The post Stablecoins Face Growth Cap Under GENIUS Act, Economist Issues Warning appeared on BitcoinEthereumNews.com. Key Insights: Stablecoins cannot compete with interest-bearing bank accounts under the GENIUS Act’s prohibition on yield payments, according to Berenberg economist Atakan Bakiskan. Migration from bank deposits to Treasury-backed stablecoins could reduce banks’ capacity to lend or purchase government debt, raising systemic concerns. Tokenized deposits and money-market funds emerged as yield-bearing alternatives that operate outside the GENIUS Act’s interest ban restrictions. Berenberg economist Atakan Bakiskan argued that stablecoins face structural limitations that prevent significant growth, despite new federal regulations designed to legitimize the sector. Bakiskan stated that stablecoins were unlikely to absorb substantial US government debt or compete effectively with bank deposits. The economist pointed to the GENIUS Act’s prohibition on interest payments as the primary constraint. The GENIUS Act bars payment stablecoin issuers from offering any form of interest or yield to holders, preventing these digital assets from matching returns available through traditional bank accounts. Bakiskan explained that, because the GENIUS Act bars issuers from paying interest, stablecoins can’t match the returns of interest-bearing bank accounts, reducing their appeal. The economist added that if deposits migrated into stablecoins backed by Treasury securities, banks could face reduced capacity to lend or purchase government debt. The GENIUS Act, signed into law on July 18, established the first federal regulatory framework for payment stablecoins. The legislation requires permitted payment stablecoin issuers to maintain reserves backing outstanding coins on a one-to-one basis, consisting solely of specified assets, including US dollars and short-term Treasuries. Regulatory Intent Behind Interest Ban The interest prohibition reflected deliberate regulatory design aimed at preventing stablecoins from functioning as high-yield deposit substitutes. Regulators explicitly recognized that payment products differed from banking products, leading to the ban on yield or interest offerings. Banking groups warned that large-scale deposit flight into stablecoins could raise funding costs and shrink balance sheets available for… The post Stablecoins Face Growth Cap Under GENIUS Act, Economist Issues Warning appeared on BitcoinEthereumNews.com. Key Insights: Stablecoins cannot compete with interest-bearing bank accounts under the GENIUS Act’s prohibition on yield payments, according to Berenberg economist Atakan Bakiskan. Migration from bank deposits to Treasury-backed stablecoins could reduce banks’ capacity to lend or purchase government debt, raising systemic concerns. Tokenized deposits and money-market funds emerged as yield-bearing alternatives that operate outside the GENIUS Act’s interest ban restrictions. Berenberg economist Atakan Bakiskan argued that stablecoins face structural limitations that prevent significant growth, despite new federal regulations designed to legitimize the sector. Bakiskan stated that stablecoins were unlikely to absorb substantial US government debt or compete effectively with bank deposits. The economist pointed to the GENIUS Act’s prohibition on interest payments as the primary constraint. The GENIUS Act bars payment stablecoin issuers from offering any form of interest or yield to holders, preventing these digital assets from matching returns available through traditional bank accounts. Bakiskan explained that, because the GENIUS Act bars issuers from paying interest, stablecoins can’t match the returns of interest-bearing bank accounts, reducing their appeal. The economist added that if deposits migrated into stablecoins backed by Treasury securities, banks could face reduced capacity to lend or purchase government debt. The GENIUS Act, signed into law on July 18, established the first federal regulatory framework for payment stablecoins. The legislation requires permitted payment stablecoin issuers to maintain reserves backing outstanding coins on a one-to-one basis, consisting solely of specified assets, including US dollars and short-term Treasuries. Regulatory Intent Behind Interest Ban The interest prohibition reflected deliberate regulatory design aimed at preventing stablecoins from functioning as high-yield deposit substitutes. Regulators explicitly recognized that payment products differed from banking products, leading to the ban on yield or interest offerings. Banking groups warned that large-scale deposit flight into stablecoins could raise funding costs and shrink balance sheets available for…

Stablecoins Face Growth Cap Under GENIUS Act, Economist Issues Warning

For feedback or concerns regarding this content, please contact us at crypto.news@mexc.com

Key Insights:

  • Stablecoins cannot compete with interest-bearing bank accounts under the GENIUS Act’s prohibition on yield payments, according to Berenberg economist Atakan Bakiskan.
  • Migration from bank deposits to Treasury-backed stablecoins could reduce banks’ capacity to lend or purchase government debt, raising systemic concerns.
  • Tokenized deposits and money-market funds emerged as yield-bearing alternatives that operate outside the GENIUS Act’s interest ban restrictions.

Berenberg economist Atakan Bakiskan argued that stablecoins face structural limitations that prevent significant growth, despite new federal regulations designed to legitimize the sector.

Bakiskan stated that stablecoins were unlikely to absorb substantial US government debt or compete effectively with bank deposits. The economist pointed to the GENIUS Act’s prohibition on interest payments as the primary constraint.

The GENIUS Act bars payment stablecoin issuers from offering any form of interest or yield to holders, preventing these digital assets from matching returns available through traditional bank accounts.

Bakiskan explained that, because the GENIUS Act bars issuers from paying interest, stablecoins can’t match the returns of interest-bearing bank accounts, reducing their appeal.

The economist added that if deposits migrated into stablecoins backed by Treasury securities, banks could face reduced capacity to lend or purchase government debt.

The GENIUS Act, signed into law on July 18, established the first federal regulatory framework for payment stablecoins.

The legislation requires permitted payment stablecoin issuers to maintain reserves backing outstanding coins on a one-to-one basis, consisting solely of specified assets, including US dollars and short-term Treasuries.

Regulatory Intent Behind Interest Ban

The interest prohibition reflected deliberate regulatory design aimed at preventing stablecoins from functioning as high-yield deposit substitutes.

Regulators explicitly recognized that payment products differed from banking products, leading to the ban on yield or interest offerings.

Banking groups warned that large-scale deposit flight into stablecoins could raise funding costs and shrink balance sheets available for loans and Treasury purchases.

At the current scale, stablecoins remained small relative to US bank deposits and the Treasury market, but the channel Bakiskan described maintained conceptual validity.

The GENIUS Act’s restrictions created a structural handicap for stablecoins competing for long-term store-of-value demand from households and corporations.

The coins could still expand as payment and trading infrastructure, but zero yield presented a ceiling for mass cash parking.

Stablecoin market cap growth in one year | Source: Artemis

However, Bakiskan’s analysis appeared incomplete when it came to tokenized deposits and similar products that circumvented the interest ban.

The GENIUS Act explicitly carved out tokenized deposits as a separate category: digital representations of bank deposits that could pay interest and remain fully within the banking system.

Banks and policymakers increasingly treated tokenized deposits, rather than GENIUS-compliant payment stablecoins, as the primary vehicle for on-chain yield.

These products allowed financial institutions to offer blockchain-based dollar instruments while maintaining the ability to pay returns.

Major banking associations, led by the American Bankers Association, urged lawmakers to extend the interest ban to affiliates and exchanges, warning that unchecked yield programs could destabilize the banking system by draining deposits used for lending.

Market Implications for Stablecoins

Bakiskan’s assessment proved directionally accurate for GENIUS-compliant payment stablecoins specifically.

The law deliberately blocked these instruments from competing as deposit replacements or primary vehicles for absorbing government debt.

However, the analysis understated the broader competitive threat from on-chain dollars.

Yield-bearing tokenized deposits and tokenized money-market funds represented more significant challenges to traditional bank deposits than non-interest payment stablecoins restricted by the Act.

Banks and crypto firms maintained divergent positions on implementation. Coinbase argued the interest ban should apply only to issuers, while major banks pushed Treasury to extend restrictions more broadly.

The regulatory framework created multiple categories of dollar-denominated digital assets with varying capabilities.

GENIUS-compliant stablecoins gained regulatory clarity and payment utility but sacrificed yield. Tokenized deposits retained interest-bearing features while operating under existing banking rules.

Financial institutions evaluated whether to launch their own stablecoins, partner with established issuers, or focus on tokenized deposit products.

The competitive landscape reflected fundamental tension between payment efficiency and yield generation in digital dollar instruments.

Although stablecoins are treated as payment rails with regulatory certainty and a zero-yield requirement, issuers are already seeking alternatives to circumvent this. As a result, it is too early to state that regulation is a hindrance to tokenized dollars.

Source: https://www.thecoinrepublic.com/2025/11/20/stablecoins-face-growth-cap-under-genius-act-economist-issues-warning/

Market Opportunity
Capverse Logo
Capverse Price(CAP)
$0.09704
$0.09704$0.09704
+0.38%
USD
Capverse (CAP) Live Price Chart
Disclaimer: The articles reposted on this site are sourced from public platforms and are provided for informational purposes only. They do not necessarily reflect the views of MEXC. All rights remain with the original authors. If you believe any content infringes on third-party rights, please contact crypto.news@mexc.com for removal. MEXC makes no guarantees regarding the accuracy, completeness, or timeliness of the content and is not responsible for any actions taken based on the information provided. The content does not constitute financial, legal, or other professional advice, nor should it be considered a recommendation or endorsement by MEXC.

You May Also Like

Tether Backs Ark Labs’ $5.2 Million Bet on Bitcoin’s Stablecoin Revival

Tether Backs Ark Labs’ $5.2 Million Bet on Bitcoin’s Stablecoin Revival

The post Tether Backs Ark Labs’ $5.2 Million Bet on Bitcoin’s Stablecoin Revival appeared on BitcoinEthereumNews.com. In brief Ark Labs secured backing from Tether
Share
BitcoinEthereumNews2026/03/12 21:44
Summarize Any Stock’s Earnings Call in Seconds Using FMP API

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
Share
Medium2025/09/18 14:40
PayPal USD Expands to TRON Network via LayerZero

PayPal USD Expands to TRON Network via LayerZero

The post PayPal USD Expands to TRON Network via LayerZero appeared on BitcoinEthereumNews.com. This content is provided by a sponsor. PRESS RELEASE. September 18, 2025 – Geneva, Switzerland – TRON DAO, the community-governed DAO dedicated to accelerating the decentralization of the internet through blockchain technology and decentralized applications (dApps), announced today that PayPal USD will be available on the TRON network through Stargate Hydra as a permissionless token, […] Source: https://news.bitcoin.com/paypal-usd-expands-to-tron-network-via-layerzero/
Share
BitcoinEthereumNews2025/09/18 23:12