| --- |
| language: en |
| license: cc-by-4.0 |
| task_categories: |
| - multiple-choice |
| - question-answering |
| size_categories: |
| - 1K<n<10K |
| pretty_name: FinDeepForecast-Historical-US |
| tags: |
| - finance |
| - economics |
| - macro |
| - forecasting |
| - federal-reserve |
| - time-series |
| - benchmark |
| dataset_info: |
| features: |
| - name: qid |
| dtype: string |
| - name: forecastType |
| dtype: string |
| - name: subtype |
| dtype: string |
| - name: indicator |
| dtype: string |
| - name: transform |
| dtype: string |
| - name: target_period |
| dtype: string |
| - name: info_cutoff |
| dtype: string |
| - name: forecast_end |
| dtype: string |
| - name: answer_release |
| dtype: string |
| - name: question |
| dtype: string |
| - name: options |
| list: string |
| - name: answer_letter |
| dtype: string |
| - name: answer_raw |
| dtype: string |
| - name: unit |
| dtype: string |
| - name: year |
| dtype: int32 |
| splits: |
| - name: '1999' |
| num_bytes: 74833 |
| num_examples: 236 |
| - name: '2000' |
| num_bytes: 104028 |
| num_examples: 328 |
| - name: '2001' |
| num_bytes: 100621 |
| num_examples: 318 |
| - name: '2002' |
| num_bytes: 107149 |
| num_examples: 337 |
| - name: '2003' |
| num_bytes: 107172 |
| num_examples: 336 |
| - name: '2004' |
| num_bytes: 101665 |
| num_examples: 322 |
| - name: '2005' |
| num_bytes: 103728 |
| num_examples: 328 |
| - name: '2006' |
| num_bytes: 101349 |
| num_examples: 321 |
| - name: '2007' |
| num_bytes: 102494 |
| num_examples: 321 |
| - name: '2008' |
| num_bytes: 113226 |
| num_examples: 352 |
| - name: '2009' |
| num_bytes: 100911 |
| num_examples: 318 |
| - name: '2010' |
| num_bytes: 106598 |
| num_examples: 335 |
| - name: '2011' |
| num_bytes: 106190 |
| num_examples: 334 |
| - name: '2012' |
| num_bytes: 105532 |
| num_examples: 333 |
| - name: '2013' |
| num_bytes: 101793 |
| num_examples: 322 |
| - name: '2014' |
| num_bytes: 104869 |
| num_examples: 331 |
| - name: '2015' |
| num_bytes: 108792 |
| num_examples: 341 |
| - name: '2016' |
| num_bytes: 113696 |
| num_examples: 356 |
| - name: '2017' |
| num_bytes: 102019 |
| num_examples: 324 |
| - name: '2018' |
| num_bytes: 111264 |
| num_examples: 348 |
| - name: '2019' |
| num_bytes: 102219 |
| num_examples: 324 |
| - name: '2020' |
| num_bytes: 107109 |
| num_examples: 334 |
| - name: '2021' |
| num_bytes: 102620 |
| num_examples: 324 |
| - name: '2022' |
| num_bytes: 109808 |
| num_examples: 343 |
| - name: '2023' |
| num_bytes: 107952 |
| num_examples: 339 |
| - name: '2024' |
| num_bytes: 108210 |
| num_examples: 339 |
| - name: '2025' |
| num_bytes: 106355 |
| num_examples: 329 |
| - name: '2026' |
| num_bytes: 49530 |
| num_examples: 155 |
| download_size: 994498 |
| dataset_size: 2871732 |
| configs: |
| - config_name: default |
| data_files: |
| - split: '1999' |
| path: data/1999-* |
| - split: '2000' |
| path: data/2000-* |
| - split: '2001' |
| path: data/2001-* |
| - split: '2002' |
| path: data/2002-* |
| - split: '2003' |
| path: data/2003-* |
| - split: '2004' |
| path: data/2004-* |
| - split: '2005' |
| path: data/2005-* |
| - split: '2006' |
| path: data/2006-* |
| - split: '2007' |
| path: data/2007-* |
| - split: '2008' |
| path: data/2008-* |
| - split: '2009' |
| path: data/2009-* |
| - split: '2010' |
| path: data/2010-* |
| - split: '2011' |
| path: data/2011-* |
| - split: '2012' |
| path: data/2012-* |
| - split: '2013' |
| path: data/2013-* |
| - split: '2014' |
| path: data/2014-* |
| - split: '2015' |
| path: data/2015-* |
| - split: '2016' |
| path: data/2016-* |
| - split: '2017' |
| path: data/2017-* |
| - split: '2018' |
| path: data/2018-* |
| - split: '2019' |
| path: data/2019-* |
| - split: '2020' |
| path: data/2020-* |
| - split: '2021' |
| path: data/2021-* |
| - split: '2022' |
| path: data/2022-* |
| - split: '2023' |
| path: data/2023-* |
| - split: '2024' |
| path: data/2024-* |
| - split: '2025' |
| path: data/2025-* |
| - split: '2026' |
| path: data/2026-* |
| --- |
| |
| # FinDeepForecast-Historical-US |
|
|
| A **historical** version of the [FinDeepForecast](https://openfinarena.com/fin-deep-forecast/) benchmark from OpenFinArena, covering **1999–2026** with ground-truth derived from real FRED time series. |
|
|
| Strictly follows the paper's two-track taxonomy: |
| - **Recurrent** — periodic numerical forecasts (CPI/GDP/Treasury/etc. value at a future date) |
| - **Non-Recurrent** — binary YES/NO forecasts on specific upcoming scheduled events (FOMC rate decisions, CPI/NFP release surprises, weekly market thresholds) |
|
|
| ## Highlights |
|
|
| | Metric | Value | |
| |---|---| |
| | Total questions | **8,437** | |
| | Recurrent | 6,366 (75.5%) — multiple choice (4 options) | |
| | Non-Recurrent | 2,071 (24.5%) — binary YES/NO | |
| | Years covered | 1999–2026 (28 splits) | |
| | Indicators | 49 US macro/market series from FRED | |
| | Avg per year | ~300 questions | |
|
|
| ## Quick Start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Single year |
| ds = load_dataset("TheFinAI/pre_test", split="2008") |
| |
| # Filter by forecast type |
| recurrent_2008 = ds.filter(lambda x: x["forecastType"] == "Recurrent") |
| non_recurrent_2008 = ds.filter(lambda x: x["forecastType"] == "Non-Recurrent") |
| ``` |
|
|
| ## Recurrent (paper-aligned periodic forecast) |
|
|
| > *"Forecast the value of [US CPI YoY Inflation Rate] for June 2010. |
| > (Information available up to 2010-04-15.)"* |
| > |
| > A) 1.45% B) 2.04% C) 2.42% D) 1.85% |
|
|
| - Format: 4-option MCQ with numeric values |
| - Generated for 49 indicators × 4 quarters/year × {level, yoy_pct, yoy_pp} transforms |
| - `info_cutoff` set ~60 days before target period |
|
|
| ## Non-Recurrent (paper-aligned binary YES/NO) |
|
|
| Follows the original paper's format: "Will [specific event] happen by [date]?" |
|
|
| ### 8 templates (all anchored to scheduled events) |
|
|
| | Template | Question pattern | Per year | |
| |---|---|---| |
| | T1 `fomc_cut` | Will FOMC cut rates ≥25bp at [date]? | ~8 | |
| | T2 `fomc_hike` | Will FOMC raise rates ≥25bp at [date]? | ~8 | |
| | T3 `fomc_hold` | Will FOMC keep rates unchanged at [date]? | ~8 | |
| | T4 `cpi_release_threshold` | Will CPI YoY for [month] exceed [threshold]%? | 12 | |
| | T5 `nfp_release_threshold` | Will NFP for [month] show change > [threshold]? | 12 | |
| | T6 `gdp_release_threshold` | Will GDP YoY for [quarter] exceed [threshold]%? | 4 | |
| | T7 `vix_weekly_spike` | Will VIX exceed [threshold] in week ending [date]? | 12 | |
| | T8 `nasdaq_weekly_gain` | Will NASDAQ gain more than [X]% in week ending [date]? | 12 | |
|
|
| ### Non-Recurrent example (real) |
|
|
| > *"Will the FOMC cut the federal funds target rate by at least 25 basis points |
| > at its meeting on 2020-03-15?"* |
| > |
| > A) YES B) NO |
| > |
| > ✓ Answer: A (YES) — Fed cut to zero on emergency Sunday meeting |
|
|
| ### YES/NO balance (across all 2,071 NR questions) |
|
|
| | Subtype | n | YES% | NO% | |
| |---|---:|---:|---:| |
| | vix_weekly_spike | 328 | 51% | 49% | |
| | nasdaq_weekly_gain | 328 | 41% | 59% | |
| | nfp_release_threshold | 326 | 49% | 51% | |
| | cpi_release_threshold | 313 | 52% | 48% | |
| | fomc_cut | 224 | 17% | 83% | |
| | fomc_hike | 224 | 19% | 81% | |
| | fomc_hold | 224 | 64% | 36% | |
| | gdp_release_threshold | 104 | 47% | 53% | |
| |
| (FOMC imbalance reflects reality: most meetings are "hold" decisions.) |
| |
| ## Schema |
| |
| | Field | Type | Description | |
| |---|---|---| |
| | `qid` | string | Unique question ID | |
| | `forecastType` | string | `Recurrent` or `Non-Recurrent` | |
| | `subtype` | string | Fine-grained subtype | |
| | `indicator` | string | Primary FRED series ID | |
| | `transform` | string | `level` / `yoy_pct` / `yoy_pp` (Recurrent), `""` (NR) | |
| | `target_period` | string | Period asked about | |
| | `info_cutoff` | string | YYYY-MM-DD — latest info allowed | |
| | `forecast_end` | string | YYYY-MM-DD — last day of horizon | |
| | `answer_release` | string | NR only — when truth becomes verifiable | |
| | `question` | string | Question text | |
| | `options` | list[string] | Length 4 (Recurrent) or 2 (NR) | |
| | `answer_letter` | string | A/B/C/D (Recurrent) or A/B (NR) | |
| | `answer_raw` | string | Underlying answer value | |
| | `unit` | string | `%`, `index`, `binary`, etc. | |
| | `year` | int | Convenience field | |
|
|
| ## Indicators (49 FRED series) |
|
|
| | Category | Examples | |
| |---|---| |
| | Inflation (8) | CPIAUCSL, CPILFESL, PCEPI, PCEPILFE, PPIACO, PPIFIS, DCOILWTICO, DCOILBRENTEU | |
| | Labor (6) | UNRATE, PAYEMS, CIVPART, EMRATIO, AHETPI, ICSA | |
| | Growth (4) | GDPC1, GDP, INDPRO, TCU | |
| | Rates (8) | FEDFUNDS, DGS3MO, DGS2, DGS5, DGS10, DGS30, T10Y2Y, MORTGAGE30US | |
| | Money (4) | M2SL, BOGMBASE, TOTBKCR, CCSA | |
| | Consumer (5) | UMCSENT, PCE, PSAVERT, RSAFS, DSPI | |
| | Housing (3) | HOUST, PERMIT, CSUSHPINSA | |
| | Manufacturing (3) | DGORDER, BOPGSTB, NEWORDER | |
| | Market (8) | SP500, NASDAQCOM, DJIA, VIXCLS, DTWEXBGS, DEXUSEU, DEXJPUS, DEXCHUS | |
|
|
| ## Coverage Notes |
|
|
| - **1999** has fewer Recurrent questions (~180) because some indicators |
| (`PPIFIS`, `SP500`, `DJIA`, `DTWEXBGS`) start later than 1999 on FRED. |
| - **2026** is a partial year (data through April/May 2026); contains only |
| questions whose ground truth is verifiable. |
| - All NR questions tied to FOMC meetings, BLS releases (CPI/NFP), BEA releases (GDP), |
| or weekly market thresholds — all from scheduled/public calendars. |
|
|
| ## Differences from the Original FinDeepForecast |
|
|
| | Aspect | Original (live, 2025-10 → 2025-12) | This dataset (historical) | |
| |---|---|---| |
| | Coverage | 10 weeks | 28 years | |
| | Markets | 8 (US/UK/CN/HK/JP/SG/DE/FR) | US only | |
| | Recurrent total | 296 macro + 699 corporate | 6,366 (macro only) | |
| | Non-Recurrent total | 128 macro + 247 corporate | 2,071 (macro only) | |
| | Ground truth | Future outcome (live) | Historical realized values | |
| | Format | Numeric (Rec) + YES/NO (NR) | **Same** — 4-option MCQ + YES/NO | |
|
|
| This historical version sacrifices the live "no memorization" property of the |
| original benchmark in exchange for reproducible offline evaluation across 28 years. |
|
|
| ## License |
|
|
| CC-BY-4.0. Underlying FRED data is in the public domain (FRED API terms). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{findeepforecast, |
| title={FinDeepForecast: A Live Benchmark for Financial Forecasting with LLMs}, |
| author={OpenFinArena}, |
| year={2026}, |
| url={https://openfinarena.com/fin-deep-forecast/} |
| } |
| ``` |
|
|
