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

Autor:	Zhu, Enmin, Yen, Jerome
Rok vydání:	2024
Předmět:	Computer Science - Computational Engineering Finance and Science
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.02029 Zobrazit plný text záznamu View this record from Arxiv