Machine Learning for Corporate Bankruptcy Prediction
School of Science | Doctoral thesis (article-based) | Defence date: 2013-05-24
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Aalto University publication series DOCTORAL DISSERTATIONS, 90/2013
AbstractCorporate bankruptcy prediction has long been an important and widely studied topic, which is of a great concern to investors or creditors, borrowing firms or governments. Especially due to the recent change in the world economy and as more firms, large and small, seem to fail now more than ever. The prediction of the bankruptcy, is then of increasing importance. There has been considerable interest in using financial ratios for predicting financial distress in companies since the seminal works of Beaver using an univariate analysis and Altman approach with multiple discriminant analysis. The big amount of financial ratios makes bankruptcy prediction a difficult high-dimensional classification problem. So this dissertation presents a way for ratio selection which determines the parsimony and economy of the models and thus the accuracy of prediction. With the selected financial ratios, this dissertation explores several Machine Learning methods, aiming at bankruptcy prediction, which is addressed as a binary classification problem (bankrupt or non-bankrupt companies). They are OP-KNN (Publication I), Delta test-ELM (DT- ELM) (Publication VII) and Leave-One-Out-Incremental Extreme Learning Machine (LOO-IELM) (Publication VI). Furthermore, soft classification techniques (classifier ensembles and the usage of financial expertise) are used in this dissertation. For example, Ensemble K-nearest neighbors (EKNN) in Publication V, Ensembles of Local Linear Models in Publication IV, and Combo and Ensemble model in Publication VI. The results reveal the great potential of soft classification techniques, which appear to be the direction for future research as core techniques that are used in the development of prediction models. In addition to selecting ratios and models, the other foremost issue in experiments is the selection of datasets. Different studies have used different datasets, some of which are publicly downloadable, some are collected from confidential resources. In this dissertation, thanks to Prof. Philippe Du Jardin, we use a real dataset built for French retails companies. Moreover, a practical problem, missing data, is also considered and solved in this dissertation, like the methods shown in Publication II and Publication VIII.
Supervising professorSimula, Olli, Prof. Aalto University, Department of Information and Computer Science, Finland
Thesis advisorLendasse, Amaury, Dr., Aalto University, Department of Information and Computer Science, Finland
Severin, Eric, Prof., University of Lille, France
bankruptcy prediction, machine learning, extreme learning machine, variable selection
- [Publication 1]: Qi Yu, Yoan Miche, Antti Sorjamaa, Alberto Guillén, Amaury Lendasse and Eric Séverin. OP-KNN: Method and Applications. Advances in Artificial Neural Systems, Volume 2010, 6 pages, February 2010.
- [Publication 2]: Qi Yu, Yoan Miche, Email Eirola, Mark van Heeswijk, Eric Séverin and Amaury Lendasse. Regularized Extreme Learning Machine for Regression with Missing Data . Neurocomputing, Volume 102, pages 45-51, June 2012.
- [Publication 3]: Dusan Sovilj, Antti Sorjamaa, Qi Yu, Yoan Miche and Eric Séverin. OPELM and OPKNN in long-term prediction of time series using projected input data . Neurocomputing, Volume 73, pages 1976-1986, June 2010.
- [Publication 4]: Laura Kainulainen, Yoan Miche, Emil Eirola, Qi Yu, Benoit Frénay, Eric Séverin and Amaury Lendasse. Ensembles of Local Linear Models for Bankruptcy Analysis and Prediction. Case Studies in Business, Industry and Government Statistics, Volume 4, November 2011.
- [Publication 5]: Qi Yu, Amaury Lendasse and Eric Séverin. Ensemble KNNs for Bankruptcy Prediction. In CEF 09, 15th International Conference: Computing in Economics and Finance, Sydney, Australia, pages 78-81, June 15-17 2009.
- [Publication 6]: Qi Yu, Yoan Miche, Eric Severin and Amaury Lendasse. Bankruptcy Prediction using Extreme Learning Machine and Financial Expertise. Accepted for publication in Neurocomputing, January 2013.
- [Publication 7]: Qi Yu, Mark van Heeswijk, Yoan Miche, Rui Nian, He Bo, Eric Séverin and Amaury Lendasse. Ensemble Delta test- Extreme Learning Machine (DT-ELM) For Regression. Accepted for publication in Cognitive Computation, February 2013.