Open Access Journal Article

Machine Learning Survival Models restrictions: the case of startups time to failed with collinearity-related issues

by Diego Vallarino a,*
a
Independent Researcher, Spain
*
Author to whom correspondence should be addressed.
JES  2023 1(3):14; https://doi.org/10.58567/jes01030001
Received: 18 August 2023 / Accepted: 10 November 2023 / Published Online: 1 December 2023
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Abstract

This research evaluates the efficacy of survival models in forecasting startup failures and investigates their economic implications. Several machine learning survival models, including Kernel SVM, DeepSurv, Survival Random Forest, and MTLR, are assessed using the concordance index (C-index) as a measure of prediction accuracy. The findings reveal that more sophisticated models, such as Multi-Task Logical Regression (MTLR) and Random Forest, outperform the standard Cox and Kaplan Meier (K-M) models in terms of predicted accuracy.

Keywords: Startups Failure; Survival Analysis; Survival Machine Learning Models; Concordance Index;
Copyright: © 2023 by Vallarino. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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ACS Style
Vallarino, D. Machine Learning Survival Models restrictions: the case of startups time to failed with collinearity-related issues. Journal of Economic Statistics, 2023, 1, 14. https://doi.org/10.58567/jes01030001
AMA Style
Vallarino D. Machine Learning Survival Models restrictions: the case of startups time to failed with collinearity-related issues. Journal of Economic Statistics; 2023, 1(3):14. https://doi.org/10.58567/jes01030001
Chicago/Turabian Style
Vallarino, Diego 2023. "Machine Learning Survival Models restrictions: the case of startups time to failed with collinearity-related issues" Journal of Economic Statistics 1, no.3:14. https://doi.org/10.58567/jes01030001
APA style
Vallarino, D. (2023). Machine Learning Survival Models restrictions: the case of startups time to failed with collinearity-related issues. Journal of Economic Statistics, 1(3), 14. https://doi.org/10.58567/jes01030001

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References

  1. Aalen, Odd O. (1989). A linear regression model for the analysis of life times. Statistics in medicine, 907-925. https://doi.org/10.1002/sim.4780080803
  2. Adnan Aziz, M and Dar, Humayon A. (2006). Predicting corporate bankruptcy: where we stand?. Corporate Governance: The international journal of business in society, 18-33. https://doi.org/10.1108/14720700610649436
  3. Alaka, Hafiz A and Oyedele, Lukumon O and Owolabi, Hakeem A and Kumar, Vikas and Ajayi, Saheed O and Akinade, Olugbenga O and Bilal, Muhammad. (2018). Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Systems with Applications, 164-184. https://doi.org/10.1016/j.eswa.2017.10.040
  4. Alhurani, Abdullah S and Hamdan-Mansour, Ayman M and Ahmad, Muayyad M and McKee, Gabrielle and O’Donnell, Sharon and O’Brien, Frances and Mooney, Mary and Saleh, Zyad T and Moser, Debra K. (2022). The Association of Persistent Symptoms of Depression and Anxiety with Recurrent Acute Coronary Syndrome Events: A Prospective Observational Study. Healthcare, 383. https://doi.org/10.3390/healthcare10020383
  5. Altman, Edward I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance, 589-609. https://doi.org/10.2307/2978933
  6. Andersen, Per Kragh and Gill, Richard. (1982). Cox's regression model for counting processes: a large sample study. The annals of statistics, 1100-1120. https://doi.org/10.1214/aos/1176345976
  7. Barboza, Flavio and Kimura, Herbert and Altman, Edward. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 405-417. https://doi.org/10.1016/j.eswa.2017.04.006
  8. Bauer, Julian and Agarwal, Vineet. (2014). Are hazard models superior to traditional bankruptcy prediction approaches? A comprehensive test. Journal of Banking & Finance, 432-442. https://doi.org/10.1016/j.jbankfin.2013.12.013
  9. Beaver, William H. (1966). Financial ratios as predictors of failure. Journal of accounting research, 71-111. https://doi.org/10.2307/2490171
  10. Beretta, Alessandro and Heuchenne, Cedric. (2019). Variable selection in proportional hazards cure model with time-varying covariates, application to US bank failures. Journal of Applied Statistics, 1529-1549. https://doi.org/10.1080/02664763.2018.1554627
  11. Bharath, Sreedhar T and Shumway, Tyler. (2008). Forecasting default with the Merton distance to default model. The Review of Financial Studies, 1339-1369. https://doi.org/10.1093/rfs/hhn044
  12. Bijwaard, Govert E and Franses, Philip Hans and Paap, Richard. (2006). Modeling purchases as repeated events. Journal of Business & Economic Statistics, 487-502. https://doi.org/10.1198/073500106000000242
  13. Box-Steffensmeier and Janet M and De Boef, Suzanna. (2006). Repeated events survival models: the conditional frailty model. Statistics in medicine, 3518-3533. https://doi.org/10.1002/sim.2434
  14. Chang, Shu-Hui and Wang, Mei-Cheng. (1999). Conditional regression analysis for recurrence time data. Journal of the American Statistical Association, 1221-1230. https://doi.org/10.1080/01621459.1999.10473875
  15. Clayton, David. (1994). Some approaches to the analysis of recurrent event data. Statistical methods in medical research, 244-262. https://doi.org/10.1177/096228029400300304
  16. Cox, David R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 187-202. https://doi.org/10.1111/j.2517-6161.1972.tb00899.x
  17. Cox, Raymond AK and Kimmel, Randall K and Wang, Grace WY. (2017). Proportional hazards model of bank failure: Evidence from USA. Journal of Economic & Financial Studies, 35-45. https://doi.org/10.18533/jefs.v5i3.290
  18. De Leonardis, Daniele and Rocci, Roberto. (2014). Default risk analysis via a discrete-time cure rate model. Applied Stochastic Models in Business and Industry, 529-543. https://doi.org/10.1002/asmb.1998
  19. Deakin, Edward B. (1972). A discriminant analysis of predictors of business failure. Journal of accounting research, 167-179. https://doi.org/10.2307/2490225
  20. Du Jardin, Philippe. (2015). Bankruptcy prediction using terminal failure processes. European Journal of Operational Research, 286-303. https://doi.org/10.1016/j.ejor.2014.09.059
  21. Duffie, Darrell and Saita, Leandro and Wang, Ke. (2007). Multi-period corporate default prediction with stochastic covariates. Journal of financial economics, 635-665. https://doi.org/10.1016/j.jfineco.2005.10.011
  22. Ejoku, Jonatha. (2020). Analysis of recurrent events with associated informative censoring: Application to HIV data. International Journal of Statistics in Medical Research. https://doi.org/10.6000/1929-6029.2020.09.03
  23. Emrouznejad, Ali and Yang, Guo-liang. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978-2016. Socio-economic planning sciences, 4-8. https://doi.org/10.1016/j.seps.2017.01.008
  24. Fotso, Stephane. (2018). Deep neural networks for survival analysis based on a multi-task framework. arXiv preprint arXiv:1801.05512.
  25. Gepp, Adrian and Kumar, Kuldeep. (2008). The role of survival analysis in financial distress prediction. International research journal of finance and economics, 13-34.
  26. Godlewski, Christophe J. (2015). The dynamics of bank debt renegotiation in Europe: A survival analysis approach. Economic Modelling, 19-31. https://doi.org/10.1016/j.econmod.2015.03.017
  27. Henriques, Iago Cotrim and Sobreiro, Vinicius Amorim and Kimura, Herbert and Mariano, Enzo Barberio. (2020). Two-stage DEA in banks: Terminological controversies and future directions. Expert Systems with Applications, 113632. https://doi.org/10.1016/j.eswa.2020.113632
  28. Hosaka, Tadaaki. (2019). Bankruptcy prediction using imaged financial ratios and convolutional neural networks. Expert systems with applications, 287-299. https://doi.org/10.1016/j.eswa.2018.09.039
  29. Hu, Dan and Zheng, Haiyan. (2015). Does ownership structure affect the degree of corporate financial distress in China? Journal of Accounting in Emerging Economies. https://doi.org/10.1108/JAEE-09-2011-0037
  30. Jabeur, Sami Ben and Serret, Vanessa. (2023). Bankruptcy prediction using fuzzy convolutional neural networks. Research in International Business and Finance, 101844. https://doi.org/10.1016/j.ribaf.2022.101844
  31. Kristanti, Farida Titik and Herwany, Aldrin. (2017). Corporate governance, financial ratios, political risk and financial distress: A survival analysis. Accounting and Finance Review, 26-34. http://dx.doi.org/10.35609/afr.2017.2.2(4)
  32. Lane, William R and Looney, Stephen W and Wansley, James W. (1986). An application of the Cox proportional hazards model to bank failure. Journal of Banking & Finance, 511-531. https://doi.org/10.1016/S0378-4266(86)80003-6
  33. LeBlanc, Michael and Crowley, John. (1992). Relative risk trees for censored survival data. Biometrics, 411-425. https://doi.org/10.2307/2532300
  34. Lee, Suk Hun and Urrutia, Jorge L. (1996). Analysis and prediction of insolvency in the property-liability insurance industry: A comparison of logit and hazard models. Journal of Risk and insurance, 121-130. https://doi.org/10.2307/253520
  35. Li, Zhiyong and Crook, Jonathan and Andreeva, Galina and Tang, Ying. (2021). Predicting the risk of financial distress using corporate governance measures. Pacific-Basin Finance Journal, 101334. https://doi.org/10.1016/j.pacfin.2020.101334
  36. Lin, Wei-Yang and Hu, Ya-Han and Tsai, Chih-Fong. (2011). Machine learning in financial crisis prediction: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C Applications and Reviews, 421-436. https://doi.org/10.1109/TSMCC.2011.2170420
  37. Luoma, Martti and Laitinen, Erkki K. (1991). Survival analysis as a tool for company failure prediction. Omega, 673-678. https://doi.org/10.1016/0305-0483(91)90015-L
  38. Mai, Feng and Tian, Shaonan and Lee, Chihoon and Ma, Ling. (2019). Deep learning models for bankruptcy prediction using textual disclosures. European journal of operational research, 743-758. https://doi.org/10.1016/j.ejor.2018.10.024
  39. Ohlson, James A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of accounting research, 109-131. https://doi.org/10.2307/2490395
  40. Parker, Susan and Peters, Gary F and Turetsky, Howard F. (2005). Corporate governance factors and auditor going concern assessments. Review of Accounting and Finance. https://doi.org/10.1108/eb043428
  41. Pölsterl, Sebastian, Nassir Navab, and Amin Katouzian. (2015). Fast training of support vector machines for survival analysis. Springer. https://doi.org/10.1007/978-3-319-23525-7_15
  42. Prentice, Ross L and Williams, Benjamin J and Peterson, Arthur V. (1981). On the regression analysis of multivariate failure time data. Biometrika, 373-379. https://doi.org/10.1093/biomet/68.2.373
  43. Shumway, Tyler. (2001). Forecasting bankruptcy more accurately: A simple hazard model. The journal of business, 101-124. https://doi.org/10.1086/209665
  44. Tam, Kar Yan and Kiang, Melody Y. (1992). Managerial applications of neural networks: the case of bank failure predictions. Management science, 926-947. https://doi.org/10.1287/mnsc.38.7.926
  45. Tian, Shaonan and Yu, Yan. (2017). Financial ratios and bankruptcy predictions: An international evidence. International Review of Economics & Finance, 510-526. https://doi.org/10.1016/j.iref.2017.07.025
  46. Tinoco, Mario Hernandez and Wilson, Nick. (2013). Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. International review of financial analysis, 394-419. https://doi.org/10.1016/j.irfa.2013.02.013
  47. Twisk, Jos WR and Smidt, Nynke and de Vente, Wieke. (2005). Applied analysis of recurrent events: a practical overview. Journal of Epidemiology & Community Health, 706-710. http://dx.doi.org/10.1136/jech.2004.030759
  48. Uno, Hajime and Cai, Tianxi and Pencina, Michael J and D'Agostino, Ralph B and Wei, Lee-Jen. (2011). On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Statistics in medicine, 1105-1117. https://doi.org/10.1002/sim.4154
  49. Van Belle, Vanya and Pelckmans, Kristiaan and Suykens, Johan AK and Van Huffel, Sabine. (2007). Support vector machines for survival analysis. Proceedings of the third international conference on computational intelligence in medicine and healthcare (cimed2007), 1-8.
  50. Wang, Ping and Li, Yan and Reddy, Chandan K. (2019). Machine learning for survival analysis: A survey. ACM Computing Surveys (CSUR), 1-36. https://doi.org/10.1145/3214306
  51. Wang, Yuling and Carson, James M. (2010). Macroeconomic factors and insurer rating transitions. Available at SSRN 1558456. http://dx.doi.org/10.2139/ssrn.1558456
  52. Wang, Zongjun and Li, Hongxia. (2007). Financial distress prediction of Chinese listed companies: a rough set methodology. Chinese Management Studies, 93-110. https://doi.org/10.1108/17506140710758008
  53. Wei, Lee-Jen and Lin, Danyu Y and Weissfeld, Lisa. (1989). Regression analysis of multivariate incomplete failure time data by modeling marginal distributions. Journal of the American statistical association, 1065-1073. https://doi.org/10.1080/01621459.1989.10478873
  54. Yang, Zijiang and You, Wenjie and Ji, Guoli. (2011). Using partial least squares and support vector machines for bankruptcy prediction. Expert Systems with Applications, 8336-8342. https://doi.org/10.1016/j.eswa.2011.01.021
  55. Yu, Chun-Nam and Greiner, Russell and Lin, Hsiu-Chin and Baracos, Vickie. (2011). Learning patient-specific cancer survival distributions as a sequence of dependent regressors. Advances in neural information processing systems
  56. Zelenkov, Yuri and Fedorova, Elena and Chekrizov, Dmitry. (2017). Two-step classification method based on genetic algorithm for bankruptcy forecasting. Expert Systems with Applications, 393-401. https://doi.org/10.1016/j.eswa.2017.07.025
  57. Zhou, Fanyin and Fu, Lijun and Li, Zhiyong and Xu, Jiawei. (2022). The recurrence of financial distress: A survival analysis. International Journal of Forecasting, 1100-1115. https://doi.org/10.1016/j.ijforecast.2021.12.005