Trading & Markets

No reviewed outcome evidence is attached to this theme. That is a gap in review, not evidence of no activity.

• Systematic equity investment signals from text and alternative dataBlackRockBlackRock Systematic uses language models to estimate sentiment from broker reports and combines traditional and alternative-data signals to rank securities for systematic investing.Scaled · reported stage
• ML-driven investment signalsTwo SigmaTwo Sigma uses machine learning and NLP to extract investment signals from structured and unstructured data. Its systematic process prepares data, models market drivers, creates forecasts and portfolio allocations, and executes strategies across diversified global markets.Scaled · reported stage
• BARXBot real-time quote preparationBarclaysRetrieves real-time pricing into trading workflows for staff to review and send to clients.Production · reported stage
• AI/ML-supported quantitative investment researchCitadelCitadel's Data Strategies Group combines alternative data, AI/ML research and quantitative modeling to produce insights for investment teams. Quantitative researchers build algorithms and models that automate, inform or enhance systematic and discretionary strategies.Production · reported stage
• Canari-powered options forecast indicatorsDeutsche BörsePublished analytics product uses AI patterns in options data to generate volatility forecasts.Production · reported stage
• Machine-learning evaluated bond pricingICEBlends trade and quote data to estimate bond prices and bid-ask spreads.Production · reported stage
• IndexGPT thematic equity-index constructionJPMorgan ChaseJ.P. Morgan uses large language models in IndexGPT to generate stock selections for client-facing thematic indices in its strategic index business.Production · reported stage
• Incident context assembly and troubleshooting in TeamsMicrosoftSmart DRI uses Semantic Kernel orchestration, domain functions, Azure AI Search plus custom similarity logic, telemetry and troubleshooting guides to assemble incident analysis. Microsoft reports over 40 teams using it; action-taking mitigation is described as future work.Production · reported stage
• Dynamic M-ELO adaptive execution timerNasdaqAdjusts order holding periods using market conditions to support midpoint executions.Production · reported stage
• AI/ML-supported quantitative investment modelsT. Rowe PriceT. Rowe Price uses quantitative models supported by AI and machine-learning algorithms in investment decisions, portfolio management and risk analysis. Its Investment Data Insights team combines alternative data, NLP and alpha-generation models to support quantitative and fundamental investment professionals.Production · reported stage
• Ai-Price machine-learning corporate-bond pricingTradewebMachine-learning bond prices combine cleaned TRACE transactions with proprietary timing and cross-market signals. A 4 November technical update reports a client comparison.Production · reported stage
• LOXM equities executionJPMorgan ChaseLearns from historical and simulated trades to execute client orders while limiting price impact.Pilot · reported stage
• NLP for Market Sentiment AnalysisCitadelCitadel applies NLP to analyze market sentiment from news feeds, social media, earnings calls, and regulatory filings. The system processes millions of text documents daily to extract trading signals and gauge market sentiment in real-time across global financial markets.Not assessed · legacy label
• AI-Driven FX TradingDeutsche BankDeutsche Bank deploys AI for foreign exchange trading optimization, using machine learning to analyze market microstructure and improve execution quality. The system provides smart order routing and pricing analytics across major currency pairs in the global FX market.Not assessed · legacy label
• AI Stock Selection ModelsFidelityFidelity uses AI and machine learning models to identify investment opportunities across global equity markets. The models process fundamental data, market signals, and alternative datasets to generate stock selection insights for portfolio managers across Fidelity's active equity strategies.Not assessed · legacy label
• AI-Powered Deal PreparationGoldman SachsGoldman Sachs uses AI to automate and accelerate investment banking deal preparation processes. The system generates initial drafts of pitch books, performs comparable company analyses, and synthesizes relevant market data to reduce the time bankers spend on routine deal work.Not assessed · legacy label
• AI Trade Execution OptimizationNomuraNomura applies AI to optimize trade execution across equity and fixed income markets. Machine learning models analyze market microstructure, order flow, and liquidity conditions to minimize market impact and improve execution quality for institutional clients.Not assessed · legacy label
• Societe Generale AI Trading AnalyticsSociete GeneraleSociete Generale deploys AI for trading analytics across its global markets division. Machine learning models analyze execution quality, market liquidity, and price dynamics to optimize trading strategies and improve client execution outcomes.Not assessed · legacy label
• AI Portfolio OptimizationTwo SigmaTwo Sigma applies advanced AI techniques to portfolio optimization, dynamically adjusting position sizes, sector exposures, and risk allocations. The system balances expected returns against multiple risk factors while accounting for transaction costs and market impact constraints.Not assessed · legacy label
• AI Index Fund OptimizationVanguardVanguard applies AI to optimize index fund tracking and rebalancing processes. Machine learning models help minimize tracking error while reducing transaction costs during index reconstitutions and portfolio transitions across Vanguard's massive index fund complex.Not assessed · legacy label