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Monte Carlo analysis considers a wide variety of hypothetical investment “simulations.” These determine the likelihood that your recommended strategy will help you achieve your financial goal—for example, managing income so your assets last through retirement.

This probability measure is called a simulation success rate. If your strategy has a 70% simulation success rate, you met your financial goal in 700 out of 1,000 simulations. In other words, you had at least $1 left at the end of retirement (and perhaps much more).

While it would be impractical to show 1,000 different outcomes, this sample Monte Carlo analysis clearly illustrates the diversity of outcomes, even though the amount of the initial investment and investment mix are the same.

While a strategy’s simulation success rate can be a useful guide, it should not be considered a guarantee of future performance.

Your Retirement Strategy Under Different Market Scenarios

Chart showing simulated returns

The projections or other information generated regarding various investment strategies are hypothetical in nature, do not reflect actual investment results, and are not guarantees of future results.

The green lines represent simulations in which you had at least $1 left at the end of retirement while the red lines represent simulations in which you prematurely ran out of money.

Monte Carlo Simulation

Monte Carlo simulations model future uncertainty. In contrast to tools generating average outcomes, Monte Carlo analyses produce outcome ranges based on probability—thus incorporating future uncertainty.

Material Assumptions Include:
  • Underlying long-term expected annual returns for the asset classes are not based on historical returns, but estimates, which include reinvested dividends and capital gains. Expected returns–plus assumptions about asset class volatility and correlations with other classes–are used to generate random monthly returns for each class over specified time periods.
  • These monthly returns are then used to generate hundreds of scenarios, representing a spectrum of possible performance for the modeled asset classes. Success rates are based on these scenarios.
  • Taxes aren’t taken into account, nor are early withdrawal penalties. But fees—average expense ratios for typical actively managed funds within each asset class—are subtracted from the expected annual returns.
Material Limitations Include:
  • Extreme market movements may occur more often than in the model.
  • Some asset classes have relatively short histories. Expected results for each asset class may differ from our assumptions—with those for classes with limited histories potentially diverging more.
  • Market crises can cause asset classes to perform similarly, lowering the accuracy of projected portfolio volatility and returns. Correlation assumptions are less reliable for short periods.
  • The model assumes no month-to-month correlations among asset class returns. It does not reflect the average periods of “bull” and “bear” markets, which can be longer than those modeled.
  • Inflation is assumed to be constant, so variations are not reflected in our calculations.
  • The analysis does not use all asset classes. Other asset classes may be similar or superior to those used.
Model Portfolio Construction

Seven model portfolios were designed for effective diversification among asset classes. Diversification theoretically involves all asset classes: equities, bonds, real estate, foreign investments, commodities, precious metals, currencies, and others. Because investors are unlikely to own all these assets, we selected those most appropriate for long-term investors: stocks, bonds, and short-term bonds. We then chose seven sub-asset classes for the model portfolios: large-cap, small-cap, and international stocks and short-term, investment-grade, high-yield, and international bonds. We did not consider real estate because of its illiquidity and investors’ potential exposure from home ownership. We believe the selected fixed-income sub-asset classes fairly represent the domestic capital markets. Short-term investment-grade bonds were chosen for stability, eliminating a cash allocation because investors are best able to decide that according to their near-term needs. The portfolios were built using the complementary behavior of sub-asset classes over long periods of time, enabling more efficient investment mixes through low correlations.

The initial withdrawal amount is the percentage of the initial value of the investments withdrawn on the first day of the first year. In subsequent years, the amount withdrawn grows by a 3% annual rate of inflation. Success rates are based on simulating 1,000 market scenarios and various asset allocation strategies. The underlying long-term expected annual return assumptions (without fees) are 10% for stocks; 6.5% for intermediate-term, investment-grade bonds; and 4.75% for short-term bonds. Net-of-fee expected returns use these expense ratios: 1.211% for stocks; 0.726% for intermediate-term, investment-grade bonds; and 0.648% for short-term bonds.

IMPORTANT: The projections or other information generated by the T. Rowe Price Investment Analysis Tool regarding the likelihood of various investment outcomes are hypothetical in nature, do not reflect actual investment results, and are not guarantees of future results. The simulations are based on assumptions. There can be no assurance that the projected or simulated results will be achieved or sustained. The charts present only a range of possible outcomes. Actual results will vary with each use and over time, and such results may be better or worse than the simulated scenarios. Clients should be aware that the potential for loss (or gain) may be greater than demonstrated in the simulations.

The results are not predictions, but they should be viewed as reasonable estimates. Source: T. Rowe Price Associates, Inc.

Copyright 2014, T. Rowe Price Investment Services, Inc., Distributor. All rights reserved.