Feature selection methods and sampling techniques to financial distress prediction for Vietnamese listed companies
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DOIhttp://dx.doi.org/10.21511/imfi.16(1).2019.22
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Article InfoVolume 16 2019, Issue #1, pp. 276-290
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The research is taken to integrate the effects of variable selection approaches, as well as sampling techniques, to the performance of a model to predict the financial distress for companies whose stocks are traded on securities exchanges of Vietnam. A firm is financially distressed when its stocks are delisted as requirement from Vietnam Stock Exchange because of making a loss in 3 consecutive years or having accumulated a loss greater than the company’s equity. There are 12 models, constructed differently in feature selection methods, sampling techniques, and classifiers. The feature selection methods are factor analysis and F-score selection, while 3 sets of data samples are chosen by choice-based method with different percentages of financially distressed firms. In terms of classifying technique, logistic regression together with SVM are used in these models. Data are collected from listed firms in Vietnam from 2009 to 2017 for 1, 2 and 3 years before the announcement of their delisting requirement. The experiment’s results highlight the outperformance of the SVM model with F-score selection method in a data sample containing the highest percentage of non-financially distressed firms.
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JEL Classification (Paper profile tab)G32, G33, G38
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References40
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Tables16
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Figures0
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- Table 1. Description of the models
- Table 2. Stepwise selection results for logistic regression models – variable set 1
- Table 3. Stepwise selection results for logistic regression models – variable set 2
- Table 4. Features selected in SVM models – variable set 1
- Table 5. Features selected in SVM models – variable set 2
- Table 6. Logistic regression classification accuracy (%)
- Table 7. Type I errors of logistic regression models (%)
- Table 8. Summary of classification – SVM models (%)
- Table 9. Type I error of SVM models (%)
- Table A1. List of independent variables – variable set 1
- Table A2. List of independent variables – variable set 2
- Table A3. Results of factor analysis – variable set 1
- Table A4. Results of factor analysis – variable set 2
- Table A5. Model’s overall significance – variable set 1
- Table A6. Model’s overall significance – variable set 2
- Table A7. Summary of C and gamma
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- 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. 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, 28(2), 131-171.
- Altman, E. I. (1984). A further empirical investigation of the bankruptcy cost question. The Journal of Finance, 39(4), 1067-1089.
- Back, B., Laitinen, T., & Sere, K. (1996). Neural Networks and Bankruptcy Prediction: Funds Flows Accrual Ratios and Accounting Data. Advances in Accounting, 14, 23-37.
- 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. H. (1966). Financial ratios as predictors of failures. Journal of Accounting Research, 4, 71-111.
- Beaver, W. H., McNichols, M. F., & Rhie, J.-W. (2005). Have financial statements become less informative? Evidence from the ability of financial ratios to predict bankruptcy. Review of Accounting Studies, 10, 93-122.
- Bharath, S., & Shumay, T. (2008). Forecasting Default with the Merton Distance to Default Model. The Review of Financial Studies, 21(3), 1339-1369.
- Bhattacharjee, A., & Han, J. (2014). Financial distress of Chinese firms: Microeconomic, macroeconomic and institutional influences. China Economic Review, 30, 244-262.
- Blum, A. L., & Langley, P. (1997). Selection of relevant features and examples in machine learning. Artificial Intelligence, 97(1-2), 245-271.
- Chen, Y. W., & Lin, C. J. (2003). Combining SVMs with various feature selection strategies. In NIPS 2003 feature selection challenge (pp. 1-10).
- Christidis, A., & Gregory, A. (2010). Some new models for financial distress prediction in the UK (Discussion paper No. 10/04). Xfi centre for finance and investment.
- Geng, R., Bose, I., & Chen, X. (2014). Prediction of financial distress: An empirical study of listed Chinese companies using data mining. European Journal of Operational Research, 241(1), 236-247.
- Gepp, A., & Kumar, K. (2015). Predicting Financial Distress: A Comparison of Survival Analysis and Decision Tree Techniques. Procedia Computer Science, 54, 396-404.
- Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.
- Hsu, C., Chang, C., & Lin, C. (2016). A Practical Guide to Support Vector Classification.
- Kim, H. S., & Sohn, S. Y. (2010). Support vector machines for default prediction of SMEs based on technology credit. European Journal of Operational Research, 201, 838-846.
- Kittler, J. (1978). Feature set search algorithms. In C. H. Chen (Ed.), Pattern Recognition and Signal Processing (pp. 41-60). Netherlands: Sijthoff and Noordhoff, Alphen aan den Rijn.
- Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97(1), 273-324.
- Koopman, S. J., & Lucas, A. (2005). Business and default cycles for credit risk. Journal of Applied Econometrics, 20, 311-323.
- 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., 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, 50, 67-80.
- Norton, C., Smith, L., & Ralph, E. (1980). A Comparison of General Price Level and Historical Cost Financial Statements in the Prediction of Bankruptcy: A Reply. The Accounting Review, 55(3), 516-521.
- Ohlson, D. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131.
- Pindado, J., Rodrigues, L., & Torre, C. (2008). Estimating financial distress likelihood. Journal of Business Research, 61, 995-1003.
- Powell, W. B. (2007). Approximate Dynamic Programming: Solving the Curses of Dimensionality. Hoboken, NJ: Wiley-InterScience.
- Rees, W. P. (1995). Financial analysis. London: Prentice-Hall.
- Sánchez, L., García, V., Marqués, A., & Sánchez, J. (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. Journal of Physics Conference Series, 979(1).
- Sayari, N., Mugan, C. S. (2016). Industry specific financial distress modeling. BRQ Business Research Quarterly, 20(1), 45-62.
- Shaonan, T., Yan, Y., & Guo, H. (2015). Variable selection and corporate bankruptcy forecasts. Journal of Banking & Finance, 52(C), 89-100.
- Song, Q., Jiang, H., Zhao, X., & Wu, X. (2017). Combination of minimum enclosing balls classifier with SVM in coal-rock recognition. PLoS One, 12(9).
- 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, 59, 276-289.
- Ugurlu, M., Aksoy, H. (2006). Prediction of corporate financial distress in an emerging market: the case of Turkey. Cross Cultural Management: An International Journal, 13(4), 277-295.
- Wan, J. W., Yang, M., & Chen, Y. J. (2015). Discriminative cost sensitive Laplacian score for face recognition. Neurocomputing, 152, 333-344.
- Yjlmaz, E. (2013). An Expert System Based on Fisher Score and LS-SVM for Cardiac Arrhythmia Diagnosis. Computational and Mathematical Methods in Medicine, 1-6.
- Zmijewski, M. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59-82.
- 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.
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