Agentic Systems

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. ...

The Evolution of Alpha in Finance Harnessing Human Insight and LLM Agents ArXiv ID: 2505.14727 “View on arXiv” Authors: Mohammad Rubyet Islam Abstract The pursuit of alpha returns that exceed market benchmarks has undergone a profound transformation, evolving from intuition-driven investing to autonomous, AI powered systems. This paper introduces a comprehensive five stage taxonomy that traces this progression across manual strategies, statistical models, classical machine learning, deep learning, and agentic architectures powered by large language models (LLMs). Unlike prior surveys focused narrowly on modeling techniques, this review adopts a system level lens, integrating advances in representation learning, multimodal data fusion, and tool augmented LLM agents. The strategic shift from static predictors to contextaware financial agents capable of real time reasoning, scenario simulation, and cross modal decision making is emphasized. Key challenges in interpretability, data fragility, governance, and regulatory compliance areas critical to production deployment are examined. The proposed taxonomy offers a unified framework for evaluating maturity, aligning infrastructure, and guiding the responsible development of next generation alpha systems. ...