Show HN: I built a website to create financial models for any stock online
A new web tool for building discounted cash flow (DCF) models for individual stocks is drawing interest for making fundamental valuation more accessible, but also criticism for naive default assumptions that produce absurd price projections for volatile companies. Commenters debate how useful DCF-based modeling really is for beating the market versus its value as an educational aid and sensitivity-analysis tool, with several urging strong disclaimers and cautioning about regulatory and legal risk. Others focus on practical improvements—better mobile design, clearer explanations of metrics, visible sample models without signup, data attribution, caching, and alert features that notify users when a stock diverges from their modeled “fair value.”
Purpose and Positioning of the Tool
- Web app to build discounted cash flow (DCF) valuation models for stocks.
- Not marketed as a stock-price predictor; users are expected to input their own assumptions.
- “AI models” in the pro tier take a user’s qualitative view of a company and translate it into DCF inputs, plus export-to-Excel.
Modeling Approach and Accuracy Issues
- Defaults often just extrapolate last year’s metrics (e.g., revenue growth) five years forward.
- This yields absurd projections for extreme recent growth (e.g., NVDA, CRSP) and negative or tiny valuations for others (e.g., Boeing, Chipotle).
- Several commenters stress that DCF output is extremely sensitive to inputs; the math is trivial, assumptions are not.
- Suggestions: add bounds to auto-filled values, hide projected prices until users adjust assumptions, or initialize with breakeven / more conservative defaults, possibly show multiple contrasting models.
Reception: Enthusiasm vs Skepticism
- Positive: people like the simple UI for DCF, the ability to tweak parameters, and find it educational or entertaining.
- Skeptical: some argue that if such a tool yielded real alpha, it wouldn’t be sold cheaply; others doubt DCF’s usefulness for predicting equity prices, especially for tech and “story” stocks.
- Counterpoints note DCF is standard in fundamental analysis, especially for steady businesses, but mainly useful for understanding sensitivities, not for guaranteed outperformance.
UX, Performance, and Data
- Reports of mobile layout problems, clunky signup (inputs obscured), ticker-selection bugs, and lack of clear explanation of how projected price is calculated.
- Strong requests to view at least some models without registration.
- Site initially hit free API limits, causing errors; suggestions to cache results and move to paid tiers.
- Data comes from a specific financial API; commenters notice missing required attribution.
Feature Requests and Extensions
- Email/alert system when market price deviates materially from a user-defined fair value.
- Metric tooltips and basic “good vs bad” guidance.
- Support for regional/segment breakouts, correlation/backtesting against historical prices, and alternate valuation heuristics.
Legal and Compliance Concerns
- Some warn about potential regulatory risk (SEC/FINRA) if naive users treat outputs as advice, especially without clear disclaimers or identifiable ownership.
- Others downplay this, arguing obviously nonsensical outputs are self-disqualifying, but this is contested.