Methodology
A public, high-level description of how this laboratory produces valuation estimates and financial quality indicators for research use.
1. Quantitative valuation
The valuation engine uses machine-learning ensembles trained on publicly disclosed fundamentals to produce a model-based market-cap estimate. From that estimate the platform derives an implied price and a relative valuation multiple (MVP).
Results are shown with industry cross-sections and historical trends so researchers can compare a name against peers rather than treat a single point estimate as a forecast.
2. Financial quality assessment
The quality system scores firms across multiple dimensions, including health score, earnings-cash-flow quality, solvency proxies (e.g. Altman Z), and risk probabilities.
Radar and trend charts make the multi-factor profile and its change over reporting periods easier to inspect for research screening.
3. Data pipeline and point-in-time design
Inputs come from publicly disclosed periodic reports and exchange market data. Indicators are recalculated on a regular schedule. The research design aims to respect announcement timing so historical analysis remains point-in-time consistent.
No non-public or insider information is used.
4. Coverage and taxonomy
The current public interface focuses on China A-shares. Industry filters use the Shenwan taxonomy; industry names remain in Chinese because that is how the source classification is stored and maintained.
Stock search accepts ticker codes or Chinese company names.
Limitations
Models are imperfect approximations. Estimates can be sensitive to accounting quality, industry structure, and regime changes. Historical fit does not imply future performance. Outputs should be interpreted as research signals, not trading instructions.
Disclaimer
All content on this site is for quantitative analysis and research purposes only. It does not constitute investment advice, a recommendation, or an offer to buy or sell any security. Users remain responsible for their own judgments and risk.