Coverage, timeliness and quality of data the major challenges for quants, research analysts and data scientists, according to a Bloomberg Research Survey. The findings also indicate a preference for moving to the cloud for historical data management needs.
The adoption of quantitative and artificial intelligence (AI)/machine learning (ML) techniques, and the growth of systematic strategies have made investment research data especially important for firms seeking alpha.
Data coverage, timeliness, and quality issues with historical data was cited as the top challenge in the industry, with nearly two-fifths (37%) of respondents selecting this option. This was followed by normalizing and wrangling data from multiple data providers (26%), and identifying which datasets to evaluate and research (15%).
In line with these challenges, Bloomberg’s survey found that 72% of respondents could evaluate only three or fewer datasets at a time, despite the need from quants and research teams to continually harness more alpha-generating data in today’s data deluge. The findings also show that the typical time it takes to evaluate a single dataset is one month or longer for more than half of respondents (65%).
Firms are still trying to figure out their optimal strategy for managing research data in the face of these hurdles. 50% of respondents reported they currently manage the data centrally with proprietary solutions versus outsourcing to third party providers (8%), with more than six in ten (62%) of respondents preferring their research data to be made available in the cloud.
Notably, 35% of respondents also would like their data to be made available via more traditional access methods such as REST API, On premise and SFTP, indicating they prefer flexibility in the choice of data delivery channels.
“From in-depth conversations with our research clients, it’s clear there is a desire for new orthogonal datasets as well as a need to harness ‘AI-ready’ data. The journey from data sourcing to extracting alpha is difficult and the continuous ingestion, cleaning, modeling and testing of data is particularly challenging,” said Angana Jacob, global head of Research Data at Bloomberg Enterprise Data. “That’s why Bloomberg is committed to building out our multi-asset Investment Research Data product suite, targeted at quantitative and quantamental research, systematic strategies and AI workflows. Our datasets with modeled Python API access enable customers to reduce their time to alpha through deep granularity, point-in-time history, broad coverage and interoperability with traditional reference and pricing data.”