Articlediamond2017

Machine Learning Methods of Bankruptcy Prediction Using Accounting Ratios

27 citations0/0 files storedOpen Journal of Business and Management

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Financial Distress and Bankruptcy Prediction Corporate Insolvency and Governance Forecasting Techniques and Applications

Abstract

The aim of bankruptcy prediction is to help the enterprise stakeholders to get the comprehensive information of the enterprise. Much bankruptcy prediction has relied on statistical models and got low prediction accuracy. However, with the advent of the AI (Artificial Intelligence), machine learning methods have been extensively used in many industries (e.g., medical, archaeological and so on). In this paper we compare the statistical method and machine learning method to predict bankruptcy with utilizing China listed companies. Firstly, we use statistical method to choose the most appropriate indicators. Different indicators may have different characteristics and not all indicators can be analyzed. After the data filtering, the indicators are more persuasive. Secondly, unlike previous research methods, we use the same sample set to conduct our experiment. The final result can prove the effectiveness of the machine learning method. Thirdly, the accuracy of our experiment is higher than existing studies with 95.9%.

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 1966Financial Ratios As Predictors of Failure 2004An application of support vector machines in bankruptcy prediction model 1994Bankruptcy prediction using neural networks 2005Have Financial Statements Become Less Informative? Evidence from the Ability of Financial Ratios to Predict Bankruptcy 2011Predicting corporate financial distress based on integration of decision tree classification and logistic regression 1988BANKRUPTCY PREDICTION ‐ AN INVESTIGATION OF CASH FLOW BASED MODELS [1]

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2024Enhancing corporate bankruptcy prediction via a hybrid genetic algorithm and domain adaptation learning architecture 2024Predicting business bankruptcy: A comparative analysis with machine learning models 2019Financial Distress Prediction: Principle Component Analysis and Artificial Neural Networks 2020Predicting the Performance of Rural Banks in Ghana Using Machine Learning Approach

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