arXiv AI
FactorEngine: A Program-level Knowledge-Infused Factor Mining Framework for Quantitative Investment
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FactorBench: A Portfolio-Aware Benchmark for Automated Factor Mining
arXiv:2610.06947v1 Announce Type: cross Abstract: Factor mining seeks to discover signals from financial data that predict future asset returns and guide portfolio construction. Automated factor mini...
arXiv AI
Oct 15
AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery
arXiv:2610.14959v2 Announce Type: new Abstract: Formulaic alpha mining seeks to discover executable expressions that capture predictive market signals in a vast, noisy search space. Recent LLM-based...
arXiv AI
Sep 1
FaVOR: LLM-Based Agentic Framework for Factor Mining via Empirical Validation
arXiv:2608.30192v1 Announce Type: new Abstract: Traditional finance relies on experts to hand-craft factors through a principled process grounded in economic rationale. Recent LLM-based multi-agent s...
arXiv AI
Jul 31
AlphaSchema: Exploring the Space of Trading Semantics for LLM-Based Alpha Mining
arXiv:2607. 26642v1 Announce Type: new Abstract: Automated alpha mining has increasingly adopted large language model (LLM) agents for factor generation and iterative discovery.
Hugging Face Trending Papers
Jul 29
AlphaSchema: Exploring the Space of Trading Semantics for LLM-Based Alpha Mining
Automated alpha mining has increasingly adopted large language model (LLM) agents for factor generation and iterative discovery. However, existing LLM-based systems often delegate both factor construction and search decisions to the agent itself, without an explicit exploration space or a principled mechanism for navigating that space.
arXiv AI
Sep 10
Alpha-R1: Alpha Screening with LLM Reasoning via Reinforcement Learning
Alpha‑R1 introduces a reinforcement‑learning aligned large language model framework that performs context‑aware alpha screening by semantically gating candidate factors against a dynamic market state description. The model, trained with group relative policy optimization using realized portfolio returns as reward, selects a sparse subset of factors whose economic rationale matches current market conditions. In a 12‑month out‑of‑sample test, Alpha‑R1 achieved annualized returns of 47.87% on the S&P 500 and 40.57% on the CSI 300, with Sharpe ratios of 1.62 and 2.23, demonstrating the effectiveness of semantic factor reranking in non‑stationary markets.