The Range Disclosure System
When congressional members disclose stock trades through PTR (Periodic Transaction Report) forms, they don't report exact dollar amounts. Instead, the STOCK Act requires disclosure using predetermined dollar ranges that provide general magnitude while obscuring precise values.
This system creates significant challenges for analysts and tracking websites attempting to quantify congressional trading activity. Every "precise" dollar figure you see on tracking sites is actually an estimate with substantial uncertainty margins.
Critical Limitation
No tracking website, including ours, knows the exact dollar amounts of congressional trades.All specific figures are mathematical estimates that may differ significantly from actual transaction values.
Understanding the Standard Range Categories
The STOCK Act establishes nine standard disclosure ranges for trade amounts. These ranges become progressively wider as dollar amounts increase:
| Range | Range Width | Midpoint Estimate | Max Error |
|---|---|---|---|
| $1,001 - $15,000 | $13,999 | $8,001 | ±$7,000 |
| $15,001 - $50,000 | $34,999 | $32,501 | ±$17,500 |
| $50,001 - $100,000 | $49,999 | $75,001 | ±$25,000 |
| $250,001 - $500,000 | $249,999 | $375,001 | ±$125,000 |
| $1,000,001 - $5,000,000 | $3,999,999 | $2,500,001 | ±$2,000,000 |
| $5,000,001 - $25,000,000 | $19,999,999 | $15,000,001 | ±$10,000,000 |
Key Observations
- •Widening Ranges: Error margins increase dramatically with trade size
- •Largest Error Potential: Multi-million dollar trades have ±$10M uncertainty
- •Percentage Error: Smaller trades have higher percentage uncertainty
Common Estimation Methods Compared
Different tracking websites and researchers use various methods to estimate trade values from disclosed ranges. Each approach has distinct advantages and limitations:
✅ Advantages
- • Simple and consistent
- • Mathematically unbiased
- • Easy to implement
- • Widely understood
❌ Limitations
- • Ignores human behavior patterns
- • May overestimate large trades
- • No consideration of market context
- • Large error margins at high ranges
Formula: Estimate = (Range_Min + Range_Max) ÷ 2
Example: $1M-$5M range → $3M estimate
✅ Advantages
- • Accounts for trading behavior
- • May be more accurate for small trades
- • Can incorporate market data
- • Reflects psychological thresholds
❌ Limitations
- • Complex to implement correctly
- • Requires assumptions about behavior
- • May introduce systematic bias
- • Difficult to validate accuracy
Concept: Assumes trades cluster toward range minimums
Example: $1M-$5M range → $1.8M estimate (weighted toward lower end)
✅ Advantages
- • Uses additional data context
- • Can estimate share quantities
- • Accounts for stock price movements
- • May identify partial vs. full sales
❌ Limitations
- • Requires extensive additional data
- • Assumptions about trading patterns
- • May not work for options/complex trades
- • Still has fundamental range limitations
Approach: Cross-reference stock prices and historical portfolio data
Example: Use stock price on trade date to estimate likely share count
Real-World Estimation Examples
Let's examine actual disclosed trades to understand how estimation methods perform and where significant uncertainties arise:
Example 1: Apple (AAPL) Sale
Disclosed Information
- Trade Date: December 20, 2024
- Asset: Apple Inc. (AAPL)
- Type: Sale
- Amount Range: $1,000,001 - $5,000,000
- Owner: Spouse
Estimation Analysis
- Midpoint Method: $3,000,001
- Uncertainty Range: ±$2,000,000
- AAPL Price (12/20): ~$190
- Implied Shares: 5,263 - 26,316 shares
- Confidence Level: Very Low
Reality Check: The actual sale could have been as small as $1,000,001 (5,263 shares) or as large as $5,000,000 (26,316 shares) - a 5x difference in magnitude.
Example 2: NVIDIA (NVDA) Option Exercise
Disclosed Information
- Trade Date: November 15, 2024
- Asset: NVIDIA Corp. (NVDA)
- Type: Exercise/Purchase
- Amount Range: $250,001 - $500,000
- Owner: Spouse
Estimation Challenges
- Option Strike: Unknown
- Exercise vs. Purchase: Ambiguous
- Share Quantity: Cannot determine
- Timing: Execution vs. disclosure date
- Market Context: Requires additional research
Complexity Factor: Option exercises involve unknown strike prices and exercise methods, making estimation significantly more difficult than simple stock purchases or sales.
Major Error Sources and Limitations
Understanding the sources of estimation error is crucial for properly interpreting congressional trading data. These limitations compound when aggregating multiple trades:
The fundamental constraint: disclosure ranges become exponentially wider at higher amounts.
Small Trades
$1K-$15K range: ±87% potential error
Large Trades
$5M-$25M range: ±67% potential error
When summing multiple trade estimates, individual errors compound unpredictably.
Example: Five trades with $1M estimated error each could result in aggregate error of $0 (errors cancel) to $5M (errors align) - impossible to predict.
Critical information absent from disclosures affects estimation accuracy:
- • Option strike prices and expiration dates
- • Whether trades represent full or partial position closures
- • Execution method (limit order, market order, etc.)
- • Exact timing within the trade date
Advanced estimation methods rely on assumptions about trading behavior that may not hold:
- • Assumption that trades cluster near range minimums
- • Behavioral patterns may differ between members
- • Market conditions affect trading decisions
- • Tax considerations influence timing and sizing
Best Practices for Interpreting Estimates
Given the inherent limitations of range-based estimation, here are evidence-based recommendations for interpreting congressional trading data:
Focus on Patterns, Not Precise Values
Use estimates to identify trading frequency, sector preferences, and timing patterns rather than calculating exact portfolio values.
Always Include Uncertainty Ranges
When citing estimated values, include the potential error range. A "$3M trade" should be described as "$1M-$5M disclosed range (est. $3M)."
Compare Methodology Across Sources
Different tracking websites may show different estimated values for the same trade. Understand which estimation method each source uses.
Weight Recent vs. Historical Data Differently
Recent trades provide better insights into current activity patterns. Historical aggregations have compounded estimation errors.
What NOT to Do
- • Don't use estimated values for investment decisions
- • Don't treat estimates as precise financial reporting
- • Don't ignore the substantial uncertainty margins
- • Don't assume different tracking sites show the same "truth"
Conclusion: Transparency with Acknowledged Limits
Congressional trading disclosures provide valuable transparency into lawmakers' financial activities, but the range-based reporting system creates inherent limitations in precision. Understanding these limitations is essential for proper interpretation of tracking data.
The most responsible approach is to use estimated values for pattern analysis and trend identification while acknowledging the substantial uncertainty in individual trade amounts. The goal should be informed transparency, not false precision.