arXiv AI By Hyeonjin Kim, Minseok Kim, Seunghyeon Jung, Sujin Pyo, Huisu Jang, Woojin Lee

FaVOR: LLM-Based Agentic Framework for Factor Mining via Empirical Validation

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

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.

By Zuoyou Jiang, Li Zhao, Rui Sun, Ruohan Sun, Zhongjian Li, Jing Li, Daxin Jiang, Zuo Bai, Cheng Hua
arXiv AI
Aug 14

AQuA: Recursively Self-Improving Quantitative Trading Research Agents

arXiv:2608. 12841v1 Announce Type: cross Abstract: We study recursive self-improvement at the level of quantitative-investment research: whether an autonomous system can use evidence from earlier experiments to improve the hypotheses and candidates proposed in later iterations.

By Jiacheng Guo, Suozhi Huang, Yunlong Gao, Zihao Li, Jian Ge, Xu Kuang, Mengdi Wang
arXiv AI
2d ago

AgenticFinBench: Benchmarking Financial Agents on Diverse Tasks from Real-World Requests

AgenticFinBench is a new benchmark comprising 230 real‑world financial tasks across eight categories, two languages, and four complexity levels, designed to capture the breadth and open‑ended nature of financial AI work. It introduces a reference‑free evaluation framework that uses question‑specific rubrics and a two‑stage claim audit to detect hallucinations, aligning assessment more closely with real‑world financial practice. Experiments on 18 agent configurations show that ignoring hallucinations inflates performance, that harness differences drive evidence grounding more than model differences, and that task complexity reduces quality scores, highlighting challenges in explaining financial impacts and comparing data consistently.

By Shu Zhou, Ziyang He, Yuxin Rao, Yudong Fang, Jiali Qian, Dongyuan Chen, Juntao Zhang, Yong Hu