tcx-valuation

name	tcx-valuation
description	Review valuation after research evidence exists. Use for valuation method selection, assumptions, market-implied expectations, scenario ranges, sensitivity, and valuation risk.

Valuation Review

Use this skill after research evidence exists and the requested universe has a supportable valuation or scenario lens.

Universe method:

• Public equity: choose DCF, comps, reverse DCF, scenario, estimate revision, or event probability methods only when the evidence supports them.
• Treat a forward per-share DCF as decision-usable when current attributable evidence supports the cash-flow base, reinvestment/CAPEX, working-capital posture, net debt or cash, diluted shares, and the relevant forecast bridge. Prefer audited filings or issuer disclosures for historical accounting facts, but allow verified OpenBB/provider-normalized fundamentals, reputable consensus estimates, and credible secondary evidence when provider, period, units, adjustments, and material conflicts are checked. Missing source-of-record evidence lowers confidence or widens sensitivity unless the unresolved input drives the conclusion. When the overall foundation is materially insufficient, prefer a reverse DCF or market-implied expectation threshold, a clearly labeled scenario screen, or abstention. Do not publish a precise target merely because assumptions can be entered into a model.
• For each selected method, state why it fits the business, which driver it tests, and which sensitivity would break the conclusion.
• ETF/index: focus on exposure, constituent/benchmark, factor, flow, and valuation-through-holdings logic when data exists.
• Crypto, macro, FX, rates, commodities, options, and credit-sensitive workflows require instrument-specific methods; if the installed support cannot underwrite the method, produce a screen-grade valuation frame or support gap rather than a false precision model.
• Always state current price or market anchor source/as-of when the user asks for risk/reward, target, entry, or action.

Expected output:

• Universe and valuation method fit
• Valuation method used
• Key assumptions
• Market-implied expectation check
• Scenario range
• Sensitivity points
• Method-selection limits and key sensitivity table or notes
• Valuation risk
• What would change the valuation
• Source/as-of posture, unsupported assumptions, and model/readiness label

Decision quality fields when applicable:

• evidence_grade, source_freshness, source_quality
• scenario_cases, contrary_evidence, update_triggers
• invalidation_conditions, decision_readiness, confidence
• forecast_required, forecast_allowed, forecast_block_reason
• forecast_target, forecast_horizon, probability, probability_range
• base_rate, evidence_ids, resolution_source, review_date

Role-specific quality:

• Choose methods that fit the business and available evidence; do not force a framework.
• State why each method is appropriate or limited.
• Include at least downside/base/upside scenario logic when evidence allows.
• Identify scenario inputs, cost and capacity assumptions, and modeling choices explicitly in prose.
• Distinguish model output, derived calculation, consensus/provider data, user input, and PM judgment.
• Label reverse-DCF break-even assumptions and scenario screens explicitly; neither becomes audited intrinsic value without the missing foundation.
• Use not-decision-ready when a missing current price, base case, valid probability, source date, or instrument-specific assumption materially prevents decision support. Do not downgrade solely because adequate evidence is provider-derived or secondary.
• State parameter sensitivity and lower confidence when the valuation range depends on fragile inputs.
• Separate valuation output from portfolio or execution recommendation.
• State what evidence would most change the range.

Write outputs under trading/reports/valuation/.