Methodology
How YieldIQ values a stock
Open methodology for the DCF, Prism scoring, and verdict bands behind every analysis.
01 — Valuation
The DCF
The core fair-value engine is a discounted-cash-flow model. Free cash flow is taken from the data pipeline — operating cash flow net of capex, cleaned for one-offs where disclosure permits. The discount rate is a sector-aware WACC: the Indian 10-year G-Sec serves as the risk-free rate, sector equity-risk premia and betas come from models/industry_wacc.py, and the cost of debt reflects the company’s own interest burden where reliable.
Terminal growth is sector-specific rather than a single blanket number. Mature FMCG and utilities are modelled at low single digits; IT services and select consumer names sit higher; cyclicals are held close to long-run nominal GDP. The intent is to avoid the single worst failure mode of generic DCFs — one terminal assumption papered across every industry.
Every valuation is published in three scenarios: bear, base, and bull. The scenarios flex growth, margin, and reinvestment jointly rather than one input at a time, so the spread reflects plausible end-states rather than sensitivity theatre.
Alongside the forward DCF, we publish a reverse DCF that solves for the growth rate implied by the current market price. When the implied number is higher than anything the business has ever delivered, the reverse DCF says so plainly.
All DCF outputs are model estimates. They are not price targets and nothing on this page should be read as a recommendation to transact.
02 — Scoring
The 6-pillar Prism
The Prism is a decomposition of business quality and valuation into six independently scored pillars. Each pillar is scored 0–10, the six are composited to a /10, and the composite is rendered as an A–F grade on a /100 scale for quick scanning.
Pulse
Short-horizon signal from recent price action and sentiment — momentum, volatility regime, and revision direction. Informative, not decisive; it sits alongside the slower-moving pillars rather than overriding them.
Quality
Return on capital employed, return on equity, operating and net margins, and the stability of reported earnings across cycles. High scores require durability, not just a good last twelve months.
Moat
Persistence of gross margin, evidence of pricing power through input-cost shocks, and the durability of return on capital versus peers. A high Moat score means the excess returns show up year after year, not as a one-period spike.
Safety
Balance-sheet resilience — leverage ratios, interest coverage, and an Altman-Z-style composite adapted for Indian reporting. Financials use bank-appropriate substitutes (capital adequacy, NPA ratios) where the standard formula does not apply.
Growth
Revenue and earnings CAGR across both 3-year and 5-year windows, blended to reward consistency over one-off spikes. Growth is reported in isolation; a high Growth score does not imply a high Value score.
Value
The DCF margin of safety combined with sigmoid-smoothed relative multiples against sector peers. Smoothing prevents extreme multiples from collapsing the score, and the MoS weight dominates so that the label tracks the model rather than the screen.
03 — Labels
Verdict bands
Verdicts are descriptive, not imperative. They describe where the current price sits relative to the modelled fair-value distribution. They do not tell anyone to buy or sell.
- Deep ValuePrice materially below the bear-case fair value. The market is pricing in an outcome worse than our most pessimistic scenario.
- Below Fair ValuePrice below the base-case fair value but above the bear. A meaningful margin of safety on the central estimate.
- Fair Value RegionPrice within the normal dispersion of the base case. No pricing edge either way on the modelled assumptions.
- Above Fair ValuePrice above the base case but below the bull. The market is implying a better outcome than our central estimate.
- OvervaluedPrice above the bull-case fair value. The market is pricing in an outcome better than our most optimistic scenario.
- Under ReviewInsufficient data to assign a band. We apply this explicitly rather than guess — thin IPO history, unit-change ambiguity in filings, or a failed validator will all land here.
04 — Inputs
Data sources
Finnhub provides real-time and delayed quotes, analyst estimates, and corporate-event metadata. It is the source of record for live prices on the site.
yfinance is still used for parts of the fundamentals pipeline. It is a pragmatic dependency, not an ideal one. We mitigate the risk with an aggressive own cache, a process-wide circuit breaker that trips on rate-limit or error bursts, and validators that reject unit-jump corruption before it reaches the model.
Aiven Postgres is the canonical store for cleaned financials, computed fair values, and Prism scores. Everything on the site reads through this layer.
DuckDB on Parquet backs the ten-year history surfaces — price panels and the aggregated fundamental history used for CAGR and stability calculations. It is fast enough for ad-hoc analytical queries and immutable enough to rely on.
XBRL filings from the exchanges are progressively replacing yfinance for fields that are reliably tagged. The rollout is line-item-by-line-item rather than a cutover, because any given filing’s quality varies by filer.
05 — Honesty
Known limitations
Recent IPOs with fewer than three years of post-listing financials are too thin for the Growth and Moat pillars to be trustworthy. These names are surfaced under Under Review rather than scored.
Unit-change events in filings (lakhs versus crores, thousands versus millions) are handled on a best-effort basis. The validator suite catches the common cases; the residual risk is real and we disclose it.
Peer selection uses a three-band market-cap bucketing — Large-cap above ₹50,000 Cr, Mid-cap between ₹10,000 Cr and ₹50,000 Cr, Small-cap below ₹10,000 Cr. Bucket boundaries are deliberate and infrequently moved, which means a stock right at a threshold can flip buckets on valuation days without a real change in its business.
Sector models are shared across their sector, not bespoke to each ticker. A bank is modelled as a bank; an IT services company as IT services. The approach is intentionally generic: bespoke per-ticker tuning is what produces post-hoc-justified valuations, which is exactly what we want the methodology to resist.
06 — Regulatory
SEBI posture
YieldIQ is not registered with the Securities and Exchange Board of India as an Investment Adviser or Research Analyst. Nothing on the site is investment advice, a recommendation, or a solicitation.
Verdicts are descriptive rather than imperative. Where data quality is insufficient, we apply an explicit Under Review label instead of forcing a call. Fair-value outputs are model estimates derived from publicly available inputs and disclosed assumptions; actual outcomes may differ materially.
Do your own research. Consult a SEBI-registered adviser before making investment decisions.