The numerical nature of financial markets makes market forecasting and portfolio construction a good use case for machine learning (ML), a branch of artificial intelligence (AI). Over the past two decades, a number of academics worldwide (mostly from the field of computer science) produced a sizeable body of experimental research. Many publications claim highly accurate forecasts or highly profitable investment strategies. At the same time, the picture of real-world AI-driven investments is ambiguous and conspicuously lacking in high-profile success cases (while it is not lacking in high-profile failures). We conducted a literature review of 27 academic experiments spanning over two decades and contrasted them with real-life examples of machine learning-driven funds to try to explain this apparent contradiction. The specific contributions our article will make are as follows: (1) A comprehensive, thematic review (quantitative and qualitative) of multiple academic experiments from the investment management perspective. (2) A critical evaluation of running multiple versions of the same models in parallel and disclosing the best-performing ones only (“cherry-picking”). (3) Recommendations on how to approach future experiments so that their outcomes are unambiguously measurable and useful for the investment industry. (4) An in-depth comparison of real-life cases of ML-driven funds versus academic experiments. We will discuss whether present-day ML algorithms could make feasible and profitable investments in the equity markets.

Artificial Intelligence (‘AI’) technologies present great opportunities for the investment management industry (as well as broader financial services). However, there are presently no regulations specifically aiming at AI in investment management. Does this mean that AI is currently unregulated? If not, which hard and soft law rules apply?Investments are a heavily regulated industry (MIFID II, UCITS IV and V, SM&CR, GDPR etc). Most regulations are intentionally technology-neutral. These regulations are legally binding (hard law). Recent years saw the emergence of regulatory and industry publications (soft laws) focusing specifically on AI. In this Article we analyse both hard law and soft law instruments.The contributions of this work are: first, a review of key regulations applicable to AI in investment management (and oftentimes by extension to banking as well) from multiple jurisdictions; second, a framework and an analysis of key regulatory themes for AI.