Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This paper introduces an agentic strategic asset allocation pipeline comprising approximately 50 specialized agents that generate capital market assumptions, construct portfolios using diverse methods, and critique each other's outputs. A researcher agent proposes new portfolio construction methods, while a meta-agent refines agent code and prompts based on historical forecast accuracy. The pipeline is governed by an Investment Policy Statement, enabling autonomous agents to be constrained and directed like human portfolio managers.
Imagine a self-improving AI portfolio manager, where agents debate investment strategies and rewrite each other's code based on past performance, all guided by a traditional Investment Policy Statement.
Agentic AI shifts the investor's role from analytical execution to oversight. We present an agentic strategic asset allocation pipeline in which approximately 50 specialized agents produce capital market assumptions, construct portfolios using over 20 competing methods, and critique and vote on each other's output. A researcher agent proposes new portfolio construction methods not yet represented, and a meta-agent compares past forecasts against realized returns and rewrites agent code and prompts to improve future performance. The entire pipeline is governed by the Investment Policy Statement--the same document that guides human portfolio managers can now constrain and direct autonomous agents.