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Empowering retail investors with institutional-grade AI analytics and real-time global market data.
Institutional-grade alternative data and quantitative signals for systematic alpha generation.
ExtractAlpha is a premier independent research firm providing high-conviction alternative data sets and quantitative signals to institutional investors. By 2026, the platform has solidified its position as a critical node in the systematic trading ecosystem, leveraging advanced NLP and machine learning architectures to filter noise from massive datasets including social media sentiment, crowdsourced earnings estimates (via Estimize), and ESG metrics. Their technical architecture is built for seamless integration into quantitative workflows, offering delivery via Snowflake, S&P Global, and direct APIs. Unlike generic data providers, ExtractAlpha focuses on 'alpha-capture'—identifying non-obvious correlations between non-traditional data sources and future stock performance. Their models are rigorously backtested and designed to minimize decay, providing institutional desks with a competitive edge in equity markets across North America, Europe, and Asia. The platform's 2026 roadmap emphasizes real-time cross-asset sentiment linkages and the integration of generative AI to provide natural language explanations for quantitative signal shifts, making high-level quant insights accessible to discretionary managers.
A quantitative model combining short-term reversal, liquidity, and sentiment factors to predict returns over a 1-10 day horizon.
Empowering retail investors with institutional-grade AI analytics and real-time global market data.
The engine for global financial market data, analytics, and workflow automation.
Powering better investment decisions through AI-driven risk analytics and ESG intelligence.
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Integration of crowdsourced earnings and economic estimates from over 100,000 contributors.
Quantifies environmental, social, and governance factors using a proprietary alpha-driven weighting system rather than simple compliance scores.
Uses BERT-based transformers to analyze global news and social feeds for equity-credit linkages.
Algorithmically identifies 'informative' insider trades while filtering out routine diversification or compensation-based sales.
A massive repository of pre-calculated, point-in-time quantitative factors for global equities.
Ensures all historical data is timestamped exactly when it was available to the market, preventing look-ahead bias.
Standard analyst estimates are often biased or slow to react.
Registry Updated:2/7/2026
Exit position 3 days post-announcement.
Most ESG data is backward-looking and lacks predictive power for returns.
Overcoming the decay of traditional technical indicators in high-frequency environments.