Nancy Pelosi is probably the most-talked-about trader in Congress. Her name shows up in headlines every time a new disclosure drops, and for good reason — the returns are hard to ignore.
But most coverage skips the actual numbers. Let’s fix that.
Her current portfolio
Based on our parsed disclosure data, here are Pelosi’s current positions by weight:
| Ticker | Weight | Side |
|---|---|---|
| NVDA | 19.2% | Long |
| AVGO | 13.9% | Long |
| AMZN | 12.6% | Long |
| AB | 11.5% | Long |
| CRM | 8.3% | Long |
| NFLX | 8.3% | Long |
| GOOG | 8.2% | Long |
| PANW | 7.5% | Long |
| TSLA | 5.5% | Long |
| MSFT | 4.9% | Long |
Her portfolio is heavily concentrated in tech (NVDA, AVGO, AMZN, GOOG, MSFT, CRM) with a tilt toward AI infrastructure plays (NVDA, AVGO) and cybersecurity (PANW). NVIDIA alone makes up nearly a fifth of the portfolio.
Backtest performance
These numbers reflect what your returns would have been if you copied each trade the moment it became public — not when Pelosi actually traded. The disclosure delay (up to 45 days under the STOCK Act) is fully baked in.
| Period | PnL | CAGR |
|---|---|---|
| 1 month | -6.81% | -57.61% |
| 3 months | -7.95% | -28.28% |
| 6 months | +1.85% | +3.75% |
| 1 year | +23.02% | +23.02% |
| 3 years | +184.19% | +41.65% |
Risk score: 0.28 · Sharpe ratio: 1.48 · Total trades processed: 117
The 3-year number is the standout: +184% cumulative return, translating to a 41.65% CAGR. Even over the past year, +23% beats the S&P 500 comfortably, all while accounting for the disclosure delay.
Short-term performance swings are expected with a concentrated tech portfolio. The -6.81% last month reflects the broader tech correction.
Recent trades
Her latest filing (disclosed January 23, 2026) shows active portfolio management:
- Sold AAPL — up to $15M (largest single trade)
- Sold NVDA — up to $3M
- Sold AMZN — up to $3M
- Sold DIS — up to $3M
- Sold GOOGL — up to $3M
- Bought AB — up to $3M
- Bought AMZN — $175K–$750K (multiple lots)
- Bought NVDA — $175K–$375K (multiple lots)
- Bought GOOGL — $375K–$750K (multiple lots)
- Bought VST — $175K (Vistra, energy/AI play)
- Bought TEM — $75K (Tempus AI, healthcare AI)
The pattern: trimming big winners (AAPL, NVDA, DIS) while simultaneously buying back smaller positions in the same names. She also added two new AI-adjacent names (VST, TEM) — smaller positions but directionally interesting.
Previous filings in 2025 show the AVGO entry ($3M+ in June 2025) and continued NVDA accumulation throughout the year.
Why she gets so much attention
Part of it is name recognition — she’s the former Speaker of the House. Part of it is the size of the trades — we’re talking about positions worth millions, not small bets. And part of it is timing. Several of her disclosures landed right before major tech earnings or policy announcements, which fuels speculation about information asymmetry.
Whether that’s skill, luck, or something else entirely is a debate that’s been going on for years. The data alone can’t settle it. What the data can tell you is how the portfolio performs when you follow along with the public information available to everyone.
How she compares
Pelosi ranks #8 among all politicians by 1-year CAGR in our database. She’s not #1 — Ashley Moody tops the list at +76.51%, and John Fetterman is at +73.07%. But Pelosi’s 3-year track record (+184%) and Sharpe ratio (1.48) are among the best, showing consistent risk-adjusted returns over time.
For the full ranking, see our top politicians page. And for the complete picture of how congressional trading works, there’s our full guide.
What to take away
Pelosi’s portfolio is real data, not speculation. The numbers above come directly from her mandatory public filings, parsed and backtested with the disclosure delay built in. The 3-year CAGR of 41.65% with a Sharpe of 1.48 is genuinely strong.
But “strong past backtests” doesn’t mean “guaranteed future returns.” The portfolio is concentrated in tech and AI — if that sector corrects hard, so will these numbers. The real value is in following the disclosures as they happen and making your own decisions from there.
