FactSquared
Sentiment data on earnings calls using both content and tone of voice analysis.
Social/Sentiment
main data source2017
Year Company Founded- 1
Discretionary Asset Manager Customers7
AAPL, AMZN, JPM, V, WMT
Hedge Fund
N/A
Washington, DC
Raw, Custom Analysis, JSON API, CSV, spreadsheet. Platform has user-facing UI, but is intended as a gateway to the data vs. the place to consume the data.
> $10k / month
2002
US
Extended Dataset Description
This tells what people mean, not just what they say. If they’re uncomfortable, it gets flagged. If rate of speech deviates, it is flagged. If they are off script, it is flagged.
The dataset has the transcripts and audio of the earnings call, along with comprehensive data indicating higher / lower areas of stress by call time and topic, in addition to increases / decreases in rate of speech and vocabulary changes.
Use Cases
The dataset provides a clean signal of deltas. If lawyers start writing the introductory text to the call, the word patterns will flag it. If different adjectives are used to describe earnings, it is flagged. Because each executive is profiled individually, unique vocal quirks and stress patterns highlight when a person is more / less comfortable discussing a particular topic, which when trended, can highlight areas of exploration or risk.