Sends Letter to CNR Board Seeking Inspection of Books and Records CNR Board is Complicit in Value-Impairing Transactions Involving Ascent Resources and its ControllingSends Letter to CNR Board Seeking Inspection of Books and Records CNR Board is Complicit in Value-Impairing Transactions Involving Ascent Resources and its Controlling

MASON CAPITAL MANAGEMENT DEMANDS ANSWERS FROM ASCENT CNR CORPORATION

2026/02/20 02:46
9 min read

Sends Letter to CNR Board Seeking Inspection of Books and Records

CNR Board is Complicit in Value-Impairing Transactions Involving Ascent Resources and its Controlling Sponsors

NEW YORK, Feb. 19, 2026 /PRNewswire/ — Mason Capital Management LLC (“Mason”), a significant, long-standing investor in Ascent Resources, LLC (“Ascent” or the “Company”), today sent a demand for the inspection of books and records of Ascent CNR Corporation (“CNR”), the entity through which Mason indirectly holds its economic interest in Ascent, to CNR’s Board of Directors (the “Board”).

The purpose of Mason’s demand is to investigate whether CNR’s Board fulfilled its fiduciary duties in connection with recent transactions involving Ascent and its controlling private equity sponsors, The Energy & Minerals Group LP and First Reserve Corporation. Mason and other stakeholders have repeatedly raised concerns regarding the valuation, conflicts, lack of process and disclosure surrounding the transactions. The CNR Board’s knowing acquiescence in, or conscious failure to respond to, the transactions directly impaired the value of CNR’s principal asset—its membership interest in Ascent—and inspection is now necessary to determine the Board’s culpability.

The full text of the letter follows:

February 19, 2026

Board of Directors
Ascent CNR Corporation
3501 NW 63rd Street
Oklahoma City, OK 73116

Ascent CNR Corporation
C/O Corporation Trust Company
1209 Orange Street
Wilmington, DE 19801

Ascent CNR Corporation
C/O Robert W. Kelly II
3501 NW 63rd Street
Oklahoma City, OK 73116

Re: Stockholder Demand for Inspection of Books and Records Pursuant to 8 Del. C. §220

Directors:

We write on behalf of Mason Capital Master Fund LP, a stockholder of Ascent CNR Corporation (“CNR” or the “Company“), and its investment manager, Mason Capital Management LLC (together with Mason Capital Master Fund LP, “Mason“). A verification executed under oath on behalf of Mason is annexed hereto as Exhibit 1. Mason has appointed the undersigned as their attorney-in-fact for purposes of this demand by the Limited Power of Attorney annexed as Exhibit 2. Documentary evidence of Mason’s beneficial ownership is attached to Exhibit 3 and is a true and correct copy of what it purports to be. Pursuant to Section 220 of the Delaware General Corporation Law (the “DGCL“), Mason hereby demands inspection of certain books and records of the Company for the proper purposes described below.

Proper Purpose

Mason’s purpose is to investigate whether CNR’s Board of Directors (the “Board” or the “CNR Board“) fulfilled its fiduciary duties in connection with material events affecting the value of CNR’s principal asset—its membership interest in Ascent Resources, LLC (“Ascent“)—including whether the CNR Board:

  • informed itself regarding those events;
  • deliberated or met to consider their impact on CNR and its stockholders;
  • considered the availability or prospect of any alternatives that would be of greater benefit to CNR and its stockholders;
  • evaluated whether to take action or exercise any contractual, governance, or informational rights on behalf of CNR and its stockholders; or
  • knowingly failed to act at all.

Mason also intends to communicate with other CNR stockholders about these issues and appropriate action that must be taken to redress the Board’s failings. Investigating potential mismanagement, bad faith, or knowing inaction by a board of directors and communicating with other stockholders regarding company malfeasance are both well-established and proper purposes under DGCL §220.

Factual and Credible Basis for Inspection

CNR’s principal asset is its membership interest in Ascent. Over the past year, Ascent has been the subject of a series of extraordinary and economically material transactions led by its controlling private equity sponsors, The Energy & Minerals Group LP (“EMG“) and First Reserve Corporation (“First Reserve“), that directly impaired the value of CNR’s investment.

As publicly disclosed and repeatedly communicated to Ascent and its advisors, First Reserve executed, and EMG is in the process of executing, continuation-vehicle transactions (the “CV Transaction” and taken together, the “CV Transactions“) pursuant to which affiliated vehicles acquired substantial portions of Ascent equity at valuations that Mason has alleged are materially discounted and not the product of any meaningful market check. These CV Transactions are not isolated or routine internal reallocations. Rather, they involve the active solicitation of other Ascent members’ interests, occur against a backdrop of sponsor level liquidity pressure and fund terminations, and result in a reset of economics uniquely favorable to the controlling sponsors, including new fee streams and extended control, at the expense of existing minority holders.

The sequencing of the CV Transactions is particularly telling. First Reserve transferred approximately 35% of Ascent through a continuation vehicle, followed shortly thereafter by a second transaction of roughly similar magnitude led by EMG. Taken together, these CV Transactions effectively allow the sponsors to retain control of Ascent while avoiding the pricing, procedural protections, and market scrutiny that would ordinarily accompany a single, contemporaneous control transaction. As previously stated, had these interests been aggregated and marketed together, they would have constituted a clear control sale requiring a control premium. Instead, the staggered structure has the effect of suppressing price, deterring thirdparty bidders, and foreclosing consideration of superior alternatives, including bona fide acquisition proposals from Kimmeridge Energy Management Company and Mason that were presented but never meaningfully engaged.

Throughout this period, Mason and other stakeholders repeatedly raised concerns regarding valuation, conflicts, lack of process, and the absence of any documented effort to evaluate market alternatives. Those concerns were not limited to Mason: limited partners in EMG’s own funds, including the Abu Dhabi Investment Council (“ADIC“), have commenced litigation against EMG challenging the fairness, disclosure, and approval process surrounding the CV Transaction. Those concerns have gone unanswered. Ascent’s Board of Managers (the “Ascent Board“), through counsel, took the position that it had no obligation to act, no role to play, and no duty to evaluate alternatives or protect minority interests in connection with these CV Transactions, notwithstanding their undeniable economic impact.

Given the scale, control implications, and public and direct communications surrounding these CV Transactions—and the fact that they directly affect the value of CNR’s sole material asset— it is reasonable to infer that CNR’s Board was aware of these developments and either affirmatively concurred in the resulting outcome or consciously failed to take any action in response.

Mason holds its economic interest in Ascent indirectly through CNR. Mason has no inspection rights at the Ascent level, and Ascent’s LLC agreement expressly modifies or eliminates fiduciary duties. CNR is therefore the sole fiduciary intermediary through which Mason can assess whether any effort was made to protect its and other CNR stockholders’ interests when control-shifting, value-impairing transactions were executed at Ascent.

Yet CNR has never disclosed whether its Board met, requested information, deliberated, sought advice, evaluated alternatives, or considered exercising any contractual or governance rights in response to these events. The absence of any observable engagement—despite repeated, detailed warnings regarding conflicts, pricing, and process—provides a credible basis to suspect that CNR’s Board either knowingly acquiesced in, or consciously failed to respond to, CV Transactions that materially impaired the value of the Company’s core asset.

Inspection is therefore necessary to determine what CNR did or did not do, what information it received, whether any process existed, and whether the Board satisfied its fiduciary obligations in the face of known, economically consequential developments.

Scope of Requested Inspection

To investigate the foregoing issues, Mason seeks inspection of the following categories of books and records, narrowly tailored to CNR’s own conduct, process, and information flow:

  1. Stockholder List
    A copy of CNR’s stock ledger and a list of all stockholders during the period January 1, 2024 through present, including such stockholders’ names, last known addresses, phone numbers and share ownership.
  2. Organizational and Governing Documents
    The Company’s certificate of incorporation, bylaws, and any amendments thereto, together with any stockholder agreements, voting agreements, governance charters, committee charters, or other governing or organizational documents in effect at any time from January 1, 2024 to present that relate to the authority, obligations, or information rights of CNR’s Board in connection with CNR’s investment in Ascent.

    Documents sufficient to identify all members of CNR’s Board of Directors at any time from January 1, 2024 to the present, including the dates of their service.

  3. Board Materials
    All minutes, written consents, resolutions, agendas, presentations, and board packages of CNR’s Board from January 1, 2024 to present that reference or relate to:
    1. Ascent;
    2. any continuation-vehicle transaction, liquidity event, valuation, or strategic alternative involving Ascent; or
    3. the impact of Ascent-level developments on CNR.
  4. Information Received from Ascent
    All materials, communications, reports, presentations, or updates provided to CNR or its directors by Ascent, its managers, sponsors, advisors, or counsel concerning:
    1. the CV Transactions;
    2. valuation or pricing matters;
    3. liquidity elections or alternatives; or
    4. strategic transactions or alternatives.
  5. Board Deliberation and Response
    Documents sufficient to show whether and how CNR’s Board:
    1. considered Ascent-level developments;
    2. discussed potential actions or responses;
    3. evaluated the exercise of any contractual, governance, or informational rights; or
    4. consciously determined not to act.
  6. Evidence of Board Inaction
    Documents sufficient to determine whether CNR’s Board held meetings, received information, or engaged in any deliberative process at all concerning the foregoing matters.

Confidentiality

Mason is prepared to enter into a reasonable confidentiality agreement governing the use of any documents produced, consistent with Delaware law and customary §220 practice.

Demand and Response

Please advise within five (5) business days whether the Company will comply with this demand and propose a prompt schedule for production. If the Company declines or fails to respond, Mason will consider all available remedies, including seeking relief in the Delaware Court of Chancery, the exclusive forum designated in CNR’s certificate of incorporation.

Nothing herein constitutes a waiver of any rights, all of which are expressly reserved.

On behalf of Mason, I affirm that the purposes for the demand inspection as set forth above constitute true and correct statements of the reasons Mason desires to review the demanded books and records, and that such demand is made in good faith, under oath and penalty of perjury. These purposes are both proper and reasonably related to Mason’s interest as stockholder of CNR.

Regards,
James C. Woolery, Esq.
Founding Partner
Woolery & Co. PLLC

About Mason Capital Management LLC
Mason Capital Management LLC is an absolute return focused investment firm that combines deep fundamental analysis with hard catalysts to drive value creation. Founded in July 2000 by Ken Garschina and Mike Martino, Mason’s strategies range from event-driven investing to corporate carve-outs and control acquisitions. Mason’s control investments include CB&I, the world’s foremost designer and builder of storage facilities, tanks and terminals for energy and industrial markets.

Cision View original content:https://www.prnewswire.com/news-releases/mason-capital-management-demands-answers-from-ascent-cnr-corporation-302692959.html

SOURCE Mason Capital Management

Market Opportunity
Sentient Logo
Sentient Price(SENT)
$0.0206
$0.0206$0.0206
-1.57%
USD
Sentient (SENT) 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 service@support.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

SM Offices investing P1B in Cebu expansion

SM Offices investing P1B in Cebu expansion

SM OFFICES, the commercial property arm of SM Prime Holdings, Inc., plans to add more than 60,000 square meters (sq.m.) of new leasable space worth about P1 billion
Share
Bworldonline2026/02/20 00:06
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
Meme Coin Frenzy Cools, Altcoins Take the Spotlight

Meme Coin Frenzy Cools, Altcoins Take the Spotlight

Pump.fun’s flagship coin PUMP dropped nearly 10% in a single day, dragging down related tokens such as TROLL and Aura, […] The post Meme Coin Frenzy Cools, Altcoins Take the Spotlight appeared first on Coindoo.
Share
Coindoo2025/09/20 00:00