ExtractAlpha's rigorously built quantitative stock selection models, listed below, leverage unique data and analytical techniques -- allowing our institutional clients to gain an investment edge.

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  • CAM1

    ExtractAlpha’s Cross-Asset Model 1 (CAM1) is an innovative quantitative stock selection model designed to capture the information contained in options market prices and volumes. The listed equity options market is composed of investors who on average are more informed and information-driven than their cash equity counterparts, due to the higher levels of conviction that are associated with levered bets.  As a result we are able to build a unique model which profits from gradual cross-asset information flows.  Here’s the CAM1 fact sheet.

  • TM1

    ExtractAlpha’s Tactical Model 1 (TM1) is a short-horizon trading and investment tool which captures the technical dynamics of single US equities over a one to ten trading day horizon.  TM1 is a tactical factor, in that it assists longer horizon investor in timing their entry or exit points, and it can also be used in combination with existing systematic or qualitative strategies with similar holding periods.  Here’s the TM1 fact sheet.


    The TRESS Expert Sentiment Signal harnesses the unique alpha in financial blogs.  Top quantitative hedge funds and institutions have incorporated TRESS into their investment processes.  Here’s the TRESS fact sheet.

  • Digital Revenue Signal

    The Digital Revenue Signal is a stock selection score designed to forecast revenue surprises based on changes in consumer demand estimated from their digital footprint trajectories. The algorithms underpinning the Digital Revenue Signal leverage ExtractAlpha partner firm alpha-DNA’s proprietary organization of near real-time commercially available online consumer behavior information across multiple web Site, Search and Social platforms.  Here’s the Digital Revenue Signal fact sheet.

  • Future models...

    ExtractAlpha is hard at work researching new alpha-generating models and data sources.  More to come!

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