The Interdependence of Planning and Investing

Expected returns are one of the most important variables in a financial plan. We can’t project the future value of anything without them.

Estimating Expected Returns

Estimating expected returns involves a few steps.

First, we figure out how tolerant you are of risk and volatility. There are plenty of ways to assess this, but the industry’s preferred method is the beloved risk tolerance questionnaire.^[i]

Second, we build your investment allocation based on that tolerance and your specific goals. For simplicity, let’s assume you have one long-term goal and are comfortable with a high degree of volatility.

Third, we allocate your investments accordingly. If you have a long-term goal, are comfortable with volatility, (and if I’m your advisor), your portfolio would likely be entirely in equities, specifically broad-market stock index funds.

All else equal, a greater equity allocation leads to higher expected returns (and volatility), and a lesser equity allocation leads to lower expected returns (and volatility). That relationship between returns and volatility must always exist. You can’t have one without the other.

For the purposes of this post, let’s say all your investments are allocated to the S&P 500 index.^[ii]

So what’s the expected return for the S&P 500? No one can predict the future, but there are three main ways to estimate it:

Historical: Use the long-term average return of the S&P 500. This is my favourite version.

Quantitative: Use a model. Statistician George Box said it best: “All models are wrong, but some are useful.” If you’re interested, models are built using tools like Mean-Variance Optimization (MVO), Reverse MVO, or Black-Litterman.

Fundamental: Estimate returns based on current valuations and risk premiums. If markets are expensive, expect a lower estimated return. If markets are cheap, expect a higher return.

Larger firms tend to combine all three methods to arrive at an expected return assumption. But often the expected return, regardless of the method used, ends up being close to the historical average.

Forecasting the Future

To recap: we have an investor who can handle volatility (or at least says so), allocates every penny of their long-term savings to the S&P 500 (not advisable because it’s not diversified enough), and wants to retire in thirty years.

With this information, we can forecast what an investor’s retirement savings could look like in 30 years. And just like calculating expected returns, there are three ways to generate a forecast:

Time Value of Money: A simple formula that compounds an initial amount at a constant expected rate of return. It gives a neat, linear projection but ignores the ups and downs that happen in real life. I do not suggest using this method because the ups and downs (best case versus worst case scenarios) are important.

Monte Carlo Simulation: This method runs thousands of possible market outcomes based on expected returns and expected volatility. It shows a range of possible futures instead of a single straight line. This used to be the preferred method by most financial planners.

Bootstrapping (or Block Bootstrapping): This method uses actual historical market data, re-sampled thousands of times, to create realistic sequences of returns. It keeps real-world market patterns and volatility intact. It’s now the preferred method and the method I tend to use.

Let’s say we use the bootstrapping method, and it tells us there is a moderate likelihood (not a strong likelihood) that the S&P 500 will provide the return required to retire successfully.

Adjusting the Plan

The investor wants certainty, though. A moderate likelihood of success isn’t good enough. What can we do? Again, three options:

Retire later or work part-time in retirement: This means later withdrawals or lower withdrawals, so the investments get more time to grow.

Increase your tolerance for risk and thus increase your expected return: This investor is entirely invested in equities. What else can we do without making expected volatility obscene?

Save more: A dollar invested today is worth more than a dollar invested tomorrow. This makes that future value higher.

That’s the substantially simplified process. It’s simple to model a plan, but it’s much harder to live through one.

When Reality Strikes

Three things make planning much harder than it looks:

The data. You need to be invested in things that have history. The more data we have, the better we can plan. Bootstrapping, Monte Carlo, expected returns all depend on historical data. Try running projections on a random mix of stocks or private holdings. You can’t because there’s no reliable data. Good planning needs good data, and good data comes from broad, diversified markets with long track records.

Life. Divorce, death, inheritance, job loss, job gain, kids, health, luck, the list goes on. Life hits everyone differently, and always unexpectedly. Markets fluctuate, people change, goals shift. The plan has to bend without breaking.

Behaviour. This one is the most important. Investment returns and investor returns aren’t the same thing. The market gives what it gives. Investors take what they can stomach. DALBAR’s research shows that the average investor underperforms the market, not because of fees or products, but because of behaviour. We chase what worked yesterday. We panic when things fall. We miss the best days.^[iii]

It’s an advisor’s job to keep you invested, hell or high water. If we don’t, your expected return calculation is useless. And if your expected return calculation is useless, then so is your financial plan.

That’s the interdependence of planning and investing. One gives direction. The other keeps you steady when everything else doesn’t.

It’s not the market that makes or breaks the plan. It’s you.