@inproceedings{li-etal-2026-findeepforecast,
title = "{F}in{D}eep{F}orecast : A Live Multi-Agent System for Benchmarking Deep Research Agents in Financial Forecasting",
author = "Li, Xiangyu and
Yao, Xuan and
Qi, Guohao and
Zhu, Fengbin and
Koa, Kelvin J.L. and
Ng, Xiang Yao and
Liu, Ziyang and
Xingyu, Ni and
Chang, Liu and
Yang, Yonghui and
Zhang, Yang and
Wang, Wenjie and
Feng, Fuli and
Wang, Chao and
Luan, Huanbo and
Xing, Xiaofen and
Xu, Xiangmin and
Chua, Tat-Seng and
Huang, Ke-Wei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.634/",
pages = "14581--14615",
ISBN = "979-8-89176-459-0",
abstract = "Deep Research (DR) Agents powered by advanced Large Language Models (LLMs) have fundamentally shifted the paradigm for completing complex research tasks. Yet, a comprehensive and live evaluation of their forecasting performance on real-world, research-oriented tasks in high-stakes domains (eg finance) remains underexplored. We introduce $FinDeepForecast$, the first live, end-to-end multi-agent system for automatically evaluating DR agents by continuously generating research-oriented financial forecasting tasks. This system is equipped with a dual-track taxonomy, enabling the dynamic generation of recurrent and non-recurrent forecasting tasks at both corporate and macro levels. With this system, we generate $FinDeepForecastBench$, a weekly evaluation benchmark over a ten-week horizon, encompassing 8 global economies and 1,314 listed companies, and evaluate 13 representative methods. Extensive experiments show that, while DR agents consistently outperform strong baselines, their performance still falls short of genuine forward-looking financial reasoning. We expect the proposed $FinDeepForecast$ system to consistently facilitate future advancements of DR agents in research-oriented financial forecasting tasks. The project website is available at https://OpenFinArena.com."
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<abstract>Deep Research (DR) Agents powered by advanced Large Language Models (LLMs) have fundamentally shifted the paradigm for completing complex research tasks. Yet, a comprehensive and live evaluation of their forecasting performance on real-world, research-oriented tasks in high-stakes domains (eg finance) remains underexplored. We introduce FinDeepForecast, the first live, end-to-end multi-agent system for automatically evaluating DR agents by continuously generating research-oriented financial forecasting tasks. This system is equipped with a dual-track taxonomy, enabling the dynamic generation of recurrent and non-recurrent forecasting tasks at both corporate and macro levels. With this system, we generate FinDeepForecastBench, a weekly evaluation benchmark over a ten-week horizon, encompassing 8 global economies and 1,314 listed companies, and evaluate 13 representative methods. Extensive experiments show that, while DR agents consistently outperform strong baselines, their performance still falls short of genuine forward-looking financial reasoning. We expect the proposed FinDeepForecast system to consistently facilitate future advancements of DR agents in research-oriented financial forecasting tasks. The project website is available at https://OpenFinArena.com.</abstract>
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%0 Conference Proceedings
%T FinDeepForecast : A Live Multi-Agent System for Benchmarking Deep Research Agents in Financial Forecasting
%A Li, Xiangyu
%A Yao, Xuan
%A Qi, Guohao
%A Zhu, Fengbin
%A Koa, Kelvin J.L.
%A Ng, Xiang Yao
%A Liu, Ziyang
%A Xingyu, Ni
%A Chang, Liu
%A Yang, Yonghui
%A Zhang, Yang
%A Wang, Wenjie
%A Feng, Fuli
%A Wang, Chao
%A Luan, Huanbo
%A Xing, Xiaofen
%A Xu, Xiangmin
%A Chua, Tat-Seng
%A Huang, Ke-Wei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F li-etal-2026-findeepforecast
%X Deep Research (DR) Agents powered by advanced Large Language Models (LLMs) have fundamentally shifted the paradigm for completing complex research tasks. Yet, a comprehensive and live evaluation of their forecasting performance on real-world, research-oriented tasks in high-stakes domains (eg finance) remains underexplored. We introduce FinDeepForecast, the first live, end-to-end multi-agent system for automatically evaluating DR agents by continuously generating research-oriented financial forecasting tasks. This system is equipped with a dual-track taxonomy, enabling the dynamic generation of recurrent and non-recurrent forecasting tasks at both corporate and macro levels. With this system, we generate FinDeepForecastBench, a weekly evaluation benchmark over a ten-week horizon, encompassing 8 global economies and 1,314 listed companies, and evaluate 13 representative methods. Extensive experiments show that, while DR agents consistently outperform strong baselines, their performance still falls short of genuine forward-looking financial reasoning. We expect the proposed FinDeepForecast system to consistently facilitate future advancements of DR agents in research-oriented financial forecasting tasks. The project website is available at https://OpenFinArena.com.
%U https://aclanthology.org/2026.emnlp-main.634/
%P 14581-14615
Markdown (Informal)
[FinDeepForecast : A Live Multi-Agent System for Benchmarking Deep Research Agents in Financial Forecasting](https://aclanthology.org/2026.emnlp-main.634/) (Li et al., EMNLP 2026)
ACL
- Xiangyu Li, Xuan Yao, Guohao Qi, Fengbin Zhu, Kelvin J.L. Koa, Xiang Yao Ng, Ziyang Liu, Ni Xingyu, Liu Chang, Yonghui Yang, Yang Zhang, Wenjie Wang, Fuli Feng, Chao Wang, Huanbo Luan, Xiaofen Xing, Xiangmin Xu, Tat-Seng Chua, and Ke-Wei Huang. 2026. FinDeepForecast : A Live Multi-Agent System for Benchmarking Deep Research Agents in Financial Forecasting. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 14581–14615, Budapest, Hungary. Association for Computational Linguistics.