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Social Market Analytics, Inc.

A Leader in Unstructured Financial Data

Analysis of Social Market Analytics Using MyDataOutlet Analytics Platform

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This blog post highlights research done on an excellent research platform My Data Outlet. My Data Outlet specializes in integrating financial data into R. They offer an API to access financial data, allowing for a single access point for extracting data.  Their tools are excellent for creating backtests and deploying models into production.  This analysis was done using the MDO.Factors Library of functions. The mdo.factors library contains samples of publicly available research factors and can be used as a template for creating new factors.  They have expertise in the Refinitive QA Direct and MarketQA platforms.  

For this research they looked at three SMA factors.  As regular blog readers know SMA quantifies the intentions of professional investors as expressed on Twitter and StockTwits.  Our S-Score metrics identify when the conversation becomes significantly more positive or negative than normal (conversations on the wings of the standard normal curve).  When conversations are at normal levels other factors are driving security price.

For this analysis they used the following SMA S-Factor metrics.

  1. SMA_RAW_S =  The raw sentiment score.  This is the summation of indicative Tweet sentiment scores over the last 24 hours (equally weighted)
  2. SMA_S = The exponentially time-weighted sentiment score.  This is the summation of indicative Tweet sentiment scores over the last 24 hours exponentially weighted with a 12 hour half life.
  3. SMA_S_SCORE is the normalized SMA_S.  This is effectively a Z-Score using a 24- hour exponential summation with a 20 day baseline.

They also divide each of these scores by the security’s price (from Datastream) to create three sentiment yield factors. Yield factors are a new and interesting application of our metrics and should be explored by users of our data.

  1. SMA_RAW_S_YLD is the SMA_RAW_S divided by price.
  2. SMA_S_YLD is the SMA_S divided by price.
  3. SMA_S_SCORE_YLD is the SMA_S_SCORE divided by price

For this test we used daily holding periods – Close to Close. They re-balance every trading day at the close.  For this analysis they used a 2:40 pm CST S-Score (20 minutes prior to close) and again, re-balanced close to close. The universe for this test was the Russell 3000 Index.  Return charts for quintiles are below.  Quantile 1 – highest scored factors and quantile 5 = lowest scored factors.

They also looked at the spread of quantiles.  Top versus bottom and top versus 2nd,3rd,and 4th quantiles. 

A lot of customers ask about sector specific breakouts. Below is an example of how MDO can quickly break out sector level performance.  Technology Sector returns are below.

Quantile Returns for the Technology Sector

MDO provides a great illustration of the predictive power of Social Market Analytics Sentiment data.  For more information ContactUS@SocialMarketAnalytics.com. 

Thank you for reading.

Joe

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Historical Accuracy Filter Applied to Sentiment Further Increases Predictive Power

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At Social Market Analytics (SMA) we create predictive signals by aggregating the intentions of professional investors as expressed on Twitter.  We have accumulated eight years of out-of-sample data illustrating the predictive nature of the data.  We publish sentiment metrics to illustrate the tone of the current conversation relative to historical conversations.  One of our key metrics is S-Score.  S-Score is effectively a Z-Score, the measure of deviation from the mean.  An |S-Score| > 2 means the current conversation is two standard deviations from the mean over the predefined lookback period. 

 In the prior blog we explored the benefits of SMA patented machine learning algorithms on return characteristics.  In this blog we incorporate rolling back tests on the predictive signal to select portfolio securities.  SMA data is predictive across sectors and industries but as with any factor there are securities that react more predictively than others.  

For chart below we use a rolling one-year accuracy metric for predicting subsequent O-C return.  The faded lines are S-Score values only.  Bolded lines represent a theoretical portfolio with accuracy filters overlaid with S-Score values.  Only select |S-Score| > 2 securities that have moved in the predicted direction 60% of the time over the last year (bold lines).  This is all out-of-sample data. 

S-Score > 2 return values are very similar for accuracy filter and S-Score only.  S-Score < -2 had a large impact from the accuracy filter.   Securities reacting negatively to negative Twitter conversation as measured by S-Score continued to underperform relative to sentiment only.  This is another example of using sentiment combined with other metrics leading to statistically significant predictive signals.   

To see how sentiment can be used in your models ContactUs@SocialMarketAnalytics.com.

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Benefits of SMA Machine Learning Algorithms

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Social Market Analytics has extensive Intellectual Property in three distinct areas:  Topic model creation, account filtering and natural language processing (NLP).  I have written blog post about SMA topic model creation capabilities and the impact of our account filtering algorithms.  This blog answers the question – “Do your machine learning algorithms really add value to the NLP process?”.  Answer -> Yes. The chart below illustrates the statistically significant benefits of Social Market Analytics Machine Learning Algorithms in isolation. 

Start date for this analysis is 11/20/2018 and the end date is 4/30/2019.  This period was chosen because of the significant market draw down in December.  We use dictionaries with three distinct rule sets.  We use a static dictionary as of the start and end dates and compare resulting predictive returns with a point-in-time dictionary (production).  Our patented NLP scores Tweets using the dictionaries at each time, S-Scores are calculated from the generated Tweet scores.  The point-in-time dictionary represents word additions, phrases, and grammatical logic as they are made. 

We isolate the impact of our NLP process by turning off account filtering applied to the Twitter stream.  To ensure we are pulling Tweets only discussing companies and securities, we are using our topic model filtering algorithms.  We regularly publish our full return charts to illustrate the impact of our entire process. 

Let us start by defining the lines in our chart.

 

Red Line = Tweets are scored using our dictionary of words and phrases as of 11/20/2018.  This illustrates the performance with no machine learning applied on a go forward basis. This is the base case. This line represents the least amount of learned information.

Black Line = Tweets are scored using words and phrases applied Point-In-Time.  This is the production feed SMA customers receive.  We use Supervised and Unsupervised Machine Learning.  There are impacts from both during this period.

Green Line = Represents the Perfect Information scenario. Take the most up to date dictionary of words and phrases (4/30/2019) and apply them backwards.  All information learned during the volatile period is included.  This represents the values expected to be received on a go forward basis.

The charts below represent the cumulative Open to Close return of securities selected based on S-Score 20 minutes prior to market open.  S-Score measures the tone of the current conversation relative to historical benchmarks.  We select securities with an |S-Score| > 2.  Securities with S-Score > 2 are purchased on the open.  Securities with S-Score < -2 are sold short on the open.  SMA Chart lines represent a theoretical long/short portfolio. Isolated long and short sides are available upon request. 

For comparison purposes S&P 500 open to close chart for the analyzed period is below.

The chart below illustrates the cumulative O-C performance illustrating the impact of our ML algorithms.  As expected, the lowest performance is the red line representing the dictionary at start date.  The back line represents SMA production data and green line represents the perfect information case. 

Again, this only looks at the impact of SMA NLP and does not include account filtering.  At SMA we believe it’s not just what is being said but who is saying it.  We employ a twelve variable algorithm to score and filter all Twitter accounts Tweeting about companies/securities to identify our approved account universe.  As you can see SMA NLP is a learning system with demonstrable impact.  To learn more please contact us at contactUs@SocialMarketAnalytics.com.

Thanks,

Joe

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Performance of SMA Data in a Volatile Market

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As we move towards the fourth quarter people have been asking how SMA data is performing in this volatile market.  Those who have been following us over the years have come to expect the Open to Close (O-C) Chart to illustrate the performance of our data.  We have been publishing this chart since the launch of the company in early 2012.  The machine learning applied to our NLP has provided increasing predictive power to our data as our out-of-sample training set continues to grow.  Even as we increased our asset class and security coverage.  Twitter has continued to be the go-to source for breaking news and conversation.  This rich and growing source of communication has allowed us to continue to improve our data.

The below charts illustrate the standard subsequent O-C performance of securities with |S-Score| > 2,  20 minutes prior to open.  As you can see even with a volatile market the Open to Close performance significantly outperforms on the long and short sides over the last year and the full history continues to perform well.  YTD the long-short portfolio has a cumulative return of 25.26% with a 5.18 Sharpe Ratio.  This is primarily driven by the long side.  Securities with an S-Score < -2 returned 1.20% – significantly underperforming the benchmark S&P.  This chart is updated through Friday 8/23 to illustrate performance during a large market down day.

YTD-8-26

Full history is below.  This chart illustrates the Machine Learning component of our data.  As more data is added to the out-of-sample historical set the training become more effective.

Fullhistory-8-26To learn more about our data please contact ContactUs@SocialMarketAnalytics.com.

Thanks,

Joe

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SMA releases ‘Crypto Fast’ Sentiment Data Feed

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Social Market Analytics (SMA) the leader in predictive social media data feeds has added the ‘Crypto Fast’ to its suite of API data feeds. SMA’s S-Factor and Activity Cryptocurrency data feeds have been in production since December 2017. “Clients asked for a bespoke feed for a shorter baseline with 1-Hour price projections. Although clients can create their own baselines and metrics with our Activity Feed, clients wanted SMA to do the development work and produce and support the product which has been named ‘Crypto Fast’.”
The SMA Crypto Fast Feed provides faster moving signals than the SMA S-Factor feed. The S-Factor Feed is a 24H lookback with a 20 baseline with decay which supports intraday out to 2-3 trading days. SMA’s Activity Feed is in isolation of what happened in each minute, which can be narrow to HFT or customized to any period including W, M, Q.
Like the calculation of SMA’s S-Score, the ‘Crypto Fast’ is a normalized sentiment score with a shorter 1H lookback period with a 12H baseline to better take into account the high volatility in the cryptocurrency market. For each crypto asset, SMA makes a 1-hour price projections based on its Crypto Fast and price momentum. SMA provides a projected return, as well as a projected range on the return with a 95% confidence interval. The accuracy field reflect how often the subsequent return has fallen within the projected return range historically.

 

Photo credit: SMA has partnered with TheTie to power sentiment www.thetie.io
SMA APIs went into production in 2011 for U.S Equities and have grown to include UK Equities, ETFs, FX, Futures, and Cryptocurrencies. SMA produces over 25 distinct APIs across 6 asset classes www.socialmarketanalytics.com
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Social Market Analytics (SMA) Partners with Coin Metrics to provide Real-Time Sentiment Data Feeds

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Coinmetrics

Coin Metrics and Social Market Analytics (SMA) announced today a partnership to incorporate SMA’s Crypto Currency Data Feed into the Coin Metrics Market Data Platform.

Alternative data such as social media platforms and data feeds have become a vital source of information for traders, particularly in the Crypto Currency Markets. The SMA Crypto Currency Sentiment Feed will offer the Crypto Currency community a tool for including social media sentiment data in their trading and portfolio strategies and expand Coin Metrics market leading Crypto Asset market and network data products.

“As the Crypto Investing market continues to mature, institutional investors are demanding data from trusted partners. These institutions are looking to make data-driven decision by accessing sources of data that they understand from their legacy investing frameworks. We believe that the power of combining sentiment data with granular network and market data is fundamental to building a deeper understanding of crypto assets. Coin Metrics is excited to partner with SMA, who has a long history of providing sentiment data to traditional capital markets participants and share Coin Metrics’ principles and values. The ability to provide an all-in-one Crypto Financial Data solution is a huge convenience for institutions.” Comments Tim Rice Co-Founder and CEO of Coin Metrics.

“Artificial intelligence and Natural Language Processing are moving into our everyday lives at light speed, and perhaps into financial markets even faster than that. We feel strongly at SMA that participants in Crypto Currency markets will benefit from our unique process in this emerging field, both in its approach to filtering social media data and in the analytical methodology used to develop our proprietary metrics. We’re excited to partner with the Coin Metrics team to offer this service through a versatile industry leading platform” said Joe Gits, Co-Founder and CEO of SMA.

About Coin Metrics

Coin Metrics was founded in 2017 as an open-source project to provide the public with actionable and transparent network data. Today, Coin Metrics delivers market and network data, analytics and research to its community and wider industry. https://coinmetrics.io/

About Social Market Analytics, Inc.
Social Market Analytics quantifies social media data for traders, portfolio managers, hedge funds and risk managers using patent pending technology to detect abnormally positive or negative changes in investor sentiment. SMA produces a family of quantitative metrics, called S-Factors™, designed to capture the signature of financial market sentiment. SMA applies these metrics to data captured from social media sources to estimate sentiment for indices, sectors, and individual securities. A time series of these measurements is produced daily and on intraday time scales. For more information, including a User Guide to S-Factors™, please visit www.socialmarketanalytics.com

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Social Market Analytics Identifies Most Accurate Twitter Accounts

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Social Market Analytics aggregates the intentions of professional investors as expressed on Twitter.  We identify these professional investors using our proprietary twelve factor ranking system.  One factor is the forward accuracy of Twitter accounts.  If a Twitter account is Tweeting bullishly based on our patented NLP process and the security subsequently moves higher over specified periods that account is deemed to be accurate over that period.  Overall accuracy is aggregated across time for each account.  We have been tracking account accuracy out-of-sample for the past seven years. – it is impossible to recreate this data.  SMA is the only provider with out-of-sample account accuracy.  We found significant variability in account accuracy for supposed professional investors.  Social Market Analytics account scoring algorithms are extremely effective in excluding non-professional professionals.

SMA’s Accurate Account algos aggregate expectations from the most accurate Twitter accounts for individual securities for a specified time period: 1-Day, 2-Day, 1-Week, and 1-Month holding periods.   Definition of ‘Accurate’ – correctly identifying directional movement of the security’s price.  We do not include size of move – their sentiment is positive and the security moved higher.

We calculate consensus expectations of these accurate accounts on individual securities.  Accurate account universes differ across holding periods. Some accounts are more accurate in the short-term (Day trades), while others are more accurate for longer holding periods (up to one month).

Securities with significant consensus for both long and short are available through our API’s, Widgets and in Reports.  Below is a widget identifying securities with the most positive and negative consensus.   In this example, SMA’s accurate account universe is currently 100 bullish on MCO over the next 24 hrs.  Positive, negative and neutral are identified separately.

accurate accounts

To discuss getting access to these or any other SMA data feed or widget please contactus@socialMarketAnalytics.com

Thanks,

Joe

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IHS Markit Analysis of Social Market Analytics LSE Equity Data

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Social Market Analytics partners with IHS Markit to distribute our S-Factor data through the IHS Markit Research Signals Feed.  Recently, IHS Markit added our LSE 1000 Equity S-Factor Feed to their data offerings.  In conjunction with the launch IHS Markit authored a research paper exploring the predictive nature of the SMA S-Factor data on LSE equity securities.   We are thrilled with the outcome of this independent research showing the predictive nature of Twitter based factors.   Below is summary of the paper conclusion section.

Download the paper here:  https://cdn.ihs.com/www/pdf/0219/Social_media_indicators_in_the_UK.pdf

IHS Markit focused primarily on the SMA’s S-Score and S-Volume sentiment metrics. SMA S-Scores open-to-close return spreads at the +/-2 tail averaged .097%, persistent to 10-day (.177%) and 20-day (.298%) holding periods.  On a cumulative basis, we report a pre-close spread return of 75% for buy rated stocks versus 6% for sell rated stocks and 9% for the market.  Results were robust to filters on minimum Tweets and to long-only strategies.  Applying the S-Volume > 1  filter, open to close spreads for the +/-2 tail strategy average .83%, and exceeded the stand-alone factor results at each of the longer holding periods.  For buy portfolios, S-Score (with S-Volume >1) open-to-close excess returns at the +2 tail averaged .044% (0.049%) and increased in general with each incremental extension in holding period reaching 0.296% (0.389%) at 20 days, confirming the benefits of signal to long-only portfolio managers.

Lastly IHS Markit researched one of their proprietary SMA based metrics, Relative Standard Deviation of Indicative Tweet Volume.  They also found strong results,  Stocks with volatile Tweet volume pre-market tend to outperform open-to-close (spread:0.224%, excess return 0.07%), while these stocks also outperform over longer time horizons, reaching a spread of .342% out to 20 days (excess return:  .196%).

For more information please ContactUs@SocialMarketAnalytics.com.

Thanks,

Joe

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Benefits of Social Market Analytics Account Filtering During 2018 Down Market

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Social Market Analytics aggregates the intentions of professional investors as expressed on Twitter.  We apply our patented filtering and natural language processing(NLP) to Tweets to proactively select Twitter accounts to use in our predictive metrics.  We track several metrics to gauge the predictive nature of our dataset.  For this blog I am going to illustrate one of these metrics.

2018 was a rough year for the SP500, it lost about 9% (rolling one year).  Given market loss and the high volatility we thought it would be an ideal dataset over which to run an experiment.  Two questions we get regularly are: How would your data perform in a bear market?  And what is the benefit of your NLP and account ratings systems? This blog will answer both questions from the perspective of 2018 market performance.

The table below illustrates performance of six theoretical portfolios.  These portfolios represent stocks with Social Market Analytics S-Scores of 2 or higher (Long signal) or Social Market Analytics S-Scores of -2 or lower (Short signal).  S-Score compares the tone of current Twitter conversations with average tone of Twitter conversations over the last twenty days.  Social Market Analytics has multiple baseline for multiple prediction periods.

Each security in our universe represents a proprietary Topic Model.  Each Topic is a collection of rules used to include or exclude specific Tweets from security buckets.  For example, if you are looking for Tweets about Ethan Allen furniture (ETH) you do not want to include Tweets about Ethereum Crypto Currency (Also symbol ETH) conversations.

We created portfolios with our account filtering algorithms and compared them with portfolios of all twitter accounts discussing our Equity Topic Models. The purpose of the run was to quantify the ability of our patented account filtering algorithms to identify professional, and hence more accurate, investors. Spoiler alert: Our account filtering improved the long/short return by 50% (18.73 for 2018 versus 12.53 NLP only)

NLP applied only:

The NLP only portfolios illustrate the power of our NLP process to accurately identify and fine grain score Tweets discussing securities and companies.  Our patented process reads each Tweet multiple times to identify if and how strongly someone is voicing a view of expected future performance.  The NLP only portfolios illustrate the predictive power of our NLP in isolation.  When you apply the Account filtering you get a predictive boost.

Account Filtered + NLP applied:

Account Filtered plus NLP portfolios illustrate the benefit of applying our account filtering metrics.  Early in the life of Social Market Analytics we learned its not just what is being said on Twitter but who is saying it. We developed proprietary metrics to identify investors more likely to be correct about the future direction of a security. When the conversation of these professional investors is significantly more positive than the average conversation over the last 20 days those securities significantly outperform.  When the conversation of these professional investors is significantly more positive than the average conversation over the last 20 days those securities significantly underperform.

 Portfolio Construction

Portfolios are constructed of securities with an S-Score of 2 or higher (long) or -2 or lower (short).  All portfolios are equally weighted.  A negative value for a short portfolio denotes a positive return to that portfolio.  Short portfolios are supposed to move lower.  All securities are entered on the Open based on a 9:10 am Eastern time S-Scores and exited on the Close.  There is no overnight exposure.

Result Analysis

We use SP500 as our performance benchmark.  SP return is calculated from open to close in the same manner as the selected securities. Using open to close performance the SP500 returned -16.89% for comparison.  As you can see from the table the S-Score > 2 outperformed the market and negative S-Score securities significantly underperformed the market (generating positive alpha).  The L/S portfolio with NLP only returned +12.54%, NLP plus account filtering improved that performance by 50% to +18.73%.  We do not illustrate this as a single factor model but removing 10% a year for slippage and commissions still significantly outperforms.

nlp-accountratingPlease contact us with any questions or to see how SMA’s NLP and filtering capabilities can be used in your investment process.  ContactUs@SocialMarketAnalytics.com

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CBOE – Social Market Analytics SMLCW Index significantly outperforms.

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Social Market Analytics aggregates the intentions of professional investors as expressed on Twitter.  SMA factors are highly predictive over various time frames.  In June of 2017 Social Market Analytics launched a weekly re-balanced large cap sentiment based index.  This index is comprised of twenty-five stocks with the highest average Twitter sentiment over the prior week selected and re-balanced Friday afternoons from the CBOE Large Cap 450 Index.  This index has been published daily since that date and is available on all major feeds.

Last year the SP500 Index had a return of -8.4%.  The CBOE SMLC Index had a return of +.87%.  Below is a comparative return chart over the last year compared to the SP500.

For more information or to license this index please contact us at ContactUs@SocialMarketAnalytics.com

smlcw performance

 

 

 

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