The doomsday scenario is universally shared by retirees and pre-retirees alike: depleting all your financial resources during your lifetime.
While some concerns may stem from insufficient savings during working years, even investors who have saved diligently face risks outside of their control. Economic conditions, the stock market, current interest rates, and the global geopolitical climate all pose hazards and opportunities for investors, especially for retirees whose financially livelihood is largely dependent on their personal retirement savings.
Although there’s no such thing as a sure thing in the financial markets, it is possible to structure a retirement strategy where the risk of outliving your assets is substantially reduced.
As part of a comprehensive retirement plan, a Monte Carlo simulation can be used to “stress test” your investment portfolio and withdrawal strategy. Fluctuations in the stock market are widely accepted as part of the risk/reward framework, so a Monte Carlo analysis seeks to emulate this volatility and identify the possible impact to the portfolio and annual income needs of the retiree.
[See: 10 of the Best Stocks to Buy for 2019.]
Most online financial calculators assume a static return; the yearly compounded growth rate is equal to the average expected return, for example 6 percent. In the Monte Carlo, the simulation could assume a 7 percent return in the first year, an 8 percent loss in the second year and a 19 percent gain in the final year, still equal to an average return of 6 percent. The sequence in which these returns occur and the disparity from the mean in any given year can dramatically impact an investor’s retirement portfolio.
Here’s how it works:
After a baseline plan is developed to define various assumptions, such as average rate of return, annual inflation, desired annual withdrawals, life expectancy, assets at retirement, and so forth, a Monte Carlo simulation can be utilized to test the viability of the strategy with hundreds of trials to mimic different market conditions.
The specific asset allocation you aim to have at retirement is used to determine an expected average annual return and the variability of returns (the standard deviation) which will also go into the analysis. For example, investors with a 90 to 10 percent equity to bond mix can expect a higher average return and a wider range of returns than someone with a 60/40 split.
Using historical data and/or a normalized distribution of returns, for each trial, the simulation projects an ending value of the portfolio under varying annual returns. For example, if an individual aims to retire at age 65 with a life expectancy of age 90, and annual income needs of $60,000, one trial of the Monte Carlo simulation would take the beginning portfolio value through 25 years of annual withdrawals and diverse investment returns, to determine how much money (if any) would be left in their portfolio at death.
[See: 8 Simple Rules for Investing in Retirement.]
Hundreds of trials are run, each with unique return assumptions every year. At the end, the results will indicate the percentage of trials where the portfolio survived all withdrawals (i.e. the retiree did not run out of money during their life) and provide details on different portfolio balances at death, from the worst-case 10th percentile to the best-case 90 percent percentile.
For a more complete analysis, simulations can also include the impact of taxes, inflation, longevity, fluctuating or adaptive withdrawals, and sequence risk. Sequence risk of returns describes how the timing of poor investment returns impacts a portfolio. For retirees making sizable withdrawals from their accounts, even a smaller loss at the beginning of retirement can be more damaging than a larger loss down the road.
For example, take a retiree who has a portfolio of $2 million and needs $90,000 per year for living expenses. If the account suffers an 8 percent loss in year 1, the remaining assets after 25 years would be less than if a 14 percent loss was incurred in year 10, assuming a 5 percent return every other year. Losses in year 1 would be even more damaging if living expenses were $100,000 annually, but if reduced to $70,000, the retiree would be better off.
The intricate nature of retirement planning requires considerable attention to detail as so many elements of the analysis are intertwined.
A Monte Carlo analysis is an extremely powerful tool, but it isn’t enough on its own. Individuals not working with an advisor will need to make sure they hold themselves accountable for spending choices and investment decisions during retirement.
As evidenced earlier, the timing of cash flows has an impact, so even one year of overspending could create issues down the line. Deviating from the asset allocation or investment strategy originally defined also plays a role in whether a retiree may run out of money during their life. One of the primary risks for investors managing their own accounts is reacting when the market is down and changing their asset allocation or going to cash.
Even if the Monte Carlo simulation doesn’t produce the desired results at first, further planning may produce other options for individuals to get closer to their goals.
[See: 8 Questions to Ask Your Financial Advisor During Volatile Markets.]
It isn’t always possible to have everything you want in retirement, so prioritize your needs and consider what trade-offs you may be willing to make, such as reducing lifestyle expenses now to save more for retirement or working longer.
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How Investors Can Have Enough Money in Retirement originally appeared on usnews.com
