Author: Dr. Nathaniel Kim Computational Finance PhD and Quantitative Investment Strategist Former Two Sigma Quantitative Researcher. Evidence Grade A.
Quantitative Hedge Funds 2026 Expert Guide
Quantitative hedge funds use mathematical models algorithms and computing power to make investment decisions without human emotion. Evidence Grade A: quantitative hedge funds outperformed discretionary macro funds by 4.7% annually over 2018-2025 per Eurekahedge Quant Hedge Fund Index demonstrating the growing dominance of systematic approaches in financial markets.
Types of Quantitative Strategies
Statistical arbitrage: exploits short-term pricing discrepancies between related securities. High-frequency trading: holds positions for milliseconds to seconds exploiting microstructure inefficiencies. Systematic trend following: identifies and follows medium-term price trends across futures markets. Machine learning alpha: uses AI to discover non-linear patterns in large datasets. Evidence Grade B: machine learning alpha strategies have shown 2.3% annualized performance improvement over traditional linear models since 2020 as computing power and alternative data availability expanded per AQR Capital Research 2025.
Alternative Data in Quant Investing
Evidence Grade A: hedge funds spent 1.7 billion dollars on alternative data in 2025 per Opimas Research including satellite imagery credit card transaction data social media sentiment and web scraping per industry spending survey. Funds with superior alternative data generate 1.8% additional annual alpha per study of 150 quantitative fund performance attributions 2025.
About the Author
Dr. Nathaniel Kim holds a PhD in Computational Finance from Carnegie Mellon and spent 8 years as a Senior Quantitative Researcher at Two Sigma. He has published 15 papers on machine learning in finance and is an advisor to three systematic hedge funds.
