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Ukraine War Impact on Corporate Filings

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Social Market Analytics released U.S. Machine Readable Filings (MRF) in partnership with S&P Global Market Intelligence in April 2020. Machine Readable Filings is the first product to provide parsed textual data of SEC Edgar Regulatory Filings at the Document, Part, Item, Section, and Notes level with historical baselines back to 2006. Global Machine Readable Filings (GMRF) was released in April 2021 in partnership with S&P Global Market Intelligence. Global Machine Readable Filings provides parsed textual data of international companies’ Annual, Semi-Annual, and Quarterly Reports and Financial Supplements across ~1.6M documents spanning over 200 countries. These magazine-style documents are parsed according to the structure of the underlying document with machine-readable text organized in multi-level JSON format.

In January of 2022, SMA released the Unstructured Data Terminal (UDT). This tool allows for real-time analysis of corporate filings in an easy-to-use interface. One feature of this product is ThemeX, which allows for real time custom topic searching of corporate filings. With the war in Ukraine just beginning, we were asked how quickly will companies react to this new conflict in their corporate filings?

Below we created a search looking at documents that must include the phrases ‘Russia’ and ‘war’ and has any mention of ‘Ukraine’. This searches all pieces of textual data throughout the entire document on all Global Filings going back 1 full year. The phrase ‘trade war’ is excluded to remove any information that is deemed irrelevant to the topic.

Below are the results of an executed search on the war in Ukraine. The search displays the trend of mentions month over month, a company breakdown, and country breakdown. ThemeX has ability to identify other related phrases and topics to the core theme as well, under the ‘Show Suggestions’ link.

 

The first chart shows the number of ThemeX hits on unique companies and filings by month.  The second table shows the number of documents hit on the ThemeX search for each company. The pie chart shows country exposure amongst the ThemeX hit companies.

If you want to explore the filing with ThemeX, you can read the section in each document where the reference is made. If you click PH Glatfelter, it shows the filing type, date, and section where the phrase was mentioned. From there you can read the entire document and browse SMA patented sentiment.

The invasion started Thursday, February 24th. In the first month, the mention of Ukraine in reference to war has already skyrocketed. Many companies have already added this topic to their Risk Factors section as there is significant worry the war will spread globally. Less than a month into the invasion, nearly 600 companies across the globe have referenced the risk of impact from the war into their corporate filings.

To learn more about this topic or explore other topics in ThemeX, email us at ContactUs@SocialMarketAnalytics.com or schedule a meeting using our 1 on 1 Meeting Signup

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Global MRF – The First of its Kind

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Social Market Analytics, Inc. (SMA) in April 2021 released Global Machine Readable Filings (GMRF) in partnership with S&P Global Market Intelligence. Global Machine Readable Filings is the first product to provide parsed textual data of over 200 Countries Company Annual Reports, Quarterly Report, Semi-Annual and Financial Supplements across ~1.6M documents.

This blog will explore details of this offering and some of unique features.  For Global filings there is no central repository of documents.  S&P archived global filings for the last 20 years.  At SMA we used our patented document processing technology to turn these files into machine readable JSON.  The structure of each document is created by individual companies, so the structure is subject to change.  For example, Company A can change formatting YoY. Since each company has its own format there are challenges from comparing Company A and Company B.  The original files come as PDFs with significant variations: “Magazine style”, scanned documents and tables without HTML tags.  Our machine learning algorithms have processed these files and successfully converted them to JSON.

Below are a few examples of parsing different file types. The SIKA 2018 Annual Report table of contents is below.

By reading the Table of Contents (TOC) we identify primary headings.  In addition, we identify subheadings under Strategic Target Markets & Risk Management based off of font and structure variations which are not available in the TOC.

The next example is Credit Suisse Group 2017 AR.

The SMA parser dynamically identifies Items, parts and subsections.

We use our Generic document parser to read and identify parts.  Below is the strategy section broken into sub-parts.

 

These are just two filings.  As we mentioned there are 1.6 million documents in the archives and updates are provided in real-time through XpressFeed, Snowflake or through the SMA Filings API.  To trial this product or learn more about other Social Market Analytics datasets please email us at ContactUs@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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Thematic Investing using Machine Readable Filings (MRF)

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Thematic Investing seeks to identify macro level trends and the underlying investments that stand to benefit from those trends. This Blog highlights ‘Drones’ as a Thematic Investing model example. Drones are now in the news on almost a daily basis and used by listed Companies across Sectors. Companies like Amazon, Allstate, and Goldman Sachs are all ‘users’ of Drones, but who are the Companies that are Drone manufactures or are providing the Technology in Drones? Extracting Themes, such as Drones, from Regulatory Filings and Company Reports is now possible through Machine Readable Filings (MRF) and Social Market Analytics, Inc. (SMA) Complex Topic Model engine. SMA can build a Thematic Investing model using SEC Edgar Regulatory Filings back to 2006 and process the Textual Data programmatically in hours.

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. SMA is currently developing the same structured data values on International Company Reports which will be released in Q1 2021.

Machine Readable Filings (MRF) used in conjunction with SMA’s ‘Complex Topic Model’ engine allows for the programmatic curation of Thematic Investing themes.

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 model capabilities you can quickly scan documents to look 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.

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

For this “Drone” Thematic blog we:
• Broke the ‘Drones’ thematic Topic Model down by Company, Filing type, and Sector. The data set covers filings from January 2006 – October 2020
• The ‘Drones’ Topic Model captures every relevant ‘Drone’ word/phrase including synonyms (inclusion) within SEC filings
• We exclude mentions of Drones where it does not apply to the Topic Model, e.g. UAS Drone Corp. mentioning their own name in a filing
• The data is run across all SEC Edgar Filings including 6-K ADR. Some of the captured Companies are overseas. Filing types can be excluded as well.

Machine Readable Filings tracks over 20 filing types. The distribution of filings containing ‘Drone’ mentions is below. Majority of the documents containing ‘Drone’ mentions since 2006 tend to be 10-Ks and 10-Qs because they are the most detailed documents. However, in 2020, the amount of 6-Ks mentioning drones almost triple from 2018, making 6-Ks the most hit document for the year so far. This suggests that drone usage for companies could be growing faster internationally than in the United States.

As illustrated by the chart below, ‘Drone’ mentions have grown more than 500% since 2006 even with just 10 months of 2020 documents included. Unique companies discussing drones in their filings has grown 400% from 40 companies in 2006 to over 160 so far in 2020.

*A mention is defined as a unique paragraph that contains one or more words from the Drones Topic Model in any SEC Filing.

Below are the top companies mentioning drones in their regulatory filings. Many of these companies are not the typical Fortune 500 companies that mention drone usage as a buzz word. The companies most frequently hit are companies that build drones and work with the drone AI technology. Amazon, one of the more media mentions of Companies ‘using’ drones through Amazon Prime Air, only mentions a drone related term once in both their most recent 10-K and 10-Q. Prime Air is not mentioned in either document. When making a thematic portfolio for drones, it would not make sense to include Amazon because Prime Air is not a central enough part of their business to mention in their filings and, as of now is not manufacturing drones.

The companies mentioning represent a variety of sectors. The top two sectors are information technology and industrials. Those are the companies that build and develop the technology for drones and use drones for delivery. Other sectors use drones in a variety of ways. For example, Allstate, in the financial sector, uses drones to analyze crash sites for their insurance claims. Goldman Sachs uses Drones in its investment banking arm. It makes sense to include companies from a variety of sectors when creating a thematic portfolio while at the same time having a higher weight on the more relevant sectors.

Today’s Macro and Technology Themes are unprecedented and cover a multitude of emerging “Themes” from Drones to ESG to Genome Research to Biometrics, 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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Machine Readable Filings (MRF): 8-K Sentiment Alpha

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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 Item, Section, Sub-section, and Notes level with historical baselines back to 2006, along with sentiment values provided by SMA’s patented Natural Language Processing (NLP) technology. Extraneous information such as page numbers, images, and tables are removed. SMA is currently developing the same structured data with sentiment values on International Regulatory Filing Reports which will be released in Q4 2020.

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.

One type of regulatory filing supported in the Machine Readable Filings (MRF) product is Form 8-K. This broad form is used to notify investors in public companies of specified events that may be important to shareholders or the SEC. This is one of the most common types of forms filed with the SEC. After a significant event like bankruptcy or departure of a CEO, a public company must file a Current Report on Form 8-K within four business days to provide an update to a previously filed 10-Q or 10-K.

We have found predictive power in virtually all MRF analysis across different filing types. Please reach out to us for white papers and research on other document types and sections. For this blog we look at the predictive power of the sentiment values of the 8-K filings. Social Market Analytics’ patented NLP technology reads and assigns fine-grain sentiment scoring to the 8-K document, down to sub-section level. The analysis below demonstrates the predictive power of sentiment values from 8-K textual data. These are not trading strategies by themselves but do illustrate the predictive power of the 8-K sentiment data.

Companies publish an average of eight 8-K filings per year.  For our analysis we looked at approximately 500,000 documents.

For our test:

  • We are analyzing the relationship between sentiment of a company’s 8-K filing and subsequent stock price movement over the next week.
  • If a company releases an 8-K, a position is entered on the Close of the same day of the release and sold 5 days later.
  • The stock universe we tested on includes all companies that filed 8-Ks after 1/1/2010 that mapped to SMA’s pricing source with a Price > $5 filter.

Quintiles:

Stock returns are bucketed into Quintiles daily depending on the value of Average Sentiment of the 8-K filings (Quintile 1 [Q1] has the lowest 20% of sentiment values. Quintile 5 [Q5] has the highest 20% of sentiment values). All quintiles display the Covid-19/Election/Trade War drawdown in March 2020.  As you can see there is a relationship between 8-K filing sentiment and subsequent price returns. Q5 which represents the highest 20% of 8-K sentiment scores significantly outperforms all other quintiles. And we see the sentiment data is monotonic. Included is also the spread between the top and bottom quintiles (Q5 – Q1). This spread is proven significant a 99% confidence level with a T-Statistics of 4.65 and a p-value of 0.00000175.

 

Thresholds:

In this part of the analysis, stock returns are bucketed into Long and Short buckets depending on the sentiment threshold set. In the following example, 8-K filings with an Average Sentiment greater than 1.5 are in the Long bucket and filings with an Average Sentiment less than 0 are in the short bucket. We see companies whose 8-Ks have positive sentiment greater than 1.5 go on to significantly outperform while negative ones tend to underperform. An equally weighted theoretical Long/Short portfolio generates a solid return and nice Sharpe ratio, even during the March 2020 drawdown.

 

Until now there has been no real way to analyze text in corporate filings. SMA’s Machine Readable Filings product allows for rapid identification of the sentiment as well as the predictive power of corporate disclosures. For more information or to start a trial ContactUs@SocialMarketAnalytics.com.

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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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