benroodman/HillStreetSample

This sample was made by sampling 1,000 rows from every table of HillStreet.

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HillStreet: A Relational Dataset for Evaluating Information Channels in Congressional Trading

HillStreet is a large-scale, longitudinal dataset and multimodal dynamic graph formalizing the intersection of Capitol Hill and Wall Street. It spans 13.5 years of mandatory STOCK Act disclosures (July 2012–December 2025), unifying the congressional trading ecosystem into a single, machine-learning-ready framework.

Dataset Summary

The dataset represents the relationship between 1,137 legislators and 6,825 companies. By framing congressional trading as a dynamic bipartite graph, HillStreet allows researchers to treat trade signal validation as an edge classification task.

• Nodes: Legislators (session-specific) and Publicly Traded Companies.
• Target Edges: Individual stock trades.
• Structural Edges: Lobbying records, campaign finance (PAC/527) contributions, and geographical/industrial-constituency alignments.

Dataset Structure

HillStreet is divided into pre-built graph objects for deep learning and a relational tabular database accessed via Hugging Face configurations.

1. Dynamic Graph Objects (.pt & .npy)

For immediate use in Graph Neural Networks (GNNs) and Temporal Graph Networks (TGNs), the core of HillStreet consists of annual PyTorch Geometric Temporal objects.

• Graph Files: hillstreet_temporal_graph_YEAR.pt
• Temporal Integrity: Every node feature and edge is instantiated based on its public disclosure date, not its reference date, ensuring a look-ahead-bias-free environment for backtesting.
• Node Features: Includes rolling DW-NOMINATE scores, SEC fiscal facts, and Census district employment statistics.
• ID Mappings: src_id_map.npy (Legislator Bioguide IDs) and dst_id_map.npy (Company Tickers).

2. Relational Tables (Hugging Face Configs)

For researchers using flat-feature models (XGBoost, LightGBM) or custom graph builders, the structural connective tissue is provided as multiple dataset configurations. You can load these individually using the Hugging Face datasets library (e.g., load_dataset("benroodman/HillStreet", "processed_events_lobbying")).

Processed Edge Tables:

• processed_events_lobbying: Mappings of legislative activity to corporate nodes.
• processed_events_campaign_finance: Itemized PAC/527 donations broadcasted to corporate sectors.
• processed_events_geographical_industry: Industrial-constituency edges linking legislators to companies in their districts.

Raw Source Tables: The raw, underlying tables are also available as distinct configurations for custom aggregations and feature engineering:

• Campaign Finance: raw_cf_* and raw_527_* configs.
• Legislator Data: raw_voteview_* configs and raw_committee_assignments.
• Corporate & Industry: raw_sec_financials, raw_naics_* crosswalks, and raw_district_industries_* configs.
• Lobbying: raw_lobbyview_* configs.

Feature Engineering & Normalization

To stabilize variance in graph training, continuous features are transformed using signed log-scaling: $$x^{\mathrm{\prime }}=\text{sign}(x)\times \log (1+\mathrm{\mid }x\mathrm{\mid })x\text{'}\; =\; \backslash text\{sign\}(x)\; \backslash times\; \backslash log(1\; +\; |x|)$$

Intended Use

• Trade Signal Validation: Determining if a trade constitutes a meaningful price signal based on political context.
• Graph Representation Learning: A benchmark for GNNs and TGNs.

Non-Intended Use: This dataset is for research purposes only. It is not designed for legal determinations of insider trading nor for real-time automated trading.