A Research Review of Financial Distress Prediction
DOI: https://doi.org/10.62517/jse.202411323
Author(s)
Ting Dai1, 2, Shizhuan Han3, Daniel Wong Barrenechea3
Affiliation(s)
1School of Economics and Management, Jiangxi University of Software Professional Technology, Nanchang, Jiangxi, China
2Philippine Christian University Center for International Education, Manila, Philippines
3School of Economics and Management, East China Jiaotong University, Nanchang, Jiangxi, China
Abstract
This study claims that two key points must be addressed in order to increase the success rate and robustness of financial distress prediction: firstly, a simplified financial prediction indicator system must be established based on a correct understanding and clearly distinguish about the concept of financial distress. Secondly, we establish a reasonable and plausible financial distress prediction model. It has been established that machine learning models can enhance financial distress prediction models' predictive power to some degree, bu the outcomes of these models vary. This is mostly due to a lack of knowledge about financial distress and a poor choice of indicators for predicting financial distress. This paper makes the case that there are three distinct, dynamic phases of financial distress. The financial strain stage is the initial phase. The financial distress at this stage is reflected in the financial indicators, as there are now some challenges with loan repayment due to the slowdown in the main business's revenue growth rate, the slowdown in operating cash flow, and the drop in the current ratio. The second stage can be called the financial crisis stage. The enterprise's ability to generate income is still declining, along with the quality of revenue and turnover rate. It’s worth noting that gearing ratio, which will be approaching 50% or above at this stage. The third stage of financial distress is distress situation. In this stage, the firm’s balance sheet structure keeps getting worse, and the gearing ratio keeps rising at an unprecedented rate. In the meantime, with growing revenue-cost-expense ratios and negative profits, profitability is still declining. At this stage, at least two of the three cash flows from financing, investing, and operating operations were negative in terms of cash flow.
Keywords
Financial Distress; Indicator System; Prediction Model; Theoretical Discussion
References
[1] Altman, Edward I. "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. " The journal of finance 23.4(1968):589-609.
[2] Altman, Edward I., Robert G. Haldeman, and Paul Narayanan. "ZETATM analysis A new model to identify bankruptcy risk of corporations. " Journal of banking & finance 1.1(1977):29-54.
[3] Beaver, William H. "Financial ratios as predictors of failure. " Journal of accounting research (1966):71-111.
[4] Back, Thomas. Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford university press, 1996.
[5] Bao, X. -Z., Tao, Q. -Y., and Fu, H. -Y. "Corporate Financial Early Warning Based on Panel Discrete Choice Modeling and Its Industry Applicability. " Journal of Systems Management 25.1(2016):36-44.
[6] Beaver, William H., Maureen F. McNichols, and Jung-Wu Rhie. "Have financial statements become less informative? Evidence from the ability of financial ratios to predict bankruptcy. " Review of Accounting studies 10(2005):93-122.
[7] Cai, Hongyan, and Han, Liyan. "A Study of Financial Condition Determination Models for Listed Companies. " Auditing Research 1(2003):62-64.
[8] Campbell, John Y., Jens Hilscher, and Jan Szilagyi. "In search of distress risk. " Journal of finance 63.6(2008):2899-2939.
[9] Casey, Cornelius, and Norman Bartczak. "Using operating cash flow data to predict financial distress: Some extensions. " Journal of Accounting Research (1985):384-401.
[10] Chen, Mu-Yen. "Predicting corporate financial distress based on integration of decision tree classification and logistic regression. " Expert systems with applications 38.9(2011):11261-11272.
[11] Chou, Jui-Sheng, et al. "Machine learning in concrete strength simulations: Multi-nation data analytics. " Construction and Building materials 73(2014):771-780.
[12] Christidis, Angela, and Alan Gregory. "Some new models for financial distress prediction in the UK. " Xfi-Centre for Finance and Investment Discussion Paper 10(2010).
[13] Dai Lijun, Song Zhigang. "Management capacity, financial distress and audit fees. "Financial and accounting newsletter 15(2022):31-34.
[14] Donker, Han, Bernard Santen, and Saif Zahir. "Ownership structure and the likelihood of financial distress in the Netherlands. " Applied Financial Economics 19.21(2009):1687-1696.
[15] Fan, Alan, and Marimuthu Palaniswami. "Selecting bankruptcy predictors using a support vector machine approach. " Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium. Vol. 6. IEEE, 2000.
[16] Guan Lili. "Industrial policy, overinvestment and corporate financial distress. "Financial and accounting newsletter 4(2023):68-71.
[17] Habib, Ahsan, Mostafa Monzur Hasan, and Haiyan Jiang. "Stock price crash risk: review of the empirical literature. " Accounting & Finance 58(2018):211-251.
[18] Helfat, Constance E., and Margaret A. Peteraf. "The dynamic resource‐based view: Capability lifecycles. " Strategic management journal 24.10(2003):997-1010.
[19] Hu, N., and Jin, Q. L. "Sociability Burden and Corporate Financial Distress Dynamics - An Examination Based on the ST System. " Accounting Research 11(2018):28-35.
[20] Jiang, Yaqi. "Financial Early Warning of Listed Tourism Companies Based on Multivariate Probit Models. " Statistics and Decision Making 3(2014):181-183.
[21] Jinchang, Z. H. A. N. G., and W. A. N. G. Dawei. "Financial distress: theoretical definition and analysis. " Collected Essays on Finance and Economics 267.12(2020):61.
[22] Johnsen, Thomajean, and Ronald W. Melicher. "Predicting corporate bankruptcy and financial distress: Information value added by multinomial logit models. " Journal of economics and business 46.4(1994):269-286.
[23] Jones, Stewart, and David A. Hensher. "Predicting firm financial distress: A mixed logit model. " the accounting review 79.4(2004):1011-1038.
[24] Khaw, Karren Lee-Hwei, et al. "Gender diversity, state control, and corporate risk-taking: Evidence from China. " Pacific-Basin Finance Journal 39(2016):141-158.
[25] Laitinen, Erkki K., and Teija Laitinen. "Bankruptcy prediction: Application of the Taylor's expansion in logistic regression. " International review of financial analysis 9.4(2000):327-349.
[26] Lee, Tsun‐Siou, and Yin‐Hua Yeh. "Corporate governance and financial distress: Evidence from Taiwan. " Corporate governance: An international review 12.3(2004):378-388.
[27] Lennox, Clive S. "The accuracy and incremental information content of audit reports in predicting bankruptcy. " Journal of Business Finance & Accounting 26.5‐6(1999):757-778.
[28] Leshno, Moshe, and Yishay Spector. "Neural network prediction analysis: the bankruptcy case. " Neurocomputing 10.2(1996):125-147.
[29] Liu, Yanwen, and Wang, Yugang. "Empirical Analysis of Expense Sticky Behavior of Chinese Listed Companies. " Management Review 21.3(2009):98-106.
[30] Ma Yannan. "Societal burdens, cash holdings and firms' future financial distress. "Financial and accounting newsletter 16(2021):84-87.
[31] Manzaneque, Montserrat, and Alba María Priego. "Corporate governance effect on financial distress likelihood: Evidence from Spain: Efecto del gobierno corporativo en la probabilidad de fracaso empresarial: evidencia española. " Revista de Contabilidad-Spanish Accounting Review 19.1(2016):111-121.
[32] Martin, Daniel. "Early warning of bank failure: A logit regression approach. " Journal of banking & finance 1.3(1977):249-276.
[33] Meyer, Paul A., and Howard W. Pifer. "Prediction of bank failures. " journal of finance 25.4(1970):853-868.
[34] Mi Wandong. "Forecasting financial distress: binary Logistic regression analysis vs. GABP algorithm comparison. "Journal of Xi′an Aeronautical University 3(2022):89-96
[35] Ning, Qingqing, and Zu, Ming. "A Wide Angle Perspective and Empirical Study of Corporate Financial Crisis Causes. " East China Economic Management 3(2013):135-139.
[36] Ohlson, James A. "Financial ratios and the probabilistic prediction of bankruptcy. " Journal of accounting research (1980):109-131.
[37] Platt, Harlan D., and Marjorie B. Platt. "Understanding differences between financial distress and bankruptcy. " Review of Applied Economics 2.1076-2016-87135(2006):141-157.
[38] Qin, Zhimin, and Guo, Wen. "A Study on the Performance Trend of Firms after A+ H-share Cross-listing. " Research on Financial Issues 4(2012):66-74.
[39] Sun, J. N., and Qi, L. "Research on financial distress prediction model based on GWO-SVM. " Microcomputer Applications 35.4(2019):95-99.
[40] Sun, Jie, et al. "Multi-class financial distress prediction based on support vector machines integrated with the decomposition and fusion methods. " Information Sciences 559(2021):153-170.
[41] Sun, Jie, et al. "Dynamic class-imbalanced financial distress prediction based on case-based reasoning integrated with time weighting and resampling. " Journal of Credit Risk 19.1(2023).
[42] Sun Jie, Li Nengfei, and Zhao Mengru. "Negative Media Coverage and Early Warning of Firms' Financial Difficulties-Based on Text Analysis and Machine Learning. " Journal of Finance and Economics 302.9(2023):80.
[43] Sun Ying, and Cui Jing. "A Study on the Identification and Early Warning of Zombie Enterprises in China. " Journal of Hohai University: Philosophy and Social Science Edition 19.5(2017):81-88.
[44] Tang, Chuanyi, et al. "A longitudinal exploration of the relations between electronic word-of-mouth indicators and firms’ profitability: Findings from the banking industry. " International Journal of Information Management 36.6(2016):1124-1132.
[45] Tsai, Chih-Fong, and Jhen-Wei Wu. "Using neural network ensembles for bankruptcy prediction and credit scoring. " Expert systems with applications 34.4(2008):2639-2649.
[46] Weaver, John Ernest, and T. J. Fitzpatrick. "Ecology and relative importance of the dominants of tall-grass prairie. " Botanical Gazette 93.2(1932):113-150.
[47] Westgaard, Sjur, and Nico Van der Wijst. "Default probabilities in a corporate bank portfolio: A logistic model approach. " European journal of operational research 135.2(2001):338-349.
[48] Wu, Shinong, and Xianyi Lu. "A study of models for predicting financial distress in China’s listed companies. " Economic Research Journal 6(2001):46-55.
[49] Yang, Cai, and Ma Haifeng. "On M&A Forecast Model of Target Firms Basing on Chinese Listed Companies. " 2009 International Conference on Information Management, Innovation Management and Industrial Engineering. Vol. 2. IEEE, 2009.
[50] Yang, Navy, and Tai Lei. "Forecasting Financial Distress of Listed Companies Based on Fuzzy Support Vector Machines. " Journal of Management Science 3(2009):102-110.
[51] Zavgren, Christine. "The prediction of corporate failure: the state of the art. " Journal of Accounting Literature 2.1(1983):1-38.
[52] Zeng, Huixiang, Li Yang, and Jing Shi. "Does the supervisory ability of internal audit executives affect the occurrence of corporate fraud? Evidence from small and medium-sized listed enterprises in China. " International Journal of Accounting & Information Management 29.1(2021):1-26.
[53] Zmijewski, Mark E. "Methodological issues related to the estimation of financial distress prediction models. " Journal of Accounting research (1984):59-82.
[54] Zhang Jinchang, Fan Ruizhen. "Theoretical analysis and empirical test of the causes of capital chain breakage. "China industrial economics 3(2012):95-107.
[55] Zhang, Ling. "A Discriminant Model for Early Warning Analysis of Financial Crisis. " Studies in Quantitative and Technical Economics 17.3(2000):49-51.
[56] Zhang, Zhiwang, et al. "A rough set-based multiple criteria linear programming approach for classification. " Computational Science–ICCS 2008:8th International Conference, Kraków, Poland, June 23-25, 2008, Proceedings, Part II 8. Springer Berlin Heidelberg, 2008.
[57] Zheng, G. J., D. J. Lin, and F. D. Zhang. "Major Shareholder Financial Distress, Hollowing Out and the Effectiveness of Corporate Governance-Evidence from Major Shareholder Financial Data. " Management World 5(2013):157-168.
[58] Zhou, M., and Wang, X. Y. "Early Warning of Corporate Financial Crisis Based on Fuzzy Preferences and Neural Networks. " Journal of Management Science 5.3(2002):86-90.
[59] Zhou, Shouhua, Yang, Jihua, and Wang, Ping. "On Early Warning Analysis of Financial Crisis - the F-Score Model. " Accounting Research 8(1996):8-11.
[60] Zhu Guanxiang. " Shanghai Bicycle Depot Mitigation Some measures to alleviate Some measures to alleviate the current financial difficulties of Shanghai SPV. " Shanghai Accounting(1989):10-11.