Algorithmic Trading vs Human-Led Strategies: Performance, Risks, and a Hybrid Path Forward

We synthesize recent evidence comparing algorithmic and human-led trading across short- and long-horizon settings, assess behavioral and systemic risks in increasingly automated markets, and propose a practical hybrid framework. In ultra-short horizons, high-frequency algorithms achieve superior risk-adjusted returns by exploiting micro-inefficiencies with disciplined inventory control. In longer horizons, performance is regime-dependent: systematic funds tended to mitigate losses during recent downturns, whereas discretionary managers captured upside in recoveries. We discuss ethical, fairness, and stability concerns (e.g., flash events, opacity, homogenized strategies) and outline a “human-in-the-loop” design that combines algorithmic consistency and speed with human judgment for regime shifts and governance. Our findings provide insights into how generative AI models can be integrated into finance for scenario generation, synthetic stress testing, and regulatory compliance, paving the way for hybrid strategies that balance efficiency with oversight.