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Upcoming Webinar: The Data Foundation for Alpha – How fragmented data is eroding hedge fund performance
Date: 23 June 2026 Time: 10:00am ET / 3:00pm London / 4:00pm CET Duration: 50 minutes Alpha depends on more than models, talent and execution. It depends on the quality, consistency and timeliness of the data behind every investment decision. Many hedge funds still operate with fragmented datasets, inconsistent identifiers and manual reconciliation processes that…
The Business Conduct Risk and Data Challenge Behind AI Adoption
Poor data preparation for artificial intelligence deployments is exposing financial institutions to greater business conduct risks that could cost them as much as US$43 million per year, according to new research. An updated report by business conduct data provider RepRisk found that such AI-related incidents are on the rise as applications are rolled out at…
Prediction Market Data: The Questions Institutional Buyers Still Cannot Answer
Are prediction markets arriving in institutional workflows faster than the industry has worked out what it is buying? The valuations are real. The distribution deals are real. The research interest is real. What is not settled is a set of questions sitting underneath the adoption story – and a closing fireside session at the recent…
Is the Real Value of Intraday Alternative Data in Risk, Not Alpha?
Intraday alternative data has long been the preserve of high-frequency and systematic desks, where speed provides an edge. But ask practitioners what decision it actually changes for a discretionary book, and the answer is rarely about generating a new trade, it is more about testing the one already on. Sizing, conviction and the risk sitting…
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…
Scraping at Scale: Where AI Actually Helps, and Where It Doesn’t Yet
“AI web scraping that doesn’t break” was the title given to a fireside chat at the recent A-Team/Eagle Alpha Alternative Data Conference London – a phrasing that is, on its own terms, aspirational. Pipelines that self-maintain through site changes, schema drift and content shifts remain a destination rather than a current reality, and the session…
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…
LexisNexis Q&A: Ensuring Data Trust, From News to Governance
Since the 1970s, LexisNexis has been providing a variety of data services to financial institutions. Data Management Insight spoke to Danielle McCormick, vice president of product, Nexis Solutions – LexisNexis, to discuss how financial institutions are approaching AI, trusted data and the future of enterprise intelligence. Data Management Insight: Hello Danielle, when were LexisNexis’ data…
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…
From Quiet Practice to Explicit Disclosure: How the Fed’s Alt-Data Use is Reshaping the Macro Stack
At the October 2025 FOMC press conference, during the federal government shutdown that had interrupted the flow of official statistics, Chair Jerome Powell did something the Federal Reserve has rarely done in public: he cited the alternative datasets from PriceStats, Adobe and ADP that the central bank was leaning on. For many observers of the…