mlsanityData to 7 Oct 2026

From Deep Learning to LLMs: A survey of AI in Quantitative Investment

arXiv 2503.21422 ↗First posted 27 Mar 2025q-fin.CP cs.AI cs.LGcited by 9

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Abstract (arXiv)

Quantitative investment (quant) is an emerging, technology-driven approach in asset management, increasingy shaped by advancements in artificial intelligence. Recent advances in deep learning and large language models (LLMs) for quant finance have improved predictive modeling and enabled agent-based automation, suggesting a potential paradigm shift in this field. In this survey, taking alpha strategy as a representative example, we explore how AI contributes to the quantitative investment pipeline. We first examine the early stage of quant research, centered on human-crafted features and traditional statistical models with an established alpha pipeline. We then discuss the rise of deep learning, which enabled scalable modeling across the entire pipeline from data processing to order execution. Building on this, we highlight the emerging role of LLMs in extending AI beyond prediction, empowering autonomous agents to process unstructured data, generate alphas, and support self-iterative workflows.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 54 of its 169 references that are in the map. First posted on arXiv 27 Mar 2025.

Builds on

54 of 169 references.

References · 54

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38 more
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50 most cited of 54 All 54 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 4.

9 papers.

  1. 2025R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimizationcited by 39
  2. 2025LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarioscited by 25
  3. 2026SoK: Security of Autonomous LLM Agents in Agentic Commercecited by 8
  4. 2025To Trade or Not to Trade: An Agentic Approach to Estimating Market Risk Improves Trading Decisionscited by 2
  5. 2025QTMRL: An Agent for Quantitative Trading Decision-Making Based on Multi-Indicator Guided Reinforcement Learningcited by 1
  6. 2026Janus-Q: End-to-End Event-Driven Trading via Hierarchical-Gated Reward Modelingcited by 1
  7. 2026AlphaCrafter: Harnessing Multi-Agent Workflows for Cross-Sectional Quantitative Tradingcited by 1
  8. 2025Large-scale portfolio optimization with variational neural annealingcited by 0
  9. 2026Institutional Equity Holdings Prediction Using Node Affinities of Dynamic Graphscited by 0