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Social Market Analytics, Inc.

A Leader in Unstructured Financial Data

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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.

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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.

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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.

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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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That’s Two Takeovers in a Week!

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Social media beats the mainstream media on a regular basis.  Last week social media beat the news wire in reporting the MSFT acquisition of LNKD (blog post below) and Tuesday Twitter broke SCTY being acquired by TSLA.  This information is not theoretical – it is actionable data in our feed!

Tesla Motors lit up Twitter, yesterday, when CEO, Elon Musk came out and said their cars can float on water.  Tuesday June 21, the electric car manufacturer took everyone by surprise when they announced their decision to buy the solar panel company SolarCity (SCTY) minutes after the markets closed. The first news article to mention this came out at 4:18 PM CDT. Twitter had already gotten wind of this development 8 minutes prior with a tweet from the account “TopstepTrader”.

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The tweet from “TopstepTrader” was deemed to be credible by Social Market Analytics’ sophisticated algorithm, which separates signal from noise to create actionable intelligence. The sentiment started to move in a positive direction the very next minute. By 16:12 CDT, SMA’s subscribers received ‘S-DeltaTM’ alerts on SCTY. The PredictiveSignalTM from SMA became positive at 16:13 CDT and at 16:18, when the first news article came out, the sentiment had already reached an extremely positive level, with Tweet volume soaring high; as was the stock price. Traders who incorporated social media sentiment from SMA into their trading models were ahead of the curve, making profits as the rest of the market was just learning of the news.

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The S-Delta metric also flagged this move.  The below chart illustrates the delta values for SCTY.  Delta represents the change in S-Score over a 15 minute lookback.  Delta values of 2 or higher are huge outliers. An SMA alarm based on Delta or S-Score would have provided an alert to this breaking news.

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To find out how you can use SMA S-Factors in your investment process contact us at Info@SocialMarketAnalytics.com

 

 

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Twitter Leads News For MSFT, LNKD Acquisition

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As is the case with most corporate events now, MSFT buying LNKD broke on Twitter first. The very first mention of this is any news article was at 7:38 AM (CDT) but Social Market Analytics (SMA) detected this 7 minutes ahead at 7:31. SMA’s patented algorithm digests, filters and evaluates Tweets in real time. The filtering process, a proprietary Tweet account filtering technology built by SMA, separates signal from noise by continuously scanning for accounts that are deemed credible to be included in the calculation process. The tweets from spam accounts are filtered right away.

The following are the 5 tweets that SMA received from these credible accounts in a matter of 13 seconds. All of them pointing towards the same positive news.

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The 7:32 AM sentiment, as a result, had already started moving positive. By the next minute, at 7:33 AM the sentiment was already positive and soaring up. By the time other news sources caught up to this news, at 7:38 AM, the sentiment was already very positive. The S-DeltaTM alerts which measures the 15 minute changes in the sentiment had started firing up at 7:33 AM as people took notice of this and the Tweet volume kept soaring.

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Contact SMA for more information about using Twitter based metrics in your investment process: info@SocialMarketAnalytics.com

 

 

 

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Twitter Sentiment and Longer Holding Periods

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Signals derived from Twitter data have typically been viewed as shorter term signals.  There are a number of reasons for this.  One reason is the lack of out of sample data to back test trading systems on.  At SMA we now have nearly four and a half years of sentiment metrics to use in the creation of longer term signals.  Long-Term is a subjective term when discussing holding periods.  For our purposes we will be looking at trading signals that generate an average holding period of one month to three months.

At SMA we do not believe that one metric provides the full tone and context of a Twitter conversation.  That is why we publish a family of metrics call S-Factors that provide a richer view of the conversation than what is available with a single metric.

With history we have been able to look at longer term metrics and changes in security prices over longer periods.  We looked at large rapid negative changes in sentiment and determined that these sentiment movements are overreactions and lead to buying opportunities. We introduce two new metrics: Velocity and Acceleration.  The universes for these back test range from 20 large Twitter followed liquid stocks to the entire equity universe.  As you can see below these strategies identify solid buying opportunities and generate healthy average profit per share.  Please contact SMA to learn more about using sentiment to generate longer holding period trading signals with sentiment data.

Below are equity curves and trading statistics net of commissions with various universes. Overall you generate much fewer trades and hold them for longer periods of time.  The 50-day S-Score chart uses the SMA S-Score, Velocity and Acceleration Metrics.  You see that the holding periods are much longer than signals typically generated by social media.  The columns represent different universe sizes.

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200 Day S-Scores returns are below.  Again, please contact SMA for more detailed information.  Slide2

To learn more please contact us 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.

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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.

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Social Topic Conversations: What You Want, When You Want It, How You Want It

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SMA is an analytics company with unique IP for filtering and quantification of social media.   SMA to date has been primarily focused on the capital markets given our extensive knowledge of this industry.   On deck for us is the natural expansion of our capabilities to a “Topic Model” format.  Right now, we use our proprietary technology to filter and quantify the conversations around stocks, commodities and foreign exchange.  But the world cares about much more and we can help.

Early on we recognized the trans-formative value of Twitter as the next frontier for breaking and disseminating news.  Its high noise to signal ratio represented an opportunity for us to apply our knowledge to generate value.   We founded SMA in early 2012 to help people in the capital markets make sense of Twitter without having to weed through individual tweets.  We could see the explosive growth trajectory of tweets – now at 750 million a day – and realized it would soon become impossible to use traditional tools to really understand the market pulse around these social conversations.   We learned to convert the Twitter fire hose into real-time streams of high signal predictive data.  We also learned that the methodology used to generate these data streams let us filter the fire hose for specific conversations in very valuable ways.

First, we filter accounts for quality based on programmatic algorithms.  We started this process to eliminate the spammers, scammers and pump-and-dump schemers.   It’s a critical step in finding the quality information.  Even with this filtering, we current certify 65,000+ Twitter accounts for capital markets conversations alone, more than one person could reasonably manage to follow.   Each approved account is then rated and weighted, again diagrammatically.  This step is interesting for a Topic Model format in that you can certify accounts for different topics to create an expert stream of signals on any topic.

Next, we generate our S-Factor metrics.  SMA Dashboard Clients are familiar with our S-Factor Alerts.  Let’s talk about what these can really do.  Let’s say you’d like to track conversations on new products, but you really only need to know when the excitement is extremely high/low, rapidly turning negative, very volatile or going viral.  By building a list of custom alerts on your specified Topics, you get only what you want, when you want it.   If you want to know of the slightest hint of trouble, you can specify tight thresholds.  Or you can set your threshold levels much higher and only get notified of extremely unusual conversation trends.   You can then drill down to the individual tweet level to get more granular level content.  You can also search on individual tweet scores and view just those tweets with high/low scores.

SMA will send you an e-mail or text alert when your specified alert limits are hit.  You can also track all changes in real-time on the Dashboard. Of course, we still have our high-powered API and all of this capability can be directly integrated into any client system.  From the start, we designed our technology to be source and search agnostic and given client demand, we’ve added additional data sources.   As we start tackling the conversations outside of finance, we welcome your interest in new Topics.

By Kim Gits, CFO

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Social Media and Investing for a New Generation

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By Kim Gits,  CFO of Social Market Analytics

Are you prepared for the shift in attitudes and expectations of the next generation of investors? What is the next step for social media use in the capital markets?  How far will you go in implementing a social strategy to retain/attract investors?  Once in house, how will you communicate with them?

Much has been said about this new generation of investors – the Millennials.  Their generation is larger than even the Boomer generation.  They will be recipients of the largest wealth transfer in history.  They grew up with cellphones and instant access to information via the internet.  They are social and mobile.

But what does this mean to firms who wish to court this generation of investors?

In the last few years, we have seen Twitter and other social media sites like StockTwits and Seeking Alpha come of age as reliable data sources.  At Social Market Analytics, we are incredibly excited about these questions and the changes in investing that are on the horizon.

I’d break down the market response to this paradigm shift in waves.  The first wave many years ago was to develop a consistent firm-wide policy for employee use of social media.  Much legal angst went into creating these edicts.  Many of the Boomer and even GenX population saw it as a fad that would pass and certainly never saw themselves as Twitter users (Deja-vu for me when thinking about the adoption of the internet in the late 80s).  This first wave marked the beginning of a new means of communication.  For us at SMA it represented a new source of data as “smart money” finally had the clearance to Tweet their thoughts.

I’d describe the second wave as the re-posting of individual Tweets – something we began seeing as early as 2012.  A few firms began streaming Twitter posts as they related to stocks and the markets.  But let me ask you – with over 500,000 tweets, about stocks, a day (and growing) is it really possible to read all of those Tweets and still get any work done?  Do you really care that your brother-in-law’s third cousin thinks Apple is going up (unless of course he’s Warren Buffet)?

The third wave was adoption of social media data by hedge funds and quant traders.  Always on the lookout for new ways to generate alpha, this group has been adding various forms of social media data to their trading strategies.  SMA and its partners have been at the forefront of research in alpha generation strategies using S-FactorsTM, our social media metrics.  Growth at this level continues with more advanced strategies using multiple asset classes.

The final wave as I see it is the native integration of social media and its rich knowledge sharing capabilities into the investing platform.  Not only will investors be alerted to what they want to know in real-time, they will also have the ability to communicate socially with their brokers and execute transactions on a mobile platform based on either alerts or social communications from their broker.

As you address these coming changes, we are prepared to help.  SMA finds the real-time “smart-money” conversations in social media.  Knowledge sharing is known to be important to this new generation of investors.  SMA’s differentiation is that our proprietary filtering eliminates the spammers, scammers and naïve-user conversations.  Our metrics are based on the social media posts of “smart-money”.  Also, SMA’s unique normalization process helps users find hidden stock conversations that might otherwise be overwhelmed by the likes of Apple, Google and Facebook.  Our data and metrics are engineered to perform at the highest levels and we offer fine-grained customization to meet the needs of your specific customer base.  User-defined thresholds of our metrics let investors listen to only the conversations that are meaningful to them.  To learn more, please contact us at info@socialmarketanalytics.com

Thanks,

Kim

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