MARA Sells 13,597 Bitcoin for Debt, AI and Buybacks; Trump Media Buys 4,359
· 6 sources
MARA sold 13,597 BTC to cut debt and fund AI, while Trump Media disclosed a 4,359 bitcoin purchase, according to BeInCrypto and Briefs Finance.
What happened
MARA sold 13,597 bitcoin, worth about $1.1 billion, according to Briefs Finance and BeInCrypto. Briefs Finance reports the proceeds are meant to pay down debt, free up collateral and finance AI spending.
Briefs Finance also says the plan includes potential share buybacks of up to $500 million. BeInCrypto reports that MARA sold the coins after its third quarter and is not done selling. TradingView's summary of key facts points to debt reduction, falling revenue, a GPU deal and a buyback.
Separately, BeInCrypto reports that Trump Media revealed a purchase of 4,359 bitcoin, coming after the president called crypto dangerous. TIKR.com reports that Compass Point set a new $20 target on MARA and said the selloff went too far.
Context
Bitcoin miners such as MARA hold bitcoin on their balance sheets and can sell it to raise cash for debt payments and other investments.
Why it matters
The two moves show companies with bitcoin exposure taking different balance-sheet approaches, which may matter to investors in MARA and Trump Media.
Stocks in this story
Price changes are shown for the trading day of the story and are a coincidence in time, not proof that the news caused them. Figures are ATLAS data from our database.
44%Probability of closing higher than today within 21 sessions.Normal range: half of the scenarios end between $5.81 and $8.27; 9 in 10, between $4.24 and $11.32.
43%Probability of closing higher than today within 21 sessions.Normal range: half of the scenarios end between $3.80 and $5.80; 9 in 10, between $2.61 and $8.45.
Fair value, probabilities and earnings for every stock. Free account, no card. Information, not advice.
Information, not advice. This content is informational and is not an investment recommendation or an offer to buy or sell securities. ATLAS estimates come from statistical models using public data, can be wrong, and past performance does not guarantee future results. Disclaimer
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