The Bank of Japan (BOJ) may raise its key interest rate at its October policy meeting.The Bank of Japan (BOJ) may raise its key interest rate at its October policy meeting.

Expectations of BOJ interest rate hike at October policy meeting grow

The Bank of Japan (BOJ) is increasingly expected to raise its key interest rate at the October policy board meeting, as speculation intensifies on the back of fresh remarks from a former board member and stronger-than-anticipated economic data.

Makoto Sakurai, a former BOJ policy board member, said this week that an October hike cannot be ruled out. He flagged the likely upward revision to Japan’s inflation and growth forecasts. His comments were the latest to suggest that the central bank would move sooner rather than later.

Money markets now indicate a 50% chance of an interest rate cut on October 29-30. This is a significantly stronger signal than earlier this month, when bets on an imminent hike were much lower.

A Reuters poll of economists echoed this outlook, with over 93% forecasting a hike of at least 25 basis points by year-end, which would lift the BOJ’s policy rate from 0.5% to 0.75%. In August, Hironori Kamezawa, CEO of Mitsubishi UFJ Financial Group, also noted that October was a plausible timeline, citing sticky inflation and easing global trade tensions.

For years, the BOJ stood apart as the only major central bank clinging to ultra-loose monetary policy. But with inflation surpassing its 2% target for more than three years, pressure is mounting to normalize policy finally.

Internal hawkish momentum builds

The BOJ’s most recent meeting on September 19 reinforced this pivot. The board voted to maintain rates at 0.5%. However, two board members, Naoki Tamura and Hajime Takata, dissented for the first time since Governor Kazuo Ueda took office. Instead, they voted for an increase to 0.75%.

Analysts said the votes against were symbolic, indicating that hawkish sentiment is gaining sway inside the nine-member board. One alliance board member, Makoto Sakurai, said the dissent might be a coordinated signal of an approaching policy shift.

The bank said it would also take steps to shrink its holdings of exchange-traded funds (ETFs) and real-estate investment trusts (REITs). That was the first step in unwinding more than half a decade of asset purchases that had characterized an era of aggressive monetary easing.

Minutes of meetings released this week add to the picture. Some argued that the federal funds rate was below what would be expected to be neutral when inflation is high and the output gap is closing. That viewpoint indicates that even more members are preparing for action.

Sakurai, who left the BOJ in 2021, said policymakers could postpone a move until December if they need clearer readings of the effect of U.S. tariffs. But he thinks that the overall trajectory points higher. He sees the policy rate rising as many as 100 basis points over the next two and a half years, reaching 1.5% before Ueda’s term expires in 2028.

LDP election could shape BOJ’s timing

Government leadership could also factor into the timing. Japan’s ruling Liberal Democratic Party (LDP) will hold its leadership election on October 4. The result could determine the mood for BOJ decisions.

One of the leading candidates, Sanae Takaichi, has been considered a supporter of easy-money policy. Last September, during a leadership contest she ultimately won, she said that a rate rise would be “absurd.” But her tone has tipped this year. She has said that the monetary tools should be left to the BOJ, while the government sets fiscal and economic direction.

Should Takaichi, or any other dovish contender, take the top job, it is possible that the BOJ will move cautiously in the near term. Market watchers say the bank could postpone a hike to avoid looking at cross purposes with the incoming leadership.

Yet the BOJ is nominally independent. Its decision will be based on inflation, wage growth, and the wider outlook for trade and consumption.

Don’t just read crypto news. Understand it. Subscribe to our newsletter. It's free.

Piyasa Fırsatı
Lorenzo Protocol Logosu
Lorenzo Protocol Fiyatı(BANK)
$0.03667
$0.03667$0.03667
-1.66%
USD
Lorenzo Protocol (BANK) 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 service@support.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

South Korea Launches Innovative Stablecoin Initiative

South Korea Launches Innovative Stablecoin Initiative

The post South Korea Launches Innovative Stablecoin Initiative appeared on BitcoinEthereumNews.com. South Korea has witnessed a pivotal development in its cryptocurrency landscape with BDACS introducing the nation’s first won-backed stablecoin, KRW1, built on the Avalanche network. This stablecoin is anchored by won assets stored at Woori Bank in a 1:1 ratio, ensuring high security. Continue Reading:South Korea Launches Innovative Stablecoin Initiative Source: https://en.bitcoinhaber.net/south-korea-launches-innovative-stablecoin-initiative
Paylaş
BitcoinEthereumNews2025/09/18 17:54
Trump Cancels Tech, AI Trade Negotiations With The UK

Trump Cancels Tech, AI Trade Negotiations With The UK

The US pauses a $41B UK tech and AI deal as trade talks stall, with disputes over food standards, market access, and rules abroad.   The US has frozen a major tech
Paylaş
LiveBitcoinNews2025/12/17 01:00
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
Paylaş
Medium2025/09/18 14:40