Public fascination with political trading has surged in recent years — driven by one persistent question: Do politicians know things before markets do? While the truth is more nuanced, congressional trading disclosures offer a rare dataset where investor behavior, policy visibility, and market timing intersect.

Using FMP's Latest Senate Financial Disclosures API and Latest House Financial Disclosures API, we examined recent congressional trades and compared their short-term price performance against standard benchmarks. The goal is not to claim alpha, but to explore whether any patterns or sector clusters emerge when political activity meets market data.

A Look at Recent Senate & House Trades

Below are three notable Senate trades and three notable House trades disclosed during November. As always, the disclosure date does not necessarily equal the transaction date — trades may occur earlier and be reported later.

Pulling the latest batch of disclosures reveals that congressional trading activity is overwhelmingly concentrated in a few areas: mega-cap technology, large financial institutions, high-risk thematic ETFs (uranium, lithium, crypto/Bitcoin), and premium consumer brands.

Notable Senate Trades

1. MSFT — Microsoft Corp. (Purchase)
   By Markwayne Mullin (OK, Joint)
   Total Purchase: $250,000 - $500,000
   Trade Date: Nov 3
   
   
2. HBAN — Huntington Bancshares (Sale)
   By spouse of Tina Smith (MN)
   Total Sale: $100,000 - $250,000
   Trade Date: Nov 21
   
   
3. BITB — Bitwise Bitcoin ETF (Purchase)
   By Dave McCormick (PA, Self)
   Total Purchase: $75,000 - $150,000
   Trade Dates: Nov 24-25
   
   

Notable House Trades

1. NFLX — Netflix Inc. (Purchase)
   By Rep. Cleo Fields (LA-06)
   Total Purchase: $200,000 - $500,000
   Trade Date: Nov 3
   
   
2. AVGO — Broadcom Inc. (Purchase)
   By Rep. Jared Moskowitz (FL-23)
   Total Purchase: $18,000 - $95,000
   Trade Date: Oct 10
   
   
3. TSM — Taiwan Semiconductor Manufacturing (Purchase)
   By Rep. Jared Moskowitz (FL-23)
   Total Purchase: $3,000 - $45,000
   Trade Date: Oct 10
   
   

Congressional trades differ from executive insider filings and should not be confused with FMP's Insider Trading API. These disclosures include the ticker, trade date, transaction type (buy/sell), estimated dollar range, ownership type, and reporting date.

By pairing these records with price and benchmark data, we can construct a simple post-trade drift analysis.

Short-Term Performance Check

Using the Full Chart API, we calculated short-term returns from each trade date based on daily closing prices. For benchmarks, we used the S&P 500 via the Index Quotes API.

Senate Trades Snapshot

Ticker	Trade Date	Type	7D Return	S&P 7D Return	Relative 7D Return
MSFT	Nov 3	Purchase	-1.14%	+0.015%	-1.155%
HBAN	Nov 21	Sale	+7.30%	+3.73%	+3.57%
BITB	Nov 24	Purchase	+4.27%	+2.15%	+2.12%

House Trades Snapshot

Ticker	Trade Date	Type	7D Return	S&P 7D Return	Relative 7D Return
NFLX	Nov 3	Purchase	+5.21%	+0.015%	5.195%
AVGO	Oct 10	Purchase	+5.55%	+2.79%	2.76%
TSM	Oct 10	Purchase	+4.93%	+2.79%	2.14%

The takeaway is mixed: some trades modestly outperform benchmarks, while others lag. There is no universal “politician alpha”, but in this sample, the dispersion is notable.

Building a Monitoring Loop Using FMP APIs

With FMP's APIs, analysts can build a repeatable screening workflow.

Before proceeding, generate your API key.

Step 1 — Pull the Latest Congressional Trades

Latest Senate Financial Disclosures API

https://financialmodelingprep.com/stable/senate-latest?page=0&limit=100&apikey=YOUR_KEY

Latest House Financial Disclosures API

https://financialmodelingprep.com/stable/house-latest?page=0&limit=100&apikey=YOUR_KEY

Example JSON response:

[

{

"symbol": "LRN",

"disclosureDate": "2025-01-31",

"transactionDate": "2025-01-02",

"firstName": "Markwayne",

"lastName": "Mullin",

"office": "Markwayne Mullin",

"district": "OK",

"owner": "Self",

"assetDescription": "Stride Inc",

"assetType": "Stock",

"type": "Purchase",

"amount": "$15,001 - $50,000",

"comment": "",

"link": "https://efdsearch.senate.gov/search/view/ptr/446c7588-5f97-42c0-8983-3ca975b91793/"

}

]

Step 2 — Fetch Price Data for Each Ticker

Use the Full Chart API to measure post-trade performance over:

• 3-day
  
  
• 7-day
  
  
• 30-day windows

Step 3 — Benchmark Against the S&P 500

• Pull ^GSPC from the Index Quote API
  
  
• Compute excess return:
  Ticker Return - Benchmark Return
  
  

Step 4 — Contextualize With Sector Data

Use the Sector Performance Snapshot API to see whether clustered political trades occur in:

• Outperforming sectors
  
  
• Laggards
  
  
• Policy-sensitive industries
  
  

Step 5 — Surface Outliers

Rank trades by:

• Relative return
  
  
• Sector concentration
  
  
• Senate + House overlap

This approach lets analysts treat congressional trades as a behavioral or anomaly signal, not a predictive model.

This analysis doesn't make alpha claims, but it does reveal dynamics worth monitoring. Congressional data becomes valuable not because politicians consistently “beat the market,” but because it allows analysts to evaluate narratives around policy-driven market behavior.

Analyze Stock-Specific Political Activity

If you want to dig deeper into a specific stock, you can use the Senate Trading Activity API and U.S. House Trades API to isolate all congressional activity tied to that individual ticker. This adds a valuable layer to the analysis: instead of looking only at broad trade scans, you can examine how frequently a particular stock appears across disclosures, who is trading it, and whether trading intensity spikes around earnings, regulatory events, or sector-wide volatility.

In practice, this becomes a useful input to a broader decision-making process — not as a predictive signal, but as contextual intelligence. Persistent or recurring political activity in a name can help analysts understand narrative pressure, policy sensitivity, or moments where congressional behavior diverges from market sentiment. As part of a multi-dataset workflow, this “ticker-level political footprint” can act as an additional diagnostic when evaluating a stock's risk, visibility, or catalyst environment.

Scaling This Workflow With FMP Plans

Once the basic workflow is in place, you can begin scaling it depending on your data needs and update frequency. The Starter Plan is typically the best entry point, since it includes full access to U.S. congressional trade disclosures and provides everything necessary to build simple watchlists, run screening logic, and experiment with post-trade drift analysis.

As your workflow grows — for example, when you begin incorporating larger watchlists, running more frequent refresh cycles, or integrating congressional data into dashboards and risk systems — moving to the Premium or Ultimate plans becomes the natural next step. These tiers offer higher data-refresh limits, bulk endpoints, and broader dataset coverage, which are essential for teams that want automated monitors, more complex models, or organization-wide dashboards.

In short, you can start small with the Starter Plan's core congressional dataset, and scale up to Premium or Ultimate as your analysis becomes more automated, more frequent, and more deeply integrated into your research or risk infrastructure.

Politics, Policy, and Price: A Continuing Research Frontier

Whether politicians outperform the market isn't a yes/no question — it's a lens. A way of understanding how policy awareness, risk sensitivity, and timing interact with broader market structure. Congressional trade data helps illuminate one behavioral dimension of investor activity, but it's most powerful when paired with other forms of drift-based analysis that explore how information travels through markets.

For example, another article on our blog provides an in-depth look at post-earnings announcement drift — a phenomenon where prices continue to move in the direction of an earnings surprise long after the headline release.

In both cases — congressional trading and earnings drift — the underlying idea is the same: markets don't always absorb information immediately. By using FMP's integrated datasets, analysts can observe how signals develop over time, measure reactions around key events, and build repeatable, data-driven frameworks that turn curiosity into structured insight.

Ultimately, political trading activity is not about proving that lawmakers “beat the market.” It's about understanding how policy knowledge, timing, sector exposure, and behavioral biases intersect — and using those intersections to enrich a broader analytical process grounded in transparency and repeatability.