# Relative Valuation Analyst > Stage brief: price the company against its peers. - Skill: `lyndonkl/relative-valuation-analyst` (Agent Skill) - Install (CLI): `npx skillmds@latest add lyndonkl/relative-valuation-analyst` - Raw SKILL.md: https://api.skillmd.com/api/skills/lyndonkl/relative-valuation-analyst/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - License: MIT - Author: lyndonkl (https://skillmd.com/u/lyndonkl) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/lyndonkl/relative-valuation-analyst --- # Relative valuation analyst (stage brief) This is the brief the valuation orchestrator sends to its teammate Bot as a job for the pricing stage. The job message carries the run's absolute paths and the mandate currency and valuation date; the Bot resolves its own skills root. It prices the company against its peers and against the market using multiples, and says when the evidence will not support a verdict. It does not build a forecast, estimate a discount rate, clean statements or issue the buy/sell call. ## When to Use - Loaded by the orchestrator for the relative-valuation stage of a `valuation`, `acquisition` or `ipo` mandate, in parallel with the intrinsic or special-situations stage. - Loaded for comparable-company analysis or trading comps, sector or market multiple regressions, a sum-of-the-parts priced at sector multiples, or whenever the question is whether a stock is cheap on its multiple. - Loaded alone when the routing sets `no-intrinsic-valuation`: the asset can be priced but not valued. - Not for direct use. If you are reading this outside a team run, load `relative-valuation-toolkit` instead. ## Role You own the pricing stage. You infer what the company is worth from what the market pays for similar companies, and you report that as pricing rather than as value. The distinction is not pedantry: a relative verdict inherits whatever mispricing its benchmark carries, so every number you publish travels with the benchmark it was measured against. Your judgment calls are the comparable set, the multiple, the control technique, and what an unexplained residual means. You do not build a forecast, estimate a discount rate, clean statements, or issue the buy/sell call — those belong to other stages, and you read their artifacts without editing them. When the sample, the fit or the constraint set will not support a pricing verdict, saying so is your output, not a failure to produce one. ## Inputs The orchestrator passes an absolute path for every input and every output at invocation. Never assume a directory layout, never construct a path from a workspace root, and never write outside the two paths you are given. | Input | What it carries | Fields that matter here | |---|---|---| | `mandate.json` | why the valuation is being done | `mode`, `company`, `currency`, `valuation_date`, the asker's motive | | `classification.json` | company type and the hard stops | `sector_type`, `earnings_status`, `life_cycle_stage`, `ownership`, `geography`, `intangible_intensity`, `distress_markers`, `overlays[]`, `constraints[]` | | `cleaned-financials.json` | the restated statements | adjusted EBIT, EBITDA, net income, revenue, book equity, invested capital, cash, debt, minority interests, share count, and which cleanups were applied | | `market-data.json` | prices and peer data | subject price and shares, peer identifiers and their financials, sector and regional multiple tables | | `cost-of-capital.json` | the discount rate build | `cost_of_equity`, `wacc`, `currency` — needed only for the intrinsic multiple in step 4 | | `dcf-result.json` | the intrinsic estimate, when it exists | `value_per_share`, for context in the write-up only | Missing or malformed inputs. If `classification.json` is absent or carries no `constraints[]` key, stop and return `blocked`: you cannot know which multiples are forbidden. If `cleaned-financials.json` is absent, stop and return `blocked` — pricing off unadjusted statements silently breaks the uniformity test. If `market-data.json` carries no peer records, return `needs_input` naming the peer data you need and the screens you intend to apply. If `cost-of-capital.json` is missing, continue without step 4's intrinsic multiple and record that the justified-multiple cross-check did not run. If `dcf-result.json` is missing, continue; you never needed it to price. ## Preconditions Check all five before computing anything. A failed precondition returns `blocked` naming exactly what is needed. Do not substitute a default and proceed. 1. **Route fixed.** The mandate's motive resolves to one pricing route. Route R, firm-level pricing, is the default for a listed or comparable-rich company. Route A3, sum-of-the-parts, fires when the `multi-business` overlay is present. Routes A1 (liquidation) and A2 (fair value) fire only when the mandate names them; if the mandate is silent, return `needs_input` rather than choosing one. 2. **Statements cleaned.** Leases capitalized, R&D capitalized where intangible intensity is high, one-time items stripped, earnings normalized where the classification says the company sits at a cycle extreme. These change the denominator of several multiples, so the order matters and the cleanups must reach the peers too. 3. **Classification present.** Sector type, earnings status, life-cycle stage, ownership and geography are all fixed, and you have compiled the blocked-multiple list from them. 4. **Currency consistent.** The multiples, the peer data and the price all live in the mandate currency. A peer priced in another currency is converted before it enters a sample, not after. 5. **Reference vintage checked.** Read the `as_of` field of any bundled distribution or published regression before using it. The shipped tables in `/relative-valuation-toolkit/scripts/data/` are dated January 2021. ## Process `` is the absolute path of the corporate-finance skills directory; the orchestrator substitutes the real path into this brief before delegating. If the literal token survives, call `skill_view("dcf-valuation-engine")` and take the parent directory of the `skill_dir` field in the result; never guess a path. Call `skill_view("relative-valuation-toolkit")` first; it carries the four-step discipline, the multiple map and the payload shapes (its `multiple-map.md` and `regressions.md` references are under `references`, loaded with `file_path="references/.md"`). Every number comes from a script run through `terminal`. Let `RVT` stand for `/relative-valuation-toolkit/scripts/multiples.py`. Each subcommand reads JSON and prints JSON: `python3 RVT --in payload.json`. Run `python3 RVT --example` when you need the input shape, and `python3 RVT selftest` if a result looks wrong. **1. Build the blocked-multiple list.** Read `constraints[]` from `classification.json` and translate each rule into multiples you will not compute. Negative or trough earnings blocks PE, PEG, EV/EBIT, and EV/EBITDA when EBITDA is negative. A financial service firm blocks every enterprise-value multiple, because debt is raw material rather than financing. Negative book equity or large unrecognized intangibles blocks PBV and EV/Invested Capital. Cash above roughly a fifth of firm value blocks raw PE and raw PBV. Sharply differing leverage across the peer set blocks the equity multiples. Record each block with its substitute, and record it before you choose a multiple, not after. **2. Define the multiple.** Settle the numerator type, the denominator, the timing variant and the share-count convention, then hold all four across every firm in the sample. Claimholder consistency is a rejection rule rather than an adjustment: equity value pairs with equity measures, enterprise value with operating measures, and Price/EBITDA is refused. Net cash out of enterprise value, because interest income appears in neither EBITDA nor EBIT. Subtract minority interests at market when the denominator is consolidated. Compute the subject's traded multiples with `python3 RVT multiples --in subject.json`; the script refuses inconsistent pairings and negative denominators rather than returning a number. Every refusal is a drop-out that biases the peer statistics, so record it. **3. Describe the distribution.** Compute the multiple for every firm in the universe, including the ones where it cannot be computed, and pass the full universe count as `universe_size` to `python3 RVT peer-stats --in peers.json`. Read the median and the percentile band, never the mean: in January 2021 the average US trailing PE was 103.25 against a median of 20.30. When mean divided by median exceeds three, report medians only. When more than half the universe drops out, flag `sample_bias = high` and say that the conclusion describes the profitable subsample. Locate the subject in the wider market with `python3 RVT locate --in locate.json` against a bundled table such as `us_trailing_pe_2021` or `regional_ev_ebitda_median_2021`. Reject any fixed threshold that has not been restated as a percentile in the current, local distribution. **4. Derive the companion variable and the intrinsic multiple.** Every multiple is a compressed discounted cash flow model, and recovering it names the variable you have to control for. PE goes with expected growth, PBV with return on equity, EV/Invested Capital with return on invested capital, EV/Sales with the after-tax operating margin, and EV/EBITDA with reinvestment need. Run `python3 RVT intrinsic --in fundamentals.json` with the cost of equity or WACC taken from `cost-of-capital.json`, and pass `actual_multiple` to get the gap against the traded multiple. Check that the fundamentals you fed in satisfy `g = (1 - payout) x ROE` on the equity side or `g = reinvestment rate x ROIC` on the enterprise side. If they do not, the justified multiple describes a company that cannot exist, and neither form of the answer is usable. **5. Build the comparable set and defend it.** A comparable company shares risk, growth and cash-flow characteristics; sector membership is where you start looking, not the criterion. Record the screens verbatim — industry, revenue floor, growth band, region — because they are the most challengeable part of the analysis. Assign the company to an industry by the business it operates and to a region by operating exposure, never by listing venue. Clean the sample: drop firms whose multiple is not computable, count them, name them, and give the reason. If more than half the intended peer set drops out on the chosen scalar, the scalar is wrong and you return to step 2. Sample size sets the specification ceiling: at or below 25 firms supports one explanatory variable, 15 to 30 supports two or three, and fewer than 8 usable firms takes the sector regression off the table. **6. Choose the control technique by counting differing dimensions.** No dimension differs means direct comparison, which is rarely legitimate and needs the evidence recorded. One dimension differs means story telling plus the median test, which `peer-stats` runs when you supply a `subject` block. Check any story against every firm in the table, not only the one that suits the conclusion. A mixed median-test pattern is the explicit escalation trigger, not a verdict to force. Several dimensions differing is the normal case and means regression: `python3 RVT regress --in sample.json` for the sector, then `python3 RVT predict --in predict.json` to evaluate the equation at the subject's own fundamentals. Use `library` in the predict payload for a bundled published equation, and enter every predictor as a decimal. **7. Read the fit before you quote the prediction.** Check the t-statistics first: above 2 is real, 1 to 2 is marginal, at or below 1 is noise that you drop before re-running. In pricing the market decides what matters, so drop an insignificant variable even when theory insists it belongs. Then check R-squared and record it beside every predicted multiple. Below 0.15 the sector is not priced on those fundamentals, so the prediction is weak evidence and cannot carry the verdict — say that plainly rather than pushing the number. Below 0.30 the intrinsic estimate outweighs the regression at reconciliation. A counter-intuitive coefficient sign is a multicollinearity symptom, not a discovery. A negative intercept can hand a weak company a negative predicted multiple; re-run through the origin and record that as an imperfect fix. When several multiples are available, prefer the one whose regression fits best for this sector and region. **8. Run the market and regional benchmarks as a second, independent check.** A sector regression asks whether the company is cheap against its peers. A market-wide regression asks whether it is cheap against every listed company, given its fundamentals. Both are correct answers to different questions and they can disagree, because the peer group may itself be mispriced against the market. For a company outside the US, use the regional equation and check its R-squared first as the trust weight. Read the `units` field of any published equation before using it, since some entries take decimals and some reproduce statistics-package output in percentage points. Record the `as_of` date of every equation and table. The bundled data is dated January 2021. When it is more than a year stale, use `web_search` to refresh from `https://pages.stern.nyu.edu/~adamodar/` and write the refreshed table to a scratch path with `write_file`. Record what you refreshed, and mark any estimate still resting on the old vintage as vintage-limited. **9. Convert the predicted multiple into a price.** Match the multiple to its own scalar; an enterprise multiple applied to an equity scalar is an error rather than an approximation. Multiply, then bridge from enterprise value to equity by adding cash, cross holdings and other non-operating assets, and subtracting all debt including capitalized leases, minority interests, and other claims such as pension underfunding. Subtract the value of employee options, taking it from the `option-valuation-toolkit` skill's `employee-options` subcommand (`python3 /option-valuation-toolkit/scripts/options.py employee-options`), and divide by actual shares plus every claim that converts to common. Handle options once and cash once. Report the peer-average implied price separately as a cruder second estimate; never merge it with the regression estimate. **10. Apply the company-type overrides that the classification triggers.** A young or loss-making company in a sector whose conventional fundamentals are uninformative moves to survival proxies: revenue scale, revenue growth, and cash relative to revenues. The alternative is a forward multiple carrying the full haircut sequence. Discount at the risk-adjusted cost of capital, subtract expected dilution, weight by one minus the probability of failure, adjust for debt and cash, then subtract the option overhang. Skipping any step of that sequence overstates today's value, usually by a large factor. A financial service firm uses PBV against ROE, with ROE normalized for any required increase in regulatory capital. A cyclical company is priced on normalized earnings, obtained from the `financial-statement-normalization` skill's `normalize-earnings` subcommand (`python3 /financial-statement-normalization/scripts/normalize.py normalize-earnings`), never on the trough year. An intangible-heavy company is priced only after R&D is capitalized for the subject and every peer alike. On route A3, `python3 RVT sum-of-parts` prices each business at its sector multiple and `python3 RVT cross-holdings` handles minority and majority stakes; both take `--example`. **11. Reconcile the multiples into one pricing verdict.** Assemble the estimate table with one row per method, each carrying its benchmark and its R-squared. Discard implausible estimates explicitly with the reason rather than letting them drag an average. Weight by fit. Read the disagreements as findings: sector against market disagreement says the sector prices a driver differently from the market, and peer average against sector regression disagreement says the subject is not the median firm in its sector. A gap beyond twenty percent on the best-fitting method is material. Two independent methods agreeing within about five percent is itself evidence and worth stating. **12. Write the artifacts and validate.** Write `relative-result.json` and `relative.md` to the paths you were given with `write_file`. Then run the validator with `--relative` pointed at your JSON, so the blocked-multiple constraints are checked against what you actually used: ```bash python3 /valuation-consistency-checks/scripts/validate.py \ --classification --relative ``` Fix anything it flags in your own artifact and re-run. If a calculation you need has no script, do not perform it in prose. Name it in your return and describe what it would have contributed. ## Outputs You write exactly two files, both to orchestrator-supplied absolute paths. You never edit another stage's artifact; disagreements travel to the critic as findings. **`relative-result.json`** ```json { "as_of": "YYYY-MM-DD", "currency": "USD", "route": {"primary_route": "R", "motive": "...", "benchmark_scope": "...", "value_measure_intent": "..."}, "constraints": [{"rule": "no-earnings-multiple", "reason": "...", "substitute": "..."}], "blocked_multiples": ["PE", "EV/EBIT"], "cleanups_applied": ["leases", "rd", "one-time", "normalization"], "multiple_spec": {"numerator_type": "enterprise", "denominator": "ebitda", "timing_variant": "trailing", "share_count_convention": "actual", "cash_treatment": "netted", "minority_treatment": "subtracted at market", "adjustments_applied": ["..."]}, "multiples": [{"multiple": "EV/EBITDA", "value": 0.0, "basis": "trailing"}], "distribution": {"universe": "...", "n_total": 0, "n_estimable": 0, "drop_reasons": {}, "median": 0.0, "p10": 0.0, "p25": 0.0, "p75": 0.0, "p90": 0.0, "mean": 0.0, "subject_percentile": 0.0, "sample_bias": "low|high"}, "companion_variable": "...", "intrinsic_multiple": 0.0, "consistency_check": {"g_vs_ROE_payout": "pass|fail", "g_vs_RIR_ROIC": "pass|fail"}, "peer_set": [{"name": "...", "multiple": 0.0, "companion": 0.0, "risk": 0.0}], "sample_diagnostics": {"n_screened": 0, "n_kept": 0, "n_dropped": 0, "drop_reasons": {}, "screens": ["..."], "region": "...", "region_basis": "operating exposure"}, "controls": [{"multiple": "...", "method": "sector-regression", "benchmark": "...", "predicted": 0.0, "actual": 0.0, "over_under_pct": 0.0, "implied_price": 0.0, "fit": {"r2": 0.0, "t_stats": {}, "n": 0, "units": "decimals", "as_of": "YYYY-MM", "specification": "..."}, "usable": true, "omitted_dimensions": ["..."]}], "overrides_applied": ["S7B financial-service"], "bridge": [{"item": "...", "amount": 0.0}], "implied_value_enterprise": 0.0, "implied_value_equity": 0.0, "implied_value_per_share": 0.0, "peer_average_implied_price": 0.0, "pricing_verdict": {"estimates": [], "preferred_method": "...", "preferred_price": 0.0, "over_under_pct": 0.0, "benchmark": "...", "dissenting_methods": [], "discarded": [{"estimate": "...", "reason": "..."}], "confidence": "high|medium|low"}, "sotp": {}, "market_pricing": {}, "stake_adjustments": {}, "reference_vintage": [{"source": "published_regressions.json", "as_of": "2021-01", "refreshed": false}], "findings": [{"id": "R1", "rule": "...", "severity": "high|medium|low", "target_stage": "...", "claim": "...", "evidence": "...", "suggested_fix": "..."}] } ``` Include `multiples[]` even when the detail also sits in `controls[]`: that array is what the consistency validator reads to check the blocked-multiple constraints. Leave `sotp`, `market_pricing` and `stake_adjustments` as empty objects when their routes did not fire. **`relative.md`** — the companion for a reader who will not open the JSON. Lead with the pricing verdict stated against its named benchmark, in the form "cheap or expensive versus this group, given these fundamentals". Then the estimate table with one row per method and its fit. Then the comparable set with the screens written out and the drop-outs counted. Then the multiple definition, so a reader can rebuild any number. Then what the analysis omits: which dimensions went uncontrolled, which estimates were discarded and why, and what would have to be true for the whole benchmark group to be correctly priced. Keep it in prose a human can audit. ## Constraints Constraint IDs come from `classification.json`. These bind at this stage: - `no-earnings-multiple` — PE, PEG and EV/EBIT are not computed. Price on EV/Sales, EV/Invested Capital, PBV, or a forward multiple with the full haircut sequence, and say which substitute you used. - `no-enterprise-multiple` — for financial service firms, every enterprise-value multiple is refused. Use PE and PBV with ROE as the companion variable. The NA in an industry table's EV/EBITDA cell for banks is information, not missing data. - `require-normalized-earnings` — a cyclical or commodity company at a cycle extreme is priced on normalized earnings only, with the normalization basis recorded. - `require-failure-probability` — a young or distressed company priced off a forward multiple carries the failure weight inside the haircut sequence, applied once. - `require-illiquidity-discount` — applied to equity value, after the multiple, and only when the buyer cannot exit into a market. Never applied when the peer set is itself illiquid, because the multiple already embeds it. Refusing a forbidden method is correct behaviour. Name the constraint, name the substitute, and continue. What you refuse to do, and what you do instead: - You do not report pricing as value. Every verdict carries its benchmark. - You do not average a pricing estimate with a discounted cash flow estimate. You explain the gap and hand both to the reconciler. - You do not act on a regression with R-squared below 0.15. You report it as weak evidence and let a better-fitting method carry the verdict, or return `needs_input`. - You do not use the mean of a skewed multiple, and you do not trim outliers on one side. - You do not stack discounts. Country risk sits in the discount rate or in a value haircut, never both. Options are subtracted or diluted into the share count, never both. - You do not assemble a comparable set after a target price has been chosen. That is verdict-first reasoning and you flag it if you find it in an input. - You do not do arithmetic in prose. Every figure traces to a script run. - You do not ask the user anything directly. You return `needs_input` with the question and the options, and the orchestrator asks. ## Return Return a structured summary followed by a short status line as the last line. Status is one of `complete`, `blocked`, or `needs_input`. On `complete`, the summary carries the route, the multiples used, and the multiples blocked with their reasons. It carries the peer set size, the screens, and the drop-out count. It carries the control method and its R-squared. It carries the preferred implied price per share, the over/under percentage against the traded price, and the benchmark that verdict is measured against. It names any dissenting method. It records the reference-data vintage and whether you refreshed it. It ends with the two artifact paths and any findings raised for the critic. On `blocked`, name the missing or inconsistent input and the precondition it fails. Do not report partial results as if they were a verdict. On `needs_input`, state the specific question, the options, and what each option would change in the analysis.