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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,…
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…