Datasets:

OpenFinMind
/

FinForesight

license: cc-by-4.0
language:
  - en
  - zh
pretty_name: FinForesight
tags:
  - finance
  - benchmark
  - deep-research
  - agent-evaluation
size_categories:
  - 1K<n<10K
configs:
  - config_name: val
    data_files:
      - split: train
        path: data/01_val/train.jsonl
    default: true
  - config_name: rank_f
    data_files:
      - split: train
        path: data/02_rank_f/train.jsonl
  - config_name: rank_t
    data_files:
      - split: train
        path: data/03_rank_t/train.jsonl
  - config_name: rank_h
    data_files:
      - split: train
        path: data/04_rank_h/train.jsonl
  - config_name: evt
    data_files:
      - split: train
        path: data/05_evt/train.jsonl
  - config_name: fund
    data_files:
      - split: train
        path: data/06_fund/train.jsonl
  - config_name: cf
    data_files:
      - split: train
        path: data/07_cf/train.jsonl
  - config_name: calc
    data_files:
      - split: train
        path: data/08_calc/train.jsonl

FinForesight

Evaluation dataset for financial Deep Research Agents (data only).

Code, builders, prompt templates, and evaluation scripts live in a separate repository. This Hub repo publishes standardized ready seeds only, loadable by subset.

Chinese documentation: README_cn.md.

Quick Start

from datasets import load_dataset

# Load by task subset (recommended)
val = load_dataset("OpenFinMind/FinForesight", "val")
fund = load_dataset("OpenFinMind/FinForesight", "fund")
cf = load_dataset("OpenFinMind/FinForesight", "cf")

print(val["train"][0]["task_id"])
print(val["train"][0]["prompt"][:200])

# Filter by time band
t2 = val["train"].filter(lambda x: x["time_band"] == "T2")

Available configs: val / rank_f / rank_t / rank_h / evt / fund / cf / calc
Default config: val.

EVT / FUND perturbations

evt and fund include perturbation items. Each is a rewrite of a base item in the same category, asking the same question with identical ground_truth, used to measure answer stability under rewrite.

variant is perturb_layout (layout reshuffle) / perturb_paraphrase (instruction paraphrase) / perturb_numeric (disclosure numbers scaled by the same factor ±1%); other variant values are base items.
metadata.perturbation.base_task_id points to the base item; metadata.perturbation.perturbation_type gives the perturbation type.
To evaluate base items only, filter on variant:

evt = load_dataset("OpenFinMind/FinForesight", "evt")
base = evt["train"].filter(lambda x: not x["variant"].startswith("perturb_"))

Scale (v0.9.0)

config	task	ready count
`val`	Valuation (VAL)	244
`rank_f`	Return Ranking F (RANK-F)	112
`rank_t`	Return Ranking T (RANK-T)	161
`rank_h`	Return Ranking H (RANK-H)	110
`evt`	Event Direction (EVT)	228
`fund`	Fundamentals (FUND)	384
`cf`	Counterfactual (CF)	30
`calc`	Calculation (CALC)	30
total		1299

Experiment identity (paper primary)

backbone_model = gpt-4.1
backbone_training_cutoff = 2024-06-30 (OpenAI states “June 2024” only; this release uses month-end conservatively)
guard_days = 30 → T1 outcome ≤ 2024-05-31, T2 origin ≥ 2024-07-30
experiment_as_of = 2026-08-17

Incompatibility note: the legacy Hub pin (339 items / model_training_cutoff=2024-06-01) is a legacy identity and must not be mixed with v0.9 paper_band / C1 tables.

Fields

One evaluation item per row (JSON object):

field	description
`task_id`	Globally unique ID
`category`	VAL / RANK / EVT / FUND / CF / CALC
`variant`	Sub-variant (e.g. F/T/H, earnings)
`cutoff_date`	Cutoff used for temporal banding
`time_band`	T1 / T2 / T3 (legacy; use `paper_band` for papers)
`forecast_origin`	Forecast origin time
`outcome_available_at`	Outcome availability date; use with `outcome_available_at_source` for observed vs estimated
`paper_band`	GPT-4.1 paper banding: T1 / T2 / T3 / CF / CALC
`status`	ready
`seed`	Structured input
`prompt`	Fully rendered prompt
`expected_output`	Output schema description
`ground_truth`	Evaluation label
`metadata`	Metadata

Temporal banding

This dataset labels legacy time_band with a reproducible rule (based on cutoff_date only):

T1: cutoff_date <= model_training_cutoff (possible pretraining leakage)
T2: model_training_cutoff < cutoff_date < reference_current_date
T3: cutoff_date >= reference_current_date (future-forecast stress)

Default legacy parameters (aligned with paper cutoff month, different rule):

model_training_cutoff = 2024-06-30
reference_current_date = 2026-08-17

For paper experiments, use sidecar paper_band (see code repo docs/schema.md / docs/c1_pairing_contract.md); do not use legacy time_band for C1.

Repo boundary

This repository only contains:

README.md (English dataset card)
README_cn.md (Chinese dataset card)
data/<config>/train.jsonl (ready seeds)

It does not include code, scripts, prompts, raw build CSVs, or run outputs.

License

CC-BY-4.0