# Market alpha and structural risk

Research plan · Work in progress

This proposed study asks whether language models and research agents can connect scattered operational, regulatory and supply-chain signals into useful probability scenarios. Summarizing public information is a different task from identifying what would change an expectation.

## The question

Can a model explain how several individually ordinary facts could combine into a material change, consider competing explanations, and assign probabilities that hold up on unseen cases? More agents or more retrieved documents would not, by themselves, answer that question.

## Planned evaluation

- Freeze each case at a stated information cutoff. Exclude later documents and outcomes from model inputs.
- Compare a single model, a multi-agent research setup, and an explicit scenario-tree approach using the same evidence and declared budgets.
- Include cases with useful signals, misleading signals and no defensible conclusion. Require source links and allow abstention.
- Score probability calibration and source accuracy separately from writing quality. Compare against simple baselines, not just other elaborate agent setups.

## Pending work

Assemble the case families, specify the scoring rules, and run held-out evaluations. No market benchmark or investment-return result is reported here. The worker-protection red-team cases motivate the broader multi-hop reasoning question; they do not establish financial forecasting performance.

[Back to the application](https://tayloramarelfellowshipapp.baltor.ai/)