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Weekly S-Volume Lookback for Daily Trading

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The goal of this research project was to aggregate sentiment factors to maximize future stock returns. The factor used for this project was an aggregate of S-Volume, which is the raw number of indicative Tweets in the previous 24 hours by company. Sentiment data is taken from the 3:40 PM ET timestamp (20 minutes before market close). Instead of using the 24-hours Tweet volume, we created a standardized S-Volume metric over the previous 7 days with a 90-day benchmark for each stock. Standardizing volume removes the ‘popular stock’ bias and gives a score relative to their recent trend. Our hypothesis is stocks with a sustained high volume of tweets over previous week (high SV-score) will perform better than those with lower volume. The calculation for the standardized S-Volume metric is shown below, with D representing the date and i representing each stock:

Stocks selected must have a Close Price greater than $5 the previous day. This eliminates low liquidity and low market cap stocks. Below is the calculation of daily Close-to-Close returns:

Using SV-score, we created a theoretical Long/Short strategy. The Long portfolio will contain the stocks with the 50 highest SV-scores, and the Short portfolio will contain the stocks with 50 lowest SV-scores. These will be selected each day 20 minutes before market close. The Long/Short portfolio combines the two portfolios to create a portfolio with reduced risk. The benchmark comparison used is the S&P 500 ETF Trust (SPY). Trading with this strategy for the past 10 years gave the following:

Looking at the graph above, the highest SV-scores consistently and significantly outperform the overall market during the 10-year period. There is a large increase in the Long portfolio during the Covid-19 pandemic recovery. Sharpe and Sortino ratios suggest this Long portfolio is a relatively low risk strategy too. At the same time, the lowest SV-scores performed consistently worse than the market. The Long/Short strategy is the most risk evasive and is still profitable over time. The recent trend with these strategies seemed to be more volatile, so I looked at them since the beginning of the current year:

Albeit negative, the highest SV-scores still outperformed the general market while the lowest SV-scores underperformed. Year-to-Date shows the Long Short strategy may be the most effective moving forward as the market has been turbulent. It also maintains the relatively low risk quality as over the 10-year period.

The SV-score measures the volume of indicative tweets surrounding a stock in the previous week compared to its normal amount. A high SV-score, therefore, indicates a stock is a hot topic on Twitter and being discussed frequently. This proves to be a positive regardless of whether sentiment is positive or negative. Conversely, a low SV-score indicates that the stock is being talked about that week less than normal. So, less volume on Twitter proves to have a negative impact on the price of the stock as there isn’t any news worth mentioning. This strategy plays into the idea that ‘There is no such thing as bad publicity’.

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

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GMRF Increases Coverage to Include ESG Documents

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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, Quarterly Reports and Financial Supplements across ~1.6M documents spanning over 200 countries. Based on client demand, we expanded our technologies to an emerging set of document types: Environmental, Social, and Governance (ESG) Reports.

We are excited to release the next iteration of Machine Readable Filings.  Our ESG data pack contains all ESG reports.  There are five types of ESG reports: Sustainability Reports, Corporate Social Responsibility Reports (CSR), Corporate Governance Reports, Environmental Reports and Task Force on Climate-Related Financial Disclosure (TCFD) Reports.

ESG Reports offer additional perspective to traditional corporate documents such as Annual Reports and SEC filings. Traditional corporate documents reflect the past year or quarter, ESG reporting has a forward-thinking perspective. Trends in ESG initiatives can impact investing decisions.

With SMA parsing technology, these magazine-style documents are parsed according to the structure of the underlying document with text organized in a multi-level JSON format. This makes it possible to run advanced textual analytics such as advanced word searches, comparisons/ red-lining, sentiment, word counts and more.

ESG reporting has become a priority for companies in recent years. The below percentages illustrate the importance of ESG initiatives to the largest companies in the world.

  • 99.2% of the Top 500 Market Cap U.S. companies have filed at least one ESG report in the last 5 years
  • 96.6% Top 500 Market Cap Global (Ex U.S.) companies have filed at least one ESG report in the last 5 years
  • 99.2% of MSCI’s Top 500 Market Cap companies have filed at least one ESG report in the last 5 years

Below is a detailed description of each document type with examples:

Sustainability Reports (19,222 documents): We see similarities in ESG goals based on a company’s industry. For example, within the food and hospitality industry, there is a huge focus on more sustainable packaging and sourcing ingredients closer to factories.

  • Sustainability Reports are the disclosure and tracking of ESG goals. Benefits include building customer confidence, improved corporate reputation, and improved risk management.
  • There are four steps in this reporting process: Define goals and metrics, measure progress, evaluate progress and manage progress
  • Popular headings include: “governance”, “stakeholder engagement”, “environment”, “employees” and “risk management”
  • Top Countries reporting this document type:
    • US (16.1% of SR universe)
    • Hong Kong (9.7% of SR universe)
    • China (8.1% of SR universe)
    • Japan (6.9% of SR universe)
    • Brazil (3.6% of SR universe)
  • Top sectors reporting this document type:
    • Industrials (20.3% of universe)
    • Materials (18.8% of universe)
    • Financials (13.7% of universe)
    • Consumer Discretionary (13.5% of universe)
    • Consumer Staples (10.0% of universe)
  • Example from CVS Health Corporations 2021 Sustainability Report regarding their sustainable packaging goals:

Corporate Social Responsibility (CSR) Reports (12,614 documents): While analyzing these documents, there was a strong emphasis on increased transparency, as well as ways to give back to their local communities, both physically and virtually.

  • CSR reports communicate a company’s impact on their communities and the environment.
  • CSR efforts can fall into four categories: Environmental, Ethical, Philanthropic and Economic
  • Popular headings include: “corporate governance”, “employees”, “human rights”, “compliance” and “health and safety”
  • Top Countries reporting this document type:
    • Japan (22.5% of CSR universe)
    • US (18.4% of CSR universe)
    • China (17.0% of CSR universe)
    • Taiwan (10.8% of CSR universe)
    • United Kingdom (3.9% of CSR universe)
  • Top sectors reporting this document type:
    • Industrials (24.3% of universe)
    • Financials (19.7% of universe)
    • Consumer Discretionary (14.9% of universe)
    • Information Technology (14.7% of universe)
    • Materials (11.8% of universe)
  • Example of Apple Inc.’s Environmental Strategy from their 2021 CSR Report:

Corporate Governance Reports (18,821 documents): While analyzing these documents, there was a strong emphasis on internal Diversity, Equity, and Inclusion initiatives, as well as addressing how their operations are contributing to the climate change risk.

  • Corporate Governance reporting allows companies to communicate how they’re monitoring decisions, business practices/policies, and actions
  • Four major sections within these documents: Corporate Strategy, Executive Compensation, Risk Oversight, and Transparency
  • Popular headings include: “Capital Structure”, “Corporate attributes”, “reasons for adoption of current corporate governance system”, “organizational composition and operation”, and “remuneration committee”
  • Top Countries reporting this document type:
    • Japan (62.7% of CG universe)
    • India (9.9% of CG universe)
    • Australia (4.9% of CG universe)
    • Sweden (2.9% of CG universe)
    • Italy (1.6% of CG universe)
  • Top sectors reporting this document type:
    • Industrials (22.3% of universe)
    • Consumer Discretionary (18.6% of universe)
    • Information Technology (12.0% of universe)
    • Materials (11.1% of universe)
    • Financials (10.5% of universe)
  • Example of Risk Oversight in Autosports Group’s 2019 Corporate Governance Report

Environmental Reports (2,760 documents): While analyzing these documents, there was a strong emphasis in the manufacturing industry surrounding supply chain sustainability and introducing future “Net-zero” emission initiatives.

  • Environmental Reporting allows companies to make better decisions about how they manage their natural resources and set environmental policy
  • Four major areas highlighted in these reports include: Purpose and Need of Proposal, Anticipated Action and Alternatives, Affected Environment, and Expected Impacts
  • Popular headings include: “emissions data/ breakdowns”, “energy”, “business strategy”, “carbon pricing” and “engagement strategy”
  • Top Countries reporting this document type:
    • Japan (31.7% of ER universe)
    • US (19.8% of ER universe)
    • United Kingdom (8.0% of ER universe)
    • Canada (3.8% of ER universe)
    • Germany (2.4% of ER universe)
  • Top sectors reporting this document type:
    • Industrials (24.1% of universe)
    • Materials (16.9% of universe)
    • Financials (16.0% of universe)
    • Consumer Discretionary (14.9% of universe)
    • Information Technology (11.7% of universe)
  • Proposed action from McDonald’s 2020 Environmental Report to make their franchises more sustainable:

TCFD Reports (432 documents): While analyzing this document type, there was a strong emphasis on managing climate-related risks and identifying opportunities on how to improve suppliers and the company’s operations in the short, medium, and long-term.

  • Assess climate-related risks to a company, its suppliers, and competitors in order to make decisions on capital allocation
  • Key elements of a TCFD Report: Governance, Strategy, Risk Management, and Metrics and Targets
  • Popular headings include: “risk management”, “metrics and targets”, “scenario analysis”, “physical risks” and “transition risks”
  • Top Countries reporting this document type:
    • US (29.2% of TCFD universe)
    • Japan (14.8% of TCFD universe)
    • Canada (10.2% of TCFD universe)
    • Australia (9.0% of TCFD universe)
    • United Kingdom (7.4% of TCFD universe)
  • Top sectors reporting this document type:
    • Financials (43.1% of universe)
    • Materials (16.7% of universe)
    • Industrials (14.4% of universe)
    • Utilities (11.8% of universe)
    • Energy (11.1% of universe)
  • Governance disclosure example from Xerox’s 2021 TCFD Report:

 

If you are interested in learning more about our MRF products or have a library of PDFs that you are trying analyze, please email us at contactus@socialmarketanalytics.com or schedule a demo using this link.

 

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Using Twitter Sentiment for Day Trading

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

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

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

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

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

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

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

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

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

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

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

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

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

The Long positions:

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

The theoretical Short positions:

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

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

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

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

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

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

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

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

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S-Factor Introduction Video Blog

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In this video we explain how our S-Factor feed uses the Twitter firehose to build trading signals, dashboards and widgets to help investors increase alpha. To find out how SMA can help your firm, email us at ContactUs@SocialMarketAnalytics.com or schedule a meeting using our 1 on 1 Meeting Signup.

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SMA Launches TSX Asset Class to Social Media Feeds

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Social Market Analytics converts textual data into quantitative signals for the investment community. To complement our U.S. Equity feed, we recently launch a Twitter based sentiment feed covering the largest TSX (Toronto Stock Exchange) equities. The initial universe included 233 TSX equities, and recently expanded to include an additional 200+ equities. The out-of-sample date for this dataset is January 20th, 2022 with history extended back to the beginning of 2020.

Similar to other asset classes in SMA’s database, the TSX asset class publishes Activity, S-Factor, Short Squeeze, and Hard to Borrow data feeds. To test the robustness of this new asset class, we conducted Daily Quintile and Threshold tests on the S-Factor feed.

TSX Equities Quintiles

In this Quintile test we look at SMA’s S-Factor feed at 3:40 pm ET (20 minutes prior to Market Close). Stocks are placed into different Quintile buckets based on the value of their S-Score. The S-Score is one of fifteen factors supplied in SMA’s S-Factor feed and provides a daily view of Social Sentiment from Twitter. S-Scores greater than 2 or less than -2 are considered extreme sentiment, while values closer to 0 indicate neutral sentiment. The lowest 20% of S-Score values are placed in Quintile 1 while the highest 20% of S-Score values are placed in Quintile 5. We look at subsequent Close-to-Close returns with each stock equally weighted within each quintile. Below is the daily cumulative return series of the TSX asset class quintiles.

After placing stocks in their proper Quintiles and taking the average return of each bucket, stocks with higher S-Scores tend to outperform stocks with lower S-Scores. The graph above shows a monotonic factor. This graph includes a large spread between Quintiles 1 and 5, which would result in a cumulative return of nearly 70% in 27 months. On average, the TSX asset class publishes 141 securities each market day. This number will only increase as SMA’s TSX universe expands.

We further explore the S-Score signal by filtering on securities that had 3 or more Tweets within the previous 24 hours (S-Volume >= 3), then conduct the same quintile analysis.

This new filter reduces the number of securities distributed in the graph from 141 to 95 securities each day. However, it also increases the Sharpe ratio and spread between Quintile 1 and Quintile 5.

TSX Thresholds

For this test, we look at all TSX securities at 3:40 pm ET and place securities in the ‘Long’ bucket if their S-Score >= 2 and place securities in the ‘Short’ bucket if their S-Score =< -2. Since 2020, there are an average of 5 stocks per day in ‘Short’ bucket and 17 stocks per day in the ‘Long’ bucket. We look at the subsequent Close-to-Close return for each security. Securities are equally weighted within each portfolio. In the Long/Short portfolio, the Long and Short baskets are equally weighted.

When only looking at securities with S-Score >= 2, the annualized return of this portfolio is greater than 45%. This portfolio also has a Sharpe Ratio of 1.57 which is well above the market benchmark (S&P/TSX Composite Index).

Many insights can be extracted using SMA’s TSX Social Media feeds. To find out more on this topic, email us at ContactUs@SocialMarketAnalytics.com or schedule a meeting using our 1 on 1 Meeting Signup.

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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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