Introducing the Social Market Analytics (SMA) 50 Long Index

Social Market Analytics has been creating security level sentiment metrics for six years.  As we build an out-of-sample history we are able to build longer holding period indexes. I have blogged about longer term factors before, this is the most comprehensive portfolio strategy built using sentiment level data.  This blog will discuss the application of sentiment to a long only 50 stock, re balanced annually, index.

SMA50 Index is a new, capitalization weighted index comprised of 50 stocks with these features:

  1. The highest average unique message source counts, from SMA’s filtered Twitter data stream, observed over a 50-day look back interval, and
  2. High daily average dollar trading volume (ADV), > $20 Mil, over a 50-day look back interval.  We are looking for liquid stocks.

The SMA50 index measures the aggregate performance of stocks with high levels of crowd sourced commentary and high market liquidity.

  1. SMA50 is reconstituted each year on March 15th.  The core constituents are selected once a year.  They are re-weighted monthly based on the below tilt methodologies.
  2. SMA50 is the “Parent Index” for SMA50 Factor Tilt Products

Below is the historical performance of the SMA50 Index.  We will add tilting to the index based on sentiment and momentum.


The following factor tilt indexes are derived from the equity universe of the SMA50 parent index.  Factor Tilt Indexes are re-balanced monthly on the first market day of the month.

SMA-MT: Momentum Tilt

– Designed to deliver the performance of an equity momentum strategy by emphasizing stocks with high risk-adjusted price momentum.

  • A momentum value is determined for each stock in the SMA50 parent index Universe by combining the stock’s recent 12-month and 6-month price performance. This is the standard implementation of a price momentum value.
  • This momentum value is then risk-adjusted to determine the stock’s Momentum Score.
  • All securities in the SMA50 Universe are weighted by the product of their Momentum Score and their market cap, as follow:

Momentum Weight for SMA-MT  = Momentum Score * Market Capitalization Weight in the SMA50.  Momentum weights are normalized to sum to 100%.


SMA-ST: Sentiment Tilt

– Using SMA’s S-Score and SV-Score as factors, emphasize stocks with positive levels of social media sentiment and intensity, while attenuating stocks with low sentiment levels.

  • A composite factor score is determined for each stock in the SMA50 parent index Universe from the linear combination of the stock’s monthly S-Score and monthly SV-Score.
  • This composite factor score is used to determine the stock’s Sentiment Score.
  • All securities in the SMA50 Universe are weighted by the product of their Sentiment Score and their market cap, as follow:

Sentiment Weight for SMA-ST  =  Sentiment Score * Market Capitalization Weight in the SMA-50.  Sentiment weights are normalized to sum to 100%.


SMA-SMT: Blended Tilt

–Define a factor which is a combination of sentiment and momentum tilts.

  • A combined factor is determined for each stock in the SMA50 parent index Universe from a linear combination of the stock’s Momentum and Sentiment scores.  Initial results for the blended tilt factor used an equal weighting of Momentum and Sentiment scores.
  • This combine factor score is then standardized and used to determine the stock’s Senti-Momentum Score.
  • All securities in the SMA50 Universe are weighted by the product of their Senti-Momentum Score and their market cap, as follow:

Senti-Momentum Weight for SMA-SMT  =  Senti-Momentum Score * Market Capitalization Weight in the SMA-50.  Senti-Momentum weights are normalized to sum to 100%.


Comparative performance for all four theoretical portfolios is below.

SMA Relative Performance

Overlaying standard benchmark performance you can clearly see the effectiveness of the SMA 50 with various tilt strategies to outperform the benchmarks.

SMA Relative Performance bench

The SMA 50 family of indexes provide a low turnover way to benefit from exposure to social sentiment.  To learn more please contact us at


Social Market Analytics – Year in Review

At Social Market Analytics we are continuously reviewing our dictionaries and account rating algorithms.  Year End is a great time to aggregate and publish performance metrics – 2017 was a great year for Social Market Analytics data.  We look at our data in many different ways.  The below tables illustrate the Open to Close performance of securities with statistically significant sentiment scores prior to market open.  Comparison S&P 500 value is open to close as well.  Securities with an S-Score > 2 ( S-Score >2 means the current conversation is much more positive than recent prior conversations)  had cumulative subsequent performance of 25.78% versus the S&P of 6.28.  Stocks with an S-Score < -2 had cumulative negative performance (positive Alpha) of -19.48.  Stocks with positive S-Scores went on to significantly outperform and stocks with negative S-Scores went on to significantly under perform.   2017 was our best year for long short performance.


Sharpe Ratios for these theoretical portfolios are below.   2017 had the best long/short Sharpe Ratio versus history (6.56), Short (-2.15) & the second best long (2.60)


The question to ask is why has performance improved over our six years of history?  At SMA we vigorously scrub the Twitter hose to aggregate the intentions of professional investors.  Historical data has provided a deep set for training and cleansing.  First, we identify who we believe are professional investors.  The universe of our certified investors has grown over history – A larger set of inputs.  The number of Tweets related to securities and products has increased – better inputs. Our dictionary has evolved and grown over time.  More inputs, better inputs and better parsing has resulted in more predictive power!  To learn more please reach out to us and I wish everyone a health and prosperous 2018.




Social Market Analytics Now Has Six Years of Out-Of-Sample History!

Social Market Analytics, Inc. (SMA) is celebrating six years of out-of-sample data in US Equities.   This data is unique in that it is a true representation of the Twitter conversation at each historical point-in-time.

Since our launch, SMA has become a leader in providing sentiment data feeds to the financial community.  Our data has become an integral part of our customers investment process.  Our customers are Quantitative Trading Firms, Hedge Funds, Sell Side Brokers, Traders and many others. SMA data is suitable for HFT, Quantitative Trading, Risk, Short Lending, Smart Beta, Fama-French Models, VAR among others.  Predictive signals range from a few minutes to quarterly.

SMA’s analytics generate high-signal data streams based on the intentions of market professionals.  Our patented machine learning process has produced six years of strongly predictive data as illustrated in the chart below.  This chart illustrates the subsequent performance of stocks based on pre-market open (9:10 am Eastern) sentiment scores.  Stocks with high sentiment subsequently out perform as illustrated by the Green line.  Stocks with strong negative sentiment go on to under perform as evidenced by the red line.  The blue line represents a theoretical equally weighted long short portfolio.  The table below illustrates Sharpe and Sortino ratios.



Deutsche Bank Research Report on Social Market Analytics S-Factor Data

Two weeks ago the Deutsche Bank Quantitative Research Team released a full research report exclusively on Social Market Analytics sentiment data.  This excellent report independently verifies the predictive nature of the Social Market Analytics Sentiment factors.  All data and charts for this blog are taken from the Signal Processing – Social Media Sentiment Report written by the Deutsche Bank Quantitative Research Team.  Over the coming weeks I will be blogging about different sections of the 35-page research report.  The full report contains much more detail than I will explore here. Reach out to me for more information.

After the introduction, there are three main sections: S and S-Score alone and with various technical indicator overlays, a monthly holding period range based strategy and a social media use in predicting security behavior after corporate events section.   Some iterations have Sharpe Ratios well over 2.0.

This blog will explore the close to close subsequent return of a quintile portfolio based on the Social Market Analytics S-Score  Factor and expand the analysis to include technical trading strategies applied to the S-Score Factor.  No attempt was made to apply multiple S-Factors to the strategy, which is a good idea for further research.  S-Scores are taken pre-market close and executions are assumed to be market-on-close.  The below table shows a quintile portfolios created based on S-Scores for a universe of Russell 3000 stocks returns a Sharpe ratio of 2.4.  The returns are monotonic in nature.  The signal decays over a longer period-of-time with correlations between factor scores and returns out to ten days.

DB Paper Blog Chart1

The below charts illustrate the growth of the long, short, and long/short portfolios.  Turnover of these portfolios is relatively high as illustrated by the turnover table.

DB Paper Blog Chart2

To refine the strategy, DB examined ways to improve the risk adjusted returns and lower turnover.   Traditional technical indicators were applied to the S-Score derived factors.  The below table illustrates the technical indicators applied to S-Score data with subsequent returns and turnover.

DB Paper Blog Chart3

Sharpe Ratios and turnover are illustrated in the table below. Bollinger Band, MACD, and 5D Score all have Sharpe Ratios above 2.  Turnover for MACD decreased significantly when compared to the S-Score portfolio.

DB Paper Blog Chart4

S-Score was used to derive these signals.  Social Market Analytics has 7 primary factors and 19 total factors in our feed.  Reach out to us to explore our data for implementation into your trading systems.  The next blog will explore monthly holding period long-short portfolios explored in the DB paper and some follow up Social Market Analytics derived research.

Stay tuned and reach out to me with any questions:

Joe Gits talks Twitter at CBOE’s Risk Management Conference

Joe Gits, CEO of Social Market Analytics, recently spoke at the 34th annual CBOE Risk Management Conference.

Gits spoke at RMC about SMA’s patented technology, the Social Sentiment Engine, and Twitter’s relevance in financial markets.

Hosted by the Chicago Board Options Exchange, the RMC is an educational forum dedicated to exploring the latest products, trading strategies and tactics used to manage risk exposure and enhance yields. The RMC is the foremost financial industry conference designed for institutional users of equity derivatives and volatility products.


Did you have a 40 Minute Head Start on Ocwen Financial Price Drop?

Social Market Analytics (SMA) aggregates the intentions of professional investors as expressed on Twitter and StockTwits.  On April 20th 2017 discussion started started on Twitter that OCN was going to be investigated in relation to sub prime mortgage concerns.  SMA Social Media Sentiment started moving lower early that morning.  In the chart below you can see the by Noon Eastern SMA sentiment had reached the -2.0 level.  The current conversation on Ocwen Financial had become more negative than 98% of conversations over the last month.  Volume of Tweets had increased significantly as well.


Below is price action during the same period.  At 12:40 pm Eastern the price was steady.  The sentiment went below the -2.0 value 40 minutes earlier.  By 2:15 eastern the stock had lost half its value.


Another example of SMA metrics leading price movements.  Contact SMA to learn more.



Decile Spreads for Twitter & StockTwits

Today I will explore decile groupings based on S-Scores, and  plot cumulative subsequent returns. We typically focus on an S-Score > 2 for subsequent positive movements in stock prices, and an S-Score < -2 for negative movements in stock price.

Our metrics identify when a conversation becomes significantly more positive or negative than normal.  Most stocks have normal conversations on any given day.  On these days there are other factors driving the security. “Normal” conversation securities will typically follow the market, as you see in the SMA data set.  High sentiment out-performs and low sentiment under-performs,  Open to Close, and Close to Close, across Twitter and StockTwits.

The only filter we add is that the prior day’s closing price must be above $5, to avoid penny stocks.  Total return time series are used for returns, and time series are equal weighted.

The first chart illustrates subsequent Open to Close returns based on S-Score deciles at 9:10 a.m. Eastern time. As you can see, the deciles are in order with top decile securities out-performing and bottom decile securities under-performing.  SPY is represented by the black line and the universe is blue.


Pre-Market Close deciles are below.  S-Scores are taken at 3:40 p.m. Eastern and Close to Close returns are calculated.  Again, high S-Score securities out-perform and low S-Score securities under-perform, with the universe in the middle.


StockTwits is the largest chat community for active traders.  Its users are professional traders discussing long and short positions. The below chart looks at S-Score decile returns based on StockTwits conversations.

Data is consistent across deciles.  A unique characteristic of the StockTwits feed is that there are significant short conversations.  The lowest two deciles have negative returns.  This is a function of the StockTwits community being able to short securities by direct short selling or taking net short options positions.

StockTwits PreOpen

Pre-market close deciles are below.

StockTwits CLose-close

To learn more about Social Market Analytics and the products we offer please visit our website, or contact us here.