Using market variables in financial distress prediction for Vietnamese listed companies

  • Vu Thi Loan Vietnam National University - Vietnam
  • Do Thi Thuy Phuong Thai Nguyen University - Vietnam
  • Ha Manh Tuan Thai Nguyen University - Vietnam
Keywords: Financial distress prediction, SVM model, Market variables


This paper aims to investigate the classification power of market variables as predictors in the financial distress prediction model for listed companies in a frontier market as Vietnam securities market. Data is collected from 70 financially distressed companies that suffer a loss in 3 consecutive years and 156 non-financially distressed companies in Vietnam from 2010 to 2017. Four different models have been constructed using Logit regression and SVM analysis technique to make a prediction in 1 to 3-year ahead. The analysis results show that combining accounting ratios with market variables such as price volatility and P/E can improve the classification ability of the ex-ante model. In addition, contrary to the results of related previous researches in emerging markets, in this study, Logit models outperform SVM models. Therefore, for future research, models that apply other machine learning classifiers such as Decision Tree (DT) or Neural Network (NN) should be investigated.


Download data is not yet available.


Metrics Loading ...


Agarwal, V., & Taffler, R. (2007). Twenty-five years of the Taffler z-score model: Does it really have predictive ability? Accounting and Business research, 37: 285–300.

Alfaro E., García N., Gámez M., Elizondo D. (2008). Bankruptcy forecasting: an empirical comparison of adaboost and neural networks. Decision Support Systems, 45: 110–122.

Alifiah M. (2014). Prediction of financial distress companies in the trading and services sector in Malaysia using macroeconomic variables. Procedia - Social and Behavioral Sciences, 129: 90 – 98.

Altman E. I. (1968). Financial Ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of finance, 23: 589-609.

Altman, E. and Saunders, A. (1997). Credit risk measurement: Developments over the last 20 years. Journal of Banking & Finance, 21(11-12): 1721-1742

Altman, E. I. (2002). Corporate Distress Prediction Models in a Turbulent Economic and Basel II Environment. NYU Working Paper, FIN-02-052, available at SSRN:

Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K.., Suvas,A. (2016). Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman’s Z-Score Model. Journal of International Financial Management & Accounting. doi:10.1111/jifm.12053

Bagher, A.N., and Milad, S. (2016). Designing a Bankruptcy Prediction Model Based on Account, Market and Macroeconomic Variables (Case Study: Cyprus Stock Exchange). Iranian Journal of Management studies. 9 (1): 125-147

Balcaen S., & Ooghe H. (2006). 35 years of studies on business failure: An overview of the classic statistical methodologies and their related problems. The British Accounting Review. 38(1): 63-93.

Beaver W. (1966). Financial ratios as predictors of failures. Journal of Accounting research, 4: 71-111.

Beaver, W. H., Correia, M., McNichols, M.F. (2010). Financial Statement Analysis and the Prediction of Financial Distress. Foundations and Trends in Accounting. 5(2): 99-173.

Beaver W., McNichols M., and Rhie J. (2005). Have financial statements become less informative? Evidence from the ability of financial ratios to predict bankruptcy. Review of Accounting Studies, 10(1): 93-122.

Bhattacharjee A., Han J. (2014). Financial distress of Chinese firms: Microeconomic, macroeconomic and institutional influences. China Economic Review, 30: 244–262.

Campbell Y., Hilscher D., and Szilagyi J.( 2011). Predicting financial distress and the performance of distressed stocks. Journal of Investment Management ,9(2): 14-34.

Cheung L., & Levy A. (1998). An Integrative Analysis of Business Bankruptcy in Australia. Economics Working Papers. 98-03, School of Economics, University of Wollongong, NSW, Australia.

Geng R., Bose I., Chen X. (2014). Prediction of financial distress: An empirical study of listed Chinese companies using data mining. European of Operational Research, 1-12.

Gepp, A. and Kumar, K. (2015). Predicting Financial Distress: A Comparison of Survival Analysis and Decision Tree Techniques. Procedia Computer Science. 54: 396 – 404

Hillegeist S., Keating E., Cram D., Lundstedt K. (2004). Assessing the probability of bankruptcy’, Review of Accounting Studies, 9: 5–34

Hill T., Susan E., and Andes S. (1996). Evaluating Firms in Financial Distress: An Event History Analysis. Journal of Applied Business Research, 12 (3): 60-71.

Koh K., Robert B., Dai L., Chang M. (2015). Financial distress: Lifecycle and corporate restructuring. Journal of Corporate Finance, 33: 19–33.

Li, Z., Crook, J., Andreeva, G., (2017). Dynamic prediction of Þnancial distress using malmquist DEA. Expert Systems With Applications. 1-28. doi: 10.1016/j.eswa.2017.03.017

Liang, D., Tsai, C., Wu, H. (2015). The effect of feature selection on financial distress prediction. Knowledge-Based Systems. 73: 289–297

Lin F., Liang D., Chen, E. (2011). Financial ratio selection for business crisis prediction. Expert Systems with Applications, 38(12): 15094–15102.

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.

Mselmi, N., Lahiani, A., Hamza, T. (2017). Financial distress prediction: The case of French small and medium-sized firms International Review of Financial Analysis.1-39. doi: 10.1016/j.irfa.2017.02.004

Ohlson, D. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1): 109–131.

Pindado J., Rodrigues L. (2004). Parsimonious models of financial distress in small companies. Small Bus Econ, 22:51–6.

Pindado J., Rodrigues L., Torre C. (2008). Estimating financial distress likelihood. Journal of Business Research, 61: 995–1003.

Sánchez, C.L., García, V., Marqués, A.I., Sánchez, J.S.(2016). Financial distress prediction using the hybrid associative memory with translation. Applied Soft Computing 44: 144–152.

Santoso, N., Wibowo,W. (2018). Financial Distress Prediction using Linear Discriminant Analysis and Support Vector Machine. J. Phys.: Conf. Ser. 979 (012089).

Sayari, N., Mugan, C.S. (2016). Industry specific financial distress modeling. BRQ Bus. Res. Q,

Schwartz J. (1997). Comment on ‘Debt-Deflation and Financial Instability: Two Historical Explorations’ by Barry Eichengreen andRichard S. Grossman. St. Martin’s Press, 100-105.

Tinoco H., Wilson N. (2013). Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. International review of financial analysis, 30: 394-419.

Tinoco, M. H., Holmes, P., Wilson, N. (2018). Polytomous response financial distress models: The role of accounting, market and macroeconomic variables International Review of Financial Analysis.1-39. doi:10.1016/j.irfa.2018.03.017

Ugurlu M.( 2006). Prediction of corporate financial distress in an emerging market: the case of Turkey Cross Cultural Management: An International Journal, 13(4): 277-295.

Wilson, R.L. and Sharda, R. (1994). Bankruptcy Prediction Using Neural Networks. Decision Support Systems. 11: 545-557.

Zhou L., Lai K., Yen. J. (2012). Empirical models based on features ranking techniques for corporate financial distress prediction. Computers & Mathematics with Applications, 64(8): 2484-2496.

How to Cite
Loan, V. T., Thuy Phuong, D. T., & Tuan, H. M. (2019). Using market variables in financial distress prediction for Vietnamese listed companies. Religación. Revista De Ciencias Sociales Y Humanidades, 4(14), 341-352. Retrieved from