IanFV Valuation Reports: Reading Guide and Methodology

2.1Stock Classification and Valuation Paths

1. New listings

   Listed ≤ 6 months
   • Methodology: Because historical time-series data is limited, this framework focuses on longitudinal comparison and semantic-mathematical processing of the financial metrics disclosed in the first two periodic reports after the IPO and in the prospectus (Forms S-1 and F-1 and the final 424B4 pricing prospectus). Sentiment from major financial media and investors across the web is introduced as a core adjustment factor.
   • Forecast horizon: A newly listed company's operations and business model are often in rapid flux (its core business may even shift materially soon after listing), so medium- to long-term historical references are unstable. The new-listing model therefore outputs only a Next Quarter target valuation and does not provide longer multi-period projections.

2. Recently listed

   Listed 6 months–2 years
   • Methodology: These stocks combine the sentiment-driven elasticity of an early growth stage with a gradually established financial track record. Once data-completeness thresholds are met, the model begins back-testing and correcting historical factors, while an LLM decision layer adaptively chooses the new-listing architecture, the seasoned-stock architecture, or a weighted hybrid.

3. Seasoned stocks

   Listed > 2 years
   • Methodology: Business models and financial structures are relatively stable. Grounded in classical modern asset pricing theory and deeply integrating the IanFV financial knowledge graph with full historical series, the framework performs systematic grid search, back-testing, and dynamic calibration of the underlying valuation factors.
   • Computation engine: It builds a multi-dimensional, higher-order factor feature matrix and applies topological space mapping and orthogonalized eigenvector reconstruction to remove collinearity and capture non-linear structure. It then solves for the probability distribution surface of the asset's value and outputs high-confidence valuations through statistical inference.

2.2Valuation Metric Definitions and Price Anchor

• Price anchor: Every multiple in the model that depends on secondary-market prices (such as P/E, P/S, and EV/EBITDA) uses the “Valuation-Date Price” in the overview table as its input anchor.
• Target price vs. confidence interval: The “Next-Quarter Target Price” and the “10% Confidence Interval” in the table may point in the same direction or diverge. The former reflects the extreme of discrete features along a defined assumption path; the latter reflects the statistical convergence of the full distribution (see the uncertainty analysis below).

6.1Multi-Scenario Factor Overlay Heatmap (New Listings and Some Recently Listed Stocks)

Shows how sensitive the base pricing model is to each external factor:

• Neutral valuation: The algorithm's baseline valuation distribution after removing short-term extreme sentiment;
• With media sentiment: The shifted valuation surface after incorporating recent sentiment indices from major financial media and social networks;
• With report deviation: The valuation distribution further adjusted for how far the first periodic report after listing deviates from the operating expectations in the prospectus.

6.2Full Target Price Density (Some Recently Listed Stocks and Seasoned Stocks)

• Violin density view: Builds the full valuation probability density surface from a high-dimensional sample space;
• Dot sampling: For legibility on mobile devices and in charts, the dots use statistically unbiased random sub-sampling (for example, N=350 representative sample points).

7.1Single-Quarter Uncertainty (New Listings and Some Recently Listed Stocks)

• Single-quarter span: Usually the window from the current report date to the next official earnings release (about 90 calendar days).
• Target price vs. statistical confidence intervals:
  • Target price: The joint-scenario point of maximum probability (Scenario-based Mode) that the model identifies after weighing the macro environment, industry trends, company fundamentals, whether expectations hold, and other dimensions—not simply the mean of a mathematical distribution;
  • Confidence intervals (10%, 50%, 80%): Output from a rigorous statistical inference surface. When market expectations are hit by extreme structural shocks, the algorithm's maximum-probability point and the center of the confidence intervals may shift in different directions; together they reveal the non-linear nature of pricing.

7.2Multi-Period Uncertainty Path (Seasoned Stocks)

• Point-in-time targets: IanFV derives target values at specific time anchors (next quarter, two quarters, one year, and two years). Because secondary markets are subject to many random shocks—macro liquidity, systemic volatility, commodity cycles, and sudden industry events—the chance that a price lands exactly on a single value on a given calendar date is, in probability terms, extremely small.
• Trend view and trading discipline: The multi-period target path is essentially a conditional expected path based on the information available at the time. It describes the direction in which the medium- to long-term value center converges and should not be mechanically treated as an execution level for short-term trades.
• Confidence bands and mean reversion: Although prices behave much like a random walk in the short term, mean reversion in the underlying value factors is statistically stable over the medium to long term. The layered confidence bands shown in the report give investors objective upper and lower pricing references for different levels of tolerance to market volatility.