A numerical value that encapsulates how consumers and investors feel about a company, stock, ETF, sector or index as expressed over social media, specifically Twitter. S-Scores are created with data gathered by social media monitoring engines to help investors make trades and to help companies with market analysis and decision making.


In 2013, NYSE Technologies and Social Market Analytics created the first S-Score to be distributed over a high-performance global network, specifically geared toward the financial sector and designed to benefit trading firms, portfolio managers, hedge funds, risk managers and brokers. Along with its trademarked S-Score, SMA offers S-Mean, S-Delta, S-Volatility, S-Buzz and S-Dispersion indicators (together called S-Factors), to track the volume, change and dispersion of social media comments. Their system filters out irrelevant and duplicate comments and spam to focus on the 10% of comments that provide meaningful information.

An S-Score of greater than +2 is associated with significant positive sentiment, while an S-Score of lower than -2 is associated with significant negative sentiment. A score greater than +3 is considered extremely positive, while one below -3 is considered extremely negative. Anything between -1 and +1 is considered neutral. Higher scores could be also associated with higher Sharpe ratios, while lower scores could be associated with lower Sharpe ratios.

Investors can use S-Scores to help them pick stocks. When S-Score changes, stock price is expected to change as well. Research by Social Market Analytics has shown that stocks with S-Scores higher than +2 significantly outperformed the S&P 500 over the period December 2011 through December 2013, while those with S scores less than -2 underperformed it significantly. As of January 2014, SMA computed S-Scores for all U.S. stocks with a meaningful amount of social media data. Examples include Whole Foods, Tesla Motors, Apple and Luluemon.