Generative AI in finance: market insight extraction

This paper covers the fundamental concepts of the RAG Workflow and it illustrates particular examples where this technology can be applied effectively in trading scenarios and analysis.

This whitepaper illustrates how the power of Large Language Models (LLMs) can be used via Retrieval Augmented Generation systems (RAGs) to refine and systematise market insight extraction by gathering relevant information from datastores at inference time to enrich prompts.

This framework facilitates diverse use cases including complex reasoning and relationship identification, trade signal extraction from news and investment reports, market sentiment modelling, topic and theme identification, report summarisation. Conceptually, LLMs function as reasoning tools and RAGs as the framework that processes the vast amounts of textual data into a Knowledge Retrieval System.

Such systems can use either pre trained open-source models entirely offline, or paywalled cloud infrastructure, affording users the capability to perform interactive financial analysis on their documents. Ideas can be easily prototyped locally, and then appropriate scaling solutions must undergo testing to meet firm-wide deployment security, compliance, and performance criteria requirements.


  • Why do we need a RAG?
  • Embeddings & similarity
  • A user query demonstration
  • Prompt engineering and signal generation
  • Driving competitive edge through advanced technology

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