Can autonomous artificial intelligence (Agentic AI), which interprets market dynamics with continuous improvement and specifies resulting trades with minimal human intervention, run an attractive portfolio? In their March 2026 paper entitled "Beyond Prompting: An Autonomous Framework for Systematic Factor Investing via Agentic AI", Allen Huang and Zheqi Fan employ Agentic AI as a self-directed quantitative researcher that translates a high-level objective, such as maximizing risk-adjusted returns while controlling for turnover/transaction costs, into buy/sell/hold decisions. Specifically, their Agentic AI model each day:
- Permutes 10 baseline variables (lagged stock return, market return, stock price, trading volume, volume relative to recent history, 20-day realized volatility, price-to-moving-average ratio, market volatility, volume growth and bid-ask spread) to discover novel candidate factors that exploitably predict next-day stock returns.
- Backtests the candidate factors and assesses their economic rationale.
- Derives stock buy/sell/hold decisions from surviving candidates.
- Updates its memory based on empirical feedback.
The Agentic AI model mitigates data snooping bias by requiring economic rationale, adjusting for multiple hypothesis testing and evaluating out-of-sample signal decay. They use historical data through December 2020 to train the model and 2021-2024 data for out-of-sample testing. They assume 0.03% trading frictions (commission plus bid-ask spread) to assess net performance. Using daily data for a broad sample of U.S. common stocks priced at least $5 and excluding extreme outliers during January 2004 through December 2024, they find that:
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