Market & Alt Data Insight Market Data, Feeds & Trading Signals The latest content from across the platform
When Correlation Breaks: Why Crowding, Not Macro, Is Testing Quant Models
In February 2025, Goldman Sachs told clients the US equity market had become a stock-picker’s market: 74% of the typical S&P 500 stock’s return was being driven by company-specific factors rather than macro forces, against a 20-year average of 58%, and the bank expected that micro-driven environment to persist. Within weeks, sweeping tariff announcements had…
Kaiko Buys Amberdata as Digital Asset Data Consolidation Picks Up Pace
Kaiko has acquired Amberdata, its fifth acquisition and its second in a fortnight, in a deal that points less to any single transaction than to the speed at which the institutional digital asset data layer is consolidating into a small number of scaled providers. The Paris-headquartered firm announced the purchase of the US-based data and…
Testing an Assumption: Do AI Signals Really Decay?
Alpha decay is one of the foundational assumptions in quantitative finance. The empirical literature, beginning with McLean and Pontiff’s 2016 study of 97 anomalies and replicated and refined across multiple subsequent studies, has repeatedly found that returns degrade out-of-sample and degrade further once published. The assumption sits inside almost every institutional model risk framework as…
Five Rings Joins BMLL Client Product Advisory Board
Financial data and analytics provider BMLL has welcomed proprietary trading firm Five Rings to its Client Product Advisory Board (CPAB). The New York-based firm will contribute its technological and market structure expertise to help shape BMLL’s future product roadmap, supporting the board’s mission to elevate historical market data standards across the industry. Five Rings utilizes…
Where is the Edge When Everyone Has the Same Alt Data?
Has the institutional alternative data market reached a phase in which the easy sources of edge have closed? Datasets that once generated standalone alpha are widely distributed, the AI tooling layered on top of them is increasingly commoditised, and the differentiator has migrated to a less glamorous middle ground: validation, transformation, kill criteria, and the…
Is the Most Expensive Failure Mode in Alt Data on the User Side?
Asked at the A-Team/Eagle Alpha Alternative Data Conference, London last week which datasets his firm had ever taken out of production, one panellist paused, considered the question, and admitted he could not think of a single one. The answer was offered without defensiveness, as a genuine reflection. It also turned out to be one of…
AI in Capital Markets Handbook 2026
AI adoption in capital markets has moved into a more disciplined phase. The priority is now controlled deployment: where AI can be used safely, where it can deliver measurable value, and how outputs can be governed, monitored and evidenced. The 2026 edition of the AI in Capital Markets Handbook examines how AI is being applied…
Recorded Webinar: Navigating the Build vs Buy Dilemma: Cloud Strategies for Accelerating Quantitative Research
For many quantitative trading firms and asset managers, building a self-provisioned historical market data environment remains one of the most time-consuming and resource-intensive steps in establishing a new research capability. Sourcing data, normalising symbologies, handling corporate actions and maintaining infrastructure can take months and absorb significant budget before a single model is tested. At the…
Where Generative AI Belongs in the Institutional Data Stack – and Where It Doesn’t
Two and a half years into the generative AI cycle, the working consensus among practitioners deploying AI against unstructured content in capital markets has quietly settled into a layered architecture. Generative models sit at the desktop, research and operational-efficiency layer. The feed, signal and modelling layer that drives systematic alpha still runs on the older,…
Now the MCP Layer is Commoditised, Are Entitlements the Next Challenge?
LSEG today added Amazon Quick to the growing list of AI-enabled workspaces in which its licensed data and analytics are exposed via a Model Context Protocol (MCP) server, with customers gaining access through natural-language and agentic queries to pricing, fundamentals, estimates, ownership data, macroeconomic indicators, ESG and analytical models. The move is the latest step…