FinanceGym — Point-in-Time Finance Deep-Research Benchmark (400 questions)

FinanceGym measures whether a finance research agent can produce a grounded, cited research report under a point-in-time constraint. Every question carries a cutoff date, and an agent may use only information published on or before that date.

Each question is graded against expert-annotated rubrics on two axes:

• Pre-cutoff — did the report correctly ground itself in the evidence that existed before the cutoff?
• Post-cutoff — did it correctly anticipate what actually happened after the cutoff?

The split between those two axes is the point of the benchmark. Across every system evaluated so far, post-cutoff scores land far below pre-cutoff — anticipating consequences from pre-cutoff evidence alone remains the open problem.

This repository holds the public 400-question slim set: the questions and their cutoffs. Rubrics are withheld (see Rubrics are not public below).

• 💻 Code and environment
• 🏆 Leaderboard — current standings
• 📥 Participate — run your agent and submit a report
• 📄 Paper — FinanceHarness: Autonomous Financial Deep Research Framework

Data Statistics

Questions	400
Split	test (single split)
Language	English
Cutoff range	2024-12-31 → 2025-11-10
Question length	32–48 words (median 41)
Withheld rubric items	2,464 total · 4–8 per question (median 6)
Rubric axes	1,496 pre-cutoff · 968 post-cutoff

Cutoff dates by month:

Month	Questions		Month	Questions
2024-12	3		2025-06	60
2025-01	11		2025-07	63
2025-02	10		2025-08	57
2025-03	20		2025-09	29
2025-04	66		2025-10	22
2025-05	52		2025-11	7

Data Format

The same 400 records ship in two identical forms:

File	Use
benchmark_400_public.jsonl	Canonical raw file — byte-identical to the copy in the code repository.
benchmark_400_public.parquet	What load_dataset and the dataset viewer read. Same rows, all three fields typed as strings.

One JSON object per line in benchmark_400_public.jsonl.

Field	Type	Description
task_id	string	Stable unique id for the question.
question	string	The research question. Used verbatim to match a submission to its rubric during grading.
cutoff	string (YYYY-MM-DD)	The point-in-time date. An agent may use only information available on or before this date.

Example line:

{
  "task_id": "69f904b728538874c086db16",
  "question": "How does Air France-KLM's accelerated transition toward a 60.5% majority stake in SAS impact its strategic bandwidth and capital allocation for the privatization of TAP Air Portugal, given the group's 2026 full-control target and ongoing European airline consolidation?",
  "cutoff": "2025-08-05"
}

Load it with datasets:

from datasets import load_dataset

ds = load_dataset("Rujun/FinanceGym", split="test")
print(ds[0]["question"], ds[0]["cutoff"])   # cutoff is a "YYYY-MM-DD" string

The Point-in-Time Rule

Agents retrieve evidence from a frozen news corpus served behind a search API that enforces a max_date filter — the server never returns documents published after the cutoff. That single hard rule is what makes the post-cutoff axis meaningful; you may otherwise run any agent architecture you like.

For each question, pass its cutoff as max_date on every search call, gather evidence, and write a cited report.

Rubrics Are Not Public

The scoring rubrics are not part of this dataset. Grading is run by maintainers against a private full-rubric set, which keeps evaluation single-blind — no tuning to the rubric is possible, and leaderboard rows stay comparable across systems.

How Submissions Are Graded

A judge scores every rubric item on a 0–4 scale:

Score	Meaning
0	NOT ADDRESSED
1	MENTIONED (no substance)
2	PARTIAL (directionally correct, missing specifics)
3	SUBSTANTIVE (correct with specifics, minor gaps)
4	FULLY GROUNDED (correct, specific, plausibly sourced)

The headline metric is macro-averaged: each question's normalized rate is s / (4n) over its n rubric items, and the score is the mean of those per-question rates, so every question is weighted equally. Pre-cutoff and post-cutoff are the same macro mean restricted to antecedent and consequent rubric items respectively, reported with a 95% bootstrap CI.

License

This dataset is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).

Non-commercial use only. FinanceGym, its benchmark datasets, and retrieval tools are provided strictly for non-commercial research and evaluation. Commercial use — including integration into commercial financial advisory services, or using generated outputs to provide commercial investment advice — is strictly prohibited.

Not investment advice. The questions and any model outputs derived from them are research artifacts, not financial advice.

Citation

@misc{xiao2026financeharnessautonomousfinancialdeep,
      title={FinanceHarness: Autonomous Financial Deep Research Framework},
      author={Yijia Xiao and Rujun Han and Yanfei Chen and Zifeng Wang and Ke Jiang and Zhongying CuiZhu and Vishy Tirumalashetty and Wei Wang and Burak Gokturk and Tomas Pfister and Chen-Yu Lee},
      year={2026},
      eprint={2607.27853},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2607.27853},
}