Total portfolio allocation and withdrawal

Overview
This article describes the "total portfolio" approach to asset allocation and withdrawal.

The total portfolio approach means that the present value of future savings and retirement income, valued using the safe bond rate, is counted as bonds in the portfolio. A fixed asset allocation is maintained on this "total portfolio." Retirement withdrawals are calculated by amortizing the total portfolio over retirement years (amortization based withdrawal).

The advantage of the total portfolio approach is that total risk is kept consistent from year to year. This has two benefits:


 * 1) The more even spreading of risk across years reduces the total risk that the retiree would need to take to achieve a given expected return.
 * 2) It prevents surprises like risk increasing unexpectedly as the real value of a pension declines and the retiree relies more heavily on the savings portfolio.

No borrowing
While the theoretical model that motivates this strategy assumes borrowing, the strategy implemented here assumes no borrowing. If the theoretical model calls for borrowing to invest (i.e. asset allocation >100% stocks, likely in early career), TPAW uses an an asset allocation of 100% stocks instead. And if the theoretical model calls for borrowing to consume (a possibility in early retirement before pensions begin), TPAW only withdraws whatever is available in the savings portfolio.

Planner with Monte Carlo Simulation
This planner uses Monte Carlo simulations to show the impact of planning choices. The user adjusts their plan until they are satisfied with the simulation results.


 * For investors in the accumulation phase:
 * Download planner: Excel
 * How to add/delete rows to customize the planner.


 * For retirees:
 * Download planner: Excel
 * How to delete rows to to customize the planner

Simulator
While the Monte Carlo simulator in the Planner repeats the simulation 500 times by drawing randomly from the given sequence of returns, this simulator calculates withdrawals for only the one given sequence of returns. This is useful for backtesting.


 * For investors in the accumulation phase: Excel or Google Sheets
 * For retirees: Excel or Google Sheets

The following spreadsheet shows a way to adjust historical returns so that its expected return becomes equal to the expected return of the return distribution you want to use for the simulation:


 * Returns for simulation: Excel or Google Sheets

Theoretical model without borrowing constraints
This spreadsheet shows the theoretical model underlying TPAW. Unlike the simulators above, it assumes that you can borrow as much as you want when you need to. This makes the model simple and elegant. Calculations can be done with formulas and don't require simulations (e.g. what is the probability of withdrawals falling below $40,000 at age 70?). But it is not directly implementable in a world with borrowing constraints. So care should be taken when using the spreadsheet to understand if and when borrowing is being assumed and the consequences of not being able to borrow as assumed. The larger your savings portfolio is relative to future income, the less likely the model is to require borrowing. The model is most likely to call for borrowing (to invest) in early career when the savings portfolio is at its smallest and future savings is at its highest. For people with large pensions relative to savings, it might also call for borrowing (to consume) in early retirement before social security and pensions start. Users are advised to use the "Planner with Monte Carlo Simulation" above to ensure that their strategy will work well without borrowing. Those who don't want to explore the ideas behind TPAW in more detail and are looking for the quickest route to an implementable strategy that does not involve borrowing can ignore this spreadsheet and rely solely on the "Planner with Monte Carlo Simulation" above to devise their strategy.


 * For investors in the accumulation phase: Excel or Google Sheets
 * For retirees: Excel or Google Sheets