Want to Find the Next Nvidia? Here’s How to Watch What ‘Big Active’ Is Buying | Trillions

Finance-Focused Summary (Markets / Investing / Portfolio Context)

This episode discusses a Bloomberg Intelligence tool (“Blue Horseshoe”) that helps investors observe what large “active” managers are buying and selling inside active ETFs. The idea is to use ETF activity as a proxy for what those managers may later do in their mutual funds, because ETF holdings are disclosed daily, while mutual fund trades/holdings can be lagged (often visible quarterly, with documents arriving weeks later).

The hosts frame this like a legal, observable alternative to “insider tip” behavior: rather than trying to get confidential information, you can follow institutional “breadcrumbs” by watching active ETF disclosures before broader index/thematic reflection catches up.

---

Key Figures, Metrics, and Timelines

• Active ETFs total AUM: about $2 trillion
• Some firms have over $100 billion in active ETF assets
• Disclosure timing
  • ETFs: daily visibility (“assets every day”)
  • Mutual funds: typically quarterly visibility; even then, reporting can arrive with weeks of delay (with monthly reports still expected but not immediately)
• Example illustration: Nvidia
  • Described as the 387th largest stock “10 years ago,” now #1 in the S&P 500
• Example cited performance: Okta
  • “Grown by 13% in 2 weeks”
• ETF-driven position growth cited (category scale/context):
  • “Stocks have gone from $11B to $33B in the last 6 months”
• Monitoring horizon: next 10 years—aimed at spotting early “small waves” before they become large

---

Tickers / Assets / Sectors / Instruments Mentioned

Equities

• Nvidia (NVDA) — cited as a major “blue horseshoe” style buying lead
• Okta (OKTA)
• Veeva Systems — mentioned as favored by JP Morgan
• Ryanair Holdings — mentioned as favored by Capital Group
• Coca-Cola — also mentioned as favored by Capital Group
• “Anacott Steel” / “Blue Horseshoe likes …” appears to be a movie quote, not a real ticker in the subtitles

Benchmarks / Indices

• S&P 500

ETFs

• iShares Innovation and Tech Active ETF — cited as buying Okta most in the prior ~30 days
• Capital Group Growth ETF — framed as mapping to behavior of an associated mutual fund team
• IDEF — discussed in the context of liquidity/impact

Manager / Firm References (not tickers)

• Fidelity, Capital Group, BlackRock, T. Rowe, Nuveen, JP Morgan
• iShares (ETF issuer/brand)
• Bloomberg Intelligence / analysts (methodology source)

---

Watch-Outs / Cautions (Methodological Limitations)

The episode emphasizes that the tool’s signals should be interpreted carefully:

• Liquidity risk & market impact
  • The tool’s impact metric may not fully capture whether ETF-driven buying can be absorbed without distorting prices.
• Crowding / over-influence
  • Some ETFs can become too large relative to the investable universe—especially in mid/small caps—creating excessive influence on a limited number of stocks.
• Impact metric refinement needed
  • The framework should better incorporate stock volume/liquidity, and consider whether both active and passive ETFs can become “too influential” (i.e., markets may “love” ETF flows too much).

---

Methodology / Step-by-Step Framework (As Described)

1. Define the universe of “active ETFs”

   • Separate true active ETFs from other ETF types; the discussion explicitly mentions excluding leveraged ETFs and buffer ETFs, focusing on more “traditional” active offerings.

2. Aggregate daily ETF holdings/flows by manager

   • Identify which influential active managers are consistently buying/selling.

3. Create an “impact metric”

   • Impact metric = (estimated money going into the stock from those ETFs) / (stock market cap)
   • Intended to detect where “belief is being formed” before broad market recognition or index inclusion.

4. Infer position-building from timing

   • Large managers often build positions gradually (days/weeks/months) due to fund size and visibility risk.
   • ETF transparency offers an earlier “peek” into where managers’ beliefs are forming before mutual funds reflect it later.

5. Curate and cross-check

   • Look for additional active ETFs that also bought the same stock.
   • Use manager overlap as confirmation.

6. Proposed improvements

   • Incorporate liquidity/volume to gauge whether flow size is supported by tradable market depth.
   • Expand the influence/integration analysis beyond active ETFs to include passive ETFs as well.

---

Presenters / Sources Mentioned

• Joel Weber
• Eric Balčunas
• Sharun Francis (Research Fellow, Bloomberg Intelligence)
• David Cohn (Mutual fund and active manager analyst)
• Magnus Hendrickson (credited as creator of Trillions)
• Keshav Pandya (credited as creator of Trillions)
• Amy Keene (executive producer)
• Cheryl Brumley (head of Bloomberg Podcast)

---

Disclosures / Disclaimers

• No explicit “not financial advice” disclaimer was present in the provided subtitles.