* Corresponding Author
Hide
Show all
Financial Distress Prediction with Stacking Ensemble Learning
https://doi.org/10.22146/ijccs.76575 Muhammad Fadhlil Hadi(1*), De-Ron Liang(2), Tri Kuntoro Priyambodo(3), Azhari SN(4)
(1) Master Program in Computer Science, FMIPA UGM, Yogyakarta
(2) National Central University, Zhongli
(3) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(4) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(*) Corresponding Author
Abstract
Previous studies have used financial ratios extensively to build their predictive model of financial distress. The Altman ratio is the most often used to predict, especially in academic studies. However, the Altman ratio is highly dependent on the validity of the data in financial statements, so other variables are needed to assess the possibility of manipulation of financial statements. None of the previous studies combined the five Altman Ratios with the Beneish M-Score. We use Stacking Ensemble Learning to classify crisis companies and perform a comprehensive analysis. This insight helps the investment public make lending decisions by mixing all the financial indicator information and assessing it carefully based on long-term and short-term conditions and possible manipulation of financial statements.
Keywords
Full Text:
PDFReferences
[1] J. Sun, H. Li, Q. H. Huang, and K. Y. He, “Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches,” Knowledge-Based Systems, vol. 57, pp. 41–56, 2014.
[2] Y. S. Huang and C. Y. Suen, “Behavior-knowledge space method for combination of multiple classifiers,” in IEEE Computer Vision and Pattern Recognition, 1993.
[3] E. I. Altman, “Financial Ratios, Discriminant Analysis and The Prediction of Corporate Bankruptcy,” The Journal of Finance, 1968.
[4] D. Liang, C.-C. Lu, C. F. Tsai, and G. A. Shih, “Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study,” European Journal of Operational Research, 2016.
[5] P. du Jardin, D. Veganzones, and E. Séverin, “Forecasting Corporate Bankruptcy Using Accrual-Based Models,” Computational Economics, 2019.
[6] K. Valaskova, R. Fedoroko, "Beneish M-Score: A Measurement of fraudulent financial transactions in global environment," SHS Web of Conferences 92, 02064, 2021
[7] E. I. Altman, M. Iwanicz-Drozdowska, E. K. Laitinen, and A. Suvas, “Distressed Firm and Bankruptcy Prediction in an International Context: A Review and Empirical Analysis of Altman’s Z-Score Model,” SSRN Electronic Journal, 2014.
[8] C. Brentani, “Financial statement analysis and financial ratios,” in Portfolio Management in Practice, Elsevier, pp. 149–163, 2004.
[9] P. A. Griffin, “Financial Statement Analysis,” in Finding Alphas: A Quantitative Approach to Building Trading Strategies, 2015.
[10] E. J. Allen, C. R. Larson, and R. G. Sloan, “Accrual reversals, earnings and stock returns,” Journal of Accounting and Economics, 2013.
[11] S. Tian and Y. Yu, “Financial ratios and bankruptcy predictions: An international evidence,” International Review of Economics and Finance, 2017.
[12] J. Barugahare, A. Amirkhanian, F. Xiao, S. Amirkhanian, “Predicting the dynamic modulus of hot mix asphalt mixtures using bagged trees ensemble,” Construction and Building Materials, 260, p.120468, 2020.
[13] H. Frydman, E. I. Altman, and D.-L. Kao, “Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress,” The Journal of Finance, vol. 40, no. 1, pp. 269–291, Mar. 1985.
[14] Tarjo, N. Herawati, “Application of Beneish M-Score Models and Data Mining to Detect Financial Fraud,” Global Conferences on Business and Social Science, Vol.211, pp. 924-930, 2015.
[15] M. P. Sesmero, A. I. Ledezma, and A. Sanchis, “Generating ensembles of heterogeneous classifiers using Stacked Generalization,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2015.
[16] W. Jiang, Z. Chen, Y. Xiang, D. Shao, L. Ma, and J. Zhang, “SSEM: A Novel Self-Adaptive Stacking Ensemble Model for Classification,” IEEE Access, vol. 7, pp. 120337–120349, 2019.
[17] S. Džeroski and B. Ženko, “Is Combining Classifiers with Stacking Better than Selecting the Best One?,” Machine Learning, vol. 54, no. 3, pp. 255–273, Mar. 2004.
[18] E. Menahem, L. Rokach, and Y. Elovici, “Troika – An improved stacking schema for classification tasks,” Information Sciences, vol. 179, no. 24, pp. 4097–4122, Dec. 2009.
[19] M.-Y. Chen, “Predicting corporate financial distress based on integration of decision tree classification and logistic regression,” Expert Systems with Applications, vol. 38, no. 9, pp. 11261–11272, Sep. 2011.
[20] A. J. Scott, D. W. Hosmer, and S. Lemeshow, “Applied Logistic Regression,” Biometrics, 1991.
[21] R. Septiani, S. Musyarofah, R. Yuliana, "Beneish M-Score Reliability as a Tool For Detecting Financial Statements Fraud," International Colloquium on Forensics Accounting and Governance, Vol.1, no.1 , 2020
[22] A.N. Fadillah, D. Liang, C.-C. Lu, “Financial Distress Prediction Based on Long-Term and Short-Term Behaviour of Taiwan List Companies,” National Central University, 2020.
[23] D. Novitasari, D. Liang, "The Role of Comprehensive Income and Accrual in Predicting Bankruptcy," National Central University, 2018
[24] M.D. Beneish, M.C. Lee, C. Nichols, "Earnings Manipulation and Expected
Returns.," Financial Analysts Journal, vol. 69 no. 2, pp. 57-82, 2013.
[25] L. Svabova, K. Kramarova, J. Chutka, L. Strakova, "Detecting earnings
manipulation and fraudulent financial reporting in Slovakia," Oeconomia Copernicana, vol.11 no.3, pp 485-508, 2020.
[26] M. Ayu, R.R. Gamayuni, M. Urbański, "The impact of environmental and social costs disclosure on financial performance mediating by earning management," Polish Journal of Management Studies, vol.21, no, 1, pp. 74-86, 2020.
[27] A. Siekelova, A. Androniceanu, P. Durana, K. Frajtova Michalikova, "Earnings
management (EM), initiatives and company size: An empirical study," Acta Polytechnica Hungarica, vol. 17, no. 9, pp. 41-56, 2020.
[28] P. M. Healy, J.M. Wahlen, , "A Review of the Earnings Management Literature
and its Implications for Standard Setting.," Accounting Horizons, vol. 13, pp. 365-383, 1999
[29] T. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE Transactions on
Information Theory, vol. 13, no. 1, pp. 21–27, 1967.
[30] C. J. C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” Data mining and knowledge discovery, vol. 2, no. 2, pp. 121–167, 1998
[31] C. Cortes and V. Vapnik, “Support Vector Networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995.
[32] J. MacCarthy, “Using Altman Z-score and Beneish M-score Models to Detect Financial Fraud and Corporate Failure: A Case Study of Enron Corporation.”, International Journal of Finance and Accounting, International Journal of Finance and, 2017.
[33] M. Warshavsky, “Analyzing Earnings Quality as a Financial Forensic Tool,” Financial Valuation and Litigation Expert Journal, vol. 39, pp. 16-20, 2012.
[34] I. Pustylnick, “Combined Algorithm of Detection of Manipulation in Financial Statements,” Far Eastern Federal University, 2009.
[35] K. Qin, S. Shi, P. Suganthan, M. Loog, “Enhanced Direct Linear Discriminant Analysis for Feature Extraction on High Dimensional Data,” Twentieth National Conference on Artificial Intelligence, pp. 851-855, 2005.
[36] F. Barboza, H. Kimura, and E. Altman, “Machine Learning Models and Bankruptcy Prediction”. Expert Systems with Applications: An International Journal, vol. 83, pp 405–417, 2017.
[37] A. Vieira, J. Duarte, B. Ribeiro, and J. Carvalho das Neves, “Accurate Prediction of Financial Distress of Companies with Machine Learning Algorithms,” International Conference on Adaptive and Natural Computing Algorithms, vol.5495, pp. 569-576, 2009.
[38] D. Liang, C. Tsai, H. Lu, L.Chang “Combining Corporate Governance Indicators with Stacking Ensembles for Financial Distress Prediction,” Journal of Business Research, vol.120, pp. 137-146, 2020.
DOI: https://doi.org/10.22146/ijccs.76575
Article Metrics
Abstract views : 1726
| 
views : 1158
Refbacks
- There are currently no refbacks.
Copyright (c) 2022 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1