CyMetica | Public Company Hidden Relationship Discovery


How feature scores are calculated for the dataset | Build a dataset

Rows contain stock symbols. Columns contain scores that represent known and hidden relationships between elements and stocks.

Elements dataset, CSV format: supercolumns-elements-nasdaq-nyse-otcbb-general-UPDATE-2017-03-01.csv


To get updates or a custom dataset built, send us an email:


What kind of things can be done with custom concept columns/features?

  • Create unique sectors or clusters based on concepts and hidden relationships and compare their gains to the S&P
  • Determine if price correlations have similar concept or keyword correlations
  • Examine symbiotic, parasitic and sympathetic relationships between equities
  • Automatically create baskets of stocks based on concepts and/or keywords
  • Detach the custom columns and append them to other proprietary inhouse datasets
  • Select a Data Context (e.g. Biological, Chemical, GeoPhysical and others) to derive different signals
  • Use stock symbols as custom concept column labels and model cross-correlations between equities
  • Create features using trending terms anywhere on the internet


  • Example use case 1: Explore hidden relationship networks similar to those described here

    Example use case 2: Returns for Playstation, Helium, Korea, Shampoo, & Coffee in comparison to the S&P 500: (interactive)


    Google Search Trends for Playstation, Helium, Korea, Shampoo, & Coffee



    Tools:

  • Google Trends as concepts
  • Concept Explorer


    Data, feature reqeusts or suggestions: cymetica@gmail.com