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ExtractAlpha is an independent research firm dedicated to providing unique, actionable alpha signals to institutional investors. We apply our extensive experience in quantitative analysis and the design of investment analytics products to interesting new data sets. Our rigorously built quantitative models are designed for institutional investors to gain a measurable edge over their competitors. We also partner with top FinTech data firms to identify investment value in their data sets and help investors profit from these unique new sources of information.
ExtractAlpha’s founding team held senior positions in the original research, development, and sales groups at StarMine and at top quantitative hedge funds including Morgan Stanley PDT.
- Hong Kong
Our datasets are priced as annual subscriptions, a la carte.
Institutional investors use our datasets and signals to improve their portfolio returns and trade timing.
We offer both raw and derived data for quantitative investors, and a front end visualization platform, Insight, for fundamental investors.
Data Sources & Collection
Data is sourced broadly through vendors, partnerships, and web scraping. Categories include sentiment, web traffic, trade timing, and ESG data.
Revenue surprise forecasts using web traffic and social media engagement for global equities
Tactical factors for trade timing for global equities
ESG and innovation data from public and government sources
China e-commerce transaction data
Crowdsourced recommendations and earnings estimates
Blogger sentiment using Natural Language Processing
Cross-asset factors which capture the information in derivatives markets
Risk model for global equities
ExtractAlpha Insight:visualizations and watchlist analytics using alternative data
Academic research digest newsletter
ExtractAlpha is unique in providing a broad set of quantitatively-proven alternative datasets. Each of our datasets have demonstrated predictive power for stock returns and fundamentals, as verified through our internal research team. This curation allows institutional investors to cut through the noise in alternative data and easily identify datasets which are most likely to be additive to their investment process.