620 research outputs found

    Predicting Corporate Bankruptcy with Financial Ratios and Macroeconomic Predictors : Evidence from Finnish data

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    Bankruptcy is a severe and permanent state of a firm where all stakeholders are facing the consequences, not just the investors. The literature of bankruptcy prediction is an extensive area where new statistical methods have been applied recently. The purpose of this thesis is to study benefits of using machine learning methods in bank-ruptcy prediction instead traditional methods such as logistic regression and Z-score by using Finnish data. Furthermore, this thesis tests the use of macroeconomic variables together with firm specific predictors. Lastly, machine learning algorithm called random forest is tested against logistic regression. The adaptation of random forest in bankruptcy prediction is not studied comprehensively. This thesis employs dataset of 96 995 Finnish firms between the years 1999 and 2019. 2595 firms of this dataset are stated as bankrupt, representing 2.7% of all observations. The finan-cial ratios are derived from Altman’s Z-score’s variables which reflect the financial state of a firm. The effect of macroeconomic events on predictability of bankruptcy, is tested by em-ploying different macroeconomic predictors such as change in gross domestic product. The robustness checks include careful data cleaning and validating models by splitting data into training and test data. The results from Finnish data encourage the use of machine learning methods in bankruptcy, especially random forest algorithm. Predictability by using random forest outperformed all other methods introduced in this thesis. Furthermore, the utilisation of macroeconomic predictor in bankruptcy prediction is justified together with firm specific predictors. Particularly, household debt as a proportion of available income shows a significant predictive power on bankruptcy. Lastly, the random forest performed better than logistic regression. This thesis provides encouraging results on bankruptcy prediction in practical purposes against traditional methods such as Z-score that are still used today

    Corporate bankruptcy prediction : can KMV-Merton model add value to support vector machines forecasts?

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    This dissertation aims to assess if the output from the KMV-Merton model, the so-called distance to default, can contribute to the support vector machines model with the ultimate goal of better forecasting the bankruptcy of a company. The considered dataset covers 248 non-financial U.S. companies between 2000 and 2018. It was found evidence that the distance to default contributes, within a given range of variables considered, to a better F1-Score using both cross-validation and percentage ratio split. Additionally, the results show that the distance to default is a better predictor than a simpler market-based variable such as the debt-to-equity ratio. This suggests that the Merton-model setup per se is useful for default prediction. As expected, taking the F1-Score as a reference, the results also indicate that using company information a year prior to default provides better results than using data two years prior to default. Lastly, given the dataset used and the assumptions stated, this study is not conclusive regarding which out-of-sample evaluation method offers better results, the percentage ratio split, or the stratified K-fold cross-validation.Esta dissertação tem como objetivo avaliar se o resultado do modelo KMV-Merton, a conhecida distância ao incumprimento, pode contribuir para o modelo de máquinas de vetor de suporte com o objetivo final de prever melhor a falência de empresas. O conjunto de dados considerado abrange 248 empresas não financeiras dos E.U.A entre 2000 e 2018. Encontra-se evidência que a distância ao incumprimento contribui, dentro de um determinado grupo de variáveis, para um melhor F1-Score utilizando tanto a validação cruzada como a divisão percentual. Além disso, os resultados mostram que a distância ao incumprimento é um melhor previsor comparativamente a uma variável de mercado mais simples tal como a dívida sobre o valor de mercado do capital próprio. Isso sugere que a configuração do modelo Merton por si só é útil para a previsão de falência. Como esperado, considerando o F1-Score como referência, os resultados também indicam que o uso de informações da empresa um ano antes da falência fornece melhores resultados do que o uso de dados dois anos antes da falência. Por fim, dado o conjunto de dados usados e as premissas assumidas, este estudo não é conclusivo em relação a qual método de avaliação out-of-sample oferece melhores resultados, a divisão percentual ou a validação cruzada

    Can bank interaction during rating measurement of micro and very small enterprises ipso facto Determine the collapse of PD status?

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    This paper begins with an analysis of trends - over the period 2012-2018 - for total bank loans, non-performing loans, and the number of active, working enterprises. A review survey was done on national data from Italy with a comparison developed on a local subset from the Sardinia Region. Empirical evidence appears to support the hypothesis of the paper: can the rating class assigned by banks - using current IRB and A-IRB systems - to micro and very small enterprises, whose ability to replace financial resources using endogenous means is structurally impaired, ipso facto orient the results of performance in the same terms of PD assigned by the algorithm, thereby upending the principle of cause and effect? The thesis is developed through mathematical modeling that demonstrates the interaction of the measurement tool (the rating algorithm applied by banks) on the collapse of the loan status (default, performing, or some intermediate point) of the assessed micro-entity. Emphasis is given, in conclusion, to the phenomenon using evidence of the intrinsically mutualistic link of the two populations of banks and (micro) enterprises provided by a system of differential equation

    Machine learning-driven credit risk: a systemic review

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    Credit risk assessment is at the core of modern economies. Traditionally, it is measured by statistical methods and manual auditing. Recent advances in financial artificial intelligence stemmed from a new wave of machine learning (ML)-driven credit risk models that gained tremendous attention from both industry and academia. In this paper, we systematically review a series of major research contributions (76 papers) over the past eight years using statistical, machine learning and deep learning techniques to address the problems of credit risk. Specifically, we propose a novel classification methodology for ML-driven credit risk algorithms and their performance ranking using public datasets. We further discuss the challenges including data imbalance, dataset inconsistency, model transparency, and inadequate utilization of deep learning models. The results of our review show that: 1) most deep learning models outperform classic machine learning and statistical algorithms in credit risk estimation, and 2) ensemble methods provide higher accuracy compared with single models. Finally, we present summary tables in terms of datasets and proposed models

    Development of a Machine Learning-Based Financial Risk Control System

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    With the gradual end of the COVID-19 outbreak and the gradual recovery of the economy, more and more individuals and businesses are in need of loans. This demand brings business opportunities to various financial institutions, but also brings new risks. The traditional loan application review is mostly manual and relies on the business experience of the auditor, which has the disadvantages of not being able to process large quantities and being inefficient. Since the traditional audit processing method is no longer suitable some other method of reducing the rate of non-performing loans and detecting fraud in applications is urgently needed by financial institutions. In this project, a financial risk control model is built by using various machine learning algorithms. The model is used to replace the traditional manual approach to review loan applications. It improves the speed of review as well as the accuracy and approval rate of the review. Machine learning algorithms were also used in this project to create a loan user scorecard system that better reflects changes in user information compared to the credit card systems used by financial institutions today. In this project, the data imbalance problem and the performance improvement problem are also explored
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