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Introducing the Social Market Analytics (SMA) 50 Long Index

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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.

SMA501

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%.

SMA50_MT

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%.

SMA50_ST

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%.

SMA50_Combined

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 ContactUs@SocialMarketAnalytics.com

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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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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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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”.

TSLA -SCTY

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.

SCTY

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.

SCTY_Delta

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 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.

Slide1

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.

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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Social Media and Smart Beta

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Smart Beta Sentiment Enhanced ETF Performance Analysis

At SMA we continuously research our data.  Below we discuss modifying weights of the SPDR SPY ETF based on sentiment values and examine the impact on return.  Please contact SMA (info@SocialMarketAnalytics.com) to learn more.

The SPDR SPY ETF is a cap-weighted ETF which closely replicates the performance of the S&P 500. Our objective is to develop a “smart beta” strategy using the social media sentiment levels of individuals ETF constituents and amplify or accentuate the weights of the constituents in the ETF while keeping the Assets under Management constant. The transaction cost assumption is ignored for both the original and the enhanced ETF.

One of the strategies explored was looking at the sentiment levels an hour before the close (2:55 PM Eastern Time) and re-balancing the weights according to that. The stocks were bought or sold (to reduce position as per new weight only, NO short selling) at the close of the day and the positions were maintained until the next day when the re-balancing was performed again. To explore the weight modification methodology please contact SMA.

Our re-balance strategy keeps the AUM constant with no need for additional funds. Another strategy explored was to use a “lagged” sentiment. The lag being a day. So, for adjusting the weights today, we looked at the sentiment at 2:55 PM yesterday, and changed the positions based on that.

The results for the cumulative returns calculated over the period extending 7/31/2013-8/31/2015 are summarized below.  Chart 1 shows the cumulative returns over the period for the “Original” which calculates fund returns using positions and closing price data. The “500% PM” makes the calculations using enhanced weights based on the pre-close sentiment. The “500% PM Lagged” has enhanced performance using pre-close sentiment from previous (trading) day.

Chart 2 shows the cumulative out performance, for the 2 “smart beta” strategies.  As you can see both strategies track the SPDR SPY ETF while outperforming performance.  You see the benefit of adding sentiment to your calculation process without increasing risk.

Chart1

Chart2

This is preliminary research we will be enhancing and updating over the coming weeks.

Regards,

SMA

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Social Media Generated Trading Ideas

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Social media is a new and rich source of trading ideas.  To illustrate this point, below are some recent  trading opportunities social media data presented.  In each case activity and sentiment increase prior to the actual event.  Social media is a leading indicator of stock performance and SMA is the leader in providing metrics based on social media.

Teva acquires Allergen

Teva Pharmaceutical Industries surged in pre-market trading on July 27, 2015 on news that the company will be acquiring Allergan’s (AGN) generic drug business. Before this happened, sentiment on Twitter had already become strongly positive. At 4:00 AM EDT, when the stock price was $66.00 there was significant positive sentiment on Twitter. The sentiment rapidly shifted positive. By 7:24 a.m., the stock was trading at $72.30. The stock opened at $67.80 when the sentiment was 3.92 and closed at $72.

Figure 1:  S-Score™ For TEVA Pre- and Post-Announcement.

TevaSentiment

TevaHistoricalSentiment

Historically, daily sentiment scores for TEVA fluctuated near 0 (Neutral), with low social media activity as indicated by the time series of the S-Volume™ metric.  This behavior started to change on July 26 with significant upticks in indicative Tweet volumes and sentiment levels.  On the morning of July 27th,TEVA’s S-Score™ increased sharply to a high positve level, coincident with a spike in S-Volume™ consistent with high social media activity, indicating that SMA’s processing technology had sucessfully detected the signature of positve sentiment for TEVA embedded in the Twitter data stream.  This high positive sentiment level persisted through the open on July 28th and then started to return to typical historical levels as the markets and social media fully integrated the effect of the announcement.

Rumored Announcement of Acquisition:  Twitter (TWTR)

On July 14, 2015 at 11:39 AM EDT, a rumor started spreading on Twitter about Twitter being acquired by Bloomberg.  At 11:40 AM, there was a Tweet from user ‘beckyhiu’ indicating that Bloomberg had offered $31 Billion to buy Twitter and that Twitter was considering the offer. This rumor caused the stock price to rise rapidly. A Tweet, about 30 seconds later, at 11.41 AM,  by ‘zerosum24’ confirmed that the rumor had reached Twitter and people had started talking about it. The sentiment had started rising rapidly by this time. The changes in S-ScoreTM and S-DeltaTM were significantly positive. At 11:42 AM, the sentiment was over 2, and was statistically significant.

It was soon realized this might be a hoax and that no offer was made. At 11:42 AM, ‘TurboResearch’ questioned the credibility of the buyout offer.

There had been no official statement from Bloomberg, and hence, both the sentiment and the stock price kept rising. At around 11:50 AM, a journalist from Bloomberg Tweeted that the news was a hoax and that it was not to be believed. At this point sentiment started declining as people starting tweeting negatively. The stock price dropped rapidly.  After that, there were mostly negative comments driven by the refuted rumor.  The figures below show SMA sentiment factors leading the stock price quite accurately.

TWTRSentimentPrice

Figure 2: TWTR S-ScoreTM vs. Price

TwitterVolumeSpike

Figure 3:  Intraday   S-Volume™ Chart for TWTR

Amazon (AMZN) Earnings Announcement 

Twitter sentiment can predict stock changes even after market close, as in the case of Amazon. Amazon reported earnings on July 23, 2015. While the market consensus was that the company would not beat expectations, the conversation on social media was different.

SMA data showed a sharp increase in sentiment metrics around 2:49 PM EDT. By 2:51 PM, the sentiment on Amazon was two standard deviations higher than its typical level. The stock was trading at $480.45 at this point. At market close, it traded at $482.18, higher than the price at the time when sentiment on Amazon became positive.

It was interesting to see how the stock traded after-hours once the company reported earnings. Amazon’s stock shot up more than 17% — to $568 — from its price at 3:51 PM EDT after the company reported a surprise quarterly profit. The hidden sentiment value in Twitter data predicted what “conventional” market speculators failed to predict.

AmazonTweets

AmazonEarnings

Figure 4:  Intraday S-Score™ And S-Volume™ Behavior across Amazon’s Earnings Event.

The progression of intraday S-Score™ and S-Volume™ metrics for Amazon is shown above from 1:00 PM EDT to 4:25 PM EDT.  Amazon’s sentiment remained positive throughout the day and became significant around 2:50 PM. The sentiment saw a sharp rise post the earnings announcement after market close.

We publish our own research and analysis.  We invite you to check our Research site for new updates and publications.

Thanks,

Joe

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