Abstract

Financial market simulation has evolved significantly with the advent of generative models, enabling high-fidelity synthetic data generation for forecasting, risk management, and algorithmic trading. This case study explores Market-GAN, a novel framework for controllable financial data generation with semantic context, and situates it within the broader paradigm shift toward generative AI in finance. We analyze Market-GAN’s architecture, training methodology, and performance against benchmarks, while also comparing it with other state-of-the-art market generators like Sig-Wasserstein GANs and VoLGAN. The study highlights key innovations such as contextual conditioning, two-stage adversarial training, and signature-based evaluation, while addressing challenges like non-stationarity, data scarcity, and evaluation metrics. Finally, we discuss future directions, including the integration of foundation models and reinforcement learning for next-generation financial simulators.

Keywords: Market-GAN; Generative adversarial networks; Financial simulation; Synthetic data; Signature methods; Contextual generation