  This study presents an innovative approach to portfolio optimization by
integrating Transformer models with Generative Adversarial Networks (GANs)
within the Black-Litterman (BL) framework. Capitalizing on Transformers'
ability to discern long-range dependencies and GANs' proficiency in generating
accurate predictive models, our method enhances the generation of refined
predictive views for BL portfolio allocations. This fusion of our model with
BL's structured method for merging objective views with market equilibrium
offers a potent tool for modern portfolio management, outperforming traditional
forecasting methods. Our integrated approach not only demonstrates the
potential to improve investment decision-making but also contributes a new
approach to capture the complexities of financial markets for robust portfolio
optimization.
