Generative artificial intelligence (GenAI)--a set of advanced technologies capable of generating texts, images, videos, programming codes, or arts from instructions via sounds or texts--has taken the society by storm and exerted wide-range influences on many aspects of the world economy (Baldassarre et al. 2023; Mannuru et al. 2023; Sætra 2023). Although it had been around for years, GenAI came to public prominence since the introduction of ChatGPT in November 2022, a chatbox able to generate answers, reasoning, and conversations at human level. Since its introduction, ChatGPT and similar large language models have quickly made their ways into the investment industry. One common use of ChatGPT for investment is to give trading recommendations directly from news about a company (such as news articles or corporate communications) (Lopez-Lira and Tang 2023). A less direct approach is to rely on similar pretrained language models such as BERT (Devlin et al. 2018) and OPT (Zhang et al. 2022) to generate a sentiment score for each company which is then used to make trading decisions.

We integrate the portfolio optimization algorithm suggested by Black / Litterman [1] into a neural network architecture. Combining the mean-variance theory [5] with the capital asset pricing model (CAPM) [7], this approach utilizes excess returns of the CAPM equilibrium to define a neutral, well balanced benchmark portfolio. Deviations from the benchmark allocation are only allowed within preset boundaries. Hence, as an advantage, there are no unrealistic solutions (e. g. large short positions, huge portfolio changes). Moreover, there is no need of formulating return expectations for all assets. In contrast to Black / Litterman, excess return forecasts are estimated by time-delay recur- rent error correction neural networks [8]. Investment decisions which comply with given allocation constraints are derived from these predictions. The risk exposure of the portfolio is implicitly controlled by a parameter-optimizing task over time (sec.