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

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.