IHS Markit has expanded its alternative data offering for asset managers with the launch of new stock selection and strategy signals covering the automotive sector.
Covering more than 30 auto manufacturers worldwide and providing more than 10 years of historical data, the 32 new factors are designed to assist in the prediction of stock returns using analytics derived from company-specific data points on sales, production and market share.
They include multiple financial factors for revenue and sales as well as factors covering production of electric vehicles and plant utilisation.
Data inputs for the signals are gathered from statistical automotive datasets from IHS Markit that include vehicle sales and production history, aggregated registration information, manufacturing volumes and vehicle pricing insight.
Stock selection and strategy signals and factor models derived from alternative datasets include 11 factors for squeeze risk derived from IHS Markit’s data on stock loan supply, demand and the cost to borrow stock.
It also includes dividend forecasts—eight factors derived from dividend forecasts on 8,000 stocks.
According to Greenwich Associates, 50 percent of institutional investors are planning to increase their usage of alternative datasets.
IHS Markit’s automotive data joins several other alternative datasets already deployed in the Research Signals factor library.
Research Signals delivers investment insights through a library of more than 600 global stock selection and strategy signals. The service covers more than 30,000 securities across 80 countries.
It extracts investment insights from a range of raw financial and industry-specific data sources to enable customers to assess intended and unintended exposures across investment themes.
David Riehl, executive director in the equities data and analytics group at IHS Markit, said: “Quantitative and fundamental investment funds are deeply interested in the unmatched scope and quality of alternative data produced by IHS Markit covering virtually every industry sector.”
He added: “Using our rich datasets in automotive to create these new factors expands our library to more than 600 signals and models and gives investors unique tools for assessing one of the world’s most important sectors.”
“The challenge for most is turning data into actionable insight. While some firms consume our raw data, many others prefer us to deliver our expertise in sourcing, structuring and validating alternative data through prepackaged analytics like the new automotive signals.”
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