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.



Six Months of SMLC

Six months ago, the CBOE in partnership with Social Market Analytics launched the first in a family of indexes derived from Social Market Analytics S-Factors.  Below is SMLC performance relative to the SPX.  For the first six months SMLC has generated 12.62% and the SPX has returned 4.64%.  In January, we launched SMLCW and it has outperformed as well.  We will be launching additional indexes this year with monthly and quarterly re-balances as well as indexes utilizing CBOE options contracts.  Please contact for more information.


2016 In Review

Last year was a good year for SMA data.  High sentiment securities outperformed and low sentiment securities underperformed with good Sharpe’s and Sortino’s.  The below tables contain returns and Sharpe/Sortino ratios for the full history of Social Market Analytics S-Factor data.   Correlations to standard factors continue to be near zero. I’m sure our data can help in your investment process, contact us to learn more.
Five-year return summary:


 Sharpe / Sortino


Weekly, Monthly Quarterly Re-balance

As we move into a new year Social Market Analytics (SMA) has acquired five years of out-of-sample data.  This real history has enabled us to build signals for longer holding periods.   In this blog we will explore the use of SMA data for weekly, monthly and quarterly holding periods.

Portfolio managers often re-select securities for their portfolios at set re-balance periods.  These periods can be weekly, monthly quarterly, yearly….  As we accumulate history we are able to create factors with longer term statistical significance.  Longer term for SMA means monthly and quarterly.   For these longer holding period portfolios we created a three factor model using Raw-S, SV-Score and S-Buzz.  These factors look at sentiment, levels of volume relative to normal conversations for that security and relative to the entire universe.   Historical baselines for these securities have been extended to 50 and 200 days.  These three factors are combined into a multi-factor score and the top and bottom stocks are selected for long, short, and a theoretical long/short portfolio.  Returns of these theoretical portfolios are below.

Each portfolio is selected from a universe of the largest 450 stocks that trade options on the CBOE.  SPY is the benchmark in each chart.  As you can see each portfolio significantly outperforms the SPY.  Each chart shows returns.  Monthly and quarterly show the diversification benefit of sentiment data by displaying beta.

Weekly returns with and without transaction cost versus SPY.




Quarterly Rebalance portfolio.



There is significant predictive power in sentiment data over longer holding periods.  SMA is unique in that we have been collecting this data for five years. Our data is free of survivorship and look ahead bias in the Tweets and universe.

For more information please contact SMA at