The post Germany pledges reforms as economy shrinks 0.3% appeared on BitcoinEthereumNews.com. Germany’s Finance Minister Lars Klingbeil told Funke Media Group this week that the coalition government will push through new reforms by the end of 2025. The pledge comes as Chancellor Friedrich Merz’s administration faces rising pressure to fix an economy that’s clearly out of breath. Klingbeil said the coalition still has what it takes to deliver actual change, promising “important decisions” in the coming months on key areas like health care and pensions. Those promises land just as fresh economic figures hit the public. Germany’s economy shrank by 0.3% in the second quarter of 2025, way worse than the early estimate of -0.1%, and far from what the government had hoped for. The Federal Statistical Office said the drop was mostly caused by weak manufacturing, which had boomed earlier in the year as companies rushed orders to the United States to avoid President Donald Trump’s tariffs. That artificial growth is gone now, and the bottom’s showing. GDP weakens as tariffs hit exports Germany’s fragile growth got knocked further by multiple hits: lower household spending, falling investment, and a declining construction sector. New data from the Statistical Office revised household consumption down to just 0.1%, citing poor performance in food, hotels, and accommodation. Meanwhile, government spending rose slightly by 0.8%, but it wasn’t nearly enough to stop the bleeding. Net exports also dropped, dragged down by weaker global demand and tariff pressure from across the Atlantic. Klingbeil said Germany needs to clean out its bureaucracy to get things moving again. “We must free life in this country from bureaucracy so that it is fun again to start a business, run an association or build a house,” he told Funke. But that message competes with the cold reality that Bundesbank already warned the country might not see any growth in the third… The post Germany pledges reforms as economy shrinks 0.3% appeared on BitcoinEthereumNews.com. Germany’s Finance Minister Lars Klingbeil told Funke Media Group this week that the coalition government will push through new reforms by the end of 2025. The pledge comes as Chancellor Friedrich Merz’s administration faces rising pressure to fix an economy that’s clearly out of breath. Klingbeil said the coalition still has what it takes to deliver actual change, promising “important decisions” in the coming months on key areas like health care and pensions. Those promises land just as fresh economic figures hit the public. Germany’s economy shrank by 0.3% in the second quarter of 2025, way worse than the early estimate of -0.1%, and far from what the government had hoped for. The Federal Statistical Office said the drop was mostly caused by weak manufacturing, which had boomed earlier in the year as companies rushed orders to the United States to avoid President Donald Trump’s tariffs. That artificial growth is gone now, and the bottom’s showing. GDP weakens as tariffs hit exports Germany’s fragile growth got knocked further by multiple hits: lower household spending, falling investment, and a declining construction sector. New data from the Statistical Office revised household consumption down to just 0.1%, citing poor performance in food, hotels, and accommodation. Meanwhile, government spending rose slightly by 0.8%, but it wasn’t nearly enough to stop the bleeding. Net exports also dropped, dragged down by weaker global demand and tariff pressure from across the Atlantic. Klingbeil said Germany needs to clean out its bureaucracy to get things moving again. “We must free life in this country from bureaucracy so that it is fun again to start a business, run an association or build a house,” he told Funke. But that message competes with the cold reality that Bundesbank already warned the country might not see any growth in the third…

Germany pledges reforms as economy shrinks 0.3%

Germany’s Finance Minister Lars Klingbeil told Funke Media Group this week that the coalition government will push through new reforms by the end of 2025.

The pledge comes as Chancellor Friedrich Merz’s administration faces rising pressure to fix an economy that’s clearly out of breath. Klingbeil said the coalition still has what it takes to deliver actual change, promising “important decisions” in the coming months on key areas like health care and pensions.

Those promises land just as fresh economic figures hit the public. Germany’s economy shrank by 0.3% in the second quarter of 2025, way worse than the early estimate of -0.1%, and far from what the government had hoped for.

The Federal Statistical Office said the drop was mostly caused by weak manufacturing, which had boomed earlier in the year as companies rushed orders to the United States to avoid President Donald Trump’s tariffs. That artificial growth is gone now, and the bottom’s showing.

GDP weakens as tariffs hit exports

Germany’s fragile growth got knocked further by multiple hits: lower household spending, falling investment, and a declining construction sector. New data from the Statistical Office revised household consumption down to just 0.1%, citing poor performance in food, hotels, and accommodation.

Meanwhile, government spending rose slightly by 0.8%, but it wasn’t nearly enough to stop the bleeding. Net exports also dropped, dragged down by weaker global demand and tariff pressure from across the Atlantic.

Klingbeil said Germany needs to clean out its bureaucracy to get things moving again. “We must free life in this country from bureaucracy so that it is fun again to start a business, run an association or build a house,” he told Funke.

But that message competes with the cold reality that Bundesbank already warned the country might not see any growth in the third quarter either. If that happens, it’ll be two quarters in a row of negative or zero growth, a textbook recession.

A brief spark at the start of 2025 had raised hopes, mostly because German firms were front-loading trade with the U.S. to dodge Trump’s new import taxes. That rush pushed up GDP early in the year but left a vacuum behind it. Now, there’s no more buffer.

The PMI data released Thursday by S&P Global gave a small sign of life, showing business activity grew in August for the third straight month, and at the fastest pace since March. But even S&P warned the improvement was modest. It’s not enough to offset what’s happening in the real economy.

Tariffs, debt limits, and global drag intensify pressure

Klingbeil and Merz’s government is trying to act. Earlier this year, it pushed through a constitutional change to the debt brake rule, allowing defense spending above 1% of GDP to escape borrowing restrictions. It also approved a €500 billion extra-budgetary fund to invest in infrastructure.

Still, those moves haven’t stopped the decline. The impact of Trump’s 15% tariffs on most European products is already being felt. And the auto industry’s hanging in limbo, waiting to see if the U.S. will bring car tariffs back down from 27.5% to 15%.

Carsten Brzeski, an economist at ING, said the tariffs and ongoing economic changes are already showing up in corporate reports. “Recent corporate results were already a painful reminder that US tariffs, but also structural transitions, were in full swing in the second quarter, weighing on company results,” Brzeski said.

He added, “This is a trend that won’t change too much in the third quarter, with US tariffs of 15% on most European goods and uncertainty over whether (and when) the 27.5% tariffs on automotives will be brought back to 15%.”

Germany sends about 10% of its exports to the United States. That’s a huge piece of the puzzle, and if that window keeps tightening, companies will feel it quarter after quarter.

This all traces back to 2022, when Russia’s invasion of Ukraine helped derail global supply chains and shook energy markets across Europe. That shock hit Germany hard, and the effects are still here. Add that to an aging population, weak global growth, and too much red tape, and the picture gets darker.

The smartest crypto minds already read our newsletter. Want in? Join them.

Source: https://www.cryptopolitan.com/germany-defends-economic-policy/

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

Bitwise CEO: In the next 6 to 12 months, the focus of the crypto field will be on the credit and lending market

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.
Share
PANews2025/09/18 17:00
Nvidia (NVDA) Stock Rises After Q4 Earnings and Guidance Beat – Data Center Revenue Up 75%

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
Share
Coincentral2026/02/26 17:12
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