Datasets:
File size: 4,866 Bytes
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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
```python
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.
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