#United States #New York #Bloomberg #Investment Research #Quantitative Teams

Quants Embrace Sector-Specific Data for Enhanced AI Adoption in Investment Research

A recent survey conducted by Bloomberg has shed light on how quantitative teams are evolving their research methodologies by embracing artificial intelligence (AI) and showcasing a notable shift towards utilizing sector-specific data. This survey, which gathered insights from over 150 quants, research analysts, and data scientists across Europe, the Middle East, Africa (EMEA), and North America, highlights pivotal changes in the investment research landscape as firms seek deeper contextual understanding to drive effective decision making.

AI's Growing Role in Investment Research

As firms look to incorporate AI into their investment workflows, the evolution of data usage is becoming increasingly critical. The survey has revealed that a substantial 72% of respondents currently invest in equities, indicating a strong market engagement. The growing trends point to a significant AI-adoption inflection point where traditional machine learning methods are being supplemented with enhanced sector-specific data. Notably, over half (54%) of respondents have yet to initiate their journey into generative AI, suggesting that data readiness remains a central challenge.

The survey findings illustrate the necessity of well-structured, contextualized data as a precondition for successful AI model deployment. This necessity is driving firms to recognize the value of incorporating deeper, sector-specific datasets—ranging from company key performance indicators (KPIs) to pharmaceutical pipelines and semiconductor revenue mixes. Such data will provide a richer analytical context that will help firms achieve alpha that is particularly concentrated within specific industry verticals.

A Shift Toward Insight Generation

The application of AI among quantitative teams is becoming more refined, with stock selection remaining the most prevalent use case at 48%. Other applications include content summarization (21%) and thematic analysis (13%). According to Angana Jacob, Global Head of Research Data at Bloomberg, the current shift is predominantly influenced by data availability, with firms prioritizing datasets that offer deeper insights into companies' operational contexts and market behaviors.

This pivotal change aligns with the ongoing evolution of Bloomberg’s Investment Research Data Solution, which aims to enrich the quantitative workflows necessary for advanced investment research by providing essential datasets. These datasets support factor-based strategy construction, backtesting, and model training, further aligning research activities with the operational needs of investors.

Key Datasets Enhancing Research Workflows

Bloomberg delivers a suite of datasets tailored for sophisticated investment research:

• - Company Financials and Estimates: This includes historical performance, consensus estimates, and company guidance for more than 100,000 public companies, enabling effective factor strategy formulation.
• - Industry Specific KPIs: Around 1200 unique KPIs are provided across various sectors, facilitating comprehensive sector analysis and benchmark comparisons.
• - Operating Segment Fundamentals: This encapsulates revenue, asset totals, and operating income per segment, thereby enhancing cross-company comparisons and industry insights.
• - Bloomberg Second Measure Transaction Analytics: A comprehensive consumer panel containing insights from over 20 million consumers, covering a multitude of public and private firms.
• - Geographic Segment Fundamentals: Empirical data is provided on global regions and countries, refining geographical exposure assessments for companies.

Future Directions for Investment Research

As AI capabilities continue to expand, the emphasis on understanding market behaviors through context-rich datasets is paramount. Bloomberg's ongoing commitment involves not only developing advanced research capabilities but also making data more accessible through Data License Plus (DL+), a next-generation platform designed for operational efficiency. By providing interconnected datasets that comply with the evolving landscapes of investment research, firms will be better equipped to drive informed decision making in real-time environments.

In conclusion, the fusion of sector-specific data with AI represents a transformative direction in the investment research realm, promising deeper insights and potentially greater financial returns for strategically aligned investments. Bloomberg’s methodology in bolstering quantitative research with specific datasets aligns perfectly with the growing demands of contemporary investors seeking precision and context within their investment strategies.