The widespread acceptance of the 4 percent rule within the investment management industry is only overshadowed by the sheer number of its shortcomings. The 4 percent rule was introduced by Larry Bierwirth in 1994 (Investing for Retirement: Using the Past to Model the Future) and expanded on by William Bengen in the same year (Determining Withdrawal Rates Using Historical Data).

In the conclusion of his paper, Bengen states that a client of age 60-65 planning for a 30-year retirement should withdraw 4 percent of the initial portfolio value, adjusted for inflation, each year.  While many variations of the rule exist, the basic idea remains the same:

1) Make assumptions for security return distributions, inflation, life expectancy, risk tolerance, taxes, transaction costs, etc.
2) Determine the fixed withdrawal percentage that minimizes the probability that the portfolio will fail to fully fund spending over the retirement horizon.

Most criticisms of the 4 percent rule focus on the impact that assumption changes have on the optimal withdrawal percentage (i.e. a 15-year vs. 30-year retirement horizon, a conservative vs. aggressive asset allocation, etc.). It is not surprising that if we change basic assumptions relating to the investor or test the withdrawal strategy over a different time horizon, the optimal withdrawal rate (with the benefit of hindsight) will differ. In fact, Bengen acknowledges in his original paper that 4 percent is specific to a 30-year retirement horizon and a 50/50 stock/bond allocation. 

The problem with the 4 percent rule lies not in the specific assumptions used for a given investor or its specific implementation, but in its underlying theory.  Most important, the 4 percent rule fails because it attempts to fund a static spending rule (fixed withdrawals adjusted for inflation) with a dynamic funding source (financial returns). 

In most aspects of our life, we try to match static (dynamic) spending rules with static (dynamic) funding sources. If my house burns down, I will need to pay to have it rebuilt. This is an example of a dynamic spending rule since I cannot predict if and when my house will burn down. To deal with this risk, most people buy a home insurance policy. The home insurance policy is a dynamic funding source since it also only pays off when my house burns down. By matching the correct dynamic funding source to the dynamic spending need, I have largely mitigated my risk.

Nobel laureate William Sharpe discusses this problem in a critique of the 4 percent rule (The 4% Rule – At What Price?). Specifically, Sharpe argues that the 4 percent rule was developed with the objective of minimizing the probability of failing to fully fund retirement withdrawals and ignored other important metrics that should be used to evaluate a withdrawal strategy. 

The most important of these metrics is the cost of surplus. The cost of surplus measures the value of unused funds at the end of retirement after accounting for any inheritance objectives. This remaining portfolio balance is considered a cost because the excess funds could have been used to increase withdrawals during retirement. Investors can on average spend more during retirement with a lower probability of prematurely exhausting retirement savings by eschewing the 4 percent rule (or a similar static variant) for a dynamic withdrawal policy that is dependent on the realized market path. 

A simple example can illustrate both the importance of considering multiple metrics and the valued added by employing simple dynamic withdrawal policies. Consider a 65-year-old retiree with $1 million in the bank to fund retirement. The retiree would like to plan for a 30-year retirement and figures that he needs $40,000 for living expenses per year. The real interest rate is assumed to be 2 percent and the retiree’s financial advisor has invested in a portfolio with an expected return of 6 percent and volatility of 13 percent. 

We used Monte Carlo methods to simulate 100,000 potential market paths. Although we make some simplifying assumptions in the simulations (normally distributed returns, constant correlations between assets, etc.), the results are still useful in comparing various withdrawal strategies. Using these paths, we can compute the failure probability, cost of surplus and total risk-adjusted present value of withdrawals. 

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