GE Vernova (GEV)

EarningsTranscriptsUpdated
Export
Copy
API
Open as TSVfor agentsOpen as JSONcolumnarOpen as Markdowntable
GET /api/v1/entity/ge-vernova

As of Oct 24, 2026, Kalshi and Polymarket have settled 42 word markets on GE Vernova’s earnings calls since 2026, on 13 different words. Next call: Oct 28, 2026 (15 words listed).

“Nuclear” on the Jul 22 call · 13:19 · said 3 times. Play the clip

Next: Earnings call ·

15 words listed · Occasion page

Who counts: Kalshi: company speakers + operator

Kalshi

GE Vernova: the words listed for Wed, Oct 28, how often each was said on past occasions, and the probability for this one
WordQ3’24Oct '24Q4’24Jan '25Q1’25Apr '25Q2’25Jul '25Q3’25year agoQ4’25Jan '26Q1’26Apr '26Q2’26last callTrendLast 8ScriptedAll-timeProbabilityMarket
Nuclear14 times: Nuclear, Q3’24 Oct '24, counts as said6 times: Nuclear, Q4’24 Jan '25, counts as said5 times: Nuclear, Q1’25 Apr '25, counts as said4 times: Nuclear, Q2’25 Jul '25, counts as said2 times: Nuclear, Q3’25 Oct '25, counts as said6 times: Nuclear, Q4’25 Jan '26, counts as said8 times: Nuclear, Q1’26 Apr '26, counts as said3 times: Nuclear, Q2’26 Jul '26, counts as saidfalling8/8100%7/888%8/1080%93%n=3+tx from settlement history blended with transcripts92¢ Kalshi
Tariff0 times: Tariff, Q3’24 Oct '240 times: Tariff, Q4’24 Jan '2519 times: Tariff, Q1’25 Apr '25, counts as said7 times: Tariff, Q2’25 Jul '25, counts as said4 times: Tariff, Q3’25 Oct '25, counts as said5 times: Tariff, Q4’25 Jan '26, counts as said10 times: Tariff, Q1’26 Apr '26, counts as said3 times: Tariff, Q2’26 Jul '26, counts as saidsteady6/875%6/875%6/1060%90%n=3+tx from settlement history blended with transcripts87¢ Kalshi
Inflationevery call2 times: Inflation, Q3’24 Oct '24, counts as said3 times: Inflation, Q4’24 Jan '25, counts as said9 times: Inflation, Q1’25 Apr '25, counts as said2 times: Inflation, Q2’25 Jul '25, counts as said1 time: Inflation, Q3’25 Oct '25, counts as said4 times: Inflation, Q4’25 Jan '26, counts as said3 times: Inflation, Q1’26 Apr '26, counts as said3 times: Inflation, Q2’26 Jul '26, counts as saidsteady8/8100%8/8100%10/10100%89%n=3+tx from settlement history blended with transcripts90¢ Kalshi
Prolec3+ times0 times: Prolec (3+ times), Q3’24 Oct '240 times: Prolec (3+ times), Q4’24 Jan '250 times: Prolec (3+ times), Q1’25 Apr '250 times: Prolec (3+ times), Q2’25 Jul '2561 times: Prolec (3+ times), Q3’25 Oct '25, counts as said23 times: Prolec (3+ times), Q4’25 Jan '26, counts as said18 times: Prolec (3+ times), Q1’26 Apr '26, counts as said9 times: Prolec (3+ times), Q2’26 Jul '26, counts as saidrising4/850%4/850%4/1040%88%transcripts from the transcript model88¢ Kalshi
Electricity4 times: Electricity, Q3’24 Oct '24, counts as said1 time: Electricity, Q4’24 Jan '25, counts as said1 time: Electricity, Q1’25 Apr '25, counts as said0 times: Electricity, Q2’25 Jul '252 times: Electricity, Q3’25 Oct '25, counts as said1 time: Electricity, Q4’25 Jan '26, counts as said3 times: Electricity, Q1’26 Apr '26, counts as said5 times: Electricity, Q2’26 Jul '26, counts as saidrising7/888%7/888%9/1090%86%n=3+tx from settlement history blended with transcripts86¢ Kalshi
Robotic / Robotics0 times: Robotic / Robotics, Q3’24 Oct '240 times: Robotic / Robotics, Q4’24 Jan '251 time: Robotic / Robotics, Q1’25 Apr '25, counts as said2 times: Robotic / Robotics, Q2’25 Jul '25, counts as said1 time: Robotic / Robotics, Q3’25 Oct '25, counts as said2 times: Robotic / Robotics, Q4’25 Jan '26, counts as said1 time: Robotic / Robotics, Q1’26 Apr '26, counts as said4 times: Robotic / Robotics, Q2’26 Jul '26, counts as saidrising6/875%6/875%6/1060%81%n=2+tx from settlement history blended with transcripts82¢ Kalshi
Data Center3+ times4 times: Data Center (3+ times), Q3’24 Oct '24, counts as said2 times: Data Center (3+ times), Q4’24 Jan '25, below the threshold7 times: Data Center (3+ times), Q1’25 Apr '25, counts as said2 times: Data Center (3+ times), Q2’25 Jul '25, below the threshold9 times: Data Center (3+ times), Q3’25 Oct '25, counts as said4 times: Data Center (3+ times), Q4’25 Jan '26, counts as said14 times: Data Center (3+ times), Q1’26 Apr '26, counts as said9 times: Data Center (3+ times), Q2’26 Jul '26, counts as saidrising6/875%4/850%6/1060%78%transcripts from the transcript model90¢ Kalshi
China0 times: China, Q3’24 Oct '243 times: China, Q4’24 Jan '25, counts as said10 times: China, Q1’25 Apr '25, counts as said0 times: China, Q2’25 Jul '251 time: China, Q3’25 Oct '25, counts as said0 times: China, Q4’25 Jan '261 time: China, Q1’26 Apr '26, counts as said1 time: China, Q2’26 Jul '26, counts as saidfalling5/862%5/862%5/1050%61%n=3+tx from settlement history blended with transcripts36¢ Kalshi
Vietnam / India3 times: Vietnam / India, Q3’24 Oct '24, counts as said3 times: Vietnam / India, Q4’24 Jan '25, counts as said1 time: Vietnam / India, Q1’25 Apr '25, counts as said0 times: Vietnam / India, Q2’25 Jul '250 times: Vietnam / India, Q3’25 Oct '251 time: Vietnam / India, Q4’25 Jan '26, counts as said5 times: Vietnam / India, Q1’26 Apr '26, counts as said0 times: Vietnam / India, Q2’26 Jul '26falling5/862%5/862%5/1050%58%n=3+tx from settlement history blended with transcripts41¢ Kalshi
Trump / Administration0 times: Trump / Administration, Q3’24 Oct '240 times: Trump / Administration, Q4’24 Jan '252 times: Trump / Administration, Q1’25 Apr '25, counts as said0 times: Trump / Administration, Q2’25 Jul '250 times: Trump / Administration, Q3’25 Oct '253 times: Trump / Administration, Q4’25 Jan '26, counts as said0 times: Trump / Administration, Q1’26 Apr '261 time: Trump / Administration, Q2’26 Jul '26, counts as saidrising3/838%1/812%3/1030%57%transcripts from the transcript model40¢ Kalshi
Iran / Middle East1 time: Iran / Middle East, Q3’24 Oct '24, counts as said0 times: Iran / Middle East, Q4’24 Jan '250 times: Iran / Middle East, Q1’25 Apr '251 time: Iran / Middle East, Q2’25 Jul '25, counts as said2 times: Iran / Middle East, Q3’25 Oct '25, counts as said2 times: Iran / Middle East, Q4’25 Jan '26, counts as said2 times: Iran / Middle East, Q1’26 Apr '26, counts as said0 times: Iran / Middle East, Q2’26 Jul '26rising5/862%4/850%6/1060%51%n=1+tx from settlement history blended with transcripts36¢ Kalshi
Blade13 times: Blade, Q3’24 Oct '24, counts as said2 times: Blade, Q4’24 Jan '25, counts as said1 time: Blade, Q1’25 Apr '25, counts as said1 time: Blade, Q2’25 Jul '25, counts as said1 time: Blade, Q3’25 Oct '25, counts as said1 time: Blade, Q4’25 Jan '26, counts as said0 times: Blade, Q1’26 Apr '260 times: Blade, Q2’26 Jul '26falling6/875%5/862%7/1070%40%n=3+tx from settlement history blended with transcripts26¢ Kalshi
BWRX-3000 times: BWRX-300, Q3’24 Oct '241 time: BWRX-300, Q4’24 Jan '25, counts as said0 times: BWRX-300, Q1’25 Apr '250 times: BWRX-300, Q2’25 Jul '251 time: BWRX-300, Q3’25 Oct '25, counts as said0 times: BWRX-300, Q4’25 Jan '260 times: BWRX-300, Q1’26 Apr '260 times: BWRX-300, Q2’26 Jul '26steady2/825%2/825%2/1020%32%transcripts from the transcript model33¢ Kalshi
Songonnever said0 times: Songon, Q3’24 Oct '240 times: Songon, Q4’24 Jan '250 times: Songon, Q1’25 Apr '250 times: Songon, Q2’25 Jul '250 times: Songon, Q3’25 Oct '250 times: Songon, Q4’25 Jan '260 times: Songon, Q1’26 Apr '260 times: Songon, Q2’26 Jul '26steady0/80%0/80%0/100%18%transcripts from the transcript model19¢ Kalshi
Oil0 times: Oil, Q3’24 Oct '240 times: Oil, Q4’24 Jan '251 time: Oil, Q1’25 Apr '25, counts as said0 times: Oil, Q2’25 Jul '250 times: Oil, Q3’25 Oct '250 times: Oil, Q4’25 Jan '260 times: Oil, Q1’26 Apr '260 times: Oil, Q2’26 Jul '26steady1/812%1/812%1/1010%15%n=2+tx from settlement history blended with transcripts18¢ Kalshi
  • times said on that call
  • counted as said (ring)
  • no call
  • probability next time, with n
  • Select a word or a cell for its quotes
What the columns mean
Last N
of the appearances shown, how many counted as said
Trend
how the count moved over these calls
Scripted
calls of the last 8 where it was said in the prepared remarks
All-time
every call on file
Probability
the calibrated probability for the next listing; n is the settled observations behind it when it was computed, '+tx' adds the transcripts, 'new' means too little history

Market prices are mid quotes from Kalshi and Polymarket, shown for context, may be delayed by several minutes and are not a signal. Quotes as of Oct 21, 2026 16:04 UTC.

Every word

1–3 of 3, most settled first
GE Vernova: every word with settled markets, most settled first
WordQ3’24Oct '24Q4’24Jan '25Q1’25Apr '25Q2’25Jul '25Q3’25Oct '25Q4’25Jan '26Q1’26Apr '26Q2’26last callTrendLast 8ScriptedAll-timeProbability
Data Centerevery call4 times: Data Center, Q3’24 Oct '24, counts as said2 times: Data Center, Q4’24 Jan '25, counts as said7 times: Data Center, Q1’25 Apr '25, counts as said2 times: Data Center, Q2’25 Jul '25, counts as said9 times: Data Center, Q3’25 Oct '25, counts as said4 times: Data Center, Q4’25 Jan '26, counts as said14 times: Data Center, Q1’26 Apr '26, counts as said9 times: Data Center, Q2’26 Jul '26, counts as saidrising8/8100%7/888%10/10100%96%n=3+tx from settlement history blended with transcripts
Administration0 times: Administration, Q3’24 Oct '240 times: Administration, Q4’24 Jan '251 time: Administration, Q1’25 Apr '25, counts as said0 times: Administration, Q2’25 Jul '250 times: Administration, Q3’25 Oct '253 times: Administration, Q4’25 Jan '26, counts as said0 times: Administration, Q1’26 Apr '261 time: Administration, Q2’26 Jul '26, counts as saidrising3/838%1/812%3/1030%58%n=3+tx from settlement history blended with transcripts
Irannever said0 times: Iran, Q3’24 Oct '240 times: Iran, Q4’24 Jan '250 times: Iran, Q1’25 Apr '250 times: Iran, Q2’25 Jul '250 times: Iran, Q3’25 Oct '250 times: Iran, Q4’25 Jan '260 times: Iran, Q1’26 Apr '260 times: Iran, Q2’26 Jul '26steady0/80%0/80%0/100%13%n=1+tx from settlement history blended with transcripts
What the columns mean
Last N
of the appearances shown, how many counted as said
Trend
how the count moved over these calls
Scripted
calls of the last 8 where it was said in the prepared remarks
All-time
every call on file
Probability
the calibrated probability for the next listing; n is the settled observations behind it when it was computed, '+tx' adds the transcripts, 'new' means too little history

Appearances and settled record

latest 10

Our transcript counts matched how 42 of 42 settled words resolved (100.0%). How we check.

Recent appearances and how their listed words settled
DateAppearanceTextWordsSettledOur count agreed
Jul 22, 2026Earnings call FY26 Q2Speaker-labelled transcript128 yes · 4 no12/12 (100%)
Apr 22, 2026Earnings call FY26 Q1Speaker-labelled transcript128 yes · 4 no12/12 (100%)
Jan 28, 2026Earnings call FY25 Q4Speaker-labelled transcript1816 yes · 2 no18/18 (100%)
Oct 22, 2025Earnings call FY25 Q3Speaker-labelled transcript0––
Jul 23, 2025Earnings call FY25 Q2Speaker-labelled transcript0––
Apr 23, 2025Earnings call FY25 Q1Speaker-labelled transcript0––
Jan 22, 2025Earnings call FY24 Q4Speaker-labelled transcript0––
Oct 23, 2024Earnings call FY24 Q3Speaker-labelled transcript0––
Jul 25, 2024Earnings call FY24 Q2Speaker-labelled transcript0––
Apr 25, 2024Earnings call FY24 Q1Speaker-labelled transcript0––

Citeparrot.markets, “GE Vernova (GEV): earnings call word history”, as of Oct 24, 2026, https://parrot.markets/earnings/GEV (CC BY 4.0)

Agents: this page as TSV at /api/v1/entity/ge-vernova · words: /api/v1/history?entity=ge-vernova · columnar JSON in #parrot-data · llms.txt · MCP