AI has changed how individual investors research stocks: you can ask a chatbot whether a company is overvalued, or let a tool draft a one-page investment summary in seconds. That is a real time saver. It is also a new risk: a language model can produce a convincing argument built on numbers that are wrong. This page explains what AI stock analysis does well, where it fails, and how to use it with a method.
Three ways to use AI for stock analysis
| Approach | Examples | Strength | Weakness |
|---|---|---|---|
| General-purpose chat assistants | ChatGPT, Gemini, Claude, Perplexity | Explain concepts, summarize documents you provide | May quote outdated or invented figures when not given sourced data |
| Quantitative screeners | Multi-criteria filters, automated scores | Scan hundreds of stocks in seconds | Do not explain why a number is good or bad |
| Hybrid tools: data + calculations + AI | Easira, AI research copilots | Numbers come from reported financials, AI helps you read them | Only as good as the data and assumptions |
The three are complementary. What matters is knowing, at every step, where a number comes from: a formula applied to the financial statements, or generated text.
The number-one risk: invented numbers
A language model generates the most likely text; it does not look up a financial database unless one is provided. Asked without data, it can:
- quote a ROIC, a margin or a debt figure that never existed;
- rely on data older than its training cut-off, sometimes by years;
- mix up fiscal years, currencies or subsidiaries;
- present an assumption as a fact, with the same confidence.
The rule is simple: every key number should trace back to a published financial statement. If a tool cannot show the source, treat the number as an assumption.
What AI does well, and what it does not
| Task | AI | Comment |
|---|---|---|
| Summarize an annual report or earnings call | Very good | Provided you give it the document |
| Explain a ratio or a valuation method | Very good | Useful for learning |
| Compute a ratio from financial statements | Average | A deterministic formula is more reliable |
| List strengths, weaknesses and risks | Good | A starting point to verify |
| Estimate fair value | Use with care | Cross-check with an explicit DCF and multiples |
| Predict the share price | No | No tool does this reliably |
How Easira uses AI
Easira keeps the steps separate:
- Data comes from market data providers and the companies' reported financials. Coverage includes more than 1,200 listed companies across US, European and Asian markets, plus ETFs and bonds.
- Metrics — ROIC, WACC, the ROIC-WACC spread, Altman Z-Score, free cash flow yield — are computed with explicit formulas. The DCF is computed in three editable scenarios.
- AI comes next: it writes the summary, lists strengths and weaknesses, and suggests a fair value estimate and a score — to be read alongside the computed DCF, never instead of it. The AI Strategist answers questions using the data of the company being analyzed.
- The conviction memo is built from the calculations (value creation, solvency, valuation), not from free text, and the analysis can be exported to Excel.
In short: AI speeds up the reading; it replaces neither the data nor your judgment. The interface is available in English and French.
Example: reading LVMH with calculations first
Easira data extracted on August 27, 2026:
| Metric | Value | Reading |
|---|---|---|
| ROIC | 13.9% | Return on invested capital |
| WACC | 7.4% | Cost of capital |
| ROIC − WACC spread | +6.5 pts | Net value creation |
| Altman Z-Score | 5.9 | Safe zone |
| Central DCF value | €524 | Versus a share price of €448.63 |
| DCF vs price | +16.8% | A margin of safety of about 14% |
Used well, AI starts from these numbers and asks the right questions: is the spread durable if Chinese demand slows? Does a stress test comparable to 2020 (revenue −16.3%, free cash flow −31%) undermine the discount? That is where AI helps — framing scenarios and risks to check, not making up the starting figures.
AI and investing: five rules
- Check the source of every key number.
- Separate calculation from interpretation: a ratio is computed, a thesis is debated.
- Cross-check valuation methods: a single estimate, human or AI, is one point in a range. Try the stock valuation calculator.
- Ask for the bear case: a good analysis states what would make it wrong.
- Keep the decision: AI is a research assistant, not an investment adviser.