mlsanityData to 7 Oct 2026

GuruAgents: Emulating Wise Investors with Prompt-Guided LLM Agents

arXiv 2510.01664 ↗First posted 2 Oct 2025cs.AIcited by 1

On the map: LLM agents and tool use → Financial trading and forecasting agents

Abstract (arXiv)

This study demonstrates that GuruAgents, prompt-guided AI agents, can systematically operationalize the strategies of legendary investment gurus. We develop five distinct GuruAgents, each designed to emulate an iconic investor, by encoding their distinct philosophies into LLM prompts that integrate financial tools and a deterministic reasoning pipeline. In a backtest on NASDAQ-100 constituents from Q4 2023 to Q2 2025, the GuruAgents exhibit unique behaviors driven by their prompted personas. The Buffett GuruAgent achieves the highest performance, delivering a 42.2% CAGR that significantly outperforms benchmarks, while other agents show varied results. These findings confirm that prompt engineering can successfully translate the qualitative philosophies of investment gurus into reproducible, quantitative strategies, highlighting a novel direction for automated systematic investing. The source code and data are available at https://github.com/yejining99/GuruAgents.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. Builds on 5 of its 21 references that are in the map. First posted on arXiv 2 Oct 2025.

Builds on

5 of 21 references.

References · 5

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  3. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,348
  4. 2023BloombergGPT: A Large Language Model for Financecited by 923
  5. 2025LLM-Enhanced Black-Litterman Portfolio Optimizationcited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2025Are Generative AI Agents Effective Personalized Financial Advisors?
  2. 2025HedgeAgents: A Balanced-aware Multi-agent Financial Trading System
  3. 2026Toward Expert Investment Teams:A Multi-Agent LLM System with Fine-Grained Trading Tasks
  4. 2024INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent
  5. 2025Integrating Traditional Technical Analysis with AI: A Multi-Agent LLM-Based Approach to Stock Market Forecasting
  6. 2024QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Model
  7. 2025The Evolution of Alpha in Finance Harnessing Human Insight and LLM Agents
  8. 2026Evaluating Investment Logic in Large Language Models: A Real-World Benchmark Towards Personalzied Financial Agents