The SMA research team has done a tremendous amount of research on Machine readable filings. This Blog is taken from a research paper authored by Koby Weisman. SMA partnered with S&P Global Market Intelligence to provide textual data in U.S. SEC EDGAR filings broken down by heading with text underneath (i.e. Parts, Items). The textual data is parsed to create historical baselines for 10-Ks, 10-Qs, 8-Ks, 20-Fs and other filings. This paper focuses on word counts, sentiment factors, and the change in those factors. There are 20 filing types in the MRF product, however this paper analyzes 10-Ks and 10-Qs building on existing academic research including Lazy Prices1.

The MRF dataset includes seven factors which are described in the table below. These factors are produced at the Item, Part, and Total Document level to provide a comprehensive view of what sections within the document have changed.

Subscribers of the MRF dataset can create derivative metrics stemming from the seven factors provided. For instance, one metric explored in this paper is Sentiment per Word. That factor is calculated by dividing Sentiment Sum by Word Count. Another factor explored is Percentage of Sentiment Hits which is calculated by dividing Sentiment Hits by Word Count. These factors and other derivative factors are calculated to normalize sentiment based on the length of document.


The MRF dataset provides word counts and sentiment factors throughout the entire document, each part, and each item of the quarterly or annual report. In order to test our hypothesis that larger changes in SEC Edgar filings underperform smaller changes, we created metrics that exemplify ‘changes’ in a report.

The authors of Lazy Prices categorized changes in filings using a variety of similarity metrics (cosine similarity, Jaccard similarity, minimum edit distance, and simple similarity). In our analysis we use raw change in word count as proxy for similarity scores. Raw change in word count is the difference between the word count in two filings. This analysis looks at the Quarter-over-Quarter changes in regulatory filings. Each 10-K and 10-Q is compared to the most recent 10-K and 10-Q from the same company.

In addition to word count, this analysis explores other factors included in the MRF dataset which contain sentiment scores, word counts categorized by sentiment, and factors that combine word counts and sentiment.

Lazy Prices makes no mention of their universe, so we used all securities over five US dollars. The benchmark used, called ‘Universe’, is the average return of all stocks in any Quintile portfolio at that point in time. The analysis begins in 2007 and concludes at the end of 2019.

When computing calendar-time portfolio returns, stocks enter buckets depending on the factor or the raw change in that factor. Stocks enter the portfolio in the month the report was released. Portfolios are rebalanced monthly to introduce new filings submitted in the most recent month. Note that average portfolio size can differ due to documents having the same value.


Results below show graphs and metrics related to calendar-time portfolio returns. ‘Q1’, or Quintile 1, contains stocks with the lowest value of the factor while ‘Q5’ encompasses stocks with the highest value of the factor.

We first looked at metrics on the total document level. This contains data embedded at the Item and Part level of a regulatory filing, which is then rolled up to the document level.

The graph and table above exemplify how Raw Change in Word Count can enhance stock selection. The green line represents securities that have the largest increase in Word Count while the red line denotes securities that have the largest decrease in Word Count. The red line, Quintile 1, outperforms all other quintiles while the green line, Quintile 5, underperforms all other quintiles.

As filings become longer or wordier compared to the company’s most previous filing, returns tend to drop compared to the universe. Regulatory filings are intended to adequately warn investors or potential investors about the company’s actions and strategies. If there are more warnings and explanations of the company’s actions, then the company isn’t as stable and thus underperforms the market.

As filings become shorter or more concise, subsequent stock returns outperform the universe. Companies that have a decrease in word count do not boast of events or products, but rather provide succinct statements. Also, one-off events that were in the company’s previous regulatory filing are taken out of the document meaning that the event was resolved.

The difference in monthly returns between the two lines (Q1 – Q5) has a T-Statistic of 3.64 and is proven significant at a 95% confidence level, thus we reject the Null Hypothesis that the Average Monthly Return equals 0.

The graph above exemplifies how a change in the number of subsections is an indicative source of future stock returns. This metric is a round integer with a small range so many stocks have the same value, which is why the average count in each bucket is uneven.

Subsections are counted at the Item level and are included if there is a specific topic to discuss. If there are more subsections included in the document (Quintile 5, green line) compared to the previous document, the stock price underperforms its peers. When there is a decrease in the number of subsections (Quintile 1, red line) the stock outperforms its peers.

Subsections are added to a regulatory filing when there’s a specific topic to discuss. Subsection Count and Word Count are correlated because as there are more topics to discuss, there are more words in the document. The addition of a new subsection means there is an event occurring and the company needs to adequately warn its investors. If there are more subsections, then the company has more events that could risk the future value of the company.

The monthly return difference between the two lines (Q1 – Q5) has a high 5.32 T-Statistic and is proven significant at a 95% confidence level. The hit rate, which is the percentage of times the return of the portfolio is greater than 0.00%, is extremely high at 68.59%. This means we reject the Null Hypothesis that the Average Monthly Return equals 0.

The above graph shows how the Total Document’s average sentiment can be a predictive source. The green line (Quintile 5) has the highest Average Sentiment value and outperforms all other stocks in the universe. Not only does Quintile 5 outperform the rest of the universe, but it also does so with the least amount of risk.

Through SMA’s Natural Language Processing all words in the document are read and assigned a score based on the sentiment of those words. If there is more positive language used throughout the document, the security tends to overperform the market. On the other side, if there is more negative language, the security underperforms its peers.

The red line (Quintile 1) underperforms its peers, but not by a significant amount. Even though the difference between Quintile 5 and Quintile 1 is not proven significant at a 95% confidence level, this factor provides additional alpha on the Long side.

We next looked at the Management Discussion & Analysis section of regulatory filings. This section is unique because of how unstructured it is compared to all other sections. It encompasses how management views the trajectory of the business and future events.

The chart above shows the Quintiles for Percentage of Sentiment Hits. This metric is calculated by dividing Sentiment Hits by Word Count. This is the percentage of the total document that had financial lexicon pertaining to sentiment (either positive or negative).

Quintile 5 (green line) represents the highest Percentage of Sentiment Hits, which outperforms all portfolios. Quintile 1 (red line) underperforms all portfolios. Companies that talk more about its performance in financial terms with sentiment are upfront. This transparency is beneficial for the company as they are forthright with investors. On the other hand, if the MD&A section has a small Percentage of Sentiment Hits that means the company is speaking about information not related to the financial status of the company. These companies don’t provide as much important information or use additional language that is not required. This lack of transparency devalues the company in the eyes of the investors.

The difference between Quintile 5 and Quintile 1 is proven significant at a 95% confidence level and provides a unique source of alpha.

The factor Sentiment per Word is calculated by dividing Sentiment Sum by Word Count. Longer documents are more likely to have an extreme value in Sentiment Sum. The rationale for this is if a document has more words, it is more likely to have more sentiment hits, thus a more extreme value for Sentiment Sum. The Sentiment per Word factor normalizes the magnitude of sentiment based on the length of the document.

Here we see Quintile 5 (green line) outperform and Quintile 1 (red line) underperform all other portfolios. The difference between the two is not proven significant, however this metric still provides insights on the Long side as Quintile 5 has the highest returns with less risk. If a company has a higher Sentiment per Word, then there is more of an upwards outlook on the future of the company and its events. A low Sentiment per Word means the company is negative when speaking about the company’s actions. This would attribute to a lack of confidence in the company’s future.

We last looked at the Risk Factors section of regulatory filings. This section generally has a negative tone and states what could go wrong in the company while adequately warning investors.

The factor plotted above, Positive and Negative Hits Difference, is the difference between Positive Hits and Negative Hits. In this graph Quintile 5 (green line) represents filings with a larger number of positive hits than negative hits, which underperforms all other portfolios. Quintile 1 (red line) contains filings that have significantly more negative hits than positive hits, which outperforms all portfolios. Filings with positive language in the Risk Factors section lack truth and transparency which leads to an underperformance. If the company is upfront about the risks of investing and doesn’t put a positive spin on the risks, the investors have more confidence in the company.


Machine Readable Filings is the most advanced and thorough product on the market for drilling into the un-tapped value of textual data in regulatory filings. These filings track how companies evolve and approach strategy in the face of micro and macro trends and the effect of these trends on their short- and long-term goals. While much in these documents do not change over successive quarters and years, the ability to quantify change and the location of change when it exists has been shown to be a predictive factor for stock selection in a portfolio.

Using previous academic research as a guide (Lazy Prices), SMA has shown the predictive nature inherent in changes in regulatory filings. The results presented in this paper show how multiple factors tend to predict future returns in securities and can be a factor for stock selection in a portfolio.

The flexibility of the raw data provided allows subscribers to create an infinite number of derivative factors at the Item, Part, and Total Document level. These factors will continue to be explored as an additional source of alpha.

Although this analysis only included factors at the Total Document level, the Management Discussion & Analysis section, and Risk Factors section, other sections within regulatory filings can provide additional insights into a security’s future return. Furthermore, we expect additional insights to be uncovered using natural language processing to quantify the sentiment of the underlying text at the various levels of the document. These analyses and more will be explored by Social Market Analytics and S&P Global in the future.


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.

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

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

smlcw performance




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)

Corn Alert

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

This year has been tough for most investment strategies.  Firms using traditional sources of data are generating the same underwhelming returns.  Two years ago, Social Market Analytics, Inc.  (SMA)  (Twitter)   launched the SMLCW index in partnership with the CBOE.  This index is re-balanced weekly and comprised of the twenty-five securities selected from the CBOE large cap universe with the highest average S-Score over the prior week.  It’s A long only index of super-cap stocks with unusually positive Twitter conversations.

SMA publishes a family of metrics providing a full representation of the Twitter conversation across equities (US and LSE), commodities, currencies, ETF’s & Cryptos.

S-Score is a normalized representation of the current Twitter conversation of professional investors as identified by Social Market Analytics patented algorithms.  SMA has access to the full Twitter feed through our licensed partnership with Twitter and listens in real-time for any mention of topics and securities of interest.  These Tweets are scanned in real-time for sentiment and influence of the poster and compared to prior conversations over the look back period.  Securities with higher S-Scores subsequently outperform and securities with negative S-Scores under-perform.

SMA S-Scores are predictive over multiple prediction periods.  With seven years of out-of-sample data we can extend our comparison baselines and predict over longer periods.

Year-To-Date the SMLCW index is up over 7.5% while the SP500 is flat.  Subtracting a couple percent for commissions/slippage and the index is still significantly positive. This is not a back-test, this index has been live and on your quote screens for nearly two years.  YTD actual performance chart from the CBOE site is below.


As mentioned, this is a long only index.  During the recent market drawdown this long index has been performing.  SMA negative S-Score stocks have been moving lower at a significant rate – generating positive alpha.  Below is a chart of the SMLCW index compared to the SP500.  for any questions or to learn more please contact us at:




Social Market Analytics (SMA) publishes real time Twitter based sentiment for nearly 300 crypto currencies including Bitcoin.  To view Bitcoin sentiment values and 35 other commodities in real time, go to the CME Active Traders website.   Twitter based sentiment has proven to be strongly predictive for Bitcoin and other commodities.

Today we will review a sentiment-based Z-Score strategy to generate profitable trades for Bitcoin.  This is similar to traditional standard deviation band strategies calculated with price.

When Twitter volume from certified investors is abnormally high use the sentiment of the abnormally large conversation to select entry points.  Strategy overview is below:

CMEBitcoin 1

A visualization of the strategy is below. When the Z-Score of Social Market Analytics Indicative Twitter volume is greater than the threshold and the tone of the conversation is significant enter or modify trades.  Sentiment  > 2 standard deviations and the volume of the conversation is high enter a position.  Positions are modified based on further extensions of the Z-Score.


Test period is from 1/1/2017 to current.  Overall results below.  For more detailed results on this and other strategies contact


SMA has examples of profitable applications of Twitter based sentiment to many coins.

Social Market Analytics (SMA) data is live on the CME Active Trader Website.  Real-time sentiment and indicative Twitter volume is used by traders to generate new ideas.  Sentiment data is predictive across various time frames.  High sentiment commodities go on to outperform and negative sentiment commodities underperform.  SMA covers 36 commodities on the CME website for: Agricultural, Equity Indexes, Energy, Metals, Interest Rates & FX.

On Monday 9/24 Gold Sentiment crossed through extreme positive at 7:30 am central time.


Clicking on the chart expands the time frame for further analysis.


To learn more about Social Market analytics commodity sentiment data or more about the CME implementation:

To receive alerts like this in real time follow us on Twitter at @sma_alpha.

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. 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.


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.


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


Overall performance with Buy & Hold Bitcoin comparison.


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


Again, please for more information on our offerings.

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






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.



Joe Gits, CEO of Social Market Analytics, recently spoke at the 34th annual CBOE Risk Management Conference.

Gits spoke at RMC about SMA’s patented technology, the Social Sentiment Engine, and Twitter’s relevance in financial markets.

Hosted by the Chicago Board Options Exchange, the RMC is an educational forum dedicated to exploring the latest products, trading strategies and tactics used to manage risk exposure and enhance yields. The RMC is the foremost financial industry conference designed for institutional users of equity derivatives and volatility products.