Methodology

How each valuation model works — formulas, assumptions, and step-by-step process.

DCF DDM Monte Carlo Multiples Fixed Income Portfolio

DCF — Discounted Cash Flow

The DCF model estimates the intrinsic value of a company by projecting future Free Cash Flows (FCF) and discounting them to present value using the Weighted Average Cost of Capital (WACC).

Firm Value = Σ FCF(t) / (1 + WACC)^t + Terminal Value / (1 + WACC)^n
Equity Value = Firm Value - Divida Liquida
Preco Alvo = Equity Value / Numero de Acoes

Step by step:

  1. Historical financial data is loaded from CVM (BR) or EDGAR (US)
  2. The user configures projection parameters: revenue growth, margins, CAPEX, working capital
  3. Two-phase convergence: Phase 1 (explicit projection, 5-10Y) → Phase 2 (convergence to steady state)
  4. FCF is calculated for each year: NOPAT + D&A - CAPEX - Delta Working Capital
  5. Terminal Value is calculated via Gordon Growth: FCF(n+1) / (WACC - g)
  6. All cash flows are discounted to present value at the WACC rate
  7. Net debt is subtracted to arrive at Equity Value
  8. Target price = Equity Value / Number of shares
Use DCF Tool →

DDM — Dividend Discount Model

The DDM values a stock based on the present value of expected future dividends. Uses the Gordon Growth Model for stocks with stable dividend policies.

P = DPS(1) / (Ke - g)
Ke = Rf + Beta × ERP + CRP

Step by step:

  1. Historical DPS (Dividend Per Share) is loaded from financial data
  2. The user sets the expected dividend growth rate (g) and cost of equity (Ke)
  3. Multi-stage DDM: explicit dividend projections for N years, then perpetuity via Gordon Growth
  4. All dividends are discounted to present value at the Ke rate
  5. Target price = Sum of discounted dividends + PV of terminal value
Use DDM Tool →

Monte Carlo — Stochastic DCF

Monte Carlo simulation runs thousands of DCF valuations with randomized parameters, producing a probability distribution of target prices instead of a single point estimate.

Para cada iteracao i = 1..N:
  Parametros ~ Normal(media, desvio_padrao)
  Preco_i = DCF(parametros_i)
Resultado = Distribuicao({ Preco_1, ..., Preco_N })

Step by step:

  1. The user configures mean and standard deviation for each DCF parameter
  2. N iterations (default 1,000) are run, each with randomly sampled parameters
  3. Each iteration runs a full DCF forecast
  4. Results are aggregated: histogram, percentiles (P10, P25, P50, P75, P90), mean, std dev
  5. Probability of upside is calculated vs. current market price
Use Monte Carlo Tool →

Multiples Valuation

Relative valuation compares a stock's trading multiples (EV/EBITDA, P/E, P/BV) against its sector peers to identify under- or over-valued companies.

EV/EBITDA = (Market Cap + Divida Liquida) / EBITDA
P/E = Preco / Lucro por Acao
P/BV = Preco / Valor Patrimonial por Acao

Preco Alvo = Multiplo_medio_peers × Metrica_empresa

Step by step:

  1. A peer group is assembled from sector companies
  2. Multiples are calculated for each peer using latest financial data
  3. Sector averages and medians are computed (excluding outliers)
  4. Implied valuation: apply peer average multiple to the target company's metrics
  5. Upside/downside is calculated vs. current market price
Use Multiples Tool →

Fixed Income — Bond Calculator

Bond pricing and analytics: pricing from yield, YTM calculation, duration, convexity, DV01, and carry analysis for Brazilian government bonds (Tesouro Direto).

Preco = Σ CF(t) / (1 + y)^t
Duration = Σ [ t × PV(CF_t) ] / Preco
Convexidade = Σ [ t × (t+1) × PV(CF_t) ] / [ Preco × (1+y)^2 ]
DV01 = Duration × Preco × 0.0001

Step by step:

  1. Macro indicators are loaded: Selic, CDI, IPCA, IGP-M, real interest rate
  2. Yield curves (ETTJ PRE, DIC, DOC) are fetched from B3
  3. Active Tesouro Direto bonds are listed with current prices and yields
  4. Bond calculator: user selects a bond and yield → price, duration, convexity, DV01, carry
  5. Scenario analysis: impact of rate changes on bond price (using duration + convexity)
Use Fixed Income Tool →

Portfolio Analytics

Portfolio analysis combines position tracking with quantitative risk and return metrics: regression-based beta, Markowitz mean-variance optimization, time-weighted returns, and concentration analysis.

Beta = Cov(Rp, Rm) / Var(Rm)
Alpha = Rp - [Rf + Beta × (Rm - Rf)]
Sharpe = (Rp - Rf) / σp
TWR = Π(1 + Ri) - 1
HHI = Σ(wi²) × 10.000

Key analytics:

  1. Regression Beta: OLS regression of portfolio monthly returns vs benchmark (IBOV or S&P 500). Provides beta, Jensen's alpha, R², and t-statistics
  2. Markowitz Optimization: Mean-variance optimization to find the efficient frontier — maximum Sharpe ratio, minimum variance, and risk parity portfolios
  3. TWR / MWR: Time-Weighted Return (eliminates cash flow distortions, GIPS standard) alongside Money-Weighted Return (reflects actual investor timing)
  4. Concentration Risk: Herfindahl-Hirschman Index (HHI), effective number of positions, maximum position and sector weights with traffic-light warnings
  5. Benchmark Comparison: Portfolio value evolution normalized to 100, overlaid with IBOV, CDI, and S&P 500 benchmarks
  6. B3 Import: Direct XLSX import from B3 Area do Investidor — positions, trades, and dividends auto-detected and mapped to our universe
Use Portfolio Tool →