Enhancing Portfolio Optimization with Transformer-GAN Integration: A Novel Approach in the Black-Litterman Framework

TL;DR

This paper introduces a novel portfolio optimization method that combines Transformer models and GANs within the Black-Litterman framework to improve predictive accuracy and investment decision-making.

Contribution

It presents an innovative integration of Transformer and GAN models into the Black-Litterman framework, enhancing predictive view generation for portfolio optimization.

Findings

Outperforms traditional forecasting methods in portfolio allocation.

Demonstrates improved investment decision accuracy.

Captures market complexities more effectively.

Abstract

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.