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

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, ag