arXiv:2606. 03918v1 Announce Type: new Abstract: AI agents can increasingly handle the mechanical tasks of financial analysis: retrieving documents, calculating formulas, updating spreadsheets.
By Eric Cho, Shawn Huang, Alice Lu, Andy Lyu
The paper introduces FABench, a scalable backtesting environment designed to evaluate forecasting agents. FABench automatically discovers events, generates questions, and resolves outcomes for any past forecast date, allowing continuous renewal beyond model training cutoffs. In initial tests, retrieval improves Brier scores across eight models, with top agents matching or surpassing market consensus on 54 Polymarket-linked questions, yet analysis shows persistent evidence‑weighting failures that highlight room for improvement.
By Yuxuan Wang, Yishu Wang, Jiaqi Deng
FinForesight is a diagnostic backtesting benchmark designed to evaluate deep research systems for financial forecasting. It tests four forecasting tasks—valuation, return ranking, event direction, and future financial fundamentals—across Chinese, U.S., and Hong Kong markets, while also including two diagnostic tasks for counterfactual reasoning and financial calculation. The framework limits evidence to what was available at the forecast date and compares pretraining-era, post-cutoff, and live evaluations to assess how well controlled backtests predict future performance.
By Fengyuan Liu, Mukai Li, Jieshun You, Yilin Zheng, Chenjia Min, Yizhou Zhang, Harper Chen, Qi Liu
arXiv:2607. 27853v2 Announce Type: replace-cross Abstract: Powered by advances in LLMs and autonomous agents, deep research has become one of the most widely adopted agentic products.
By Yijia Xiao, Rujun Han, Yanfei Chen, Zifeng Wang, Ke Jiang, Zhongying CuiZhu, Vishy Tirumalashetty, Wei Wang, Burak Gokturk, Tomas Pfister, Chen-Yu Lee
The paper introduces an agentic forecasting environment built on 2,100+ resolved Polymarket questions, where a language model (Qwen3.5-35B-A3B) learns to gather evidence during rollout via web search, page reading, and financial time series, all filtered to avoid post‑cutoff leaks. Training with single‑epoch GRPO and a Brier‑score reward improves calibration by 30‑40% and reduces search attempts, while the trained policy outperforms four frontier models in evidence‑based forecasting, achieving lower soft‑Brier scores at roughly 5% of the inference cost. The authors release the environment, dataset, and per‑rollout records as a reusable harness for temporal forecasting agents.
By Yusuf Afifi, Artur Kiulian, Anton Polishko, Mykola Khandoga, Hamudi Naanaa, Alina Krasnobrizha
arXiv:2608. 11683v1 Announce Type: new Abstract: AI agents are increasingly deployed for professional investment research, yet no benchmark captures the complexity of the full investor workflow.
By Yuhao Zhang, O. Ozan Koyluoglu, Thejas Venkatesh, Richard Diehl Martinez, Vishank Bhatia, Arash Alidoust, Ashwin Paranjape