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, Bloomberg says.
With these strategies on the rise, Bloomberg polled over 150 quants, research analysts and data scientists in a survey conducted during a global series of client workshops to understand key trends and challenges in investment research.
Data coverage, timeliness, and quality issues with historical data was cited as the top challenge in the industry, with nearly two-fifths (37 per cent) of respondents selecting this option. This was followed by normalising and wrangling data from multiple data providers (26 per cent), and identifying which datasets to evaluate and research (15 per cent).
In line with these challenges, Bloomberg’s survey found that 72 per cent 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 per cent).
Firms are still trying to figure out their optimal strategy for managing research data in the face of the aforementioned hurdles. 50 per cent of respondents reported they currently manage the data centrally with proprietary solutions versus outsourcing to third party providers (8 per cent), with more than six in ten (62 per cent) of respondents preferring their research data to be made available in the cloud. Notably, 35 per cent 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,” says Angana Jacob, Global Head of Research Data, 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.”
