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Journal of Statistics and Management Systems

(Since 1998)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510

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Monthly Journal: Publishes peer-reviewed aticles on theoretical and applied statistics and management systems, expoloring industrial statistics, actuarial and decision sciences.

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Journal of Statistics and Management Systems
Open Access Research Article

A predictive model for bankruptcy: ANN, LSTM and CNN approaches

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pp. 67–86Vol. 26Issue 1December 2022DOI: 10.47974/JSMS-948XML
Published Online:
31 Dec 2022
Article type:
Research Article
Language:
EN
Article no.:
JSMS-948
Pages:
67–86

Abstract

Predicting bankruptcy is the focus of our research, which is one of the important aspects of research, as due to bankruptcy both company’s goodwill and shareholders’ benefits are affected. In order to predict bankruptcy, reliable models are required. The focus of this paper is based on different deep learning models. However, developing deep learning models for forecasting bankruptcy is one of the challenging tasks as most of the datasets are imbalanced in nature. So we first try to balance the dataset. US Bankruptcy Prediction Data set (1971-2017) is taken here, which is very imbalanced in nature. To balance the dataset both undersampling and oversampling and one hybrid method are used. In this research, a comparison is made among three different types of models like CNN, LSTM and ANN by applying all balancing techniques on each classifying model, for the prediction of bankruptcy. Here we get that the ANN model gives better results than the other two whatever balancing technique may be used and among the balancing techniques oversampling is far better than undersampling, but here the hybrid sampling method outperformed all in each classification model as compared to the others.

Keywords

BankruptcyUndersamplingOversamplingSMOTESMOTETOmekArtificial neural networks(ANN)Recurrent neural network(RNN)Long short term memory networks (LSTM)Convolutional neural network(CNN)

Subject Classifications

68: Computer Science

References

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