This group of signals rely on non-standard datasets or specialty insights sourced across markets, many of which are proprietary to IHS Markit.
Proprietary & Alternative Factors
CDS to Equity Factors
CDS to Equity Factors are proprietary measures of credit risk and momentum using IHS Markit CDS spread pricing data. We link CDS spread pricing data to Equity securities in order to produce measures of credit risk using daily market prices of risk from the credit market. We create 4 factors measuring risk, changes in risk, slope of the credit risk term structure, and the divergence between equity and credit market prices.
History: 20 years dataGet Whitepaper
Cybersecurity Factors (for US & for Global)
IHS Markit 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.
History: 7 years data
Dividend Forecast Factors
A suite of eight factors using underlying data from IHS Markit Dividend Forecast dataset. Factors include forecasted yield, growth and payout ratios.
IHS Markit’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 dataGet Whitepaper
Scores sourced from ASSET4, measuring economic, environmental, social, and governance. 23 factors and composites based on underlying 250 key performance indicators related to ESG.
History: 17 years dataGet Whitepaper
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 dataGet Whitepaper
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)
History:15 years dataGet Whitepaper
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 IHS Markit 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
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.
- Equities: US Total Cap, LSE
- Cryptocurrencies: 700+ cryptos
History: 10 years data
Institutional Ownership factors
IHS Markit’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.
History: 15 years data
Shipping (Maritime & Trade) Factors
Stock selection signals created using proprietary IHS Markit 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.
History: January 2007
Mr. Hammond is the global head of the Research Signals product. He leads the team's research and new product development strategy and oversees a team of quantiative analysts. He is experienced with numerous quantitative strategies and datasets, and has led the team's innovation into areas such as short squeeze signals, social media sentiment, and cybersecurity risk.He is a member of the CFA Society of Chicago and the Chicago Quantitative Alliance. As a graduate in 2007, Mr. Hammond joined Quantitative Services Group, which in 2011 was acquired by Markit, and is now part of IHS Markit. He earned his B.S.B.A. in Finance from Washington University in St. Louis, US.
- Financial Services
- Alternative Data
- Environmental, Social and Governance (ESG)
- Equity Capital Flows
- Equity Markets
- Quantitative Research
Ms. Lipatova is a quantitative product specialist who joined IHS Markit in August 2016. Previously, she held a variety of roles in risk management and quantitative investing at Morgan Stanley, Serengeti Asset Management, S&P Capital IQ, Misys. While at S&P Capital IQ-NY, Sydney, Tokyo, London, she had global responsibilities. Ms. Lipatova holds a Bachelor of Arts in economics-operations research and mathematics from Columbia University, United States.
Mr. Sharma is a quantitative product specialist who joined IHS Markit in April 2021. Previously, he has held Data Science and Strategy roles with global remit at Western Union Business Solutions in London and Social Market Analytics, Inc in Chicago. Mr. Sharma has worked closely on projects involving machine learning and natural language processing application in Finance.Mr. Sharma holds a Master of Science Degree in Finance from Illinois Institute of Technology, United States and Bachelor of Technology Degree in Electronics and Communication Engineering from National Institute of Technology, India. He is a current member of CFA UK Society.
Mr. Unni leads a team of quantitative research analysts in areas such as new research, alternative data evaluation and the development of analytic tools for the buy-side. He joined IHS Markit via the acquisition of Quantitative Services Group, prior to which he was focused on quantitative research and risk management for various equity strategies at Wintrust Capital Management in Chicago. Mr. Unni holds a Master's in businesses administration from the University of Illinois at Chicago, and a Bachelor's in engineering from Cochin University in India.