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Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables

Hernandez Tinoco, Mario; Wilson, Nicholas - In: International review of financial analysis 30 (2013), pp. 394-419

Persistent link: https://www.econbiz.de/10010461542

Corporate bankruptcy prognosis : an attempt at a combined prediction of the bankkruptcy event and time interval of its occurrence

Philosophov, Leonid V.; Philosophov, Vladimir L. - In: International review of financial analysis 11 (2002) 3, pp. 375-406

Persistent link: https://www.econbiz.de/10001715991

Optimization of corporate capital structure : a probabilistic Bayesian approach

Philosophov, Leonid V.; Philosophov, Vladimir L. - In: International review of financial analysis 8 (1999) 3, pp. 199-214

Persistent link: https://www.econbiz.de/10001495519

Bankruptcy prediction : application of the Taylor's expansion in logistic regression

Laitinen, Erkki K.; Laitinen, Teija - In: International review of financial analysis 9 (2000) 4, pp. 327-349

Persistent link: https://www.econbiz.de/10001545822

Chinese corporate distress prediction using LASSO : the role of earnings management

Li, Chunyu; Lou, Chenxin; Luo, Dan; Xing, Kai - In: International review of financial analysis 76 (2021), pp. 1-21

Persistent link: https://www.econbiz.de/10012804735

Consumer bankruptcy prediction using balanced and imbalanced data

Brygała, Magdalena - In: Risks : open access journal 10 (2022) 2, pp. 1-13

This paper examines the usefulness of logit regression in forecasting the consumer bankruptcy of households using an … imbalanced dataset. The research on consumer bankruptcy prediction is of paramount importance as it aims to build statistical … models that can identify consumers in a difficult financial situation that may lead to consumer bankruptcy. In the face of …

Persistent link: https://www.econbiz.de/10012805903

An optimal model of financial distress prediction : a comparative study between neural networks and logistic regression

Zizi, Youssef; Jamali-Alaoui, Amine; El Goumi, Badreddine; … - In: Risks : open access journal 9 (2021) 11, pp. 1-24

In the face of rising defaults and limited studies on the prediction of financial distress in Morocco, this article aims to determine the most relevant predictors of financial distress and identify its optimal prediction models in a normal Moroccan economic context over two years. To achieve...

Persistent link: https://www.econbiz.de/10012704037

Empirics of Korean shipping companies' default predictions

Park, Sunghwa; Kim, Hyunsok; Kwon, Janghan; Kim, Taeil - In: Risks : open access journal 9 (2021) 9, pp. 1-18

In this paper, we use a logit model to predict the probability of default for Korean shipping companies. We explore numerous financial ratios to find predictors of a shipping firm’s failure and construct four default prediction models. The results suggest that a model with industry specific...

Persistent link: https://www.econbiz.de/10012612618

Bankruptcy prediction with a doubly stochastic poisson forward intensity model and low-quality data

Berent, Tomasz; Rejman, Radosław - In: Risks : open access journal 9 (2021) 12, pp. 1-24

With the record high leverage across all segments of the (global) economy, default prediction has never been more important. The excess cash illusion created in the context of COVID-19 may disappear just as quickly as the pandemic entered our world in 2020. In this paper, instead of using any...

Persistent link: https://www.econbiz.de/10012794088

Determinants and predictors of SMEs' financial failure: a logistic regression approach

Zizi, Youssef; Oudgou, Mohamed; El Moudden, Abdeslam - In: Risks : open access journal 8 (2020) 4/107, pp. 1-21

This paper aims to identify the determinants and predictors of Small and Medium-sized Enterprises (SMEs)' financial failure. Within this framework, we have opted for a quantitative method based on a sample of healthy and failing SMEs of a Moroccan bank. The main results of the different optimal...

Persistent link: https://www.econbiz.de/10012384411