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Users can view all videos on the SMA Vimeo Page: http://vimeopro.com/user23551542/social-market-analytics-inc
Social Market Analytics (SMA) uses patent-pending technology to estimate sentiment for specific stocks, industries, sectors and indices from social media using Twitter’s data stream.
Sentiment is the expression of an opinion of what is yet to come.
An example of emotion versus sentiment is below.
SMA’s algorithms filter Twitter’s data steam of Tweets and generate sentiment scores for individual securities, industries, sectors and indices. The filtered Tweets are “indicative” of social sentiment.
Twitter has over 500 Million Tweets per day. Social Market Analytics filters the Twitter data stream using qualitative and quantitative measures to identify tweets regarding financial trading. Accounts are qualitatively filtered to confirm the source of information while individual tweets are qualitatively reviewed to confirm relevancy.
Tweets that are filtered through our 3-step process are deemed “indicative” of social sentiment for an individual stock, industry, sector or index.
S-FactorsTM are a family of metrics used to quantify social sentiment for individual stock symbols. The leading indicator of sentiment for a particular symbol is the S-Score. Stocks with S-Scores greater than +2 have a high probability to beat the market. Stocks with S-Scores less than -2 have a high probability to lag the market.
To download a comprehensive guide to our S-Factors, click here.
New to the SMA EDGE Report? Download the usage guide.
SMA currently tracks and provides sentiment estimates on approximately 8,000 securities. Tweet volumes change significantly over the course of an average week. Tweet volumes on weekends and holidays are particular low. Before the market open on Monday mornings, it is common to have signals on approximately 1,200 stocks. On Friday mornings, we usually have signals on over 1,600 securities.
S-Score
Our backtested data shows that S-Scores greater than +2.0 and S-Scores less than -2.0 are statistically significant.
S-V Score
As with S-Score, S-V Scores greater than +2.0 and S-Score less than -2.0 are statistically significant.
S-Dispersion
S-Dispersion can range from 0.0 to 1.0. More popular stocks (APPL, GOOG) tend to see a dispersion level between 0.5 and 0.7.
S-Volume
Just as you would pay attention to an usual volume of trades against a stock in one day, you should consider the amount of social media volume regarding a stock. S-Volume shows the aggregate number of indicative Tweets of relevance to a specific stock, or symbol, over a 24-hour period.
More popular stocks (APPL, GOOG) can have an S-Volume of 500 or more. Smaller or less popular stocks may have typical S-Volume of 25. Monitoring the average S-Volume score for a particular stock over the course of a few days can help you determine the S-Volume score for your alert.
Normalized representation means that sentiment is measured on a common scale, for the stocks in our universe. Normalizing our metrics allows you to compare the sentiment measurement for multiple stocks on a common measurement scale
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