A GAN-Based Framework for Synthetic Financial Data Generation, Risk Forecasting, and Portfolio Optimization under Uncertainty

Abstract

This article proposes a financial risk dynamic prediction and decision optimization model based on Generative Adversarial Network (GAN). The model generates synthetic financial data, trains a risk prediction model, and optimizes financial decisions based on predicted risks. Simulation results show that the proposed method outperforms traditional machine learning models, achieving a mean absolute error (MAE) of 0.012 and a mean squared error (MSE) of 0.002, indicating high prediction accuracy. The model achieves an average risk of 4.5% and an average return of 8.2%, surpassing conventional algorithms. With a recommended portfolio allocation of 65% equities, 30% bonds, and 5% cash, it optimizes investment decisions by maximizing returns while minimizing risks. Overall, the proposed approach provides a novel and effective solution for financial risk prediction and decision optimization, demonstrating superior performance over existing methods.