Assessing Your Future Needs
Here’s the worst-case scenario for most investors: Running out of money during retirement, or seeing investments depleted to the point that drastic lifestyle changes are required. Diligent savers may believe they are at little risk of asset depletion. But even a large nest egg can be affected by factors other than spending, including bad investment decisions, inflation, interest rates, sustained periods of market decline, and unforeseen major expenses (because life happens).
Foundations, endowments, and trusts with fiduciary responsibilities face similar concerns about how to protect and preserve assets for long-term income. These organizations seek ways to sustain spending over much longer periods than the typical retiree who is generally looking at a 20- to 30-year timeframe. We advise and create investment plans for many such organizations, but for this discussion we’ll focus on the individual investor.
The inherent challenge in planning for the future is uncertainty. Markets do not behave predictably, which is why Cardiff Park uses a variety of financial models to demonstrate best and worst-case scenarios. With accurate input, these models can offer guidance and side-by-side comparisons of outcomes based on different savings, spending and allocation scenarios. They also can provide guidance on a host of other factors that enter into individual financial planning.
How reliable are the modeling projections? For years, one of the most popular methods in financial planning has been the Monte Carlo simulation. Another method uses “bootstrapped” samples drawn from many different historical samples of returns. Far more conservative return estimates can be applied to both models as a portfolio “stress-test.” We’ll discuss these models in more detail in a moment. But to be clear, uncertainty can never completely be planned away. .
A Framework for Assessment
While investors have widely varying needs and goals, most share one overarching aim: build a portfolio that can be relied upon to support a set level of spending, adjusted for inflation, for a defined number of years. DFA calls this the future “consumption stream,” but you might call it “spending your assets.” The point is how to make the stream smooth, removing uncertainty to the extent possible. And that raises many questions. How much should you save? How much can you afford to spend later? How will inflation affect things? How aggressively do you need to invest to reach your goals? When can you retire? Should you work a few more years?
Savings, spending and asset allocation are three key factors that affect the probability of achieving long-term investment goals. These factors are not static and may change over the course of a lifetime due to changing circumstances. They are closely intertwined in portfolio design decisions and used in all models that project future outcomes.
Let’s take a closer look at three pieces of this puzzle.
Spending. When approaching retirement, some believe a safe strategy is to spend a pre-determined fraction of their savings each year, say 4%. This strategy may work well until the markets take a sharp dip and a 4% withdrawal rate means you end up with 20% less income than the previous year. A sustained downturn would make matters even worse. And over time, that 4% will decline in real dollars as investments are drawn down. Others opt to spend a fixed amount of real income each year after retirement, market performance notwithstanding. This is a riskier solution and puts one at risk of eventually running out of money.
Savings and Asset Allocation. Spending decisions (and future spending projections) cannot be made without knowing how much one has saved. And for most investors, savings have a great deal to do with how investments have been allocated before and after retirement. Some investors shy away from equity investing after retirement, for fear of losing their nest eggs. And yet this defies logic. We know that investing in equities pays a higher premium but comes with greater market risk. Fixed income investing lowers market risk, but increases the possibility that an investor may fall short of savings goals and eventually have to curtail spending.
You can read more about asset allocation in other parts of our website. For this discussion, suffice to say that the risk-reward tradeoff is present throughout life, but feels particularly acute when one’s savings years are over and the spending years begin. Sudden or prolonged market downturns, interest rate hikes and rising inflation early in retirement can put pressure on a portfolio and planned spending. Those who are heavily allocated in stocks with the intent of leaving an end-of-life bequest may leave behind a smaller bequest if markets turn down for a prolonged period. Of course, just the opposite could also be true if markets go up.
Risk management. There’s no way around risk. The necessity of taking greater risk to earn higher returns must be balanced by each investor’s personal risk tolerance and risk capacity. Risk tolerance measures emotional comfort with respect to financial risk, while risk capacity evaluates the capacity to shoulder financial setbacks. At Cardiff Park we thoroughly assess risk capacity by considering net worth, age, investment knowledge, taxes, time horizon, liquidity requirements, current and probable future cash flows, pension benefits, regular and retirement assets, insurance, retirement account contributions, and other relevant information.
When there is a mismatch between risk tolerance and goals—for example, an investor has risk-averse attitude paired with ambitious goals—we’ll find a trade-off by adjusting the mix of risk-taking, savings and portfolio goals.
There’s no golden answer to how to perfectly combine savings, spending and asset allocation decisions for a fail-proof outcome. Investors have different goals, resources, and timelines. Life throws curveballs. This is why it’s so important to build a robust, well-diversified strategy that maximizes savings in order to achieve a targeted level of future consumption. Here are a few proven tactics:
- Get into the habit of spending less and saving more. The earlier the better.
- Invest in equities as far as your risk tolerance allows. If you take little risk in investing, your ability to spend at a desired level during retirement will be diminished.
- Higher spending may be necessary for short periods, but may necessitate future lower spending. Be aware of the tradeoffs.
Is there a perfect planning model?
We promised a quick review of various planning models, provided below. I am always happy to discuss the pros and cons of each model with clients, and to run simulations using multiple models. These models offer competing ways to solve the same problem, but cannot capture the subjective information needed to tackle individual risk management issues and concerns. And to answer the question: no, there is no perfect planning model that can tell you exactly what the future holds.
Monte Carlo: This is one of the most well-known methods for simulating the future viability of various savings, spending and asset allocation strategies. Many variables can be entered into the simulation, including retirement age, retirement spending, downsizing, pension options, stock options, and other factors that affect future outcomes. With side-by-side comparisons, investors can see how changes in asset allocation and spending might affect these future outcomes. Monte Carlo analysis is popular because it is easy to run particularly if one is willing to assume that returns are normally distributed. But many people are uncomfortable with the assumption of normality because historical returns tend to exhibit a greater likelihood of extreme events (fat tails) similar to the financial crisis that can have devastating effects on portfolio returns. One alternative is to search for a distribution that better fits the historical returns. But says Jim Davis (DFA) there is a danger of over-fitting the data; the distribution that best fits returns in one sample may be a poor fit for another sample.
Bootstrapped samples: To avoid choosing a probability distribution, an investor can use bootstrapped samples. Following is an explanation of the bootstrap method by James Davis (Spending Rates, Asset Allocation and Probability of Failure, 2010). “Instead of drawing returns from a hypothetical probability distribution (the Monte Carlo technique), the bootstrap simply draws simulated return histories from the historical sample of returns. Monthly returns are drawn at random, with replacement, from the historical sample, and these returns are used to construct a (typically large) number of simulated return histories of the desired length. These bootstrapped return histories inherit many of the characteristics of the original sample, including any skewness and fat tails.”
Davis goes on to explain that bootstrap samples have their own complications. “One of the characteristics that the bootstrapped samples inherit is the sample mean. In other words, the bootstrapped samples treat the historical average returns of the various asset classes as their expected returns. This may not be entirely appropriate. For example, Fama and French (2002) provide evidence that the average US stock market return for 1950-2000 is probably higher than investors expected at the beginning of the period. This suggests that historical average returns can be biased estimates of expected returns. Even if the historical average is not biased, it still may not be appropriate to treat it as the expected return.”
Conservative estimates: Regardless of the simulation framework selected, the results are very sensitive to underlying assumptions about future expected returns. Small changes in the expected return or standard deviation can have dramatic effects. See Lee (2009) for a detailed examination of the importance of the various assumptions that go into Monte Carlo analysis. People want to know the worst-case scenario, and the likelihood of it happening. How bad can things get? How much will they have to cut spending? The historical average return is one estimate of future expected returns, but may be too high. As explained by Jim Davis at DFA, there is an approximately 5% probability that the true mean of the distribution is more than 1.645% standard errors below the sample mean. Therefore, its useful to stress-test a portfolio by conducting adjusted mean simulations to see if it can support a given spending rule.
A final word: At Cardiff Park we’ve experimented with each of these competing approaches. Despite their relative strength and weaknesses, we have found consistent results when using the same estimates of returns. It stands to reason that failure rates increase dramatically when using the most conservative returns. This should heighten investors’ appreciation of the importance of saving and appropriate allocation over a lifetime of investing. We are happy to stress-test any portfolio to support a given spending objective.