| --- |
| 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. |
|
|
