Machine learning models and bankruptcy prediction
By Flavio Luiz de Moraes Barboza, Herbert Kimura, Edward I. Altman. Expert systems with applications, 2017
799 citations recorded by the source corpus.
Subfields: Accounting, Finance
View Paper: https://www.semanticscholar.org/paper/3af7252f7e3ca8e9bc8f45e6cbf567b10ecb5d95
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Accounting
- Tuong Le
- Mi Young Lee
- Sung Wook Baik
- Camila C. Pereira
- Suneel Sharma
- Xiaojun Sun
- Yalin Lei
- Koilakuntla Maddulety
In Finance
- Tuong Le
- Mi Young Lee
- Sung Wook Baik
- Camila C. Pereira
- Suneel Sharma
- Xiaojun Sun
- Yalin Lei
- Koilakuntla Maddulety
Citations in the corpus
- Machine Learning in Banking Risk Management: A Literature Review (2019, result): “Barboza et al. (2017) test machine learning models to predict bankruptcy one year prior to the event comparing the performance with results from traditional methods.”
- Research on financial early warning of mining listed companies based on BP neural network model (2021, positive): “For example, Barboza et al. (2017) selected the financial data of North American companies in the whole industry from 1985 to 2013, and used artificial intelligence model to predict the financial crisis of more than 10000 companies.”
- A Cluster-Based Boosting Algorithm for Bankruptcy Prediction in a Highly Imbalanced Dataset (2018, methodology): “For the business management domain, bankruptcy forecasting [24–26], a model to predict enterprises that will crash in the near future, and fraud detection [21] are the two most attractive topics.”
- Machine Learning in Agriculture: A Review (2018, background): “Year by year, ML applies in more and more scientific fields including, for example, bioinformatics [2,3], biochemistry [4,5], medicine [6–8], meteorology [9–11], economic sciences [12–14], robotics [15,16], aquaculture [17,18], and food security [19,20], and climatology [21].”
- Oversampling Techniques for Bankruptcy Prediction: Novel Features from a Transaction Dataset (2018, methodology): “[19] implemented and tested several classification models, including SVM with linear and radial basis function (RBF) kernels, artificial neural networks (ANN), logistic regression, boosting, Random Forest, as well as bagging, to predict bankruptcy.”
- Textual analysis in finance (2019, background): “For the same purpose, Barboza, Kimura, and Altman (2017) compare alternative machine learning methods in 7 the estimation, also incorporating the context in the analyses.”