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:

returns2016

 Sharpe / Sortino

sharpes

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That’s Two Takeovers in a Week!

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

 

 

Twitter Leads News For MSFT, LNKD Acquisition

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.

Tweets

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.

SentimentVisuals

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

 

 

 

Twitter Sentiment and Longer Holding Periods

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

Sentiment Leading Recent Market Volatility

The chart below looks at the percentage of positive Tweets versus the percentage of negative Tweets over the last couple of weeks.  There are usually significantly more positive than negative Tweets so the fact that the negative percentage was so high is valuable data in itself.  As you can see the percentage of negative Tweets increased prior to days with significant market downtrends.

The black lines on the chart represent market activity.   The red and green bars represent negative and positive Tweet percentages.  Sentiment is captured by Social Market Analytics 24×7; you can see the growth in negative sentiment prior to the Monday (8/24 draw down).  On 8/24 the market started strong and fell significantly at session end.

The universe of Tweets is so large that when you aggregate it you get a terrific view of what people believe is going to happen.  This data is only available from Social Market Analytics.  Please contact us for more information on our market leading data sets or visit our Research Page.

PosNegTweets

Thanks,

Joe

Predictive Power of SMA S-Factors

Every quarter we review performance returns and statistical ratios for our family of S-Factors.   S-Score is a normalized representation of sentiment over a pre-defined look back period and is a key metric.  Below are some charts that look at the full history and YTD performance of our data across the entire universe.

Anyone can pick specific securities and instances where sentiment leads price movement; it’s a lot harder to consistently predict movements over the entire universe over a long period of time.  We pride ourselves on statistical consistency of our data over what is now 3.5 years of history.    We are the only company to track and publish these metrics, providing the most transparency.

We view S-Score >2 and S-Score <-2 as statistically significant.  An S-score of 2 means the current conversation on social media is more positive than 97 percent of prior conversations as filtered by our proprietary metrics.   When this happens the security moves higher with statistically significant consistency. The green line below represents the full history cumulative open to close return chart of stocks with a high S-Score (S-Score >2) prior to market open.  The Red line represents the full history cumulative open to close return of stocks with an extreme negative S-Score (S-Score <-2) prior to market open.  The black line represents the open to close return of stocks in the SP500.  The Sharpe and Sortino ratios for the green line (Pre-Open S-Score >2) are 1.37 and 2.23 respectively.  Sharpe and Sortino ratios for the red line (Pre-Open S-Score <-2) are -.54 and -.86. Benchmark SP500 Sharpe = .69 and Sortino = 1.08.

FullHistory

Below is the exact same chart for YTD 2015.  Sharpe and Sortino ratios show the benefit of our evolving filtering and scoring criteria.

returnYTD

SharpeYTD

Price and Tweet volume filters are commonly added when filtering stocks for sentiment.  Tweet volume represents indicative Tweet volume, once all Tweets are filtered indicative volume typically represents only 10% of the total volume of Tweets.  The below chart is the same return chart represented above with the added filter of Price day close price >5 and indicative Tweet volume > 5.  As you can see the Sharpe and Sortino ratios increase dramatically by adding simple filters.

PriceVolumeFilter

PriceFilterSort

Social media analytics is a learning process.  Our filtering and cleansing algorithms are continuously evolving.  We maintain our history as it was at each time and we keep dictionaries and accounts as a time series.

We have many more statistics employing other S-Factors and filtering criteria; please contact us for a more detailed briefing on SMA data and products.

Thanks,

Joe

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

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