TLDR Circle Q4 revenue rose 77% year over year to $770M, driven by $733M in reserve income. USDC circulation increased 72% to $75.3B, with 28% stablecoin marketTLDR Circle Q4 revenue rose 77% year over year to $770M, driven by $733M in reserve income. USDC circulation increased 72% to $75.3B, with 28% stablecoin market
Circle Stock Surges 35% After Strong Q4 and USDC Growth
TLDR
- Circle Q4 revenue rose 77% year over year to $770M, driven by $733M in reserve income.
-
USDC circulation increased 72% to $75.3B, with 28% stablecoin market share.
-
On-chain USDC transaction volume surged 247% to $11.9T in Q4 2025.
-
CRCL shares jumped over 35% premarket after EPS of $0.43 beat $0.16 estimates.
Circle Internet Group shares jumped more than 35% before the opening bell on Wednesday. The rally followed a strong fourth-quarter earnings report and continued expansion of USDC circulation. CRCL closed near $61.37 previously and traded around $71 in premarket activity. Investors reacted to accelerating revenue growth and improved profitability metrics.
Q4 Earnings Beat Drives CRCL Rally
Circle reported total revenue and reserve income of $770 million for the fourth quarter. That marked a 77% increase compared to the same period last year. Reserve income alone reached $733 million during the quarter.
Net income from continuing operations rose to $133 million. In the prior year quarter, net income was $4 million. Adjusted EBITDA climbed 412% to $167 million, reflecting improved operating leverage.
Diluted earnings per share came in at $0.43. Analysts had expected $0.16 per share. The earnings surprise supported the strong premarket reaction in CRCL stock.
For the full year 2025, Circle reported $2.7 billion in total revenue and reserve income. Adjusted EBITDA for the year reached $582 million, up 104% year over year.
USDC Circulation and Volume Expand Rapidly
USDC in circulation ended 2025 at $75.3 billion. That represented a 72% increase year over year. The stablecoin now holds about 28% market share in the sector.
On chain USDC transaction volume totaled $11.9 trillion in the fourth quarter. That figure rose 247% compared to the previous year. Growth in circulation and volume remains central to Circle’s business model.
Average USDC balances help drive reserve income. As balances rise, interest like income from backing assets increases. Circle said other revenue reached $37 million in Q4, up $34 million year over year.
Stablecoin market capitalization dipped earlier this year but has since recovered toward $75 billion. Investors appear focused on continued enterprise and institutional adoption.
Enterprise Partnerships and Platform Expansion
Circle highlighted partnerships with Visa and Intuit during its earnings update. Visa now allows US issuers and acquirers to settle using USDC. This enables settlement outside traditional banking hours.
The company also discussed progress on its Arc platform. The public testnet includes more than 100 participants. Daily average transaction volumes reached 2.3 million as of February 20.
Circle said the Arc testnet has processed over 166 million transactions with near 100% uptime. A mainnet launch remains planned for later this year. The company also received conditional approval to establish a national trust bank.
CEO Jeremy Allaire said the firm is building infrastructure for automated digital payments. He stated, “We are entering a world where tens or hundreds of billions of AI agents will interact and perform economic functions over the internet.” He added that Circle Gateway supports automated cross chain USDC transfers.
The post Circle Stock Surges 35% After Strong Q4 and USDC Growth appeared first on CoinCentral.
Piyasa Fırsatı
USDCoin Fiyatı(USDC)
$1
$1$1
USD
USDCoin (USDC) Canlı Fiyat Grafiği
Sorumluluk Reddi: Bu sitede yeniden yayınlanan makaleler, halka açık platformlardan alınmıştır ve yalnızca bilgilendirme amaçlıdır. MEXC'nin görüşlerini yansıtmayabilir. Tüm hakları telif sahiplerine aittir. Herhangi bir içeriğin üçüncü taraf haklarını ihlal ettiğini düşünüyorsanız, kaldırılması için lütfen crypto.news@mexc.com ile iletişime geçin. MEXC, içeriğin doğruluğu, eksiksizliği veya güncelliği konusunda hiçbir garanti vermez ve sağlanan bilgilere dayalı olarak alınan herhangi bir eylemden sorumlu değildir. İçerik, finansal, yasal veya diğer profesyonel tavsiye niteliğinde değildir ve MEXC tarafından bir tavsiye veya onay olarak değerlendirilmemelidir.
Ayrıca Şunları da Beğenebilirsiniz
Bitwise CEO: In the next 6 to 12 months, the focus of the crypto field will be on the credit and lending market
PANews reported on September 18 that Bitwise CEO Hunter Horsley tweeted that over the next six to 12 months, the focus of the cryptocurrency sector will shift to credit and lending. This sector is expected to experience explosive growth in the next few years. He pointed out that the current cryptocurrency market capitalization is approaching $4 trillion and continues to grow. When people can borrow against cryptocurrency, they will choose to borrow rather than sell. Furthermore, the market capitalization of publicly traded stocks in the United States exceeds $60 trillion. With the tokenization of assets, individuals holding $7,000 worth of stocks will be able to borrow against them on-chain for the first time. Horsley believes that cryptocurrency is redefining capital markets, and this is just the beginning.
Paylaş
PANews2025/09/18 17:00
Nvidia (NVDA) Stock Rises After Q4 Earnings and Guidance Beat – Data Center Revenue Up 75%
TLDR Nvidia beat Q4 earnings estimates with EPS of $1.62 adjusted vs $1.53 expected Total revenue hit $68.13 billion, up 73% year-over-year Data center revenue
Paylaş
Coincentral2026/02/26 17:12
Summarize Any Stock’s Earnings Call in Seconds Using FMP API
Turn lengthy earnings call transcripts into one-page insights using the Financial Modeling Prep APIPhoto by Bich Tran
Earnings calls are packed with insights. They tell you how a company performed, what management expects in the future, and what analysts are worried about. The challenge is that these transcripts often stretch across dozens of pages, making it tough to separate the key takeaways from the noise.
With the right tools, you don’t need to spend hours reading every line. By combining the Financial Modeling Prep (FMP) API with Groq’s lightning-fast LLMs, you can transform any earnings call into a concise summary in seconds. The FMP API provides reliable access to complete transcripts, while Groq handles the heavy lifting of distilling them into clear, actionable highlights.
In this article, we’ll build a Python workflow that brings these two together. You’ll see how to fetch transcripts for any stock, prepare the text, and instantly generate a one-page summary. Whether you’re tracking Apple, NVIDIA, or your favorite growth stock, the process works the same — fast, accurate, and ready whenever you are.
Fetching Earnings Transcripts with FMP API
The first step is to pull the raw transcript data. FMP makes this simple with dedicated endpoints for earnings calls. If you want the latest transcripts across the market, you can use the stable endpoint /stable/earning-call-transcript-latest. For a specific stock, the v3 endpoint lets you request transcripts by symbol, quarter, and year using the pattern:
https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={q}&year={y}&apikey=YOUR_API_KEY
here’s how you can fetch NVIDIA’s transcript for a given quarter:
import requestsAPI_KEY = "your_api_key"symbol = "NVDA"quarter = 2year = 2024url = f"https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={quarter}&year={year}&apikey={API_KEY}"response = requests.get(url)data = response.json()# Inspect the keysprint(data.keys())# Access transcript contentif "content" in data[0]: transcript_text = data[0]["content"] print(transcript_text[:500]) # preview first 500 characters
The response typically includes details like the company symbol, quarter, year, and the full transcript text. If you aren’t sure which quarter to query, the “latest transcripts” endpoint is the quickest way to always stay up to date.
Cleaning and Preparing Transcript Data
Raw transcripts from the API often include long paragraphs, speaker tags, and formatting artifacts. Before sending them to an LLM, it helps to organize the text into a cleaner structure. Most transcripts follow a pattern: prepared remarks from executives first, followed by a Q&A session with analysts. Separating these sections gives better control when prompting the model.
In Python, you can parse the transcript and strip out unnecessary characters. A simple way is to split by markers such as “Operator” or “Question-and-Answer.” Once separated, you can create two blocks — Prepared Remarks and Q&A — that will later be summarized independently. This ensures the model handles each section within context and avoids missing important details.
Here’s a small example of how you might start preparing the data:
import re# Example: using the transcript_text we fetched earliertext = transcript_text# Remove extra spaces and line breaksclean_text = re.sub(r'\s+', ' ', text).strip()# Split sections (this is a heuristic; real-world transcripts vary slightly)if "Question-and-Answer" in clean_text: prepared, qna = clean_text.split("Question-and-Answer", 1)else: prepared, qna = clean_text, ""print("Prepared Remarks Preview:\n", prepared[:500])print("\nQ&A Preview:\n", qna[:500])
With the transcript cleaned and divided, you’re ready to feed it into Groq’s LLM. Chunking may be necessary if the text is very long. A good approach is to break it into segments of a few thousand tokens, summarize each part, and then merge the summaries in a final pass.
Summarizing with Groq LLM
Now that the transcript is clean and split into Prepared Remarks and Q&A, we’ll use Groq to generate a crisp one-pager. The idea is simple: summarize each section separately (for focus and accuracy), then synthesize a final brief.
Prompt design (concise and factual)
Use a short, repeatable template that pushes for neutral, investor-ready language:
You are an equity research analyst. Summarize the following earnings call sectionfor {symbol} ({quarter} {year}). Be factual and concise.Return:1) TL;DR (3–5 bullets)2) Results vs. guidance (what improved/worsened)3) Forward outlook (specific statements)4) Risks / watch-outs5) Q&A takeaways (if present)Text:<<<{section_text}>>>
Python: calling Groq and getting a clean summary
Groq provides an OpenAI-compatible API. Set your GROQ_API_KEY and pick a fast, high-quality model (e.g., a Llama-3.1 70B variant). We’ll write a helper to summarize any text block, then run it for both sections and merge.
import osimport textwrapimport requestsGROQ_API_KEY = os.environ.get("GROQ_API_KEY") or "your_groq_api_key"GROQ_BASE_URL = "https://api.groq.com/openai/v1" # OpenAI-compatibleMODEL = "llama-3.1-70b" # choose your preferred Groq modeldef call_groq(prompt, temperature=0.2, max_tokens=1200): url = f"{GROQ_BASE_URL}/chat/completions" headers = { "Authorization": f"Bearer {GROQ_API_KEY}", "Content-Type": "application/json", } payload = { "model": MODEL, "messages": [ {"role": "system", "content": "You are a precise, neutral equity research analyst."}, {"role": "user", "content": prompt}, ], "temperature": temperature, "max_tokens": max_tokens, } r = requests.post(url, headers=headers, json=payload, timeout=60) r.raise_for_status() return r.json()["choices"][0]["message"]["content"].strip()def build_prompt(section_text, symbol, quarter, year): template = """ You are an equity research analyst. Summarize the following earnings call section for {symbol} ({quarter} {year}). Be factual and concise. Return: 1) TL;DR (3–5 bullets) 2) Results vs. guidance (what improved/worsened) 3) Forward outlook (specific statements) 4) Risks / watch-outs 5) Q&A takeaways (if present) Text: <<< {section_text} >>> """ return textwrap.dedent(template).format( symbol=symbol, quarter=quarter, year=year, section_text=section_text )def summarize_section(section_text, symbol="NVDA", quarter="Q2", year="2024"): if not section_text or section_text.strip() == "": return "(No content found for this section.)" prompt = build_prompt(section_text, symbol, quarter, year) return call_groq(prompt)# Example usage with the cleaned splits from Section 3prepared_summary = summarize_section(prepared, symbol="NVDA", quarter="Q2", year="2024")qna_summary = summarize_section(qna, symbol="NVDA", quarter="Q2", year="2024")final_one_pager = f"""# {symbol} Earnings One-Pager — {quarter} {year}## Prepared Remarks — Key Points{prepared_summary}## Q&A Highlights{qna_summary}""".strip()print(final_one_pager[:1200]) # preview
Tips that keep quality high:
Keep temperature low (≈0.2) for factual tone.
If a section is extremely long, chunk at ~5–8k tokens, summarize each chunk with the same prompt, then ask the model to merge chunk summaries into one section summary before producing the final one-pager.
If you also fetched headline numbers (EPS/revenue, guidance) earlier, prepend them to the prompt as brief context to help the model anchor on the right outcomes.
Building the End-to-End Pipeline
At this point, we have all the building blocks: the FMP API to fetch transcripts, a cleaning step to structure the data, and Groq LLM to generate concise summaries. The final step is to connect everything into a single workflow that can take any ticker and return a one-page earnings call summary.
The flow looks like this:
Input a stock ticker (for example, NVDA).
Use FMP to fetch the latest transcript.
Clean and split the text into Prepared Remarks and Q&A.
Send each section to Groq for summarization.
Merge the outputs into a neatly formatted earnings one-pager.
Here’s how it comes together in Python:
def summarize_earnings_call(symbol, quarter, year, api_key, groq_key): # Step 1: Fetch transcript from FMP url = f"https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={quarter}&year={year}&apikey={api_key}" resp = requests.get(url) resp.raise_for_status() data = resp.json() if not data or "content" not in data[0]: return f"No transcript found for {symbol} {quarter} {year}" text = data[0]["content"] # Step 2: Clean and split clean_text = re.sub(r'\s+', ' ', text).strip() if "Question-and-Answer" in clean_text: prepared, qna = clean_text.split("Question-and-Answer", 1) else: prepared, qna = clean_text, "" # Step 3: Summarize with Groq prepared_summary = summarize_section(prepared, symbol, quarter, year) qna_summary = summarize_section(qna, symbol, quarter, year) # Step 4: Merge into final one-pager return f"""# {symbol} Earnings One-Pager — {quarter} {year}## Prepared Remarks{prepared_summary}## Q&A Highlights{qna_summary}""".strip()# Example runprint(summarize_earnings_call("NVDA", 2, 2024, API_KEY, GROQ_API_KEY))
With this setup, generating a summary becomes as simple as calling one function with a ticker and date. You can run it inside a notebook, integrate it into a research workflow, or even schedule it to trigger after each new earnings release.
Free Stock Market API and Financial Statements API...
Conclusion
Earnings calls no longer need to feel overwhelming. With the Financial Modeling Prep API, you can instantly access any company’s transcript, and with Groq LLM, you can turn that raw text into a sharp, actionable summary in seconds. This pipeline saves hours of reading and ensures you never miss the key results, guidance, or risks hidden in lengthy remarks. Whether you track tech giants like NVIDIA or smaller growth stocks, the process is the same — fast, reliable, and powered by the flexibility of FMP’s data.
Summarize Any Stock’s Earnings Call in Seconds Using FMP API was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story
Paylaş
Medium2025/09/18 14:40