The Graph Theoretical Approach to Bankruptcy Prediction
Keywords:
Financial Solvency Matrix; Permanent FunctionAbstract
Objectives – This paper explores the applicability of the graph theoretical methods to bankruptcy prediction.
Prior Work – Various statistical techniques have been used to predict business failure including univariate analysis, multivariate discrimination analysis, logit model, probit model, and neural networks.
Approach – This paper employs undirected graph and matrix methods based on the Graph Theory.
Results – The empirical findings confirmed the validity of the proposed methods for predicting bankruptcy.
Implications - The proposed method in this paper provides an insight into the development of a new approach to the assessment of financial solvency of a company.
Value – This paper contributes to the literature by introducing the graph theoretical approach to bankruptcy prediction.
References
Altman, E. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, 23, 589-609.
Altman, E. (1993). Corporate financial distress and bankruptcy. 2nd. Ed., Wiley, New York.
Aziz, M., & Dar, H. (2006). Predicting corporate bankruptcy: where we stand? Corporate Governance: The international journal of business in society.
Babalola, Y., & Abiola, F. (2013). Financial ratio analysis of firms: A tool for decision making. International journal of management sciences, 1(4), 132-137.
Barnes, P. (1987). The Analysis and Use of Financial Ratios: A Review Article. Journal of Business Finance and Accounting, 14(4), 449-461.
Beaver, W. (1966). Financial ratios as predictors of failure. Journal of Accounting Research (Supplement), 4, 71-102.
Bellovary, J., Giacomino, D., & Akers, M. (2007). A review of bankruptcy prediction studies: 1930 to present. Journal of Financial Education, 1-42.
Casey, C., & Bartczak, N. (1984). Cash flow: it’s not the bottom line. Harvard Business Review, 4, 60- 66. Deakin, E. (1972). A discriminant analysis of predictors of business failure. Journal of Accounting Research, 10, 167-179.
Choe, K. & Neureuther, (2020). Graph theoretical approach to bankruptcy prediction of retailers. IABE-2020 Conference Proceedings, 20(1), 45.
Choe, K. (2017). Network analysis of Transportation Improvement strategies, Review of Business
Research, 17(1), 49-60, 2017.
Choe, K. & Zhou, L. (2015). A study of structural balance in stock portfolio graphs. Journal of International Finance and Economics, 15(1), 13-20.
Deakin, E. (1972). A discriminant analysis of predictors of business failure. Journal of accounting research, 167-179.
Fitzpatrick, P. (1932). A comparison of ratios of successful industrial enterprises with those of failed companies, Certified Public Accountant, pp 598-605, 656-662, &721-731.
Gombola, M. & Ketz, J. (1983). Financial ratio patterns in retail and manufacturing organizations. Financial Management, 45-56.
Grover, S., Agrawal, V., & Khan, I. (2004). A digraph approach to TQM evaluation of an industry. International Journal of Production Research, 42(19), 4031-4053.
Holtemöller, O. & Muradoglu, Y. (2020). Corona shutdown and bankruptcy risk (No. 3/2020). IWH Online.
Hossari, G., & Rahman, S. (2005). A comprehensive formal ranking of the popularity of financial ratios in multivariate modeling of corporate collapse. Journal of the American academy of business, 6(1), 321-327.
Jewell, J., & Mankin, J. (2011). What is your ROA? An investigation of the many formulas for calculating return on assets. Academy of Educational Leadership Journal, 15, 79-91.
Klepáč, V., & Hampel, D. (2016). Prediction of bankruptcy with SVM classifiers among retail business companies in EU. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 64(2), 627-634.
Kulkarni, S. (2005). Graph theory and matrix approach for performance evaluation of TQM in Indian industries. The TQM magazine.
Lennox, C. (1999). Identifying failing companies: A re-evaluation of the logit, probit and MDA approaches. Journal of Economics and Business, 51(4), 347-364.
Lin, F., Liang, D., Yeh, C., & Huang, J. (2014). Novel feature selection methods to financial distress prediction. Expert Systems with Applications, 41(5), 2472-2483.
Lifschutz, S., & Jacobi, A. (2010). Predicting bankruptcy: evidence from Israel. International Journal of Business and Management, 5(4), 133.
Mey, M. & Lamprecht, C. (2020). The many faces of earnings before interest, tax, depreciation and amortisation (EBITDA): Assessing the decision usefulness of EBITDA disclosure by Johannesburg Stock Exchange-listed companies.
Mohamed, S., Li, A., & Sanda A. (2001). Predicting corporate failure in Malaysia: An application of the Logit Model to financial ratio analysis. Asian Academy of Management Journal, 6(1), 99-118.
Mohammed, A., & Kim-Soon, N. (2012). Using Altman’s model and current ratio to assess the financial status of companies quoted in the Malaysian Stock Exchange. International Journal of Scientific and Research Publications, 2(7), 1-11.
Nair, J. (2019). Corporate Distress and Bankruptcy Prediction--A Critical Review of Statistical Methods and Models. Abhigyan, 37(2), 10-21.
Nalurita, F. (2017). The effect of profitability ratio, solvability ratio, market ratio on stock return. Business and Entrepreneurial Review, 15(1), 73-94.
Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18, 109-131.
Prabowo, S. (2019). Analysis On The Prediction Of Bankruptcy Of Cigarette Companies Listed In the Indonesia Stock Exchange Using Altman (Z-score) Model And Zmijewski (X-score) Model. Jurnal Aplikasi Manajemen, 17(2), 254-260.
R. Anbanandam, D.K. Banwet and Ravi Shankar, Evaluation of Supply Chain Collaboration: A Case of Apparel Retail Industry in India, International Journal of Productivity and Performance Management, Vol. 60 No. 2, 2001, pp. 82-98.
Rao, R. & Gandhi, O. (2002). Digraph and matrix methods for the machinability evaluation of work materials. International Journal of Machine Tools and Manufacture, 42(3), 321-330.
Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. Journal of Business, 74 (1), 101-124.
Rashid, A. & Abbas, Q. (2011). Predicting Bankruptcy in Pakistan. Theoretical & Applied Economics, 18(9).
Rose, P. & Giroux, G. (1984). Predicting corporate bankruptcy: an analytical and empirical evaluation. Review of Financial Economics, 19(2), 1.
Son, H., Hyun, C., Phan, D., & Hwang, H. J. (2019). Data analytic approach for bankruptcy prediction. Expert Systems with Applications, 138, 112816.
Talib, F., Rahman, Z., & Qureshi, M. (2011). Assessing the awareness of total quality management in Indian service industries. Asian Journal on Quality, 12(3), 228-243.
Thakkar, J., Kanda, A., & Deshmukh, S. (2008). Evaluation of buyer‐supplier relationships using an integrated mathematical approach of interpretive structural modeling (ISM) and graph theoretic matrix. Journal of Manufacturing Technology Management, 19(1), 92–124.
Wu, Y., Gaunt, C., & Gray, S. (2010). A comparison of alternative bankruptcy prediction models. Journal of Contemporary Accounting & Economics, 6(1), 34-45.
Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting research, 59-82.
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