A new frontier in data availability

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.