Systematic investing has evolved for decades, fueled by growing data volume, speed, and diversity. Today’s markets offer vast opportunities, with the next generation of alternative investments poised to unlock new sources of systematic return.
A new frontier in data availability
Key points
01.
From big data to insight
Big data’s scale and complexity create vast investment insights for those able to analyze diverse, fast-moving data sources effectively.
02.
Decades of research
This decades-old approach includes economic, AI, and machine learning techniques to identify opportunities and improve investment decisions.
03.
Alternative investments
Systematic investing could reshape how investors access and invest in alternative assets, the same way it revolutionized investing in public markets.
Early origins
Systematic investing applies data, analysis, and structured decision-making to investment management, explicitly balancing risk and return when building portfolios.
The approach gained momentum in the 1980s, when researchers discovered that financial metrics such as valuation ratios could help forecast stock returns and risk. Early systematic strategies used these insights to tilt portfolios toward factors like value, momentum, and smaller company size while managing benchmark risk.
Since then, systematic investing has expanded across global equity markets, fixed income, and currencies. Advances in data and research have continually improved return forecasting, incorporating insights ranging from analyst expectations to fundamental measures beyond price trends.
Systematic alpha
Today, advances in machine learning and large language models (LLMs) are creating new ways to extract investment insights. For example, BlackRock Systematic applies LLMs to analyze broker reports and estimate market sentiment, building on more than a decade of innovation in natural language processing and data-driven investing.
New Frontiers
Systematic investment strategies are set to expand even further. Today’s strategies use text analytics to extract macro signals, LLMs to identify thematic baskets of related securities, and AI-driven models to analyze illiquid asset classes such as private equity and real estate. While sourcing attractive deals once depended heavily on a manager’s personal network, advances in data science have broadened access, enabling managers to proactively identify promising opportunities through predictive analytics.
Authors
Raffaele Savi
Global Head of BlackRock Systematic and Co-CIO of Systematic Active Equities
Dr. Ronald N. Kahn
MD, Global Head- Systematic Investment Research, BlackRock
Tom Parker, CFA
Managing Director, Chief Investment Officer of Systematic Fixed Income
Jeff Shen, PhD
Co-Head and Co-CIO of BlackRock Systematic Active Equities
Explore more Systematic insights
BlackRock Systematic combines alternative data, data science and deep human expertise to help modernize the way we invest and construct portfolios on behalf of our clients.