12 Augmenting investment decisions: LLM-guided portfolio allocation
This chapter covers
In chapter 10, we built a quantitative engine that processes the market’s numerical signals: price movements, momentum, and cross-asset correlations. In chapter 11, we constructed a qualitative engine that extracts signals from text: news sentiment, policy tone, and emerging risks. Each engine captures different information. The quantitative engine detects statistical patterns but can’t explain the drivers behind price movements. The qualitative engine interprets narratives but lacks systematic numerical rigor.
This chapter combines these approaches into a decision-support workflow, but not by simply merging two signal streams. Instead, we elevate the role of the large language model's (LLM). In chapter 11, the LLM acted as a signal extractor, reading text and outputting scores. Here, the LLM becomes a decision advisor: an agent that receives structured inputs, reasons within explicit constraints, and produces actionable recommendations for human review. This architectural shift from extraction to interpretation lays the groundwork for the fully agentic workflows we’ll explore in chapter 14.