Relevant bibliographies by topics / Financial distress prediction models

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

Academic literature on the topic 'Financial distress prediction models'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Financial distress prediction models"

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Ashraf, Sumaira, Elisabete G. S. Félix, and Zélia Serrasqueiro. "Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?" Journal of Risk and Financial Management 12, no. 2 (April 4, 2019): 55. http://dx.doi.org/10.3390/jrfm12020055.

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Purpose: This study aims to compare the prediction accuracy of traditional distress prediction models for the firms which are at an early and advanced stage of distress in an emerging market, Pakistan, during 2001–2015. Design/methodology/approach: The methodology involves constructing model scores for financially distressed and stable firms and then comparing the prediction accuracy of the models with the original position. In addition to the testing for the whole sample period, comparison of the accuracy of the distress prediction models before, during, and after the financial crisis was also done. Findings: The results indicate that the three-variable probit model has the highest overall prediction accuracy for our sample, while the Z-score model more accurately predicts insolvency for both types of firms, i.e., those that are at an early stage as well as those that are at an advanced stage of financial distress. Furthermore, the study concludes that the predictive ability of all the traditional financial distress prediction models declines during the period of the financial crisis. Originality/value: An important contribution is the widening of the definition of financially distressed firms to consider the early warning signs related to failure in dividend/bonus declaration, quotation of face value, annual general meeting, and listing fee. Further, the results suggest that there is a need to develop a model by identifying variables which will have a higher impact on the financial distress of firms operating in both developed and developing markets.
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Chen, Jianguo, Ben R. Marshall, Jenny Zhang, and Siva Ganesh. "Financial Distress Prediction in China." Review of Pacific Basin Financial Markets and Policies 09, no. 02 (June 2006): 317–36. http://dx.doi.org/10.1142/s0219091506000744.

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We use four alternative prediction models to examine the usefulness of financial ratios in predicting business failure in China. China has unique legislation regarding business failure so it is an interesting laboratory for such a study. Earnings Before Interest and Tax to Total Assets (EBITTA), Earning Per Share (EPS), Total Debt to Total Assets (TDTA), Price to Book (PB), and the Current Ratio (CR), are shown to be significant predictors. Prediction accuracy achieves a range from 78% to 93%. Logit and Neural Network models are shown to be the optimal prediction models.
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Mitchell, M. R., R. E. Link, Li-Tze Lee, Chiang Ku Fan, Hsiang-Wen Hung, and Yu-Chun Ling. "Analysis of Financial Distress Prediction Models." Journal of Testing and Evaluation 38, no. 5 (2010): 102759. http://dx.doi.org/10.1520/jte102759.

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Tew, You Hoo, and Enylina Nordin. "Predicting corporate financial distress using logistic regression : Malaysian evidence." Social and Management Research Journal 3, no. 1 (June 1, 2006): 123. http://dx.doi.org/10.24191/smrj.v3i1.5108.

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This study attempts to construct and test financial distress prediction model for Malaysian Companies. The samplefor this study consists of84 companies listed on Bursa Malaysia that became financially distressed in 200/ and 2002 and a matched (by industry and firm size) sample 0/ 84 financially healthy companies. The model is constructed by employing logistic regression analysis based on pooled data of5 years prior tofinancial distress. The model isfirst derived using the estimation sample andthen tested using the validation sample. Adding to the existing research onfinancial distress prediction models, the current model utilizes measures ofshareholders' equity to total liabilities, shareholders' equity to total assets, current liabilities to total assets, total borrowings to total assets andinventory turnover. The results are encouraging, as the model developed/or predicting corporatefinancial distress in Malaysia is reliable up to 5 years prior to financial distress. II is also believed thai the prediction model can be useful to different groups of users such as policy makers, financial institutions, creditors, managers, bankers, investors and shareholders.
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El-ansari, Osama, and Lina Bassam. "Predicting Financial Distress for Listed MENA Firms." International Journal of Accounting and Financial Reporting 9, no. 2 (April 15, 2019): 51. http://dx.doi.org/10.5296/ijafr.v9i2.14542.

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Financial distress prediction gives an early warning about defaulting risk for firms; thus, it is a real concern of the entire economy.Purpose: To examine the determinants of financial distress across MENA region countries, by using definitions of distress and historical data from active listed firms in the region.Methodology: logistic regression is run on firm-specific variables and a set of macroeconomic variables to develop a prediction model to examine the effect of these predictors on the probability of financial distress.Findings: it has been found that after controlling for country effects, accounting ratios, firm size, and macroeconomic variables provided an acceptable prediction model for listed MENA firms.Originality: a gap exists in the literature of developing countries’ prediction for financial distress. Many studies addressed bankruptcy prediction for a certain country in the region, however, a limited number of researches approached predicting distressed models for listed firms in the region.
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Ma’aji, Muhammad M., Nur Adiana Hiau Abdullah, and Karren Lee-Hwei Khaw. "Predicting Financial Distress among SMEs in Malaysia." European Scientific Journal, ESJ 14, no. 7 (March 31, 2018): 91. http://dx.doi.org/10.19044/esj.2018.v14n7p91.

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Predicting financial distress among Small and Medium Enterprises (SMEs) can have a significant impact on the economy as it serves as an effective early warning signal. The study develops distress prediction models combining financial, non-financial and governance variables which were used to analyze the influence of major corporate governance characteristics, like ownership and board structures, on the likelihood of financial distress. Multiple Discriminant Analysis (MDA) model as one of the extensively documented approaches was used. The final sample for the estimation model consists of 172 companies with 50 percent non-failed cases and 50 percent failed cases for the period between 2000 to 2012. The prediction models perform relatively well especially in MDA model that incorporate governance, financial and non-financial variables, with an overall accuracy rate of 90.7 percent in the estimated sample. The accuracy rate in the holdout sample was 91.2 percent for the MDA model. This evidence shows that the models serve as efficient earlywarning signals and can thus be beneficial for monitoring and evaluation. Controlling shareholder, number of directors, and gender of managing director are found to be significant predictors of financially distressed SMEs.
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Ahmadreza Ghasemi, Ahmadreza, Mohsen Seyghalib, and Maryam Moradi. "PREDICTION OF FINANCIAL DISTRESS, USING METAHEURISTIC MODELS." Financial and credit activity: problems of theory and practice 1, no. 24 (March 30, 2018): 238–49. http://dx.doi.org/10.18371/fcaptp.v1i24.128242.

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Listyarini, Fitri. "ANALISIS PERBANDINGAN PREDIKSI KONDISI FINANCIAL DISTRESS DENGAN MENGGUNAKAN METODE ALTMAN, SPRINGATE, DAN ZMIJEWSKI." Jurnal Bina Akuntansi 7, no. 1 (January 31, 2020): 1–20. http://dx.doi.org/10.52859/jba.v7i1.71.

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This study aims to: 1) Determine the accuracy of the Altman model, the springate model and the zmijewski model in predicting financial distress conditions in manufacturing companies in Indonesia, 2) To find out the most accurate prediction models in predicting financial distress conditions in manufacturing companies in Indonesia. This study compares three financial distress prediction models, the Altman, Springate and Zmijewski models. The population of this study is the financial statements of manufacturing companies listed on the Indonesia Stock Exchange for the period 2011-2014. The sampling technique is pair matching sampling with a total sample of 28 companies, consisting of 14 companies experiencing financial distress and 14 companies not experiencing financial distress. Comparisons of the three financial distress prediction models are made by analyzing the accuracy of each model based on the company's real conditions. The results show that the zmijewski model is the most accurate model for predicting financial distress in manufacturing companies in Indonesia because it has the highest level of accuracy compared to other models, which is 100%, followed by the Springate model which has an accuracy rate of 89.29% and the Altman model by 75%.
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Zhuang, Qian, and Lianghua Chen. "Dynamic Prediction of Financial Distress Based on Kalman Filtering." Discrete Dynamics in Nature and Society 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/370280.

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The widely used discriminant models currently for financial distress prediction have deficiencies in dynamics. Based on the dynamic nature of corporate financial distress, dynamic prediction models consisting of a process model and a discriminant model, which are used to describe the dynamic process and discriminant rules of financial distress, respectively, is established. The operation of the dynamic prediction is achieved by Kalman filtering algorithm. And a generaln-step-ahead prediction algorithm based on Kalman filtering is deduced in order for prospective prediction. An empirical study for China’s manufacturing industry has been conducted and the results have proved the accuracy and advance of predicting financial distress in such case.
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Munawarah, Munawarah, and Keumala Hayati. "ACCURACY OF SPRINGATE, ZMIJEWSKY AND GROVER AS LOGISTIC MODELS IN FINDING FINANCIAL DIFFICULTY OF FINANCING COMPANIES." ACCRUALS 3, no. 1 (March 29, 2019): 1–12. http://dx.doi.org/10.35310/accruals.v3i1.36.

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This study aims to determine both the Springate model, Grover and Zmijewski able to predict the condition of financial distress in finance companies listed on the Indonesia Stock Exchange. And of the three models can be known which model is the most accurate in predicting financial distress. The population in this study are companies in the financing sector listed on the Indonesia Stock Exchange in the period 2013 to 2017 as many as 17 companies. By using purposive sampling technique, a total sample of 85 financing companies was obtained. The data used are secondary data sourced from the company's annual financial reports. The analysis model used is logistic regression. Simultaneously, all predictive models for Springate, Zmijewski, and Grover affect the probability of financial distress. While partially only Zmijewski can influence the prediction of financial distress conditions in Financing sub-sector companies listed on the Indonesia Stock Exchange. Nagelkerqe Square value shows 0.606 meaning that only 60.6% variation of the accuracy of these three models in predicting financial distress conditions of finance companies. While the remaining 39.4% can be explained by other models not examined in this study
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Dissertations / Theses on the topic "Financial distress prediction models"

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Stulpinienė, Vaida. "Financial distress prediction model of family farms." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2013~D_20140123_133545-56537.

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Designed financial distress prediction model is intended directly for the farmer (decision-maker) in order to diagnose the farm’s financial condition and predict the likelihood of financial distress, by using financial information of his farm. There are identified family farm characteristics in which family farms have higher risks to run in financial distress and are guidelines for the family farms that intend to more carefully monitor and control their financial condition. The aim of the research: after analysing the conception of financial distress and identifying the factors determining the financial condition as well as related indicators and prediction models, to methodologically justify and design financial distress prediction model of family farms.
Parengtas finansinio išsekimo prognozavimo modelis tiesiogiai skirtas ūkininkui, kuris panaudodamas savo ūkio finansinę informaciją, galėtų diagnozuoti ūkio finansinę būklę ir iš anksto numatyti finansinio išsekimo grėsmę. Disertacijoje nustatytos ir įvardintos ūkininkų ūkių charakteristikos, kurioms esant ūkiai turi didesnes grėsmes finansiškai išsekti, yra gairės ūkininkų ūkiams, kurie ketina atidžiau stebėti savo veiklą ir kontroliuoti finansinę būklę. Tyrimo tikslas – ištyrus finansinio išsekimo sampratą, identifikavus finansinę būklę sąlygojančius veiksnius, indikatorius ir prognozavimo modelius, metodologiškai pagrįsti ir parengti ūkininkų ūkių finansinio išsekimo prognozavimo modelį.
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Mselmi, Nada. "Financial distress prediction and equity pricing models : Theory and empirical evidence in France." Thesis, Orléans, 2017. http://www.theses.fr/2017ORLE0502.

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Cette thèse porte sur la prédiction de la détresse financière et son impact sur le rendement des actions. L’objet principal de cette thèse est de : (i) prédire la détresse financière des petites et moyennes entreprises françaises en utilisant plusieurs spécifications économétriques tels que, le modèle Logit, les réseaux de neurones artificiels, la méthode SVM et la régression des moindres carrés partiels, et (ii) d’identifier les facteurs de risque de détresse financière à caractère systématique, explicatifs des rendements des actions, et additionnels au modèle de Fama et French (1993) tels que le momentum, la détresse relative, la liquidité et la Value-at-Risk, sur le marché boursier Français. Cette étude comporte deux parties. La première partie, composée de 2 chapitres, s’interroge sur les principaux indicateurs discriminants entre les petites et moyennes entreprises françaises saines et celles en détresse financière un an et deux ans avant la défaillance. Elle mobilise différentes approches de prédiction et aboutit à des résultats empiriques qui font l’objet d’analyse. La deuxième partie, composée aussi de 2 chapitres, étudie le pouvoir explicatif, du modèle de Fama et French (1993) augmenté de certains facteurs de risque, mais aussi des modèles alternatifs à cette approche dans le contexte français. Les tests portent aussi sur le caractère systématique des facteurs de risque additionnels ou alternatifs, explicatifs des rendements des actions. Les résultats empiriques obtenus font l’objet d’analyse et permettent de proposer des implications managériales aux décideurs
This thesis focuses on financial distress and its impact on stock returns. The main goal of this dissertation is: (i) to predict the financial distress of French small and medium-sized firms using a number of techniques namely Logit model, Artificial Neural Networks, Support Vector Machine techniques, and Partial Least Squares, and (ii) to identify the systematic risk factors of financial distress that can explain stock returns, in addition to those of Fama and French (1993) such as the momentum, the relative distress, the liquidity, and the Value-at-Risk in the French stock market. This study has been concretized in two parts. The first part, composed of 2 chapters, wonders about the main indicators that can discriminate between distressed and non-distressed French small and medium-sized firms one and two years before default. It mobilizes different prediction techniques and leads to the empirical results that are the subject of the analysis. The second part, composed also of 2 chapters, investigates the explanatory power of Fama and French (1993) model augmented by a number of risk factors, as well as alternative models in the French context. The tests also focus on the systematic nature of the additional or alternative risk factors, explaining the stock returns. The obtained empirical results are analyzed and propose managerial implications to decision makers
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Omar, Mohd Azmi. "The sensitivity of distress prediction models to the nonnormality of bounded and unbounded financial ratios : an application in Malaysia." Thesis, Bangor University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239854.

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Malíková, Pavlína. "Finanční analýza společnosti Metrostav a.s." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-76786.

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The thesis aim is to examine and evaluate the Metrostav a.s financial health during the years 2005 and 2009 even in the context of economic crisis. The thesis is divided into two main parts. The first one, theoretical - methodological part, describes the various methods of financial analysis, which are gradually being applied in the practical part. The content of the practical part is a brief description of the company and the construction sector, followed by the very core of financial analysis. At the end there are summarized learned knowledge of applied methods and interpreted results of financial analysis.
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Sova, Lukáš. "Predpoveď finančnej tiesne podniku pomocou bankrotných a bonitných modelov." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-193213.

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In the diploma thesis we are examining possibilities of utilization of financial standing models and bankruptcy models for the purpose of prediction of financial distress of a company. We start the analysis with a broad description of methods provided by financial analyses used for prediction of the financial distress, followed by a more particular investigation into the problematics of financial standing and bankruptcy models. In the thesis we aim to define 9 of these models including their variations followed by application onto four companies in the time scale of five years up front the incoming financial distress. Whilst applying the models we will have a closer look at the discrepancies coming from the different nature of the predictive formulas, meanwhile observing how the key changes in financial statements transfer into the scores. Moreover, we will try to point out the key elements causing deformation in the relevance of the models. In the conclusion we will summarize the findings and confront the assumptions.
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Jan, Yitzung, and 詹益宗. "Comparison Between Financial Distress Prediction Models." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/86423538258426182202.

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碩士
國立交通大學
財務金融研究所
94
Based on the data of Taiwan corporations trading in TSE and OTC, this study used financial accounting variables and market variables to construct financial distress prediction models, such as Logit model, MDA model and discrete-time hazard model. With such methodology, I examined whether the added-in market variables could enhance the model’s discrimination ability and predicting capability or not, furthermore, I compared the accuracy of three statistical models. This study classified the variables into four categories, which are financial accounting variable group, financial accounting variable plus market variable group, market variable group and Shumway’s variable group, separately. The methods used in analyzing the models’ prediction accuracy are the default probability table, misclassification table, ROC curve and AUC, and EMC. 　　The empirical results showed that the best model to discriminate in-sample data is Logit model composed of financial accounting variable plus market variable group; however, the best model to predict out-sample data is composed by financial accounting variable plus market variable group and Shumway’s variable group, but there are no difference between three statistical models in predicting capabilities. In summary, adding market variables does really enhance discrimination ability of in-sample data, but it doesn’t obviously enhance the prediction ability of out-sample data. Moreover, it is better to use financial distress prediction models alternatively in judging the tendency of the out-sample default firms.
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Hung, Min-Yu, and 洪旻郁. "When Will Financial Distress Prediction Models Fail?" Thesis, 2009. http://ndltd.ncl.edu.tw/handle/60003156094196645699.

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碩士
臺灣大學
財務金融學研究所
98
he thesis investigates reasons behind failed distressed prediction models. Since variables of models reflect the current status of a company’s operating and financing situation. The quality of inputted information is quite influential for an effective distressed model. We compare our defined best accounting and market distressed forecasting models under five hypotheses to see the relative performance of both models. Our defined best accounting and market distressed prediction models have reached statistical significant in forecasting default. Particularly, the market model gets extra information after adjusting industry effect. On the other hand, accounting distressed forecasting model failed when the management has higher incentive and capability to manipulate information. Moreover, for small firms, the accounting model fails because of high information asymmetry. Nevertheless, market model fails when liquidity is too high and investors are too optimistic toward growth stocks. The thesis provides some empirical reasons for failed distressed prediction models. It also provides some references for people who will use these forecasting results in the future.
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Sera, Roxana. "Financial distress prediction for portuguese SMEs." Master's thesis, 2020. http://hdl.handle.net/1822/69982.

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Dissertação de mestrado em Finance
In Portugal, small and medium-sized enterprises (SMEs) represent 99.9% of the total number of companies and are key generators of employment and contributors to the country`s economy. Given their key role and the fact that their main source of funding comes from financial institutions, it is vital that they have easy access to diversified financing instruments as well as the capacity of presenting their activity and results in an efficient way in order to gain access to them. In this context, a way of interpreting the information available about a company in a clear, concise and efficient manner is through the application of an accounting - based financial distress model. The analysis provided by such an instrument is beneficial to both financial institutions, that can use the results in order to understand the general situation of the company, and to the company`s management, who can foresee and prevent eventual financial problems. The objective of this study is to identify the main financial ratios that are relevant in order to discriminate between financially distressed and healthy companies and estimate financial distress prediction models based on them then use the estimated parameters to predict the probability of financial distress in Portuguese SMEs. In order to obtain a more balanced data set of companies the propensity score method, with matching of one-to-one as well as one-to-many, was applied. The model estimation was made with insolvent companies` data from one year prior to insolvency. Validation tests were performed on data samples for one, two and three years before insolvency, as well as for years one to three in a joint data set and also for the entire set of insolvent companies available, up to six years prior to insolvency. The five variables found to be the best predictors of insolvency are Current Assets to Total Assets, Operating Cash Flow to Total Assets, Operating Cash Flow to Debt, Retained Earnings to Total Assets and Equity to Debt. The overall forecasting accuracy of the final model was of over 85%, by which we conclude that the model could be successfully applied to the Portuguese market, in the context of the SMEs.
Em Portugal, as Pequenas e Médias Empresas (PMEs) representam 99.9% do número total de empresas e são um fator chave para a geração de emprego, com uma contribuição elevada para a economia geral do país. Considerando o papel estratégico desempenhado e o fato de que a maior fonte de recursos para as PMEs são as instituições financeiras, é fundamental que essas tenham tanto facilidade de acesso à instrumentos financeiros diversificados, quanto a possibilidade de apresentar a sua atividade e resultados obtidos de uma maneira adequada que lhes garante acesso a esses instrumentos. Nesse contexto, a aplicação de um modelo de previsão de insolvência baseado na análise de rácios financeiros é uma maneira de interpretar a informação disponível sobre uma empresa de uma forma clara, concisa e eficiente. A análise facilitada por tal instrumento beneficia tanto as instituições financeiras, que podem interpretar os resultados obtidos para melhor entender a situação geral da empresa, quanto os gestores da empresa, para quais facilita a detecção e prevenção de eventuais problemas financeiros. O objetivo deste estudo é identificar os principais rácios financeiros relevantes para distinguir entre empresas em dificuldades financeiras e empresas saudáveis, estimar com base neles um modelo de previsão de insolvência e utilizar os parámetros estimados para previsão de dificuldades financeiras nas PMEs portuguesas. Para obter uma amostra mais equilibrada de empresas foi aplicado o método Propensity Score Matching, com pareamentos de um-para-um e um-para-muitos. O modelo foi estimado com base nos dados financeiros de empresas insolventes de um ano antes da insolvência. Testes de validação foram feitos em amostras de um, dois e três anos antes da insolvência, amostra de um a três anos antes da falência, bem como no inteiro conjunto de empresas com dados disponíveis, até seis anos antes da insolvência. As cinco variáveis que mostraram melhor capacidade de previsão da insolvência são: Ativo Corrente/ Total do Ativo, Fluxo de Caixa Operacional/ Total do Ativo, Fluxo de Caixa Operacional/ Total do Ativo, Resultados Transitados/ Total do Ativo e Patrimônio Líquido/ Total do Passivo. A capacidade total preditiva do modelo é acima de 85%, o que leva à conclusão de que o modelo pode ser aplicado ao mercado Português, no contexto das PMEs.
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Wen, Tsou Hui, and 鄒惠雯. "Financial Distress Prediction Model." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/13836405838104904375.

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碩士
健行科技大學
國際企業管理研究所
101
In this study, logical construct financial distress logistic regression model for the study period from 2007 to 2011, the Hong Kong enterprises as the research object, assess Hong Kong''s corporate financial variables on the early warning model predictive ability; empirical results show that the financial ratio variables debt and total asset turnover ratio greater impact on the enterprise; insufficient if the company''s profitability, debt ratio is higher, but will cause cash flow problems of the situation, the enterprise is the higher the likelihood that the financial crisis. In this study, Logica logistic regression model prediction accuracy, the closer point in time of financial distress, the higher the predictive ability of the model overall accuracy rate of 76.6%.
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XIE, MEI-SHUANG, and 謝美霜. "The study of sample designing in financial distress prediction models." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/85829856569285848926.

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Books on the topic "Financial distress prediction models"

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Almeida, Heitor. The risk-adjusted cost of financial distress. Cambridge, MA: National Bureau of Economic Research, 2005.

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Almeida, Heitor. The risk-adjusted cost of financial distress. Cambridge, MA: National Bureau of Economic Research, 2005.

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Lin, Feng Yu. A data mining approach to the prediction of financial distress. [S.l: The Author], 2004.

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Ramaswamy, Srichander. One-step prediction of financial time series. Basle, Switzerland: Bank for International Settlements, Monetary and Economic Dept., 1998.

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Ogawa, Kazuo. Financial distress and employment: The Japanese case in the 90s. Cambridge, Mass: National Bureau of Economic Research, 2003.

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Andrade, Gregor. How costly is financial (not economic) distress?: Evidence from highly leveraged transactions that became distressed. Cambridge, MA: National Bureau of Economic Research, 1997.

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E, Weinstein David, and National Bureau of Economic Research., eds. The myth of the patient Japanese: Corporate myopia and financial distress in Japan and the US. Cambridge, MA: National Bureau of Economic Research, 1996.

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Berg, Andrew. Are currency crises predictable?: A test. [Washington, D.C.]: International Monetary Fund, Research Department, 1998.

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Application of quantitative techniques for the prediction of bank acquisition targets. Singapore: World Scientific, 2006.

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Pasiouras, Fotios. Application of quantitative techniques for the prediction of bank acquisition targets. Singapore: World Scientific, 2005.

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Book chapters on the topic "Financial distress prediction models"

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Camska, Dagmar. "Industry Specifics of Models Predicting Financial Distress." In Contributions to Statistics, 113–23. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56219-9_8.

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Yeh, Ming-Feng, Chia-Ting Chang, and Min-Shyang Leu. "Financial Distress Prediction Model via GreyART Network and Grey Model." In Lecture Notes in Electrical Engineering, 91–100. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12990-2_11.

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García, Vicente, Ana I. Marqués, L. Cleofas-Sánchez, and José Salvador Sánchez. "Model Selection for Financial Distress Prediction by Aggregating TOPSIS and PROMETHEE Rankings." In Lecture Notes in Computer Science, 524–35. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32034-2_44.

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Pozzoli, Matteo, and Francesco Paolone. "The Models of Financial Distress." In Corporate Financial Distress, 11–28. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67355-4_3.

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Agostini, Marisa. "The Role of Going Concern Evaluation in Both Prediction and Explanation of Corporate Financial Distress: Concluding Remarks and Future Trends." In Corporate Financial Distress, 119–26. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78500-4_5.

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Alaminos, David, Sergio M. Fernández, Francisca García, and Manuel A. Fernández. "Data Mining for Municipal Financial Distress Prediction." In Advances in Data Mining. Applications and Theoretical Aspects, 296–308. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95786-9_23.

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Sun, Jie, and Xiao-Feng Hui. "Financial Distress Prediction Based on Similarity Weighted Voting CBR." In Advanced Data Mining and Applications, 947–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811305_103.

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Chen, Ning, Armando S. Vieira, João Duarte, Bernardete Ribeiro, and João C. Neves. "Cost-Sensitive Learning Vector Quantization for Financial Distress Prediction." In Progress in Artificial Intelligence, 374–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04686-5_31.

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Briggs, William M. "Testing, Prediction, and Cause in Econometric Models." In Econometrics for Financial Applications, 3–19. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-73150-6_1.

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Chou, Tsung-Nan. "A Practical Grafting Model Based Explainable AI for Predicting Corporate Financial Distress." In Business Information Systems Workshops, 5–15. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36691-9_1.

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Conference papers on the topic "Financial distress prediction models"

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Guo-ming, Qian, Feng Yuan, and Zhou Ling. "Financial Distress Prediction Models of China's Listed Companies." In 2007 International Conference on Management Science and Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icmse.2007.4422105.

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Zheng, Qin, and Jiang Yanhui. "Financial Distress Prediction Based on Decision Tree Models." In 2007 IEEE International Conference on Service Operations and Logistics, and Informatics. IEEE, 2007. http://dx.doi.org/10.1109/soli.2007.4383925.

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Ribeiro, Bernardete, Catarina Silva, Armando Vieira, A. Gaspar-Cunha, and Joao C. das Neves. "Financial distress model prediction using SVM+." In 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 2010. http://dx.doi.org/10.1109/ijcnn.2010.5596729.

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Durica, Marek, and Lucia Svabova. "MDA FINANCIAL DISTRESS PREDICTION MODEL FOR HUNGARIAN COMPANIES." In 5th International Scientific Conference ERAZ - Knowledge Based Sustainable Development. Association of Economists and Managers of the Balkans, Belgrade, Serbia, 2019. http://dx.doi.org/10.31410/eraz.s.p.2019.199.

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Geng, Zhaoyuan, Lan Tan, Xiaoli Gao, Yining Ma, Lufeng Feng, and Jiaying Zhu. "Financial Distress Prediction Models of Listed Companies by Using Non-Financial Determinants in Bayesian Criterion." In 2011 International Conference on Management and Service Science (MASS 2011). IEEE, 2011. http://dx.doi.org/10.1109/icmss.2011.5998341.

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Durica, Marek, Peter Adamko, and Katarina Valaskova. "MDA financial distress prediction model for selected Balkan countries." In 2nd International Scientific Conference - Economics and Management: How to Cope With Disrupted Times. Association of Economists and Managers of the Balkans, Belgrade, Serbia; Faculty of Management Koper, Slovenia; Doba Business School - Maribor, Slovenia; Integrated Business Faculty - Skopje, Macedonia; Faculty of Management - Zajecar, Serbia, 2018. http://dx.doi.org/10.31410/eman.2018.969.

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Zi-nan, Chang, Ge Jun, and Chen Ai-ping. "Research and Application of the Bayesian financial distress prediction model." In 2011 International Conference on E-Business and E-Government (ICEE). IEEE, 2011. http://dx.doi.org/10.1109/icebeg.2011.5884522.

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Yuzhu, Hao, Li Zengxin, and Huo Zaiqiang. "Financial Distress Prediction Model of Small and Medium-sized Listed Companies." In 2011 International Conference on Information Management, Innovation Management and Industrial Engineering (ICIII). IEEE, 2011. http://dx.doi.org/10.1109/iciii.2011.50.

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Zhuang, Qian, and Liang-hua Chen. "Research on financial distress prediction model based on Kalman filtering theory." In 2012 First National Conference for Engineering Sciences (FNCES). IEEE, 2012. http://dx.doi.org/10.1109/nces.2012.6543485.

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Zhuang, Qian, and Liang-hua Chen. "Research on Financial Distress Prediction Model Based on Kalman Filtering Theory." In 2013 Conference on Education Technology and Management Science. Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/icetms.2013.304.

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