IHS Markit Eyes Advanced Analytics
Lance Uggla, chief executive of IHS Markit, said the data provider is in a good position to grow as more firms are using advanced analytics to inform their decisions.
Uggla was interviewed at the Sibos conference in London today.
— Lance Uggla (@LanceUggla) September 23, 2019
The chief executive said that since he had the idea to launch an information provider 20 years ago the quality and quantity of data has improved. In addition, types of data have changed with an increase in unstructured data such as satellite imagery and drone photography.
“More information is being used in decision making so we are in a good place to grow,” Uggla added. “The speed of calculations and output is changing so firms are using advanced analytics to look for patterns and signals.”
Natural language processing can discern patterns more quickly and is less prone to errors.
Uggla gave the example of IHS Markit’s Commodities at Sea analytics suite which provides data-driven intelligence to help determine prices. Commodities at Sea uses trade data and ship movements to provide an indicator of supply and demand in the market.
“We use satellite imagery to determine the size of storage facilities and the size of ships,” he added.
Other analytics also provide risk scores for countries and events such as geopolitical turbulence.
IHS Markit has also partnered with Caixin in China to calculate the country’s General Manufacturing PMI (Purchasing Managers’ Index).
“We have a great opportunity to expand in Asia,” said Uggla. “China has a more digital economy than any other country I have visited.”
Fund managers use of data
Research provider Morningstar said in a report that asset managers are increasingly using big data to get an informational edge.
How Fund Managers Are Using Big Data. https://t.co/hEq2fn2uYO
— Morningstar, Inc. (@MorningstarInc) September 23, 2019
Linda Abu Mushrefova, manager research analyst for Morningstar, said in the report that about one third of the signals used to forecast stock prices in asset manager models are now based on non-traditional data. In comparison, a decade ago none of these signals used alternative data sources.
However she also warned that a large quantity of data introduces additional noise.
“Having access to a large and unique data set isn’t like finding a bag full of magic market-beating beans,” said Mushrefova. “The ability to access, test, validate, and implement this unstructured data into an investment process is critical to making the data useful.”
As a result, buy-side firms now have to attract quantitative researchers with programming experience and data science backgrounds in addition to strong investment talent.
Mushrefova continued that crowding and signal decay are risks, especially as more investors pile into the same factors or exposures.
“While the use of new data and tools is exciting, it is also untested over a full market cycle, and while we are optimistic on some of the benefits, we also maintain a healthy level of skepticism,” said Mushrefova. “Specifically, it is not just the availability of the data but what is done with it.”