Stock Value Analyzer

Problem It Solves

Value investing conclusions can be misleading when based on a single-year report, a low PE ratio, or one valuation multiple. A low PE does not automatically mean cheap, a high ROE may be driven by leverage, and a current price can be contaminated by analyst targets. stock-value-analyzer turns the test of good industry, good company, and good price into an auditable workflow, using quantitative models to set boundaries and qualitative frameworks to judge whether the direction is reasonable.

How It Works

• Dual-layer evaluation: industry, company, and price are assessed quantitatively first, then qualitatively. The quantitative layer produces testable ranges; the qualitative layer checks business logic.
• Valuation anchors: reverse DCF estimates the growth rate implied by the current price, while scenario-weighted valuation, EPV, and industry-specific valuation routing provide cross-checks. When A-share inputs lack detailed financial statements, the skill marks models as applicable=False instead of forcing a conclusion.
• Financial-quality filters: DuPont decomposition, ROIC vs WACC, Piotroski F-Score, and earnings-quality checks separate operating performance from leverage or accounting effects. Beneish M-Score and Altman Z-Score can trigger rejection when applicable.
• Time-series validation: reports should reference 5-10 year trends in revenue, profit, gross margin, ROE, and FCF rather than a single-year snapshot.
• Data discipline: API-first retrieval, multi-source validation, source-tier labeling, recent-event scanning, and pre-delivery checks reduce reliance on unreliable numbers.

Boundaries

It is intended for medium- to long-term value assessment, not short-term forecasting, technical analysis, or specific timing advice. Data availability varies by market; some quantitative models may be downgraded. Users should still apply their own judgment, risk tolerance, and position sizing.