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SOLVE Expands Fixed Income Data and Trading Workflow Tools
SOLVE has introduced a series of enhancements to its fixed income platform, spanning TRACE market data, structured credit workflows and relative value analytics. The new TRACE Data Product Suite brings reported trade activity into SOLVE’s MBS Source workflow alongside collateral characteristics, historical offers, BWIC results and other security-level information. Coverage includes agency and non-agency MBS…
FutureSports and MLB Pave Way for Team Performance Futures
FutureSports has agreed with Major League Baseball (MLB) to create rules-based financial indexes tracking the performance of each of the league’s 30 teams, potentially providing the basis for a new class of sports-related derivatives. Under the agreement, MLB will supply Official League Data for the creation of CME FutureSports Performance Indexes (FSPI). CME Group plans…
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…
BMLL Adds Kalshi Data as Prediction Markets Move Deeper into Institutional Workflows
BMLL, the market data and analytics provider, has added historical prediction market data from Kalshi to its global market data platform, giving quantitative and systematic investment firms a standardised dataset for incorporating event probabilities into macro research, backtesting and trading models. Under the partnership announced today, Kalshi’s historical order book will be normalised into the…
Rethinking Market Data Distribution for a New Generation of Markets
Pyth Network has expanded rapidly from its roots in blockchain-based market data into a broader financial data platform, bringing together more than 138 institutional publishers and thousands of price feeds across equities, FX, commodities, crypto and other asset classes. Its ecosystem now includes names ranging from Coinbase and Fidelity to Kalshi, Revolut, Polymarket and Hyperliquid….
SIX Launches API-Based European Market Data Service
SIX has launched SIX Market Signal, a new API-based marketplace providing real-time and historical European market data to institutional and retail users. Its core product, 1BBO, provides a real-time European cash equities stream showing best bid and ask prices using order-book data from SIX Swiss Exchange and BME Exchange, alongside 16 European markets covered by…
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…