Leverage Social Market Analytics’ patented technology to identify and aggregate market-moving tweets. Independent research has shown that SMA’s social media-based factors are predictive at statistically significant levels and can provide alpha enhancement and risk reduction to most quantitative models.
Incorporate SMA data into multi factor Quantitative models, Algo Execution, Market Making, Indices, Barra and VarRisk models and Research. Predictive signals range in frequency from real-time to quarterly. Data for sectors and industries is aggregated from the security level. SMA has point in time out of sample data dating back to 2011 for accurate backtests.
Identify professional investors with SMA proprietary algorithms. SMA’s process is driven by two U.S. patents that extract, evaluate, and calculate nearly 1B textual data points per day to provide real time APIs. SMA’s financial machine learning NLP has been built over the past seven years using supervised and unsupervised training. All SMA data is out-of-sample and all changes are applied on a go forward basis.
Data Feed Details
Social Market Analytics (SMA) S-Factor metrics provide quantitative measures of the intentions of professional investors as expressed on Twitter. SMA's metrics provide raw sentiment as well as a measure of sentiment on the security level relative to its history. Historical back-testing, independent research, and customer feedback support the fact that SMA insights are tradable and can deliver alpha. The SMA Sentiment Data Feed represents a new uncorrelated source of predictive information that can add value to predictive models.
Social Market Analytics, Inc. (SMA) was founded in early 2012 to create predictive quantitative signals from unstructured data. SMA's patented process develops proprietary metrics using a unique approach that filters social media and textual data. SMA extracts detailed information from sources around the world using algorithms that rely on machine learning and natural language processing.