Proprietary & Alternative Factors

Cybersecurity Factors (for US & for Global) 

S&P Global has partnered with BitSight, the standard in security ratings, to provide asset managers with critical cybersecurity intelligence on organizations worldwide. BitSight captures cybersecurity risk through a proprietary process, providing quantifiable security ratings. These ratings are the key to assessing cyber risk in companies’ ecosystems. Much like credit ratings (ranging from 250 to 900, with a higher rating indicating better security performance), this approach allows asset managers to gain insight into the security of companies, driving stock selection and risk management decisions.

Research Signals’ cybersecurity datasets contains a suite of 35 factors that quantify cybersecurity risks to enhance stock and portfolio risk management. Factors include: the key BitSight Rating, 18 scores from the BitSight risk vectors, and 16 derived factors measuring changes and volatility in ratings, z-scores, industry and sector positioning and impact of data breaches.

Coverage: Global

History: 7 years data

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• Cybersecurity factors powered by BitSight

Dividend Forecast Factors 

A suite of eight factors using underlying data from S&P Global Dividend Forecast dataset. Factors include forecasted yield, growth and payout ratios.  

S&P Global’s Dividend Forecast dataset provides announcements and forecasts for dividend amounts and dates for more than 28,000 stocks globally. Forecasts are calculated using a rigorous methodology based on fundamental analysis and the latest market news, taking into account a broad range of inputs enhanced by proprietary data.

Coverage: US, EMEA, APAC

History: 10 years data

ESG Factors 

Scores sourced from ASSET4, measuring economic, environmental, social, and governance. 23 factors and composites based on underlying 250 key performance indicators related to ESG.

Coverage: Global

History: 17 years data

Index Rebalance Forecast

A rules-based process that aims to forecast the membership lists, changes, confidence scores and weight allocations of the major Russell US Indexes: Russell 1000 and Russell 2000 (Russell 3000).

Coverage: Russell 1000, Russell 2000

History: 25 years data

Short Sentiment Factor Suite 

This is a suite of timely, global, short sentiment factors covering 3MM+ intraday transactions, spanning $12 trillion of securities in the lending programs of over 20,000 institutional funds globally – captures ~90% of the securities lending market in developed markets

It allows better detection of negative driven sentiment around a company’s prospects via the securities lending market. Short Sentiment indicators illuminate an opaque market segment, providing daily data ranging from supply and demand to borrow rates and market shares. The model is designed to capture performance at the extremes (more pertinent to short sentiment indicators)

Coverage: Global

History:15 years data

Short Squeeze Model 

This is a multifactor model designed to predict short squeeze events. The model utilizes capital constraint (short position PNL) indicators based on transaction-level securities loan data with event indicators to predict squeezes and generate excess alpha from a universe of highly shorted names in the S&P Global US total Cap universe. Factors created using transaction level data are available globally.

The creation of the model is based on a hypothesis that squeezes are more likely to occur with stocks where short sellers are experiencing capital constraints (actual or potential losses), the model helps to identify those names at risk of a squeeze, improve the accuracy of short interest signals and provide deeper insight into short positions.

Coverage: Global: US, Developed Europe and Asia Pacific

History: 10 years data

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• Expanding our Short Squeeze Model to global markets
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Social Media Indicators

A suite of 22 stock sentiment indicators used to gauge investor outlook on firms and identify potential buy and sell candidates. These sentiment indicators are derived from proprietary S-Scores™ provided by our partner, Social Market Analytics (SMA) – a data services company that provides analysis of social media data from Twitter to estimate market sentiment at the stock level.

Our unique social media indicators capture timely information gleaned from Twitter posts such as Tweet sentiment, Tweet Volume, Relative Value, Changing Sentiment and Dispersion (see report for full list), over several weighting methodologies (unweighted, exponential & normalized) w/data coverage beginning Dec 1st, 2011. As not all tweets are useful, SMA utilizes a proprietary extraction, evaluation and calculation algorithm that parses and analyzes daily tweet data polled from Twitter & GNIP API’s with access to 500MM+ daily tweets. The system delivers S-Scores that are filtered for financial trading relevance & scored for market sentiment from “indicative” tweets posted by confirmed accounts. Aggregated tweet scores for each stock produce a sentiment measurement from which the overall indicators are derived.

The dataset supports the equities and cryptocurrency asset classes.

Coverage:

• Equities: US Total Cap, LSE
• Cryptocurrencies: 700+ cryptos

History: 10 years data

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• Social media indicators in the UK
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Institutional Ownership factors

S&P Global’s Equity Point-in-Time Ownership data provides daily insights into global institutional and fund owned security positions, flow of funds and activity globally across developed and emerging markets. Ownership is sourced from 13Fs, global mutual funds, daily ETF holdings, annual reports, and major stakeholder exchange announcements for equity securities. We combine our Research Signals team’s quantitative research capability with key elements of this proprietary data, specifically looking for factors that are drivers of stock price performance. In total, we introduce 17 factors capturing ownership concentration, changes in holdings, institutional and hedge fund holdings and liquidity flow ratios.

Coverage: Global

History: 15 years data

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Shipping (Maritime & Trade) Factors 

Stock selection signals created using proprietary S&P Global Maritime & Trade division’s Bill of Lading data that covers imports and exports that come into major US ports. 48 factors constructed from four underlying import and export data items including shipping volume, shipping weight, shipping value and total shipments. Factors also include shipping trends, revenue impact, sector/industry relative shipping activities.

Coverage: Global

History: January 2007

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