Bankruptcy prediction using imaged financial ratios and convolutional neural networks

By Tadaaki Hosaka. 2019

295 citations recorded by the source corpus.

Abstract Convolutional neural networks are being applied to identification problems in a variety of fields, and in some areas are showing higher discrimination accuracies than conventional methods. However, applications of convolutional neural networks to financial analyses have only been reported in a small number of studies on the prediction of stock price movements. The reason for this seems to be that convolutional neural networks are more suitable for application to images and less suitable for general numerical data including financial statements. Hence, in this research, an attempt is made to apply a convolutional neural network to the prediction of corporate bankruptcy, which in most cases is treated as a two-class classification problem. We use the financial statements (balance sheets and profit-and-loss statements) of 102 companies that have been delisted from the Japanese stock market due to de facto bankruptcy as well as the financial statements of 2062 currently listed companies over four financial periods. In our proposed method, a set of financial ratios are derived from the financial statements and represented as a grayscale image. The image generated by this process is utilized for training and testing a convolutional neural network. Moreover, the size of the dataset is increased using the weighted averages to create synthetic data points. A total of 7520 images for the bankrupt and continuing enterprises classes are used for training the convolutional neural network based on GoogLeNet. Bankruptcy predictions through the trained network are shown to have a higher performance compared to methods using decision trees, linear discriminant analysis, support vector machines, multi-layer perceptron, AdaBoost, or Altman’s Z′′-score.

Subfields: Accounting, Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

Deep Learning for Financial Applications : A Survey (2020, methodology): “[102] Financial statements of several companies from Japanese stock market 2002-2016 Financial ratios CNN F1-score, AUROC -”
Research on financial early warning of mining listed companies based on BP neural network model (2021, unknown): “The main research methods include neural network model, multi case reasoning system, support vector machine and other methods (Hosaka, 2019; Iturriaga and Sanz, 2015; Lee and Choi, 2013; Chen and Du, 2009; Sartori et al., 2016; Ahn and Kim, 2009; Chaudhuri and De, 2011; Yang et al., 2011).”
Remaining Financially Healthy and Competitive: The Role of Financial Predictors (2020, methodology): “…(discriminant analysis, logistic regression or principal component analysis), some researchers attempt to use newer significant methods, e.g. Technique for Order Performance by the Similarity to Ideal Solution (Ouenniche et al., 2018) or convolutional neural networks (Hosaka, 2019; Vochozka, 2017).”