Social Market Analytics – Social Sentiment and the S-Score

If you have spent time on the trading floor you know that higher sound levels usually precede periods of increased volatility. This increased energy was the main signal that something was about to happen or already happened in the market. This was beneficial because traders could only see quantities being offered to buy or sell at the current bid and ask. So, an increase in sound level could be an indicator of future price changes, increasing or decreasing in magnitude at a similar level that the animations and bellows of traders increase. Higher sound levels could also mean that order volume may increase as well as concerted efforts for brokerages to manage their inventories.

Now that the days of open out-cry are almost non-existent the sound signals as a trading indicator are largely extinct. However, some believe social media can mimic the sounds and signals of the trading pits by indicating warning of potential market moves through the process of social listening.

Lightspeed brings to our clients a research tool developed by Social Market Analytics (SMA) that analyzes Twitter data to capture the financial market’s sentiment about a specific equity or commodity. This Social Sentiment is represented by the S-Score.

What is the S-Score?

The normalized representation of a security’s social sentiment

SMA’s S-Score is one of 15 S-Factors delivered through an API to SMA’s quantitative systemic trading clients. The S-Score is expressed as a deviation from the normal tone of conversation on a standard normal scale of -4.5 to +4.5. A |S-Score| > 2 represents the conversation is 98.6% more negative or positive over the past 24 hours compared to a 20-day baseline. At levels above +2.0 and below -2.0, conversations become statistically significant and the securities start seeing movement in the direction of sentiment over the next 1 hour to 1 day.

5 S-Factors are available to Lightspeed Clients

S-Score – The normalized representation of a security’s social sentiment

S-Volume – The volume of indicative tweets contributing to a security’s S-Score

SV-Score – The normalized representation of a security’s indicative Tweet volume

S-Dispersion – The measure of Twitter source diversity contributing to a security’s S-Score

S-Delta – The change in a security’s S-Score over a 15-minute period

How can the S-Score be used?
The S-Score and other S-Factors have been properly analyzed by Social Markets Analytics (SMA) and can be used as a powerful tool in your research toolbox as an additive factor when deciding to buy or sell a security.

How can traders use the S-Score and its factors?
If you are an active professional or quantitative trader these factors created by Twitter data is new content for alpha generation. Securities that have a high sentiment scores have the tendency to outperform at a statistically significant level and securities with a low or negative sentiment score tend to underperform at a statistically significant level.

It is also a great tool for identifying new securities to trade or monitoring current positions.

To learn more about SMA on Lightspeed Trader, try a demo and see how it works.


Lightspeed Financial Services Group LLC is not affiliated with these third-party market commentators/educators or service providers. Data, information, and material (“content”) are provided for informational and educational purposes only. This content neither is, nor should be construed as an offer, solicitation, or recommendation to buy or sell any securities or contracts. Any investment decisions made by the user through the use of such content is solely based on the users independent analysis taking into consideration your financial circumstances, investment objectives, and risk tolerance. Lightspeed Financial Services Group LLC does not endorse, offer nor recommend any of the services or commentary provided by any of the market commentators/educators or service providers and any information used to execute any trading strategies are solely based on the independent analysis of the user.

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