Comparative Study of Methods for Bankruptcy Prediction: Empirical Evidence from Finland
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School of Business | Master's thesis
Information and Service Management (ISM)
95 + 7
AbstractIn business analytics and the financial world, bankruptcy prediction has been an interesting and widely researched topic over the past few decades. The accuracy of bankruptcy predictions play a crucial role for financiers, business owners, shareholders, and supply chain managers alike. With much on the line, being able to predict bankruptcies is the basis for timely and well-founded strategic business decisions. Academic research has developed bankruptcy prediction models, belonging into two major categories: statistical and machine learning models. Statistical models include logistic regression and multiple discriminant analysis, and machine learning models include neural networks, decision trees, and support vector machines, to name a few. While the research on bankruptcy prediction has yielded numerous different prediction models, there are no clear winners. Each of the models has its pros and cons, and academic research has reached contradicting results while comparing the same models with each other. This thesis aims to further compare the prediction accuracy of the most popular bankruptcy prediction models with Finnish private limited manufacturing company data. This thesis compares the following models: Altman Z-score, Logistic Regression, two Decision Trees (C5 and CART), Neural Networks, and Support Vector Machines (SVM). The results show that with the sample data chosen, SVM is the best performing bankruptcy prediction method; measured both in terms of overall prediction accuracy and F-measure. SVM provides the most accurate predictions both in short-term and long-term predictions. Logistic regression provides the second most accuracy, falling just behind SVM by a small margin. It is worthwhile mentioning, however, that the differences in every models’ prediction accuracy and F-measures are relatively small during the first year prior to bankruptcy. SVM and logistic regression seem to sustain their prediction performance better than the other models when the prediction horizon gets longer. Yet, by stretching the prediction horizon to five years or more, it seems that no model provides results, which would be more accurate than flipping a coin. The study contributes toward a more thorough understanding of the advantages and disadvantages of the bankruptcy prediction models, and delivers insights on how the various models perform compared to each other with the Finnish private manufacturing company data.
Thesis advisorMalo, Pekka
bankruptcy prediction, decision trees, discriminant analysis, financial ratios, logistic regression, machine learning, neural networks, predictive modeling