Trump, Dell and Intel: Reading the Overlaps in Public Data

• Scope: This guide focuses on Trump-related data, including the U.S. government portfolio, Trump's stock mentions, Trump's personal portfolio, and companies connected to Trump Accounts and White House ballroom donations. For the broader congressional context, see the guide to politician-trade filing delays.

1/4: Finding meaningful overlaps

• Method: Look for “overlaps” between different public datasets. The image above shows examples.
• Myth: Trump holds a little of everything, so there is no way to tell which positions have additional context.
• Fact: The best-performing positions in this dataset overlap with government activity, positive public mentions, corporate donor relationships, and recent news.
• How: Compare the U.S. government portfolio, Trump Mentions, and Trump sponsors. A stock appearing in more than one dataset has multiple documented Trump-related connections.

2/4: Are there filing delays?

• U.S. government stock activity is disclosed on the same day. So are Trump's public stock mentions and corporate donations to Trump Accounts or the White House ballroom.
• Myth: Trump's personal filings arrive late, so Trump-related market data cannot be analyzed promptly.
• Fact: Three of the four datasets considered here are published on the same day and appear earlier than Trump's personal trade disclosures.

3/4: Do Trump's stock mentions matter?

• Not every mention is equal. The dataset includes strongly worded examples such as “go out and buy a Dell” and the Palantir post that placed the ticker symbol in parentheses, as well as overlaps with other Trump-related data.
• Myth: The mentions are only pump-and-dump activity.
• Fact: A historical model assigning $1,000 of positive exposure to every positive mention and $1,000 of negative exposure to every negative mention is up 103% since January 2025 in this dataset, compared with 32.8% for the S&P 500.
• Note: Timing matters, and an old mention is not current guidance. Dell is up 324% since the first “go out and buy a Dell” remark in this dataset. The overlap and timeline provide context for how much the price has already changed.

4/4: What about ETFs?

• It helps to know exactly what a Trump-related ETF contains.
• Timing: An ETF based on these datasets provides exposure at current prices, not at the historical prices shown in the source data. In the example given, a $100 allocation to a U.S. Government ETF would place $92 in Intel at its current price.
• Coverage: Check how the fund defines its holdings. The product known as the “Pelosi ETF,” for example, includes all Democrats rather than only trades reported by Nancy Pelosi's husband.