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
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.
To discuss getting access to these or any other SMA data feed or widget please contactus@socialMarketAnalytics.com
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.
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.
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.
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.
Please 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
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
Social Market Analytics (SMA) tracks real-time sentiment on equities, commodities, currencies, ETF’s and crypto currencies. SMA has the most powerful and customizable Alerting API combining Twitter sentiment and pricing metrics. Users receive custom real-time sentiment alerts on instruments in their watch list. For example, on December 11, 2018, SMA’s alerting system sent an alert on Corn at 12:12 pm CT when corn was @ $385.25. Below is the email and mobile alert.
Subsequent to the alert, corn moved lower starting at 12:17pm CT. The price continued to move lower the remainder of the day and closed at $383.25. (See chart below)
The above alert was based on SMA’s rolling 24-hour sentiment. SMA also calculates a Long-term sentiment with longer price projection periods. Corn’s long-term S-Factor flipped from positive to negative on November 14th. 12/10 was the first day the long-term S-Factor for corn reached a significantly negative level of -1.5 standard deviations more negative than the longer-term baseline conversation. For more information please contactUs@SocialMarketAnalytics.com