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Budgeting With An Actuarial Approach

Better Budgeting With An Actuarial Approach

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Q3 hedge fund letters, conference, scoops etc

This is a follow-up to my article of May 29, 2017 encouraging financial advisors to use the actuarial budget benchmark (ABB) to develop sustainable spending plans (SSPs) to better serve and retain your clients. This article will discuss:

  • “Real-world” situations frequently ignored by retirement researchers and other retirement experts when illustrating or testing proposed distribution/spending approaches;
  • Budgeting/planning limitations of sustainable withdrawal plans (SWPs) and Monte Carlo modeling currently in use; and
  • How the ABB can be used together with our recommended smoothing algorithm to help you develop more robust SSPs for your clients that will handle most situations. I call this SSP the “smoothed ABB.”

I also present an example to demonstrate how the smoothed ABB works over a 10-year projection period for a hypothetical couple. Finally, I encourage advisors to experiment with the smoothed ABB to see if you should add it to your consulting toolkit.


I discussed how the ABB is calculated in our May 29, 2017 article. Briefly, it involves an annual mark-to-market calculation (using assumptions inherent in inflation-adjusted annuity pricing) to solve for the current and present value of future spending budgets that balances the present value of clients’ assets with the present value of their spending liabilities. Refer to that article for additional details.

Unfortunately, good budgeting is not that simple

Developing a reasonable spending budget is not as simple as we are sometimes led to believe. Real-world complications frequently ignored (or assumed away) by retirement researchers and experts include:

  • Many individuals are part of a couple and plan as a couple;
  • The individuals in a couple may not be the same age and may not retire at the same time;
  • Income sources before retirement don’t always stop at the same time and income sources after retirement don’t always start (or stop) at the same time;
  • Couples generally don’t live the same period of time and income needs generally change upon the first death within the couple;
  • Future expenses are generally not the same each year. Some future expenses may be non-recurring, some may be recurring, some may increase at a rate higher than general inflation and some at a lower rate. This is a significant, frequently-ignored, issue that will be discussed in more detail below;
  • Some income sources in retirement may be paid in fixed dollars and some in inflation-adjusted dollars;
  • Individuals will generally not spend exactly their spending budget each year; and
  • Individuals experience one pattern of future investment returns, not an average of 10,000 patterns generated for Monte Carlo modeling based on historical returns.

Ignoring these complications can lead to ineffective client budgets and financial plans.

Budget/planning limitations of SWPs and Monte Carlo modeling

Because SWPs don’t coordinate with other sources of income and are simply drawdown algorithms, their use is frequently inconsistent with a couple’s (or single individual’s) retirement goals in these real-world situations. Since SSPs focus on spending, and not withdrawals from accumulated savings, they can be tailored to address these real-world situations to better meet retirement spending objectives. And while the SPP I discuss below is more complicated than most simple SWPs, you can use my spreadsheets to perform the annual present value calculations necessary to make it work and obtain a better result for your client.

Monte Carlo models frequently used by financial advisors are great tools for developing probabilities of certain outcomes, but these models are not necessarily effective in developing a current-year spending budget or for determining when that budget should be revised. A Monte Carlo model will typically inform a client that if she invests her assets in a certain way, she has a 90% probability of being able to spend $X per year for the rest of her life. The model generally doesn’t separate recurring and non-recurring spending and it doesn’t really give your client a plan of action if her assets decrease by 30% in one year (or increase by 30%). The model results imply that, irrespective of actual investment experience, your client should stay the course with respect to her investment strategy and keep spending $X per year come hell or high water (and not worry about the 10% failure probability). Thus, it is more of an “implied plan.” This is a potential shortcoming for many Monte Carlo models. Michael Kitces addressed this shortcoming in his post of December 7, 2015, Is Financial Planning Software Incapable of Formulating an Actual Financial Plan, when he wrote,

… virtually no financial plan today actually constitutes a real “plan” for anything. After all, the whole point of planning is to formulate the strategy of how to handle a range of possible future scenarios. If A happens, then we’ll do B. If C happens, we’ll do D instead. Yet financial plans today, and the financial planning software that supports the process, is incapable of illustrating such scenarios and the appropriate responses! Answering a simple planning question like “how much do the markets have to decline before I need to cut spending in retirement, and how much would I need to adjust my spending to get back on track” cannot be easily answered with any financial planning software available today!

Ten-Year Spending Projection for Bob and Sue

Budgeting With An Actuarial Approach

Read the full article here by Ken Steiner, Advisor Perspectives

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