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Why Agentic AI Is Shifting Attention Back to the Data Layer
The rapid improvement of large language models is changing the way financial institutions approach agentic AI. Model capability remains important, alongside the practical challenges of connecting AI to the right data, providing sufficient context, embedding agents into investment workflows and controlling what they can access and do. These issues featured prominently during a panel discussion…
Can Alternative Data Close the Credit Risk Visibility Gap?
Credit risk has traditionally been monitored through a familiar collection of fundamentals, ratings and market prices. But as private and public credit markets become more interconnected, investors are looking across a much wider range of data for signs that a borrower – or an entire sector – may be running into trouble. That search for…
As AI Lowers the Bar for Alternative Data, Where Does the Edge Move Next?
Alternative data has spent much of the past decade moving from the margins of investment research towards the mainstream. Now, artificial intelligence is accelerating that process, making datasets easier to discover, evaluate, process and combine while allowing investment teams to tackle sources that would previously have required considerable engineering resources. But that creates a new…
How QuantumStreet AI Tackles the Journey From Signal to Portfolio
AI is becoming increasingly sophisticated at identifying patterns and generating investment signals from disparate sources of financial and market data. But for systematic investors, those signals still have to be converted into something much more concrete: a portfolio that determines which assets to hold, in what proportions, and with what degree of conviction. Doing that…
Moving Up the Value Chain: 3AI on AI, Alternative Data and Investment Intelligence
Traditional financial and market information now sits alongside news, sentiment, analyst estimates and a growing universe of alternative datasets, creating an increasingly rich information environment for investment firms. But access to more data doesn’t necessarily translate into better investment decisions. Firms still have to establish which information has genuine predictive value, turn disparate datasets into…
Beyond the API: FactSet on Making Financial Data Ready for Agentic AI
The effectiveness of an AI agent ultimately depends on the quality, context and structure of the information it can access. For investment firms, that puts familiar challenges around data quality, provenance and integration firmly back on the agenda – while potentially changing what they expect from their data providers. “When you’re selling data, you’re not…
Bridgewise and the Expanding Universe of Sentiment Data
Investment firms today face a growing challenge with alternative data: how to turn an ever-expanding universe of unstructured content into something that can be analysed systematically. Social media, news articles, disclosure documents and, increasingly, podcasts can all contain information relevant to investment decisions. But before that material can be useful to quantitative or discretionary investors,…
Alternative Data in Capital Markets Handbook 2026
Alternative data adoption in capital markets has moved into a more disciplined phase. Simply possessing a dataset is no longer a differentiator – the priority now is how data is sourced, synthesised and deployed, and how quickly granular, disaggregated signals can be turned into validated, decision-ready insight. The 2026 edition of the Alternative Data in…
Rethinking Alternative Data Infrastructure with Aerospike
Investment firms have never had access to more data. From corporate filings and social media to satellite imagery, transaction data and other alternative sources, the range of information that can potentially feed the investment process continues to expand. But as AI becomes embedded more deeply into research and investment workflows, the challenge is shifting. Simply…
How EventVestor Puts Alternative Data in Context
For investment firms looking to extract signals from alternative data, finding an interesting dataset is only part of the challenge. Moving that data from research into production brings a different set of requirements around quality, timeliness, consistency and historical accuracy. Those requirements are particularly important for quantitative investment firms, where apparently small discrepancies in timestamps,…