Generative AI-Driven Financial Market Simulation and Forecasting Using Cloud-Based Computational Frameworks

The article presents a novel artificial intelligence based computational cloud-based financial market simulator and prediction model. The proposed system incorporates the state-of-the-art generative models, including the Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), diffusions alongside combinations of hybrid time-series forecasting networks, including Prophet-LSTM or Transformer-based architectures. The framework operates at scale on the distributed cloud platforms (aws, Azure, GCP) that help to train and serve in parallel and provide training and real-time inference. According to the provided experimental analysis of the use of the S and P 500 and cryptocurrency as models reveals a higher accuracy of 94.7% in prediction, a lower mean error of 15.3% and a reduced time of 3.2 longer to train when trained on the basis of parallelization in a cloud-based environment. The system exhibits high scenarios bearing capabilities which are 10,000 synthetics produced in a second at statistical accuracy up to the past tendencies. The research contributes to the creation of financial technology through synergistic integration of generative AI, cloud computing, and quantitative finance methods. Authors retain all © copyrights 2025 IJEW. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.