Data augmentation of credit default swap transactions based on a sequence GAN. Issue 3 (May 2022)

• Record Type: Journal Article
• Authors: Fan, Xi; Guo, Xin; Chen, Qi; Chen, Yishuang; Wang, Tongyao; Zhang, Yuxin
• Is Part Of: Information processing & management. Volume 59:Issue 3(2022)
• Publication Date: 2022-05
• Subjects: Generative adversarial network -- Synthetic ranking agreement index -- Credit default swap transaction; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2022.102889
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: We can use the GAN model to generate more similar order data based on limited data currently available. We will design some evaluation criteria so that we can measure the outcome of our simulated data. We will show results after training to test whether our new data has the same behaviors as the original data does, which proves that our model could really help simulate limit order data. Abstract: Credit default swap transaction data repositories are frequently applied with credit default swap spread estimation and financial market risk assessment. However, in practical applications, there is poor liquidity, some missing data, and inaccurate definitions. Small samples tend to lead to poor prediction accuracy and poor adaptability of the statistical algorithm. Data generation can effectively increase the sample size and improve the effect of the risk assessment model. In this paper, a credit default swap data generation algorithm based on a sequence generative adversarial network (SeqGAN) is proposed, and the policy gradient algorithm in reinforcement learning is introduced to optimize the traditional generative adversarial network (GAN) algorithm to solve the gradient disappearance and poor data adaptability problems in the traditional algorithm. Gradient disappearance is due to the generator network in GAN being designed to be able to adjust the output continuously, which does not work on discrete data generation. The optimization algorithm proposed in this paper is used to train randomly distributed sequence data and generate credit default swap transactions with diversity and good model applicability. The credit default swap data generated in this paper are verified by the synthetic ranking agreement (SRA) index. The results show that SeqGAN can effectively synthesize various simulation samples, which can provide support for the risk discrimination model.