Today's top news highlights: The South Korean National Assembly has passed two legislative amendments to regulate security tokens. The People's Bank of China (PBOCToday's top news highlights: The South Korean National Assembly has passed two legislative amendments to regulate security tokens. The People's Bank of China (PBOC

PA Daily Report | Bitcoin spot ETF saw net inflows of $844 million yesterday; Binance Alpha launched on Xueqiu and Rensheng K-line.

2026/01/15 17:28

Today's top news highlights:

The South Korean National Assembly has passed two legislative amendments to regulate security tokens.

The People's Bank of China (PBOC) announced a 0.25 percentage point reduction in interest rates for various structural monetary policy tools.

Infinex: Public offering subscriptions exceeded $7.2 million; TGE to be held on January 30.

Binance Alpha launched on Xueqiu and Rensheng K-line.

Xunlei has sued its former CEO Chen Lei, alleging that he misappropriated company funds for illegal cryptocurrency trading.

Binance Wealth Management, One-Click Crypto Buying, Instant Exchange, Margin Trading, and Futures Trading will be available on Frax (FRAX).

Arthur Hayes predicts that dollar liquidity will increase in 2026, and Bitcoin will rise accordingly.

Bitcoin spot ETFs saw a total net inflow of $844 million yesterday, marking the third consecutive day of net inflows.

Regulatory news

Central Bank: There is still room for reserve requirement ratio and interest rate cuts this year.

Zou Lan, spokesperson and vice governor of the People's Bank of China, stated at a press conference held by the State Council Information Office on January 15 that there is still room for further reductions in the reserve requirement ratio and interest rates this year. Regarding the statutory reserve requirement ratio, the current average ratio for financial institutions is 6.3%, leaving room for further cuts. As for policy interest rates, external constraints include a relatively stable RMB exchange rate and a declining US dollar interest rate, meaning the exchange rate does not pose a strong constraint. Internal constraints include signs of stabilization in banks' net interest margins since 2025, and a significant amount of 3-year and 5-year long-term deposits maturing in 2026. The upcoming reductions in interest rates for various structural monetary policy tools will help lower banks' interest costs, stabilize net interest margins, and create room for further interest rate cuts.

The People's Bank of China (PBOC) announced a 0.25 percentage point reduction in interest rates for various structural monetary policy tools.

Zou Lan, spokesperson and vice governor of the People's Bank of China, stated at a press conference held by the State Council Information Office on January 15 that the interest rates for various structural monetary policy tools have been lowered by 0.25 percentage points, with the one-year interest rate for various relending maturities reduced to 1.25%, and other maturities adjusted accordingly. This aims to improve structural tools and increase support to further facilitate the transformation and optimization of the economic structure.

The South Korean National Assembly has passed two legislative amendments to regulate security tokens.

According to Digital Asset, the South Korean National Assembly has passed amendments to the Capital Markets Act and the Electronic Securities Act, marking the formal establishment of a framework for the issuance and circulation of security tokens (STOs) approximately three years after financial regulators issued relevant guidelines.

The core amendments include the introduction of the distributed ledger concept, allowing issuers meeting certain conditions to directly issue and manage tokenized securities through electronic registration, and establishing a new "Issuance Account Management Institution." Furthermore, atypical securities such as investment contracts will be brought under the regulatory scope of the Capital Markets Act, and their trading in the over-the-counter market will be permitted through the establishment of a new over-the-counter brokerage business. The revised Capital Markets Act will take effect from the date of promulgation. However, provisions related to investment inducement guidelines will take effect six months after promulgation, and provisions related to over-the-counter trading will take effect one year after promulgation.

Project Updates

Binance Wealth Management, One-Click Crypto Buying, Instant Exchange, Margin Trading, and Futures Trading will be available on Frax (FRAX).

According to the official announcement, Binance Wealth Management, One-Click Crypto, Instant Exchange, Leverage, and Binance Futures will be listed on Frax (FRAX) according to the timeline listed below.

  • Binance Wealth Management: The FRAX Principal-Protected Earnings product will be launched on the Binance Principal-Protected Earnings platform and open for subscription on January 15, 2026 at 16:00 (UTC+8).

  • One-click buy & sell: Within one hour of FRAX being listed on Binance Spot, you can buy and start trading with one click.

  • Instant Exchange Platform: Within one hour of FRAX being listed on Binance Spot, users can exchange FRAX for BTC, USDT, or other tokens supported by the instant exchange platform, with no transaction fees.

  • Binance Leverage: Binance will add FRAX as a borrowable asset and FRAX/USDT and FRAX/USDC cross and isolated margin trading pairs on January 15, 2026 at 16:00 (UTC+8).

  • Binance Futures: Binance will launch FRAX 1-50x USDT-margined perpetual contracts on January 15, 2026 at 16:00 (UTC+8).

Binance Alpha will list OWL tokens at 6 PM today, with an airdrop threshold of 241 points.

According to the official announcement, Binance Alpha will list Owlto Finance (OWL), and Alpha trading will officially begin on January 15, 2026 at 18:00 (UTC+8). Users with at least 241 Binance Alpha Points can receive an airdrop of 667 OWL tokens on a first-come, first-served basis. If the reward pool is not fully distributed, the points threshold will automatically decrease by 5 every 5 minutes. Please note that claiming the airdrop will consume 15 Binance Alpha Points. Users must confirm their claim on the Alpha event page within 24 hours, otherwise they will be considered to have forfeited their claim.

Infinex: Public offering subscriptions exceeded $7.2 million; TGE to be held on January 30.

Infinex announced on its X platform that its public sale has ended. The sale had 868 participants, raising 7.214 million USDC. Approximately $5 million (5% of the INX supply) was allocated, and approximately $2.21 million was refunded. After identifying and removing approximately $1.2 million in funds from "Tyrant" addresses, the maximum allocation per participant was $245,000, and 99.5% of participants received their full allocation. Refunds have been credited to users' Infinex accounts. Furthermore, TGE will take place on January 30th.

Binance Alpha will list Sport.Fun (FUN)

According to the official announcement, Binance Alpha will list Sport.Fun (FUN) on January 15th. Eligible users can claim the airdrop using Binance Alpha Points on the Alpha event page after trading opens. More details will be announced soon.

Moonbirds unveils its Birbillions strategy: integrating Memecoin and physical collectibles to create a Web3 version of Pop Mart.

Moonbirds announced its Birbillions strategy on the X platform, aiming to create a "Web3 version of Pop Mart" by integrating Memecoin and physical collectibles to build a crypto-native consumer brand with annual revenue of $1 billion. The core of this plan is a dual-engine approach: using the Memecoin BIRB as a cultural dissemination and community coordination layer to drive marketing and communication; and leveraging the manufacturing and distribution capabilities of its physical collectibles company, Orange Cap Games (OCG), to convert online attention into sustainable physical merchandise revenue and penetrate the IP into the mainstream consumer market.

In terms of strategic execution, OCG generated approximately $8 million in revenue through the sale of physical collectibles in its second year of operation. Card game (TCG) products alone generated over $6 million in sales within 12 months, while related Telegram stickers generated over $1.4 million in demand. Currently, OCG's products have entered North America's largest hobby distribution network, establishing a stable retail channel. Regarding ecosystem growth data, since acquiring the Moonbirds IP, the number of related independent wallets has grown from approximately 10,000 to nearly 400,000, demonstrating a significant expansion of its community.

Binance Alpha launched on Xueqiu and Rensheng K-line.

According to information on the official page, Binance Alpha has listed Meme Coin Snowball (Snowball) and Life K-line (Life K-line).

Xunlei has sued its former CEO Chen Lei, alleging that he misappropriated company funds for illegal cryptocurrency trading.

According to The Paper, Xunlei Corporation (Nasdaq: XNET) has filed a lawsuit against its former CEO Chen Lei and his core team, accusing them of misappropriating company funds and seeking damages of up to 200 million yuan. The case has been accepted by a court in Shenzhen. Sources close to the case told reporters that Chen Lei joined Xunlei as CTO in 2014 and became CEO in 2017. In 2020, Xunlei dismissed him on suspicion of embezzlement. Chen Lei is also suspected of misappropriating tens of millions of yuan of company funds for illegal cryptocurrency trading, which is prohibited by the state. To evade investigation, Chen Lei left the country in early April 2020.

Hyperliquid launches Monero futures trading, supporting up to 5x leverage.

According to an official announcement from Hyperliquid, in response to community requests, the platform has now launched Monero ($XMR) perpetual contract trading, allowing users to go long or short with leverage up to 5x.

Opinions & Analysis

Bank of England Deputy Governor: The UK may need to provide a similar guarantee mechanism for stablecoin deposits as for bank deposits.

According to Bloomberg, Bank of England Deputy Governor Dave Ramsden stated that the UK may need to provide a similar guarantee mechanism for stablecoin deposits as bank deposits. Ramsden noted that the central bank is considering how to maintain public confidence in the currency should a systemically important stablecoin collapse. He suggested that long-term trust in stablecoins might require establishing a scheme similar to bank deposit insurance and ensuring stablecoin holders have priority as creditors in bankruptcy proceedings through statutory liquidation arrangements.

Ramsden's comments suggest that the Bank of England may extend its current protections for bank deposits to widely used stablecoins. The Bank of England has already raised the cap on term cash deposits for British citizens from £85,000 to £120,000 to protect against bank failures. The Bank of England plans to implement stablecoin regulatory rules by the end of the year.

Arthur Hayes predicts that dollar liquidity will increase in 2026, and Bitcoin will rise accordingly.

In his latest article, BitMEX co-founder Arthur Hayes predicts that as the Federal Reserve's balance sheet expands and bank lending and mortgage rates decline, dollar liquidity will increase in 2026, and Bitcoin will rise accordingly.

JPMorgan Chase: Crypto market inflows are projected to reach a record $130 billion in 2025, with further increases expected this year.

According to The Block, JPMorgan analysts stated in a recent report that following a record inflow of nearly $130 billion in 2025, cryptocurrency market inflows are expected to increase further in 2026, primarily driven by institutional investors. Analysts anticipate that the enactment of further crypto regulations, including the U.S. Clarity Act, will support this growth and potentially further boost institutional adoption of digital assets, as well as venture capital, M&A, and IPO activity in areas such as stablecoin issuers, payment companies, and exchanges.

The report analyzes that inflows in 2025 were primarily driven by purchases from Bitcoin and Ethereum ETFs (likely dominated by retail investors) and digital asset treasury firms other than Strategy. However, the pace of purchases by treasury firms has slowed significantly since last October. Meanwhile, while crypto venture capital saw a slight increase in 2025, the number of deals plummeted, and early-stage funding activity slowed markedly.

Important data

Bitcoin spot ETFs saw a total net inflow of $844 million yesterday, marking the third consecutive day of net inflows.

The Bitcoin spot ETF with the largest single-day net inflow yesterday was BlackRock ETF IBIT, with a net inflow of $648 million. IBIT's historical total net inflow has reached $63.11 billion. This was followed by Fidelity ETF FBTC, with a net inflow of $125 million. FBTC's historical total net inflow has reached $12.31 billion.

Ethereum spot ETFs saw a total net inflow of $175 million yesterday, marking the third consecutive day of net inflows.

The Ethereum spot ETF with the largest single-day net inflow yesterday was the BlackRock ETF ETHA, with a net inflow of $81.6032 million. ETHA's historical total net inflow has reached $12.773 billion. This was followed by the Grayscale Ethereum Mini Trust ETF ETH, with a single-day net inflow of $43.4695 million. ETH's historical total net inflow has reached $1.61 billion.

Binance's spot crypto trading market share has fallen to 25%, the lowest level since the beginning of 2021.

According to Bloomberg, CoinDesk data shows that in December 2025, Binance's market share in cryptocurrency spot trading fell to 25%, the lowest level since January 2021, far below its peak of nearly 60% in 2023. Its market share in derivatives trading also dropped from a peak of nearly 70% to approximately 35%. Analysts point out that trading activity flowing out of Binance has primarily shifted to non-US exchanges such as Bybit, HTX, and Gate, while trading volume growth on US exchanges, including Coinbase, has been relatively limited. Meanwhile, on-chain trading platforms like Hyperliquid are attracting more derivatives trading, indicating a profound shift in market structure.

Huang Licheng continued to reduce his ETH long positions, and his account had a floating profit of $876,000.

According to HyperInsight monitoring, Maji (Huang Licheng) address has been continuously reducing its ETH long positions in the past 30 minutes. Currently, it has 9620.13 ETH (approximately US$32.03 million) long with 25x leverage, with an average entry price of US$3217.71 and a floating profit of US$1.07 million; and 310,000 HYPE long with 10x leverage, with an average entry price of US$25.62 and a floating loss of US$194,000.

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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
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Medium2025/09/18 14:40