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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

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