Datasets:

lfqian
/

pre_test

license: mit
task_categories:
  - multiple-choice
  - question-answering
language:
  - en
tags:
  - finance
  - forecasting
  - macroeconomics
  - recall-vs-reasoning
  - time-series
size_categories:
  - 10K<n<100K
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 12,838-question multiple-choice benchmark built from 49 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. All questions are 4-option (random baseline = 25%).

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

Two broad kinds of question:

Predictable (condition = forecast): the next periodic value of an indicator, which a model can in principle extrapolate from the pre-cutoff trend.
Unpredictable (condition = event / blind / revealed): outcomes that cannot be derived from the pre-cutoff trend, so above-25% accuracy on them signals memorized recall.

`condition`	count	predictable?	what it is
`forecast`	7,868	yes	periodic numeric forecast of an indicator (recurrent, quarterly cadence)
`event`	2,824	no	scheduled events with unpredictable outcomes — FOMC rate, NFP, CPI, GDP, weekly VIX peak, weekly NASDAQ return. Present every year as a stable floor.
`blind`	1,073	no	aftermath of a real black-swan shock, event name hidden — clean recall probe
`revealed`	1,073	no	the same shock question, event name given — situational reasoning

blind/revealed are paired 1:1 via pair_id (same options, same answer) and cluster in crisis years. The event floor guarantees every year has a ~25–30% unpredictable share (crisis years rise to ~50% once black swans stack on top). Answer letters are balanced: A/B/C/D ≈ 25% each. The recurrent base is ~280–312/year (1999 and 2026 are lighter — fewer indicators existed in 1999; 2026 is partial).

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 / event / blind / revealed (the experiment types above).
subtype — recurrent / shock_aftermath / event subtypes (fomc_rate, nfp_change, cpi_yoy, gdp_yoy, vix_weekly, nasdaq_weekly).
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

Accuracy vs. year — group by year; a cliff at 2026 (→ ~25% chance level) is the clean signal that pre-2026 accuracy is partly memorization.
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.