Intelligent Agent-Based Market Research: Cloud-Orchestrated Large Language Models as Financial Analysts

Abstract

The rapid convergence of cloud computing, data science, and intelligent agents has redefined the landscape of financial research. In the emerging paradigm of Agentic Market Research, Large Language Models (LLMs) such as ChatGPT, DeepSeek, and Claude are evolving from passive analytical tools into cloud-orchestrated autonomous financial analysts. These agents integrate massive, heterogeneous data sources—ranging from market microstructure signals and corporate disclosures to social sentiment and macroeconomic narratives—within distributed cloud environments to generate adaptive, explainable insights. This paper provides a critical review of how cloud-based architectures enable the deployment, coordination, and scalability of LLM-driven agents for stock market analysis. It explores the interplay between data-centric Artificial Intelligence (AI) pipelines, real-time decision systems, and human-in-the-loop supervision in constructing hybrid ecosystems of human-AI collaboration. Furthermore, the review identifies key methodological challenges—including latency, interpretability, bias propagation, and regulatory compliance—and discusses future directions, including federated learning, agentic reasoning, and sustainable AI infrastructure. By mapping the technological evolution from traditional data analytics to self-learning, cloud-orchestrated financial agents, this work positions Agentic Market Research as a transformative framework for the next generation of intelligent, transparent, and scalable financial analysis. Methodologically, the review follows a structured narrative synthesis approach, combining systematic literature screening of recent peer-reviewed and preprint studies (2023-2025) with industry case analysis, in order to capture both academic advances and real-world deployments in a rapidly evolving technological domain.