Fed Decision & Big Tech Earnings: Building Your AI Analysis Framework

This Week’s Focus: Markets and AI Analysis

Two major events align this week: the Federal Reserve’s policy decision and quarterly results from the largest technology companies. Both come amid limited visibility, with the government shutdown delaying key data releases and the U.S. launching a new tariff probe into China’s 2020 trade commitments.

In this edition, we explore structured prompting through two key themes, Fed policy and Big Tech earnings, to show how to frame analysis under uncertainty.

It’s designed to help you:

1. Define measurable scenarios.

2. Apply consistent logic across time periods.

3. Focus on metrics that drive valuation.

4. Keep a transparent record of reasoning.

AI in Action

📌 Prompt Example 1: Fed Decision Post-Meeting Analysis

The Fed concludes its two-day meeting on Wednesday, October 29, 2025, with a policy decision expected at 2 p.m. EST, followed by Chair Jerome Powell’s press conference at 2:30 p.m. Markets anticipate a 25-basis-point rate cut, shifting the target range from 4.00-4.25% to 3.75-4.00%. The September Consumer Price Index (CPI) came in at 3.0% year over year (YoY), just below the 3.1% consensus, while core inflation was also 3.0%. These readings support a modest case for easing. Yet, with employment and retail-sales data still missing, the Fed must lean on inflation and surveys, making Powell’s tone almost as influential as the rate move itself.

To turn that uncertainty into structured analysis, let’s outline how to build a potential AI prompt step by step.

Step 1. Role

Assign a role to the AI model and define the analytical lens:

This keeps the analysis interpretive, not predictive.

Step 2. Context

Provide the model with detailed situational backdrop:

This grounds the model in what’s known, what’s uncertain and what matters most.

Step 3. Clarity

Lay out what to produce. For example, request three scenarios:

• Baseline (Neutral) — Tone consistent with prior guidance.

• Hawkish — Greater emphasis on inflation risks or a slower path of cuts.

• Dovish — Stronger focus on downside risks or flexibility to ease.

For each scenario, summarize:

1. Key tone indicators in the statement and remarks.

2. Immediate reactions in the S&P 500, 10-year Treasury yield, U.S. dollar, and gold.

3. Sector shifts within 48 hours.

4. One short-term portfolio takeaway.

This replaces open-ended guessing with structured, comparable outcomes.

Step 4. Output Constraints

It keeps the output disciplined and usable in professional settings.

When you put those pieces together, the full prompt reads along the lines of:

You are a market strategist reviewing the Federal Reserve’s [month, year] policy meeting. Markets expected a [X] basis-point move, with inflation near [X]% year over year. With incomplete data, attention turns to the Fed’s communication. Develop three scenarios, i.e., Baseline, Hawkish, and Dovish. For each, summarize tone indicators, short-term market reactions (S&P 500, 10-year yield, USD, gold), likely sector leadership and one portfolio consideration. Rely only on official communications and market data. Keep each scenario concise in 3-4 sentences and factual.

Adaptation note: This framework applies equally to other central banks. Simply substitute instruments or variables while keeping the same four-step structure. 

📌 Prompt Example 2: Apple Earnings Review  

Microsoft (MSFT), Alphabet (GOOG; GOOGL), Meta (META), Apple (AAPL) and Amazon (AMZN) all report this week, representing roughly a quarter of S&P 500 market capitalization. Investors are focused on whether recent AI-driven capital expenditure is translating into durable revenue growth and margin stability.

Apple’s fourth-quarter 2025 results on October 30 will show whether last quarter’s strength is holding. In fiscal Q3 2025, Apple reported $94 billion in revenue, up 10% YoY with iPhone sales rising 13% to $44.6 billion and Services revenue up 13% to a record $27.4 billion. Yet gross margin narrowed to 46.5% on $800 million in tariff costs, set to reach $1.1 billion in Q4. Management highlighted record upgrades and a rebound in Greater China, yet both developments are now under scrutiny as global demand cools.

To analyze it systematically, let’s walk through how to build the earnings prompt.

Step 1. Role

Set the tone for the analytical lens.

It centers the output on earnings durability rather than short-term noise.

Step 2. Context

Anchor the model in what the market already knows:

This creates a factual baseline for assessment.

Step 3. Clarity

Specify what to extract, the core checkpoints:

1. Did iPhone 16 demand remain resilient or cool off?

2. Did gross margin hold as tariff costs rose toward $1.1 billion?

3. Did Greater China’s recovery extend beyond one quarter?

4. What tone did management take on holiday-quarter demand and the 2026 outlook?

Each question turns “analyze earnings” into measurable tasks.

Step 4. Output Constraints

Guide structure and tone. In this case, keep the analysis concise and evidence-based, using only reported figures and direct commentary. Highlight one data point or quote that reflects management confidence or caution, and conclude with a brief view on how guidance may shape sentiment into early 2026.

When you put those pieces together, the full prompt for APPL reads:

You are an equity analyst reviewing Apple’s fiscal Q4 2025 results and management commentary from the October 30 earnings release. Assess earnings quality and sustainability by addressing: (1) Did iPhone 16 demand continue to drive growth or slow? (2) Did gross margin hold as tariff costs neared $1.1 billion? (3) Did Greater China’s recovery persist? (4) What tone did management take on holiday-quarter demand and the 2026 outlook? Use only reported data and direct statements released by the company. Keep the response under 400 words, highlight one telling data point or quote. End with a brief forward view on how sentiment may shift into early 2026.

Adaptation note: This Apple example illustrates structure, not content. For other companies, substitute metrics relevant to their business models such as Azure growth, AWS margins, or advertising revenue, while keeping the same sequence: Context → Role → Clarity → Output.

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Closing Thoughts

This is the essence of structured prompting. Instead of asking an AI to generically “summarize the call,” you guide it to answer the precise questions that inform your trading decisions. This approach creates a repeatable framework you can rely on meeting after meeting, quarter after quarter and cycle after cycle.

Thank you for reading AI in Investment Research & Finance. Here’s to spotting tomorrow’s market stories today.

Until next time