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.
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)