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Machine Readable Filings (MRF): Total Document, MD&A, and Risk Factors Alpha

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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.

Methodology

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

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.

Conclusion

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.

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Predictive Sentiment on Machine Readable Filings (MRF)

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In April 2020, S&P Global Market Intelligence and Social Market Analytics, Inc. (SMA) launched ‘Machine Readable Filings’ (MRF), a sophisticated textual data offering which applies Parsing and Natural Language Processing to generate machine readable text extracted from SEC Regulatory Filings. Machine Readable Filings allows businesses and investors to incorporate more qualitative measures of company performance into their investment strategy by using machine readable text from full or individual sections of regulatory filings to enhance their analysis of companies. The parsed textual data allows firms to drill down on both historical and new filings in near real-time.  My last blog introduced the product and illustrated some basic return characteristics present in filings word count.

This blog explores the predictive nature of filings using SMA patented NLP and machine learning. For our analysis we used all active securities with a price greater than 5 dollars. Our analysis starts in 2006.  Securities are broken into quintiles based on each factor.  These factors are samples of the extensive metrics that can be created with this data.  Quintiles are re-balanced monthly based on each company’s most recent filing. 10-Q’s are compared to prior 10-Q’s and 10-Ks are compare to prior 10-K’s.  These are not meant to be trading models. They illustrate the predictive power of the data and use as broad a universe as possible. Two interesting distributions are below: distribution of word counts for 10-K (mean 36,000) and distribution of average sentiment.  As you can see companies try and keep the 10-K as upbeat as possible.

Our first factor is Change in Sentiment Hits. Sentiment hits are the number of times our NLP was able to identify a word or segment in a sentence. Positive hits + Negative hits + Neutral hits.  The green line represents filings with the largest increase in sentiment hits while the red line represents filings with the largest decrease in sentiment hits. Large increases in sentiment hits tend to under perform and large decreases in sentiment hits tend to outperform its peers.

The quintile performance characteristics are below.   Although quintile 2 and 3 are out of order you see the average values for those quintiles are near zero.  Quintile 1 outperforms quintile 5 by 3% annualized.

The next factor we are analyze is what percentage of the document does the parser hit.  Many filings are filled with general information not necessarily providing meaningful statements.   The green line represents filings with the highest percentage of sentiment hits in the document while the red line represents filings with the lowest percentage of sentiment hits in the document. A higher percentage of sentiment hits tend to  outperform and a lower percentage of sentiment hits tend to under perform its peers.   Companies with documents containing more meaningful content outperform companies with documents with less meaningful content by about 3.5% annualized.

Quintile 5 – Quintile 1 annualized is 3.5%

The third factor we are exploring is changes in negative hits.  Companies with increasing negative hits are discussing more negative information than prior quarters, they subsequently under perform.  The green line represents filings with the largest increase in negative hits while the red line represents filings with the largest decrease in negative hits. A large increase in negative hits tend to under perform and a large decrease in negative hits tend to outperform its peers.

The last factor we explore is cumulative document sentiment.  Quintiles are based on summations of all sentiment hits in the document.  More common analysis of sentiment is by section.  We identify parts sections and subsections in this product providing a myriad of ways to analyze the data.  At the most aggregated level sentiment is predictive.   Document length has a large impact on overall sentiment.  Z-Scores of this factor are a good way to compare prior documents.  As you can see in the chart companies with more positive total document sentiment tend to outperform companies with more negative total sentiment.

Quintile 5 outperforms quintile 1 by 1.7 percent annualized.

There are many ways to analyze the MRF data set. Filings are parsed by Item, Section, and sub-Sections to 2006 for historical back testing. This analysis looked at only 10-K’s and Q’s ‘Machine Readable Filings’ (MRF) cover 20 types of SEC filings. This blog covers a small portion of the research. The U.S. SEC Edgar Data is live on the S&P Xpressfeed. International Reports will be released later in 2020. To learn more or to start a trial please ContactUS@SocialMarketAnalyitcs.com.

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S&P Global – Social Market Analytics Machine Readable Filings (MRF)

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S&P Global and Social Market Analytics today launched Machine Readable Filings (MRF), a sophisticated new data offering which applies Parsing and Natural Language Processing to generate machine readable text extracted from SEC Regulatory Filings. Machine Readable Filings allows businesses and investors to incorporate more qualitative measures of company performance into their investment strategy by using machine readable text from full or individual sections of regulatory filings to enhance their analysis of companies. The parsed textual data allows firms to drill down on both historical and new filings in near real-time.

Machine Readable Filings features the following:

  • 23 Years of History
  • 48,000+ Companies
  • 20 Filing Types
  • 4 Million Documents

The product feed contains three levels of detail:

  • Parsed Filings
    • A normalized JSON of all financial documents. Parts, Items, and subsections are normalized
    • Document and Section Summaries
  • Summaries for each section and the whole document, with their respective changes over time, including
    • Word Counts
    • Numbers of Positive Words
    • Numbers of Negative Words
  • Sentiment Feed
  • Full Patented SMA Sentiment Feed and associated metrics

As regular readers will attest, my previous blogs have focused on NLP and parsing Twitter and StockTwits based messages. This blog breaks new ground for Social Market Analytics as our first piece featuring Machine Readable Filings. We processed all 10-Ks and 10-Qs mapped to a pricing source (~150,000 documents) and looked at subsequent returns based on two word count based factors.

The word count factors we explored were Raw Change and Magnitude Change. Raw Change is the difference between the number of words in a filing and the number of words in the most recent filing of the same type for the same company. Magnitude Change is the absolute value of Raw Change, so it does not account for the direction of change. Below is the formula for the factors created where i represents the company, j represents the filing type (10-K or 10-Q), and k represents the period of the filing.

 

First, we looked at the Management Discussion & Analysis (MD&A) section because this section has the largest variability across all companies. This section addresses the company’s performance in a qualitative manner. Each value is carried forward from the previous filing until a new filing is released or until the data is 3 months old. The chart below represents a Quintile plot of Magnitude Change of word count in the MD&A section from January 2010 to December 2019.

 

 

Quintile 1 contains filings with the least amount of change in the MD&A section. Average Magnitude Change of word count in MD&A section for the lowest quintile group is 118 words. Quintile 5 represents the largest Magnitude Change of word count in MD&A section. Average Magnitude Change of word count in this quintile is 3,073 words. This graph shows that in the MD&A section, smaller changes in word count tend to outperform the market and larger changes in word count tend to underperform the market. The hypothetical Long/Short of this variable (Q1 – Q5) is proven significant at a 95% confidence level meaning the average monthly return is greater than 0%.

Next we looked at how changes in word count across an entire document can impact future returns. The largest increases in the number of words in the total document is represented by the green line. The largest decreases in word count is represented by the red line.

 

 

As you can see if there is a large increase in the number of words in the document, the stock subsequently underperforms its peers. On the other hand, if there is a large decrease in word count throughout the document, the stock tends to outperform its peers.

Although this analysis only includes the change in word count of the whole document and the MD&A 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.

S&P and SMA are excited about the launch of this new product. This is the first product to break out documents into component parts and provide a full historical analysis. To learn more about or schedule a trial please ContactUs@SocialMarketAnalytics.com.

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SMA Futures

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SMA S-Score Strength on the Wings

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Social Market Analytics (SMA) calculates metrics describing the current conversation on Twitter relative to historical baselines using statistical Z-Score metrics. Most Twitter conversations are neutral on a given day. If the conversation on Twitter is neutral, stock price is being driven by something other than social media. We find that Twitter derived metrics illustrate statistically significant predictive power when the conversation becomes more positive or negative than normal levels. The table below is a good illustration of Z-Score. Points on the curve denote the positive or negative levels of conversation. If the SMA S-Score is greater than 1 the current conversation is more positive or negative than 68% of conversations over the look back period. That is a bullish (S-Score > 1) or bearish (S-score < -1) signal. If the S-Score is > 2 then the current conversation is more positive/negative than 95% of conversations over the look back period. S-Score > 3 moves the percentages out to 99.7 percent.

The below chart has six lines representing S-Scores (Z-Scores) of +2, +1.5, +1, -1, -1.5, -2. All SMA data is out-of-sample, we never recalculate any values. All historical data is pulled from update archives. As the conversation becomes more positive or negative than normal the subsequent security returns become more extreme. S-Scores are sampled at 9:10 Eastern time, 20 minutes prior to market open. We look at subsequent open to close returns bucketed by six S-Score levels. As the chart clearly illustrates the more significant the S-Score the more extreme the subsequent returns. Negative returns of negative S-Scores represent positive alpha.

Sharpe an Sortino ratios become more significant as S-Scores become more extreme. SMA publishes 19 different metrics in our S-Score feed. Each metric adds more clarity to the tone of the conversation. SV-Score for example is a metric comparing Twitter volume relative to historical levels for that security. If Twitter volume is higher than normal and the conversation is more positive or negative than normal the signal is stronger.

To learn more about SMA contact us at ContactUs@SocialMarketAnalytics.com

 

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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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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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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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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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