Extracting Information from Sequences of Financial Ratios with Markov for Discrimination: An Application to Bankruptcy Prediction
Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
In this paper, we propose a method that extracts information from sequences of financial ratios and investigate the usefulness of this information for bankruptcy prediction, which constitutes an important class of financial services. We use the annual financial reports available from an external financial information services provider to extract predictors based on the Markov for Discrimination (MFD) methodology. These predictors are used as inputs in a binary classification model, which applies logistic regression to estimate the odds of bankruptcy. The results suggest that MFD-based predictors can achieve substantial predictive performance in terms of the AUC and the 5-percent predictive lift, which are two relevant performance metrics in our case.
Companies, Markov processes, Predictive models, Data mining, Logistics, Biological system modeling, Analytical models, financial services, bankruptcy predicition, sequence analysis, Markov for Discrimination
Andrey Volkov, Dirk Van den Poel, "Extracting Information from Sequences of Financial Ratios with Markov for Discrimination: An Application to Bankruptcy Prediction", ICDMW, 2012, 2013 IEEE 13th International Conference on Data Mining Workshops, 2013 IEEE 13th International Conference on Data Mining Workshops 2012, pp. 340-343, doi:10.1109/ICDMW.2012.137