کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
11021200 1715031 2019 40 صفحه PDF سفارش دهید دانلود رایگان
عنوان انگلیسی مقاله ISI
Bankruptcy prediction using imaged financial ratios and convolutional neural networks
ترجمه فارسی عنوان
پیش بینی ورشکستگی با استفاده از نسبت های مالی تصویر و شبکه های عصبی کانولوشن
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کلمات کلیدی
یادگیری عمیق، شکست کسب و کار، بیانیه مالی، تصویربرداری،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 117, 1 March 2019, Pages 287-299
نویسندگان
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