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

SMA Signals for Monthly & Quarterly Holding Periods

Since our founding in 2012 we have been acquiring out-of-sample history to use in development of factor models.  We now have five years of history, with this history we can create statistically significant signals with monthly and quarterly holding periods.

This blog will explore the use of sentiment data for monthly and quarterly holding periods.  A trading system is explored by looking at exhaustion in social media measured by acceleration and velocity of sentiment as an indicator of price movements.  My next blog will look at traditional monthly and quarterly holding period performance based on a multi-factor sentiment model.

Acceleration and Velocity Trading System

Acceleration and velocity metrics can identify shifting sentiment.   To build these metrics, we created a new 50 period signal in addition to our traditional 20 day signal.  Raw-S is the net sentiment over a 24-hour look back period from a point of observation, derived from Tweets captured during the look back period. By aggregating over 50 periods, we create a 50-period Raw-S we call R50 as follows,


For the purpose of this blog, we use 3:40 PM US Eastern Time factors to create the new metrics. At 3:40 PM US Eastern, a signal is generated near Market Close but with enough time to enter trades and execute at the Close. All trades reported in this paper are assumed to be executed at the Closing price of the day.

The R50 Factor is a raw 50 period sentiment measure.  To normalize the factor, we compute a standardized measure, using the following formula,



S50 is the Z-Score of R50

MA50 (R50) is 50-day moving average of the R50

SD50 (R50) is 50-day moving standard deviation of R50

S50 is a way to represent the R50 raw sentiment estimate on a standard normal curve.

 The New Metrics

We want to identify when the sentiment trend changes direction.  We derive new Velocity and Acceleration metrics from the S50 factor to identify changes in long-term sentiment and the rate of change of long-term sentiment.

We define V50, the velocity, as the one period change in S50,


In our research, the velocity and the rate of change of velocity are equally important in identifying the exhaustion of a sentiment trend.

We call the rate of change of velocity, the acceleration, A50, (the second derivative of the S50),


Building A Trading Strategy Using Velocity and Acceleration

We observed a mean reverting phenomenon with longer-term sentiment. A high positive peak in the S50 sentiment was typically followed by a decrease in price. Consequently, a local minimum of S50 was followed by price appreciation.

This is the foundation of the trading signals developed.

We used various portfolio sizes to test this trading strategy. Portfolios ranged from 20 highly followed stocks on Twitter to the full SMA equity universe.

Using the metrics defined above, we used the following entry and exit signals,



Rational for using these signals is as follows:

  1. A mean reverting relation between S50 and Closing Price.
  2. We want to capture the change and rate of change rather than just the absolute level of sentiment.
  3. A V50 of 0.5 means a change in S50, of 0.5 standard deviations and A50 < 0 is deceleration, A50 > 0 is acceleration.
  4. Due to the mean reverting nature of S50, we wanted to enter when the sentiment was decelerating and had reduced by over 0.5 standard deviations in one day.
  5. We wanted to exit when the sentiment was accelerating and had already increased by 0.5 standard deviations.


This was a successful trading strategy across all universes selected.  Below are performance statistics for various universe sets.  20 liquid stock portfolio universe is a universe of stocks that are highly followed on Twitter.  100 liquid stock universe is a universe of 100 stocks that are highly followed on Twitter.  Top 1000 mkt. cap and universe are self-explanatory.


This data is statistically significant, out of sample, and reproducible.  To learn more about the possibilities of sentiment data in your model please contact SMA at