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Research
AI Agents in Financial Markets: Why Memory Is the Real Edge
Agent memory — not model size — is emerging as the decisive factor in financial AI performance. From crypto trading to portfolio optimization, memory-augmented agents consistently outperform their memoryless counterparts.
References
- [1] 2025: The year the AI market blinked and predictions for 2026 https://bemagentiq.com/2025-the-year-the-ai-market-blinked/
- [2] MacroHFT: Memory Augmented Context-aware Reinforcement Learning for High-Frequency Trading https://dl.acm.org/doi/10.1145/3637528.3672064
- [3] Artificial Intelligence Models for Predicting Stock Returns Using Semantic-Algorithmic Fusion https://pmc.ncbi.nlm.nih.gov/articles/PMC12191900/
- [4] How to Build AI Agents That Actually Remember - Salesforce Research https://www.salesforce.com/blog/agentic-memory-agents/
- [5] Memory in LLM-based Agents: Building Stock-Trading Workflows https://medium.com/@prabhuss73/memory-in-llm-based-agents-bu...
- [6] Why Multi-Agent Systems Need Memory Engineering - MongoDB https://www.mongodb.com/company/blog/technical/why-multi-age...
- [7] Agentic AI in Commodity Trading: A Comparative Simulation Study https://thesai.org/Downloads/Volume16No11/Paper_2-Agentic_AI...
- [8] 7 Agentic AI Trends to Watch in 2026 - Machine Learning Mastery https://machinelearningmastery.com/7-agentic-ai-trends-to-wa...