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Social Market Analytics, Inc.

A Leader in Unstructured Financial Data

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

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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.

 

Fullhistory

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Joe Gits talks Twitter at CBOE’s Risk Management Conference

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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.

 

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Decile Spreads for Twitter & StockTwits

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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.

Twitter-Pre-Open

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.

Twitter-Pre-Close-Close

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.

Thanks,

Joe

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Weekly, Monthly Quarterly Re-balance

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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.

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Quarterly Rebalance portfolio.

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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 ContactUs@SocialMarketAnalytics.com

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SMA Predicts Brexit Two Weeks Before The Vote

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People seem surprised that Britain voted to exit the EU.  We at SMA with our partners the CBOE are not nearly as surprised as everyone else.  Russell Rhoads from the CBOE has been blogging and Tweeting with SMA data for two weeks that it looks like the Brexit is going to happen.  Let’s look at the timeline.  Again, this is not a post analysis, these Tweets were out there 2 weeks ago!

Brexit Post on June 8, 2016:

Russell Rhoads, from CBOE wrote a blog about Brexit using the using SSE, the results indicated that an Exit is going to be the result of the vote.

Brexit1

The update from our partners at CBOE talked about the huge increase in Twitter volume about #brexit. One of the key observations was the #VoteLeave campaign had gained far more popularity than the remain campaign. To everyone who was looking, Twitter had shown the signs of a British Exit.

Brexit2

The final post on June 22 talked about strong social media indicators towards the exit. The #VoteLeave campaign has dwarfed the conversations of every other opinion, including the BBC debate. The prediction turned out to be true.

Brexit3

Twitter is the premier leading source of information and SMA can help you make sense of it.  Please contact SMA for more information at contactus@socialmarketanalytics.com

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2015 In Review

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Wow, what a ride 2015 was with the S&P 500 closing slightly down for the year.  As we head into 2016 are you going to continue to look at the same factors as everyone else or maybe try something new?

Below are the returns for stocks with significantly positive and negative pre-market open S-Scores.   Stocks with High pre-market open sentiment scores had a cumulative return of 12.19% versus an SP 500 open to close return of -.38%.  Stocks with a low pre-market open sentiment score had a cumulative open to close performance of -34%.  Stocks with high sentiment scores outperformed and stocks with low sentiment scores under-performed.  With significant Sharpes and Sortinos.  Combining S-Factors with your selection criteria and risk management can add a dynamic new factor to your security selection.

These charts use the S-Factor S-Score.  SMA publishes and family of S-Factors  to clearly identify the tone of the social media conversation.  To learn more go to: https://socialmarketanalytics.com/process.

returns2015

 

Returns2015Tables

Returns2015FullHistory

Returns2015FullHistoryTable

Social Market Analytics has been publishing the performance characteristics of stocks with high and low sentiment over the last four years.  Last year it was difficult to find success with traditional factors.  SMA S-Factors helped our customers generate out-performance.   Please contact Social Market Analytics to explore how sentiment based factors can be included in your models: ContactUs@SocialMarketAnalytics.com.

 

 

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Long/Short Research on Russell 1000 Stocks

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People ask about the persistence of SMA sentiment signals over time.   The signal length is dependent on the S-Factor used.   S-Mean for example represents a 20 day look back period and is generally used as a longer term signal.  We looked at a theoretical strategy using a universe of the Russell 1000 and S-Factors: S-Score, S-Volume and price.  It is not intended as a proposal of a trading strategy using S-Factors as a single factor in an alpha model it does illustrate the persistence of the signal over time.

We primarily looked at the signal prior to market open instead of prior days close to include the most recent social media conversation in the metrics.  If you use a sentiment score from the prior days close you don’t include overnight information such as news, foreign markets, and indication of market open…

The universe of stocks considered is the Russell 1000 representation as of 06/26/2015.  The back test period is 2011-12-01 to 2015-11-12.  There are no transaction costs/impact costs assumptions included in this analysis.

The simulation uses S-Score > 3 / S-Score < -3 AND S-Volume > 5 AND Price at Entry > $5.  An S-Score > 3 means the current conversation is more positive than 99% of conversations over the look back period (20 days).  The reason for choosing the threshold of 3 for S-Score is motivated, in part; on research results presented by Markit using the SMA S-Factors (contact us for the research).  The position holding period is Open to Close (+2), (i.e. close 2 business days from now).  The S-Factors signals used were selected at 09:10 AM (ET).

In the below chart, the Green Curve shows the results of going long on Positive Signal (S-Score positive with the thresholds used).  Similarly, the Red Curve shows the returns of the negative (S-Score <-3, negative return is good in this case). The Blue Curve is the result of going long on Positive Signal Portfolio and short the Negative Signal Portfolio each day.   Black Curve is the ONEK ETF (Russell 1000 SPDR ETF) and serves as a market reference for the back test period.  Risk adjusted performance measures (Sharpe, Sortino) and average daily returns are presented for each filter.

The results of Open to Close (+2) Strategy using |S-Score|> 3 & S-Volume > 5 & Entry Price > $5 signal at 09:10 AM are below.  To test the strength of a signal with strong threshold, we decided to hold the position for 3 days, (buying at the open when the signal met requirements of the filter and selling it at the close 2 business days later).  If a stock appears on 2 consecutive days, the trades are made in isolation, irrespective of the position that is held due to previous signal. The Positive and Long Short outperform the ONEK benchmark.  A large spread between Positive results and the benchmark is evident.

In our simulation the positive S-Score portfolio returns 23 bps per day versus the benchmark of 9 bps per day.  The long/short portfolio returns 28 bps versus the 9 bps per day for the benchmark.  Certainly significant reason for further analysis of adding sentiment signals to your portfolio.

These results illustrate that adding S-Volume and price filters, in conjunction with S-Score, yields significant positive returns performance.

Please contact us for much more detail and other back test or to explore how sentiment metrics can be used in your environment.

R1000 S-Score

RtnsTable

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