Deep belief networks for predicting corporate defaults

By Shu-Hao Yeh, Chuan-Ju Wang, Ming-Feng Tsai. 2015

17 citations recorded by the source corpus.

This paper provides a new perspective on the default prediction problem using deep learning algorithms. Via the advantages of deep learning, the representable factors of input data will no longer need to be explicitly extracted, but can be implicitly learned by the deep learning algorithms. We consider the stock returns of both default and solvent companies as input signals and adopt one of the deep learning architecture, Deep Belief Networks (DBN), to train the prediction models. The preliminary results show that the proposed approach outperforms traditional machine learning algorithms.

Subfields: Accounting, Finance, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

1. A. Ozbayoglu
2. Omer Berat Sezer
3. M. Ugur Gudelek

In Finance

1. M. Ugur Gudelek
2. A. Ozbayoglu
3. Omer Berat Sezer
4. Weiwei Jiang
5. Longbing Cao

In Management Science and Operations Research

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Weiwei Jiang
5. Longbing Cao

Citations in the corpus

• Deep Learning for Financial Applications : A Survey (2020, methodology): “[101] used the stock returns of default and solvent companies as inputs to RBM used as SAE, then the output of RBM was used as input to DBN to predict if the company was solvent or default.”
• An innovative neural network approach for stock market prediction (2018, result): “The result showed that the effect of DBN was higher than the traditional machine learning algorithm [22].”