Relevant bibliographies by topics / Business Bankruptcy Prediction

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Business Bankruptcy Prediction'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Business Bankruptcy Prediction"

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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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Dissertations / Theses on the topic "Business Bankruptcy Prediction"

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Haensly, Paul J. "The Application of Statistical Classification to Business Failure Prediction." Thesis, University of North Texas, 1994. https://digital.library.unt.edu/ark:/67531/metadc278187/.

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Bankruptcy is a costly event. Holders of publicly traded securities can rely on security prices to reflect their risk. Other stakeholders have no such mechanism. Hence, methods for accurately forecasting bankruptcy would be valuable to them. A large body of literature has arisen on bankruptcy forecasting with statistical classification since Beaver (1967) and Altman (1968). Reported total error rates typically are 10%-20%, suggesting that these models reveal information which otherwise is unavailable and has value after financial data is released. This conflicts with evidence on market efficie
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Charraud, Jocelyn, and Saez Adrian Garcia. "Bankruptcy prediction models on Swedish companies." Thesis, Umeå universitet, Företagsekonomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-185143.

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Bankruptcies have been a sensitive topic all around the world for over 50 years. From their research, the authors have found that only a few bankruptcy studies have been conducted in Sweden and even less on the topic of bankruptcy prediction models. This thesis investigates the performance of the Altman, Ohlson and Zmijewski bankruptcy prediction models. This research investigates all Swedish companies during the years 2017 and 2018.  This study has the intention to shed light on some of the most famous bankruptcy prediction models. It is interesting to explore the predictive abilities and usa
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Moore, Ronald K. (Ronald Kenneth). "Prediction of Bankruptcy Using Financial Ratios, Information Measures, National Economic Data and Texas Economic Data." Thesis, North Texas State University, 1987. https://digital.library.unt.edu/ark:/67531/metadc331133/.

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The main purpose of this study is to develop a bankruptcy prediction model for the small business firm. Data was collected from the Dallas Small Business Administration (SBA), making this study specific to its decision makers. Existing research has produced models which predominately use financial ratios and information measures either independently or combined, and a few research models have used economic trends. This study varies from past studies in that it includes regional economic variables from the states of Texas. A sample of three-year data for 138 firms included fifteen bankrupt fir
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Aly, Ibrahim M. Mohamed. "Prediction of Business Failure as a Criterion for Evaluating the Usefulness of Alternative Accounting Measures." Thesis, North Texas State University, 1986. https://digital.library.unt.edu/ark:/67531/metadc332393/.

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This study examines the usefulness of general price level information (GPL) and current cost information (CC) originally provided by SFAS No. 33 as compared to historical cost information (HC) in predicting bankruptcy. The study also examines the usefulness of GPL data versus CC data when each supplements HC data. In addition, this study tests the usefulness of the three types of information systems combined in one model (HC, GPL, and CC) versus HC data in predicting bankruptcy. The study focuses on the predictability of business failure using financial ratios as predictors. A comparison of th
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Mota, Stephen Kopano. "A matched study to determine a conditional logistic model for prediction of business failure in South Africa." Thesis, Stellenbosch : Stellenbosch University, 2001. http://hdl.handle.net/10019.1/52084.

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Thesis (MBA)--Stellenbosch University, 2001<br>ENGLISH ABSTRACT: The subject of prediction of business failure from an academic point of view dates back to the turn of the century with the development of a single ratio, the current ratio, as an evaluation of credit-worthiness. Subsequently studies conducted have become complex using different statistical techniques and more than one variable to predict failure. The challenge in these studies has been to establish a reliable model to predict failure. The aim of this report was to find out which financial factors best predicted failure in
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Rosendal, Lars, and Christofer Lilja. "Förvaltningsberättelsen : och dess nytta vid konkursförutsägelser." Thesis, Umeå University, Umeå School of Business, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-1702.

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<p>År 2007 gick 5 791 svenska företag i konkurs. Företagskonkurser kan få svåra ekonomiska konsekvenser för företagens intressentkrets. Utöver den uppenbara ekonomiska förlusten för företagsledningen och ägarna drabbas även andra intressenter såsom bolagets kreditgivare, leverantörer, kunder, staten och naturligtvis bolagets anställda. Följderna av en företagskonkurs kan dock mildras om företagets intressenter får en möjlighet att förbereda sig. Genom att förutse ett företags konkurs ges intressenterna en chans att vidta nödvändiga åtgärder för att skydda sina intressen. Traditionellt sett har
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Weller, Paula. "The Application of Altman, Zmijewski and Neural Network Bankruptcy Prediction Models to Domestic Textile-Related Manufacturing Firms: A Comparative Analysis." NSUWorks, 2010. http://nsuworks.nova.edu/hsbe_etd/115.

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Some of the largest United States bankruptcies of publicly-traded non-financial firms have occurred within the last decade. The continuing need to improve bankruptcy prediction has generated numerous research studies utilizing various prediction models. The purpose of this study is to test the usefulness of the multiple discriminant, probit, and artificial neural network (ANN) models in predicting bankruptcy in the United States textile-related industry. Financial data is examined for 47 bankrupt and 104 non-bankrupt publicly-traded firms in the textile-related industry during the time period
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Ahmeti, Laureta, and Azra Zubanovic. "The predictive power of financial ratios on bankruptcy : A quantitative study of non-listed limited liability SMEs companies in Sweden." Thesis, Internationella Handelshögskolan, Jönköping University, IHH, Företagsekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-48628.

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Abstract Background - Bankruptcy is an issue that not only affects the company that is registered as bankrupt, but also the society since it has an impact on the economy. Previous studies have been focusing on larger listed companies outside of Sweden hence there is a lack of empirical findings about Swedish companies in this research area. Small and medium companies represent most of the Sweden's labor force and therefore the bankruptcy issue is important to investigate for these companies. Purpose - The purpose of the thesis is to find out which financial information distinguishes bankrupt f
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Lebedžinskaitė, Renata. "Bankroto diagnozavimo įmonėse tyrimas." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2007. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2007~D_20070816_153211-47644.

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Darbo tikslas – sukurti modifikuotą bankroto diagnozavimo modelį ir jį patikrinti Lietuvos įmonių pavyzdžiu. Darbo uždaviniai: 1) ištyrus bankroto veiksnius, nustatyti jo atsiradimo priežastis; 2) atlikti teorinių bankroto tikimybės įvertinimo modelių analizę; 3) atlikus bankroto diagnozavimo modelių teorinę analizę, sukurti ir patikrinti modifikuotą bankroto diagnozavimo modelį. Darbo objektas – bankroto diagnozavimas. Tyrimo metodai: mokslinės literatūros analizė; loginė lyginamoji analizė bei sintezė; dokumentų, turinio analizė, apibendrinimo metodas; statistinė įmonių finansinių rodiklių a
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Nordin, Dan, and Olof Nordgaard. "Pohlman Prognostiserade och Zaida Spådde : -Vad kan vi förutsäga?" Thesis, Umeå University, Umeå School of Business, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-18676.

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<p> </p><p> </p><h1>Sammanfattning</h1><h1>Företag har alltid drabbats av ekonomiska svårigheter och gått i konkurs. Detta faktum harlänge intresserat forskare och legat till grund för en mängd studier inom området.Kassaflödesanalysens användbarhet vid konkursprognostisering har länge diskuterats ochtidigare forskning har visat på varierande resultat. Eftersom en kassaflödesanalys visar på detfaktiska in- och utflödet av pengar i företaget tror vi att den kan utgöra en viktigare indikatorpå om ett företag befinner sig i kris än resultat- och balansräkningen, som lättare kanmanipuleras. De fles
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Books on the topic "Business Bankruptcy Prediction"

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Alan, Fox. A comparison of the Zeta model to a failure prediction model incorporating the effects of recession and stress related factors. University College Dublin, Graduate School of Business, 1998.

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Altman, Edward I. Corporate financial distress and bankruptcy: A complete guide to predicting & avoiding distress and profiting from bankruptcy. 2nd ed. Wiley, 1993.

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Kazakova, Nataliya. Financial security of the company. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1908969.

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The textbook provides theoretical and practical training of business analysts on the financial security of companies. Considers the regulatory legal and methodological basis for the diagnosis of bankruptcy of organizations, as well as corporate fraud as a type of economic crimes; analytical tools for assessing the level of financial security based on a risk-oriented approach, the basics of building an internal financial security control system, including monitoring of the company's business processes affecting its financial security, as well as methods for assessing the risks of corporate frau
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Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction. Cambridge University Press, 2008.

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(Editor), Stewart Jones, and David A. Hensher (Editor), eds. Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction (Quantitative Methods for Applied Economics and Business Research). Cambridge University Press, 2008.

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(Editor), Stewart Jones, and David A. Hensher (Editor), eds. Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction (Quantitative Methods for Applied Economics and Business Research). Cambridge University Press, 2008.

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Archibald, Robert B. The Rhetoric of Higher Education in Crisis. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190251918.003.0001.

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Crisis rhetoric dominates the conversation about higher education. This chapter provides a few fictional stories about the future of colleges and universities facing today’s stresses. It introduces the threats that US higher education faces. These include internal threats, classified as those that come from conducting business as usual in the traditional model of producing a college education; environmental threats, broader economic changes in the world outside of higher education that make the current financial model for colleges and universities more challenging; and technological threats, t
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Book chapters on the topic "Business Bankruptcy Prediction"

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Anandarajan, Murugan, Picheng Lee, and Asokan Anandarajan. "Bankruptcy Prediction Using Neural Networks." In Business Intelligence Techniques. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24700-5_7.

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Babič, František, Cecília Havrilová, and Ján Paralič. "Knowledge Discovery Methods for Bankruptcy Prediction." In Business Information Systems. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38366-3_13.

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Feldman, Konrad, Jason Kingdon, Anoop Mangat, Renato Arisi, and Orsio Romagnoli. "Bankruptcy Prediction: An Intelligent Systems Approach." In Rapid Application Generation of Business and Finance Software. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1369-4_7.

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Meisa Putri, Amanda, and Imo Gandakusuma. "Bankruptcy prediction of manufacturing companies using Altman and Ohlson model." In Business Innovation and Development in Emerging Economies. CRC Press, 2019. http://dx.doi.org/10.1201/9780429433382-10.

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Svabova, Lucia, and Tomas Kliestik. "Analysis of Predictors in Bankruptcy Prediction Models for Slovak Companies." In Economy, Finance and Business in Southeastern and Central Europe. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-70377-0_23.

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Aliano, Mauro, Greta Cestari, and Salvatore Madonna. "Business Crisis and Bankruptcy Prediction Models: The Other Side of Sustainability." In Sustainable Finance. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-74193-7_9.

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Mahardini, Nikke Yusnita, Bandi Bandi, Payamta, and Setianingtyas Honggowati. "Financial Distress and Bankruptcy Prediction Research in Indonesia: Last 20 Years." In Proceedings of the International Colloquium on Business and Economics (ICBE 2022). Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-066-4_20.

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Georgescu, Vasile, and Florentina Mihaela Apipie. "Evolving Improved Neural Network Classifiers for Bankruptcy Prediction by Hybridization of Nature Inspired Algorithms." In Lecture Notes in Business Information Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40506-3_5.

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Musa, Hussam, Frederik Rech, Fanchen Meng, and Zdenka Musova. "COVID-19 Impact on Bankruptcy Prediction for Visegrad Four Manufacturing Companies: A Multiyear Approach." In Springer Proceedings in Business and Economics. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-76658-9_14.

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Dewaelheyns, Nico, Sofie De Prijcker, and Karen Van Den Heuvel. "Predicting Business Failure." In Turnaround Management and Bankruptcy. Routledge, 2017. http://dx.doi.org/10.4324/9781315738116-4.

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Conference papers on the topic "Business Bankruptcy Prediction"

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Huang, Ziyue. "Evaluating Hybrid Machine Learning Models Bankruptcy Prediction." In 2025 7th International Symposium on Computational and Business Intelligence (ISCBI). IEEE, 2025. https://doi.org/10.1109/iscbi64586.2025.11015371.

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Khudhur, Ahmed Abdulaziz, and Minwir Al-Shammari. "The Trend of Published Research for Predicting Bankruptcy Via Machine Learning: A Bibliometric Analysis." In 2024 5th International Conference on Data Analytics for Business and Industry (ICDABI). IEEE, 2024. https://doi.org/10.1109/icdabi63787.2024.10800117.

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Zelenkov, Yuri. "Bankruptcy Prediction Using Survival Analysis Technique." In 2020 IEEE 22nd Conference on Business Informatics (CBI). IEEE, 2020. http://dx.doi.org/10.1109/cbi49978.2020.10071.

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Mažintienė, Aurelija, and Daiva Burkšaitienė. "The Need of Bankruptcy Prediction in the Company." In The 7th International Scientific Conference "Business and Management 2012". Vilnius Gediminas Technical University Publishing House Technika, 2012. http://dx.doi.org/10.3846/bm.2012.019.

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Sponerova, Martina. "FINANCIAL DISTRESS PREDICTION FOR MANUFACTURING AND COMMERCIAL COMPANIES." In NORDSCI Conference Proceedings. Saima Consult Ltd, 2021. http://dx.doi.org/10.32008/nordsci2021/b2/v4/18.

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"A large number of studies on bankruptcy prediction are published every year. The topic of SME failure prediction has evolved over the past decades into a relevant research area that has grown exponentially across many disciplines, including finance and management, for obvious reasons. This has been motivated by the massive toll on SMEs caused by the global crisis of 2007-2009, the recent COVID-19 crisis and the resulting need to update indicators of SME failure. Many authors during the last fifty years have examined several possibilities to predict business failure. They have studied bankrupt
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Bakar, Suzaida, Irene Ting Wei Kiong, and Annuar Md Nassir. "Examining and validating a bankruptcy prediction models in Malaysia." In 2012 International Conference on Statistics in Science, Business and Engineering (ICSSBE2012). IEEE, 2012. http://dx.doi.org/10.1109/icssbe.2012.6396560.

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Saragih, Fitriani, Elizar Sinambela, and Eka Sari. "Bankruptcy Prediction By Using The Grover Method." In Proceedings of the 1st International Conference on Economics, Management, Accounting and Business, ICEMAB 2018, 8-9 October 2018, Medan, North Sumatra, Indonesia. EAI, 2019. http://dx.doi.org/10.4108/eai.8-10-2018.2288689.

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Bednárová, Juliána, and Kamila Sotáková. "Bankruptcy Prediction Models in Prešov Region of the Slovak Republic." In EDAMBA 2022: 25th International Scientific Conference for Doctoral Students and Post-Doctoral Scholars. University of Economics in Bratislava, 2023. http://dx.doi.org/10.53465/edamba.2022.9788022550420.52-63.

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The current business environment, not only in the Slovak Republic, is characterized by the very frequent existence of crises of business entities. It is no different even during the pandemic, which was characterized by various measures or restrictions affecting the smooth running of companies. If the company can adapt to the constant changes in the market and thus survive, it needs to know in which financial condition it is and what is its perspective in the market. Companies can use financial analysis, which monitors not only the company's management in the past period, but also with a view t
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Shuang, Qing, Yongbo Yuan, Mingyuan Zhang, and Dandan Yu. "Bankruptcy prediction in construction companies via Fisher's Linear Discriminant Analysis." In 2011 International Conference on E-Business and E-Government (ICEE). IEEE, 2011. http://dx.doi.org/10.1109/icebeg.2011.5881984.

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Chang, Hanxu. "The Application of Machine Learning Models in Company Bankruptcy Prediction." In ICSEB 2019: 2019 The 3rd International Conference on Software and e-Business. ACM, 2019. http://dx.doi.org/10.1145/3374549.3374550.

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