Methodology
FrontierLab is a research filter for growth stocks in frontier sectors (AI, semis, cloud, energy, space, AgriTech and more). A grade ranks a name on evidence-based quality signals so you know what to look at first — it is not a buy/sell call, a price forecast, or financial advice.
The signals behind a grade
- Gross profitability (GP/assets) — the most robust cross-sectional quality signal in the academic literature (Novy-Marx, 2013). The level is the signal, so it stays absolute, never sector-relative.
- Rule of 40 — revenue growth + margin, the practitioner balance of growth vs. profitability for software/growth names.
- Revenue growth & trajectory — durable top-line growth and whether margins are improving.
- Dilution — share-count change, calibrated to the evidence that heavy issuers underperform and repurchasers do better (Fama-French style buckets).
- Cash runway & burn — for early-stage names, can the company fund itself, and how efficiently does it burn?
- Distress gates — names showing distress are disqualified from a top grade regardless of growth.
Early-stage and established companies are graded on different rubrics — a young, speculative name is ranked among its peers ("worth a closer look first"), never against a mature company's scale.
What we deliberately do NOT do
- No "fair value" or price targets. Single-number DCFs and analyst targets run systematically optimistic and are often wide of the mark — we don't publish them.
- No RSI-as-a-sell-signal. Momentum context is shown as context only, never a timing call.
- No fake precision. Every track-record figure is shown with its sample size (N) and window; small samples are labeled "too early to tell."
- Insider selling is not treated as bearish — it's usually routine diversification. Only cluster buying is highlighted as a positive.
- End-of-day data only. No intraday, no real-time, no stop-loss simulation — and we never present cached data as live.
- Grades are frozen at entry. When you log a paper trade, the grade that applied that day is recorded immutably, so the track record can't be rewritten after the fact.
- Peer context is display-only — how a name compares to its sector is shown for context but never feeds the grade itself.
Does it work?
We publish a forward-looking track record — measuring each grade tier's later return versus the S&P 500, recorded daily, with confidence intervals. It is directional evidence, not a promise, and it discloses its own survivorship limits.
AI assistance, disclosed
News digests and on-demand research summaries are drafted by AI from the data above and clearly labeled; they explain, they don't predict. The grade math itself is deterministic and identical between the data pipeline and the dashboard.