Author(s) :

Matthew Balaban.

Volume/Issue :

Abstract :

Artificial intelligence (AI) is rapidly transforming the asset management industry by enhancing investment strategy formulation, portfolio construction, and risk management. This study investigates the performance impacts of AI integration in asset management using a ten-year panel (2015–2024) of institutional fund holdings derived from SEC Form 13F filings via Whalewisdom. Funds were classified as traditional or quantitative based on turnover and holdings concentration, serving as proxies for AI adoption. Literature review findings reveal that leading institutions such as JPMorgan and BlackRock leverage AI for alternative data analysis, risk oversight, and hybrid human–machine decision-making. Results show that quantitative funds consistently exhibited higher volatility and greater market sensitivity (beta), while both fund types saw declining downside-adjusted performance (Sortino ratio). Notably, Sortino ratios converged over time, suggesting narrowing differences in downside-risk efficiency. Alpha was persistently negative for quantitative funds, indicating limited excess returns beyond market exposure. Overall performance differences were negligible except during the onset of COVID-19, when quantitative funds briefly outperformed. These findings highlight AI’s dual role as both a source of adaptability and a driver of higher systemic risk, underscoring the importance of governance and transparency in its deployment.