Abstract

This paper studies congressional activity as a potentially informative signal for systematic market positioning. The report frames the problem as one of extracting structured, event-driven information from public political developments and turning it into a reproducible trading workflow. The central idea is that public legislative and policy activity can be encoded into a systematic event score, then mapped into position sizing and holding-period decisions.

Introduction

Congressional activity often contains market-relevant information before it is fully reflected in prices. Committee hearings, bill introductions, sector-specific testimony, and voting patterns can all affect sentiment in industries exposed to regulation, procurement, defense spending, energy policy, and digital assets. The challenge is not whether the information exists, but how to transform it into a consistent signal that can be traded without discretion.

This paper treats congressional events as a structured stream of observable inputs and applies a rules-based framework for ranking event importance, filtering false positives, and controlling exposure.

Motivation

Three practical observations motivate the strategy:

1. Policy-sensitive assets react asymmetrically: sectors such as defense, healthcare, financials, semiconductors, and crypto often move more sharply on political headlines than broad market indices.
2. Event timing is public: hearings, votes, and hearings calendars are announced in advance, making them suitable for repeatable signal construction.
3. Discretion is fragile: an explicit score-and-filter framework is easier to backtest, audit, and deploy than a subjective news-reading process.

The research question is whether congressional data can be organized into a signal with enough structure to support event-driven positioning after transaction costs and timing constraints.

Key Equations

Congressional signal score:

where is hearing intensity, is bill relevance, is vote sensitivity, is media amplification, and is negative policy surprise.

Event credibility filter:

where is lead/lag noise, and is realized volatility over the lookback window.

Position sizing rule:

Net return after costs:

Event window return aggregation:

Algorithm Blueprint

Trade rules:

• Tradable only when event score clears threshold and noise filters pass
• Position size scales with conviction but remains capped
• Exit when signal decays, volatility spikes, or the event window closes

Results

Signal behavior across event types:

| Event Type | Relative Signal Strength | Expected Market Sensitivity | |---|---|---| | Committee hearing | High | Sector-specific | | Bill introduction | Medium | Theme-dependent | | Vote announcement | High | Broad if policy-critical | | Routine floor activity | Low | Usually filtered |

Event scoring summary:

| Metric | Value | |---|---| | Tradable event threshold | | | Volatility cap | | | Maximum position | | | Holding horizon | Event-dependent | | Transaction cost model | Entry + exit + slippage |

The strategy is designed to favor fewer, higher-conviction trades rather than broad event coverage. This keeps the framework closer to a research-grade signal filter than a headline-chasing system.

Contributions

• Introduces a deterministic scoring framework for congressional event selection and portfolio positioning
• Separates signal strength, timing noise, and realized volatility into explicit trade filters
• Converts legislative and policy activity into a reproducible, backtestable event-driven process
• Provides a structure that can be extended to sector baskets, single names, or thematic ETFs

Paper

Author

Frankline Misango Oyolo Arithmax Research Frankline@arithmax.com — Published: May 4, 2026