Last updated: 13 March 2026

Objective:

Scan news and analyst reports weekly for early signs of newly emerging market narratives and connect them to sectors and stocks that may be positively and negatively influenced. A preliminary screening workflow to resolve emerging situations into potentially actionable ideas or understand risks and opportunities regarding existing portfolio positions.

Explanation:

In equity markets, some of the biggest winners and losers are shaped by new narratives that begin to change how investors interpret the forward outlook for particular stocks. To see these impacts in action you only have to look at how the “AI eating software / SaaSpocalypse” narrative emerged in 2025 and de-rated the entire software sector globally.

The challenge is that the most important narratives rarely arrive fully formed. They emerge first as fragments across news flow, company commentary, analyst notes, industry reporting, geopolitical developments and specialist market discussion, often before consensus has clearly recognized their significance. For investors, identifying these shifts early matters because these narratives can alter the market’s view of growth durability, margins and valuation multiples well before the full earnings impact is visible in reported results. By the time a theme is universally understood, much of the repricing has often already occurred.

I have introduced a new workflow into the Inferential Investor AI-assisted Research Library which is designed to improve that early detection process by using live web research and AI-assisted synthesis to separate genuinely new narrative formation from stale consensus thinking. This workflow is designed for investors to run every week to scan for new weak or emerging narrative signals that may provide opportunity.

Rather than relying on internal training data to recycle familiar market stories, the workflow strictly imposes a live research regime upon the AI model to scan recent reporting for fresh signals, test whether a theme is truly emerging, and then map the transmission mechanism from the narrative to sectors, industries and individual stocks. AI is especially useful in this context because it can rapidly consolidate dispersed evidence, clarify where the primary and secondary impacts are likely to fall, and draw on historical analogies to frame how similar episodes have propagated through markets in the past. The result is a more structured, forward-looking process that helps investors move from vague macro or thematic noise to a clearer view of which stock groups may benefit, which may be exposed, and why.

Link to blog post explanation:

Preferred Model(s):

ChatGPT 5.4 Extended Thinking.

Gemini: Note that testing in Gemini shows the model regularly fails to actually complete live web searching even though it reports that it does. This bug appeared in 2026 with the launch of later variants of Gemini and appears related to system prompt instructions Google has overlaid in Gemini when user select “thinking” modes (Thinking and PRO). Gemini is layering a system instruction not to complete web searches in order to focus the model on reasoning instead in these modes (web RAG can distract). For Gemini users “Fast” mode gets web search live again, however the synthesized response is lower quality. This results in surfaced market themes being drawn from training data which are outdated. Hopefully this problem, which is now being widely recognized, is ironed out in later updates.

Important Execution Notes:

• Copy / past the prompt and run.

• Deep Research mode can be used to generate an even more comprehensive report backed by more extensive web searches.

Sample Output:

Emerging Market Narratives: February 27, 2026 – March 13, 2026 (example report at bottom of blog post)

Copy/Paste Prompt Set:

Important note: Subscribers can use this prompt set for their own analysis. However, the prompt is copyrighted by The Inferential Investor, paywalled, and must not be shared without permission.

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Last updated: 18 December 2025

Objective:

Generate a rigorous, metrics-driven equity analysis of any Retailer or Consumer Packaged Goods company by systematically extracting, calculating, and benchmarking institutional-grade industry-specific operating, financial, and capital efficiency metrics across recent periods. The analysis is designed to translate changes in demand, margins, inventory discipline, and cash generation into clear implications for future growth durability, risk, and valuation.

Explanation:

This analysis is designed to deconstruct a Retail or Consumer Packaged Goods business into its core economic drivers, moving beyond headline revenue growth to assess the quality, sustainability, and profitability of that growth. By examining metrics such as comparable sales, traffic, average order value, price versus mix, and sales productivity, the framework distinguishes between demand-led growth and price- or promotion-driven growth, which has materially different implications for durability and valuation. Gross margin, markdown intensity, inventory turnover, and weeks of supply are used to evaluate merchandising discipline and brand pricing power, while store-level economics and customer metrics such as repeat purchase rates, lifetime value, and acquisition costs provide insight into whether growth is value-accretive at the unit level.

The analysis then links operating performance to financial outcomes through cost structure, cash flow, and capital efficiency metrics, including SG&A leverage, operating margin, free cash flow conversion, return on invested capital, and lease-adjusted balance sheet risk. These metrics determine whether scale translates into operating leverage, whether inventory and working capital are sources of cash or risk, and whether the company can fund growth, withstand cyclical pressure, and return capital to shareholders. By benchmarking these measures against peers and historical performance, the framework explicitly ties operational execution to valuation outcomes, clarifying when a company merits a premium multiple due to durable growth and strong returns versus when deteriorating demand, margin pressure, or balance sheet risk justify multiple compression.

As always, be aware that models can make mistakes. At each step, examine the response and challenge information or conclusions that appear erroneous before proceeding to any subsequent steps. If in doubt use a second model with the same prompt to verify the information and generate challenge questions and answers (CoVe process) to correct interpretations of data.

Link to blog post explanation:

N/A

Preferred Model(s):

ChatGPT-5.2+ Deep Research Mode for strongest analysis. Gemini 3 for most accurate data pulls outside provided sources (eg valuation multiples).

Expect these highly detailed industry specific reports to take approximately 10 minutes to complete.

Important Execution Notes:

• Inset the stock ticker, name and exchange where indicated

• Choose sub-industry type

• State periods to analyze

• Provide 5-10 peers to benchmark against

Sample Output:

Starbucks Corporation (sbux) – Q3 ^0 Q4 Fy2024 Vs Fy2025 Investment Analysis

464KB ∙ PDF file

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Copy/Paste Prompt Set:

Important note: Subscribers can use this prompt set for their own analysis. However, the prompt is copyrighted by The Inferential Investor, paywalled, and must not be shared without permission.

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Last updated: 17 December 2025

Objective:

This prompt is designed to produce an institutional-grade analysis of any SaaS or cloud stock by systematically extracting, calculating, and benchmarking industry-specific operating and financial metrics across the last three periods. The objective is to translate these metrics into clear insights on growth durability, unit economics, and valuation support, enabling an investor to assess both the current quality of the business and the conditions required for future upside or downside.

Explanation:

This prompt is structured to replicate the way professional technology investors evaluate SaaS and cloud businesses, starting from disciplined source extraction and moving through metric calculation, benchmarking, and synthesis. It forces the analysis to anchor on primary disclosures such as financial statements, earnings materials, and transcripts, while explicitly identifying and filling disclosure gaps through targeted web research where necessary. By calculating each key metric consistently across the last three periods, the prompt emphasizes not just absolute performance but direction of travel, allowing the analyst to distinguish between durable structural strength and short-term noise.

The metrics analyzed span growth, revenue durability, unit economics, efficiency, profitability, and valuation context, which together define the economic quality of a SaaS or cloud model. Measures such as ARR growth, NRR, and RPO capture the sustainability and visibility of future revenue, while gross margin, sales efficiency, and free cash flow margins reveal whether scale is translating into economic leverage. Customer metrics like LTV and CAC and LTV/CAC ratios are examined. Benchmarking these metrics against peers and best-in-class ranges enables a forward-looking assessment of valuation support, clarifying which metrics must improve, stabilize, or defend current levels for the stock to justify multiple expansion or avoid compression.

As always, be aware that models can make mistakes. At each step, examine the response and challenge information or conclusions that appear erroneous before proceeding to any subsequent steps. If in doubt use a second model with the same prompt to verify the information and generate challenge questions and answers (CoVe process) to correct interpretations of data.

Link to blog post explanation:

N/A

Preferred Model(s):

TOP: ChatGPT 5.2+ preferred for its depth of reasoning, analysis and comprehensiveness of reported metrics. Reports can be less user friendly as they are very detail dense.

Claude Opus 4.6 provides more user friendly report generation and readability. Note that Claude can struggle with the complexity of the task and may require some nudging via “retry” or “continue” when conversation lengths hit limits. You can get the output though with persistence. Also note that Claude will frequently report outdated valuation metrics as the model often refers to memory (past training cut off)

Gemini 3 for the fastest output however I’ve noted it sacrifices detail from the attached source documents for speed. Deep Research mode is required for greater detail however that is usually sourced from web search.

Important Execution Notes:

• Insert the stock, ticker, name and exchange where indicated

• Specify the sub-industry to align to most appropriate metrics for the business and the periods to analyze

• Optional: specify 5-10 tickers of competitor stocks to benchmark against where able.

• Attached to the prompt the last 2 sets of accounts (eg 10-Ks, 10-Q,s semi or annual reports). Also attached last 2 presentations where available and last 2 call transcripts (for KPIs and guidance not disclosed in filings)

Sample Output:

Servicenow Saas Kpi Deep Dive And Peer Benchmarking

295KB ∙ PDF file

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Copy/Paste Prompt Set:

Important note: Subscribers can use this prompt set for their own analysis. However, the prompt is copyrighted by The Inferential Investor, paywalled, and must not be shared without permission.

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Last updated: 16 December 2025

Objective:

The objective of this prompt is to rigorously analyze a stock’s share price and valuation multiple over time by identifying all major and minor turning points and decomposing each move into valuation-driven versus fundamental-driven factors. The analysis aims to link observed price and multiple reversals to underlying market, macro, and company-specific conditions in order to clarify what truly drove past inflections and inform forward-looking investment judgment.

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Explanation:

This prompt functions much like the approach of a history professor examining a sequence of past events, not to memorize dates, but to understand causality, context, and consequence. Just as history is studied to distinguish between structural forces and one-off shocks, the prompt dissects a stock’s price and valuation history to separate enduring drivers from temporary distortions. By identifying major and minor turning points and reconstructing the conditions that surrounded them, the analysis treats the chart as a historical record, asking not just what happened, but why it happened at that moment and what forces were already in motion beneath the surface.

What the prompt ultimately achieves is a disciplined framework for learning from market “history” without falling into hindsight bias. Like a historian distinguishing economic cycles, political decisions, and social sentiment, the analysis separates valuation multiple changes from fundamental business performance, clarifying whether reversals were driven by shifts in perception, changes in underlying reality, or both. In doing so, it provides investors with a more durable intuition for how this stock tends to behave across different regimes, using the past not as a prediction of the future, but as a set of recurring patterns and warning signals that can inform better forward-looking judgment.

As always, be aware that models can make mistakes. At each step, examine the response and challenge information or conclusions that appear erroneous before proceeding to any subsequent steps. If in doubt use a second model with the same prompt to verify the information and generate challenge questions and answers (CoVe process) to correct interpretations of data.

Link to blog post explanation:

N/A

Preferred Model(s):

Gemini 3 PRO performs best for its multi-modal chart interpretation capabilities.

Important Execution Notes:

• Insert the stock ticker and name where indicated

• Specify the date range of the chart(s) (10+ years is preferred for greater understanding)

• Attach/upload either a single chart with two panels (one for the stock price and one for the valuation multiple) or two separate charts (price and multiple) ensuring they have the same date ranges and are file labeled as to content.

• Outline where indicated, what is depicted in each panel of the uploaded chart (eg Panel 1: Stock Price; Panel 2: P/Sales multiple)

Sample Output:

The following sample output is derived from an analysis of the following 2 panel chart:

Meta Historical Drivers Of Stock Price And Multiple Re Rates And De Rates

324KB ∙ PDF file

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Copy/Paste Prompt Set:

Important note: Subscribers can use this prompt set for their own analysis. However, the prompt is copyrighted by The Inferential Investor, paywalled, and must not be shared without permission.

ROLE & MISSION
You are a senior equity research analyst specializing in technical-fundamental synthesis. Your task is to analyze an attached chart image for a user-specified stock, which shows:
1. the share price over time, and
2. a valuation multiple over the same period (e.g. P/E, forward P/E, P/Sales, or EV/Sales).

Your goal is to identify all major and minor turning points in both price and multiple, then explain what drove each inflection by disentangling:
* valuation re-rating / de-rating, and
* underlying fundamental performance (earnings, sales, margins, growth expectations).

INPUTS (User must provide)

* Stock ticker / EXCHANGE / COMPANY NAME: [INSERT TICKER, EXCHANGE AND COMPANY NAME]
* Time period covered by the chart: [ 10 YEARS TO 16 DECEMBER 2025]
* Chart image attached description: [price + multiple over identical time axis]
  * Chart Panel 1: [STOCK PRICE CHART]
  * Chart Panel 2: [FORWARD PRICE / EARNINGS RATIO]
***User note: if two separate chart images are attached, adjust prompt descriptions above and identify each file and its contents instead of Panels.

SOURCE HANDLING & ASSUMPTIONS

* Treat the chart image as the primary source for timing of turning points.
* Use external market and company context only to explain why those turning points occurred.
* If multiple plausible drivers exist, rank them by importance rather than speculating.
* Clearly label uncertainty where causal attribution is ambiguous.
* Do not assume causality purely from correlation. Explicitly justify linkages.

TASKS

1. Chart Interpretation & Turning Point Identification
   From the chart image:

* Identify major turning points (structural peaks/troughs with sustained trend reversals).
* Identify minor turning points (shorter-term counter-trend moves, corrections, or rebounds).
* For each turning point, record:

  * Approximate date / period
  * Direction (peak or trough)
  * Whether price and multiple moved together or diverged

2. Turning Point Attribution Framework
   For each identified turning point, analyze using the following structure:

A. What Happened in the Price?

* Magnitude and speed of the move
* Whether it marked trend continuation or reversal
* Volatility regime change (if evident)

B. What Happened in the Multiple?

* Expansion or compression
* Rate of change (gradual vs abrupt)
* Whether the multiple led or lagged price

3. Driver Decomposition (Critical Requirement)
   For each turning point, separate drivers into two distinct buckets:

A. Multiple-Driven Factors (Valuation)
Examples:

* Interest rate changes or discount-rate repricing
* Risk-on / risk-off market regime shifts
* Sector rotation or factor exposure (growth vs value)
* Narrative shifts (AI optimism, regulatory fears, competitive threats)
* Change in perceived durability of growth or margins

B. Fundamental Metric-Driven Factors
Depending on the multiple used:

* For P/E: earnings growth, margins, operating leverage, guidance changes
* For P/Sales: revenue growth, pricing power, volume growth, customer expansion
  Include:
* Reported results vs expectations
* Forward guidance changes
* Unit economics or cost structure shifts

Explicitly state:

* What moved expectations vs what moved reported results
* Whether fundamentals justified the multiple change ex post or not

4. Catalyst Identification
   For each reversal, identify key catalysts:

* Earnings releases
* Guidance updates
* Macro events (rates, inflation, policy)
* Industry-specific developments
* Company-specific news (M&A, product launches, regulation)

Distinguish between:

* Trigger events and
* Underlying conditions already in place

5. Synthesis & Pattern Recognition
   After reviewing all turning points:

* Identify recurring drivers behind peaks and troughs
* Assess whether reversals are historically driven more by:

  * Multiple re-rating cycles, or
  * Fundamental acceleration/deceleration
* Highlight periods where:

  * Price rose despite multiple compression
  * Price fell despite improving fundamentals

OUTPUT STRUCTURE

Section 1: Summary Table of Turning Points
A table with:

* Date / period
* Major vs minor
* Peak or trough
* Price driver (multiple vs fundamentals)
* Key catalyst(s)

Section 2: Detailed Turning Point Analysis
Sub-sections for each turning point, following the decomposition framework above.

Section 3: Valuation vs Fundamentals Attribution
A high-level assessment of:

* What proportion of the stock’s volatility over the period was driven by valuation changes vs operating performance
* How sensitive the stock appears to macro vs company-specific factors

Section 4: Investor Takeaways

* What this history implies about how the stock should be analyzed going forward
* Which indicators historically matter most for anticipating the next turning point
* Lessons about timing, valuation discipline, and expectation management

FINAL CONSTRAINTS

* Be analytical, not narrative-heavy.
* Use clear causal language.
* Avoid hindsight bias where possible.
* Write for a professional investor audience.

Last updated: 16 December 2025

Objective:

The largest share price opportunities come from situations where investor’s identify impactful changes to a stock’s operating environment early. This leads to a period where the key value drives of a company either accelerate or decelerate, changing the market’s view, and drive large changes (re-rating or de-rating) in a company’s valuation multiple and resultant share price. This prompt workflow uses detailed Context Engineering by way of 40 synthesized historic examples to feed the AI model with sufficient context to use its pattern recognition capabilities to identify similar situations across the market.

Explanation:

This prompt is designed to function as a high-level pattern-matching engine, using context engineering (specifically “many-shot prompting”) to train the AI to mimic the reasoning of a fundamental equity analyst.

By providing a diverse set of detailed historical examples—such as Oracle’s shift to AI cloud infrastructure or Celsius’s distribution deal—the prompt moves beyond simple instruction and instead demonstrates the precise causal chain it seeks: an external catalyst (operating environment change) triggering an acceleration in internal value drivers (revenue/margins), which inevitably forces the market to assign a higher valuation multiple to the stock. This effectively calibrates the model to ignore generic “cheap” stocks and instead hunt for structural discontinuities—specific moments where a company’s future earnings power is decoupling from its historical trend, creating a temporary pricing inefficiency that the market has not yet recognized.

For investors, these opportunities are the “holy grail” because they offer asymmetric returns driven by a multiplier effect: the share price rises not only because earnings grow, but because the market decides to pay more for every dollar of those earnings (multiple expansion). The AI spots these by synthesizing qualitative data—such as backlog acceleration, regulatory approvals, or strategic pivots—with quantitative disconnects, looking for situations where a stock is priced like a low-growth legacy business (e.g., an auto parts supplier) but is silently transitioning into a high-growth vertical (e.g., AI data center cooling). This approach allows the model to identify “coiled spring” setups where the current valuation fails to reflect the imminent change in the company’s economic reality.

Important Note: This prompt is designed as an initial stock screen - not to produce investment recommendations and is subject to our Disclaimer (accessible from top menu). As always, be aware that models can make mistakes. At each step, examine the response and challenge information or conclusions that appear erroneous before proceeding to any subsequent steps. If in doubt use a second model with the same prompt to verify the information and generate challenge questions and answers (CoVe process) to correct interpretations of data.

Link to blog post explanation:

N/A

Preferred Model(s):

· Gemini 3 + Deep Research

Important Execution Notes:

• Input the geographic and market cap focus range where indicated.

• Ensure Deep Research is selected.

Sample Output:

Hedge Fund Investment Research Plan (1)

421KB ∙ PDF file

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Copy/Paste Prompt Set:

Important note: Subscribers can use this prompt set for their own analysis. However, the prompt is copyrighted by The Inferential Investor, paywalled, and must not be shared without permission.

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Last updated: 16 December 2025

Objective:

The objective of this analysis is to rigorously evaluate the company’s financial performance, financial position, and cash generation by systematically analyzing its income statement, balance sheet, and cash flow statement across multiple periods. This analysis aims to synthesize the drivers of growth, profitability, returns, and capital structure sustainability to assess the company’s ability to sustain future growth, defend returns on capital, and maintain long-term financial health.

Explanation:

This prompt is designed to produce a disciplined, institution-grade financial statement analysis that mirrors how professional equity research teams assess a company’s underlying economic performance. By requiring the user to attach complete financial statements and enforcing strict data-verification rules before analysis begins, the prompt prioritizes numerical accuracy and internal consistency. It systematically dissects income statement, balance sheet, and cash flow dynamics across multiple periods, decomposing growth rates, margin movements, and return metrics into their underlying drivers rather than relying on surface-level ratios. Segment-level analysis, working capital behavior, cash earnings quality, and leverage structure are explicitly examined, ensuring the analysis distinguishes between structural trends and transitory effects such as one-offs, accounting changes, or cyclical tailwinds.

The resulting analysis produces a coherent, forward-looking synthesis rather than a collection of disconnected metrics. By integrating profitability, returns, cash flow strength, and capital structure sustainability, the framework highlights whether recent growth is repeatable, whether margins and returns are defensible, and whether balance sheet risk is increasing or receding. This allows the analyst to infer implications for future growth trajectories, reinvestment capacity, and downside resilience across economic cycles. Ultimately, the prompt enables a high-confidence assessment of financial health and value creation durability, providing clear signals about potential acceleration, normalization, or deterioration in the company’s long-term fundamentals.

As always, be aware that models can make mistakes. At each step, examine the response and challenge information or conclusions that appear erroneous before proceeding to any subsequent steps. If in doubt use a second model with the same prompt to verify the information and generate challenge questions and answers (CoVe process) to correct interpretations of data.

Link to blog post explanation:

N/A

Preferred Model(s):

· Gemini 3 + or ChatGPT 5.2+

Important Execution Notes:

• Use Deep Research Mode when you want more detailed analysis. Thinking mode will provide results faster however with less detail.

• Input the stack ticker, exchange and name where insidcated

• Attach preferably 3 years of financials (eg last 2 10-K reports)

Sample Output:

Financial Statement Analysis Sample Oracle

229KB ∙ PDF file

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Copy/Paste Prompt Set:

Important note: Subscribers can use this prompt set for their own analysis. However, the prompt is copyrighted by The Inferential Investor, paywalled, and must not be shared without permission.

### Role & Objective

You are a senior equity research analyst conducting a full financial statement analysis to assess a company’s growth quality, profitability, financial health, and capital structure sustainability. Your task is to carefully parse the company’s Income Statement, Balance Sheet, and Cash Flow Statement, analyze trends and drivers, and synthesize implications for future growth and returns.

Your analysis must be numerically precise, internally consistent, and evidence-based.

### Required Inputs (User Must Attach)

Ticker / Exchange / Company Name: [INSERT STOCK CODE / EXCHANGE / COMPANY NAME]
Please attach:
* The latest full financial statements (Income Statement, Balance Sheet, Cash Flow Statement), ideally covering at least 3–5 periods
* Segment disclosures, if available
* Notes on debt, leases, and off-balance-sheet items, if available

If any required statement or period is missing, explicitly state what is missing and proceed only with available data.

### Data Handling & Verification Rules

1. Extract all relevant line items explicitly before analysis.
2. Cross-check consistency:
   * Net income vs cash flow from operations
   * Changes in balance sheet accounts vs cash flow reconciliation
   * Segment totals vs consolidated totals
3. Flag any:
   * Missing items
   * Restatements
   * Structural changes (new segments, accounting changes, acquisitions)
4. Do not infer or estimate missing numbers unless clearly stated and justified.

## Analysis Structure (Use These Headings)

## 1. Income Statement Analysis
* Analyze period-to-period revenue growth and the management discussion and analysis comments on revenue to identify the key drivers of growth and their relative contributions. 
* Where possible, decompose revenue growth from disclosures and commentary into:
  * Volume vs pricing vs mix
  * Organic vs inorganic growth
* Calculate Gross, Operating and Net margins. Examine changes in margins period to period and decompose those margin changes at each level into:
  * Pricing
  * COGS leverage and the components of COGS
  * Operating leverage from each expense line.
  * Examine earnings and cost normalizations: Identify one-off factors contributing to GAAP vs Non-GAAP differences.
* Assess earnings quality:
  * Operating vs non-operating contributions
  * Accruals analysis and cash conversion of earnings
* Synthesize detailed insights on earnings growth and its sustainability in future periods from this analysis.

## 2. Segment Analysis
* Break down revenue and operating profit by segment
* Compare:
  * Segment growth rates
  * Segment margins
  * Segment relative Capital intensity (where data permits)
* Identify:
  * Which segments are driving headline growth
  * Which segments are dilutive or accretive to margins
* Discuss future implications:
  * Likely margin expansion or compression at the group level
  * Portfolio mix evolution

## 3. Balance Sheet & Capital Structure Analysis
* Analyze asset composition and trends:
  * Tangible vs intangible intensity
  * Goodwill and acquisition risk
* Capital structure:
  * Debt vs equity mix
  * Leverage trends over time
* Liquidity position:
  * Cash, revolvers, short-term obligations
* Identify **structural balance sheet risks or strengths**

## **4. Profitability & Returns Analysis**
* Calculate and Analyze trends in:
  * ROIC
  * ROA
  * ROE (Perform a DuPont decomposition: margins, asset turnover, leverage)
* Identify:
  * Primary drivers of return changes
  * Whether returns are improving due to operational quality or financial leverage
* Compare returns to:
  * Cost of capital (qualitatively if WACC not provided)
  * Historical levels
* Synthesize insights on the implications of changes in the various return metrics for future growth and/or capital requirements to sustain current growth rates.

## 5. Working Capital Trends
* Analyze changes in:
  * Receivables and receivable days
  * Inventory and inventory days
  * Payables and payables days
* Assess:
  * Cash conversion efficiency. Does the business consume or produce cash as it grows. Are there any working capital trends of concern?
  * Signs of demand stress, payment stress or channel stuffing
* Synthesize Implications for:
  * Near-term cash generation as the business grows
  * Earnings quality

## 6. Cash Flow Statement & Cash Earnings
* Triage cash flows for the last 3 periods into:
  * Operating
  * Investing
  * Financing
* Compare:
  * Operating cash flow vs net income
  * Calculate and examine Free cash flow trends
* Assess:
  * Capital expenditure intensity and its trends
  * Maintenance vs growth capex (if inferable)
* Provide insights on how the business is funding itself, where cash is being consumer and how cashflow productive it is. Comment on cash earnings quality and reinvestment discipline.

## 7. Debt & Leverage Analysis
* Calculate and Analyze:
  * Gross vs net debt
  * Leverage ratios and trends
  * Interest coverage and debt servicing capacity
* Review:
  * Debt maturity ladder
  * Refinancing risk
  * Covenant headroom (examine attached accounts for disclosures)
* Identify:
  * Off-balance-sheet obligations (leases, guarantees)
* Assess and comment on trends in debt, any concerns, debt sustainability across cycles and debt servicing insights.

## 8. Ratio Analysis (With Context)
Compute and interpret key ratios, grouped by:
* Profitability
* Liquidity
* Leverage
* Efficiency

Where possible:
* Compare to historical ranges and known benchmarks
* Focus on **directional insight rather than absolute judgment**
* Provide insights on what the ratio analysis suggests regarding future growth sustainability.

## 9. Integrated Synthesis & Forward Implications
Provide a concise but deep synthesis addressing:

* Growth Outlook
  * Can the company sustain recent growth rates? Are there signs of accelerated investment that may accelerate growth or vice versa?
  * Are there signs of acceleration, normalization, or slowdown?
* Margin Trajectory
  * Does the business and financial analysis suggest Structural expansion vs peak margins?
* Return Sustainability
  * Are ROIC/ROE defensible or mean-reverting? Does the business produce returns above its cost of capital and is the trend in returns positive or falling?
* Financial Health
  * Balance sheet resilience under stress and any debt servicing concerns
* Capital Structure Sustainability
  * Does the business have the ability to fund growth without value-destructive leverage

End with a clear investment-relevant conclusion, grounded strictly in financial statement evidence.

### Tone & Output Standards
* Analytical, precise, and professional
* No marketing language
* No unsupported speculation
* Clearly separate facts, analysis, and implications

Last updated: 10 December 2025

Objective:

The objective of this analysis is to evaluate a stock’s business model through the lens of Hamilton Helmer’s 7 Powers framework to identify structural, durable competitive advantages that can lead to superior returns through time. This assessment will quantify the strength of the company’s economic moat and its ability to generate sustained differential growth, margins and returns against current and future competitors.

2 variants of this prompt are presented:

1. A single company deep research assessment

2. A head-to-head variant to compare two competitors and understand which has the stronger business model and where individual weaknesses lie.

Explanation:

Hamilton Helmer’s 7 Powers framework is a strategic tool used to identify the structural competitive advantages that allow a business to generate persistent differential returns. These powers include Scale Economies, where unit costs decline with volume; Network Economies, where the value of a service increases for all users as more join; Switching Costs, which lock customers in due to the expense or effort of changing suppliers; and Counter-Positioning, where a new business model is adopted that incumbents cannot mimic without damaging their existing business.

The remaining powers are Branding, which allows a company to charge a premium for an objectively similar product due to customer perception; Cornered Resource, involving preferential access to a scarce and valuable asset; and Process Power, which stems from embedded, hard-to-replicate operational processes that yield superior efficiency or quality.

For a stock, the presence of one or more of these powers implies a durable economic “moat” that protects market share and profitability from competitors. This structural advantage often translates into higher-than-average returns on invested capital over the long term, making the company more resilient to competitive pressures. Consequently, stocks with strong powers are often valued at a premium, as investors anticipate sustained earnings growth and reduced risk of disruption.

As always, be aware that models can make mistakes. At each step, examine the response and challenge information or conclusions that appear erroneous before proceeding to any subsequent steps. If in doubt use a second model with the same prompt to verify the information and generate challenge questions and answers (CoVe process) to correct interpretations of data.

Link to blog post explanation:

N/A

Preferred Model(s):

Gemini 3+ PRO with Deep Research mode

Important Execution Notes:

• Variant 1 - single company: Simply enter the company’s name where indicated and run.

• Variant 2 - head-to-head: Replace the names of Companies A and B where indicated

Indicative Sample Output:

7 Powers Comparison Alphabet Vs Microsoft

2.41MB ∙ PDF file

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Copy/Paste Prompt Set:

Important note: Subscribers can use this prompt set for their own analysis. However, the prompt is copyrighted by The Inferential Investor, paywalled, and must not be shared without permission.

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Last updated: 14 November 2025

Objective:

• This procedure outlines a mandatory loop to ensure the accuracy of objective data points, such as statistics or financial figures, before they are used in a response.

• It requires retrieving an initial data point from a high-authority source and then cross-verifying it against at least two other independent sources to either confirm a consensus, report a discrepancy, or state if the data is unverifiable.

Explanation:

This data integrity procedure establishes a formal verification loop designed to ensure the factual accuracy of any specific, objective data (like statistics, dates, or financial figures) retrieved for a response. It achieves this by mandating that every data point be cross-referenced against at least three independent, credible sources, forcing a reconciliation of any discrepancies. This process is critically important in high-stakes situations—such as financial analysis, medical reporting, or academic research—where presenting unverified or inaccurate data could lead to significant negative consequences, erode trust, and spread misinformation.

As always, be aware that models can make mistakes. At each step, examine the response and challenge information or conclusions that appear erroneous before proceeding to any subsequent steps. If in doubt use a second model with the same prompt to verify the information and generate challenge questions and answers (CoVe process) to correct interpretations of data.

Link to blog post explanation:

N/A

Preferred Model(s):

An optional addedum to prompts requiring data retrieval in any model

Important Execution Notes:

• Paste into the prompt at the end as a final step.

Sample Output:

N/A

Copy/Paste Prompt Set:

Important note: Subscribers can use this prompt set for their own analysis. However, the prompt is copyrighted by The Inferential Investor, paywalled, and must not be shared without permission.

Read more

Last updated: 14 November 2025

Objective:

• The prompt’s objective is to retrieve all current analyst reports and related analyst news for a specific stock since a user-defined date, using only real-time web searches with citations.

• It then directs the model to comprehensively analyze this data to summarize analyst ratings, price target changes, key themes, and sentiment, and to identify any lead/lag relationships between analyst actions and the stock’s price.

Explanation:

Tracking changes in analyst recommendations provides a crucial barometer for measuring evolving market expectations. Analysts act as professional information processors, synthesizing complex data on company fundamentals, industry trends, and macroeconomic factors into simplified ratings and price targets. When these estimates are revised, it signals a tangible shift in expert opinion, offering a quantifiable proxy for the direction of institutional sentiment. By aggregating these changes, investors can measure the consensus view and, more importantly, the momentum of expectations—whether the smart money is collectively becoming more optimistic or pessimistic about a stock’s future earnings power, often before this shift is fully reflected in the share price.

Beyond the simple “Buy” or “Sell” rating, this analysis extracts deeper insights into why expectations are changing. The commentary within analyst reports reveals the key “bull” and “bear” narratives driving the stock, highlighting specific catalysts or risks the market is focused on, such as margin trends, competitive positioning, or product cycles. Furthermore, by comparing the timing of recommendation changes against price movements, investors can identify “leading” analysts whose calls precede price action versus “lagging” analysts who merely follow the trend. This process uncovers the market’s core valuation debates and helps filter high-conviction, predictive insights from herd-following noise.

As always, be aware that models can make mistakes. At each step, examine the response and challenge information or conclusions that appear erroneous before proceeding to any subsequent steps. If in doubt use a second model with the same prompt to verify the information and generate challenge questions and answers (CoVe process) to correct interpretations of data.

Link to blog post explanation:

N/A

Preferred Model(s):

Gemini 2.5 PRO Deep Research mode - note Gemini’s stronger web retrieval capabilities will improve results.

Important Execution Notes:

• Insert Company ticker/name where indicated

• Insert lookback period start date (eg the last reporting date to assess changes post results)

• Deep Research modes will direct the model to examine more sources for data and improve insights however if fast results are needed, the prompt can be run without this.

• This report cannot look through paywalls so only publicly available ratings or news reports on paywalled brokers can be identified. However with well research stocks, available sources provide good insights.

Sample Output:

Analyst Intelligence Report Pypl

255KB ∙ PDF file

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Copy/Paste Prompt Set:

Important note: Subscribers can use this prompt set for their own analysis. However, the prompt is copyrighted by The Inferential Investor, paywalled, and must not be shared without permission.

**Role:** You are an expert-level Equity Research Assistant.

**Objective:** Conduct a comprehensive, real-time analysis of analyst actions and sentiment for:
**Company Name/Ticker & exchange in non US:** [INSERT Company Name/Ticker]*
from
**Start Date**: [Start Date: YYYY-MM-DD] 
to the present.

**Critical Execution Rules:**
1. You must perform a live web search to find reliable sources. Do NOT use pre-existing training data without new verification. Use of pre-existing training data results in outdated information and misleading conclusions that can be costly to an investor.
2. Ground your answer using the latest online sources available as of Today's Date.
3. Cite every factual statement with a direct link to the source used for verification.

### **Section 1: Analyst Activity Scan & Summary**

First, retrieve the last closing share price for the specified stock.

Next, perform a live web search to scan the web for an updated view of all news, press releases, and reports detailing analyst actions for the specified stock on or after the specified [Start Date].

Present this data in the following summary table:

| Date | Broker/Analyst | Rating | Change | EPS Forecast (if cited) | PT (Old) | PT (New) | % Change PT | Source |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| | | | | | | | | |

### **Section 2: Aggregate Analysis & Price Context**

Using **only** the data from Section 1:

1.  **Aggregate Statistics:** Calculate and report the following:
    * **Total Ratings Changes:** (e.g., 3 Upgrades, 2 Downgrades, 5 Reiterations)
    * **Consensus Rating:** (e.g., “Strong Buy,” “Hold”)
    * **Price Target Range:** (Min: $X, Max: $Y)
    * **Mean & Median PT:** (Mean: $X, Median: $Y)
    * **Average PT % Change:** (Average % increase/decrease from “PT (Old)” to “PT (New)”)

2.  **Price Context:**
    * **Last Close:** $ [Last Closing Price]
    * **Position in Range:** “The last closing price of $ [Price] sits at the [e.g., 45th percentile] of the current analyst PT range ($ [Min PT] - $ [Max PT]).”

3.  **Broker-by-Broker Summary:** Briefly summarize the key changes for each broker listed in the table (e.g., “Goldman Sachs reiterated ‘Buy’ but lowered its PT by 5% due to margin concerns.”).

### **Section 3: Key Themes, Catalysts, & Events**

Analyze the retrieved reports to extract the following:

1.  **Key Analyst Themes (Summarized):**
    * **Bullish:** (List the 3-5 most common positive arguments)
    * **Bearish:** (List the 3-5 most common negative arguments)

2.  **Upcoming Catalysts:**
    * Isolate and list any specific upcoming events analysts are monitoring (e.g., product launches, investor days, regulatory decisions).

3.  **Next Earnings Date:**
    * Report the next expected earnings release date for **[Ticker]**.

### **Section 4: Influential Analyst Commentary**

Extract and present 2-3 direct quotes from the most influential analysts (e.g., from major banks or those making significant rating changes) that best encapsulate the *forward-looking* sentiment on the stock.

> **[Broker/Analyst]:** “[Quote]” (Source: URL)
>
> **[Broker/Analyst]:** “[Quote]” (Source: URL)

### **Section 5: Sentiment & Trend Analysis**

1.  **Historical Sentiment Baseline:** Conduct a search to determine the *general* analyst sentiment (e.g., “Positive,” “Neutral,” “Negative”) for **[Ticker]** during the month *prior* to your start date (i.e., **[Month-1, YYYY]**).
2.  **Sentiment Shift Analysis:** Compare that baseline to the current sentiment derived from your Section 1 data. Describe the change (e.g., “Sentiment has become significantly more bullish since...”).
3.  **Consistency Check:** Analyze whether this sentiment shift is consistent with the *direction* of:
    * Consensus rating changes.
    * Aggregate EPS forecast revisions (if available).
    * Aggregate price target changes.

### **Section 6: Lead/Lag Analysis**

1.  **Retrieve Price Trend:** Find a data source or summary of the share price trend for **[Ticker]** from **[Start Date]** to the present.
2.  **Correlate Actions to Price:** Compare the *dates* of analyst PT changes (from your Section 1 table) against the stock’s price chart.
3.  **Identify Leads & Lags:**
    * **Report Leading Analysts:** Identify any analysts who made significant PT or rating changes *before* a corresponding major share price movement.
    * **Report Lagging Analysts:** Identify any analysts who only adjusted their PT or rating *after* a significant share price move had already occurred.

### **Section 7: Final checks**

1. Check that you have performed live web searches to answer with the latest available information as of today's date. 
2. Provide a final note to the report specifying how many web searches were performed to answer this query.
3. If the check reveals that no web searches have been performed, then perform the searches required to answer each stage of the task and update the response, discarding information obtained from training data.

Last updated: 12 November 2025

Objective:

• Synthesize an updated and source referenced set of financial forecasts to compare against consensus from any company’s earnings presentations, transcripts and result releases.

• Use AI’s reasoning and analysis capabilities to identify, extract and incorporate all recent trends and forward looking statements in arriving at an unbiased and comprehensive set of financial forecasts

Explanation:

Management guidance often comes in subtle and fragmented forms, spread across slide decks, transcripts, and regulatory filings using language that can be opaque, forward-looking, and qualified by corporate jargon. Forecast signals may be embedded in segment-specific commentary, relative performance descriptors, or conditional statements that are easily overlooked or misunderstood by investors with pre-existing biases or heuristics.

This prompt enables AI to systematically surface and interpret all such forward-looking clues, stripping out narrative noise and identifying the quantitative and qualitative anchors that actually drive revenue, margin, cost and earnings trajectories. By parsing both structured data (tables, guidance ranges, segment metrics) and unstructured text (verbal statements, tone, and directional indicators), the model can generate a comprehensive, internally consistent set of financial forecasts that can be free from the distortions of selective perception or confirmation bias.

Because generative models can integrate natural-language understanding with quantitative trend analysis, they can bridge the gap between narrative insight and financial modeling. This allows the AI to recognize when management implies moderation rather than acceleration, or when shifts in pricing mix or cost base signal margin changes not explicitly stated. The resulting forecasts, synthesized from multi-modal evidence, save analysts hours of manual cross-referencing while providing a rigorous foundation for discussion.

Importantly, the process does not end there: these initial AI-generated forecasts can be refined interactively through follow-on prompting, where the user adjusts assumptions, stress-tests sensitivities, or adds new data points—creating a dynamic human-AI forecasting loop that continuously improves analytical accuracy and insight.

As always, be aware that models can make mistakes. At each step, examine the response and challenge information or conclusions that appear erroneous before proceeding to any subsequent steps. If in doubt use a second model with the same prompt to verify the information and generate challenge questions and answers (CoVe process) to correct interpretations of data.

Link to blog post explanation:

N/A

Preferred Model(s):

Gemini 2.5+ Deep Research with attached source documents

Important Execution Notes:

• Specify Company, Ticker and Exchange where indicated

• Attached source documents (I have tested this with up to 8 attachments incl last full year and a further 3 quarterly presentations and result press releases)

• Set the model running - Deep Research can take 5-10 minutes.

• Perform follow-on prompting to sensitize forecasts for nuances that may not have been picked up or additional metrics required for specific companies.

• Treat the first set of forecasts as an initial first take for fine tuning if necessary.

• The model can be prompted to show sensitvities (eg the effect on EPS of an additional 1% revenue growth)

Sample Output:

Paypal Holdings, Inc

320KB ∙ PDF file

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Copy/Paste Prompt Set:

Important note: Subscribers can use this prompt set for their own analysis. However, the prompt is copyrighted by The Inferential Investor, paywalled, and must not be shared without permission.

Read more

Last updated: 10 November 2025 (v1)

Objective:

• To identify and prioritize the most material strategic, financial, and competitive issues influencing a company’s near-term performance, and to translate those insights into a focused, evidence-based set of up to 15 questions for management.

• This enables the investor to test key assumptions underlying the market’s expectations, refine their investment thesis, and extract decision-relevant information during a management meeting.

Explanation:

You have a 45 minute 1-on-1 meeting with the CEO of a company. What do you ask to get the most out of the meeting and make good investment decisions? How do you ask the questions to elicit direct and informative answers?

This prompt functions as a structured analytical workflow for equity analysts or investors preparing to meet company management to first identify the key, current issues, rank them by priority and then structure them into questions.

By guiding the model to synthesize insights from recent earnings reports, transcripts, analyst notes, and market developments, it distills the core strategic, financial, risk and competitive issues that are most relevant to the stock’s near-term performance. It enforces a disciplined approach: identifying the range of potential issues then prioritizing them by materiality to share price and recency. The result is a set of questions that are not generic or conversational, but instead grounded in data, valuation levers, and decision-critical uncertainties.

For investors, this prompt serves as a maximum efficiency preparation tool that replaces intuition with structured inference grounded in research. It enables the investor to identify the issues that are likely to truly drive the next phase of performance and to interrogate management on those points directly. The generated questions drive to what assumptions underpin the market’s consensus, where sentiment or risk perception may shift, and what quantitative triggers to monitor in coming quarters. Used consistently, it transforms meetings from soft narratives into focused data-gathering sessions that test the integrity of one’s investment thesis and improve both conviction and timing.

As always, be aware that models can make mistakes. At each step, examine the response and challenge information or conclusions that appear erroneous before proceeding to any subsequent steps. If in doubt use a second model with the same prompt to verify the information and generate challenge questions and answers (CoVe process) to correct interpretations of data.

Link to blog post explanation:

N/A

Preferred Model(s):

Gemini 2.5+ and ChatGPT-5+

Important Execution Notes:

• The best results are achieved by attaching recent transcripts and presentations. It is often good to attached them for the last 2 quarters to bring additional context from the last 3 months into consideration

• Where indicated in the Inputs section, simply put company name, ticker and exchange and Yes/No as to whether reports are attached to be used in preference to web retrieval (greater risk of hallucinations)

Sample Output:

Management Q A Builder For Meetings

166KB ∙ PDF file

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Copy/Paste Prompt Set:

Important note: Subscribers can use this prompt set for their own analysis. However, the prompt is copyrighted by The Inferential Investor, paywalled, and must not be shared without permission.

Prompt: Management Q&A Builder for an Equity Research Analyst

ROLE: You are a senior equity research analyst preparing for a face-to-face meeting with management. Your task is to analyze the company and generate a focused, prioritized list of up to 15 incisive questions grouped by topic. You must ground your output in primary sources and very recent secondary materials.

0) User Inputs

Fill these before running:

* Company name, Ticker, Exchange: <COMPANY_NAME, TICKER, EXCHANGE>
* Documents uploaded? <YES/NO>

  * If YES, identify the type of information in files provided
  * If NO, retrieve documents as required in “Source Rules.”

1) Source Rules (Use in this strict order)

1. Uploaded documents take precedence. If current and complete, use them; retrieve only missing items.
2. Primary filings & company materials: Latest two quarterly earnings releases and two call transcripts; recent press releases and IR presentations within the last six weeks.
3. Secondary corroboration (last six weeks): Major analyst reports, reputable news, CEO/CFO interviews, and credible stock write-ups.
4. Citation discipline:** Attribute key facts to source and date. Prefer quotes or numeric data from primary sources; summarize brokers/news with attribution.
5. De-duplication: If multiple sources repeat the same fact, keep the primary or most authoritative.
6. Recency filter: Heavily weight developments within T-6 weeks. Flag anything older as “background”.

> If browsing/tools are available: prioritize company IR site, SEC/official exchange filings, SEDAR/EDGAR equivalents, major wire services, and top-tier broker notes.

2) Tasks & Analytical Scope

A) Identify current, material issues (evidence-based) across:

* Strategy & products/roadmap
* Growth drivers & demand signals
* Competition** (narrow definition; see Section B)
* Financial trends: revenue/units/ASP, GM/OM, FCF, capex, working capital, cash taxes
* Price & volume drivers (stock-specific flows, catalysts)
* Margin drivers: mix, utilization, pricing, input costs, FX, learning curves, operating leverage
* Balance sheet: debt stack, covenants, maturities, interest burden, refinancing risk
* Risks: operational, financial, regulatory, legal, geopolitical, supply chain
* Seasonality: patterns and near-term setup into next quarter
* Management bench & turnover: departures/appointments, incentives alignment
* Regulatory/legal: investigations, approvals, rule changes, litigation milestones

B) Map key competitors (narrow definition)

* Define “narrow competition” as firms with:

  1. overlapping end-customer/job-to-be-done,
  2. similar price band and distribution, and
  3. substitutable offering in customers’ buying process.
* List 3–6 most relevant competitors with their current key issues from the last six weeks (e.g., guide changes, product launches, outages, regulatory actions) and state why each matters for the subject company (share shift, pricing umbrella, input bottlenecks, etc.).

C) Prioritize issues by stock price materiality

* Score each issue on a 1–5 scale for:

  * Impact (valuation sensitivity: revenue/GM/FCF delta or multiple effect)
  * Likelihood (probability within 6 months)
  * Timeframe (near-dated catalysts within next quarter = higher)
* Compute Priority Score = Impact × Likelihood × Timeframe. Rank descending.
* Note explicit catalysts (earnings date, product launch, regulatory ruling, contract renewal, capacity ramp, M&A close, lock-up).

D) Frame bull vs bear (1–2 paragraphs total)

* Bull case: core thesis, 2–3 pivotal assumptions, and what must be true.
* Bear case: opposing thesis, 2–3 pivotal assumptions, and what breaks.
* Tie both to the specific issues above and to analyst Q&A focal points** seen in the last two calls.

E) Generate management questions (up to 15)

* Group by headings aligned to the highest-priority topics.
* Questions must be:

  * Non-leading, falsifiable, and specific (aim for numbers, mechanisms, or decision rules).
  * Tied to a decision or model lever (unit growth, price/mix, utilization, COGS line item, opex cadence, capex phasing, churn, retention, LTV/CAC, cohort behavior).
  * Time-boxed (next quarter/half), with follow-up if management gives vague answers.
* For each question, include a one-line “What we’re testing” (the hypothesis) and “Signals to listen for” (KPIs, ranges, commitments).

3) Output Format (Markdown)

A. Company Snapshot (3–6 bullets)

* What the company does, monetization model, and the single most important earning driver for the next 1–2 quarters.

B. Issues Inventory & Priority Table

| # | Topic | Issue (one line) | Impact (1–5) | Likelihood (1–5) | Timeframe (1–5) | **Priority** | Catalyst(s) | Key Source(s) |
| - | ----- | ---------------- | ------------ | ---------------- | --------------- | -----------: | ----------- | ------------- |
| 1 | …     | …                | …            | …                | …               |        **…** | …           | …             |

C. Competitor Lens (Narrow Definition)

* Competitor A — current issue(s) in last 6 weeks; **Why it matters** → share/price/cost/mix.
* Competitor B — …
* Competitor C — …

D. Bull vs Bear (≤2 paragraphs)

* Integrate analysts’ live concerns and management guidance inflections.

E. Management Meeting Questions (max 15, grouped)

1) Margins & Cost Structure

1. *Question:* …

   * What we’re testing: …
   * Signals to listen for: ranges, unit economics, cost bridges, hedges, learning curves.

2. *Question:* …

2) Demand, Mix & Pipeline
3. Question: …

* What we’re testing: …
* Signals to listen for: bookings→billings conversion, cohort retention, deal cycle days.

3) Competitive Dynamics & Pricing
…(continue until ≤15 total)

> After the last group, include a short **“Parking Lot (if time)” list of 2–3 bonus questions.

F. Appendix: Source Notes

* Bullet list with **source, date, and 1-line relevance. Quote sparingly.

4) Quality & Guardrails

* No speculation. Every claim links to a cited source or is labeled as an explicit hypothesis.
* Numbers first. Prefer quantified questions and answer targets to narrative.
* Clarity over volume. If trade-offs arise, reduce count, increase sharpness.
* Avoid “tell us about the strategy” questions. Ask how decisions are made and what thresholds trigger different actions.
* Keep total questions ≤15.

---

(Optional) Quick Starter Examples (replace with your content to include a specific question in the report)

* High-impact margin question:
  *“On gross margin into next quarter, what are the quantified bridges by price/mix, utilization, and input costs, and what variability bands should we use if volumes are ±5% vs plan?”*
  Testing: sensitivity of GM to volume/mix; Signals: bridge %, guardrails, contingency levers.

* Competitive intensity question:
  “Where has realized win-rate diverged most vs last quarter’s plan and what specific pricing or feature moves did you take in those segments?”
  Testing: real share shift; *Signals:* measured win-rates, take-rate changes, concrete actions.

---

Run Instructions (to the model)

1. Confirm inputs. If uploads exist, list them and state whether they cover the “latest two” requirement; retrieve only missing or more recent items (≤6 weeks).
2. Build Issues Inventory, then Priority Table.
3. Draft Bull vs Bear in ≤2 paragraphs tied to priority items.
4. Produce ≤15 grouped questions with What we’re testing and Signals to listen for.
5. Deliver the output strictly in the Output Format above.

Last updated: 26 October 2025

Objective:

Research in depth a Thematic or Industry for opportunities and generate a comprehensive report. Map from top level manufacturers (OEMs) or players down the supply chain with company profiles, exposure assessments, news and developments, and synthesis of the best placed potential winners. Also examine the Bill of Materials for key products in an industry with profile, functions and suppliers of each component.

Explanation:

This prompt acts as a professional-grade research framework for thematic investing by deconstructing entire industries from the top down, linking technologies, products, and suppliers into one continuous chain of insight.

Instead of isolating single companies, the generated report maps how value is created and transferred across a sector: from the firms building end-products to the upstream component suppliers that enable them. By building and tracing the bill of materials, development timelines, and capital flows, it reveals where the real leverage sits including who captures margin as an emerging theme matures, which companies are at risk of displacement, and where the next inflection points in commercialization may arise. The prompt ensures that analysis is not just descriptive but structured and actionable, connecting innovation trends to the companies most exposed to them in quantifiable, source-anchored ways and examining the materiality of the thematic to that company.

In the context of thematic investing, this approach transforms abstract narratives like “AI robotics,” “energy storage,” or “climate tech” into investable ecosystems. It provides a method for identifying not just the headline beneficiaries but the often-overlooked enablers such as firms supplying the key inputs, technologies, or components that quietly scale with the theme’s adoption.

The research them boils down the entire mapped industry chain to potential investment angles to help investors position ahead of the curve, highlighting the layers of the value chain and associated public companies most likely to compound returns as the theme matures, taking into account relative materiality, estimated time horizons and leadership within sub-segments of the chain.

Note the options to deepen this research even further in “Execution Notes”

Link to blog post explanation:

Thematic Investing in Practice

Preferred Model(s):

Gemini 2.5 PRO Deep Research Mode over ChatGPT. I have found Gemini to provide a far more structured framework and presentation for long format deep research than ChatGPT which helps highlight and absorb the implications.

However, running the prompt in both models simultaneously captures greater breadth of companies involved in the industry and verification of the information presented and opportunity identification.

Important Execution Notes:

• Simply Enter the Industry or thematic name in the User Inputs section.

• There is an option to further specify a sub-industry focus if desired or delete this line.

• The prompt is designed to be run as is, with a copy / past click. This will likely generate 30-40 pages of output. However I have found that splitting the prompt and running 2-3 deep research tasks (replicating the system prompt at the top each time) surfaces even deeper information and more complete company lists than doing as a single pass. Stock identification can be more complete from this approach.

• This is all about user preference. How deep do you want to go. The sample report provided was aggregated from a 3 split approach as follows: Industry overview and Top Level OEMs; Bill of Materials and Components; Supply Chain companies.

Sample Output:

Humanoid Robot Industry And Supply Chain

1.05MB ∙ PDF file

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Copy/Paste Prompt Set:

Important note: Subscribers can use this prompt set for their own analysis. However, the prompt is copyrighted by The Inferential Investor, paywalled, and must not be shared without permission.

Read more

Last updated: 24 October 2025

Objective:

Explain short term share price movements in stocks with reference to both fundamental, technical and flow related factors to help decide whether its an opportunity or a risk.

Explanation:

This prompt provides traders and investors with a structured framework to diagnose the immediate drivers of a stock’s short-term price movement—typically over hours to days. By combining quantitative market data (price, volume, volatility) with qualitative catalysts (news, filings, analyst actions, or macro events), it identifies which developments most plausibly explain the stock’s deviation from prior expectations. The insight it delivers is not merely descriptive (i.e., “the stock fell 3%”) but inferential: it connects timing, sentiment, and information flow to price impact. This allows a trader to distinguish between transient reactions (e.g., options-related gamma squeezes) and information-driven repricing (e.g., revised earnings outlooks or regulatory risks), helping refine positioning or trade timing decisions.

The rationale for examining such diverse factors lies in how modern price formation is multi-causal and path-dependent. Fundamental news alters valuation expectations; broker revisions and corporate announcements influence institutional flow; and derivatives activity, particularly changes in implied volatility, open interest, and dealer gamma exposure, can amplify or suppress price swings independent of fundamentals. Meanwhile, liquidity, sector correlation, and macro data releases contribute cross-asset shocks that ripple through correlated names. By mapping these dynamics into a coherent short-term narrative, the prompt gives investors a probabilistic understanding of why a stock moved the way it did, grounding tactical trades in evidence rather than noise.

As always, be aware that models can make mistakes. At each step, examine the response and challenge information or conclusions that appear erroneous before proceeding to any subsequent steps. If in doubt use a second model with the same prompt to verify the information and generate challenge questions and answers (CoVe process) to correct interpretations of data.

Link to blog post explanation:

N/A

Preferred Model(s):

ChatGPT 5 over Gemini. GPT’s advanced reasoning can provide an additional edge.

Important Execution Notes:

• Insert the stock code where indicated

• Set the lookback period (eg 1 or 2 trading days or as desired). Default is set to 1 trading day.

• Run the prompt.

• Remember you can follow up the initial analysis with further questions to understand concepts such as OpEx influences, extract crowded option expiry levels have the market interpret those for you, undertake a technical analysis study of the stock etc.

Sample Output:

N/A

Copy/Paste Prompt Set:

Important note: Subscribers can use this prompt set for their own analysis. However, the prompt is copyrighted by The Inferential Investor, paywalled, and must not be shared without permission.

ROLE: You are a professional stock trader.
TASK: Explain short-term price action for a single stock.

Inputs:
- Ticker: {TICKER}
- Lookback: {PERIOD | default: 1 day}
- Timezone for timestamps: {TZ | default: your local}

Instructions:
1) Fetch the price/volume path over {PERIOD}: open→close %, intraday high/low, gap vs prior close, and volume vs 20-day avg. Note any halts.
2) Scan the LAST {PERIOD}+1 day for catalysts:
   • Company: press releases, filings (8-K/ASX/SGX), guidance, earnings, buybacks, insider trades.
   • External: broker rating/estimate changes, M&A/rumors, regulatory actions, litigation, macro prints, sector moves, peers’ earnings.
   • Flow/market microstructure: options flow (IV, skew, notable OI/volume, gamma/zero-gamma proximity, put-call ratio), short interest/borrow cost, dark-pool or block prints, index rebalances.
3) Map catalysts to moves (timed cause→effect): “Event → immediate move → persistence/mean-reversion,” with source links + exact timestamps.
4) Synthesize the 2–4 most likely drivers with confidence levels. Distinguish fundamental vs flow-driven effects. Note alternative hypotheses you ruled out.
5) Conclude with expected near-term path (base case + key if/then triggers).

Output (keep tight):
• Summary (≤120 words) in plain English.
• Top Drivers (ranked, confidence %).
• Evidence Table: [Time | Event | Source | Price reaction | Note].
• Options/Positioning: IV change, notable strikes, gamma context.
• Peer/Index Context: sector/market contribution (bps).
• Risks/Unknowns + Next Checks (specific sources/times).

Last updated: 1 October 2025

Objective:

Identify potential red flag issues that an investor may wish to investigate further regarding a stock.

Explanation:

Investors often focus on growth opportunities and valuation upside, but the biggest risks to capital frequently emerge from overlooked red flags hidden in company accounts, management disclosures, or the broader information environment. This prompt is designed to systematically surface those risks by interrogating financial statements, earnings transcripts, and credible external sources such as news, credit agencies, research reports, and corroborated social commentary.

The resulting report organizes findings into clear risk categories and traffic light materiality assessment, ranging from accounting quality and credit stress to management turnover, shareholder dilution, and litigation exposure, flagging where evidence exists and noting when none is found. By structuring the analysis this way, the report helps investors move beyond headlines to assess whether material red flags exist, how they could impair earnings, cash flow, or valuation, and what signals to monitor going forward.

As always, be aware that models can make mistakes. At each step, examine the response and challenge information or conclusions that appear erroneous before proceeding to any subsequent steps. If in doubt use a second model with the same prompt to verify the information and generate challenge questions and answers (CoVe process) to correct interpretations of data.

Link to article:

Preferred Model(s):

Gemini 2.5 FLASH over ChatGPT-5+ due to length of attached information

Important Execution Notes:

• The prompt works best with the more information you attach to support the analysis

• It is recommended to attach the last three earnings report and last three earnings call transcripts.

• For this reason Gemini’s architecture and longer context window is preferable as is its integrated google search capability. However, the FLASH version of Gemini often works best over PRO as the complexity of the task can stall the system. (This can be avoided by pre-processing the documents - see separate utility prompt).

• Insert the company’s TICKER, EXCHANGE and COMPANY name in the Inputs section

• Specify the lookback window for online information sources (default 12 months).

Sample Output:

Red Flags Report Orcl

3.86MB ∙ PDF file

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Copy/Paste Prompt Set:

Important note: Subscribers can use this prompt set for their own analysis. However, the prompt is copyrighted by The Inferential Investor, paywalled, and must not be shared without permission.

Role & Mission
You are a red-flag risk screener for public equities. Your job is to interrogate primary sources (accounts, filings, earnings transcripts) and sweep credible public sources (news, research write-ups, reputable blogs, and social posts) to identify issues that could impair a stock’s fundamentals, valuation, or liquidity. Deliver a source-linked, category-based risk report with severity scoring and a concise, decision-useful conclusion.

Inputs (fill before running)
•	Company: <TICKER, EXCHANGE, COMPANY NAME>
•	Lookback window for external sources: <12 MONTHS> (use longer for ongoing litigation/regulatory processes)
•	Files provided: <attach latest 10-K/20-F/Annual Report, 10-Q/HY, MD&A, notes, proxy/DEF 14A/Remuneration Report, investor presentations, and the last 3 earnings call transcripts>
•	Benchmark / Peer set (optional): <tickers>
•	Materiality guardrails: Revenue base <value / currency>, EBITDA <value / currency>, Market Cap <value / currency>
•	Language/region: <default English/global>
Source Prioritization & Verification
1.	Primary: Company filings, audited financials, notes, MD&A, proxy/comp report, earnings call transcripts.
2.	Regulatory/legal: Court dockets, regulator releases, consent orders, enforcement actions.
3.	Reputable media & research: Major financial outlets, recognized industry trades, broker/independent research (link to public copy only), credible corporate governance trackers.
4.	Social & blogs: Consider only when corroborated by primary or reputable secondary sources.
Rule: Every non-trivial factual claim must carry a citation with publisher, title, and date. Prefer the most recent source for ongoing items. Flag any conflicting accounts.

Output Requirements
•	Use markdown with the category headings listed below.
•	Under each category, list Findings → Evidence → Why it matters → Materiality (Low/Med/High).
•	If no issues are found: write “No notable evidence surfaced in research.”
•	Provide a Summary Heatmap and an Overall Conclusion at the end.
•	Put all links inline after each finding and include publication dates.
•	Keep claims concise; avoid duplicative entries—cross-reference instead.

Red-Flag Taxonomy & What to Look For

1) Accounting Issues (Earnings Quality, Policy Changes, Short-Seller Claims)
Tests & cues
•	Accruals quality: NI vs. CFO/FCF divergence (rising accruals, CFO/NI < 0.8 persistently, FCF negative while NI positive).
•	Revenue recognition: Bill-and-hold, channel stuffing signals (DSO↑ >> sales growth, rising contract assets vs. revenue).
•	Capitalized costs: Aggressive capitalization of R&D/S&M; intangibles growth outpacing revenue.
•	Non-GAAP add-backs: Recurring “one-time” items; stock-based comp excluded despite structural significance.
•	Policy changes: New revenue/expense recognition, depreciation lives, segment re-mapping; quantify impact.
•	Short-seller theses: Summarize specific claims; test each against primary evidence.
Deliverable: Bullet each suspected issue with quant (trend charts optional), citations, and materiality.

2) Transcripts: Material Issues Affecting Growth
Detect Q&A by cues like “We’ll now open the line for questions,” names of analysts, or operator hand-offs.
Extract: capacity constraints, demand softness, churn, pricing pressure, pushouts, supply chain, competitive losses, product delays, quality incidents, salesforce turnover, AI/tech disruption, customer concentration.
Report management vs. analyst framing, evidence of walk-backs from prior guidance, and new risk disclosures.

3) Earnings Revisions
Scan: consensus downgrades, guide-downs, withdrawn guidance, negative pre-announcements.
Explain drivers: end-market weakness, mix shifts, GM compression, Opex creep, FX, working capital stress, customer deferrals.
Quantify: NTM revenue/EPS delta over lookback; direction, magnitude, timing.

4) Credit Risks & Liquidity
Check: interest coverage (EBIT/Interest), FCF/Net debt, short-term debt walls, covenant headroom (leverage, interest cover, liquidity), securitization reliance, customer prepayment obligations, factoring/receivables sales.
Watch: variable-rate exposure vs. rate path; undrawn RCF conditions; rating outlook changes.

5) Management & Personnel
Signals: abrupt C-suite turnover, CFO churn, departures tied to investigations, headcount cuts beyond restructuring narrative, Glassdoor / employee review inflections (if available), attrition spikes, HR/legal complaints.
Cross-link to performance periods and strategic pivots.

6) Shareholding & Equity Structure
Assess:
•	SBC intensity: SBC/Revenue and SBC/CFO; persistent high levels diluting owners.
•	Net share issuance vs. buybacks; repeated equity raises to fund operations or M&A.
•	Insider behavior: broad-based employee selling, 10b5-1 patterns, concentrated insider disposals around events.
•	Ownership concentration and governance control (dual-class, staggered board) that may entrench management.

7) Litigation & Regulation
Capture: active lawsuits (securities, IP, product liability, antitrust, labor), expected damages or settlement ranges; regulatory probes, consent decrees, sector rule changes (safety, data, privacy, AML/CTF, environmental).
Explain: how outcomes could alter unit economics, TAM, or capital needs; timing of milestones.

8) Other Operational/Financial/Counterparty Risks
Look for: customer concentration >10%, key supplier dependency, warranty/quality spikes, cyber incidents, data breaches, ESG controversies affecting permits or customers, off-balance sheet exposures (leases, SPVs), tax authority disputes, FX/commodity mismatch, geopolitical exposure, going-concern language.

9) Additional Red Flags (Analyst Discretion)
Anything outside the above that could impair cash flow durability, force capital raises, or compress valuation multiples.

Methods & Heuristics (apply consistently)
•	Triangulate numbers across income statement, cash flow, and notes. Reconcile NI ↔ CFO ↔ FCF.
•	Trend deltas: Compute YoY and sequential changes for DSO/DPO/DIO, contract assets, deferred revenue, inventory obsolescence reserves.
•	Common thresholds (indicative, not absolute):
o	CFO/NI < 0.8 for ≥2 years, or FCF negative while NI positive → Medium/High concern.
o	DSO growth > revenue growth by >10–15pp for ≥2 quarters → Medium.
o	SBC/Revenue > 10% for mature software; >20% for growth stage without deceleration → Medium/High.
o	Net leverage > 3× with negative FCF; interest cover < 3× → High.
o	Customer > 20% of sales or top-3 > 50% → Medium/High depending on contracts/tenure.
•	Corroborate social claims with filings/transcripts before inclusion.
•	Date all facts; prefer the most recent datapoint for the conclusion.
Scoring & Materiality
For each finding assign:
•	Severity: Low / Medium / High (impact on EPS/FCF multiple or solvency).
•	Timing: Near-term (≤12m) / Mid-term (12–24m) / Long-dated.
•	Confidence: Low / Medium / High (based on source quality & corroboration).
Sum up with a category heat rating: 🟢 Low | 🟠 Medium | 🔴 High.

Required Deliverables (in order)
1.	Executive Snapshot (bulleted, ≤8 bullets): the 20% of issues that drive 80% of downside risk.
2.	Category Sections (1–9 as above): each with Findings → Evidence → Why it matters → Materiality. If none, say “No notable evidence surfaced in research.”
3.	Revisions Table: timeline of guidance/consensus changes with drivers and links.
4.	Liquidity & Debt Ladder: maturities, covenants, coverage metrics, and commentary.
5.	Insider/SBC & Dilution Table: SBC intensity, net share issuance, notable insider transactions.
6.	Summary Heatmap: category vs. severity (🟢/🟠/🔴).
7.	Overall Conclusion (1–3 paragraphs):
o	Presence/absence of red flags, their mechanisms (how they hit cash flow, multiple, or solvency), and materiality.
o	Link to established theory: e.g., accrual anomaly (low earnings quality predicts underperformance), limits to arbitrage (risks persist despite awareness), credit cycle transmission to equities, dilution vs. per-share value.
o	What to monitor next (leading indicators, dates/milestones) and decision implication (position sizing, hurdle for upside case, catalysts required).

Style & Quality Bar
•	Citations after every finding (Publisher – Title – Date).
•	No speculation without clear labeling and probability language.
•	Prefer concise bullets, not prose walls.
•	De-duplicate and cross-reference; avoid double counting.
•	If data are insufficient, say so and state what would change your view.

Fallbacks & Edge Cases
•	If multiple conflicting sources exist, present both views, assess evidence, and assign Confidence accordingly.
•	If a category has legacy issues but no longer material, mark “Resolved/immaterial as of <date>” with evidence.
•	If filings are stale but social chatter is active, do not upgrade severity without corroboration.
________________________________________
Template (paste this below your inputs)
Executive Snapshot
•	…
1) Accounting Issues
•	Finding:
Evidence:
Why it matters:
Materiality: Low / Medium / High
(Repeat bullets as needed; write “No notable evidence surfaced in research” if none.)
2) Transcripts: Growth-Affecting Issues
•	Finding:
Evidence (quotes + date):
Why it matters:
Materiality: Low / Medium / High
3) Earnings Revisions
•	Event / Date:
Direction & magnitude:
Driver:
Source:
4) Credit Risks & Liquidity
•	Metric / Covenant / Maturity:
Observation:
Headroom / Coverage:

Materiality:
5) Management & Personnel
•	…
6) Shareholding & Equity Structure
•	…
7) Litigation & Regulation
•	…
8) Other Operational/Financial/Counterparty Risks
•	…
9) Additional Red Flags
•	…

Summary Heatmap
Category	Severity
Accounting	🟢/🟠/🔴
Transcripts	…
Revisions	…
Credit/Liquidity	…
Management	…
Equity/Dilution	…
Legal/Reg	…
Other	…
Additional	…

Overall Conclusion
•	Do red flags rise to thesis-changing? Why?
•	Transmission channels: (earnings quality → multiple; liquidity → survival risk; dilution → per-share economics).
•	Theory links: accrual anomaly; credit cycle impacts; governance and valuation discounts.
•	Monitors & milestones: dates, KPIs, disclosure events.
•	Decision guidance: sizing, risk controls, catalysts required to negate flagged risks.

Last updated: 17 September 2025

Objective:

This prompt summarizes news on any stock over a specified period, making a materiality assessment and synthesizing key recurring themes with links to the primary value drivers of the stock.

Explanation:

In fast-moving markets, investors are inundated with headlines, yet only a fraction of them truly move the needle for a company’s valuation. Distinguishing between noise and genuinely price-sensitive developments is critical for sharpening conviction and avoiding misallocation of attention. This prompt is designed to cut through the clutter by systematically gathering company news within a chosen lookback window, assessing each item’s likely materiality, and ranking them by impact. It not only produces concise, source-linked summaries but also highlights cross-article themes and connects them directly to the firm’s key value drivers and potential change signals, giving investors a structured view of what really matters for the stock’s outlook.

As always, be aware that models can make mistakes. At each step, examine the response and challenge information or conclusions that appear erroneous before proceeding to any subsequent steps. If in doubt use a second model with the same prompt to verify the information and generate challenge questions and answers (CoVe process) to correct interpretations of data.

Link to blog post explanation:

N/A

Preferred Model(s):

ChatGPT-5+, Gemini 2.5 PRO or Grok

Important Execution Notes:

• Fill out Inputs section specifying Ticker, Exchange and Company name

• Specify Lookback window. Default is 30 days but other examples may be 3 Months.

• Specify language if necessary

Sample Output:

News Impact Research

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Important note: Subscribers can use this prompt set for their own analysis. However, the prompt is copyrighted by The Inferential Investor, paywalled, and must not be shared without permission.

Role
You are an equity research assistant. Your job is to find, rank, and summarize impactful news about a specified publicly listed company over a defined lookback period, assess price sensitivity/materiality, and produce a sorted, source-linked brief. Then synthesize cross-article themes and map them to the company’s value drivers and change drivers (signals that the outlook is shifting).

Inputs (fill before running)
•	Company: <TICKER, EXCHANGE, COMPANY NAME> (e.g., “NVDA, NASDAQ, NVIDIA Corporation”)
•	Lookback window: <N DAYS / WEEKS / MONTHS> (default: 30 days)
•	Region/Language: <default English/global>
•	Primary value drivers (comma-separated; if unknown, infer): <e.g., DC GPU shipments; data-center margins; LT contracts; price/mix; opex>
•	Peers/Benchmarks (optional): <tickers>
•	Risk-free rate / Index benchmark (optional): <e.g., UST10Y / S&P 500>
•	Inclusion preferences (optional): press releases | regulatory filings | reputable news | trade media | credible blogs | social (only if corroborated)

Source & Verification Rules
1.	Prioritize primary/official: SEC/EDGAR, company IR, regulator notices.
2.	Then top-tier outlets; then trade media. Use social posts only if verified by primary/major sources.
3.	De-duplicate near-identical coverage; keep the most detailed/earliest article as primary; link notable follow-ups.
4.	Cite every fact with date, publication, and working URL. No uncited claims.
Materiality & Price-Sensitivity Rubric
Score each article ImpactScore 1–5 and bucket ImpactBucket = High / Medium / Low:
•	5 (High): Guidance updates; earnings pre-announcements; regulator actions/inquiries; M&A (announce/close/termination); large contract wins/losses; major product/technology launches or failures; CFO/CEO changes; significant litigation rulgings/settlements; capital raises/buybacks/dividend policy changes; data breaches; safety issues; major customer/supplier disruptions. Likely price-sensitive.
•	3–4 (Medium): Notable analyst downgrades/upgrades with new thesis; mid-sized customer wins; production ramps/slips; early trial/readouts; credible competitive threats; important macro/policy items with clear channel to the company; meaningful but not definitive regulatory steps. Sometimes price-sensitive.
•	1–2 (Low): Human-interest pieces; speculative rumors without corroboration; incremental marketing; small partnerships; general industry commentary without direct company linkage. Unlikely price-sensitive.
Also label:
•	PriceSensitive: true/false (your reasoned judgment per rubric)
•	Direction: Positive | Negative | Mixed | Unclear
•	Confidence: 0.0–1.0 (evidence strength/clarity)
•	ChangeDriverTags: choose one or more: Demand, Pricing/Mix, Supply/Capacity, Cost/OpEx, Product/Tech, Regulatory/Legal, Capital/BalanceSheet, Mgmt/Gov, Strategy/M&A, ESG/Reputation, Macro/FX.

Tasks
1.	Search within the lookback window; gather distinct, reputable articles.
2.	For each article, produce a standalone card with: Headline; Date (ISO); Publication; SourceType; URL; One-paragraph Summary; Why it matters (analysis); ImpactScore + ImpactBucket; PriceSensitive judgment; Direction; Confidence; ChangeDriverTags; Linked coverage (optional).
3.	Sort the cards by ImpactBucket (High→Medium→Low), and within each bucket sort by Date (new→old).
4.	Conclude with a cross-article synthesis:
o	Key Themes observed this period.
o	Implications for value drivers (quant/qual linkages to revenue growth, gross margin, operating leverage, FCF, WACC, multiple).
o	Change Signals: what is shifting vs. prior period (leading indicators vs. lagging).
o	Risks & Counterpoints (bearish reads) and Opportunities & Catalysts (bullish reads).
o	Watch-list KPIs and Next checks (events, filings, datapoints).
5.	Quality guardrails: No speculation beyond sources; note uncertainties; distinguish facts vs. inference explicitly.
Output — Markdown Brief (human-readable)
•	Title: <Ticker> Impactful News Brief — <Lookback>
•	At-a-Glance: counts by ImpactBucket; quick legend.
•	High-Impact (sorted new→old): list of article cards.
•	Medium-Impact (sorted new→old)
•	Low-Impact (sorted new→old)
•	Concluding Synthesis:
o	Themes → Value Driver Mapping (bullets or compact table)
o	Change Signals (what improved/deteriorated; why)
o	Implications for Model (revenue/margin/FCF/multiple directionality)
o	Catalysts (± 1–3 months / 3–12 months)
o	Open Questions & To-Dos

Formatting Rules
•	Dates must be ISO-8601 (YYYY-MM-DD).
•	Sorting: ImpactBucket High→Medium→Low; within each bucket date desc.
•	Keep each article summary to ~120–180 words.
•	Use neutral tone in summaries; reserve judgment for “why it matters.”
•	Bold the ImpactBucket and italicize the Direction in the Markdown brief.
•	Flag paywalled sources with (paywalled) after the publication name.

Example (one article card in Markdown)
•	[High] — Positive — 2025-09-20 — Reuters — Headline goes here

Last updated: 26 September 2025

Objective:

Synthesize a logical bull land bear case from stock write-ups across the web to encapsulate the primary viewpoints on the stock. Break these down to critical components and examine those vs evidence to help judge on balance of probabilities which case is more likely.

Explanation:

When assessing any stock, a disciplined investor must weigh both the bull and bear cases, since markets reflect the tension between competing narratives rather than a single viewpoint. By explicitly breaking each case into its underlying “must-be-true conditions,” an investor can better evaluate which assumptions are credible, which are fragile, and how they compare in probability. This structured approach transforms debate into analysis, forcing clarity on what evidence would validate or falsify each side. The prompt provides a comprehensive framework to gather and synthesize current perspectives from across news, research, and social sources, then organizes them into a clear bull-versus-bear comparison with catalysts and probabilities, helping investors make balanced, evidence-anchored judgements.

As always, be aware that models can make mistakes. At each step, examine the response and challenge information or conclusions that appear erroneous before proceeding to any subsequent steps. If in doubt use a second model with the same prompt to verify the information and generate challenge questions and answers (CoVe process) to correct interpretations of data.

Link to blog post explanation:

N/A

Preferred Model(s):

ChatGPT-5+ or Gemini 2.5 PRO

Important Execution Notes:

• Insert Ticker, Exchange and Company name in Inputs section

• Set the time horizon from 6 -24 months for the bull and bear cases (default 12 mths) under inputs

• Set the lookback period for case sources. under inputs (default 90 days)

• Set the primary valuation metric for the stock (P/E, P/Sales, EV/EBITDA etc)

• Optional: Add a peer set of tickers, for the prompt to scrape competitor comments that assist identifying conditions

Sample Output:

Bull And Bear Cases Soun

211KB ∙ PDF file

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Important note: Subscribers can use this prompt set for their own analysis. However, the prompt is copyrighted by The Inferential Investor, paywalled, and must not be shared without permission.

Prompt Text:

Role & Mission
You are an equity research synthesizer. Your job is to gather current viewpoints from across the public web and social platforms, reconcile them with reputable primary sources, and produce the strongest possible bull and bear cases on a specified stock, structured under C.I.V.C. (What’s Changing → Implications → Valuation → Catalysts). You must show how each case could plausibly be correct, not just repeat headlines. Every factual claim that is not trivial must carry a source, link, and publication date.
________________________________________
0) Inputs (fill before running; use defaults if not given)
• Choose Company: <TICKER, EXCHANGE, COMPANY NAME> (e.g., “NVDA, NASDAQ, NVIDIA Corp.”)
• Set Time horizon for thesis: <6-24 MONTHS> (default 12 months)
• Set Lookback for sources: <3-6 MONTHS> (default 90 days; allow older if structural/evergreen)
• SetPrimary valuation lens: <EV/Sales | P/E | EV/EBITDA | P/B | FCF yield> (default: what market most uses today)
• Peer set (optional): <TICKERS OF PEERS>
• Language/region: <defaults to English/global>
• Risk-free rate & index benchmark (optional): <e.g., UST 10Y; S&P 500 or ASX 200>
________________________________________
1) Source Discovery & Recency Rules
1. Aggregate broadly:
o Company IR (press releases, transcripts), reputable newswires, major newspapers, trade press.
o Social/investing media: X/Twitter threads, Substack posts/newsletters, Seeking Alpha articles (incl. comment sentiment), Motley Fool, Zacks, public analyst blogs, forum DD (Reddit, etc.) when substantive.
2. Recency first: Prioritize items ≤90 days old. If older pieces are structurally important (e.g., product roadmap, regulatory shift), include and label them as “evergreen/structural.”
3. Diversity & balance: Ensure multiple independent sources per major claim and include contrary evidence where found.
4. Quality filters: Prefer sources with demonstrated expertise/reputation; treat anonymous posts as hypotheses needing corroboration.
5. Metadata capture: For each source store: title | author/handle | outlet | link | publish date (YYYY-MM-DD) | stance (bull/bear/mixed) | key claims.
________________________________________
2) Viewpoint Extraction & Normalization
• From each source, extract explicit claims (what, why, magnitude, timing), evidence type (data, management quote, 3rd-party dataset, anecdote), and asserted impact (revenue driver, margin, multiple, balance sheet, regulatory).
• De-duplicate near-identical claims; cluster by theme (e.g., AI tailwinds, mix shift to subscription, competitive pricing pressure, regulatory risk).
• Assign each claim a confidence tag: High (multi-source, primary data), Medium (some corroboration), Low (single/opinion/anecdotal).
________________________________________
3) Build the Bull & Bear Cases (C.I.V.C.)
For each case, follow this exact structure and labeling:
Thesis (≤120 words; plain English)
State why the stock is likely to outperform (bull) or underperform (bear) the market over the stated horizon. Tie explicitly to earnings trajectory and valuation multiple path. Identify the business conditions and changes underpinning the view.
C — What’s Changing?
Summarize recent developments that suggest consensus is too low (bull) or too high (bear). Types of change to consider:
• Industry cycle (acceleration/slowdown), competitor behavior, product/tech launches, pricing power, channel checks, mgmt/strategy shifts, M&A/integration, regulatory moves, input costs, capacity, capex, financing/capital structure, customer cohort behavior, macro exposure.
For each change theme:
• Evidence bullets with dates and links.
• Tie each theme to quantifiable levers (units, ARPU/ASP, churn/retention, wins/losses, backlog/TAI, bookings/billings, utilization, mix).
I — Implications (Top 3–4 value drivers)
Without producing full forecasts, reason how the changes likely influence the company’s key value drivers:
• Typical drivers: Revenue growth, gross/operating margin trajectory, mix (higher/lower-margin products, regions, customers), capital intensity/FCF conversion, working capital, risk profile.
• For each driver: a short paragraph linking evidence → mechanism → likely direction/scale (qualitative ranges OK).
V — Valuation (Two-sided lens)
• Multiple: Identify the primary valuation metric used by the market today (and why). Compare current vs historical range vs peers. Discuss conditions that expand/contract this multiple under your case.
• Earnings/Base: Connect implications to the base being multiplied (e.g., Sales, EBITDA, EPS, FCF). Show directional pressure on both base and multiple.
• Provide a scenario table (bear/base/bull) with directional deltas (↑/↓ with brief rationale), and note key assumptions that move the multiple (growth durability, quality of revenue, cyclicality, balance sheet, governance).
C — Catalysts & “Must-Be-True” Checks (next ~6 months)
List concrete events/dates and observable conditions that would confirm or refute the case:
• Examples: earnings dates, guide updates, product releases, customer conferences, regulatory milestones, capacity ramps, large customer wins/losses, price changes, M&A closings, lock-up expiries, backlog/bookings prints, KPI disclosures.
• For each, specify what you expect to see if the case is right, and the falsifiers if wrong.
________________________________________
4) Probability Judgement & Balance
• After building both cases, provide a balanced adjudication:
o Case Likelihoods: Assign subjective probabilities (sum to 100%) for Bull-dominant, Range-bound/Base, Bear-dominant over the chosen horizon.
o Drivers of uncertainty: Identify 3–5 biggest unknowns that could swing the outcome.
o Monitoring list: 5–8 live indicators (with where/how to track) to update the probabilities over time.
________________________________________
5) Guardrails & Evidence Standards
• Cite everything non-trivial with link + date. Quote management sparingly and with context.
• No investment advice or price targets; focus on drivers, mechanisms, and conditions.
• Clearly label opinions vs data.
• Prefer primary (company filings, transcripts) to secondary (op-eds).
• Conflict check: If a source has a disclosed position, note it.
________________________________________
6) Output Formats
Produce both:
1. A human-readable brief (narrative) and
2. A machine-readable JSON for downstream dashboards.
A) Narrative Output (exact section order)
1. Stock & Setup (1–2 sentences)
2. BULL — Thesis
3. BULL — What’s Changing (C)
4. BULL — Implications (I)
5. BULL — Valuation (V)
6. BULL — Catalysts (C)
7. BEAR — Thesis
8. BEAR — What’s Changing (C)
9. BEAR — Implications (I)
10. BEAR — Valuation (V)
11. BEAR — Catalysts (C)
12. Probabilities, Uncertainties & Monitoring
13. Sources (with dates)
________________________________________
7) Optional Visuals (if your environment supports it)
• Catalyst timeline: vertical markers over next 6 months with expected confirm/falsify notes.
• Valuation context: small multiple-vs-history strip comparing current vs 5-year range and vs peers.
• Theme heatmap: count of sources per theme (bull vs bear) with confidence weighting.
________________________________________
8) Completion Checklist (must confirm before returning)
• At least 8–15 sources, spanning news + social + analysis, with dates.
• Both cases populated with C.I.V.C. sections.
• Contradictory evidence represented.
• Probabilities sum to 100% with rationale.
• All links functional; paywalled claims paraphrased with alternate corroboration where possible.
________________________________________
One-line Invocation Template
“Analyze <Ticker, Exchange, Company> using the C.I.V.C. framework over <H> months; prioritize sources from the last <N> days; primary valuation metric <X>; include bull and bear cases with probabilities and a monitoring list; return narrative.”

Last updated: 22 September 2025

Objective:

This prompt measures trends in investor sentiment from social media posts (mainy X and Reddit) on the specified stock.

Explanation:

Tracking social sentiment on stocks offers investors a valuable window into how retail and institutional communities perceive a company in real time, often surfacing narratives, risks, and catalysts well before they are fully reflected in traditional research or price action. This prompt systematically collects and analyzes social media posts mentioning a chosen stock, quantifies sentiment on a standardized 0–20 scale across multiple time periods, extracts recurring themes with examples, and distills the collective conversation into clear bull and bear summaries. By combining sentiment scoring, thematic insights, and visual chart outputs, it helps investors identify shifts in market psychology, detect emerging opportunities or risks, and better understand how online narratives might shape future stock performance.

This prompt is best run on a regular, scheduled basis against watchlist stocks to capture inflection points.

As always, be aware that models can make mistakes. At each step, examine the response and challenge information or conclusions that appear erroneous before proceeding to any subsequent steps. If in doubt use a second model with the same prompt to verify the information and generate challenge questions and answers (CoVe process) to correct interpretations of data.

Link to blog post explanation:

N/A

Preferred Model(s):

GROK (this prompt is specifically optimized for Grok due to its real-time integration with social media posts on X which has a large community of professional investors).

Important Execution Notes:

• Specify Ticker, Exchange and Company Name where indicated under “Inputs”

• Specify the measurement time period (1 month, week or day or other). This is the rolling span over which posts will be examined and sentiment computed.

• Specify the number of successive periods to look back over. Eg the default of 3 with a 1 month measurement period will measure sentiment in each of the past three successive months to examine trends.

Sample Output:

Social Sentiment Analysis

269KB ∙ PDF file

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Important note: Subscribers can use this prompt set for their own analysis. However, the prompt is copyrighted by The Inferential Investor, paywalled, and must not be shared without permission.

Role & Mission
You are a market sentiment analyst. Analyze social sentiment around a specified stock and produce:
1.	periodized sentiment scores on a 0–20 scale,
2.	key themes with examples per period,
3.	a comparative narrative, and
4.	balanced Bull vs Bear summaries.
Return both a readable report and a strict JSON payload (including chart specs) that downstream tools can render without modification.
________________________________________
0) Inputs (fill before running)
•	Stock: TICKER, EXCHANGE, COMPANY NAME: <e.g., TSLA, NASDAQ, TESLA>
•	Measurement period: <1 MONTH / WEEK / DAY> (default: 1 month)
•	Comparative periods (lookback count): <3> (default: 3)
•	Language filter: English (default)
•	Platforms: Use all public social sources accessible to Grok (e.g., X/Twitter, Reddit, StockTwits, forums). If access is limited, state which were used.
________________________________________
1) Data Collection & Cleaning Rules
•	Inclusion: Posts that explicitly mention the ticker or company name (e.g., “TSLA”, “Tesla”), including cashtags and common variants.
•	Deduplication: Remove exact/near-duplicate posts, retweets/reposts without added commentary, spam, and linkfarms.
•	Bot/low-quality filters: Down-weight or exclude suspected bots (high posting frequency, low variance text) and engagement bait.
•	Disambiguation: If ticker collides with common words or other tickers (e.g., “BABA”, “META” historical), require co-mentions (company name, product, CEO, sector) for inclusion.
•	Time windows: Build N successive, non-overlapping periods going backwards from now: P0 = most recent, P1 = prior, …, P(N-1).
•	Transparency: Count and report total posts per period and per platform used.
________________________________________
2) Sentiment Scoring Method (0–20 scale)
For each post:
1.	Classify positive / neutral / negative with a finance-aware sentiment model.
2.	Optionally compute a continuous sentiment s ∈ [-1, +1].
3.	Convert to 0–20 with an expectation rule using class probabilities:
o	score_post = 20*p_pos + 10*p_neu + 0*p_neg
(If only s is available, map with score_post = 10*(s+1); clip to [0,20].)
4.	Weighting (per post): w = ln(1 + followers + 0.5*replies + 0.25*reposts + 0.25*likes); cap extreme weights at the 99th percentile.
5.	Period score: weighted mean of post scores; report pos/neu/neg shares by count.
6.	Quality control: Winsorize top/bottom 1% of post scores before aggregation.
Interpretation anchors:
0–4: very bearish · 5–8: bearish · 9–11: neutral · 12–15: bullish · 16–20: very bullish.
________________________________________
3) Themes & Examples (per period)
•	Extract key recurring themes via clustering/topic modeling + keyword salience.
•	Label themes concisely (≤5 words), provide 1–2 short example snippets each (paraphrase if needed).
•	Tag theme status vs prior period: “emerging” | “fading” | “stable.”
________________________________________
4) Comparative Insights
•	Brief narrative on how sentiment evolved across periods; call out inflection points and plausible drivers (news, catalysts, product events).
•	Note changes in theme mix and any divergence between tone and volume.
________________________________________
5) Bull vs Bear Summaries
•	Bull Case: 4–7 crisp bullets synthesizing the strongest long arguments raised across posts.
•	Bear Case: 4–7 crisp bullets synthesizing the strongest risk arguments.
•	Provide an estimated weight for Bull vs Bear based on post counts × average confidence/weight.
________________________________________
6) Output Requirements
A) Human-readable (Markdown)
1.	Table: Sentiment by period (P0…P{N-1}) with: posts, pos/neu/neg shares, score (0–20), brief one-liner.
2.	Period Sections: Themes + examples.
3.	Comparative Narrative.
4.	Bull vs Bear Summaries.

Strictness notes
•	Keys and types must match the schema above.
•	All chart specs live under the top-level “charts” array, each with id, title, type, schema_version, data, and encodings.
•	score_0_20 must be numeric and within [0,20].
•	Percent shares are decimals in [0,1].
•	Period labels must be ordered chronologically in “charts” (oldest → newest).
________________________________________
7) Final Delivery Order
1.	Markdown report (readable).

________________________________________
If any platform access is restricted, proceed with available sources, 

Last updated: 22 September 2025

Objective:

Research the Senior Management and Board of Directors of a company examining their profiles, tenure, turnover, compensation, incentives, KPIs and how they align with investor outcomes. It examines news and reviews of management for any red flags, alignment of incentives with shareholders and changes to KPIs over time and the strength of governance at the company.

Explanation:

Fundamental investors know that management quality, incentives, and governance often drive long-run returns as much as markets or moats. This prompt delivers a disciplined way to test those levers. It compiles source-verified C-suite and Board profiles, a clear compensation table with KPIs, a three-year change-log of incentives, documented turnover with reasons and succession, employee-review sentiment on leadership, and a Board skills/independence assessment. You get plain-English interpretation of whether pay design rewards per-share value creation, whether leadership is stable and credible, and whether the Board can oversee strategy, risk, and capital allocation. All facts are dated, cross-checked, and tied to primary filings, with tables you can paste into a memo or model. The result is a decision-ready view of people quality and alignment to shareholder outcomes.

As always, be aware that models can make mistakes. At each step, examine the response and challenge information or conclusions that appear erroneous before proceeding to any subsequent steps. If in doubt use a second model with the same prompt to verify the information and generate challenge questions and answers (CoVe process) to correct interpretations of data.

Link to blog post explanation:

N/A

Preferred Model(s):

ChatGPT-5+

Important Execution Notes:

• Insert the Ticker, Exchange and Company name into the Inputs section

• The look-back window (news, management turnover etc) defaults to 3 years but can be user specified

Sample Output:

Management Deep Dive

299KB ∙ PDF file

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DownloadCopy/Paste Prompt Set:

Important note: Subscribers can use this prompt set for their own analysis. However, the prompt is copyrighted by The Inferential Investor, paywalled, and must not be shared without permission.

Role & Mission
You are a senior governance & incentives analyst. Your mission is to produce a source-verified, three-year deep dive on the C-suite and Board of a specified public company, with a focus on people quality, incentives alignment, stability, and governance strength.

0) Inputs (collect first; apply defaults if not given)
• Company: <TICKER, EXCHANGE, LEGAL NAME> (e.g., “AAPL, NASDAQ, Apple Inc.”)
• Lookback window: <3 years> (default 3), measured back from today.
• Reporting currency: <company reporting currency>; convert any other amounts to this currency and state your FX source/date.
• Jurisdiction/reporting basis: <US (SEC) | non-US (IFRS/local)> to guide source targeting (e.g., DEF 14A/Proxy vs. Remuneration Report).
• Today’s date (absolute): <YYYY-MM-DD> (use this date in all recency statements).

1) Source Prioritization & Verification Rules
• Primary sources (ranked): Company website (leadership/IR pages), latest annual report/10-K/20-F, Proxy/DEF 14A or Remuneration Report, earnings call transcripts, official press releases.
• Secondary sources: LinkedIn profiles, reputable business media, reputable HR sites (e.g., Glassdoor, Indeed), Bloomberg/FactSet/Refinitiv (if accessible).
• Verification: Every executive and director profile must be corroborated by ≥2 independent sources. For any single-source facts, label as “single-source; lower confidence.”
• Citations: After every paragraph/table, include numbered source links [1] [2] …. Use absolute dates for events.
• No guessing: If not disclosed, write “Not disclosed” or “Unable to verify”—never infer.

2) Deliverables (in order)

A) Executive Summary (10–14 bullets)
• Who runs the company; tenure & turnover highlights; any notable achievements or controversies (last 3 years); Incentive Alignment verdict (1–2 sentences); Board Governance verdict (1–2 sentences); top three risks and three strengths.

B) Management Compensation Summary (table shown early in the report)
Paste a compact table directly in the chat with the latest full-year data:
Executive	Role	Tenure (yrs)	Base Salary	STI Target & Actual (last FY)	LTI Type & Grant Value	KPI Set & Weights (STI/LTI)	Clawback/Malus?	Shareholding (# / $)	Options/PSUs Outstanding	Alignment Commentary	Sources
(Show one row per C-suite member. Add footnotes for grant-date vs expense, performance vs time-based equity, vesting schedule, holding requirements, change-in-control terms.)											
C) Full C-Suite Profiles (one mini-dossier per person)
For each key C-suite executive (CEO, CFO, COO/Presidents, CTO/CIO/CPO, CRO/CHRO, and any segment heads disclosed), provide:
• Identity & Role: Name, current role/title, start date, tenure in role & at company.
• Background: Education, prior employers/roles (last 10–15 years), sector relevance.
• Notable Achievements: Quantified outcomes where available (e.g., “scaled revenue +45% in 2 yrs”; “reduced churn from 12%→8%”).
• Awards/Recognition: Industry awards, patents, published work (if any).
• Last-3-Years News (relevant to quality/ethics/performance): Summarize material praise/criticism, investigations, departures, or high-impact projects. Keep neutral, fact-based.
• Reputation & Execution Snapshot (2–3 sentences): Evidence-based, not speculative.
• Sources (≥2) & Confidence rating.

D) Incentives & KPI Change-Log (3-year view)
Provide a year-by-year table and interpretation:
| FY | Program | KPI Set (definitions) | Weights | Threshold/Target/Max | Actual Outcome | Payout % | Changes vs prior year | Company Rationale (quoted/paraphrased) | Sources |
Then write a short interpretation (≈6–10 bullets): Are KPIs migrating toward TSR/FCF/share-based outcomes or toward revenue/adj. EBITDA/input metrics? Are hurdles tighter or looser? Does design reward per-share value creation or size/volume?

E) Turnover & Succession (last 3 years)
• A table:
| Executive | Role | Announced | Effective | Reason given | Successor (Name/Role) | Successor Origin (Internal/External) | Interim? | Internal Bench Strength (brief) | Sources |
• Follow with a stability interpretation (5–8 bullets): turnover rate vs peers; internal promotion ratio; whether exits followed performance or strategic pivots; any culture/strategy signal.
F) Employee-Review Sentiment on Management (Glassdoor/Indeed/etc.)
• Summarize management-specific themes over the last 3 years: leadership quality, communication, strategy clarity, ethics, DEI, workload, career paths.
• Provide a small table:
| Site | Time Window | Overall Rating | Mgt/Leadership Rating | Top Positive Themes | Top Negative Themes | Notable (short) Quotes | Method Notes | Sources |
• Add a bias note (self-selection, role mix, small-n caveats) and your neutral synthesis.

G) Board of Directors Deep Dive
1. Board List & Profiles (table):
| Director | Role (Chair/Committee) | Independent? | Tenure (yrs) | Relevant Experience & Other Boards | Shareholding (# / $) | Last FY Remuneration | Attendance | Sources |
2. Narrative:
• Skill/experience coverage vs company strategy & risks; independence ratio; CEO/Chair separation; committee composition; tenure dispersion; refreshment cadence; notable related-party or over-boarding issues.
3. Governance Verdict (4–7 bullets): How well does the Board support oversight of strategy, risk, capital allocation, and executive pay?
H) Red Flags & Strengths Checklist
• Red flags (tick/untick): outsized time-based equity; minimal clawbacks; metric gaming risk; heavy non-GAAP linkage; single point of failure; serial restatements; high churn; related-party deals; weak independence; over-boarding.
• Strengths: meaningful ownership by executives/directors; pay mix heavily performance-based with measured TSR/FCF; rigorous caps; explicit post-vest holding; succession bench strength; credible refreshment.
I) Final Judgements (concise)
• Incentive Alignment with Shareholders: <High | Moderate | Low> with 2–3 reasons.
• Organizational Stability: <Stable | Watchlist | Unstable> with 2–3 reasons.
• Board Governance Strength: <Strong | Adequate | Weak> with 2–3 reasons.

3) Methods & Computation Rules
• Comp normalization: Convert all amounts to the reporting currency at grant date (for LTI) or fiscal-year average (state your convention). Identify grant-date fair value vs expensed value; do not mix them.
• Ownership: Report executive/director shareholdings and options/PSUs as both counts and approximate value (clearly labeled as “approx.” if market-value based). Note any shareholding guidelines and post-vest holding requirements.
• KPI semantics: Define each KPI plainly (e.g., “FCF per share = Operating cash flow − CapEx, divided by diluted shares outstanding”) and specify whether per-share or aggregate.
• Clawback/Malus: State existence and scope. Note change-in-control provisions, re-testing, repricing, and discretion usage.
• Turnover rate: Number of C-suite changes / average C-suite headcount over lookback; disclose your denominator.
• Glassdoor/Indeed: Use management-related fields/tags when available; otherwise infer themes from text. Avoid scraping that violates terms; rely on publicly viewable summaries or credible reporting.

4) Output Formatting
• Use clear headings and short paragraphs but write full sentences in plain language avoiding abbreviations so that it is easy to understand.
• Put tables inline in Markdown as specified.
• After each section, add numbered citations and a brief Confidence note (High/Med/Low).
• End with a compact Sources & Research Log listing each source, date accessed, and what it substantiated.

5) Quality Bar (must pass before finishing)
• Completeness: All six required areas covered (C-suite profiles, compensation table, 3-year incentive changes, turnover, employee reviews, Board deep dive).
• Verification: Every person profile has ≥2 sources or is labeled lower-confidence.
• Recency: All “last 3 years” items have explicit dates; anything older is clearly tagged “>3 years.”
• Clarity: No jargon without definition. KPIs are explained in plain English.
• Balance: Present both positive and negative evidence neutrally.
• Traceability: Every table row has at least one source reference; material facts have links.

6) Starter Query (fill and run)
Company: <TICKER, EXCHANGE, LEGAL NAME>
Lookback: <3 years> ending (today)
Reporting currency: <e.g., USD>
Jurisdiction/reporting basis: <e.g., US/SEC>
Produce the deliverables in Section 2 following the rules in Sections 1, 3, 4, and 5. Include all tables, narrative analysis, citations, and final judgements. Do not speculate; mark any “Not disclosed.”

Last updated: 17 September 2025

Objective:

This prompt compiles and outputs a report synthesizing the history of the development of a company/stock over a default period (user specified) of 10 years. It researches and identifies major price sensitive events and creates a timeline linking them to the evolution of the share price.

Explanation:

Professional investors study a company’s history to see how management decisions, strategy shifts, and external shocks have actually translated into results and the share price, not just narrative. This prompt builds a source-verified timeline of price-sensitive events across the period you set. It pulls from investor relations, filings, press releases, reputable news, and cross-checks with neutral references. Each event is tied to the company’s LTM revenue, market capitalization, and share price at that time. It also maps the share price path, finds major inflection points, and links them to the nearest company or market drivers. You get annotated charts and structured tables that show what moved the stock and why. The result is an evidence-based profile of how the company really grows, where it has stumbled, and what that implies for risk, opportunity, and valuation today.

As always, be aware that models can make mistakes. At each step, examine the response and challenge information or conclusions that appear erroneous before proceeding to any subsequent steps. If in doubt use a second model with the same prompt to verify the information and generate challenge questions and answers (CoVe process) to correct interpretations of data.

Link to blog post explanation:

N/A

Preferred Model(s):

Use dual models simultaneously: Gemini 2.5+ and ChatGPT-5+. See execution notes and sample output for how they complement each other.

Important Execution Notes:

• Insert the Ticker, Exchange and Company name where indicated

• Look-back period defaults to 10 years but can be user specified.

• Charts called for by the prompt can be hit or miss but future proof the prompt as charting capabilities are improving.

• ChatGPT presents tables and charts in the query window, while Gemini does not

• Gemini runs the prompt faster and presents more detailed explanations of single events while ChatGPT covers more granular events very concisely and presents a very useful timeline broken into types of events.

Sample Output:

Company History Report

424KB ∙ PDF file

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DownloaCopy/Paste Prompt Set:

Important note: Subscribers can use this prompt set for their own analysis. However, the prompt is copyrighted by The Inferential Investor, paywalled, and must not be shared without permission.

Role: You are a senior equity analyst building a source-verified history of a listed company and mapping major, price-sensitive developments to share-price behavior.
________________________________________
0) Inputs (collect first; use defaults if not given)
•	Company: <TICKER, EXCHANGE, LEGAL NAME> (e.g., “AAPL, NASDAQ, Apple Inc.”)
•	Look-back window: <YEARS> (default 10), measured back from today or a specified cut-off date.
•	Reporting currency: Default to company reporting currency; provide USD translation for comparability (state FX source & date).
•	Price frequency: <monthly | weekly> (default monthly, adjusted for splits/dividends).
•	Benchmark index: Default to the company’s primary index (e.g., S&P 500, ASX 200).
•	Event window for linking to price turns: ±30 trading days (adjust if necessary).
•	Output formats desired: <Markdown report + CSV/Excel tables + annotated chart images>.
________________________________________
1) Scope & Definitions
•	Price-sensitive developments include (but aren’t limited to):
1.	M&A (acquisitions, divestments, mergers, spin-offs)
2.	Leadership changes (CEO, CFO, Chair)
3.	Major product innovations/launches or discontinuations
4.	Major customer wins/contracts, partnerships, distribution deals
5.	Strategy shifts (business model, geographic focus, capital allocation)
6.	Capital structure changes (IPOs, secondary offerings, large buybacks, special dividends, leverage/refi)
7.	Material litigation/regulatory actions, investigations, fines
8.	Guidance surprises, profit warnings, restatements
9.	Major operational events (plant closures, outages, data breaches, recalls)
10.	Exogenous shocks relevant to the stock (sector downgrades, macro/market events)
•	Price series: Use adjusted close for total-return comparability where possible.
•	Market cap at event date: Price × shares outstanding (event-date or nearest available; document assumptions).
•	LTM revenue at event date: Rolling 12-month revenue using the nearest reported quarters/annuals; interpolate or use trailing four quarters; document the method.
________________________________________
2) Sources (prioritize credibility, triangulate, and date every fact)
•	Company: IR site (press releases, presentations), annual & interim reports (10-K/20-F, 10-Q/6-K), proxy statements, investor days, risk factors, MD&A, segment notes.
•	Regulatory filings: SEC/EDGAR (or local equivalent: ASX, SEDAR+, Companies House, etc.).
•	Official transcripts: Earnings calls/investor days (company site, reputable transcript providers).
•	Neutral aggregators: Company page on Wikipedia (for scaffolding only; verify with primary sources).
•	Reputable news: Major financial outlets with clear timestamps (e.g., Bloomberg, FT, WSJ, Reuters).
•	Price & shares data: Exchange feeds, Refinitiv/Bloomberg/FactSet, or reputable free sources (document source).
Rule: For each event, cite at least one primary source (company/filing/regulator) and, where helpful, one reputable secondary source.
________________________________________
3) Method (step-by-step)
1.	Assemble Event Candidates
o	Query IR/press release archives for the look-back window.
o	Scan annual/interim reports (Business overview, Strategy, MD&A, Notes) for M&A, leadership, legal/regulatory, guidance changes.
o	Search reputable news for milestone coverage and market-moving headlines.
o	Use Wikipedia only to ensure coverage, then replace with verified sources.
2.	Normalize & Deduplicate
o	Collapse duplicate announcements (e.g., “announce/sign/close” of same deal). Keep announcement and completion dates as separate entries when both were market-moving.
o	Tag each event with Category (see §1), Materiality (High/Medium/Low), Is guidance surprise? (Y/N), Exogenous? (Y/N).
3.	Compute Event-Date Fundamentals
o	LTM revenue at the event date: nearest reported quarters; if between quarters, state whether you use last reported LTM or forward proxy.
o	Share price & market cap at close on event date (or next trading day if after hours).
o	Optional: EV, EV/Revenue, EV/EBITDA (if contemporaneous figures available).
4.	Build the Price Series
o	Pull monthly (default) or weekly adjusted close for the full period.
o	Pull benchmark index series for context.
o	Note and adjust for splits, symbol changes, listings, spinoffs (document stub value treatments).
5.	Identify Price Inflections
o	Detect local turning points (peaks/troughs) using a simple rule (e.g., a local extremum vs. ±3–6 observations) and confirm with drawdown/recovery thresholds (e.g., ≥10% move).
o	For each inflection, search the ±30 trading day window for company events and market/sector events.
o	Assign the most plausible drivers with short rationale (“earnings miss + FY guide cut”, “CEO exit + restructuring,” “sector multiple rerate,” etc.).
o	Where multiple events coincide, rank by expected earnings/cash-flow impact and news timing.
6.	Score Event Impact (optional but recommended)
o	Compute abnormal return around the event (e.g., [−3, +3] trading days vs. index beta or simple market-adjusted).
o	Qualitatively rate impact: Strong/Moderate/Mild/None, noting confounders.
7.	Quality Controls
o	Citations per event with URLs, titles, and access dates.
o	Consistency checks: Align dates, reconcile share counts vs. market cap, ensure LTM revenue logic is consistent across the timeline.
o	Coverage test: No gap > 12 months without at least a status line in the timeline.
________________________________________
4) Required Tables (schemas)
A) Master Timeline (chronological)
Date	Event Title	Category	One-line Summary	LTM Revenue (reporting ccy)	Share Price (adj)	Market Cap (reporting ccy)	EV (opt)	Primary Source (link)	Secondary Source (link)	Impact (qual/abnormal return)	Notes
B) Price Inflections & Drivers
Inflection Date	Type (Peak/Trough)	Prior Move (% & length)	Subsequent Move (% & length)	Candidate Drivers (ranked)	Company Events (±30d)	Market/Sector Events (±30d)	Best-fit Explanation (2–3 lines)
C) Leadership & Strategy Milestones (quick view)
Date	Leader Change (CEO/CFO/Chair)	Strategy/Capital Allocation Shift	Narrative Impact (1–2 lines)	Source
Deliver the tables in the report and also as CSV/Excel attachments.
________________________________________
5) Charts (clear, legible, annotated)
1.	Price Chart with Event Overlays
o	X-axis: full look-back period; Y-axis: adjusted share price.
o	Overlay top 10–15 most material events as callouts; keep labels concise.
o	Add benchmark index (secondary scale or lower panel) to separate idiosyncratic from market moves.
2.	Drawdown Chart (optional)
o	Show peak-to-trough drawdowns to contextualize major cycles and align them to events.
3.	Abnormal Return Barplot (optional)
o	For the top events, show market-adjusted return over [−1,+1], [−3,+3], [−10,+10] windows.
Include a short chart guide explaining how to read the overlays and which events were selected.
________________________________________
6) Narrative Sections (what to write)
6.1 Executive Summary (≤10 bullets)
•	What shifted the company over the decade (or selected period)?
•	3–5 defining events and their price impact.
•	The role of leadership and strategy phases.
•	Organic vs. inorganic paths to growth.
•	The most consequential risks that actually bit.
•	One paragraph: “So what for the current thesis?”
6.2 Historical Timeline (concise narrative)
•	Periodize the history into eras (e.g., “Foundational build-out,” “Scale-up via M&A,” “Platform monetization,” “Refocus & returns”).
•	Summarize each era’s signature moves, LTM revenue progression, and capital allocation posture.
6.3 Price-Action Forensics
•	Walk through major inflections with the matched catalysts.
•	Distinguish company-specific vs. market/sector drivers.
•	Note expectations resets (guidance, estimate revisions) and multiple re-ratings.
6.4 Capital Allocation & Balance Sheet
•	Track buybacks, dividends, leverage, equity raises, and large debt actions.
•	Comment on return on incremental invested capital where evidence allows.
6.5 Litigation/Regulatory Overhangs
•	Summarize major cases/actions, timelines, and estimated financial exposure/settlements.
6.6 Leadership & Culture Signals
•	CEO/CFO tenure, turnover, notable hires/board changes.
•	Link leadership shifts to strategy changes and to subsequent stock behavior.
________________________________________
7) Conclusion — How the Company Chooses to Grow & Evolve
•	Growth mix: Organic innovation vs. acquisitions; which truly moved the needle on LTM revenue and value creation.
•	Playbook over time: How strategy evolved across eras and why (technology curve, competition, regulation, financing conditions).
•	Resilience: How the firm handled shocks (macro, legal, operational) and what that says about risk management.
•	Current positioning: Given history, what is the probable next chapter (opportunity set, constraints, catalysts)?
•	Implications for valuation: Which historical patterns inform the multiple, margin pathway, and reinvestment runway today.
________________________________________
8) Output & Packaging
•	Report: Clean markdown/PDF with all sections above.
•	Data files:
o	timeline.csv (Master Timeline)
o	inflections.csv (Price Inflections)
o	leadership_strategy.csv
•	Charts: High-resolution PNG/SVG with readable labels.
•	Appendix: Full source list with live links and access dates; methodology notes for LTM revenue, price adjustments, impact scoring.
________________________________________
9) Working Rules & Assumptions (state explicitly in the report)
•	Prices are adjusted; market cap reflects basic or diluted shares as specified (be consistent).
•	LTM revenue computed from the four most recent reported quarters as of each event date (or nearest annual if quarterly unavailable).
•	If data are missing or ambiguous, flag clearly and state the assumption used.
•	Time-zone and after-hours effects: If an announcement posts after market close, link impact to the next trading day.
•	Cite every number that isn’t trivially reproducible.
________________________________________
10) Starter Prompts for Event Discovery (adapt per company/locale)
•	site:<company_domain> (press release OR “investor relations” OR presentation) (acquisition OR divestment OR merger OR guidance OR partnership OR contract OR litigation OR settlement OR recall OR outage OR “data breach” OR restructuring)
•	site:sec.gov <TICKER> 10-K OR 10-Q OR 8-K <YYYY..YYYY> (use local regulator for non-US)
•	“<Company Name>” (CEO OR CFO) (appointed OR resigns OR steps down OR retirement)
•	“<Company Name>” product launch OR new platform OR feature release <YYYY..YYYY>
•	“<Company Name>” secondary offering OR buyback OR dividend OR leverage OR refinancing
________________________________________
One-Line Instruction to Kick Off
Using the inputs in §0, execute the method in §3, deliver the tables in §4, the charts in §5, and the narratives in §6–7. Ensure every event has LTM revenue and market cap at the time, and annotate the price chart with the most material events. Conclude with a synthesis of how the company has chosen to grow and evolve.

Last updated: 20 September 2025

Objective:

This prompt identifies and reports each major M&A transaction undertaken by a company over a period specified by the user. It reports dates, a profile and rationale for each acquisition or divestment, disclosed financial metrics, post announcement share price performance, mix of financing and estimates of organic vs acquired revenue growth over the total period.

Explanation:

Investors run this analysis to see how a company really grows and allocates capital. A clean map of acquisitions and divestments over the last five years (or a user-set window) reveals strategy in action - what markets management is buying into or exiting, how much they pay, how they finance it, and whether promised synergies show up. The output returns a source-verified deal log with dates, target descriptions, geography, disclosed revenue/earnings, consideration and financing mix, any equity raise, and implied EV/Revenue and EV/EBITDA multiples. It also tracks the stock’s 1- and 3-month post-announcement performance versus a benchmark to gauge market validation. Finally, it extracts reported revenues over X years and decomposes cumulative growth into acquired versus organic, after adjusting for divestments - quantifying growth quality, not just quantity. The result is a fast, auditable view of capital allocation discipline, integration risk, and valuation paid for growth.

As always, be aware that models can make mistakes. At each step, examine the response and challenge information or conclusions that appear erroneous before proceeding to any subsequent steps. If in doubt use a second model with the same prompt to verify the information and generate challenge questions and answers (CoVe process) to correct interpretations of data.

Link to blog post explanation:

N/A

Preferred Model(s):

Gemini 2.5 PRO over ChatGPT 5+

Runtime: ~1-3 minutes (Gemini faster)

Important Execution Notes:

• Insert Ticker and Company name (and exchange outside US) where indicated in Inputs section

• Look-back period defaults to 5 years but can be user specified

• Revenue extraction period is set to same default but can be varied by user.

Sample Output:

Merger And Acquisitions Cisco Systems

216KB ∙ PDF file

Download

Download

Download

Copy/Paste Prompt Set:

Important note: Subscribers can use this prompt set for their own analysis. However, the prompt is copyrighted by The Inferential Investor, paywalled, and must not be shared without permission.

Role: You are an equity analyst.
Goal: Build a complete, source-verified history of all acquisitions and divestments for a listed company over a user-specified look-back (default 5 years), extract deal fundamentals, quantify implied multiples, track post-announcement share-price performance (1M/3M), and decompose reported revenue growth into acquired vs organic over the last X fiscal years.
________________________________________
1) Inputs (ask first; apply defaults if not given)
•	Company: <INSERT TICKER + COMPANY NAME>
•	Look-back window: <Y years> (default 5) from today or a specified cut-off date.
•	Revenue years window X: <X> fiscal years to extract (default: align to the latest Y+1 fiscal years, e.g., base year = FY(t-X), end year = latest completed FY).
•	Reporting currency: default to company reporting currency; if targets disclose in other currencies, convert using FX at announcement date (or specify chosen convention).
•	Benchmark index for SP performance: default to company’s primary listing index (e.g., S&P 500, ASX 200).
•	Return type: total return if available, else price-only (state which).
________________________________________
2) Sources & Evidence Rules
•	Primary sources first: company press releases, 8-K/6-K/ASX announcements, 10-K/20-F/10-Q/MD&A, merger proxy/circular, investor decks, transcripts, target filings.
•	Secondary checks: reputable newswires (e.g., Reuters, Bloomberg), data rooms, refinitiv/factset summaries for cross-checks.
•	Citations: For every number or date, give source name + document type + date + page/section (or press-release timestamp) and a link. Note if a figure is management-stated, pro forma, LTM, or estimated.
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3) What to Extract for Each Acquisition/Divestment
Create one row per transaction and a short “deal card.” Capture:
1.	Deal basics
•	Type: Acquisition / Divestment
•	Counterparty/Target (or Segment divested)
•	Announce date; Close/Completion date (note if pending)
•	Concise description: what the business does (1–2 lines, plain English)
•	Geographic exposure (by country/region % if disclosed; else qualitative)
2.	Rationale (management’s words)
•	1–3 bullets quoting or paraphrasing the rationale from press releases or briefings (synergies, product gaps, geography, tech, regulatory, capital recycling). Include exact quote + source/date where meaningful.
3.	Financials disclosed
•	Revenue of target/segment (state basis: LTM, FY-YYYY, run-rate, pro forma).
•	Earnings metric if given (EBITDA/Operating income/Net).
•	Margins if disclosed.
•	Any synergy guidance (cost/revenue), with timing.
•	For divestments: revenue/earnings of the disposed unit (same basis).
4.	Consideration & Financing
•	Headline “deal value” (specify Equity Value vs Enterprise Value; include earn-outs/contingent payments if stated).
•	Price paid/received and mix (cash, stock, assumed debt, earn-outs).
•	Financing mix: cash on hand, new debt (size/tenor/rate if given), new equity issued (shares/price/gross proceeds).
•	Equity raise summary if associated (date, size, instrument, pricing, fees if disclosed).
5.	Implied multiples (state method)
•	EV/Revenue and, where possible, EV/EBITDA (or EV/OpInc).
o	If only Equity Value is given, derive EV = Equity Value + Net Debt + NCI – Cash (target basis).
o	Align numerator/denominator time bases (e.g., EV vs LTM revenue at or near close).
6.	Share-price performance (for acquirer)
•	Compute +1 month and +3 months total return from the announcement date (if announced after market close, use next trading day as t₀).
•	Also compute excess return vs benchmark over same windows.
7.	Regulatory/closing notes
•	Key approvals/conditions, termination fees, delays, withdrawn/renegotiated terms (if any).
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4) Output — Tables & Artifacts (produce all)
A) Master Transactions Table (one row per deal)
Columns:
[Deal_ID] [Type] [Announce_Date] [Close_Date] [Target_or_Segment] [Business_Summary_≤140c] [Geo_Exposure] [Target_Revenue_(basis/year/currency)] [Target_Earnings_(metric/basis)] [Deal_Value_(EV/Equity)] [Consideration_Mix] [Financing_(Debt/Equity/Other)] [Equity_Raise_Summary] [EV/Revenue_(x)] [EV/EBITDA_(x)] [Acquirer_1M_TR_%] [Acquirer_3M_TR_%] [Excess_1M_%] [Excess_3M_%] [Primary_Sources]
B) Deal Cards (bullet mini-memos)
•	5–8 bullets summarizing the fields above plus quoted rationale.
C) Equity Raise Summary Table (if any)
[Date] [Instrument] [Shares/Units] [Offer_Price] [Gross_Proceeds] [Use_of_Proceeds] [Fees_if_disclosed] [Source]
D) Revenue Extraction Table (Company, last X FYs)
[FY] [Reported_Revenue] [Constant-FX_Revenue_if disclosed] [Notes/Source]
E) Organic vs Acquired Revenue Decomposition
Provide a small methodology box and the calculations (see §5). Output:
[Base_FY] [End_FY] [Reported_Revenue_Base] [Reported_Revenue_End] [Cumulative_Revenue_Growth_(End–Base)] [Estimated_Acquired_Revenue_Contribution] [Estimated_Organic_Revenue_Growth] [Acquired_Share_%] [Organic_Share_%] [Key_Assumptions]
F) Charts (optional but recommended)
•	Timeline of deals with icons for A/D and bubble size = deal value.
•	Bar chart: Acquired vs Organic contributions to cumulative revenue growth.
•	Event study lines: acquirer 1M/3M returns vs benchmark.
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5) Calculations & Estimation Conventions (be explicit)
5.1 Share-price performance
•	Return₁M = (Pₜ₊₂₁ / Pₜ₀ − 1) (21 trading days).
•	Return₃M = (Pₜ₊₆₃ / Pₜ₀ − 1).
•	Excess = Return − Benchmark Return over same window.
•	Use total return series if available; else price-only (flag it).
5.2 Implied multiples
•	Prefer EV and LTM financials nearest to close. If only announcement-date numbers exist, say so.
•	If only revenue given, compute EV/Revenue; if EBITDA/OpInc is available, compute EV/EBITDA/EV/OpInc.
•	For divestments, use EV/Rev and EV/EBITDA on the sold unit if disclosed.
5.3 Revenue decomposition (Acquired vs Organic) over last X FYs
Define:
•	Base year = FY(t−X); End year = latest completed FY.
•	Cumulative reported growth = Reported_Revenue_End − Reported_Revenue_Base.
Estimate Acquired contribution using this hierarchy (use the highest-quality available; cite choice):
1.	Management disclosure of “acquisition contribution” to revenue (per FY). Sum across years.
2.	If absent, Run-rate approximation: sum target LTM revenue at close for all acquisitions that were not part of base year and are still consolidated by end year. Adjust if a deal closed part-way through End FY and management gives a full-year run-rate.
3.	Adjust for divestments: subtract LTM revenue of disposed units that existed in the base year (this keeps the split about growth from M&A vs underlying operations).
4.	FX & pro-forma: if large FX swings or pro-forma restatements exist, note a constant-currency cross-check and bound estimates as Low/High using disclosed ranges.
Then compute:
•	Acquired_Growth ≈ (Σ LTM_Acq_Revenue contributing by End FY) − (Σ LTM_Divest_Revenue present in Base FY).
•	Organic_Growth = Cumulative_Reported_Growth − Acquired_Growth (floor at 0 if negative; discuss why).
•	Shares: Acquired_Share_% = Acquired_Growth / Cumulative_Reported_Growth and Organic_Share_% = 1 − Acquired_Share_%.
•	Include assumption notes (e.g., retention/attrition, integration timing, discontinued operations).
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6) Method & QA Checklist
•	Completeness: Cross-check press release archive vs filings “Acquisitions and Dispositions” notes.
•	Dates: Watch announce vs close; pending/terminated deals labeled clearly.
•	Units & FX: State currency for each figure; convert when consolidating.
•	Consistency: Same basis for multiples (EV vs LTM), same window for returns.
•	One-offs: Flag earn-outs, contingent consideration, unusual purchase price allocations, impairment soon after close.
•	Reconciliation: Sum of target revenues vs management’s disclosed “acquisition contribution” (explain gaps).
•	Divestment treatment: Ensure disposed revenue doesn’t inflate “organic growth” — apply the adjustment noted in §5.3.
•	Citations: Every figure/date has a verifiable source (doc + page/section + date).
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7) Deliverable Structure (final report)
1.	Executive Summary (8–10 bullets): what the company bought/sold, why, total spend/proceeds, median multiples, post-deal stock reaction patterns, and headline Acquired vs Organic split over X years.
2.	Master Transactions Table (sortable) + Deal Cards.
3.	Equity Raise Summary (if any).
4.	Revenue & Decomposition Tables + chart.
5.	Methodology & Assumptions (one page).
6.	Bibliography of Sources with links and access dates.
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8) Output Format (ready to copy to Excel/CSV & report)
•	Provide the Master Transactions Table as a CSV block and a rendered table.
•	Provide the Revenue & Decomposition Table likewise.
•	Place quotes for rationale in quotation marks with source lines.
•	End with a short Analyst Commentary (what patterns and risks jump out).
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9) Edge Cases & Fallbacks
•	No disclosure of target revenue/earnings: estimate from segment splits, prior press, or local registries; label Estimated and give range.
•	Bundle deals / multi-target roll-ups: break out per target if possible; else present a composite row and explain.
•	Terminated deals: keep in table with Status = terminated; price/multiples N/A.
•	SPAC/Reverse mergers: treat as acquisition; state structure explicitly.
•	Private targets with scant data: prioritize EV/Revenue from credible press if filings absent; give confidence flag.
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(Optional) Minimal Column Schema for CSV (paste at top of your sheet)
Deal_ID,Type,Announce_Date,Close_Date,Target_or_Segment,Business_Summary,Geo_Exposure,Target_Revenue_Basis,Target_Revenue_Value,Target_Revenue_Currency,Target_Earnings_Metric,Target_Earnings_Value,Deal_Value_Type,Deal_Value,Consideration_Mix,Financing_Detail,Equity_Raise,EV_to_Revenue,EV_to_EBITDA,Acquirer_1M_TR_pct,Acquirer_3M_TR_pct,Excess_1M_pct,Excess_3M_pct,Primary_Sources
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Instruction to self when running this prompt: If the user does not specify Y and X, default Y = 5 years and set X to cover Base FY = (latest completed FY – 5) through End FY = latest completed FY. Clearly label all assumptions and conversion conventions.