. This study applied chaos theory to bankruptcy prediction using a pair-matched sample of bankrupt firms. Given that healthy systems exhibit more chaos than unhealthy ones, it was hypothesized that the returns of firms nearing bankruptcy would exhibit significantly less chaos, measured with Lyapunov exponents, than at an earlier period. Successful univariate and multivariate bankruptcy prediction models were then constructed using the obtained Lyapunov exponents.</span>

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Klepáč, Václav, and David Hampel. "Prediction of Bankruptcy with SVM Classifiers Among Retail Business Companies in EU." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 64, no. 2 (2016): 627–34. http://dx.doi.org/10.11118/actaun201664020627.

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Article focuses on the prediction of bankruptcy of the 850 medium-sized retail business companies in EU from which 48 companies gone bankrupt in 2014 with respect to lag of the used features. From various types of classification models we chose Support vector machines method with linear, polynomial and radial kernels to acquire best results. Pre-processing is enhanced with filter based feature selection like Gain ratio, Chi-square and Relief algorithm to acquire attributes with the best information value. On this basis we deal with random samples of financial data to measure prediction accurac

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Barbullushi, Erjola, Albana Kastrati, and Aurora Kinka. "Insolvency Pattern Trends in Albanian Enterprises: An in-depth Investigation." WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS 22 (May 2, 2025): 845–59. https://doi.org/10.37394/23207.2025.22.74.

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Predicting bankruptcy has become a significant focus for researchers, especially during periods of economic instability such as the COVID-19 pandemic and the ongoing Russian-Ukrainian conflict. Early detection of financial distress enables stakeholders to make more informed decisions, minimizing the adverse effects of business failures. In Albania, studies on bankruptcy prediction are scarce, particularly for economic entities such as limited liability companies and joint-stock companies, which dominate the market. This paper aims to develop a bankruptcy prediction model tailored to the Albani

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Kanász, Róbert, Peter Gnip, Martin Zoričák, and Peter Drotár. "Bankruptcy prediction using ensemble of autoencoders optimized by genetic algorithm." PeerJ Computer Science 9 (June 8, 2023): e1257. http://dx.doi.org/10.7717/peerj-cs.1257.

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The prediction of imminent bankruptcy for a company is important to banks, government agencies, business owners, and different business stakeholders. Bankruptcy is influenced by many global and local aspects, so it can hardly be anticipated without deeper analysis and economic modeling knowledge. To make this problem even more challenging, the available bankruptcy datasets are usually imbalanced since even in times of financial crisis, bankrupt companies constitute only a fraction of all operating businesses. In this article, we propose a novel bankruptcy prediction approach based on a shallow

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Mehreen, Mehreen, Maran Marimuthu, Samsul Karim, and Amin Jan. "Proposing a Multidimensional Bankruptcy Prediction Model: An Approach for Sustainable Islamic Banking." Sustainability 12, no. 8 (2020): 3226. http://dx.doi.org/10.3390/su12083226.

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The main purpose of this study is to conceptualize a sustainable banking model for Islamic banking by blending three essential business aspects namely financial performance, Islamic corporate governance, and sustainability practices dimension. In the case of Islamic banking, evidence shows that a Shariah-based bankruptcy prediction model for apprehending the true bankruptcy prediction is over-sighted. This study offers an efficient Shariah-based bankruptcy prediction model by first, reviewing the previously applied conventional bankruptcy prediction models; secondly, by developing and proposin

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GAVUROVA, Beata, Miroslava PACKOVA, Maria MISANKOVA, and Lubos SMRCKA. "PREDICTIVE POTENTIAL AND RISKS OF SELECTED BANKRUPTCY PREDICTION MODELS IN THE SLOVAK BUSINESS ENVIRONMENT." Journal of Business Economics and Management 18, no. 6 (2017): 1156–73. http://dx.doi.org/10.3846/16111699.2017.1400461.

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In our study, we focused on the assessment of four bankruptcy prediction models, to figure out which model is most appropriate in the conditions of the Slovak business environment. Based on the previous research within the Slovak conditions, we set a portfolio of 4 models to be assessed: Altman model (1984), Ohlson model (1980), indexes IN01 and IN05 that were validated on the sample of 700 Slovak companies. Based on previous studies we expected that IN indexes are superior to Ohlson and Altman model. The excellency of our research lies in validation and assessing the accuracy of bankruptcy pr

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Sfakianakis, Evangelos. "Bankruptcy prediction model for listed companies in Greece." Investment Management and Financial Innovations 18, no. 2 (2021): 166–80. http://dx.doi.org/10.21511/imfi.18(2).2021.14.

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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

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Zetzsche de Abreu Gomes, Amanda, Cristina Lopes, and Rui Bertuzi da Silva. "Advancements in Bankruptcy Prediction Models and Bibliometric Analysis." European Conference on Research Methodology for Business and Management Studies 23, no. 1 (2024): 82–91. http://dx.doi.org/10.34190/ecrm.23.1.2428.

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Since the economic downturn of the 1930s, there has been a growing interest in predicting company bankruptcies. Though not a new topic, the prospect of business bankruptcy has gained increasing relevance due to globalisation. This study explores various methodologies employed in predicting bankruptcy. Preventing bankruptcies also bolsters economic stability by averting the adverse effects of insolvency on the community. Companies with a solid and flexible economic foundation are more likely to succeed. This article reviews existing literature, discusses prevalent predictive models, and present

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Vlaović-Begović, Sanja, Stevan Tomašević, and Dajana Ercegovac. "Selection of variables in the function of improving the bankruptcy prediction model." Ekonomika 68, no. 3 (2022): 45–59. http://dx.doi.org/10.5937/ekonomika2203045v.

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The significance of early disclosure of the probability of launching a bankruptcy proceeding leads the authors to develop a model of high prediction power. In this way, the authors use different variables and statistical tools, and techniques. The impact of the economic environment and data availability limits the introduction of certain variables in bankruptcy prediction models. The paper aims to explore attitudes in existing literature regarding the selection of variables used to develop models for predicting bankruptcy, their characteristics, limitations, and impact on the power of predicti

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Hašková, Simona. "Fuzzy-based non-bankruptcy trend forecast." SHS Web of Conferences 132 (2022): 01004. http://dx.doi.org/10.1051/shsconf/202213201004.

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Many companies face an economic downturn due to the Covid-19 pandemic outbreak, which makes their future uncertain. The practical aim of the paper is to establish a procedure for an effective prediction of a business tendency to bankrupt in the short-term period. The tool is a three-stage fuzzy model formulated in the theoreticalmethodological part and applied on the real data of an examined company. The model input parameters are objective and subjective measured data between 2008-2020 of a nature affecting the output. The output is an interval of subjectively expected values determining the

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Moreira, Cândido Jorge Carvalho Peres, Mário Alexandre Guerreiro Antão, Catarina Carvalho Peres Terrinca, and João Manuel Afonso Geraldes. "EFFECTIVENESS OF THE BUSINESS FAILURE PREDICTION MODELS IN THE IBERIAN TRANSPORT COMPANIES." International Journal of Professional Business Review 9, no. 2 (2024): e04392. http://dx.doi.org/10.26668/businessreview/2024.v9i2.4392.

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Purpose: validate, in the existing models, its effectiveness and ability to provide useful information for decision-making, allowing the choice of one that presented as the best alternative for predicting bankruptcy for companies in the transport economic sector (NACE H) up to 6 years before it occurs. Theoretical Framework: in the last decades, since Beaver's (1966) preliminary work in bankruptcy prediction, followed by Altman (1968) models, numerous authors developed different techniques and models to this purpose. Of all the techniques used and developed in almost 60 years of bankruptcy pre

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Blanco-Oliver, A., A. Irimia-Dieguez, M. D. Oliver-Alfonso, and M. J. Vázquez-Cueto. "Hybrid model using logit and nonparametric methods for predicting micro-entity failure." Investment Management and Financial Innovations 13, no. 3 (2016): 35–46. http://dx.doi.org/10.21511/imfi.13(3).2016.03.

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Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods (Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and Multilayer Perceptron, of

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Yadav, Rakesh, Ameya Patil, and Rajeev Sengupta. "An analysis of Satyam case using bankruptcy and fraud detection models." SocioEconomic Challenges 7, no. 4 (2023): 24–35. http://dx.doi.org/10.61093/sec.7(4).24-35.2023.

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Bankruptcy, which occurs due to inability of a business, to repay its debts and obligations has caught the interest of investors and practitioners alike. Predicting bankruptcy prior to the occurrence of event has become crucial in the field of investment and lending, especially in the light of events such as the global financial crisis of 2008. Early bankruptcy prediction models used traditional statistical techniques via financial ratios. Since then there has been a constant endeavour to develop models with enhanced predictive performance. Satyam Inc. was Indian listed business which went ban

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Máté, Domicián, Hassan Raza, and Ishtiaq Ahmad. "Comparative Analysis of Machine Learning Models for Bankruptcy Prediction in the Context of Pakistani Companies." Risks 11, no. 10 (2023): 176. http://dx.doi.org/10.3390/risks11100176.

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This article presents a comparative analysis of machine learning models for business failure prediction. Bankruptcy prediction is crucial in assessing financial risks and making informed decisions for investors and regulatory bodies. Since machine learning techniques have advanced, there has been much interest in predicting bankruptcy due to their capacity to handle complex data patterns and boost prediction accuracy. In this study, we evaluated the performance of various machine learning algorithms. We collect comprehensive data comprising financial indicators and company-specific attributes

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Karpac, Dusan, and Viera Bartosova. "Prediction of Financial Health of Business Entities of Selected Sector Using Balance Analysis II. by Rudolf Doucha and Verification of Its Predictive Ability through ROC." SHS Web of Conferences 91 (2021): 01006. http://dx.doi.org/10.1051/shsconf/20219101006.

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Forecasting business failure is a worldwide known term, in a global notion, and there is a lot of prediction models constructed to compute financial health of a company and, by that, state whether a company inclines to financial boom or bankruptcy. A healthy financial management of a business entity is very important for the proper operation of the business, and it is therefore very important to know how to assess financial health and to anticipate possible problems that will be easier to eliminate in advance. Globalized prediction models compute financial health of companies, but the vast maj

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Giriūniene, Gintare, Lukas Giriūnas, Mangirdas Morkunas, and Laura Brucaite. "A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania." Economies 7, no. 3 (2019): 82. http://dx.doi.org/10.3390/economies7030082.

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Different economic environments differ in their characteristics; this prevents the usage of the same bankruptcy prediction models under different conditions. Objectively, the abundance of bankruptcy prediction models gives rise to the idea that these models are not in compliance with the changing business conditions in the market and do not meet the increasing complexity of business tasks. The purpose of this study is to assess the suitability of existing bankruptcy prediction models and the possibilities to increase the effectiveness of their application. In order to analyze theoretical aspec

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Kušter, Denis. "Financial ratio indicators as early predictors of business failure: Evidence from Serbia." Anali Ekonomskog fakulteta u Subotici, no. 00 (2022): 5. http://dx.doi.org/10.5937/aneksub2200005k.

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The problem of corporate bankruptcies has intrigued the scientific community for years due to its practical significance. There is no country whose economic well-being is not affected by business failures. The research problem stems from the lack of analyses related to the issue of business failures in the the Republic of Serbia. The main aim of this research paper is to determine whether ratio indicators are relevant in predicting business failure one, two and three years before bankruptcy proceedings start. The research was conducted on a sample of 100 companies from the territory of Serbia.

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Supitriyani, Supitriyani, Astuti Astuti, and Khairul Azwar. "Implementation of Springate, Altman, Grover and Zmijewski Models in Measuring Financial Distress." INTERNATIONAL JOURNAL OF TRENDS IN ACCOUNTING RESEARCH 3, no. 1 (2022): 001–8. http://dx.doi.org/10.54951/ijtar.v3i1.169.

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Companies that have improved company performance will have good prospects in the future. In addition, the company also needs some good strategy and planning to stay afloat in running its business. One of the strategies carried out by the company is to avoid the occurrence of a level of financial difficulties (financial distress). The goal of the study was to determine bankruptcy predictions and find out the most accurate methods for measuring bankruptcy among Springate, Altman, Grover and Zmijewski models. The data collection techniques used are documentation techniques while the data analysis

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Halek, Vitezslav. "Practical application of the CCB model in Czechia." Zbornik radova Ekonomskog fakulteta u Rijeci: časopis za ekonomsku teoriju i praksu/Proceedings of Rijeka Faculty of Economics: Journal of Economics and Business 39, no. 2 (2021): 299–323. http://dx.doi.org/10.18045/zbefri.2021.2.299.

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This research aimed to present a new bankruptcy prediction model and apply this original prediction method in practice. The Come Clean Bankruptcy (or CCB) model uses relevant financial indicators and ratios to detect the signs of impending financial distress in time so that the management can take appropriate measures to avoid it. The model was applied to the data reported by 199 entities operating in the textile/clothing industry in the Czech Republic. Analyzing data reported for the previous seven years enabled us to predict which companies are more likely to end in a difficult financial sit

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Sági, Judit, Nick Chandler, and Csaba Lentner. "Family businesses and predictability of financial strength: a Hungarian study." Problems and Perspectives in Management 18, no. 2 (2020): 476–89. http://dx.doi.org/10.21511/ppm.18(2).2020.39.

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The aim of this study is to examine how bankruptcy prediction models forecast financial strength for family businesses. Three predictive tests are used to study financial strength for three consecutive years (2016, 2017 and 2018) for a sample of 462,200 active Hungarian companies using the Amadeus database and expert data. Complex statistical model tests for credit assessment (bankruptcy predictions) are performed by size and ownership of the companies. It is found that the revised Altman model is impeded by a superfluous high weighting on net working capital; therefore, IN05 Quick Test predic

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Moreira, Cândido Jorge Carvalho Peres, Mário Alexandre Guerreiro Antão, João Manuel Afonso Geraldes, and Catarina Carvalho Peres Terrinca. "Business bankruptcy prediction: the multisectorial models’ efficiency in the Old Blue Economy Iberian companies’." Revista de Gestão e Secretariado (Management and Administrative Professional Review) 14, no. 9 (2023): 15459–81. http://dx.doi.org/10.7769/gesec.v14i9.2803.

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In the risk evaluation by the stakeholders it’s crucial to understand the causes of bankruptcy or breach in corporate sustainability, as well as more efficient ways of predicting it. These techniques, mainly supported by financial analysis, over time, have given rise to a diversity of approaches in particular to the Multivariate Discriminant Analysis. We selected, 54 iberian companies from that sector, divided them in two samples, one of the bankrupt companies and another of the healthy ones, matched by size and business' volume. Were applied 21 multisectoral models from a diversity of countri

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Mckee, Thomas E. "Predicting Bankruptcy via Induction." Journal of Information Technology 10, no. 1 (1995): 26–36. http://dx.doi.org/10.1177/026839629501000104.

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Research has consistently shown that auditors disclose going-concern problems for less than 50% of all business failures. As evidenced by widespread litigation, investors and creditors believe that this performance needs to be improved. This paper reports on research which analysed financial data for 60 public companies via an inductive inferencing algorithm. The end result was a simple and theoretically consistent model that was 97% accurate in predicting bankruptcy. Auditors, investors and creditors may find the model useful in improving their bankruptcy prediction capability.

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Lee, Ming-Chang. "Business Bankruptcy Prediction Based on Survival Analysis Approach." International Journal of Computer Science and Information Technology 6, no. 2 (2014): 103–19. http://dx.doi.org/10.5121/ijcsit.2014.6207.

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Kovacova, Maria, Martin Lacny, and Jaroslav Gonos. "Impact of earnings management on bankruptcy prediction." SHS Web of Conferences 129 (2021): 03016. http://dx.doi.org/10.1051/shsconf/202112903016.

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Research background: Managers of the companies intentionally manipulate business earnings to achieve the required status of the company. Earnings management is a legal and widely preferred phenomenon of business finance that financial managers use to maintain and improve the company´s competitiveness. The consequence of these activities is to provide a positive view for the owners, encourage the profitability for the creditor and the investors as well as demonstrate economic strengths to competitors. Consequently, these activities lead to the modification of financial statements of the compani

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Saputra, Dasriyan, and Fazria Nur Daluh Arisyah. "ANALISIS PERBANDINGAN KETEPATAN PREDIKSI KEBANGKRUTAN ANTARA ALTMAN DAN MODEL SPRING GATE DI PERUSAHAAN YANG DELISTING DI BEI." Jurnal GeoEkonomi 15, no. 1 (2024): 26–37. http://dx.doi.org/10.36277/geoekonomi.v15i1.301.

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The aim of this research is to analyze significant non-uniformities in the bankruptcy prediction process through the use of the Altman Z score, and which bankruptcy prediction model is most suitable for predicting business failure. In this research, the data used comes from company financial reports available on the Indonesia Stock Exchange website. The focus of this research is on companies that have experienced delisting and have been listed on the Indonesia Stock Exchange during the 2019-2021 period. To identify comparisons between the six listed companies, data analysis will involve the ap

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Ni, Jinlan, Wikil Kwak, Xiaoyan Cheng, and Guan Gong. "The Determinants of Bankruptcy for Chinese Firms." Review of Pacific Basin Financial Markets and Policies 17, no. 02 (2014): 1450012. http://dx.doi.org/10.1142/s021909151450012x.

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The global financial crisis in 2008 increased the number of business failures in the U.S. as well as in China. The Chinese economy has also been affected by the recent global financial crisis given the fact that the Chinese economy depends heavily on international trade. Our study tries to find the determinants of bankruptcy in Chinese firms. Both logit and survival model analyses provide consistent results on the determinants in predicting distressed firms in China. Our results suggest that firms with liquidity problems and firms experiencing a decline in profits are more likely to file for b

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Hassan, Ehsan ul, Zaemah Zainuddin, and Sabariah Nordin. "A Review of Financial Distress Prediction Models: Logistic Regression and Multivariate Discriminant Analysis." Indian-Pacific Journal of Accounting and Finance 1, no. 3 (2017): 13–23. http://dx.doi.org/10.52962/ipjaf.2017.1.3.15.

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In corporate finance, the early prediction of financial distress is considered more important as another occurrence of business risks. The study presents a review of literature for early prediction of financial bankruptcy. It contributes to the formation of a systematic review of the literature regarding previous studies done in the field of bankruptcy. It addresses two most commonly used financial distress prediction models, i.e. multivariate discriminant analysis and logit. Models are discussed with their advantages and disadvantages. After methodological review, it seems that logit regressi

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Yayu Kusdiana. "Predicting Bankruptcy Using Altman's Z-Score And Zmijewski Models." Journal of Multidisciplinary Science 2, no. 1 (2023): 69–75. http://dx.doi.org/10.58330/prevenire.v2i1.124.

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Oil and natural gas (oil and gas) are strategic natural resources and have an important role in national development. The need for oil and gas is expected to continue to increase in line with economic and population growth. Therefore, oil and gas business activities must be managed as well as possible to meet current and future supplies. Financial crises (financial distress) can occur in various companies and can be a signal of bankruptcy that the company may experience. The purpose of this study is to determine the bankruptcy prediction of Sub-Oil and Gas companies in Indonesia that are liste

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Kuběnka, Michal, Jan Čapek, and František Sejkora. "A NEW LOOK AT BANKRUPTCY MODELS." E+M Ekonomie a Management 24, no. 3 (2021): 167–85. http://dx.doi.org/10.15240/tul/001/2021-3-010.

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New models for bankruptcy prediction are constantly being formulated and tested against the current ones and current ones are tested to assess their current accuracy and to allow users to determine the reliability of the results when using the model. These models use accounting information as input data. Accounting systems, for example, US GAAP, or IFRS, contain rules that may be applied differently from one company to another without being breached. This leads to input data uncertainty. Likewise, uncertainties may arise due to errors in recording and transcribing input data or in translating

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Kanapickienė, Rasa, Tomas Kanapickas, and Audrius Nečiūnas. "Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector." Risks 11, no. 5 (2023): 97. http://dx.doi.org/10.3390/risks11050097.

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Credit-risk models that are designed for general application across sectors may not be suitable for the construction industry, which has unique characteristics and financial risks that require specialised modelling approaches. Moreover, advanced bankruptcy-prediction models are often used to achieve the highest accuracy in large modern datasets. Therefore, the aim of this research is the creation of enterprise-bankruptcy prediction (EBP) models for Lithuanian micro and small enterprises (MiSEs) in the construction sector. This issue is analysed based on classification models and the specific t

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Nurahayu, Choirunnisa, Evi Yuniarti, and Nurmala Nurmala. "Analisis Model Prediksi Kebangkrutan Altman Z-Score. Springate dan Zmijewski untuk Menilai Kelangsungan Usaha PT Enseval Putera Megatrading Tbk." Jurnal Ilmiah ESAI 12, no. 2 (2018): 128–32. http://dx.doi.org/10.25181/esai.v12i2.1101.

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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

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Horváthová, Jarmila, and Martina Mokrišová. "Risk of Bankruptcy, Its Determinants and Models." Risks 6, no. 4 (2018): 117. http://dx.doi.org/10.3390/risks6040117.

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In this paper, the following research problem was addressed: Is DEA (Data Envelopment Analysis) method a suitable alternative to Altman model in predicting the risk of bankruptcy? Based on the above-mentioned research problem, we formulated the aim of the paper: To apply DEA method for predicting the risk of bankruptcy and to compare its results with the results of Altman model. The research problem and the aim of the paper follow the research of authors aimed at the application of methods which are appropriate for measuring business financial health, performance and competitiveness as well as

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Adamko, Peter, and Jan Chutka. "Company bankruptcy and its prediction in conditions of globalization." SHS Web of Conferences 74 (2020): 05002. http://dx.doi.org/10.1051/shsconf/20207405002.

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The internationalisation and globalisation of today’s world, especially in business, brings whole new range of opportunities, challenges and also many kinds of risks. Today´s global market in process of globalization offers many different ways of doing business and also whole new ranges of methods how to analyse optimize and also minimize it´s risks. The issue of bankruptcy models is still relevant given by the high competition in the markets and the increasingly frequent crises. Not only in the world, but also in our country, we can see a huge number of bankruptcies of businesses. If the comp

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Musah, Alhassan, and Josephine Agyimaa Agyirakwah. "Application of Altman Bankruptcy prediction model in Ghana." Jurnal Perspektif Pembiayaan dan Pembangunan Daerah 7, no. 1 (2019): 63–72. http://dx.doi.org/10.22437/ppd.v7i1.7558.

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The study examined the applicability of the Altman Z-score model in predicting bankrupt companies or financially distressed companies on the Ghana Stock Exchange. A sample 10 listed firms were selected and one other company to be used for validation purposes. The validation process involved data for 2016 and 2017 for Aluworks which represented a distressed company and GOIL Ghana Limited which represented non-distressed company. The final analysis was based on a random sample of 10 listed firms using their 2017 financial statement. The results of the initial prediction showed 50 percent of the

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Papana, Angeliki, and Anastasia Spyridou. "Bankruptcy Prediction: The Case of the Greek Market." Forecasting 2, no. 4 (2020): 505–25. http://dx.doi.org/10.3390/forecast2040027.

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Financial bankruptcy prediction is an essential issue in emerging economies taking into consideration the economic upheaval that can be caused by business failures. The research on bankruptcy prediction is of the utmost importance as it aims to build statistical models that can distinguish healthy firms from financially distressed ones. This paper explores the applicability of the four most used approaches to predict financial bankruptcy using data concerning the case of Greece. A comparison of linear discriminant analysis, logit, decision trees and neural networks is performed. The results sh

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Kesuma, Muhammad Ramadhani, Felisitas Defung, and Anisa Kusumawardani. "Bankruptcy Prediction And Its Effect On Stock Prices As Impact Of The COVID-19 Pandemic." Technium Social Sciences Journal 25 (November 9, 2021): 567–82. http://dx.doi.org/10.47577/tssj.v25i1.4964.

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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 mo

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Cassim, Ronel Juliana'. "Bankruptcy prediction indicators approach: A tool for measuring steinhoff’s risk." Journal of Accounting and Investment 25, no. 3 (2024): 1061–87. http://dx.doi.org/10.18196/jai.v25i3.19578.

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Research aims: The objective of this article is to ascertain whether Steinhoff International Holdings could have successfully employed the Bankruptcy Prediction Indicators Approach model as an instrument for measuring business continuity risk prior to its significant share price fall in 2012.Design/Methodology/Approach: The study utilized a qualitative research design and document analysis approach to examine Steinhoff International Holdings' financial data from 2012-2021, using the Integrated Real-time Equity System (IRESS BFA McGregor) and the Bankruptcy Prediction Indicators Approach. In MS

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Le, Tuong, Minh Thanh Vo, Bay Vo, Mi Young Lee, and Sung Wook Baik. "A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction." Complexity 2019 (August 5, 2019): 1–12. http://dx.doi.org/10.1155/2019/8460934.

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The diagnosis of bankruptcy companies becomes extremely important for business owners, banks, governments, securities investors, and economic stakeholders to optimize the profitability as well as to minimize risks of investments. Many studies have been developed for bankruptcy prediction utilizing different machine learning approaches on various datasets around the world. Due to the class imbalance problem occurring in the bankruptcy datasets, several special techniques would be used to improve the prediction performance. Oversampling technique and cost-sensitive learning framework are two com

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Siddiqui, Hafeez Ur Rehman, Beatriz Sainz de Abajo, Isabel de la Torre Díez, et al. "Predicting bankruptcy of firms using earnings call data and transfer learning." PeerJ Computer Science 9 (January 4, 2023): e1134. http://dx.doi.org/10.7717/peerj-cs.1134.

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Business collapse is a common event in economies, small and big alike. A firm’s health is crucial to its stakeholders like creditors, investors, partners, etc. and prediction of the upcoming financial crisis is significantly important to devise appropriate strategies to avoid business collapses. Bankruptcy prediction has been regarded as a critical topic in the world of accounting and finance. Methodologies and strategies have been investigated in the research domain for predicting company bankruptcy more promptly and accurately. Conventionally, predicting the financial risk and bankruptcy has

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Milić, Dragan, Nedeljko Tica, Vladislav Zekić, et al. "The assessment of bankruptcy risk of companies from the sector of drying and storage of crops and plants." Journal on Processing and Energy in Agriculture 25, no. 3 (2021): 86–90. http://dx.doi.org/10.5937/jpea25-32138.

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Contemporary market conditions make all business entities confront various risks - credit risk, liquidity risk, cash flow risk and market risk. Companies should predict these particular business risks and manage them adequately in order to minimize their influence. The assessment of creditworthiness and bankruptcy risk has a major role in the process of prediction and management of company risks. The creditworthiness of companies presents their ability to meet financial obligations to their creditors contemporary business conditions recognize several methods for assessment of credit ability of

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Kušter, Denis, Bojana Vuković, Sunčica Milutinović, Kristina Peštović, Teodora Tica, and Dejan Jakšić. "Early Insolvency Prediction as a Key for Sustainable Business Growth." Sustainability 15, no. 21 (2023): 15304. http://dx.doi.org/10.3390/su152115304.

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This research aimed to determine whether and how financial analysis combined with machine learning can support decision-making for sustainable business growth. This study was conducted using a sample of 100 Serbian companies whose bankruptcies were initiated between 2019 and 2021 to identify key factors that distinguish solvent from insolvent companies. Two neural networks (NNs) were trained and tested to predict these discriminating factors one year (Y-1) and two years (Y-2) before bankruptcy initiation. Initially, a total of 37 predictor variables were included, but prior to modeling, variab

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