AI analyst for pennies: how 10 prompts for Claude replace expensive stock experts
The market for analytical services is undergoing a tectonic shift. While investment firms churn out multi-million dollar reports, a powerful tool has emerged in the community that questions the very need to maintain a staff of expensive analysts. This refers to a specialized collection of 10 prompts for Claude, which allows for deep fundamental analysis of companies and crypto projects at the level of leading consulting firms. And most importantly, these queries do not provide "buy" or "sell" recommendations, but merely offer structured analytics for making your own decision.
The set covers the full research cycle: from a general business overview to a detailed assessment of risks and management quality. Each prompt assigns Claude a strictly defined role and a set of parameters for analysis.
The First Five: From General Overview to Valuation
The first prompt turns the neural network into a senior analyst preparing a research report on a ticker that is understandable for a beginner. It covers the business model, revenue sources, industry trends, competitors, financial results, valuation, growth drivers, risks, and bull/base/bear scenarios. The key requirement is to rely on recent public sources, indicate dates, and clearly separate facts from assumptions.
The second prompt breaks down the company's latest earnings call: five main takeaways, changes in revenue and margins, management guidance, management tone, analyst concerns, pleasant and unpleasant surprises. Additionally, a table of key metrics is generated with the current and previous results and an explanation of why this is important.
The third prompt is a skeptical analyst who looks for red flags in revenue quality, margins, cash flow, debt, dilution, insider actions, and management wording. Each issue is assigned a severity rating, and a total risk score from 1 to 10 is provided at the end.
The fourth and fifth prompts focus on competitive advantages and valuation. One assesses the company's "moat" — brand, network effects, switching costs, scale, intellectual property — on a scale and compares it with competitors. The second compares the company with peers using multiples (P/E, forward P/E, EV/revenue, EV/EBITDA) and explains whether it looks cheap, fairly valued, or expensive.
The Second Five: From DCF Model to Beginner's Checklist
The sixth prompt helps build realistic assumptions for a discounted cash flow (DCF) model: bear, base, and bull scenarios for revenue growth, margins, tax rate, capital expenditures, and discount rate, with an explanation of the logic behind each assumption.
The seventh prompt creates a catalyst calendar for 3, 6, and 12 months: reports, product launches, investor days, regulatory decisions, lawsuits, macro events, management changes, buybacks, and dividends. For each event, timelines, impact, upside and downside risks, confidence level, and source are indicated.
The eighth prompt evaluates the management team: the CEO's track record, the CFO's credibility, forecast accuracy, transparency, capital allocation, acquisitions, insider ownership size, and compensation.
The ninth prompt is a simulation of an investment committee debate. Claude creates a bull analyst and a bear analyst, and a neutral judge explains at the end whose position is better supported.
The tenth prompt turns Claude into a patient teacher who explains the company in simple terms: what it does, how it makes money, what could go right and wrong, and its profitability, growth, debt, and valuation. A beginner's checklist is generated at the end.
My analysis: This collection is not just a set of queries, but a ready-made methodology that structures research. However, for all the power of AI, final data verification and decision-making remain with the investor. Claude can provide a brilliant structure, but it does not eliminate the need to check facts and form your own opinion. The analyst market is changing — those who do not adapt risk being left without a job.