Dolla — Dollar Basis Analyzer

## What Monte Carlo simulates Analyzer's **Monte Carlo engine** runs many randomized market paths to estimate the probability your plan succeeds (meets spending goals without depleting assets). Each path applies your return assumptions, inflation, contributions, withdrawals, and tax-aware cash flows. ## Success probability **Success probability** is the share of simulated paths that complete the planning horizon without running out of investable assets. It is an educational metric—not a guarantee of future results. Values around 80–90% are commonly treated as comfortable; very high values may mean you could safely spend more. ## Reading the fan chart The **fan chart** shows the spread of simulated portfolio balances over time as percentile bands—the median path in the middle, with optimistic and pessimistic percentiles above and below. A wide fan means outcomes are highly uncertain; the lower band shows what a poor market sequence could look like. ## Sequence of returns in simulations The engine explicitly models **sequence of returns risk** by drawing different return sequences each trial. Early-retirement withdrawals combined with bad early returns reduce success rates even when average returns look acceptable. ## Income in simulations The **income panel** shows how detailed income lines—salary, Social Security, pensions—net against spending in each simulated year. Income that covers more of your expenses reduces portfolio withdrawals, which typically raises success probability. ## Taxes and scenarios in simulations Monte Carlo runs are **tax-aware**: withdrawals from tax-deferred accounts generate tax drag, and scenario forks (such as a Roth conversion plan) carry their tax treatment into the simulation. Comparing the same plan with and without a scenario shows the strategy's effect on success probability. ## Spending outcomes Beyond success probability, the panel shows a **lifetime spending distribution** (real P10 / median / P90 in today's dollars) and the **deepest real spending cut** across paths. Success probability only measures running out of money; these figures show how much you actually get to **spend**. Flexible withdrawal policies raise success by **trimming spending in poor markets**, which a single success percentage hides — a fixed-real plan shows a near-zero cut but carries its risk as depletion instead. ## Valuation mean-reverting returns By default the engine draws returns from a **fixed average** each year. The **Valuation mean-reverting returns** toggle (off by default) instead lets today's market **valuation (CAPE)** shape returns: an expensive market depresses **early-retirement** returns, which then **mean-revert** as valuation normalizes, and an early crash lets forward returns **recover**. This makes the simulation more **sequence-risk aware** and is what gives valuation-based strategies traction. The **valuation-adjusted** withdrawal policy always runs this regime (the toggle shows forced-on). With the toggle off, results match the standard fixed-mean simulation. ## Parameters you can adjust On the Monte Carlo panel you can tune path count, return volatility presets, the valuation return regime, and horizon alignment with your household plan. Results update when assumptions or portfolio balances change. ## Why results change between runs Each run draws fresh random paths, so success probability can shift slightly between runs—more paths give more stable results. Larger swings usually mean an input changed: portfolio balance, spending, horizon, or a scenario fork. ## SEPP and success probability A scenario **SEPP 72(t)** can **raise** success probability even when **ending wealth** falls. Success requires every year to clear spending and taxes without a **liquidity shortfall**; SEPP schedules penalty-free tax-deferred income before age 59½, which helps pessimistic return paths fund early retirement. See the SEPP help article for how this differs from maximizing terminal net worth.