Mastercard (MA)

EarningsTranscriptsUpdated
Export
Copy
API
Open as TSVfor agentsOpen as JSONcolumnarOpen as Markdowntable
GET /api/v1/entity/mastercard

As of Oct 24, 2026, Kalshi has settled 12 word markets on Mastercard’s earnings calls since 2026, on 12 different words. Next call: Oct 29, 2026 (13 words listed).

Next: Earnings call ·

13 words listed · Occasion page

Who counts: Kalshi: company speakers + operator

Kalshi

Mastercard: the words listed for Thu, Oct 29, how often each was said on past occasions, and the probability for this one
WordQ3’24Oct '24Q4’24Jan '25Q1’25May '25Q2’25Jul '25Q3’25year agoQ4’25Jan '26Q1’26Apr '26Q2’26last callTrendLast 8ScriptedAll-timeProbabilityMarket
Stablecoin0 times: Stablecoin, Q3’24 Oct '243 times: Stablecoin, Q4’24 Jan '25, counts as said6 times: Stablecoin, Q1’25 May '25, counts as said5 times: Stablecoin, Q2’25 Jul '25, counts as said7 times: Stablecoin, Q3’25 Oct '25, counts as said6 times: Stablecoin, Q4’25 Jan '26, counts as said14 times: Stablecoin, Q1’26 Apr '26, counts as said15 times: Stablecoin, Q2’26 Jul '26, counts as saidrising7/888%6/875%10/7813%93%n=1+tx from settlement history blended with transcripts95¢ Kalshi
Tokenized / Tokenization7 times: Tokenized / Tokenization, Q3’24 Oct '24, counts as said3 times: Tokenized / Tokenization, Q4’24 Jan '25, counts as said8 times: Tokenized / Tokenization, Q1’25 May '25, counts as said2 times: Tokenized / Tokenization, Q2’25 Jul '25, counts as said4 times: Tokenized / Tokenization, Q3’25 Oct '25, counts as said5 times: Tokenized / Tokenization, Q4’25 Jan '26, counts as said6 times: Tokenized / Tokenization, Q1’26 Apr '26, counts as said3 times: Tokenized / Tokenization, Q2’26 Jul '26, counts as saidsteady8/8100%8/8100%42/7854%88%transcripts from the transcript model90¢ Kalshi
Cybersecurity7 times: Cybersecurity, Q3’24 Oct '24, counts as said0 times: Cybersecurity, Q4’24 Jan '258 times: Cybersecurity, Q1’25 May '25, counts as said8 times: Cybersecurity, Q2’25 Jul '25, counts as said3 times: Cybersecurity, Q3’25 Oct '25, counts as said4 times: Cybersecurity, Q4’25 Jan '26, counts as said4 times: Cybersecurity, Q1’26 Apr '26, counts as said6 times: Cybersecurity, Q2’26 Jul '26, counts as saidsteady7/888%6/875%28/7836%87%n=1+tx from settlement history blended with transcripts90¢ Kalshi
Agent Pay0 times: Agent Pay, Q3’24 Oct '240 times: Agent Pay, Q4’24 Jan '252 times: Agent Pay, Q1’25 May '25, counts as said1 time: Agent Pay, Q2’25 Jul '25, counts as said4 times: Agent Pay, Q3’25 Oct '25, counts as said3 times: Agent Pay, Q4’25 Jan '26, counts as said6 times: Agent Pay, Q1’26 Apr '26, counts as said7 times: Agent Pay, Q2’26 Jul '26, counts as saidrising6/875%6/875%6/788%86%n=1+tx from settlement history blended with transcripts90¢ Kalshi
International2 times: International, Q3’24 Oct '24, counts as said2 times: International, Q4’24 Jan '25, counts as said3 times: International, Q1’25 May '25, counts as said2 times: International, Q2’25 Jul '25, counts as said1 time: International, Q3’25 Oct '25, counts as said1 time: International, Q4’25 Jan '26, counts as said2 times: International, Q1’26 Apr '26, counts as said3 times: International, Q2’26 Jul '26, counts as saidsteady8/8100%8/8100%51/7865%82%n=1+tx from settlement history blended with transcripts88¢ Kalshi
Regulation / Regulatory0 times: Regulation / Regulatory, Q3’24 Oct '242 times: Regulation / Regulatory, Q4’24 Jan '25, counts as said2 times: Regulation / Regulatory, Q1’25 May '25, counts as said0 times: Regulation / Regulatory, Q2’25 Jul '252 times: Regulation / Regulatory, Q3’25 Oct '25, counts as said2 times: Regulation / Regulatory, Q4’25 Jan '26, counts as said4 times: Regulation / Regulatory, Q1’26 Apr '26, counts as said0 times: Regulation / Regulatory, Q2’26 Jul '26rising5/862%2/825%57/7873%67%n=1+tx from settlement history blended with transcripts63¢ Kalshi
Holiday0 times: Holiday, Q3’24 Oct '240 times: Holiday, Q4’24 Jan '251 time: Holiday, Q1’25 May '25, counts as said1 time: Holiday, Q2’25 Jul '25, counts as said0 times: Holiday, Q3’25 Oct '250 times: Holiday, Q4’25 Jan '261 time: Holiday, Q1’26 Apr '26, counts as said1 time: Holiday, Q2’26 Jul '26, counts as saidrising4/850%4/850%21/7827%42%n=1+tx from settlement history blended with transcripts58¢ Kalshi
Rate Cap / 10% Cap0 times: Rate Cap / 10% Cap, Q3’24 Oct '240 times: Rate Cap / 10% Cap, Q4’24 Jan '250 times: Rate Cap / 10% Cap, Q1’25 May '250 times: Rate Cap / 10% Cap, Q2’25 Jul '250 times: Rate Cap / 10% Cap, Q3’25 Oct '253 times: Rate Cap / 10% Cap, Q4’25 Jan '26, counts as said0 times: Rate Cap / 10% Cap, Q1’26 Apr '260 times: Rate Cap / 10% Cap, Q2’26 Jul '26rising1/812%0/80%1/781%41%n=1+tx from settlement history blended with transcripts17¢ Kalshi
M&A / Merger0 times: M&A / Merger, Q3’24 Oct '240 times: M&A / Merger, Q4’24 Jan '250 times: M&A / Merger, Q1’25 May '250 times: M&A / Merger, Q2’25 Jul '254 times: M&A / Merger, Q3’25 Oct '25, counts as said0 times: M&A / Merger, Q4’25 Jan '260 times: M&A / Merger, Q1’26 Apr '260 times: M&A / Merger, Q2’26 Jul '26steady1/812%0/80%22/7828%33%n=1+tx from settlement history blended with transcripts15¢ Kalshi
Fiserv0 times: Fiserv, Q3’24 Oct '240 times: Fiserv, Q4’24 Jan '250 times: Fiserv, Q1’25 May '251 time: Fiserv, Q2’25 Jul '25, counts as said0 times: Fiserv, Q3’25 Oct '250 times: Fiserv, Q4’25 Jan '261 time: Fiserv, Q1’26 Apr '26, counts as said0 times: Fiserv, Q2’26 Jul '26steady2/825%2/825%8/7810%24%n=1+tx from settlement history blended with transcripts58¢ Kalshi
World Banknever said0 times: World Bank, Q3’24 Oct '240 times: World Bank, Q4’24 Jan '250 times: World Bank, Q1’25 May '250 times: World Bank, Q2’25 Jul '250 times: World Bank, Q3’25 Oct '250 times: World Bank, Q4’25 Jan '260 times: World Bank, Q1’26 Apr '260 times: World Bank, Q2’26 Jul '26steady0/80%0/80%0/780%16%transcripts from the transcript model15¢ Kalshi
SpendingPulse0 times: SpendingPulse, Q3’24 Oct '240 times: SpendingPulse, Q4’24 Jan '250 times: SpendingPulse, Q1’25 May '250 times: SpendingPulse, Q2’25 Jul '250 times: SpendingPulse, Q3’25 Oct '250 times: SpendingPulse, Q4’25 Jan '260 times: SpendingPulse, Q1’26 Apr '260 times: SpendingPulse, Q2’26 Jul '26steady0/80%0/80%46/7859%13%n=1+tx from settlement history blended with transcripts20¢ Kalshi
Trumpnever said0 times: Trump, Q3’24 Oct '240 times: Trump, Q4’24 Jan '250 times: Trump, Q1’25 May '250 times: Trump, Q2’25 Jul '250 times: Trump, Q3’25 Oct '250 times: Trump, Q4’25 Jan '260 times: Trump, Q1’26 Apr '260 times: Trump, Q2’26 Jul '26steady0/80%0/80%0/780%13%n=1+tx from settlement history blended with transcripts10¢ 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 20, 2026 15:46 UTC.

Every word

1–1 of 1, most settled first
Mastercard: every word with settled markets, most settled first
WordQ3’24Oct '24Q4’24Jan '25Q1’25May '25Q2’25Jul '25Q3’25Oct '25Q4’25Jan '26Q1’26Apr '26Q2’26last callTrendLast 8ScriptedAll-timeProbability
Tokenization5 times: Tokenization, Q3’24 Oct '24, counts as said2 times: Tokenization, Q4’24 Jan '25, counts as said6 times: Tokenization, Q1’25 May '25, counts as said1 time: Tokenization, Q2’25 Jul '25, counts as said4 times: Tokenization, Q3’25 Oct '25, counts as said3 times: Tokenization, Q4’25 Jan '26, counts as said2 times: Tokenization, Q1’26 Apr '26, counts as said2 times: Tokenization, Q2’26 Jul '26, counts as saidfalling8/8100%7/888%39/7850%86%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 12

Our transcript counts matched how 12 of 12 settled words resolved (100.0%). How we check.

Recent appearances and how their listed words settled
DateAppearanceTextWordsSettledOur count agreed
Jul 30, 2026Earnings call FY26 Q2Speaker-labelled transcript0––
Apr 30, 2026Earnings call FY26 Q1Speaker-labelled transcript0––
Jan 29, 2026Earnings call FY25 Q4Speaker-labelled transcript127 yes · 5 no12/12 (100%)
Oct 30, 2025Earnings call FY25 Q3Speaker-labelled transcript0––
Jul 31, 2025Earnings call FY25 Q2Speaker-labelled transcript0––
May 1, 2025Earnings call FY25 Q1Speaker-labelled transcript0––
Jan 30, 2025Earnings call FY24 Q4Speaker-labelled transcript0––
Oct 31, 2024Earnings call FY24 Q3Speaker-labelled transcript0––
Jul 31, 2024Earnings call FY24 Q2Speaker-labelled transcript0––
May 1, 2024Earnings call FY24 Q1Speaker-labelled transcript0––
Jan 31, 2024Earnings call FY23 Q4Speaker-labelled transcript0––
Oct 26, 2023Earnings call FY23 Q3Speaker-labelled transcript0––

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

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