*.parquet/README.md

metadata

license: mit
task_categories:
  - multiple-choice
  - question-answering
language:
  - en
tags:
  - finance
  - forecasting
  - macroeconomics
  - recall-vs-reasoning
  - time-series
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: mcq_all.parquet
  - config_name: by_year
    data_files:
      - split: y1999
        path: mcq_1999.parquet
      - split: y2000
        path: mcq_2000.parquet
      - split: y2001
        path: mcq_2001.parquet
      - split: y2002
        path: mcq_2002.parquet
      - split: y2003
        path: mcq_2003.parquet
      - split: y2004
        path: mcq_2004.parquet
      - split: y2005
        path: mcq_2005.parquet
      - split: y2006
        path: mcq_2006.parquet
      - split: y2007
        path: mcq_2007.parquet
      - split: y2008
        path: mcq_2008.parquet
      - split: y2009
        path: mcq_2009.parquet
      - split: y2010
        path: mcq_2010.parquet
      - split: y2011
        path: mcq_2011.parquet
      - split: y2012
        path: mcq_2012.parquet
      - split: y2013
        path: mcq_2013.parquet
      - split: y2014
        path: mcq_2014.parquet
      - split: y2015
        path: mcq_2015.parquet
      - split: y2016
        path: mcq_2016.parquet
      - split: y2017
        path: mcq_2017.parquet
      - split: y2018
        path: mcq_2018.parquet
      - split: y2019
        path: mcq_2019.parquet
      - split: y2020
        path: mcq_2020.parquet
      - split: y2021
        path: mcq_2021.parquet
      - split: y2022
        path: mcq_2022.parquet
      - split: y2023
        path: mcq_2023.parquet
      - split: y2024
        path: mcq_2024.parquet
      - split: y2025
        path: mcq_2025.parquet
      - split: y2026
        path: mcq_2026.parquet

pre_test — Historical Financial Forecasting MCQ

A 5,443-question multiple-choice benchmark built from 50 US macro / market indicators (FRED, 1999–2026). Every correct answer is a real historical value independently re-derivable from raw FRED data — not model-generated.

The dataset is designed to separate genuine forecasting from memorized recall: by comparing a model's accuracy across years (especially 2026, which is past most models' training cutoff and acts as a clean baseline), and across "blind" vs "revealed" framings of the same shock event.

Composition

condition	count	what it tests
forecast	3,297	periodic numeric forecast of an indicator (recurrent)
blind	1,073	aftermath of a market shock, event name hidden — clean recall probe
revealed	1,073	the same question, event name given — situational reasoning

blind and revealed rows are paired 1:1 via pair_id (same options, same answer). Answer letters are balanced: A/B/C/D ≈ 25% each.

Fields

Input to the model:

• question — the full prompt, options already appended.
• options — the four "A) …" strings (also for programmatic use).

The answer:

• answer_letter — "A"/"B"/"C"/"D".
• answer_raw — the numeric value of the correct option (e.g. "+0.32pp").

Metadata / analysis labels (NOT shown to the model):

• condition — forecast / blind / revealed (the 3 experiment types above).
• subtype — recurrent / shock_aftermath.
• forecastType — Recurrent / Non-Recurrent.
• indicator — FRED series id (e.g. CPIAUCSL).
• transform — level / yoy_pct / yoy_pp.
• target_period, info_cutoff — what is being predicted, and the information cutoff (strictly before the target → it is forecasting, not lookup).
• unit, year.
• pair_id, event_label, event_date — present on shock rows; link the blind/revealed twins.

Recommended analysis

1. Accuracy vs. year — group by year; a cliff at 2026 (→ ~25% chance level) is the clean signal that pre-2026 accuracy is partly memorization.
2. Blind vs. revealed — join on pair_id; a gap suggests the model relies on the event name (memory) rather than the pre-cutoff numbers (reasoning). (Auxiliary signal — note the date itself can leak event identity.)

Loading

from datasets import load_dataset

# all rows
ds = load_dataset("lfqian/pre_test", split="train")

# one year at a time (config "by_year", splits y1999 … y2026)
ds_2008 = load_dataset("lfqian/pre_test", "by_year", split="y2008")

# every year as a DatasetDict
by_year = load_dataset("lfqian/pre_test", "by_year")

Files

• mcq_all.parquet — all rows (default config, train split).
• mcq_all.jsonl / mcq_all.json — same data, JSONL and single-array JSON.
• mcq_<year>.parquet — per-year files (config by_year, splits y1999…y2026).
• mcq_<year>.json — per-year files in JSON.

Provenance

Indicators and values from the Federal Reserve Economic Data (FRED). Questions, distractors, and answer-letter balancing generated by the OpenFinArena competitions_historical pipeline. 100/100 stratified audit re-derived every sampled answer from raw FRED.