Which Assets Finance YouTube Was Bullish On Last Month

Every stock, fund, coin and commodity named in 305 top finance videos (26 Aug to 26 Sep 2026), with sentiment. Charts, an explorer and the CSV.

By TubeExtract team · 8 min read

Finance YouTube runs on opinions: what to buy, what to dump, what is about to move. We wanted to know what those opinions added up to, so we took the most-viewed English finance videos of the last month and pulled out every asset they named and how the speaker felt about it.

The result covers 305 videos from 198 channels: 3,566 mentions of 1,902 different stocks, funds, coins, indexes, bonds and commodities. Across all of them, 42% of mentions were positive, 23% negative and 35% neutral. The whole dataset is below to explore, and free to download.

Free dataset · CC BY 4.0
Download the full data as CSV

3,566 rows, one per video and asset · 305 videos · 592 KB · opens in Excel and Google Sheets

Download CSV

What finance YouTube talked about

Gold was named in more videos than any single stock (82), just ahead of NVIDIA (79) and Bitcoin (77). The S&P 500, silver and oil complete the top six. Big tech follows as a group, and most of the rest is a long tail: most assets were named in a single video.

The 12 assets named in the most videos, from Gold (82) down, split into positive, neutral and negative mentions.Gold82Nvidia79Bitcoin77S&P 50068Silver50Oil47META3810-Year Treasury37Apple34Amazon33Ethereum32Microsoft31positiveneutralnegativevideos naming the asset
The 12 assets named in the most videos. Each video counts once per asset, with the sentiment it expressed most.

Bullish and bearish

Net sentiment is the share of an asset’s videos that were positive minus the share that were negative. Among assets named in at least ten videos, the most one-sided calls were on AI chips and crypto.

  • AMD: 22 positive, 0 negative across 27 videos (+81%). NVIDIA was talked about far more but split more often: 48 positive, 14 negative (+43%). Palantir came in at +55%.
  • Crypto beyond Bitcoin. Solana (15 positive, 0 negative), Ethereum (23 to 0) and XRP (+64%) were the most positive coins; Bitcoin landed at +55%.
  • Precious metals. Silver +48% and gold +38%.
  • On the bearish side: the 10-year Treasury (-27%, and lower still under the other names it was called by), oil (-23%), diesel (-36%), Nike (-25%) and Oracle (-11%).
Net sentiment for the 35 assets named in at least 10 videos, from AMD at +81% to 10-Year at -57%.-100%-50%0%+50%+100%AMD+81% · 27Solana+79% · 19Ethereum+72% · 32XRP+64% · 28Zcash+62% · 13Hyperliquid+60% · 10Broadcom+60% · 10Robin Hood+60% · 10Bitcoin+55% · 77Palantir+55% · 22Silver+48% · 50Nvidia+43% · 79META+42% · 38Gold+38% · 82Micron+33% · 21Precious Metals+30% · 10Intel+27% · 11Apple+26% · 34Tesla+18% · 28CoreWeave+18% · 11Google+17% · 30Microsoft+13% · 31Nasdaq+13% · 23S&P 500+7% · 68Amazon+6% · 33S&P0% · 25Oracle-11% · 18Alphabet-17% · 12Oil-23% · 47NIKE-25% · 1210-Year Treasury-27% · 37Diesel-36% · 1410-Year Yield-50% · 12Us Treasuries-50% · 1010-Year-57% · 14
Net sentiment for every asset named in at least ten videos, with the number of videos after each value. Names are counted as spoken, so “S&P” and “S&P 500”, or “Google” and “Alphabet”, have rows of their own.

Explore the data

Every asset named in two or more videos. Click one to see the videos behind it, how each speaker felt, and every spelling it arrived under in the captions.

AssetVideosSentimentNet
Goldcommodity82
+38%
NvidiaNVDA79
+43%
Bitcoincrypto77
+55%
S&P 500index68
+7%
Silvercommodity50
+48%
Oilcommodity47
-23%
METAMETA38
+42%
10-Year Treasurybond37
-27%
AppleAAPL34
+26%
AmazonAMZN33
+6%
Ethereumcrypto32
+72%
MicrosoftMSFT31
+13%
Googlestock30
+17%
TeslaTSLA28
+18%
XRPcrypto28
+64%
AMDAMD27
+81%
S&Pindex25
0%
Nasdaqindex23
+13%
PalantirPLTR22
+55%
MicronMU21
+33%
Solanacrypto19
+79%
OracleORCL18
-11%
Dieselcommodity14
-36%
10-Yearbond14
-57%
Zcashcrypto13
+62%
25 of 188 assets
commodity

Gold

43 positive · 12 negative · 27 neutral

gold ×77Gold ×5

How we built it

1. The videos

YouTube’s own search, filtered to “This month” and sorted by view count, across 24 finance queries: stocks to buy, AI stocks, dividend stocks, ETFs, index funds, bitcoin, gold, silver, interest rates, the bond market and more. We kept English videos of 4 to 90 minutes whose title names a finance subject, at most ten per channel, and took the most viewed. This edition covers 305 of them that named at least one asset.

2. The extraction

Each video went through the TubeExtract API with the schema below, 50 videos per request. Every row is one asset in one video: the name as the speaker said it, its type, and the speaker’s sentiment towards it.

schema
the one used for this dataset
{
"multiple": true,
"max_rows": 30,
"columns": [
{
"name": "asset",
"type": "verbatim-string",
"description": "A specific, publicly traded asset the speaker names: a stock, an ETF, a mutual fund, an index, a cryptocurrency, a government bond or a commodity."
},
{
"name": "type",
"type": "enum",
"options": [
"stock",
"ETF",
"mutual fund",
"index",
"crypto",
"bond",
"commodity"
],
"description": "What kind of asset it is"
},
{
"name": "sentiment",
"type": "enum",
"options": [
"positive",
"negative",
"neutral"
],
"description": "How the speaker talks about this asset"
},
{
"name": "ticker",
"type": "string",
"description": "The asset's ticker symbol, if the speaker says it or it is unambiguous"
}
]
}

3. One label per asset

Captions spell things the way they sound: “palanteer”, “salana”, “cloudfare”. Every name was first matched to a ticker where one exists, checked against the US exchange symbol directory. The remaining names were grouped automatically: two names become one label when their embeddings are close (cosine similarity of at least 0.90) and they are spelled as the same asset, measured by Jaro-Winkler similarity and edit distance, word by word, with numbers required to match. Tested on 54 pairs from this data whose answer we know, the rule matched 46 and joined no two different assets. The largest merged groups:

Nvidianvidia ×74 · nvidia stock ×1 · nvidas ×1 · nvidia vr nlv72 ai computers ×1 · nvidia exemplar cloud status ×1 · nvidia gpu ×1
S&P 500s&p 500 ×66 · snp 500 ×1 · s&p 500 etf ×1
METAmeta ×35 · meta platforms ×3
10-Year Treasury10-year treasury ×33 · 10-year treasuries ×3 · the 10-year treasury ×1
Appleapple ×33 · apple stock ×1
Microsoftmicrosoft ×30 · microsoft stock ×1
Teslatesla ×26 · tesla stock ×2
Nasdaqnasdaq ×22 · the nasdaq ×1
Palantirpalanteer ×12 · palantir ×10
Solanasolana ×14 · salana ×5
10-Year10-year ×11 · 10 years ×2 · 10 year ×1
10-Year Yield10-year yield ×11 · 10-year yields ×1

4. The numbers

A video counts once per asset, with the sentiment it expressed most often about it. Net sentiment is positive videos minus negative videos, as a share of all videos naming the asset.

NOTE
Sentiment here is what the speaker said, not whether they were right, and nothing on this page is financial advice.

Download the data

finance-youtube-2026-08-26_to_09-26.csv: 3,566 rows, one per video and asset, with the columns video_url, video_title, channel, views, asset_as_spoken, asset, ticker, type and sentiment. Filter the asset column to see every video behind a single asset. Free to use under CC BY 4.0, with a link back to this page.

Free dataset · CC BY 4.0
Download the full data as CSV

3,566 rows, one per video and asset · 305 videos · 592 KB · opens in Excel and Google Sheets

Download CSV

Run it on your own videos

The whole dataset is one schema and a list of URLs. Swap in your own channels, a different month, or other columns (a price target, a time horizon, the reason given) and you have your own tracker. You describe the columns, send up to 200 videos per request, and get typed rows back, at 10 credits per started minute of video.

curl https://api.tubeextract.dev/v1 \
  -H "X-API-Key: $TUBEEXTRACT_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "videos": ["https://www.youtube.com/watch?v=...", "... up to 200 URLs"],
    "schema": {
      "multiple": true,
      "columns": [
        { "name": "asset", "type": "verbatim-string",
          "description": "A publicly traded asset the speaker names" },
        { "name": "type", "type": "enum",
          "options": ["stock", "ETF", "mutual fund", "index", "crypto", "bond", "commodity"],
          "description": "What kind of asset it is" },
        { "name": "sentiment", "type": "enum",
          "options": ["positive", "negative", "neutral"],
          "description": "How the speaker talks about this asset" }
      ]
    }
  }'

Start with the quickstart, or read how to write a schema for the column wording that shapes the rows.

Try it on your own videos

Describe the columns, send up to 200 video URLs, get typed rows back. New accounts start with 3,000 credits, about 300 minutes of video.

Get an API keyRead the docs