Self-Evolving Multi-Agent Symbolic Discovery for Financial Fundamental Analysis
While symbolic regression (SR) has been successfully used in science to discover new equations, its use in financial valuation is hindered by several limitations. Whereas the natural sciences provide objectively correct relationships, financial valuation constitutes a distinct class of symbolic discovery problems, as it admits multiple valid perspectives, operates under non-stationary market conditions, and involves noisy, continuous performance signals. In this work, we propose Multi-Agent Fundamental Analysis with Symbolic Adaptive learning (MUFASA), a hierarchical multi-agent framework for symbolic discovery in finance. MUFASA introduces (1) disentangled equation discovery via speciali...
| Authors | Kelvin J. L. Koa, Filip Orestav, Shengqiong Wu et al. |
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| Primary category | cs.MA |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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