The Graph Theoretical Approach to Bankruptcy Prediction

  • Kwangseek Choe SUNY Plattsburgh
  • Samy Garas SUNY Plattsburgh
Keywords: Financial Solvency Matrix; Permanent Function

Abstract

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

Agarwal, V., & Taffler, R. (2008). Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking & Finance, 32(8), 1541-1551.

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.
Published
2020-11-04
Section
Articles