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Social Market Analytics, Inc.

A Leader in Unstructured Financial Data

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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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Using Twitter Social Media for One-Month Holding Periods

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

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

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

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

 

 

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

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

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

 

 

 

 

About Social Market Analytics:

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

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

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

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

Contact:Doug Hopkins at doug@socialmarketanalytics.com

 

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A Returns Analysis on StockTwits Sentiment Metrics

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This paper discusses the analysis of a Returns Study done on US Equities using social media sentiment factors from Social Market Analytics (SMA) in a single factor trading model environment. In a comparison to a market reference on a long only strategy, the results indicate an outperformance in returns for positive sentiment stocks and an underperformance for negative sentiment stocks. The long short strategy outperforms the market reference consistently over multiple testing periods.

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