Machine Readable Filings enables businesses and investors to incorporate the impact of COVID-19 on company performance into their business forecasts New York, Apr. 09 2020 — S&P Global Market Intelligence announced today the launch of S&P Global Machine Readable Filings, a new data offering that applies cleansing and parsing techniques to generate machine readable text extracted from SEC Regulatory Filings. As part of the growing suite of Textual Data, Machine Readable Filings can help investors and market participants better identify and interpret a company’s impact from unp ..read more

People have been asking about performance of sentiment data over the past few weeks.  We track performance for all asset classes multiple ways.  A lot of recent volatility has been overnight.  The chart below illustrates performance of S-Factor S-Score data relative to pre-market close sentiment S-Scores. Criteria for this chart is as follows. Minimum $5 filter. S-Scores are snapped at (3:40 PM EST).  20 minutes before close. We multiply S-Score with the subsequent Close-to-Open returns to calculate ‘Stock Score’.For example. AAPL has an afternoon Pre-Close S-Score of 3.0 and has a Close-to ..read more

Social Market Analytics (SMA) calculates metrics describing the current conversation on Twitter relative to historical baselines using statistical Z-Score metrics. Most Twitter conversations are neutral on a given day. If the conversation on Twitter is neutral, stock price is being driven by something other than social media. We find that Twitter derived metrics illustrate statistically significant predictive power when the conversation becomes more positive or negative than normal levels. The table below is a good illustration of Z-Score. Points on the curve denote the positive or negati ..read more

This blog post highlights research done on an excellent research platform My Data Outlet. My Data Outlet specializes in integrating financial data into R. They offer an API to access financial data, allowing for a single access point for extracting data.  Their tools are excellent for creating backtests and deploying models into production.  This analysis was done using the MDO.Factors Library of functions. The mdo.factors library contains samples of publicly available research factors and can be used as a template for creating new factors.  They have expertise in the Refinitive QA Direct and ..read more

At Social Market Analytics (SMA) we create predictive signals by aggregating the intentions of professional investors as expressed on Twitter.  We have accumulated eight years of out-of-sample data illustrating the predictive nature of the data.  We publish sentiment metrics to illustrate the tone of the current conversation relative to historical conversations.  One of our key metrics is S-Score.  S-Score is effectively a Z-Score, the measure of deviation from the mean.  An |S-Score| > 2 means the current conversation is two standard deviations from the mean over the predefined lookback pe ..read more

Social Market Analytics has extensive Intellectual Property in three distinct areas:  Topic model creation, account filtering and natural language processing (NLP).  I have written blog post about SMA topic model creation capabilities and the impact of our account filtering algorithms.  This blog answers the question – “Do your machine learning algorithms really add value to the NLP process?”.  Answer -> Yes. The chart below illustrates the statistically significant benefits of Social Market Analytics Machine Learning Algorithms.  Start date for this analysis is 11/20/2018 and the end ..read more

As we move towards the fourth quarter people have been asking how SMA data is performing in this volatile market.  Those who have been following us over the years have come to expect the Open to Close (O-C) Chart to illustrate the performance of our data.  We have been publishing this chart since the launch of the company in early 2012.  The machine learning applied to our NLP has provided increasing predictive power to our data as our out-of-sample training set continues to grow.  Even as we increased our asset class and security coverage.  Twitter has continued to be the go-to source for bre ..read more

Social Market Analytics (SMA) the leader in predictive social media data feeds has added the ‘Crypto Fast’ to its suite of API data feeds. SMA’s S-Factor and Activity Cryptocurrency data feeds have been in production since December 2017. “Clients asked for a bespoke feed for a shorter baseline with 1-Hour price projections. Although clients can create their own baselines and metrics with our Activity Feed, clients wanted SMA to do the development work and produce and support the product which has been named ‘Crypto Fast’.” The SMA Crypto Fast Feed provides faster moving signals than the ..read more

Coin Metrics and Social Market Analytics (SMA) announced today a partnership to incorporate SMA’s Crypto Currency Data Feed into the Coin Metrics Market Data Platform. Alternative data such as social media platforms and data feeds have become a vital source of information for traders, particularly in the Crypto Currency Markets. The SMA Crypto Currency Sentiment Feed will offer the Crypto Currency community a tool for including social media sentiment data in their trading and portfolio strategies and expand Coin Metrics market leading Crypto Asset market and network data products. “As the Cry ..read more

Social Market Analytics aggregates the intentions of professional investors as expressed on Twitter.  We identify these professional investors using our proprietary twelve factor ranking system.  One factor is the forward accuracy of Twitter accounts.  If a Twitter account is Tweeting bullishly based on our patented NLP process and the security subsequently moves higher over specified periods that account is deemed to be accurate over that period.  Overall accuracy is aggregated across time for each account.  We have been tracking account accuracy out-of-sample for the past seven years. – it i ..read more