The New Quant: A Survey of Large Language Models in Financial Prediction and Trading

ArXiv ID: 2510.05533 “View on arXiv”

Authors: Weilong Fu

Abstract

Large language models are reshaping quantitative investing by turning unstructured financial information into evidence-grounded signals and executable decisions. This survey synthesizes research with a focus on equity return prediction and trading, consolidating insights from domain surveys and more than fifty primary studies. We propose a task-centered taxonomy that spans sentiment and event extraction, numerical and economic reasoning, multimodal understanding, retrieval-augmented generation, time series prompting, and agentic systems that coordinate tools for research, backtesting, and execution. We review empirical evidence for predictability, highlight design patterns that improve faithfulness such as retrieval first prompting and tool-verified numerics, and explain how signals feed portfolio construction under exposure, turnover, and capacity controls. We assess benchmarks and datasets for prediction and trading and outline desiderata-for time safe and economically meaningful evaluation that reports costs, latency, and capacity. We analyze challenges that matter in production, including temporal leakage, hallucination, data coverage and structure, deployment economics, interpretability, governance, and safety. The survey closes with recommendations for standardizing evaluation, building auditable pipelines, and advancing multilingual and cross-market research so that language-driven systems deliver robust and risk-controlled performance in practice.

Keywords: Quantitative investing, LLMs, Retrieval-augmented generation, Agentic systems, Time series prompting, Equities

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper discusses sophisticated mathematical and computational concepts like transformer architectures, efficient tuning (LoRA, quantization), and multimodal reasoning, but its summary and excerpt focus on surveying research, proposing taxonomies, and identifying challenges (e.g., temporal leakage, governance) rather than presenting specific backtests, statistical metrics, or implementation details.

  flowchart TD
    A["Research Goal:<br>Assess LLMs for<br>Equity Prediction & Trading"] --> B["Data & Inputs:<br>Unstructured Info<br>e.g., News, Filings"]
    B --> C["Core Methodology:<br>Task-Centered Taxonomy"]
    C --> D["Computational Processes:<br>RAG, Time Series Prompting, Agentic Systems"]
    D --> E["Key Findings & Outcomes:<br>Standardized Benchmarks, Auditable Pipelines, Risk-Controlled Signals"]