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How Balyasny Asset Management built an AI research engine

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By combining rigorous model evaluation, full-platform use of OpenAI, and agent workflows, Balyasny is reinventing investment research.

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arXiv AI
Sep 23

The Self Driving Portfolio: Agentic Architecture for Institutional Asset Management

The paper introduces an agentic strategic asset allocation pipeline called the Self Driving Portfolio, where 44 specialized agents generate market assumptions, 21 competing methods construct portfolios, and agents critique and vote on each other's outputs. A researcher agent can propose new construction methods, while a meta agent evaluates past forecasts against realized returns and rewrites agent code and prompts to enhance future performance. The entire process is governed by an Investment Policy Statement, mirroring the document that guides human portfolio managers, thereby constraining and directing autonomous agents.

By Andrew Ang, Nazym Azimbayev, Andrey Kim