arXiv Machine Learning
FactorBench: A Portfolio-Aware Benchmark for Automated Factor Mining
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arXiv AI
Sep 16
FactorEngine: A Program-level Knowledge-Infused Factor Mining Framework for Quantitative Investment
arXiv:2603.16365v3 Announce Type: replace Abstract: We study alpha factor mining, the automated discovery of predictive signals from noisy, non-stationary market data-under a practical requirement th...
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.
arXiv AI
Sep 25
AlphaDiverse: Post-Training Local Quantitative Research Agents for Diverse Exploration in Alpha Factor Mining
AlphaDiverse is a framework that enhances large language model–based multi‑agent systems for alpha factor mining by addressing cost, availability, and confidentiality constraints. It generates diverse research paths through varied environments and post‑training local agents, then fine‑tunes these agents with supervised learning and optimizes them jointly using a GRPO method that balances predictive quality and diversity. The approach limits research feedback to inner‑period data and evaluates a frozen model on outer‑period data to avoid test‑set tuning, demonstrating competitive prediction and broader exploration across four Chinese stock universes.