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A Leader in Unstructured Financial Data

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General Market Indicator: Volatility of Sentiment Helpful in Predicting Overall Market Performance

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The target of this research was to find an indicator that helps predict the direction of the overall US Equity market for the next week using sentiment data from the previous week. The hypothesis is when there is high volatility in sentiment over the previous week, which means investors have differing opinions, the subsequent week overall market performance will underperform. When volatility on sentiment is low or neutral, the crowd has reached a consensus and the general market will outperform over the next week. The sentiment metric used to represent volatility is Raw-Volatility in SMA’s S-Factor data feed, which captures the volatility of the sentiment from Twitter conversations. All Raw-Volatility data points were taken from the 3:40 pm ET timestamp (20 minutes before the market close). We calculated the summation of Raw-Volatility for each date as a proxy to represent the volatility of Twitter social sentiment on the entire market. The exact calculation is as follows, where “N” is the number of companies with sentiment on that date and “D” is the date:

We then created a 7-day standardized volatility using a 91-day benchmark:

This Z_Volatility score follows a roughly normal distribution.

Using the S&P 500 ETF Trust (SPY) as a proxy of general market performance, we then look at the relationship between Z_Volatility and SPY’s return series. The daily close-to-close return is calculated as:

Hypothesis: When Z_Volatility for the previous closing Date is high, the subsequent market performance will be lower. When Z_Volatility is low or neutral, the next day’s market performance will be higher.

To test this, our strategy is to open short position of SPY when Z_Volatility > 1. When Z_Volatiltiy is =< 1, the portfolio treats SPY as a long position. This hypothetical portfolio is then compared to SPY over the past 10 years:

Prior to the COVID-19 pandemic, which began in early 2020, SPY outperformed the modified portfolio. However, since then the behavior of this factor changed drastically. Here is the same graph as above starting in 2020:

Taking a closer look, the separation since the beginning of 2020 is quite significant. Adding a short position to SPY when volatility on sentiment is high, has enhanced the portfolio’s return. Even though many of the days will maintain a long position, the Z-Volatility is predictive of downturns in the market since 2020. Traders could use this metric as an indicator to stay out of the market, or at the very least trade with more caution. The COVID-19 Pandemic led to a large amount of uncertainty surrounding the stock market and the direction its heading. A high Z_Volatility score indicates the public’s opinion is more uncertain about the direction of various stocks. This research shows the value of sentiment from Social Market Analytics in predicting macro-level events and price movements.

If you are interested in learning more about how SMA’s S-Factor data can help your trading strategies, please email us at contactus@socialmarketanalytics.com or schedule a demo using this link.

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Using Twitter Sentiment for Day Trading

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My name is Campbell Taylor. I am a rising senior and a Statistics major at The Ohio State University. Through my first few weeks as a Quantitative Research Intern for Social Market Analytics I’ve been exposed to alternative data and its applications in the financial market. In this research, I created a day trading strategy built around changes in sentiment on twitter.

Social Market Analytics (SMA) captures unstructured data through alternative sources such as Twitter. Using unique Natural Language Processing sentiment analysis, SMA rates tweets in real time and create metrics that enhance insights into equities’ market movements.

Sentiment factors used for this analysis are distributed through SMA’s S-Factor feed. The factors are:

  • S-Score: normalized representation of a stock’s sentiment on twitter over 24 hours
  • SV-Score: normalized representation of a stock’s indicative tweet volume over 24 hours
  • S-Buzz: measurement of unusual Twitter activity compared to the universe of stocks

A large S-Score (> 2) is associated with extreme positive sentiment on Twitter, while a small S-Score (< -2) is associated with extreme negative sentiment. The same applies for SV- Score. S-Buzz ranges from 0 to 4.5, with 1 being the statistical mean. The goal of this research is to use these sentiment factors overlaid with pricing momentum to develop a profitable daily trading strategy.

The momentum used for this research is defined as the following:

This isolates the pricing momentum to strictly overnight movement. Similarly, I used the differences in sentiment to capture the overnight sentiment changes. The two sentiment timestamps are taken at 9:10 AM EST of the current trading day and 3:40 PM EST of the previous trading day, both 20 minutes prior to market open and close. Subtracting the previous day’s closing sentiment from the current day’s opening sentiment isolates the overnight sentiment change.The target return can be defined as:

A popular trading strategy is buying securities with rising momentum and selling them when the momentum has appeared to be exhausted. My original hypothesis was that positive overnight sentiment movements would enhance the overnight pricing momentum. More specifically: a positive S-Score difference (increased positivity), SV-score difference (increased volume of tweets), or S-Buzz difference (more unusual activity) would lead to the positive momentum continuing until close of the trading day, and vis versa for negativity.

To find which sentiment movement was most significant in predicting returns, I built a logistic regression model. This models the probability of a discrete outcome given the input variables. In this case, the probability of positive open to close returns given the various overnight sentiment changes and the overnight pricing momentum. The idea being parameters that increase the probability of positive returns will create a trading strategy that would be more profitable than the market over time.

Before selecting the model, I checked the distribution of the variables to ensure there was no abnormalities in their distribution. The histograms below show the following distributions (left to right, up to down): Overnight Difference in S-Score, Overnight Difference in SV-Score, Overnight Difference in S-Buzz, Overnight Pricing Momentum, and Open to Close Returns. All the variables appear to be roughly normally distributed, which is beneficial for statistical modeling and taking the tails of the distribution.

Using the four remaining variables, I used a stepwise information criteria method to aid the selection of the best parameters for trading. The information criteria measure the model’s performance while considering the number of parameters used. To my surprise, the model showed that positive (negative) overnight momentum decreased (increased) the probability of positive open to close returns for the next trading day. Additionally, overnight momentum was more significant as a factor variable rather than a continuous variable. Meaning the sign of the momentum is more important than the magnitude of the momentum. Each of the sentiment changes were significant in predicting the return. A positive difference in S-Score and S-Buzz increased the probability of positive returns, while a positive difference in SV-score lowered it. While each variable is significant, it is important to consider the number of stocks that will pass the conditions of all parameters when trading. Very few daily stocks will satisfy all 3 specific sentiment parameters, which will lead to a large variance in results. Thus, it makes sense to narrow the model to one S-Factor variable. Selecting the S-Factor variable that has the most occurrences of extreme changes will give the most robust results. The difference in S-Score had more than double the number of stocks with extreme changes than SV-Score and S-Buzz. Therefore, the final trading strategy will be built around the difference in S-Score overlaid with overnight pricing momentum.

Since the difference in S-Score is a continuous variable that follows a normal distribution, I only wanted to trade on stocks with extreme overnight changes. I defined extreme changes as above 2 and below -2, like the S-Score variable itself. A difference in S-Score over 2 indicates there is an extreme increase in sentiment surrounding that stock on Twitter. Similarly, a difference in S-Score below -2 indicates an extreme decrease in sentiment.

I calculated the cumulative returns of 4 different trading strategies and the S&P 500 ETF trust (SPY) as the benchmark for the general market. Each of the strategies enter at market open and exit at market close with an equal weight placed on each stock. Two of the strategies will be long positions and two of them will be theoretical short positions. The long positions have parameters that increase the probability of positive returns, while the short positions have parameters that lower it.

The Long positions:

  • Trading only on stocks that had negative overnight momentum
  • Trading only on stocks that had negative overnight momentum, but an extreme increase in sentiment (difference in S-Score > 2)

The theoretical Short positions:

  • Trading only on stocks that had positive overnight momentum
  • Trading only on stocks that had positive overnight momentum, but an extreme decrease in sentiment (difference in S-Score < -2)

Trading with these 4 different strategies quantifies the effect that sentiment movement has on the overnight momentum. I expected the two long positions to give positive cumulative returns and the short positions to have negative cumulative returns. Based on the model, the long position with sentiment should give the highest returns while the short position with sentiment should give the lowest returns. Before calculating returns, I looked at the number of trades per day in the strategies with sentiment to ensure the trades won’t be too heavily weighted on one stock (top-down).

The x-axis of the histograms shows the number of trades made in a day, while the y-axis shows the number of days with that number. Both distributions suggest there will be some volatility in the number of trades per day. However, the mean and median number of trades for both strategies are high enough to ensure diversity for many of the days. There will be days where there are less than 10 trades, but those will be less than 15% of the trading days in a 10-year span. Therefore, the low volume days will be spread out and not affect the strength of the results. The average also isn’t too high to the point where it is impossible to execute the trades at the markets open. Knowing the number of trades was solid, I used these strategies to trade from December 1st 2011 to June 3rd 2022.

The time series graph shows the cumulative return of the strategies over time. Between April and June of 2020 there is a sharp increase in returns for the negative momentum with sentiment increase strategy. The abnormality can be attributed to the market condition following the beginning of lockdowns for the COVID-19 Pandemic. While markets were turbulent during this time, the long position with sentiment performed very well. Overnight sentiment movement had a significant impact on the pricing momentum. The long positions both gave positive cumulative returns, and the theoretical short positions gave negative cumulative returns. As the model suggested, trading stocks that had negative momentum with an extreme increase in sentiment gave the best returns. This strategy produced a cumulative return over 1400% in the 10-year time frame. The Sharpe and Sortino ratios suggest that the above-average returns are worth the potential volatility of this strategy. A Sharpe above 1 and Sortino above 2 are considered good for a portfolio. For the long positions, adding sentiment movement increased the Annualized Return by nearly 12%. While the effects were not as strong, adding sentiment decreased the Annualized Return of the short position by close to 7%. I then looked at how this strategy has performed since start of 2020.

The jump at the beginning is also during the lockdowns of the COVID-19 Pandemic. Each of the strategies jumped further in the direction the model predicted during this time. This time series graph follows the same behavior as the 10-year trend. The impact of the negative sentiment change on the positive momentum is more evident on this plot. Recently, the long position with sentiment strategy has performed even better than over the 10-year period. While maintaining strong Sharpe and Sortino ratios, the annualized return climbed to nearly 41%. Trading with this strategy would have given a 140% cumulative return since the first trading day of 2020. The short position with sentiment strategy also performed better in this time period. The negative overnight sentiment lowered the annualized returns by nearly 8%. Trading on the long/short positions with sentiment has been an effective trading strategy over time and shows no signs of slowing down.

The limitation with this strategy is the opening of the market being used as a part of the overnight momentum calculation and as the entry point for the trade. Therefore, there will be a delay in executing the trade. In practice this results in adding 5 cents to the opening price for the long positions and removing 5 cents to the opening price for the short positions. The returns will be a bit smaller than the ones calculated but will be very close.

Stocks have generally shown to revert to their mean following overnight movement. Adding sentiment changes appears to enhance the probability and magnitude of reversion. That is why trading on stocks where the overnight sentiment contradicts the overnight pricing momentum is a very profitable strategy. Following this strategy also removes holding stocks overnight where there is risk of news and events breaking after the market close. This research also exemplifies the predictive power of the S-Factors from Social Market Analytics. The overnight S-Score movement proved to have a significant impact on the open to close returns. Capturing the sentiment movement allows traders to identify securities where the price has not yet followed the direction of the public opinion.

To learn more about Social Market Analytics email us at ContactUs@SocialMarketAnalytics.com or schedule a demo using this link.

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Using Twitter Social Media for One-Month Holding Periods

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Social Market Analytics, Inc. (SMA) has been warehousing and normalizing social media signals, called S-Factors, since December 2011. SMA has collected nearly 10 years of out-of-sample data, which allows back tests of longer holding periods. SMA’s typical S-Factors are a daily view on social sentiment and is great at predicting subsequent price movement in securities 1-3 days out. By aggregating S-Factors over longer time horizons, testers / quantitative models can generate social sentiment signals that are more suitable for trading over weekly, monthly, or even quarterly holding periods.

This blog tests our hypothesis that heavily talked about securities with positive sentiment over the past month will outperform the market. We also expect heavily talked about securities with negative sentiment over the past month will underperform the market.

The strategy below is called the ‘Monthly Multifactor Strategy’ because the signal comprises of 3 S-Factors: Raw-S, S-Volume, and S-Buzz. These 3 factors are a view of sentiment and tweet volume over the previous 24-hour period. By taking these factors once a day, you can aggregate the data over any time horizon to create long-term factors at your desired length. Below are the Long-Term factors created for this strategy:

  • Long-term sentiment, ‘S50’, is calculated by taking the sum of Raw-S over the past 50 market days and standardizing it over a 200-market day baseline
  • Long-term volume, ‘SV50’, is calculated by taking the sum of S-Volume over the previous 50 market days and standardized to a 200-market day baseline
  • S-Buzz is a measure of unusual s-volume activity compared to the rest of the universe

 

 

This is a Monthly Long Only or Long/Short strategy.Trades are entered on the Market on Close price of the last market day of each month. Securities are selected 20 market days prior to trade execution. The Top 50 stocks with the highest Multifactor Score are Longs and the bottom 50 stocks with the lowest Multifactor Score are Shorts.

Stocks are weighted within each portfolio based on the rank of the Multifactor Score. Stronger scores have a higher rank numeric value (ranges from 50 to 1).

The combination of the two portfolios is the Long/Short. The Long and Short baskets have an equal dollar amount invested.

 

 

 

 

About Social Market Analytics:

SMA is a leader in unstructured financial data from Social Media to Machine Readable Filings (MRF). SMA is a preferred Twitter Finance partner and Stocktwits vendor. SMA partners with S&P Global Market Intelligence on Machine Readable Filings (MRF) across U.S. Edgar Filings and Global Annual Reports.

Founded in 2011, Social Market Analytics, Inc. goal is to harness the massive amount of unstructured financial data across both Alternative and Traditions sources into machine readable textual data feeds for Quantitative and Quantamental analysis. SMA’s DNA goes back to its predecessor firm Quantitative Analytics,

Inc. (QA Direct) which was acquired by Thomson Reuters (Refinitiv).

SMA’s Patented processes are unique in the emerging field of Machine Learning, Financial Sentiment Natural Language Processing Textual Parsing, and Source Accuracy.

Contact:Doug Hopkins at doug@socialmarketanalytics.com

 

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Rare Earth Metals – Machine Readable Filings (MRF) Expose U.S. Domestic Shortage

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Overview

Smart phones, electric/ hybrid cars and X-Rays have undoubtedly taken an important role in today’s society. All three essential items depend on a limited resource called Rare Earth Metals. The use of Rare Earth Elements (REE) does not stop there. REEs are also used for refining crude oil, building wind turbines, making televisions and computer screens, and are necessary for production of many other important products. There is no doubt Rare Earth Metals play an important role in the world, therefore, from an investing standpoint, you might wonder who is mining these crucial metals and who is using them? This blog answers those questions by using Machine Readable Filings (MRF) and Social Market Analytics, Inc. (SMA) Complex Topic Model engine. The dataset is run across all 10-K, 10-Q, 8-K, 20-F, 6-K and 40-F filings from SEC Edgar.

Social Market Analytics, Inc. (SMA) has partnered with S&P Global Market Intelligence on ‘Machine Readable Filings’ (MRF). Machine Readable Filings is the first product to provide parsed textual data of SEC Edgar Regulatory Filings at the Total Document, Part, Item, Sub-section, and Notes level with historical baselines back to 2006. The Textual Data, Sentiment NLP, and Word Count values are provided by SMA’s patented technology. Extraneous information such as page numbers, images, and tables are removed. S&P Global and SMA also developed the same structured data values on International Company Reports.

SEC regulatory filings are formal documents reported to the U.S. Securities and Exchange Commission (SEC) that contain important information about companies such as financial statements, forward looking statements, risks associated with the company, etc. Every U.S. publicly traded company is required to submit regulatory filings to the SEC.

Companies discuss their forward focus in these documents, and you can glean a lot about a company’s direction or risks by the topics they discuss in filings. With SMA’s topic modeling capabilities you can quickly scan documents for curated topics. If you are searching for companies investing in a specific topic, this functionality makes the process much more efficient. As an example, if you want to find companies with negative exposure to Covid-19 you can search for Covid-19 mentions in the Risk Factors section of a 10-K or 10-Q. If you would like to find a company’s strategy for handling their business during Covid-19, you may want to search in the Management Discussion & Analysis section.

Machine Readable Filings (MRF) used in conjunction with SMA’s Complex Topic Model engine allows for the programmatic curation of Thematic Investing themes. Our topic modeling allows us to capture ever relevant ‘Rare Earth’ terms within SEC filings and exclude terms that are not relevant. For example, U.S. Rare Earth Minerals, Inc. mentioning their own name in a filing would not be considered relevant. SMA’s proprietary Complex Topic Model engine uses smart synonym and proximity search to make sure you capture all relevant information.

Example of the Dataset

The data is available through an API, CSV reports or other customizable options to be easily read. Below is a picture of part of the data set used for this blog. The circled context reads: “We manufacture and sell an array of plain surface prestressed steel materials and rare earth coated and zinc coated prestressed steel materials…”

In this blog we will be illustrating the power in SMA’s topic searching by identifying companies with the most mentions of the topic ‘Rare Earth’ in their filings. The Topic Model data extraction across ~1.4 Million documents going back to 2006 only takes a few minutes. The MRF textual data structure also enables the creation of baselines.

Full Rare Earth Universe

To answer who is mining and using these Rare Earth Materials, we first must look at the topic model on a macro level. Below is a chart illustrating mentions from each sector. A hit is defined as the frequency of which a term in mentioned within an SEC filing. The topic contains model contains over 40,000 hits from January 1, 2006- May 19, 2021.

The chart demonstrates that 65% of the hits come from the Materials sector. Of the Materials Sector hits, 24,768 (96%) of the hits from materials are from the Metals and Mining Industry. Using that information, we can determine that the large majority of the Materials Sector are the suppliers. The rest of the sectors appear to be the consumers. The top two consumer sectors are Health Care (4,329 hits, 11% of total hits) and Information Technology (2,908 hits, 7% of total hits). We will look in depth at the determined largest suppliers and two largest consumers.

Materials Sector

Zooming in on the suppliers, we see that the three most mentioned Rare Earth Metals are scandium with 5,191 hits, neodymium with 1,266 hits and yttrium with 1,237. The most popular term hit by far was ‘Rare Earth’ with 11,654 hits. Knowing these are the three most talked about metals by suppliers could possibly imply that they are the three most profitable or abundant.

We know which sector the supplier is in and which metals they are discussing the most. Which specific companies are discussing these most frequently? The graph below shows the top 20 companies mentioning Rare Earth terms the most in their filings from January 1, 2006 to May 19, 2021. The bars are color coded by which metal they discuss. Scandium International, a Canadian company is the most popular and they talk about scandium an overwhelming majority of the time. It might be inferred they mention their name a lot in their filings, using by SMA’s Complex Topic Model engine we are able to exclude the mentions of “Scandium International” and only capture the relevant mentions of scandium. Scandium International is very centered on scandium and discusses the metal a lot within their filings. Molycorp (last filed in 2016), although no longer publicly traded is a close second, discusses a variety of metals. The third most popular is Texas Mineral Resources Corp which has the most mentions of any current U.S. publicly traded stock.

The top two suppliers of metals are a Canadian company and an American company that are no longer publicly traded. The next three companies are all trading below $5 a share. This could imply that the U.S. is not a great supplier of Rare Earth Metals. Many articles suggest that China has the majority of Rare Earth Metals. The top companies in this chart support that the majority of the supply of Rare Earth Metals are not mined by American companies.

Consuming Sectors of Rare Earth

Now that we have looked in depth into the suppliers, we will shift to the consumers. The most popularly talked about terms in these filings from January 1, 2006 to May 19, 2021 are yttrium with 800 hits, gadolinium with 708 hits and holmium with 695.  There is a large drop to lutetium with 473 hits. This is vastly different from the suppliers. Scandium and neodymium are the 13th and 8th most talked metals within the consumers. Both suppliers and consumer talk about yttrium in their top three metals, however, the difference in conversation between the rest of the metals could hint at a disconnect.

After looking at the most metals, we can transition to which sectors use which metals. The graph below demonstrates which sectors discuss which metals. The Health Care sector discusses 6 of these metals more than any other sector. Their top being yttrium with 800 hits, gadolinium with 708 hits and holmium with 695 hits. These results are not surprising as yttrium is used in cancer treatments, gadolinium is used in MRI machines and holmium is used in holmium lasers, which are used in surgeries. Information technology talks about 5 different metals more than any other sector. The most popular being ytterbium with 358 hits, erbium with 339 hits and yttrium with 238 hits. These metals are all used in alloys to strengthen or soften metals.

The companies with the most hits from January 1, 2006 to May 19, 2021 within the healthcare sector include Vivos Inc., Trimedyne, Inc. and Immunomedics, Inc. These companies mention yttrium and holmium the most. That could imply that these companies have large dependencies on those metals.

Below shows the top 20 companies within the IT Sector. The top 3 companies include IPG Photonics, Western Digital and II-VI Incorporated. IPG Photonics develops fiber lasers, Western Digital manufactures hard drives and data center equipment, and II-VI Incorporated manufactures optical materials and semi-conductors.

It might be surprising that large companies like Apple or Intel do not make the list for top 20 mentions of rare earth metals, considering they are used in building microchips and cell phones. That could mean that they are not dependent enough on rare earth elements to be mentioned in their Risk Factors or Business portions of their filings. In that manner it is just as important to look at which companies are not mentioning Rare Earth Metals in their filings. A shortage in Rare Earth Metals may not drastically impact their business. However, these other companies are dependent enough that a shortage and an event revolving around Rare Earth Metals could impede their business.

Conclusion

How does knowing who is and who is not mentioning Rare Earth Metals help from an investment standpoint? Investing in the suppliers could be beneficial if you believe the consumer businesses will grow. If the consumers business grows, then the demand will increase, which will allow these mining companies to sell more Rare Earth Metals at higher prices, likely increasing the value of these companies. Knowing which companies are the most and least dependent on Rare Earth Metals could give you an understanding of the risk in these companies if Rare Earth Metals become scarcer due to geo-political risk, mining accidents, civil disputes or many other reasons. Although this data gives the picture of Rare Earth from American, Canadian, and ADR SEC Filings, the data is still very powerful. SMA’s Complex Topic Model engine will soon include Global filings and Sustainability Reports.

In April 2021, SMA released in partnership with S&P Global Market Intelligence ‘Global Machine Filings’ cover +200 Countries Company Annual Reports (AR). SMA will be running a Rare Earth analysis on ARs shortly.

Today’s Macro and Technology Themes are unprecedented and cover a multitude of emerging “Themes” from Drones to ESG to Genome Research to Rare Earth, etc. The investment potential is unlimited. Thematic Investing is becoming a powerful vehicle across investments wrappers such as ETFs and Structured Products, as well as, in Portfolio holdings by Hedge Funds and Asset Management. Prior to Machine Readable Filings (MRF), capturing data within Regulatory Filings and Company Reports was difficult to impossible. The MRF data is broad across Sectors and can include Global and or U.S. listed Companies. The data can be run on any timeframe including Monthly and Quarterly for portfolio reconstitution. As you can see Social Market Analytics’ ability to read and classify documents is a powerful tool for your investment process. To learn how Social Market Analytics (SMA) can help you instantly search for Thematic Investing themes using Machine Readable Filings (MRF) SEC Edgar Filings and SMA’s Complex Topic Model engine, please ContactUs@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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Cboe – SMA Large Cap Weekly Index continues to outperform in Volatile Market

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Social Market Analytics, Inc. (SMA) partnered with the Cboe in January 2017 to release the SMLCW Index ‘Cboe – SMA Large Cap Weekly Index’. The SMLCW Index is a Long Only Index that has outperformed since it was released and has continues to outperform in the recent market volatility and sell-off. In the chart below the S&P500 is flat for the year and SMLCW is up nearly 5% YTD.

SMA has two U.S. Patents around its machine learning and NLP processes that produce predictive analytics at the security level across U.S. and UK stocks, ETFs, FX, Futures, and Crypto Currencies

The SMLCW portfolio is an equally-weighted Long Only portfolio of 25 stocks drawn from the CBOE Large-Cap Universe with the highest average 5-period S-Scores. Stocks in this universe (a) are in the top 15% capitalization tranche of stocks that are the underlying for options listed on the CBOE (approximately 3000 stocks) and (b) have a market capitalization greater than or equal to $10 billion.

SMLCW YTD

The CBOE Large-Cap Universe is reconstituted quarterly on the third Friday of the month. The SMLCW portfolio is reconstituted every Friday at 8:30 am CT, based on average 5-period SMA S-Scores at 8:10 am CT. A period is a date on which there is sufficient social media data to derive SMA S-Scores. Stocks are deemed sold and purchased at market-on-open prices. The portfolio is held until 8:30 am CT on the next Friday. If Friday is a business holiday, the portfolio is rebalanced on the preceding Thursday.

To learn more, visit SMA at www.socialmarketanalytics.com or the Cboe website at http://www.cboe.com/products/social-media-indexes

 

 

 

 

 

 

 

 

 

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Social Market Analytics Receives Second Patent

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Most of my blogs center around the predictive nature of Social Market Analytics data. This blog is different.  At Social Market Analytics we are continuously expanding and improving our technology.  These innovations sometime lead to such unique technology that a patent application is warranted.  As many of you know this is a lengthy and challenging process.  We are proud to announce SMA recently received our second patent as an extension of our original patent.  SMA now has two granted U.S. Patents.

Patent

Our first Patent was on our three-component system for extraction, evaluation and publication of metrics on social media feeds. The high-level diagram with Twitter as an input is below.

process

Each process above uses SMA created technology.  Extractor allows for the rapid ingestion of data.  Evaluator filters noise on both the author and content level and Calculator creates custom predictive metrics for multiple time frames and purposes.  As our processes evolve we apply for patents to protect this unique technology.  Our second granted U.S. Patent revolves around publication of metrics and alerting customers to breaking information available through our Twitter metrics and other sources. Although, we are exciting about our 2nd U.S. Patents, we already have our 3rd patent application in preparation!

Thanks for reading,

Joe

 

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UIUC Bitcoin Trading System Practicum Presentation

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Every year Social Market Analytics (SMA) is proud to work with the University of Illinois Masters of Science in Financial Engineering Students on a practicum project. In the past we have explored looking at sentiment to predict the VIX, enhancements to traditional indexes and smart beta ETF’s. This year we decided to tackle the most popular topic of the last year – Bitcoin Trading!   We worked with RCM Capital’s Strategy Studio Platform for back testing to develop a Bitcoin trading strategy combining price momentum with sentiment to keep you in the market when Bitcoin is trading up and minimizing draw downs when Bitcoin retreats as it did in early 2018.

Social Market Analytics tracks sentiment on the top 275 market cap currencies, the below Bitcoin strategy performs similarly on other Crypto currencies.

The students did a wonderful job in strategy construction and explanation.  I will undoubtedly leave something important out.  ContactUs@SocialMarketAnalytics.com for details.

At it’s core the strategy buys on a price breakout with a sentiment confirmation.  Exit when price breaks down and is confirmed with sentiment.  Buy when the price crosses above (K) standard deviations over a 21 day moving average of price.  Variable K ranged from .5 to 2. Results shown use a .5 standard deviation multiplier.  Strategy visualization is below.

BitcoinStrategyVisual

Your first trigger is a breakout above K- Standard deviations of the 21 day moving average.

The confirming signal is based on the Social Market analytics S-Score value.  S-Score is a normalized representation of Bitcoin’s Sentiment time series over a look back period and is updated every minute.  It measures the tone of the conversation on Twitter relative to the benchmark time period.  If Bitcoin is breaking out and the sentiment is 2 standard deviations more positive than normal you initiate or add to your position by 50%.  If the conversation is 1 standard deviation more positive than normal  increase the position 25%.  If the standard deviation price break out is not confirmed by sentiment then no position change.

There was no short position initiated with futures.  Exit criteria are opposite entry criteria.  Price break below K – Standard deviations below a moving average. Confirmation with S-Score.

BitcoinResults

Dollar P/L results indicated this portfolio successfully navigates the the bitcoin draw down of early 2018.   2018 in isolation is below.

Bitcoin-2018

Overall performance with Buy & Hold Bitcoin comparison.

BitcoinStats.png

Sharpe ratio and draw down improve dramatically with the momentum and sentiment confirmation.

stats2

Again, please ContactUs@SocialMarketAnalytics.com for more information on our offerings.

Thanks again to the University of Illinois MSFE students and RCM  Capital Markets for contributing to this project.

Regards,

Joe

 

 

 

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Introducing the Social Market Analytics (SMA) 50 Long Index

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Social Market Analytics has been creating security level sentiment metrics for six years.  As we build an out-of-sample history we are able to build longer holding period indexes. I have blogged about longer term factors before, this is the most comprehensive portfolio strategy built using sentiment level data.  This blog will discuss the application of sentiment to a long only 50 stock, re balanced annually, index.

SMA50 Index is a new, capitalization weighted index comprised of 50 stocks with these features:

  1. The highest average unique message source counts, from SMA’s filtered Twitter data stream, observed over a 50-day look back interval, and
  2. High daily average dollar trading volume (ADV), > $20 Mil, over a 50-day look back interval.  We are looking for liquid stocks.

The SMA50 index measures the aggregate performance of stocks with high levels of crowd sourced commentary and high market liquidity.

  1. SMA50 is reconstituted each year on March 15th.  The core constituents are selected once a year.  They are re-weighted monthly based on the below tilt methodologies.
  2. SMA50 is the “Parent Index” for SMA50 Factor Tilt Products

Below is the historical performance of the SMA50 Index.  We will add tilting to the index based on sentiment and momentum.

SMA501

The following factor tilt indexes are derived from the equity universe of the SMA50 parent index.  Factor Tilt Indexes are re-balanced monthly on the first market day of the month.

SMA-MT: Momentum Tilt

– Designed to deliver the performance of an equity momentum strategy by emphasizing stocks with high risk-adjusted price momentum.

  • A momentum value is determined for each stock in the SMA50 parent index Universe by combining the stock’s recent 12-month and 6-month price performance. This is the standard implementation of a price momentum value.
  • This momentum value is then risk-adjusted to determine the stock’s Momentum Score.
  • All securities in the SMA50 Universe are weighted by the product of their Momentum Score and their market cap, as follow:

Momentum Weight for SMA-MT  = Momentum Score * Market Capitalization Weight in the SMA50.  Momentum weights are normalized to sum to 100%.

SMA50_MT

SMA-ST: Sentiment Tilt

– Using SMA’s S-Score and SV-Score as factors, emphasize stocks with positive levels of social media sentiment and intensity, while attenuating stocks with low sentiment levels.

  • A composite factor score is determined for each stock in the SMA50 parent index Universe from the linear combination of the stock’s monthly S-Score and monthly SV-Score.
  • This composite factor score is used to determine the stock’s Sentiment Score.
  • All securities in the SMA50 Universe are weighted by the product of their Sentiment Score and their market cap, as follow:

Sentiment Weight for SMA-ST  =  Sentiment Score * Market Capitalization Weight in the SMA-50.  Sentiment weights are normalized to sum to 100%.

SMA50_ST

SMA-SMT: Blended Tilt

–Define a factor which is a combination of sentiment and momentum tilts.

  • A combined factor is determined for each stock in the SMA50 parent index Universe from a linear combination of the stock’s Momentum and Sentiment scores.  Initial results for the blended tilt factor used an equal weighting of Momentum and Sentiment scores.
  • This combine factor score is then standardized and used to determine the stock’s Senti-Momentum Score.
  • All securities in the SMA50 Universe are weighted by the product of their Senti-Momentum Score and their market cap, as follow:

Senti-Momentum Weight for SMA-SMT  =  Senti-Momentum Score * Market Capitalization Weight in the SMA-50.  Senti-Momentum weights are normalized to sum to 100%.

SMA50_Combined

Comparative performance for all four theoretical portfolios is below.

SMA Relative Performance

Overlaying standard benchmark performance you can clearly see the effectiveness of the SMA 50 with various tilt strategies to outperform the benchmarks.

SMA Relative Performance bench

The SMA 50 family of indexes provide a low turnover way to benefit from exposure to social sentiment.  To learn more please contact us at ContactUs@SocialMarketAnalytics.com

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Social Market Analytics Now Has Six Years of Out-Of-Sample History!

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Social Market Analytics, Inc. (SMA) is celebrating six years of out-of-sample data in US Equities.   This data is unique in that it is a true representation of the Twitter conversation at each historical point-in-time.

Since our launch, SMA has become a leader in providing sentiment data feeds to the financial community.  Our data has become an integral part of our customers investment process.  Our customers are Quantitative Trading Firms, Hedge Funds, Sell Side Brokers, Traders and many others. SMA data is suitable for HFT, Quantitative Trading, Risk, Short Lending, Smart Beta, Fama-French Models, VAR among others.  Predictive signals range from a few minutes to quarterly.

SMA’s analytics generate high-signal data streams based on the intentions of market professionals.  Our patented machine learning process has produced six years of strongly predictive data as illustrated in the chart below.  This chart illustrates the subsequent performance of stocks based on pre-market open (9:10 am Eastern) sentiment scores.  Stocks with high sentiment subsequently out perform as illustrated by the Green line.  Stocks with strong negative sentiment go on to under perform as evidenced by the red line.  The blue line represents a theoretical equally weighted long short portfolio.  The table below illustrates Sharpe and Sortino ratios.

 

Fullhistory

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