Abstract

This study analyzes the synergistic impact of AI-driven strategies on retail options trading in the European derivatives market through comparative analysis of market efficiency and liquidity compared to the US setting. This study employs the mixed-methods approach, and we analyze quantitative data daily trades volume, bid-ask spread, and volatility indices (VIX/VSTOXX)-as provided by EUREX, CBOE, CME, and Bloomberg, and qualitative data sourced from industry and scholarly publications. The findings indicate that US markets, characterized by more liquid pools of liquidity (average daily SPX contracts of 1.2 million vs. 300,000 SX5E contracts) and narrower spreads (0.25% vs. 0.40%), enable AI algorithms to calculate more economically than in Europe (CBOE Global Markets, 2024; EUREX, 2024). Further, AI technologies improve price discovery chartered by a rise in options volume of 18% vs. 9% after CPI announcement (Bloomberg, 2024)-and minimize behavioral biases among retail traders. However, structural fragmentation and lower AI take-up in Europe constrain these benefits (Acuiti, 2023). We conclude that AI enhances liquidity exploitation and efficiency on both sides of the Atlantic, but that market structure is once again a significant moderator. We suggest additional research on model specific performance, sectoral research, stress system risk, and coverage to emerging markets.

Keywords: AI-driven strategies, Retail options trading, Liquidity, Quantitative data, Market efficiency