Investment Without Optimization: LLM-as-a-Judge Tournaments and Evolution

This work explores whether large language models can replicate the qualitative reasoning processes used by investment committees, instead of relying on numerical optimizers.

The first component is a correlation-aware selection method that repurposes a hierarchical clustering dendrogram as a tournament bracket. At each internal node, the LLM allocates selection slots between clusters and performs structured eliminations within correlation regimes.

The second component is a portfolio evolution loop that contains no objective function, expected returns, covariance matrices, or solvers. Instead, the model compares variants using a qualitative rubric (business quality, durability, thematic alignment, drawdown resilience, diversification) and accepts improvements through iterative reasoning.

Both mechanisms are fully text-explainable: every elimination, selection, and mutation is auditable.

Adding a bit of detail: this work tries to replace the standard numerical pipeline (expected returns → covariance → optimizer) with structured reasoning steps.

Two components: • A correlation tree is repurposed as a tournament bracket. At each node, the LLM allocates “selection slots” across branches and performs eliminations inside correlation regimes. • A qualitative evolution loop compares portfolio variants using a rubric (business quality, durability, diversification, resilience) and accepts improvements iteratively — without any explicit optimization objective.

The interesting aspect is not the performance but the explainability: every elimination and mutation step is text-auditable.

Curious whether others have experimented with LLM-based reasoning loops as substitutes for classical optimization in areas outside finance.