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Once geopolitical tensions ease, a global supply glut is anticipated to drive oil prices downOnce geopolitical tensions ease, a global supply glut is anticipated to drive oil prices down
Beneath the headlines on the Middle East conflict: Why the market fall may soon be over
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Wall Street on March 16, 2026 closed sharply higher as the price of oil pulled back below US$95 a barrel (bbl). News reports then explain this to be due to US President Donald Trump’s strong call for “efforts to reopen the Strait of Hormuz.”
Asian markets like ours opened higher last Tuesday morning, March 17, tracking the rebound of Wall Street following the retreat in the price of oil overnight — a welcome development from concerns regarding the ongoing US-Iran conflict.
The Nikkei 225 index, the leading index of Japan’s top 225 companies traded on the Tokyo Stock Exchange (TSE) rose 0.75% in early trading, while the broader Topix index or Tokyo Stock Price Index, composed of major capitalization-weighted firms listed on the Prime Market (formerly the first section) of the TSE, also showed gains of over 1%.
The KOSPI Index or Korea Composite Stock Price Index, the representative market index tracking all common stocks traded on the primary division of the Korea Exchange (KRX), also opened strongly, jumping over 2.9% in morning trading.
Australia’s S&P/ASX 200 added 0.27% in early trades. The S&P/ASX 200 is the benchmark stock market index for the Australian Securities Exchange (ASX) that measures the performance of the 200 largest, most liquid, and publicly listed companies in Australia. The S&P/ASX 200 likewise represents approximately 80% of the total market capitalization of the Australian market, that is used as well to gauge the overall health of the local economy.
The broader MSCI Asia Pacific Index for the region, rose up by nearly 1%, boosted, also by the pullback in oil prices.
Even India’s GIFT Nifty (Gujarat International Finance Tec-City Nifty), which indicates the opening for the Indian market, also traded higher by about 111 points or 0.53%, suggesting a positive start following a strong recovery in the previous session. The GIFT Nifty is a US dollar-denominated futures contract based on the NSE Nifty 50 index, traded on the NSE International Exchange (NSE IX) in Gujarat, India.
The NSE Nifty 50 index is the flagship benchmark index of the National Stock Exchange of India (NSE), representing the weighted average of 50 of the largest, most liquid Indian blue-chip companies across 13 key sectors. It acts as a barometer for the Indian economy, covering roughly 54% of the NSE’s free-float market.
It also provides nearly 20-hour trading hours (6:30 am to 2:45 am IST) for global investors to hedge risks or speculate on the Indian market, particularly acting as a pre-market indicator before Indian equity markets open.
Lastly, both the PSEi or Philippine Stock Exchange Index and the broader All shares Index also ended the day with positive gains with the former closing at 6,026.01, up 19.46 points or 0.32% and the latter at 3,349.75, up 8.06 or 0.24%; all sectors were also up except property and financials.
On Tuesday, March 17, Wall Street again ended higher for the second consecutive day, as investors continued to rotate back into equities, despite renewed volatility in oil prices and a cautious tone ahead of the Federal Reserve’s policy decision. Similarly, the PSEi and All Shares index ended on higher territory for the second day.
As of mid-morning on Wednesday, March 18, Wall Street futures were trading higher, poised to extend a multi-day rally as investors looked past heightened Middle East tensions and focus on upcoming Federal Reserve policy updates.
In spite of this surprising market uptick, overall investor sentiments continuous to be fragile, as situations could also change rapidly due to the intense volatility of the ongoing conflict in the Middle East.
Global oil supply glut
Behind the market’s fall due to the present tensions in the Middle East, there are some fundamental factors that may explain why the market is able to easily bounce back, one of which is about the oil supply situation in the world.
While the present conflict in the Middle East has sent the price of Brent to surge past US$105/bbl and WTI or West Texas Intermediate to over $100/bbl, the forecast on global oil supply for 2026 to 2028 is expected to exceed demand — a situation expected to create a downward pressure on the price of oil.
Once geopolitical tensions ease, a global supply glut is anticipated to drive oil prices down, according to J.P. Morgan Chase &Co. (J.P. Morgan). It views the current triple-digit prices of oil as a “geopolitical panic” only that will fade once shipping routes normalize.
As of Monday, March 16, the two-year oil price futures (or contracts for delivery in March 2028) are as follows: Brent crude for March 2028 delivery was placed at $72.88 per barrel (bbl), while WTI Crude is “in the high US$60s as in Dec 2028, contracts were at US$65.49.”
The US Energy Information Administration (IEA) also has a lower price estimate from the current conflict highs to “around US$64/bbl” or a little above for 2027.” IEA explains that current prices include a significant risk premium due to the closure of the Strait of Hormuz; however, based on the two-year futures prices of oil, this premium is expected not to persist for too long.
Here are the revisions made by the other major investment banks on their long-term oil price targets as of March 16, 2026:
- J.P. Morgan
It is maintaining a $60/bbl average price for oil in 2026 despite the spike. For 2027, it expects the price of Brent at $57/bbl and WTI at $53/bbl, at the same time warning that prices could drop into the $30s/bbl by late 2027 if a massive global surplus develops post-conflict. It has the most bearish outlook among the major banks.
- The Goldman Sachs Group, Inc
It has hiked its March/April Brent forecast to $98 to $110/bbl, up from approximately $70/bbl. It also raised its Q4 2026 Brent to $71, up 7.58% from $66/bbl and WTI to $67, up 8.06% from $62/bbl for 2027/2028.
- Bank of America (BofA)
It raised its forecast for Brent by 27% to $77.50 from $61/bbl and warned of a $130/bbl peak if the conflict persists through the second half of 2026, while also expecting a retreat to $65/bbl by 2027 once the war ends.
- Standard Chartered PLC
It has the most aggressive revision: it increased its 2026 Brent average price to $85.50 from $70/bbl, while raising its average price for 2027 to $77.50/bbl, citing lasting damage to regional energy infrastructure that will limit supply even after a ceasefire.
- Citi Research
It ramped its Q1 price of Brent to $75 and Q2 to $78/bbl, from $73 and $70/bbl, respectively. However, it is maintaining a bear case of $50 to $60/bbl for late 2026/2027, driven by weak Chinese demand and rapid EV adoption.
Thus, while the near-term “front-month” prices of oil for March 2026 are elevated due to supply disruptions from the ongoing US-Israel war on Iran, the “back end” of the futures curve is seen to be significantly lower by the said major banks, reflecting market expectations of a return to surplus conditions.
In other words, the long-term outlook for oil prices is much cheaper than the current spot prices of $93 to $102 because the market views the current supply shock as temporary.
Need to end the war without being the first to quit
As of Wednesday, March 18, US President Donald Trump has been quoted by reporters that the conflict will be “wrapped up soon” or will be ending the war in the “very near future” because the Iranian military has been “obliterated.” He has asserted as well that Iran “wants to make a deal” and is seeking a ceasefire. However, Tehran’s Minister of Foreign Affairs Abbas Araghchi has flatly rejected this claim, calling the assertion “delusional” and stating that Iran is prepared to fight “as long as it takes.”
Analysts believe that Trump’s statements are but a sign that he wanted already to end the conflict not only because of the global energy crisis and high oil prices the war has caused, it could also hurt his party’s prospects in the upcoming November US midterm elections, and weaken his grip on both houses of the US Congress.
Aside from the six non-American flag vessels previously attacked at the Strait of Hormuz by Iran last March 11 and 12, and despite the defiant statements of Aragchi, no other vessel has been bombed except the Kuwait-flagged liquefied petroleum gas (LPG) tanker, Gas Al Ahmadiah. It was hit last Tuesday, March 17, by an “unknown projectile” while anchored east of Fujairah, UAE; the vessel sustained minor structural damage and no injuries were reported.
To analysts of the conflict, this dichotomy of Iran’s defiant rhetoric and less acts of aggression may be also a subtle indication of its desire to end the conflict without being the first to quit and be called “chicken.” (TACO: Why Trump walked back on 19% reciprocal tariff on Philippine agriculture)
However, emotions on both sides may just prevail, prolonging the war more than it should be. But come to think of it, with the massive global economic havoc the conflict is creating, on top of the huge costs shouldered at the moment by each of the parties involved, the war may just be ending “soon.”
This could mean, too, that “The market fall may soon be over.” – Rappler.com
(The article has been prepared for general circulation for the reading public and must not be construed as an offer, or solicitation of an offer to buy or sell any securities or financial instruments whether referred to herein or otherwise. Moreover, the public should be aware that the writer or any investing parties mentioned in the column may have a conflict of interest that could affect the objectivity of their reported or mentioned investment activity. You may reach the writer at densomera@yahoo.com)
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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.
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