Refining understanding of corporate failure through a topological data analysis mapping of Altman’s Z-score model
•Introduce Topological Data Analysis Ball Mapper for examining creditworthiness.•Example taken from seminal Altman (1968) Z-Score model and ratios therefrom.•Failing firms shown to only occupy a subset of the “distress zone” of risky Z-Scores.•Visualizing data cloud removes the perceived “black box”...
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| Vydané v: | Expert systems with applications Ročník 156; s. 113475 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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15.10.2020
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | •Introduce Topological Data Analysis Ball Mapper for examining creditworthiness.•Example taken from seminal Altman (1968) Z-Score model and ratios therefrom.•Failing firms shown to only occupy a subset of the “distress zone” of risky Z-Scores.•Visualizing data cloud removes the perceived “black box” of data science.•Practitioners can quickly see how credit seekers place and review credit accordingly.
Corporate failure resonates widely, leaving practitioners searching for understanding of default risk. Managers seek to steer away from trouble, credit providers to avoid risky loans and investors to mitigate losses. Applying Topological Data Analysis tools, this paper explores whether failing firms from the United States organise neatly along the five predictors of default proposed by the Z-score models. Each firm is represented as a point in a five-dimensional point cloud, each dimension being one of the five predictors. Visualising that cloud using Ball Mapper reveals failing firms are not always located in similar regions of the point cloud, that is they are not concentrated in an easily split out area of the space. As new modelling approaches vie to better predict firm failure, often using black boxes to deliver potentially over-fitting models, a timely reminder is sounded on the importance of evidencing the identification process. Value is added to the understanding of where in the parameter space failure occurs, and how firms might act to move away from financial distress. Further, lenders may find opportunity amongst subsets of firms that are traditionally considered to be in danger of bankruptcy, but which the Ball Mapper plots developed herein clarify actually sit in characteristic spaces where failure has not occurred. |
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| AbstractList | Corporate failure resonates widely, leaving practitioners searching for understanding of default risk. Managers seek to steer away from trouble, credit providers to avoid risky loans and investors to mitigate losses. Applying Topological Data Analysis tools, this paper explores whether failing firms from the United States organise neatly along the five predictors of default proposed by the Z-score models. Each firm is represented as a point in a five-dimensional point cloud, each dimension being one of the five predictors. Visualising that cloud using Ball Mapper reveals failing firms are not always located in similar regions of the point cloud, that is they are not concentrated in an easily split out area of the space. As new modelling approaches vie to better predict firm failure, often using black boxes to deliver potentially over-fitting models, a timely reminder is sounded on the importance of evidencing the identification process. Value is added to the understanding of where in the parameter space failure occurs, and how firms might act to move away from financial distress. Further, lenders may find opportunity amongst subsets of firms that are traditionally considered to be in danger of bankruptcy, but which the Ball Mapper plots developed herein clarify actually sit in characteristic spaces where failure has not occurred. •Introduce Topological Data Analysis Ball Mapper for examining creditworthiness.•Example taken from seminal Altman (1968) Z-Score model and ratios therefrom.•Failing firms shown to only occupy a subset of the “distress zone” of risky Z-Scores.•Visualizing data cloud removes the perceived “black box” of data science.•Practitioners can quickly see how credit seekers place and review credit accordingly. Corporate failure resonates widely, leaving practitioners searching for understanding of default risk. Managers seek to steer away from trouble, credit providers to avoid risky loans and investors to mitigate losses. Applying Topological Data Analysis tools, this paper explores whether failing firms from the United States organise neatly along the five predictors of default proposed by the Z-score models. Each firm is represented as a point in a five-dimensional point cloud, each dimension being one of the five predictors. Visualising that cloud using Ball Mapper reveals failing firms are not always located in similar regions of the point cloud, that is they are not concentrated in an easily split out area of the space. As new modelling approaches vie to better predict firm failure, often using black boxes to deliver potentially over-fitting models, a timely reminder is sounded on the importance of evidencing the identification process. Value is added to the understanding of where in the parameter space failure occurs, and how firms might act to move away from financial distress. Further, lenders may find opportunity amongst subsets of firms that are traditionally considered to be in danger of bankruptcy, but which the Ball Mapper plots developed herein clarify actually sit in characteristic spaces where failure has not occurred. |
| ArticleNumber | 113475 |
| Author | Rudkin, Simon Qiu, Wanling Dłotko, Paweł |
| Author_xml | – sequence: 1 givenname: Wanling surname: Qiu fullname: Qiu, Wanling email: wanling.qiu@liverpool.ac.uk organization: School of Management, University of Liverpool, United Kingdom – sequence: 2 givenname: Simon surname: Rudkin fullname: Rudkin, Simon email: p.t.dlotko@swansea.ac.uk organization: Economics Department, Swansea University, United Kingdom – sequence: 3 givenname: Paweł surname: Dłotko fullname: Dłotko, Paweł email: s.t.rudkin@swansea.ac.uk organization: Mathematics Department, Swansea University, United Kingdom |
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| Cites_doi | 10.1111/jifm.12053 10.1016/j.eswa.2019.112827 10.1016/j.eswa.2016.01.015 10.1257/jep.31.2.87 10.1073/pnas.1102826108 10.2307/2490171 10.1016/j.physa.2017.09.028 10.1016/j.eswa.2018.05.026 10.1111/j.1468-0394.2012.00642.x 10.1016/j.eswa.2019.07.033 10.2307/2490395 10.1111/j.1540-6261.1968.tb00843.x 10.1007/s00454-008-9053-2 10.32614/CRAN.package.BallMapper 10.1016/j.eswa.2016.04.001 10.1016/j.eswa.2017.07.036 10.1023/A:1022627411411 10.1016/j.eswa.2017.04.006 |
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| References | Barboza, Kimura, Altman (b0025) 2017; 83 Ziba, Tomczak, Tomczak (b0130) 2016; 58 Altman (b0005) 1968; 23 Altman, Iwanicz-Drozdowska, Laitinen, Suvas (b0020) 2017; 28 Petropoulos, Chatzis, Xanthopoulos (b0110) 2016; 53 Pearson, P., Muellner, D., & Singh, G. (2015). TDAmapper: Analyze High-Dimensional Data Using Discrete Morse Theory. R package version 1.0. Choi, Son, Kim (b0045) 2018; 110 Dlotko, P. (2019). BallMapper: Create a Ball Mapper graph of the input data. R package version 0.1.0. Altman (b0010) 1983 Cawley, Talbot (b0040) 2010; 11 Vejdemo-Johansson, M., Carlsson, G., Lum, P.Y., Lehman, A., Singh, G., & Ishkhanov, T. (2012). The topology of politics: voting connectivity in the us house of representatives. In NIPS 2012 Workshop on Algebraic Topology and Machine Learning. Beaver (b0030) 1966; 4 Gidea, Katz (b0070) 2018; 491 Mullainathan, Spiess (b0085) 2017; 31 Son, Hyun, Phan, Hwang (b0120) 2019; 138 Singh, Mémoli, Carlsson (b0115) 2007 Beaver (b0035) 1968; 43 Li, Sun, Li, Yan (b0075) 2012; 30 Altman, Iwanicz-Drozdowska, Laitinen, Suvas (b0015) 2017; 28 Niyogi, Smale, Weinberger (b0095) 2008; 39 De Bock (b0055) 2017; 90 Ohlson (b0100) 1980; 18 Nicolau, Levine, Carlsson (b0090) 2011; 107 Dłotko, P. (2019). Ball mapper: a shape summary for topological data analysis. arXiv preprint arXiv:1901.07410. Liu, Xie, Zhao, Xie, Liu (b0080) 2019; 138 Cortes, Vapenik (b0050) 1995; 20 Nicolau (10.1016/j.eswa.2020.113475_b0090) 2011; 107 Altman (10.1016/j.eswa.2020.113475_b0010) 1983 10.1016/j.eswa.2020.113475_b0060 10.1016/j.eswa.2020.113475_b0065 Cawley (10.1016/j.eswa.2020.113475_b0040) 2010; 11 Beaver (10.1016/j.eswa.2020.113475_b0030) 1966; 4 Li (10.1016/j.eswa.2020.113475_b0075) 2012; 30 Petropoulos (10.1016/j.eswa.2020.113475_b0110) 2016; 53 Choi (10.1016/j.eswa.2020.113475_b0045) 2018; 110 Gidea (10.1016/j.eswa.2020.113475_b0070) 2018; 491 Son (10.1016/j.eswa.2020.113475_b0120) 2019; 138 Singh (10.1016/j.eswa.2020.113475_b0115) 2007 Cortes (10.1016/j.eswa.2020.113475_b0050) 1995; 20 Ohlson (10.1016/j.eswa.2020.113475_b0100) 1980; 18 Beaver (10.1016/j.eswa.2020.113475_b0035) 1968; 43 Liu (10.1016/j.eswa.2020.113475_b0080) 2019; 138 Niyogi (10.1016/j.eswa.2020.113475_b0095) 2008; 39 Altman (10.1016/j.eswa.2020.113475_b0020) 2017; 28 Altman (10.1016/j.eswa.2020.113475_b0005) 1968; 23 De Bock (10.1016/j.eswa.2020.113475_b0055) 2017; 90 10.1016/j.eswa.2020.113475_b0125 Altman (10.1016/j.eswa.2020.113475_b0015) 2017; 28 Barboza (10.1016/j.eswa.2020.113475_b0025) 2017; 83 Mullainathan (10.1016/j.eswa.2020.113475_b0085) 2017; 31 10.1016/j.eswa.2020.113475_b0105 Ziba (10.1016/j.eswa.2020.113475_b0130) 2016; 58 |
| References_xml | – volume: 30 start-page: 385 year: 2012 end-page: 397 ident: b0075 article-title: Forecasting business failure using two-stage ensemble of multivariate discriminant analysis and logistic regression publication-title: Expert Systems – volume: 23 start-page: 589 year: 1968 end-page: 609 ident: b0005 article-title: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy publication-title: The Journal of Finance – volume: 107 start-page: 7265 year: 2011 end-page: 7270 ident: b0090 article-title: Topology based data analysis identifies a group of breast cancers with a unique mutational profile and excellent survival publication-title: Proceedings of the National Academy of Sciences – volume: 18 start-page: 109 year: 1980 end-page: 131 ident: b0100 article-title: Financial ratios and the probabilistic prediction of bankruptcy publication-title: Journal of Accounting Research – volume: 58 start-page: 93 year: 2016 end-page: 101 ident: b0130 article-title: Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction publication-title: Expert Systems with Applications – volume: 28 start-page: 131 year: 2017 end-page: 171 ident: b0020 article-title: Financial distress prediction in an international context: A review and empirical analysis of Altman’s Z-score model publication-title: Journal of International Financial Management & Accounting – volume: 4 start-page: 71 year: 1966 end-page: 111 ident: b0030 article-title: Financial ratios as predictors of failure publication-title: Journal of Accounting Research – volume: 110 start-page: 1 year: 2018 end-page: 10 ident: b0045 article-title: Predicting financial distress of contractors in the construction industry using ensemble learning publication-title: Expert Systems with Applications – volume: 43 start-page: 113 year: 1968 end-page: 122 ident: b0035 article-title: Alternative accounting measures as predictors of failure publication-title: The Accounting Review – volume: 53 start-page: 87 year: 2016 end-page: 105 ident: b0110 article-title: A novel corporate credit rating system based on student’s-t hidden Markov models publication-title: Expert Systems with Applications – volume: 31 start-page: 87 year: 2017 end-page: 106 ident: b0085 article-title: Machine learning: an applied econometric approach publication-title: Journal of Economic Perspectives – volume: 138 year: 2019 ident: b0120 article-title: Data analytic approach for bankruptcy prediction publication-title: Expert Systems with Applications – volume: 20 start-page: 273 year: 1995 end-page: 297 ident: b0050 article-title: Support-vector networks publication-title: Machine Learning – volume: 138 year: 2019 ident: b0080 article-title: Novel evolutionary multi-objective soft subspace clustering algorithm for credit risk assessment publication-title: Expert Systems with Applications – reference: Pearson, P., Muellner, D., & Singh, G. (2015). TDAmapper: Analyze High-Dimensional Data Using Discrete Morse Theory. R package version 1.0. – volume: 83 start-page: 405 year: 2017 end-page: 417 ident: b0025 article-title: Machine learning models and bankruptcy prediction publication-title: Expert Systems with Applications – volume: 28 start-page: 131 year: 2017 end-page: 171 ident: b0015 article-title: Financial distress prediction in an international context: A review and empirical analysis of Altman’s Z-score model publication-title: Journal of International Financial Management & Accounting – reference: Vejdemo-Johansson, M., Carlsson, G., Lum, P.Y., Lehman, A., Singh, G., & Ishkhanov, T. (2012). The topology of politics: voting connectivity in the us house of representatives. In NIPS 2012 Workshop on Algebraic Topology and Machine Learning. – reference: Dlotko, P. (2019). BallMapper: Create a Ball Mapper graph of the input data. R package version 0.1.0. – volume: 90 start-page: 23 year: 2017 end-page: 30 ident: b0055 article-title: The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles publication-title: Expert Systems with Applications – year: 1983 ident: b0010 article-title: Corporate financial distress: A complete guide to predicting, avoiding, and dealing with bankruptcy – reference: Dłotko, P. (2019). Ball mapper: a shape summary for topological data analysis. arXiv preprint arXiv:1901.07410. – start-page: 91 year: 2007 end-page: 100 ident: b0115 article-title: Topological methods for the analysis of high dimensional data sets and 3d object recognition publication-title: SPBG – volume: 39 start-page: 419 year: 2008 end-page: 441 ident: b0095 article-title: Finding the homology of submanifolds with high confidence from random samples publication-title: Discrete & Computational Geometry – volume: 11 start-page: 2079 year: 2010 end-page: 2107 ident: b0040 article-title: On over-fitting in model selection and subsequent selection bias in performance evaluation publication-title: Journal of Machine Learning Research – volume: 491 start-page: 820 year: 2018 end-page: 834 ident: b0070 article-title: Topological data analysis of financial time series: Landscapes of crashes publication-title: Physica A: Statistical Mechanics and its Applications – volume: 11 start-page: 2079 issue: Jul year: 2010 ident: 10.1016/j.eswa.2020.113475_b0040 article-title: On over-fitting in model selection and subsequent selection bias in performance evaluation publication-title: Journal of Machine Learning Research – year: 1983 ident: 10.1016/j.eswa.2020.113475_b0010 – ident: 10.1016/j.eswa.2020.113475_b0125 – volume: 28 start-page: 131 issue: 2 year: 2017 ident: 10.1016/j.eswa.2020.113475_b0020 article-title: Financial distress prediction in an international context: A review and empirical analysis of Altman’s Z-score model publication-title: Journal of International Financial Management & Accounting doi: 10.1111/jifm.12053 – volume: 138 year: 2019 ident: 10.1016/j.eswa.2020.113475_b0080 article-title: Novel evolutionary multi-objective soft subspace clustering algorithm for credit risk assessment publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2019.112827 – volume: 53 start-page: 87 year: 2016 ident: 10.1016/j.eswa.2020.113475_b0110 article-title: A novel corporate credit rating system based on student’s-t hidden Markov models publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2016.01.015 – volume: 31 start-page: 87 issue: 2 year: 2017 ident: 10.1016/j.eswa.2020.113475_b0085 article-title: Machine learning: an applied econometric approach publication-title: Journal of Economic Perspectives doi: 10.1257/jep.31.2.87 – volume: 107 start-page: 7265 year: 2011 ident: 10.1016/j.eswa.2020.113475_b0090 article-title: Topology based data analysis identifies a group of breast cancers with a unique mutational profile and excellent survival publication-title: Proceedings of the National Academy of Sciences doi: 10.1073/pnas.1102826108 – volume: 4 start-page: 71 year: 1966 ident: 10.1016/j.eswa.2020.113475_b0030 article-title: Financial ratios as predictors of failure publication-title: Journal of Accounting Research doi: 10.2307/2490171 – volume: 491 start-page: 820 year: 2018 ident: 10.1016/j.eswa.2020.113475_b0070 article-title: Topological data analysis of financial time series: Landscapes of crashes publication-title: Physica A: Statistical Mechanics and its Applications doi: 10.1016/j.physa.2017.09.028 – volume: 110 start-page: 1 year: 2018 ident: 10.1016/j.eswa.2020.113475_b0045 article-title: Predicting financial distress of contractors in the construction industry using ensemble learning publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2018.05.026 – volume: 30 start-page: 385 issue: 5 year: 2012 ident: 10.1016/j.eswa.2020.113475_b0075 article-title: Forecasting business failure using two-stage ensemble of multivariate discriminant analysis and logistic regression publication-title: Expert Systems doi: 10.1111/j.1468-0394.2012.00642.x – volume: 138 year: 2019 ident: 10.1016/j.eswa.2020.113475_b0120 article-title: Data analytic approach for bankruptcy prediction publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2019.07.033 – ident: 10.1016/j.eswa.2020.113475_b0060 – volume: 18 start-page: 109 year: 1980 ident: 10.1016/j.eswa.2020.113475_b0100 article-title: Financial ratios and the probabilistic prediction of bankruptcy publication-title: Journal of Accounting Research doi: 10.2307/2490395 – volume: 23 start-page: 589 issue: 4 year: 1968 ident: 10.1016/j.eswa.2020.113475_b0005 article-title: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy publication-title: The Journal of Finance doi: 10.1111/j.1540-6261.1968.tb00843.x – volume: 39 start-page: 419 issue: 1–3 year: 2008 ident: 10.1016/j.eswa.2020.113475_b0095 article-title: Finding the homology of submanifolds with high confidence from random samples publication-title: Discrete & Computational Geometry doi: 10.1007/s00454-008-9053-2 – ident: 10.1016/j.eswa.2020.113475_b0105 – ident: 10.1016/j.eswa.2020.113475_b0065 doi: 10.32614/CRAN.package.BallMapper – volume: 43 start-page: 113 year: 1968 ident: 10.1016/j.eswa.2020.113475_b0035 article-title: Alternative accounting measures as predictors of failure publication-title: The Accounting Review – volume: 58 start-page: 93 year: 2016 ident: 10.1016/j.eswa.2020.113475_b0130 article-title: Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2016.04.001 – volume: 90 start-page: 23 year: 2017 ident: 10.1016/j.eswa.2020.113475_b0055 article-title: The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2017.07.036 – volume: 20 start-page: 273 year: 1995 ident: 10.1016/j.eswa.2020.113475_b0050 article-title: Support-vector networks publication-title: Machine Learning doi: 10.1023/A:1022627411411 – volume: 83 start-page: 405 year: 2017 ident: 10.1016/j.eswa.2020.113475_b0025 article-title: Machine learning models and bankruptcy prediction publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2017.04.006 – volume: 28 start-page: 131 issue: 2 year: 2017 ident: 10.1016/j.eswa.2020.113475_b0015 article-title: Financial distress prediction in an international context: A review and empirical analysis of Altman’s Z-score model publication-title: Journal of International Financial Management & Accounting doi: 10.1111/jifm.12053 – start-page: 91 year: 2007 ident: 10.1016/j.eswa.2020.113475_b0115 article-title: Topological methods for the analysis of high dimensional data sets and 3d object recognition |
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| SubjectTerms | Bankruptcy Bankruptcy prediction Credit scoring Data analysis Data visualization Default Failure analysis Loans Mapping Three dimensional models Topological data analysis Topology |
| Title | Refining understanding of corporate failure through a topological data analysis mapping of Altman’s Z-score model |
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