Build a Monte Carlo Yield-on-Cost Calculator Inspired by 10,000-Simulation Sports Models

Hook: Stop guessing — model thousands of dividend outcomes the way elite sports models run 10,000 simulations

If you build a dividend portfolio and wonder whether your yield-on-cost will be 3%, 6%, or 12% in a decade, you’re not alone. The painful gap for many investors is not a lack of stock picks, but a lack of systems that translate uncertain dividend growth into probabilities. Inspired by sports models that run 10,000 simulations per game to identify edges, this guide shows you how to build a Monte Carlo yield-on-cost (YOC) calculator in a spreadsheet that runs thousands of dividend-growth scenarios, models DRIP and cuts, and produces actionable probabilities you can use to plan income, taxes, and retirement withdrawals.

Executive summary — what you’ll get

• A step-by-step spreadsheet blueprint (Google Sheets or Excel) to run 1,000–10,000 Monte Carlo simulations of dividend outcomes.
• Formulas and layout for modeling annual dividend growth, dividend cuts, DRIP, price paths (optional), and after-tax income.
• How to summarize outputs: percentile YOC, probability of hitting income thresholds, fan charts and histograms.
• Three example portfolios (Conservative, Dividend Growth, Opportunistic High Yield) with suggested growth/volatility inputs tuned for 2026 trends.
• Advanced notes on correlation, inflation-adjusted income, and performance tips for large simulation counts.

Why Monte Carlo for YOC — and why now (2026 context)

After a multi-year period of elevated interest rates and sector rotation that intensified dividend volatility (late 2024–2025), dividend outcomes are not as predictable as a single CAGR projection. In 2025 many sectors — energy, materials and some REITs — returned to stronger dividend growth, while others saw cuts or freeze decisions. In 2026, planning for a range of outcomes is essential:

• Distribution of outcomes matters: median, 10th and 90th percentiles tell you whether your plan is robust.
• Probability is actionable: you can hedge, choose higher-quality payers, or accept higher risk based on probability, not hope.
• DRIP and reinvestment change compounding: small differences in dividend growth volatility compound to large YOC differences over 10+ years.

How the sports-model analogy helps

Sports analytic models (e.g., those simulating 10,000 game outcomes) do three things well: define distributions for uncertain inputs, run many scenarios, and summarize probabilities. Apply the same framework to dividends:

1. Define input distributions (mean growth, volatility, cut probability).
2. Run many simulations with random draws for each year.
3. Aggregate to get percentiles and probabilities (e.g., chance YOC > 6% after 10 years).

Design overview — the model you’ll build

This calculator simulates, for each simulation run and each year:

• Annual dividend growth rate for each holding (drawn from a distribution).
• Dividend per share evolution and total dividends received.
• Optionally, share-price returns to model DRIP purchase prices.
• After-tax dividends and inflation-adjusted income (optional).
• Final yield-on-cost (YOC) and cumulative dividend totals.

Step 1 — Spreadsheet layout (simple, scalable)

Create a workbook with three sheets: Inputs, Simulations, and Analysis.

Inputs sheet

• Horizon (years) — e.g., 10 or 20.
• Simulations — e.g., 10,000.
• Portfolio table with columns: Ticker, Shares, Cost basis per share, Current dividend per share (D0), Starting yield (D0 / cost), Sector/class.
• Assumptions per ticker or per bucket: Mean annual dividend growth (mu), volatility (sigma), annual probability of a dividend cut (p_cut), expected cut severity given a cut (cut_pct).
• Global settings: tax rate on dividends, inflation rate, enable price simulation? (Y/N), price-return mu and sigma (if enabled).

Simulations sheet

Rows = simulations (1..N). Columns = years (0..Horizon) and computed metrics.

Column group per holding (or use aggregated portfolio approach):

• Year 0: set D0 and shares.
• Year 1..H: growth draw for year t: e.g., g_t = NORM.INV(RAND(), mu, sigma)
• If simulating cuts: draw uniform U(0,1) and if < p_cut then apply cut: g_t = -cut_pct OR set D_t = D_{t-1} * (1 - cut_pct).
• Dividend per share: D_t = D_{t-1} * (1 + g_t) with a floor at zero.
• Dividends received = D_t * shares (if DRIP, add shares purchased = dividends_received / simulated_price).
• YOC at year t = (D_t * 12) / (cost_basis_per_share) if annualized D reported monthly — more typically YOC = D_t_annual / cost_basis.

Analysis sheet

Aggregate across all simulations to compute:

• Mean, median, 10th, 25th, 75th, 90th percentiles for YOC at each year (use PERCENTILE.EXC or PERCENTILE.INC).
• Probability that YOC > threshold (COUNTIF / N).
• Expected cumulative dividends and after-tax expected income.
• Charts: fan chart (percentile bands), histogram for final-year YOC, time-series percentiles.

Step 2 — Concrete formulas and example cells (Google Sheets / Excel)

Below are practical formulas you can copy. Assume Inputs sheet has per-security mu in B2 and sigma in B3, and Simulation row starts in row 10.

• Random annual growth (cell C10 for year1):

  =NORM.INV(RAND(), Inputs!$B$2, Inputs!$B$3)

  Use =MAX(-0.9999, ...) to prevent more-than-100% negative draws.
• Apply cut (if you model cuts):

  =IF(RAND() < Inputs!$B$4, -Inputs!$B$5, NORM.INV(RAND(), Inputs!$B$2, Inputs!$B$3))

  Where B4 is p_cut and B5 is cut_pct.
• Dividend per share progression (D0 in B9):

  =B$9 * (1 + C10)

  For multi-year chain, use previous cell multiplication: D_t = D_{t-1} * (1 + g_t).
• Annual dividends received (if paid quarterly, multiply appropriately):

  =D_t * Shares

• DRIP shares purchased (if simulating price P_t):

  =Dividends_received / P_t

  Then cumulative shares = initial_shares + SUM(DRIP_shares_each_year).
• Yield-on-cost (annualized) at year t:

  = (D_t * 1) / Cost_basis_per_share

  If D_t is quarterly, multiply by 4. Express as percent.

Step 3 — Running 10,000 simulations efficiently

Filling 10,000 rows with RAND draws is straightforward but can be slow. Tips:

• Use Google Sheets for small portfolios < 1,000 sims. For 10k+, use Excel with 64-bit and plenty of RAM.
• Turn calculation to manual while filling formulas, then calculate once.
• Consider batching runs (e.g., 1,000 rows * 10 batches) and append results to avoid massive single-sheet recalculation.
• Use VBA or Google Apps Script to speed up: a short script can generate random draws and write values rather than volatile RAND formulas.

Step 4 — Summarize outcomes and answer the questions that matter

Ask and answer:

• What is the median YOC after 10 years?
• What is the 10th percentile YOC (stress-case)?
• What is the probability YOC > target (e.g., 6%)?
• How much of the portfolio income is at risk if a recession-driven cut occurs?

Use these functions:

• PERCENTILE.EXC(range, 0.5) — median
• COUNTIF(finalYOCRange, ">0.06") / simulations — probability above 6%
• AVERAGE, MEDIAN, STDEV to summarize distributions

Practical example: three model portfolios (inputs tuned for 2026)

Below are compact example inputs you can copy into Inputs sheet. These reflect market realities in early 2026: moderate dividend growth in quality names, higher volatility for cyclical and high-yield names, and a small but non-zero cut risk for some sectors.

1) Conservative Income (utilities & staples)

• Mean dividend growth (mu): 3.0% per year
• Volatility (sigma): 5.0%
• Annual cut probability: 2%
• Cut severity (if cut): 25%
• DRIP: off (many income investors take cash)

Outcome (illustrative): 10-year median YOC rising from 3.5% to ~4.7%; 90th percentile ~6.2%; probability YOC >6% ≈ 18%.

2) Dividend Growth Focus (quality dividend growers)

• Mean dividend growth: 6.0%
• Volatility: 7.5%
• Annual cut probability: 3%
• Cut severity: 30%
• DRIP: on

Outcome (illustrative): 10-year median YOC from 2.8% → ~5.15% with DRIP; probability YOC >6% ≈ 35%.

3) Opportunistic High Yield (REITs, MLP-like)

• Mean dividend growth: 2.5%
• Volatility: 20%
• Annual cut probability: 12%
• Cut severity: 50%
• DRIP: optional

Outcome (illustrative): fat-tailed distribution — median YOC may increase modestly, but downside risk is material: 10th percentile could be negative (dividend cuts), probability of sustaining yields >8% may be low. Monte Carlo reveals sizing rules: limit high-yield weight to manage downside probability.

Advanced: correlation, price simulation & inflation-adjusted income

If you want to model co-movement (e.g., energy names moving together), you can incorporate correlation. In spreadsheets:

• Create a correlation matrix for growth rates across holdings.
• Generate independent standard normals and apply a Cholesky decomposition matrix to induce correlation. That requires array math (MMULT) and a Cholesky function or pre-computed decomposition. For many users, sector-level aggregated simulations reduce the need for per-stock correlation.

Price simulation (optional) helps if you model DRIP purchases or want total return alongside income. Use a geometric Brownian motion step:

P_t+1 = P_t * EXP((mu_price - 0.5 * sigma_price^2) + sigma_price * NORM.S.INV(RAND()))

Inflation-adjusted (real) income: after you compute nominal dividends, divide by (1 + inflation)^t to express purchasing power.

Visuals and reporting — make the numbers tell a clear story

Create these charts:

• Fan chart: plot median and percentile bands over years (10th, 25th, 75th, 90th).
• Histogram of final-year YOC to show skew and tail risk.
• Probability table: rows = years, columns = probabilities (P(YOC > 4%), P(YOC > 6%), P(YOC > 8%)).

Interpretation: translate outputs into decisions

Use Monte Carlo outputs to:

• Size positions: cap weighting in high-cut-probability names to keep downside probability acceptable.
• Set income goals: if the probability of exceeding your income target is only 25%, raise savings or pick different assets.
• Tax planning: model after-tax dividends to set withdrawal schedules and Roth conversion timing.

Common pitfalls and how to avoid them

• Over-confidence in single-point forecasts: always show percentiles. A single CAGR hides skew and tail risk.
• Unrealistic volatility: calibrate mu and sigma to sector history (last 5–10 years) but adjust for 2025’s regime shifts.
• Ignoring cut probabilities: high yields often come with higher cut risk; model it explicitly.
• Performance issues: 10,000 sims are doable, but use scripts to speed up and avoid constant re-calculation.

Downloadable template & example portfolios

We created a ready-to-use template with the full Inputs / Simulations / Analysis structure, pre-filled example portfolios described above, and chart sheets you can copy. The template contains two modes: “Fast” (1,000 sims) and “Deep” (10,000 sims). Use Fast to iterate on assumptions and Deep for final probability statements.

How to use the template: copy Inputs → set your tickers, cost basis and initial dividend; choose the horizon and simulation count; choose whether to DRIP; run calculations and review the Analysis sheet’s probability table and fan chart’s probability table and fan chart.

Quick checklist to implement in a weekend

1. Copy the template and set Calculation mode to Manual.
2. Populate portfolio positions and cost basis.
3. Set mu and sigma per holding or use bucketed defaults (conservative / growth / high-yield).
4. Run 1,000 sims and sanity-check distributions; adjust mu/sigma if results look implausible.
5. Run 10,000 sims for final analysis; export percentiles and fan chart PNG for your plan.

Final considerations for 2026 and beyond

As we move deeper into 2026, expect a mix of opportunities: companies that paused raises during high-rate years could resume growth, while structurally challenged sectors will face renewed pressure. Monte Carlo models allow you to incorporate shifting expectations quickly — change mu and sigma and re-run thousands of scenarios in minutes to see how your plan reacts.

“Probability is the language of preparedness.” Use it to turn dividend wishlists into verifiable income plans.

Actionable takeaways

• Build or download a Monte Carlo YOC template: run at least 1,000 simulations to smooth randomness; 10,000 for robust percentiles.
• Model cut risk explicitly: include a cut probability and severity — it materially changes tail outcomes.
• Use DRIP simulation: reinvestment compounds dividend growth into share ownership and can boost YOC significantly.
• Translate outputs into rules: size high-yield positions based on downside probability, not yield alone.

Call to action

Ready to stop guessing and start planning with probabilities? Download our free Monte Carlo Yield-on-Cost spreadsheet template (Fast & Deep modes) and the three example portfolios tuned for 2026. Run your first 1,000 simulations today — then upgrade to 10,000 when you’re ready to formalize your income plan. Sign up for our newsletter to get template updates, new example portfolios, and short walkthrough videos that show the spreadsheet in action.

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