Computational Economics

, Volume 53, Issue 2, pp 851–872 | Cite as

Improving Financial Distress Prediction Using Financial Network-Based Information and GA-Based Gradient Boosting Method

  • Jiaming Liu
  • Chong WuEmail author
  • Yongli LiEmail author


Previous studies on financial distress prediction have chiefly used financial indicators which derived from financial statements as explanatory variables, so some potentially useful information that contained in the financial network was not considered. The listed companies can be represented as a complex financial network which the firms are regarded as nodes and the links account for stock returns correlation. The purpose of this study is to investigate whether network-based variables can improve the predictive power of financial distress prediction. Therefore, this study proposed a genetic algorithm (GA) approach to parameter selection in gradient boosting decision tree and integrated network-based variables for financial distress prediction. In order to verify the prediction capability of network-based variables and GA-based gradient boosting method in financial distress prediction, empirical study based on Chinese listed firms’ real data is employed, and comparative analysis is conducted. The experiment results indicate that the introduction of network-based variables and GA-based gradient boosting method for financial distress prediction can enhance predictive performance in terms of accuracy, recall, precision, F-score, type I error, and type II error.


Financial distress prediction Financial network Network-based variable Gradient boosting method Genetic algorithm 



This work is supported by the National Natural Science Foundation of China (Nos. 71271070, 71501034) and Hei Longjiang Natural Science Foundation (No. G2016003).


  1. Abbasimehr, H., Setak, M., & Soroor, J. (2013). A framework for identification of high-value customers by including social network based variables for churn prediction using neuro-fuzzy techniques. International Journal of Production Research, 51(4), 1279–1294.Google Scholar
  2. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609.Google Scholar
  3. Beaver, W. H. (1968). Alternative accounting measures as predictors of failure. The Accounting Review, 43(1), 113–122.Google Scholar
  4. Benoit, D. F., & Van den Poel, D. (2012). Improving customer retention in financial services using kinship network information. Expert Systems with Applications, 39(13), 11435–11442.Google Scholar
  5. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.Google Scholar
  6. Caraiani, P. (2017). The predictive power of local properties of financial networks. Physica A: Statistical Mechanics and its Applications, 466, 79–90.Google Scholar
  7. Castro, J. L., Navarro, M., Sanchez, J. M., & Zurita, J. M. (2011). Introducing attribute risk for retrieval in case-based reasoning. Knowledge-Based Systems, 24(2), 257–268.Google Scholar
  8. Chaudhuri, A., & De, K. (2011). Fuzzy support vector machine for bankruptcy prediction. Applied Soft Computing, 11(2), 2472–2486.Google Scholar
  9. Chauhan, N., Ravi, V., & Chandra, D. K. (2009). Differential evolution trained wavelet neural networks: Application to bankruptcy prediction in banks. Expert Systems with Applications, 36(4), 7659–7665.Google Scholar
  10. Chen, M. Y. (2011). Predicting corporate financial distress based on integration of decision tree classification and logistic regression. Expert Systems with Applications, 38(9), 11261–11272.Google Scholar
  11. Chen, W. S., & Du, Y. K. (2009). Using neural networks and data mining techniques for the financial distress prediction model. Expert Systems with Applications, 36(2), 4075–4086.Google Scholar
  12. Chen, L. H., & Hsiao, H. D. (2008). Feature selection to diagnose a business crisis by using a real GA-based support vector machine: An empirical study. Expert Systems with Applications, 35(3), 1145–1155.Google Scholar
  13. Chen, H. L., Yang, B., Wang, G., Liu, J., Xu, X., Wang, S. J., et al. (2011). A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method. Knowledge-Based Systems, 24(8), 1348–1359.Google Scholar
  14. Chi, K. T., Liu, J., & Lau, F. C. (2010). A network perspective of the stock market. Journal of Empirical Finance, 17(4), 659–667.Google Scholar
  15. Chiu, C., Ku, Y., Lie, T., & Chen, Y. (2011). Internet auction fraud detection using social network analysis and classification tree approaches. International Journal of Electronic Commerce, 15(3), 123–147.Google Scholar
  16. Chiu, W. C., Peña, J., & Wang, C. W. (2013). Do structural constraints of the industry matter for corporate failure prediction? Investment Analysts Journal, 42(78), 65–81.Google Scholar
  17. Freeman, L. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41.Google Scholar
  18. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of statistics, 29(5), 1189–1232.Google Scholar
  19. Gao, Y. C., Wei, Z. W., & Wang, B. H. (2013). Dynamic evolution of financial network and its relation to economic crises. International Journal of Modern Physics C, 24(02), 1350005.Google Scholar
  20. Geng, R., Bose, I., & Chen, X. (2015). Prediction of financial distress: An empirical study of listed Chinese companies using data mining. European Journal of Operational Research, 241(1), 236–247.Google Scholar
  21. Georg, C. P. (2013). The effect of the interbank network structure on contagion and common shocks. Journal of Banking & Finance, 37(7), 2216–2228.Google Scholar
  22. Gepp, A., Kumar, K., & Bhattacharya, S. (2010). Business failure prediction using decision trees. Journal of Forecasting, 29(6), 536–555.Google Scholar
  23. Gopikrishnan, P., Rosenow, B., Plerou, V., & Stanley, H. E. (2001). Quantifying and interpreting collective behavior in financial markets. Physical Review E, 64(3), 035106.Google Scholar
  24. Guelman, L. (2012). Gradient boosting trees for auto insurance loss cost modeling and prediction. Expert Systems with Applications, 39(3), 3659–3667.Google Scholar
  25. Gu, R., Xiong, W., & Li, X. (2015). Does the singular value decomposition entropy have predictive power for stock market?—Evidence from the Shenzhen stock market. Physica A: Statistical Mechanics and its Applications, 439, 103–113.Google Scholar
  26. Hsieh, T. J., Hsiao, H. F., & Yeh, W. C. (2012). Mining financial distress trend data using penalty guided support vector machines based on hybrid of particle swarm optimization and artificial bee colony algorithm. Neurocomputing, 82, 196–206.Google Scholar
  27. Iturriaga, F. J. L., & Sanz, I. P. (2015). Bankruptcy visualization and prediction using neural networks: A study of US commercial banks. Expert Systems with Applications, 42(6), 2857–2869.Google Scholar
  28. Kumar, P. R., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques—A review. European Journal of Operational Research, 180(1), 1–28.Google Scholar
  29. Lau, A. H. L. (1987). A five-state financial distress prediction model. Journal of Accounting Research, 25(1), 127–138.Google Scholar
  30. Mantegna, R. N. (1999). Hierarchical structure in financial markets. The European Physical Journal B-Condensed Matter and Complex Systems, 11(1), 193–197.Google Scholar
  31. Onnela, J. P., Chakraborti, A., Kaski, K., & Kertesz, J. (2003). Dynamic asset trees and Black Monday. Physica A: Statistical Mechanics and Its Applications, 324(1), 247–252.Google Scholar
  32. Onnela, J. P., Chakraborti, A., Kaski, K., Kertesz, J., & Kanto, A. (2003). Dynamics of market correlations: Taxonomy and portfolio analysis. Physical Review E, 68(5), 056110.Google Scholar
  33. Peron, T. K. D. M., da Fontoura Costa, L., & Rodrigues, F. A. (2012). The structure and resilience of financial market networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 22(1), 013117.Google Scholar
  34. Sandoval, L., & Franca, I. D. P. (2012). Correlation of financial markets in times of crisis. Physica A: Statistical Mechanics and its Applications, 391(1), 187–208.Google Scholar
  35. Sun, J., & Li, H. (2008). Data mining method for listed companies’ financial distress prediction. Knowledge-Based Systems, 21(1), 1–5.Google Scholar
  36. Taieb, S. B., & Hyndman, R. J. (2014). A gradient boosting approach to the Kaggle load forecasting competition. International Journal of Forecasting, 30(2), 382–394.Google Scholar
  37. Verbeke, W., Martens, D., & Baesens, B. (2014). Social network analysis for customer churn prediction. Applied Soft Computing, 14, 431–446.Google Scholar
  38. Yang, B., Li, L. X., Xie, Q., & Xu, J. (2001). Development of a KBS for managing bank loan risk. Knowledge-Based Systems, 14(5), 299–302.Google Scholar
  39. Zhang, Y., & Haghani, A. (2015). A gradient boosting method to improve travel time prediction. Transportation Research Part C: Emerging Technologies, 58, 308–324.Google Scholar
  40. Zheng, Z., Zha, H., Zhang, T., Chapelle, O., Chen, K.,&Sun, G. (2008). A general boosting method and its application to learning ranking functions for web search. In Advances in neural information processing systems (pp. 1697–1704).Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.School of ManagementHarbin Institute of TechnologyHarbinChina
  2. 2.School of Business AdministrationNortheastern UniversityShenyangChina

Personalised recommendations