Leveraging Generative Adversarial Networks for Addressing Data Imbalance in Financial Market Supervision

ArXiv ID: 2412.15222 “View on arXiv”

Authors: Unknown

Abstract

This study explores the application of generative adversarial networks in financial market supervision, especially for solving the problem of data imbalance to improve the accuracy of risk prediction. Since financial market data are often imbalanced, especially high-risk events such as market manipulation and systemic risk occur less frequently, traditional models have difficulty effectively identifying these minority events. This study proposes to generate synthetic data with similar characteristics to these minority events through GAN to balance the dataset, thereby improving the prediction performance of the model in financial supervision. Experimental results show that compared with traditional oversampling and undersampling methods, the data generated by GAN has significant advantages in dealing with imbalance problems and improving the prediction accuracy of the model. This method has broad application potential in financial regulatory agencies such as the U.S. Securities and Exchange Commission (SEC), the Financial Industry Regulatory Authority (FINRA), the Federal Deposit Insurance Corporation (FDIC), and the Federal Reserve.

Keywords: Generative Adversarial Networks (GANs), Data Imbalance, Synthetic Data Generation, Risk Prediction, Financial Market Supervision, General Financial Markets (Equities/Bonds)

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 5.0/10
• Quadrant: Holy Grail
• Why: The paper presents a standard GAN minimax formulation and architectural diagram, indicating moderate mathematical density, while its focus on generating synthetic data for risk prediction with comparisons to traditional methods suggests a moderate level of empirical testing and implementation focus.

  flowchart TD
    A["Research Goal: Improve risk prediction in financial markets"] --> B["Input: Imbalanced Financial Market Data<br/>High-risk events rare<br/>e.g., Market Manipulation"]
    B --> C["Methodology: Generative Adversarial Networks GANs"]
    C --> D["Computational Process: Generate Synthetic Data<br/>Balances minority class distribution"]
    D --> E["Experimental Results<br/>vs. Traditional Oversampling/Undersampling"]
    E --> F["Outcome: Enhanced Prediction Accuracy<br/>Improved Risk Detection for Regulatory Bodies<br/>SEC, FINRA, FDIC, Federal Reserve"]