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TheFinAI
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pre_test

Tasks:
Multiple Choice
Question Answering
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Tags:
finance
economics
macro
forecasting
federal-reserve
time-series
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metadata 
language: en
license: cc-by-4.0
task_categories:
  - multiple-choice
  - question-answering
size_categories:
  - 1K<n<10K
pretty_name: FinDeepForecast-Historical-US
tags:
  - finance
  - economics
  - macro
  - forecasting
  - federal-reserve
  - time-series
  - benchmark
dataset_info:
  features:
    - name: qid
      dtype: string
    - name: forecastType
      dtype: string
    - name: subtype
      dtype: string
    - name: indicator
      dtype: string
    - name: transform
      dtype: string
    - name: target_period
      dtype: string
    - name: info_cutoff
      dtype: string
    - name: forecast_end
      dtype: string
    - name: answer_release
      dtype: string
    - name: question
      dtype: string
    - name: options
      list: string
    - name: answer_letter
      dtype: string
    - name: answer_raw
      dtype: string
    - name: unit
      dtype: string
    - name: year
      dtype: int32
  splits:
    - name: '1999'
      num_bytes: 74833
      num_examples: 236
    - name: '2000'
      num_bytes: 104028
      num_examples: 328
    - name: '2001'
      num_bytes: 100621
      num_examples: 318
    - name: '2002'
      num_bytes: 107149
      num_examples: 337
    - name: '2003'
      num_bytes: 107172
      num_examples: 336
    - name: '2004'
      num_bytes: 101665
      num_examples: 322
    - name: '2005'
      num_bytes: 103728
      num_examples: 328
    - name: '2006'
      num_bytes: 101349
      num_examples: 321
    - name: '2007'
      num_bytes: 102494
      num_examples: 321
    - name: '2008'
      num_bytes: 113226
      num_examples: 352
    - name: '2009'
      num_bytes: 100911
      num_examples: 318
    - name: '2010'
      num_bytes: 106598
      num_examples: 335
    - name: '2011'
      num_bytes: 106190
      num_examples: 334
    - name: '2012'
      num_bytes: 105532
      num_examples: 333
    - name: '2013'
      num_bytes: 101793
      num_examples: 322
    - name: '2014'
      num_bytes: 104869
      num_examples: 331
    - name: '2015'
      num_bytes: 108792
      num_examples: 341
    - name: '2016'
      num_bytes: 113696
      num_examples: 356
    - name: '2017'
      num_bytes: 102019
      num_examples: 324
    - name: '2018'
      num_bytes: 111264
      num_examples: 348
    - name: '2019'
      num_bytes: 102219
      num_examples: 324
    - name: '2020'
      num_bytes: 107109
      num_examples: 334
    - name: '2021'
      num_bytes: 102620
      num_examples: 324
    - name: '2022'
      num_bytes: 109808
      num_examples: 343
    - name: '2023'
      num_bytes: 107952
      num_examples: 339
    - name: '2024'
      num_bytes: 108210
      num_examples: 339
    - name: '2025'
      num_bytes: 106355
      num_examples: 329
    - name: '2026'
      num_bytes: 49530
      num_examples: 155
  download_size: 994498
  dataset_size: 2871732
configs:
  - config_name: default
    data_files:
      - split: '1999'
        path: data/1999-*
      - split: '2000'
        path: data/2000-*
      - split: '2001'
        path: data/2001-*
      - split: '2002'
        path: data/2002-*
      - split: '2003'
        path: data/2003-*
      - split: '2004'
        path: data/2004-*
      - split: '2005'
        path: data/2005-*
      - split: '2006'
        path: data/2006-*
      - split: '2007'
        path: data/2007-*
      - split: '2008'
        path: data/2008-*
      - split: '2009'
        path: data/2009-*
      - split: '2010'
        path: data/2010-*
      - split: '2011'
        path: data/2011-*
      - split: '2012'
        path: data/2012-*
      - split: '2013'
        path: data/2013-*
      - split: '2014'
        path: data/2014-*
      - split: '2015'
        path: data/2015-*
      - split: '2016'
        path: data/2016-*
      - split: '2017'
        path: data/2017-*
      - split: '2018'
        path: data/2018-*
      - split: '2019'
        path: data/2019-*
      - split: '2020'
        path: data/2020-*
      - split: '2021'
        path: data/2021-*
      - split: '2022'
        path: data/2022-*
      - split: '2023'
        path: data/2023-*
      - split: '2024'
        path: data/2024-*
      - split: '2025'
        path: data/2025-*
      - split: '2026'
        path: data/2026-*

FinDeepForecast-Historical-US

A historical version of the FinDeepForecast benchmark from OpenFinArena, covering 1999–2026 with ground-truth derived from real FRED time series.

Strictly follows the paper's two-track taxonomy:

  • Recurrent — periodic numerical forecasts (CPI/GDP/Treasury/etc. value at a future date)
  • Non-Recurrent — binary YES/NO forecasts on specific upcoming scheduled events (FOMC rate decisions, CPI/NFP release surprises, weekly market thresholds)

Highlights

Metric Value
Total questions 8,437
Recurrent 6,366 (75.5%) — multiple choice (4 options)
Non-Recurrent 2,071 (24.5%) — binary YES/NO
Years covered 1999–2026 (28 splits)
Indicators 49 US macro/market series from FRED
Avg per year ~300 questions

Quick Start 

from datasets import load_dataset

# Single year
ds = load_dataset("TheFinAI/pre_test", split="2008")

# Filter by forecast type
recurrent_2008 = ds.filter(lambda x: x["forecastType"] == "Recurrent")
non_recurrent_2008 = ds.filter(lambda x: x["forecastType"] == "Non-Recurrent")

Recurrent (paper-aligned periodic forecast)

"Forecast the value of [US CPI YoY Inflation Rate] for June 2010. (Information available up to 2010-04-15.)"

A) 1.45% B) 2.04% C) 2.42% D) 1.85%

  • Format: 4-option MCQ with numeric values
  • Generated for 49 indicators × 4 quarters/year × {level, yoy_pct, yoy_pp} transforms
  • info_cutoff set ~60 days before target period

Non-Recurrent (paper-aligned binary YES/NO)

Follows the original paper's format: "Will [specific event] happen by [date]?"

8 templates (all anchored to scheduled events)

Template Question pattern Per year
T1 fomc_cut Will FOMC cut rates ≥25bp at [date]? ~8
T2 fomc_hike Will FOMC raise rates ≥25bp at [date]? ~8
T3 fomc_hold Will FOMC keep rates unchanged at [date]? ~8
T4 cpi_release_threshold Will CPI YoY for [month] exceed [threshold]%? 12
T5 nfp_release_threshold Will NFP for [month] show change > [threshold]? 12
T6 gdp_release_threshold Will GDP YoY for [quarter] exceed [threshold]%? 4
T7 vix_weekly_spike Will VIX exceed [threshold] in week ending [date]? 12
T8 nasdaq_weekly_gain Will NASDAQ gain more than [X]% in week ending [date]? 12

Non-Recurrent example (real)

"Will the FOMC cut the federal funds target rate by at least 25 basis points at its meeting on 2020-03-15?"

A) YES B) NO

✓ Answer: A (YES) — Fed cut to zero on emergency Sunday meeting

YES/NO balance (across all 2,071 NR questions)

Subtype n YES% NO%
vix_weekly_spike 328 51% 49%
nasdaq_weekly_gain 328 41% 59%
nfp_release_threshold 326 49% 51%
cpi_release_threshold 313 52% 48%
fomc_cut 224 17% 83%
fomc_hike 224 19% 81%
fomc_hold 224 64% 36%
gdp_release_threshold 104 47% 53%

(FOMC imbalance reflects reality: most meetings are "hold" decisions.)

Schema

Field Type Description
qid string Unique question ID
forecastType string Recurrent or Non-Recurrent
subtype string Fine-grained subtype
indicator string Primary FRED series ID
transform string level / yoy_pct / yoy_pp (Recurrent), "" (NR)
target_period string Period asked about
info_cutoff string YYYY-MM-DD — latest info allowed
forecast_end string YYYY-MM-DD — last day of horizon
answer_release string NR only — when truth becomes verifiable
question string Question text
options list[string] Length 4 (Recurrent) or 2 (NR)
answer_letter string A/B/C/D (Recurrent) or A/B (NR)
answer_raw string Underlying answer value
unit string %, index, binary, etc.
year int Convenience field

Indicators (49 FRED series)

Category Examples
Inflation (8) CPIAUCSL, CPILFESL, PCEPI, PCEPILFE, PPIACO, PPIFIS, DCOILWTICO, DCOILBRENTEU
Labor (6) UNRATE, PAYEMS, CIVPART, EMRATIO, AHETPI, ICSA
Growth (4) GDPC1, GDP, INDPRO, TCU
Rates (8) FEDFUNDS, DGS3MO, DGS2, DGS5, DGS10, DGS30, T10Y2Y, MORTGAGE30US
Money (4) M2SL, BOGMBASE, TOTBKCR, CCSA
Consumer (5) UMCSENT, PCE, PSAVERT, RSAFS, DSPI
Housing (3) HOUST, PERMIT, CSUSHPINSA
Manufacturing (3) DGORDER, BOPGSTB, NEWORDER
Market (8) SP500, NASDAQCOM, DJIA, VIXCLS, DTWEXBGS, DEXUSEU, DEXJPUS, DEXCHUS

Coverage Notes

  • 1999 has fewer Recurrent questions (~180) because some indicators (PPIFIS, SP500, DJIA, DTWEXBGS) start later than 1999 on FRED.
  • 2026 is a partial year (data through April/May 2026); contains only questions whose ground truth is verifiable.
  • All NR questions tied to FOMC meetings, BLS releases (CPI/NFP), BEA releases (GDP), or weekly market thresholds — all from scheduled/public calendars.

Differences from the Original FinDeepForecast

Aspect Original (live, 2025-10 → 2025-12) This dataset (historical)
Coverage 10 weeks 28 years
Markets 8 (US/UK/CN/HK/JP/SG/DE/FR) US only
Recurrent total 296 macro + 699 corporate 6,366 (macro only)
Non-Recurrent total 128 macro + 247 corporate 2,071 (macro only)
Ground truth Future outcome (live) Historical realized values
Format Numeric (Rec) + YES/NO (NR) Same — 4-option MCQ + YES/NO

This historical version sacrifices the live "no memorization" property of the original benchmark in exchange for reproducible offline evaluation across 28 years.

License

CC-BY-4.0. Underlying FRED data is in the public domain (FRED API terms).

Citation 

@article{findeepforecast,
  title={FinDeepForecast: A Live Benchmark for Financial Forecasting with LLMs},
  author={OpenFinArena},
  year={2026},
  url={https://openfinarena.com/fin-deep-forecast/}
}