GuruAgents: Emulating Wise Investors with Prompt-Guided LLM Agents
Researchers unveiled GuruAgents, a system where AI agents use large language models and tailored prompts to replicate investment strategies of icons like Warren Buffett and Benjamin Graham. The Buffett-inspired agent posted a striking 42.2 percent annual growth, beating the NASDAQ-100 and S&P 500. The study shows prompt engineering can translate qualitative investment wisdom into automated, quantitative decisions. While results are strong, the research is limited to one market and a short time frame, raising questions about wider use.
What it examines
This paper introduces GuruAgents, AI agents powered by large language models and prompt engineering, designed to mimic the investment strategies of famous investors. The study tests whether these agents can turn qualitative investment philosophies into systematic, quantitative portfolio decisions using financial data and deterministic reasoning.
What it concludes
GuruAgents successfully translate legendary investors' strategies into reproducible investment outcomes, with prompt engineering as the key driver. These agents can be used for automated portfolio management, educational tools, or financial research. Future work includes improving philosophical alignment and creating ensembles of agents for more robust strategies.
Evidence objects
AI agents called 'GuruAgents,' powered by large language models and guided by detailed prompts, successfully mimic investment strategies of legends like Warren Buffett and Benjamin Graham, generating portfolios reflecting their real-world philosophies.
key_findings bullet 1 · key_findings · validation V0
The Buffett-inspired agent delivered a striking 42.2% annual growth rate, outperforming major benchmarks such as the NASDAQ-100 and S&P 500, while other agents also showed strong, though varied, investment results.
key_findings bullet 2 · key_findings · validation V0
This research highlights prompt engineering as a bridge between qualitative investment wisdom and quantitative automation, but notes limitations due to focus on a single market and short time frame, raising questions about broader applicability.
key_findings bullet 3 · key_findings · validation V0
This paper introduces 'GuruAgents,' a novel approach where prompt-guided LLM agents systematically encode legendary investors qualitative doctrines into quantitative, backtestable strategies. By operationalizing investment philosophies through prompt engineering and deterministic reasoning, it uniquely bridges AI and quantitative finance, offering compelling, reproducible results that surpass traditional LLM applications and demonstrate practical investment impact.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
- … of LLM-based agents to not only interpret financial data but … The application of LLMs to the financial domain is a … analyzing the sentiment of financial news to forecast stock …
Source row: 1010 · abstract type: snippet