| Abstract: |
The intersection of artificial intelligence and alternative investments has emerged as one of the most consequential shifts in contemporary finance. This paper synthesizes a decade of empirical and theoretical work on how machine learning, deep neural architectures, and reinforcement learning algorithms are reconfiguring strategy design across hedge funds, private equity, real assets, and digital asset classes. Drawing on a meta-analysis of 87 peer-reviewed studies published between 2013 and 2023, we map the trajectory of AI adoption across asset classes, examine risk-adjusted performance differentials between AI-assisted and conventional strategies, and identify persistent methodological gaps in the literature. Our findings suggest that ensemble methods and transformer-based models yield statistically significant alpha generation in illiquid markets, yet their opacity introduces systemic risks that existing regulatory frameworks were never designed to address. The paper further traces how data diversity satellite imagery, social media sentiment, supply-chain telemetry has expanded the investable information set beyond anything previously available. We conclude that AI-driven strategies are not merely an efficiency improvement over traditional quant approaches; they represent a structural reconfiguration of how risk premier are identified, priced, and harvested across global markets. Understanding this reconfiguration matters not just for practitioners, but for regulators, academics, and anyone with exposure to capital markets. |
