Power of Predictive Alpha in a Bear Market

This year has been tough for most investment strategies.  Firms using traditional sources of data are generating the same underwhelming returns.  Two years ago, Social Market Analytics, Inc.  (SMA)  (Twitter)   launched the SMLCW index in partnership with the CBOE.  This index is re-balanced weekly and comprised of the twenty-five securities selected from the CBOE large cap universe with the highest average S-Score over the prior week.  It’s A long only index of super-cap stocks with unusually positive Twitter conversations.

SMA publishes a family of metrics providing a full representation of the Twitter conversation across equities (US and LSE), commodities, currencies, ETF’s & Cryptos.

S-Score is a normalized representation of the current Twitter conversation of professional investors as identified by Social Market Analytics patented algorithms.  SMA has access to the full Twitter feed through our licensed partnership with Twitter and listens in real-time for any mention of topics and securities of interest.  These Tweets are scanned in real-time for sentiment and influence of the poster and compared to prior conversations over the look back period.  Securities with higher S-Scores subsequently outperform and securities with negative S-Scores under-perform.

SMA S-Scores are predictive over multiple prediction periods.  With seven years of out-of-sample data we can extend our comparison baselines and predict over longer periods.

Year-To-Date the SMLCW index is up over 7.5% while the SP500 is flat.  Subtracting a couple percent for commissions/slippage and the index is still significantly positive. This is not a back-test, this index has been live and on your quote screens for nearly two years.  YTD actual performance chart from the CBOE site is below.


As mentioned, this is a long only index.  During the recent market drawdown this long index has been performing.  SMA negative S-Score stocks have been moving lower at a significant rate – generating positive alpha.  Below is a chart of the SMLCW index compared to the SP500.  for any questions or to learn more please contact us at:  ContactUs@SocialMarketAnalytics.com.




Social Market Analytics (SMA) Trading Strategy on Natural Gas Futures

Social Market Analytics, Inc. (SMA) has been the leading provider of predictive quantitative signal in the alternative data space for 7 years. Over the years, we have developed patented algorithms that use machine learning and natural language processing to provide content that generates alpha. Our NLP is unique because of our proprietary processes that tag sentiment weights based on the language used in finance per asset class. The processing of tweets for futures and commodities is different from what we use for equities.

There have been a lot of conversations around Natural Gas futures in social media lately. Through our partner CME Group , people have been monitoring social sentiment from SMA in real time on their active trader site.

Recently, we did as study on using SMA signals to create a Long Short trading strategy using Natural Gas futures. In the example here, we are using front month futures contract and trading daily using a combination of S-Score (a measure of unusual sentiment) and SV-Score (a measure of unusual twitter volume activity).

The strategy buys contracts when the sentiment (S-Score) is positive. We scale up the long position when the sentiment is positive, and the volume of tweets is also significantly high (SV-Score). Conversely, when the sentiment is negative, we sell contracts and go short. We sell more contracts when sentiment is negative with significantly high number of tweets. For this study, we use a maximum of 100 contracts when going long and 100 contracts when taking short positions.

The sentiment strategy performs significantly better than the Natural gas prices, returning 87.42% YTD. We also avoid the volatility in the price and get a Sharpe of 3.53 on the strategy.


A PnL curve of investment of $300,000 in 100,000 contracts on Jan 1, 2018 is shown in the chart below.

The strategy is profitable throughout the  period, and the maximum drawdown is only 7.19%, which is significantly better than the 29.72% drawdown in the natural gas prices YTD.


Social Market Analytics (SMA) on CME Group Active Trader

The CME Group has partnered with Social Market Analytics, Inc. (SMA) on the CME Active Trader platform to provide predictive sentiment data analytics across six asset classes and thirty-six commodities. The CME Group is leveraging SMA’s Patented processing technology in machine learning and natural language processing (NLP) to provide users with new alternative sentiment data to a trader’s tool kit.

The CME Active Trader cover six asset classes: Equity Index, Energy, Metals, Interest Rates, FX, and Agriculture. Traders can now add Sentiment to their tool kits to decide when to enter and exit positions, hedge or spread, and as a factor in best execution practices.

NatGas Blog

Over the past several years, social media sources like Twitter are being used more frequently to distribute company news, information, and analysis of Futures and Options. There are 800 Million Tweets a Day across the globe. Social media often raises awareness of news and information more quickly than traditional news sources. SMA is the leader in social media predictive data analytics using forward looking Tweets.

Social Sentiment is measured by the S-Score calculated by Social Market Analytics, Inc. What is S-ScoreTM? The S-Score uses Patented machine learning, natural language processing and account rating algorithms to produce predictive metrics in real time. The S-Score is expressed as deviation from normal tone of conversation on a standard normal scale of -4.5 to +4.5. An S-Score of -2.0 or +2.0 represent that the conversation is 98.6% more negative or positive over the past 24 hours compared to a 20-day baseline. Values around 0 can be interpreted as conversation sentiment same as the past 20 days. Any value above 0 represent a positive deviation as compared to last 20 days and values below 0 represent the conversations turning negative. At levels above +2 and below -2, conversations become statistically significant and the securities start seeing movement in the direction of sentiment over the next 1 hour to 1 day.

The Sentiment in the Product page has a Pop Out where both Sentiment S-Score and Tweet Volume can be viewed over 1D, 1W, 1M, 6M, and 1Y.

NatGas Blog 2

contact: doug@socialmarketanalytics.com http://www.socialmarketanalytics.com