Bankruptcy prediction using imaged financial ratios and convolutional neural networks

作者:

Highlights:

• Convolution networks can predict bankruptcy by inputting financial ratios as an image.

• Predictive accuracy improves with correlated financial ratios placed in the vicinity.

• Deeper network configuration improves predictive accuracy.

• Creating artificial financial data does not ensure the same effect as using real data.

• Convolution-network-based bankruptcy prediction outperforms traditional methods.

摘要

•Convolution networks can predict bankruptcy by inputting financial ratios as an image.•Predictive accuracy improves with correlated financial ratios placed in the vicinity.•Deeper network configuration improves predictive accuracy.•Creating artificial financial data does not ensure the same effect as using real data.•Convolution-network-based bankruptcy prediction outperforms traditional methods.

论文关键词:Deep learning,Business failure,Financial statement,Imaging

论文评审过程:Received 16 May 2018, Revised 20 July 2018, Accepted 17 September 2018, Available online 18 September 2018, Version of Record 2 October 2018.

论文官网地址:https://doi.org/10.1016/j.eswa.2018.09.039