Social Market Analytics produces a family of metrics, called S-Factors – designed to capture the signature of financial market sentiment. SMA applies these metrics to data captured from social media sources to estimate sentiment for indices, sectors, and individual securities.
SMA’s processing engine is made up of 3 different components.
Extractor - The Extractor accesses the API web services of Twitter and GNIP, a microblogging data aggregator. A Data Acquisition process polls these sources to capture tweets containing commentary on the members of the SMA stock universe. The polling process continuously cycles through the universe list, adaptively polling for securities with current content in the message stream.
Evaluator - The Evaluator analyzes each tweet for financial market relevance to the entities in the SMA stock universe. These are called “indicative” tweets, as these indicate expressions of market trading sentiment for these stocks. The Evaluator uses proprietary natural language processing algorithms to asses each message. SMA also uses a proprietary algorithm to further filter specific twitter accounts. In short, SMA utilizes tweet content and characteristics of the individuals tweeting to distill the intentions of professional investors.
Calculator - The Calculator determines the sentiment signatures for each member of the SMA stock universe. A bucketing and weighting process operates on an entity’s indicative tweets and groups these into time period buckets based on the arrival time of each tweet. A Normalization and Scoring process calculates the S-Score™ and other S-Factors™ for each entity with active content at the time of the estimate.