Survey results also show a preference for moving to the cloud for historical data management needs.
The introduction of quantitative and artificial intelligence (AI)/machine learning (ML) technologies and the growth of systematic strategies have made investment research data especially important for companies seeking alpha. As these strategies proliferate, Bloomberg surveyed more than 150 quantitative, research analysts in a survey conducted during a series of global client workshops to understand the key trends and challenges in investment research. , conducted a survey targeting data scientists.
Issues with data coverage, timeliness, and quality of historical data were cited as the industry’s biggest challenges, with nearly two-fifths (37%) of respondents choosing this option. This was followed by normalizing and wrangling data from multiple data providers (26%) and identifying datasets to evaluate and explore (15%).
In line with these challenges, the Bloomberg survey found that despite the need from quants and research teams to continue leveraging more alpha-generating data in today’s data deluge, 72 of respondents % were able to evaluate no more than three datasets at a time. The findings also show that the time it typically takes to evaluate a single dataset is more than a month for more than half (65%) of respondents.
Companies are still trying to find the best strategies to manage research data while facing the aforementioned hurdles. 50% of respondents say they currently centralize data using their own solutions rather than outsourcing to third-party providers (8%), and more than 6 in 10 respondents (62%) would like their research data to be available in the cloud. Notably, 35% of respondents want their data to be available through more traditional access methods such as REST APIs, on-premises, and SFTP, indicating they prefer flexibility in choosing data delivery channels.
“From our in-depth conversations with our research clients, it is clear that there is a desire for new orthogonal datasets and a need to leverage ‘AI-enabled’ data. is especially difficult to continuously ingest, clean, model and test,” said Angana Jacob, Global Head of Research Data, Bloomberg Enterprise Data. “That’s why Bloomberg is focused on building a multi-asset investment research data product suite for quantitative and quantitative research, systematic strategies, and AI workflows. Our datasets enable customers to accelerate time to alpha through deep granularity, point-in-time history, broad coverage, and interoperability with traditional reference and pricing data.”
New additions to Bloomberg’s investment research data suite include the following corporate research data products:
Industry-Specific Company KPIs and Estimates: Bloomberg’s Industry-Specific Company KPIs and Estimates product provides point-in-time data on more than 1,200 unique key performance indicators (KPIs) for a wide range of companies, providing detailed sector and industry enable investigation. Data is also available via Per Security, giving customers the flexibility to leverage customized universes. Stock Prices Point-in-Time: Bloomberg’s Stock Prices Point-in-Time product uses security master data from the world’s universe of publicly traded companies to provide point-in-time daily end-of-day data. We offer a composite price for.
Clients can link these datasets with other Bloomberg corporate research data products to seamlessly build corporate and industry knowledge graphs that enable alpha discovery and extraction.
Click here to view the full survey results.
About Bloomberg Investment Research Data Solutions
Bloomberg’s suite of enterprise investment research data products provides end-to-end solutions to power your research workflows. The solution includes company financials, quotes, pricing and point-in-time data, operating segment fundamental data, industry-specific company KPIs and quote data products, providing broad company coverage and deep actionable insights. I will. The product suite also includes quantitative pricing with cross-asset tick history and bars. Additional solutions will be available in 2025, including basic geographic segment data, company segment and detailed quote data, and pharmaceutical product and brand data products. All of these data solutions are interoperable and can seamlessly connect with other datasets, including alternative data. It’s available through a variety of delivery mechanisms, including the cloud and API. Learn more about these solutions here.
About Bloomberg
Bloomberg is a global leader in business and financial information, providing trusted data, news and insights that bring transparency, efficiency and fairness to markets. The company helps connect communities of influence across the global financial ecosystem through reliable technology solutions that help customers make more informed decisions and foster better collaboration. Masu. For more information, visit Bloomberg.com/company or request a demo.
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