IDEAS home Printed from https://ideas.repec.org/r/eee/jbfina/v40y2014icp432-442.html
   My bibliography  Save this item

Are hazard models superior to traditional bankruptcy prediction approaches? A comprehensive test

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Kalak, Izidin El & Azevedo, Alcino & Hudson, Robert & Karim, Mohamad Abd, 2017. "Stock liquidity and SMEs’ likelihood of bankruptcy: Evidence from the US market," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1383-1393.
  2. Xiaobo Tang & Shixuan Li & Mingliang Tan & Wenxuan Shi, 2020. "Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 769-787, August.
  3. Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
  4. Christophe Schalck & Meryem Yankol-Schalck, 2021. "Predicting French SME failures: new evidence from machine learning techniques," Applied Economics, Taylor & Francis Journals, vol. 53(51), pages 5948-5963, November.
  5. Ben Jabeur, Sami, 2017. "Bankruptcy prediction using Partial Least Squares Logistic Regression," Journal of Retailing and Consumer Services, Elsevier, vol. 36(C), pages 197-202.
  6. Stef, Nicolae & Zenou, Emmanuel, 2021. "Management-to-staff ratio and a firm's exit," Journal of Business Research, Elsevier, vol. 125(C), pages 252-260.
  7. Ruben Loaiza-Maya & Michael Stanley Smith, 2017. "Variational Bayes Estimation of Discrete-Margined Copula Models with Application to Time Series," Papers 1712.09150, arXiv.org, revised Jul 2018.
  8. De Bock, Koen W. & Coussement, Kristof & Lessmann, Stefan, 2020. "Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach," European Journal of Operational Research, Elsevier, vol. 285(2), pages 612-630.
  9. Simone Pizzi & Fabio Caputo & Andrea Venturelli, 2020. "Does it pay to be an honest entrepreneur? Addressing the relationship between sustainable development and bankruptcy risk," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 27(3), pages 1478-1486, May.
  10. El Kalak, Izidin & Hudson, Robert, 2016. "The effect of size on the failure probabilities of SMEs: An empirical study on the US market using discrete hazard model," International Review of Financial Analysis, Elsevier, vol. 43(C), pages 135-145.
  11. Grodecka-Messi, Anna & Kenny, Seán & Ögren, Anders, 2021. "Predictors of bank distress: The 1907 crisis in Sweden," Explorations in Economic History, Elsevier, vol. 80(C).
  12. Boratyńska, Katarzyna & Grzegorzewska, Emilia, 2018. "Bankruptcy prediction in the agribusiness sector: Lessons from quantitative and qualitative approaches," Journal of Business Research, Elsevier, vol. 89(C), pages 175-181.
  13. Sigrist, Fabio & Hirnschall, Christoph, 2019. "Grabit: Gradient tree-boosted Tobit models for default prediction," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 177-192.
  14. Cathcart, Lara & Dufour, Alfonso & Rossi, Ludovico & Varotto, Simone, 2020. "The differential impact of leverage on the default risk of small and large firms," Journal of Corporate Finance, Elsevier, vol. 60(C).
  15. Ugur, Mehmet & Solomon, Edna & Zeynalov, Ayaz, 2022. "Leverage, competition and financial distress hazard: Implications for capital structure in the presence of agency costs," Economic Modelling, Elsevier, vol. 108(C).
  16. Jarmila Horváthová & Martina Mokrišová, 2018. "Risk of Bankruptcy, Its Determinants and Models," Risks, MDPI, vol. 6(4), pages 1-22, October.
  17. Bátiz-Zuk Enrique & Mohamed Abdulkadir & Sánchez-Cajal Fátima, 2021. "Exploring the sources of loan default clustering using survival analysis with frailty," Working Papers 2021-14, Banco de México.
  18. M. Simona Andreano & Roberto Benedetti & Andrea Mazzitelli & Federica Piersimoni, 2018. "Spatial autocorrelation and clusters in modelling corporate bankruptcy of manufacturing firms," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 45(4), pages 475-491, December.
  19. Sigrist, Fabio & Leuenberger, Nicola, 2023. "Machine learning for corporate default risk: Multi-period prediction, frailty correlation, loan portfolios, and tail probabilities," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1390-1406.
  20. Christopoulos, Andreas D., 2017. "The composition of CMBS risk," Journal of Banking & Finance, Elsevier, vol. 76(C), pages 215-239.
  21. Surbhi Bhatia & Manish K. Singh, 2022. "Fifty years since Altman (1968): Performance of financial distress prediction models," Working Papers 12, xKDR.
  22. Marui Du & Yue Ma & Zuoquan Zhang, 2021. "A Meta Path Based Evaluation Method for Enterprise Credit Risk," Papers 2110.11594, arXiv.org, revised May 2022.
  23. Tian, Shaonan & Yu, Yan, 2017. "Financial ratios and bankruptcy predictions: An international evidence," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 510-526.
  24. Yukiko Konno & Yuki Itoh, 2016. "An alternative to the standardized approach for assessing credit risk under the Basel Accords," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1220119-122, December.
  25. Adriana Csikosova & Maria Janoskova & Katarina Culkova, 2020. "Application of Discriminant Analysis for Avoiding the Risk of Quarry Operation Failure," JRFM, MDPI, vol. 13(10), pages 1-14, September.
  26. Mselmi, Nada & Lahiani, Amine & Hamza, Taher, 2017. "Financial distress prediction: The case of French small and medium-sized firms," International Review of Financial Analysis, Elsevier, vol. 50(C), pages 67-80.
  27. Koen W. de Bock & Kristof Coussement & Stefan Lessmann, 2020. "Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach," Post-Print hal-02863245, HAL.
  28. Ahmad, Abd Halim, 2019. "What factors discriminate reorganized and delisted distressed firms: Evidence from Malaysia," Finance Research Letters, Elsevier, vol. 29(C), pages 50-56.
  29. Leccadito, Arturo & Tunaru, Radu S. & Urga, Giovanni, 2015. "Trading strategies with implied forward credit default swap spreads," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 361-375.
  30. Koresh Galil & Neta Gilat, 2019. "Predicting Default More Accurately: To Proxy or Not to Proxy for Default?," International Review of Finance, International Review of Finance Ltd., vol. 19(4), pages 731-758, December.
  31. Ampudia, Miguel & Busetto, Filippo & Fornari, Fabio, 2022. "Chronicle of a death foretold: does higher volatility anticipate corporate default?," Working Paper Series 2749, European Central Bank.
  32. John Nkwoma Inekwe, 2016. "Financial Distress, Employees’ Welfare and Entrepreneurship Among SMEs," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 129(3), pages 1135-1153, December.
  33. ARATA Yoshiyuki, 2018. "Bankruptcy propagation on a customer-supplier network: An empirical analysis in Japan," Discussion papers 18040, Research Institute of Economy, Trade and Industry (RIETI).
  34. Michał Thor & Łukasz Postek, 2024. "Gated recurrent unit network: A promising approach to corporate default prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1131-1152, August.
  35. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.
  36. Alam, Nurul & Gao, Junbin & Jones, Stewart, 2021. "Corporate failure prediction: An evaluation of deep learning vs discrete hazard models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
  37. Li, Huan & Wu, Weixing, 2024. "Loan default predictability with explainable machine learning," Finance Research Letters, Elsevier, vol. 60(C).
  38. Floros, Ioannis & White, Joshua T., 2016. "Qualified residential mortgages and default risk," Journal of Banking & Finance, Elsevier, vol. 70(C), pages 86-104.
  39. Nico Dewaelheyns & Cynthia Van Hulle & Yannick Van Landuyt & Mathias Verreydt, 2021. "Labor Contracts, Wages and SME Failure," Sustainability, MDPI, vol. 13(14), pages 1-15, July.
  40. Ampudia, Miguel & Busetto, Filippo & Fornari, Fabio, 2022. "Chronicle of a death foretold: does higher volatility anticipate corporate default?," Bank of England working papers 1001, Bank of England.
  41. Mohammad Mahdi Mousavi & Jamal Ouenniche, 2018. "Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions," Annals of Operations Research, Springer, vol. 271(2), pages 853-886, December.
  42. Koresh Galil & Margalit Samuel & Offer Moshe Shapir & Wolf Wagner, 2023. "Bailouts and the modeling of bank distress," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 46(1), pages 7-30, February.
  43. Avino, Davide E. & Conlon, Thomas & Cotter, John, 2019. "Credit default swaps as indicators of bank financial distress," Journal of International Money and Finance, Elsevier, vol. 94(C), pages 132-139.
  44. Datta, Pratik & Surya Prakash B. S. & Sane, Renuka, 2017. "Understanding Judicial Delay at the Income Tax Appellate Tribunal in India," Working Papers 17/208, National Institute of Public Finance and Policy.
  45. Evangelos C. Charalambakis & Ian Garrett, 2019. "On corporate financial distress prediction: What can we learn from private firms in a developing economy? Evidence from Greece," Review of Quantitative Finance and Accounting, Springer, vol. 52(2), pages 467-491, February.
  46. Li, Zhiyong & Li, Aimin & Bellotti, Anthony & Yao, Xiao, 2023. "The profitability of online loans: A competing risks analysis on default and prepayment," European Journal of Operational Research, Elsevier, vol. 306(2), pages 968-985.
  47. Li, Tangrong & Lin, Hui, 2021. "Credit risk and equity returns in China," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 588-613.
  48. Katarina Valaskova & Dominika Gajdosikova & Jaroslav Belas, 2023. "Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries," Oeconomia Copernicana, Institute of Economic Research, vol. 14(1), pages 253-293, March.
  49. Afik, Zvika & Arad, Ohad & Galil, Koresh, 2016. "Using Merton model for default prediction: An empirical assessment of selected alternatives," Journal of Empirical Finance, Elsevier, vol. 35(C), pages 43-67.
  50. Gila Burde, 2018. "Improved Methods for Predicting the Financial Vulnerability of Nonprofit Organizations," Administrative Sciences, MDPI, vol. 8(1), pages 1-8, February.
  51. Christopoulos, Andreas D. & Barratt, Joshua G., 2016. "Credit risk findings for commercial real estate loans using the reduced form," Finance Research Letters, Elsevier, vol. 19(C), pages 228-234.
  52. Trushin, Eshref & Ugur, Mehmet, 2018. "Ecosystem complexity, firm learning and survival: UK evidence on intra-industry age and size diversity as exit hazards," Greenwich Papers in Political Economy 19095, University of Greenwich, Greenwich Political Economy Research Centre.
  53. Iwanicz-Drozdowska, Małgorzata & Jackowicz, Krzysztof & Kozłowski, Łukasz, 2018. "SMEs' near-death experiences. Do local banks extend a helping hand?," Emerging Markets Review, Elsevier, vol. 37(C), pages 47-65.
  54. Alessandro Bitetto & Stefano Filomeni & Michele Modina, 2021. "Understanding corporate default using Random Forest: The role of accounting and market information," DEM Working Papers Series 205, University of Pavia, Department of Economics and Management.
  55. Stefano Filomeni & Udichibarna Bose & Anastasios Megaritis & Athanasios Triantafyllou, 2024. "Can market information outperform hard and soft information in predicting corporate defaults?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 3567-3592, July.
  56. Brezigar-Masten, Arjana & Masten, Igor & Volk, Matjaž, 2021. "Modelin-g credit risk with a Tobit model of days past due," Journal of Banking & Finance, Elsevier, vol. 122(C).
  57. Yang Liu & Qingguo Zeng & Bobo Li & Lili Ma & Joaquín Ordieres‐Meré, 2022. "Anticipating financial distress of high‐tech startups in the European Union: A machine learning approach for imbalanced samples," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1131-1155, September.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.