Using Large Language Models to Generate Quantitative Factors

Summary

This training overview outlines a proposed workflow for using multimodal large language models in quantitative investing. It combines time-series databases, knowledge graphs, and reinforcement learning in a pipeline that connects data, signals, and portfolio decisions. Its examples focus on extracting signals from unstructured sources such as news, financial reports, and satellite imagery, including changes in annual-report language that might indicate management priorities or supply-chain developments.

The document also describes generating dynamic factors by encoding policy text into measures of industry sensitivity, and coordinating separate agents for market monitoring, tail-risk alerts, and portfolio optimization. These are architectural ideas and illustrative use cases, rather than a documented implementation with a reproducible method. The page provides no factor definitions, validation results, or performance evidence, so it does not establish that the suggested signals are predictive or that the multi-agent setup improves investment outcomes.

Key ideas

• Large language models can be used to extract candidate trading signals from news, reports, and imagery.
• Text embeddings of policy material are proposed as inputs for industry policy-sensitivity factors.
• The suggested system combines language models with time-series data, knowledge graphs, and reinforcement learning.
• Separate agents are assigned market monitoring, risk alerts, and portfolio optimization roles.
• The overview provides concepts and examples but no validation results or evidence of investment performance.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.