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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,…
Kadoa and the New Economics of Web Data
For investment firms seeking an informational edge, the web represents an extraordinarily rich source of potential data. Product inventories and pricing, corporate activity, technology adoption, infrastructure development and countless other indicators can all provide insights that may not be available through conventional market and financial data sources. Extracting that information reliably, however, has traditionally required…
Multiplying the Value of Unstructured Data
Financial institutions have spent years expanding the range of data available to investment, trading and risk teams. Increasingly, however, the challenge is not obtaining more data but making sense of what they already have. Unstructured data is estimated to account for 80-90% of the information generated withinfinancial institutions, encompassing everything from regulatory filings and earnings-call…
How ExtractAlpha Builds Trust in the Age of AI Signals
When a data provider sells raw data, the buyer can inspect what they are getting. When a provider sells a signal – a ranking, a score, a forecast of forward returns – the buyer is being asked to trust a research process they cannot necessarily see. That asymmetry sits at the centre of the systematic…
From noise to knowledge: why financial AI needs trusted news
Decisions in financial markets are increasingly shaped by machines. Yet the information feeding those decisions is arriving in unprecedented volume, velocity and variety. As a result, the true differentiator is no longer access to more content, but access to news that is trusted, relevant and correct to the moment in time. With AI rapidly becoming…
Selling the Proof, Not the Data
For most of the past decade, a vendor specialising in natural-language processing (NLP) could sell a hedge fund something it could not easily make for itself. Sentiment scored from news, filings and earnings calls, provided as a clean feed, mapped to individual stocks and timestamped for point-in-time use. The work of pulling that signal out…
A-Team Group Announces Capital Markets Technology APAC Awards 2026 Winners and Launches ‘State of the Market’ Report
A-Team Group today announced the highly anticipated winners of the Capital Markets Technology APAC Awards 2026. These prestigious awards celebrate the most innovative solution providers and financial institutions that are reshaping the capital markets technology landscape across the dynamic Asia Pacific region. In conjunction with the awards, A-Team Group has also launched the “State of…
Institutions Are Already in Prediction Markets – As Data Consumers, Not Traders
The framing around institutional prediction markets assumes a gap that needs bridging: the markets exist, institutions are circling, and what stands between them is a stretch of missing infrastructure – clearing, margin, prime brokerage and surveillance. Build the scaffolding, the assumption runs, and the institutions follow. A panel at A-Team Group’s recent TradingTech Summit New…
How Much Hedge Fund Alpha Is Lost Before the Model Even Runs?
How often is a hedge fund’s apparent model failure actually a data failure in disguise? A model that stops working. A backtest that does not replicate. A risk number that needs explaining. How to tell one from the other – before reaching for the model first – was the recurring question of an hour-long discussion…
Recorded Webinar: The Data Foundation for Alpha – How fragmented data is eroding hedge fund performance
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 slow research, distort signals and increase operational risk. As firms scale across strategies, regions and asset…