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

 

 

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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

 

 

 

StockTwits based S-Factor Return Charts

Social Market Analytics (SMA) aggregates the intentions of  investors as expressed on the StockTwits platform.   SMA creates proprietary S-Factor metrics that quantitatively describe the current conversation relative to historical benchmarks.  This data provides strong predictors of future price movement.  This blog will focus on the deterministic nature of the StockTwits data set when aggregated into SMA S-Factors.    StockTwits is a community for active traders to share ideas enabling you to tap into the pulse of the market:  http://stocktwits.com/

The charts and tables below illustrate the subsequent open to close return of stocks that are being spoken about abnormally positively or abnormally negatively on StockTwits twenty minutes prior to market open.  Sharpe and Sortino ratios for the theoretical portfolios are included as well.  The SMA S-Score looks at the current conversation relative to historical benchmarks and creates effectively a Z-Score.

The Green line below is an index of subsequent open to close return of stocks with abnormally positive conversations on StockTwits prior to the market open.  The Red line is an index of the subsequent open to close return of stocks with an abnormally negative conversation prior to market open.  The black line represents the market open to close return and the blue line represents a theoretical long/short portfolio.

These charts clearly illustrate the predictive information present in the StockTwits message stream. If there was no predictive power in the StockTwits data set the Green, Red, and Black lines would be nearly identical -statistically not the case.  These signals are available at 9:10 am Eastern time well before the market open.

The chart below looks at the full SMA history of StockTwits based S-Factors.  The theoretical long portfolio has a Sharpe Ratio of 1.53, theoretical short portfolio -.82 Sharpe and LS portfolio has a Sharpe of 3.68.   Sortino Ratios are above one as well.  There is strong predictive power in this data.

FullHistoryStockTwits

The last year has been particularly challenging for the Hedge Fund community.  Below is a chart with the performance of the theoretical portfolios broken out from 1/1/2015 to current.  As you can see these portfolios performed well in this volatile market period.

LastYearStockTwits

For more information on these data sets please contact Pierce Crosby:  (pierce@stocktwits.com)  or Joe Gits: (joeg@socialmarketanalytics.com)

Regards,

Joe

2015 In Review

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.

 

 

Long/Short Research on Russell 1000 Stocks

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

Social Media and Smart Beta

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

Social Market Analytics Visualization Tools

Social Market Analytics has been offering API access to our data since inception. Fewer people know we also offer powerful visualization and screening tools.  We offer two ways to access our data without programming.

First, we have a robust Excel Add-in that allows for Real-Time screening and historical retrieval.  This functionality is ideal for integrating sentiment data into Excel based research platforms.  You can screen for user defined pricing and sentiment criteria or upload a watch list and monitor sentiment activity on these securities in real-time.

Add-In

The SMA Sentiment Dashboard is a real-time visual representation of sentiment changes for the entire universe or your watch list.  the below screen provides a real-time view of stocks with large changes is social media.  Users can set criteria for filtering for the most relevant securities.

Dashboard1

The dashboard tracks sentiment for Stocks, commodities, currencies, indexes, sectors and industries. Below is an illustration of industry level sentiment.

Industry

In, addition users can set screening criteria for real-time alerting by email, text or private Tweet.   Alerts can be specified for individual securities, watch list of securities or the entire universe.

ExistingAlerts

These are just some of the visualization tools SMA offers.  For a full demonstration of trial contact us at Sales@SocialMarketAnalytics.com

Thanks,

SMA