How AerInvest Actually Values a Stock

1. The framework is chosen, then stated

There is no single formula applied to everything. The engine picks a valuation framework from what the asset actually is and what data came back for it — an operating company, an ETF and a crypto asset are not valued the same way — and then prints that choice on the page instead of leaving you to assume one. For NVDA the framework is: Valuation engine: DCF cash-flow model, Free-cash-flow yield capitalization, Forward earnings power, weighted by data quality. Confidence 50/100; the signal is checked against cash flow, profitability, revenue trend and the 200-day trend before it is shown.. The inputs feeding it, with what each one is actually used for:

• Current price: USD 210.96 — Primary anchor for scenario analysis.
• 52W position: 59/100 — Used as a momentum and drawdown context proxy.
• Market cap: N/A — Used as a rough liquidity and business-scale proxy.
• P/E ratio: Unavailable — Used as one valuation sanity check, not a complete model.

2. What it will not fill in

A missing field is treated as uncertainty, not quietly replaced with a synthetic number. When an input is absent the estimate widens and the page says which input was missing, rather than returning a narrow, confident-looking figure built on a guess. Inside the app the same rule is visible as a number: the company page lists every valuation method the engine tried, the weight each one carried, and a plain-English line for any method that had to be skipped — a method with no data behind it shows a weight of 0%, not a filled-in value.

Peer context for NVDA: Peer valuation needs comparable-company data for a stronger conclusion.

3. The score is a separate number from the price

The research score out of 100 is not the fair-value estimate and does not feed into it. It is a read on business quality across several fundamental factors, kept deliberately separate so that a good company at a bad price and a weak company at a cheap price do not collapse into the same number. The factor breakdown for this analysis:

• Fundamental Intelligence52/100 — Balanced contribution with 34% model weight and explicit data-quality penalties.
• Market Intelligence62/100 — Balanced contribution with 18% model weight and explicit data-quality penalties.
• Sentiment Intelligence49/100 — Defensive contribution with 12% model weight and explicit data-quality penalties.
• Macro Intelligence55/100 — Balanced contribution with 14% model weight and explicit data-quality penalties.
• Risk Intelligence30/100 — Defensive contribution with 22% model weight and explicit data-quality penalties.

The three scenarios, not one number

A single fair-value figure hides how much of the estimate depends on assumptions that could go either way. The range above shows that spread explicitly — downside path USD 51.97, cautious path USD 76.12, growth path USD 76.12. If the downside and growth paths are close together, confidence is higher; if they're far apart, that spread itself is information.

What could break this thesis

• Company-specific risks need fundamentals that are not available for this ticker right now — see Data Quality below.