| --- |
| license: cc-by-4.0 |
| task_categories: |
| - time-series-forecasting |
| - tabular-regression |
| - text-generation |
| - question-answering |
| language: |
| - en |
| size_categories: |
| - 1M<n<10M |
| tags: |
| - finance |
| - macroeconomic |
| - multimodal |
| - benchmark |
| - sec-edgar |
| - xbrl |
| - rentcast |
| - small-cap |
| - russell-2000 |
| - private-valuation |
| - scenario-conditioned-forecasting |
| pretty_name: MacroLens |
| configs: |
| - config_name: panel_daily |
| data_files: |
| - split: train |
| path: data/daily/panel_train.parquet |
| - split: test |
| path: data/daily/panel_test.parquet |
| - config_name: panel_weekly |
| data_files: |
| - split: train |
| path: data/weekly/panel_train.parquet |
| - split: test |
| path: data/weekly/panel_test.parquet |
| - config_name: panel_monthly |
| data_files: |
| - split: train |
| path: data/monthly/panel_train.parquet |
| - split: test |
| path: data/monthly/panel_test.parquet |
| - config_name: scenarios_daily |
| data_files: data/daily/scenarios.parquet |
| - config_name: valuation_inputs_daily |
| data_files: data/daily/valuation_inputs.parquet |
| - config_name: private_valuation_inputs_daily |
| data_files: data/daily/private_valuation_inputs.parquet |
| - config_name: generation_inputs_daily |
| data_files: data/daily/generation_inputs.parquet |
| - config_name: generation_ground_truth_daily |
| data_files: data/daily/generation_ground_truth.parquet |
| - config_name: generator_eval_inputs_daily |
| data_files: data/daily/generator_eval_inputs.parquet |
| - config_name: generator_eval_ground_truth_daily |
| data_files: data/daily/generator_eval_ground_truth.parquet |
| - config_name: scenario_forecast_ground_truth_daily |
| data_files: data/daily/scenario_forecast_ground_truth.parquet |
| - config_name: real_estate_train |
| data_files: data/real_estate/re_train_properties.parquet |
| - config_name: real_estate_eval |
| data_files: data/real_estate/re_eval_inputs.parquet |
| --- |
| |
| # MacroLens |
|
|
| A benchmarking corpus for **contextual financial reasoning under macroeconomic scenarios** across **4,416 U.S. small- and micro-cap equities (2021-01-04 — 2026-03-31)**. MacroLens unifies seven tasks over a single point-in-time panel: contextual time-series forecasting, public valuation, financial-statement generation, scenario-conditioned return forecasting, private-company valuation, generator evaluation from natural-language descriptions, and real-estate valuation. |
|
|
|  |
|
|
| | Task | Type | Output | |
| |---|---|---| |
| | **T1** Contextual Forecasting | Time-series | Horizon-length close trajectory | |
| | **T2** Public Valuation | Tabular regression | Equity market cap | |
| | **T3** Financial Statement Generation | Structured generation | 11 canonical XBRL fields per (ticker, fiscal year) | |
| | **T4** Scenario-Conditioned Return | Event forecasting | 63-day post-event return percentage | |
| | **T5** Private-Company Valuation | Tabular regression (price-stripped) | Equity value w/o market data | |
| | **T6** Generator Evaluation | NL→ structured | Same 11 fields from a natural-language company description | |
| | **T7** Real-Estate Valuation | Cross-domain regression | Rent + price per RentCast address | |
|
|
| Every instance carries a 131-numeric / 141-column point-in-time panel (prices, 46.8M XBRL accounting facts, 53 macroeconomic series, filing recency, derived ratios), an optional macroeconomic scenario object (1,130 events across 49 types), and optional SEC filings + financial-news context. Temporal alignment is strictly point-in-time: every observation visible at prediction timestamp $t$ was publicly available by $t$. |
|
|
| ## Quickstart |
|
|
| ```python |
| import macrolens as ml |
| |
| # 1. Load (X, y, meta) — identical schema across train/test |
| X_train, y_train, meta_train = ml.load("T1", "train", granularity="daily") |
| X_test, y_test, meta_test = ml.load("T1", "test") |
| |
| # 2. Fit + Predict |
| model = ml.methods.LightGBMRegressor(task="T1") |
| model.fit(X_train, y_train, seed=42) |
| y_pred = model.predict(X_test) |
| |
| # 3. Score (cluster-bootstrap CIs by ticker for T1; adaptive n_boot) |
| metrics = ml.score("T1", y_test, y_pred) |
| print(metrics["mse"]["value"], metrics["mse"]["ci_lo"], metrics["mse"]["ci_hi"]) |
| ``` |
|
|
| 10 lines per task; swap the model class to compare methods. |
| ## Dataset structure |
|
|
| ``` |
| data/ |
| ├── daily/ # primary granularity (4.84M panel rows) |
| │ ├── panel_train.parquet # T1, T4 train side |
| │ ├── panel_test.parquet # T1, T4 eval side |
| │ ├── scenarios.parquet # 1,130 macroeconomic events |
| │ ├── valuation_inputs.parquet # T2 features |
| │ ├── valuation_ground_truth.parquet # T2 ground truth (market cap) |
| │ ├── private_valuation_inputs.parquet # T5 features (price-stripped) |
| │ ├── private_valuation_ground_truth.parquet # T5 ground truth |
| │ ├── generation_inputs.parquet # T3 fundamentals snapshot |
| │ ├── generation_ground_truth.parquet # T3 long-form (ticker, FY, field, value) |
| │ ├── generator_eval_inputs.parquet # T6 NL company descriptions |
| │ ├── generator_eval_ground_truth.parquet # T6 long-form |
| │ └── scenario_forecast_ground_truth.parquet # T4 ground truth |
| ├── weekly/ # Friday-close resampled (1.01M rows) — same file set as daily/ |
| ├── monthly/ # Last-trading-day resampled (232k rows) — same file set as daily/ |
| ├── real_estate/ |
| │ ├── re_train_properties.parquet # T7 train (53,804 unique addresses) |
| │ ├── re_eval_inputs.parquet # T7 eval (23,190 unique addresses) |
| │ └── re_eval_ground_truth.parquet # T7 ground truth (rent + price) |
| ├── xbrl/ # 46.8M standardized XBRL facts, 92.6% ticker coverage |
| ├── filings/ # 295,860 SEC filings (10-K, 10-Q, 8-K, 20-F, 6-K, N-CSR, N-CSRS) — markdown + PDF |
| ├── prices/ # OHLCV + adjusted close (yfinance) |
| ├── fundamentals/ # README placeholder only; raw CSVs reproducible via code/collect_fundamentals.py (see note below) |
| └── macro/ # 46 FRED + 6 EIA series |
| |
| manifest.json # SHA-256 over every parquet (provenance) |
| ``` |
|
|
| > **Note on `data/fundamentals/`**: Raw per-ticker fundamentals CSVs (~12,000 files: `{TICKER}_balance.csv`, `{TICKER}_income.csv`, `{TICKER}_cashflow.csv`) are **not shipped directly** here because HF enforces a hard cap of 10,000 files per directory. They are reproducible end-to-end via the released pipeline: |
| > |
| > ```bash |
| > python code/collect_fundamentals.py |
| > ``` |
| > |
| > The aggregated/processed versions used by the benchmark API (e.g. `valuation_inputs.parquet`, `private_valuation_inputs.parquet`, `generation_inputs.parquet`) are shipped directly under `data/daily/`, `data/weekly/`, `data/monthly/`, and `data/real_estate/`. |
| |
| ## Data sources & access requirements |
| |
| **What's bundled in this HF release** (no user credentials required): |
| |
| | Source | Bundled artifact | License | |
| |---|---|---| |
| | SEC EDGAR | `filings/` (295k docs), `xbrl/` (46.8M facts) | Public domain (US gov) | |
| | FRED | 46 macroeconomic series | Public domain | |
| | EIA | 7 commodity series | Public domain | |
| | yfinance | `prices/` (OHLCV), `fundamentals/` (quarterly) — derived features | Non-commercial (yfinance ToU) | |
| | RentCast | `real_estate/` (address-level derived features only — rent + price targets, property attributes) | RentCast ToU — derived only | |
| | Macroeconomic events | `scenarios.parquet` (1,130 events × 49 types) | Curated by us, CC-BY-4.0 | |
|
|
| **What's NOT bundled** (gated — user credentials required for raw re-fetch via `collect_*.py`): |
|
|
| | Source | Status | User-side requirement | |
| |---|---|---| |
| | **Financial-news provider** | **Excluded** — provider ToU prohibits redistribution. The release ships derived counts (`filing_8k_count_30d`, `news_count_7d`, `has_press_release_7d`) only. | **User's own news-API key required** for `collect_news.py` | |
| | **RentCast raw listings** | **Excluded raw** — proprietary. Derived features bundled. | **User's own RentCast subscription** required for `collect_real_estate.py` raw mode | |
|
|
| ## Universe |
|
|
| The 4,416-ticker universe combines: full Russell 2000 (1,923 IWM holdings), full S&P SmallCap 600 (72 IJR-only additions), iShares Micro-Cap (225 IWC additions), and 2,196 small-cap NASDAQ/NYSE tickers outside all three indices. The split is **3,857 operating companies + 333 funds + 226 SPACs**, with `security_type` recorded for applicability-aware stratification. |
|
|
| ## Splits |
|
|
| - **Forecasting (T1, T4)**: chronological 70/30 split at **2024-09-03**. |
| - **Valuation + generation (T2, T3, T5, T6)**: **30% company-level holdout = 1,324 tickers** (seed = 42), each contributing its latest valid snapshot. |
| - **Real-estate (T7)**: 30% address-level holdout (random, seeded), with per-property time-axis features. |
|
|
| Cluster-bootstrap 95% CIs are computed per task: by `ticker` (T1/T2/T3/T5/T6), `scenario_id` (T4), or `address` (T7). Number of bootstrap resamples is adaptive in [1k, 10k] until `(ci_hi - ci_lo) / |mean| < 0.05`. |
|
|
| ## Methods (panel) |
|
|
| The release ships a 18-method baseline panel across 7 families: 4 naive, 2 classical, 3 deep sequence, 3 zero-shot TSFM, 2 LLM-adapted multi-task systems, 3 zero-shot frontier LLMs (gpt-oss-120b, gpt-5.1, gemini-3-flash + qwen35). Every method registers via `@register(name=…, family=…, tasks=…)` and exposes the sklearn-style `(fit, predict, save, load)` contract. |
|
|
| ```python |
| ml.list_methods() # all registered methods |
| ml.list_methods(task="T1") # methods that support T1 |
| ml.list_methods(family="naive") # naive baselines |
| ``` |
|
|
| ## License |
|
|
| - **Data**: CC-BY-4.0 (derived features + curated panel) |
| - **Code**: MIT (`code/macrolens/`, `code/dataloader/`, `code/methods/`, `code/eval.py`, `code/experiments/`) |
| - **Vendored libraries** (under `code/methods/_vendored/`): |
| - `tslib/` — MIT (DLinear, iTransformer source) |
| - `moderntcn/` — Apache 2.0 (ModernTCN source) |
| - **Reconstruction scripts** (`collect_*.py`) provided for sources with redistribution restrictions: SEC filings (re-fetch from EDGAR), financial news (re-fetch from provider), real-estate (re-fetch from RentCast). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{macrolens2026, |
| title = {{MacroLens}: A Multi-Task Benchmark for Contextual Financial Reasoning under Macroeconomic Scenarios}, |
| author = {<authors>}, |
| booktitle = {<arxiv>}, |
| year = {2026} |
| } |
| ``` |
|
|
| ## Reproducibility |
|
|
| When `code/experiments/run_all.py` is executed, each method run produces a `RunRecord` JSON under `code/experiments/results/` recording: `git_sha`, `lib_versions`, `hardware`, `artifact_sha256` (SHA-256 of every parquet read), `timestamp`, and `deterministic_mode`. Predictions are also persisted at `code/experiments/predictions/<method>_<task>_seed<seed>.pkl` so eval logic can be re-applied via `code/experiments/re_evaluate.py` without re-running the models. These output directories are not shipped on Hugging Face — reviewers reproduce them by running the released code. |
|
|
| ## Reconstruction (raw filings + news) |
|
|
| The release ships derived features and reconstruction scripts; raw artifacts subject to redistribution restrictions remain re-fetchable: |
|
|
| ```bash |
| python collect_universe.py # iShares ETF holdings + NASDAQ Trader directory |
| python collect_filings.py # SEC EDGAR (10-K, 10-Q, 8-K, 20-F, 6-K, N-CSR, N-CSRS) |
| python collect_fundamentals.py # XBRL company facts via SEC EDGAR |
| python collect_prices.py # yfinance OHLCV + adjusted close |
| python collect_news.py # provider-specific (~215k articles) |
| python collect_real_estate.py # RentCast (100 metros, 139,855 properties) |
| python collect_macro.py # FRED + EIA series |
| python preprocess.py |
| python assemble_benchmark.py |
| python generate_scenarios.py |
| python enrich_benchmark.py |
| python build_valuation_tasks.py |
| python validate_all.py |
| ``` |
|
|
| ## Authors / Contact |
|
|
| Anonymous. Contact at `<email>` after author notification. |
|
|
