Predicting Bankruptcy with Support Vector Machines

The purpose of this work is to introduce one of the most promising among recently developed statistical techniques – the support vector machine (SVM) – to corporate bankruptcy analysis. An SVM is implemented for analysing such predictors as financial ratios. A method of adapting it to default probability estimation is proposed. A survey of practically applied methods is given. This work shows that support vector machines are capable of extracting useful information from financial data, although extensive data sets are required in order to fully utilize their classification power.support vector machine, classification method, statistical learning theory, electric load prediction, optical character recognition, predicting bankruptcy, risk classification

Machine Learning for Corporate Bankruptcy Prediction

Corporate 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

An overview of bankruptcy prediction models for corporate firms: a systematic literature review

Purpose: The aim of this paper is to conduct a literature review of corporate bankruptcy prediction models, on the basis of the existing international academic literature in the corresponding area. It primarily attempts to provide a comprehensive overview of literature related to corporate bankruptcy prediction, to investigate and address the link between the different authors (co-authorship), and to address the primary models and methods that are used and studied by authors of this area in the past five decades. Design/methodology: A systematic literature review (SLR) has been conducted, using the Scopus database for identifying core international academic papers related to the established research topic from the year 1968 to 2017. Findings: It has been verified, firstly, that bankruptcy prediction in the corporate world is a field of growing interest, as the number of papers has increased significantly, especially after 2008 global financial crisis, which demonstrates the importance of this topic for corporate firms. Secondly, it should be mentioned that there is little co-authorship in this researching area, as researchers with great influence were barely working together during the last five decades. Thirdly, it has been identified that the two most frequently used and studied models in bankruptcy prediction area are Logistic Regression (Logit) and Neural Network. However, there are many other innovative methods as machine learning models applied in this field lately due to the emerging technology of computer science and artificial intelligence. Originality/value: We used an approach that allows a better view of the academic contribution related to the corporate bankruptcy prediction; this serves as the link among the different elements of the concept studied, and it demonstrates the growing interest in this area.Peer Reviewe

Early bankruptcy prediction using ENPC

Bankruptcy prediction has long time been an active research field in finance. One of the main approaches to this issue is dealing with it as a classification problem. Among the range of instruments available, we focus our attention on the Evolutionary Nearest Neighbor Classifier (ENPC). In this work we assess the performance of the ENPC comparing it to six alternatives. The results suggest that this algorithm might be considered a good choice.Publicad

Application of support vector machines on the basis of the first Hungarian bankruptcy model

In our study we rely on a data mining procedure known as support vector machine (SVM) on the database of the first Hungarian bankruptcy model. The models constructed are then contrasted with the results of earlier bankruptcy models with the use of classification accuracy and the area under the ROC curve. In using the SVM technique, in addition to conventional kernel functions, we also examine the possibilities of applying the ANOVA kernel function and take a detailed look at data preparation tasks recommended in using the SVM method (handling of outliers). The results of the models assembled suggest that a significant improvement of classification accuracy can be achieved on the database of the first Hungarian bankruptcy model when using the SVM method as opposed to neural networks

Hybrid model using logit and nonparametric methods for predicting micro-entity failure

Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods (Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic variables complement financial ratios for bankruptcy prediction

Predicting financial distress:A comparison of survival analysis and decision tree techniques

AbstractFinancial distress and then the consequent failure of a business is usually an extremely costly and disruptive event. Statistical financial distress prediction models attempt to predict whether a business will experience financial distress in the future. Discriminant analysis and logistic regression have been the most popular approaches, but there is also a large number of alternative cutting – edge data mining techniques that can be used. In this paper, a semi-parametric Cox survival analysis model and non-parametric CART decision trees have been applied to financial distress prediction and compared with each other as well as the most popular approaches. This analysis is done over a variety of cost ratios (Type I Error cost: Type II Error cost) and prediction intervals as these differ depending on the situation. The results show that decision trees and survival analysis models have good prediction accuracy that justifies their use and supports further investigation