How ML Stats Calculates Portfolio Modeler And Retirement Planner

This page explains the core calculation flow used by both planning tools. Both tools run deterministic, month-by-month simulations based on your inputs and assumptions.

Portfolio Modeler: Calculation Flow

1. Build the portfolio from target weights and optional growth/yield overrides.
2. For each month, apply price growth to holdings.
3. Calculate monthly dividends and add them to cash.
4. Fund monthly income target from cash first.
5. If needed, sell holdings according to withdrawal strategy (lowest growth, highest growth, or equal).
6. Track monthly shortfall if assets cannot fully fund the target.
7. If DRIP is on, reinvest leftover cash by current weights.
8. If rebalance is scheduled, rebalance holdings back to target weights.

Retirement Planner: Calculation Flow

1. Build monthly expenses from base spending plus recurring and one-time events.
2. Apply inflation to base spending and inflation-enabled annual events.
3. Add fixed income sources (Social Security and pension streams).
4. Advance each investment account for growth and dividends.
5. Fill any remaining gap using withdrawal order rules.
6. Apply account-level DRIP and rebalance settings after withdrawals each month.
7. Record shortfalls, first shortfall timing, and ending net worth.

Retirement Planner Withdrawal Order

1. Cash accounts first (priority order, draw-enabled only).
2. Taxable Brokerage accounts.
3. Traditional retirement accounts: 401k and IRA.
4. Roth IRA accounts.

Inside each investment account, the model uses account cash first, then sells holdings based on that account's withdrawal strategy.

Retirement Age Gating And Penalties

• Retirement account withdrawals are gated by each account's configured withdrawal start age.
• The current model does not automatically apply a fixed 10% early withdrawal penalty.
• If you want to model a penalty effect, you can add equivalent expense events or increase spending assumptions.

Important Assumptions And Limitations

• Assumptions are deterministic. The tools do not model market randomness in these projections.
• Taxes are not fully modeled as a tax engine.
• Transaction costs, slippage, and advisor fees are not explicitly modeled.
• Results are informational and educational, not guarantees or personalized financial advice.

Related Methodology

• How ML Stats Calculates Portfolio Projections