Goal Tracker for Schwab Intelligent Portfolios

Introduction

Schwab Intelligent Portfolios provides you with a convenient way to invest and save for an investment goal. The goal can be to accumulate savings, or to take distributions for an income need.

To reach a goal, a plan and clear, concrete, trackable steps help in almost any area of life. Investing is no exception. We believe that financial planning and goal setting are critically important. A good plan should include a clear goal, an understanding of the risks and benefits, and a look at "what if" scenarios. A careful asset allocation and automatic savings schedule puts the plan into action.

It's one thing to invest. It's another thing to invest with a goal in mind. Whether you are saving for retirement or an upcoming expense, the Goal Tracker helps you monitor your investment goal, which is set up based on your risk profile, investment amount, and your planned monthly contributions. If you are seeking to generate retirement income, the Goal Tracker also allows you to set up your target monthly withdrawal amount and will monitor whether your income stream will last as long as you need it to based on the information that you provide.

After you set up a goal, the Goal Tracker will help project hypothetical portfolio performance and will monitor whether your goal is "on target," "off target," or "at risk" so that you can track your progress over time and make any necessary adjustments. It is as simple as going to the Goal tab of your Schwab Intelligent Portfolios dashboard and with a few clicks, you can see how to get back on track.

Goal Tracker can help you anticipate how your account could perform, within a range of hypothetical outcomes, to help you work towards your goal. The rest of this whitepaper describes how Goal Tracker projects hypothetical portfolio performance and tracks your account in relation to your goal.

What is Goal Tracker?

Goal Tracker is a tab in your Schwab Intelligent Portfolios dashboard that helps you set up and track an investment goal for an account. It projects how your portfolio might perform using a range of outcomes to help you see how close you are to reaching your future goal.

Keep in mind, the hypothetical illustrations provide projections, not guarantees, but they can help you consider how your portfolio could perform, if things go as projected over time.

Goal Tracker saves your goal and monitors daily whether your portfolio is "on target," "at risk," or "off target." These graphics are dynamic, and you can make adjustments to variables, such as your contributions, and see how those changes impact your potential to reach your goal.

Goal Tracker methodology

One relatively straight-forward way to project performance of a portfolio is to estimate an average annual or monthly return for your portfolio, and then use that to calculate your returns, every month, for the rest of your stated investment horizon. This method is called straight-line appreciation.

However, returns are almost never average. Markets are inherently unpredictable and volatile, so we can't assume an average return every month or even every year. Instead, the Goal Tracker uses Monte Carlo simulation to provide probability analysis on the likelihood that your assets will reach your savings goal or that your income will last until a selected end date.

What is Monte Carlo simulation?

Goal Tracker uses a statistical simulation technique, used widely in finance, engineering and other fields, to simulate a range of paths for portfolio performance, based on long-term return estimates and annual volatility around those estimates.

For example, a hypothetical portfolio might be estimated to deliver an average 6% annual return per year over time. However, in any single year, it might deliver -10%, 15%, -3%, 8% or a wide range of other possible paths to get there. Each path may be different.

It'd be great if we could assume that a portfolio would receive the average estimated return every month, and every year, like clockwork. Unfortunately that's not the way markets work. In some months and years, market returns are up, and in some months and years, they are down. Over time, however, we anticipate that they'll be positive over time, if you have a long enough time horizon.

Not every portfolio will end up generating the average estimated annual return over the planning time horizon. Some portfolios may experience a "better" than average series of returns, and some portfolios may receive a "worse" than average series of returns. As noted above, this is part of the inherent uncertainty of investing.

Monte Carlo simulation is a bit like writing down on slips of paper various ranges of projected returns that we think you could receive in a month and putting them into a hat. For each month of your goal, we'd pull out one piece of paper and use that as the projected return for that month, based on a range of projected returns. We do this for every month until the end of your stated time horizon to calculate a hypothetical or projected ending balance. We repeat this series of draws 999 times, to give us a range of possible outcomes. This helps us simulate the uncertainty of markets and ranges of performance.

The exhibit below shows five such trials. Goal Tracker runs many more and then summarizes them in a simpler chart to stress test the likelihood of meeting your goal.

Exhibit 1: Monte Carlo simulates multiple scenarios

Long-term return estimates

To create the simulations described above, Goal Tracker uses long-term return estimates provided by Charles Schwab Investment Management Inc. (CSIM) to estimate the performance of your portfolio. In other words, Goal Tracker doesn't just choose a random range of numbers to guess on how your portfolio might perform. It bases the simulated returns on long-term return estimates, within a range of possible paths, to show projected performance.

To create these long-term return estimates, CSIM uses a set of factors including interest rates, earnings, dividends and others to estimate future returns and risk on a wide range of asset classes including equities, bonds, and commodities, etc. The long-term return estimates will vary based on your portfolio.

Keep in mind, these are estimates, and actual performance will vary and may be less predictable over short time horizons. The performance projections assume monthly rebalancing and reinvestment of interest and dividends. They do not include inflation, fees or taxes.

The performance and volatility of your portfolio and the ending balance will almost certainly vary, each year, and over time. Past performance or projections are not guarantees of future performance, but we believe that we can give you a range based on how we think the future will unfold.

Better, average, worse, and very poor market

Since no one knows how markets or portfolios will perform in the future with certainty, it helps to project a range of possible outcomes, using our best estimates of how your portfolio might perform over time.

Goal Tracker uses the simulations to chart a range of possible performance, from a "better," "average," "worse," and "very poor" market. This will help you track your goal over time and the range of possible outcomes. The ranges are described below.

• The "better" market shows your projected ending balance in the 75th percentile best outcome, based on the many simulations of your portfolio's performance from the Monte Carlo simulation. In other words, the 750th best out of 1,000 ending balances. You would only beat this in 25% of the simulations.
   
• The "average" market shows the ending balance in the 50th percentile best outcome in the simulations. In other words, the 500th best out of 1,000 ending results. This is the median outcome in the 50th percentile (or median) simulation.
   
• The "worse" market shows the ending balance in the 25th percentile best outcome. In other words, the 250th best of 1,000 ending results. You would achieve this in 75% of the simulations.
   
• The "very poor" market shows the ending balance in the 10th percentile best outcome. In other words, the 100th best of 1,000 ending results. You would achieve this in 90% of the simulations.