The Supreme Court has refused to support President Donald Trump in his attempt to fire Federal Reserve Governor Lisa Cook, after justices raised serious doubts The Supreme Court has refused to support President Donald Trump in his attempt to fire Federal Reserve Governor Lisa Cook, after justices raised serious doubts

Supreme Court rejected Trump’s attempt to fire Fed Governor Lisa Cook

The Supreme Court has refused to support President Donald Trump in his attempt to fire Federal Reserve Governor Lisa Cook, after justices raised serious doubts about the legal grounds and the threat it posed to the Fed’s independence.

Trump’s lawyers argued that Lisa could be fired “for cause” based on uncharged mortgage fraud allegations. They also claimed no court review was needed. That set off alarms inside the courtroom.

Justice Brett Kavanaugh told Trump’s solicitor general, D. John Sauer, that the argument could seriously damage the Fed’s structure. He said the idea that “the president alone” can decide what counts as cause, with no process or legal check, would “weaken, if not shatter, the independence of the Federal Reserve.”

Lisa sat inside the courtroom as this unfolded. She had sued Trump in September, saying his claim to fire her violated the Federal Reserve Act, which only allows firing “for cause.” The law doesn’t define the term clearly, but it’s always meant serious wrongdoing during someone’s time in office, not before.

Justices question speed of firing and lack of hard evidence

Justice Ketanji Brown Jackson pressed Sauer hard. She asked, “Do you have evidence other than the president’s view?” Sauer answered that Lisa’s presence was damaging to the Fed’s public image.

Jackson wasn’t convinced. She asked if the public was really being harmed by her staying in her role while the case was still ongoing in district court.

Justice Samuel Alito, one of the conservatives usually aligned with Trump, also showed doubt. He asked why the White House, the district court, and the appeals court all pushed the process forward so quickly. “Is there any reason why this whole matter had to be handled… in such a hurried manner?” Alito asked. He also said that when the issue was in the executive branch, it was dealt with “in a very cursory manner.”

Lisa is the first Black woman to serve on the Fed board. She was first appointed by President Joe Biden in 2022, to complete an unfinished term. In 2023, Biden reappointed her for a full 14-year term.

Trump didn’t mention her interest rate stance when he said he was firing her. He pointed instead to claims by Federal Housing Finance Director Bill Pulte that Lisa had lied on old mortgage applications. Those claims predate her time on the Fed board. No charges were filed.

Lisa’s lawyer, Paul Clement, told the court there’s no reason to treat the Fed like any regular federal agency. He said the court itself had called the Fed a “uniquely structured, quasi-private entity” in a recent ruling.

“There’s no rational reason to go through all the trouble of creating this unique, quasi-private entity… just to give it a removal restriction that is as toothless as the president imagines,” Clement said.

He argued that if the removal rules had any actual power, then the Supreme Court should reject Trump’s request to fire her immediately.

Judge Jia Cobb, who reviewed the case in district court, already ruled that Lisa can stay on the job for now. Cobb said Lisa has a strong case that Trump’s action violated the Federal Reserve Act. She wrote that the best way to read the “for cause” rule is to apply it only to actions that happen while someone is serving on the board, not to anything that came before.

Also present in court was Fed Chair Jerome Powell, who is now facing a criminal investigation over his role in a multibillion-dollar renovation of the Fed’s Washington, D.C. headquarters. Powell said the investigation is politically motivated, pointing to Trump’s anger at the Fed keeping interest rates steady last year.

Lisa supported Powell in that decision. After the hearing, she said, “This case is about whether the Federal Reserve will set key interest rates guided by evidence and independent judgment or will succumb to political pressure.”

She added, “Research and experience show that Federal Reserve independence is essential to fulfilling the congressional mandate of price stability and maximum employment. That is why Congress chose to insulate the Federal Reserve from political threats, while holding it accountable.”

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Summarize Any Stock’s Earnings Call in Seconds Using FMP API

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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. 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Medium2025/09/18 14:40