Black-Litterman Portfolio Optimization with Asset Universe Given by Large Language Models.

Generally, the Black-Litterman portfolio model relies heavily on the expert investment opinions, where high-quality investment opinions would improve model performance whereas low-quality investment opinions would result in poor model performance. Essentially, expert investment opinions are highly subjective, sourcing from these financial experts' information and knowledge. ChatGPT, as an advanced generative AI model, could extract and analyze information from huge multi-modal data, which is beneficial to build AI investment opinions for Black-Litterman portfolio model. In this study, we construct and analyze the large language model-based Black-Litterman portfolios, ChatGPT-BL and BARD-BL, where the goals of minimum variance and mean-variance trade-off are taken into account. Computational results show that the ChatGPT-based portfolios tend to be conservative, which is suitable for risk-averse investors, while the BARD-based portfolios are aggressive, which is appropriate for risk-seeking investors. Also, the superiority of Black-Litterman model using investor views generating from gradient boosting regression and GJR-GARCH algorithm is illustrated by the efficient frontiers. [ABSTRACT FROM AUTHOR]