CHICAGO--(BUSINESS WIRE)--Social Market Analytics (SMA), the world leader in using big data to provide the financial industry with near real time sentiment signals that consistently and reliably predict specific stock movement (up or down), today announced that it has begun selling aggregated and weighted social sentiment signals for virtually all commonly traded commodities and foreign exchanges.
SMA’s equity sentiment signals have been incorporated into trading models of many buy-side traders for more than two years. The company’s patented technology and algorithms have consistently proven to help traders outperform the market.
SMA’s Daily Equity Sentiment Indicators Consistently Outperform the Street
“If you had traded our daily pre-market open social sentiment ‘winners’ over the last two years, you’d be up more than 50 percent for the period,” said Joe Gits, President and Chief Executive Officer of SMA. “You’d also have won if you had traded the pre-market open social sentiment ‘losers.’” It’s equity data streams over Markit’s financial data platform to many of the largest banks and institutions in the world.
New Commodities Sentiment Signals Track Ags, Currencies, Metals, Treasuries and Indices
Gits noted that several major hedge fund customers beta tested the commodities sentiment service ultimately buying the service before market introduction. The SMA commodities product tracks pro-trader sentiment across all leading commodities (agriculture and energy futures, currencies and precious metals, treasury notes and indices).
The company now tracks social sentiment of identified and vetted professional traders and market movers and instantly evaluates those intentions against statistical real world norms established for each stock or commodity. SMA’s patented algorithms also enable the company to instantly identify the strength and pervasiveness of indicative tweets. A sentiment signal of a standard deviation of +/- 2 or more from the norm is considered highly indicative of a stock or commodities pricing direction.
Seven Proprietary Sentiment Metrics:
SMA’s algorithm produces a family of seven (7) metrics, called S-factors that can help active traders quickly understand the validity, importance and pervasiveness of that sentiment against standard deviation norms:
S-Score™: is the weighted normalized representation of our sentiment time series over a lookback period.
S-Mean™: is a smoothed weighted average of the S-Score™ over a lookback period.
S-Delta™: is the percent change in S-Score™ over a lookback period, a first order measurement of the sentiment trend.
S-Volatility™: is a percent measurement of the variability of the sentiment level over a lookback period.
S-Volume™: is the volume of indicative tweets contributing S-Factors™ calculations at an observation time.
S-V Score™: is a measurement of unusual volume activity.
S-Dispersion™: is a measurement of the tweet source concentration factor contributing to an S-Score™.
In general, positive S-Scores™ are associated with favorable changes in investor sentiment, while negative levels are associated with unfavorable changes. SMA has proven that investor sentiment changes reliably result in stock price changes. The company also has observed larger changes in investor sentiment to be associated with larger stock price changes.
About Social Market Analytics
Social Market Analytics is a privately held Chicago-based big data company that now provides active individual and institutional investors (banks, hedge funds, brokerages) with streaming up-to-the-minute social sentiment quantification and analysis for both equities and commodities that can help them to outperform the market. Its patented technology and algorithms have consistently proved that the tweets of professional traders can help both institutional and individual traders outperform the market. SMA was selected by Markit to provide their members’ access to reliable, up-to-the-minute social data sentiment signals.