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
- Underlying long–term rates of return for the asset classes are not directly based on historical returns. Rather, they represent assumptions that take into account, among other things, historical returns. They also include our estimates for reinvested dividends and capital gains.
- These assumptions, as well as an assumed degree of fluctuation of returns around these long–term rates, are used to generate random monthly returns for each asset class over specified time periods.
- The monthly returns are then used to generate 1,000 different scenarios, representing a spectrum of possible return outcomes for the modeled asset classes. Analytical results are directly based on these scenarios.
- The analysis relies on return assumptions, combined with a return model that generates a wide range of possible return scenarios from these assumptions. Despite our best efforts, there is no certainty that the assumptions and the model will accurately predict asset class return ranges going forward. As a consequence, the results of the analysis should be viewed as approximations, and users should allow a margin for error and not place too much reliance on the apparent precision of the results. Users should also keep in mind that seemingly small changes in input parameters (the information the user provides to the tool, such as age or contribution amounts) may have a significant impact on results, and this (as well as the mere passage of time) may lead to considerable variation in results for repeat users.
- Extreme market movements may occur more often than in the model.
- Some asset classes have relatively short histories. Actual long–term results for each asset class going forward may differ from our assumptions—with those for asset classes with limited histories potentially diverging more.
- Market crises can cause asset classes to perform similarly, lowering the accuracy of our projected return assumptions and diminishing the benefits of diversification (that is, of using many different asset classes) in ways not captured by the analysis. As a result, returns actually experienced by the investor may be more volatile than those projected in our analysis.
- The model assumes no month–to–month correlations among asset class returns ("correlation" is a measure of the degree in which returns are related or dependent upon each other). It does not reflect the average duration of “bull” and “bear” markets, which can be longer than those in the modeled scenarios.
- 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.
- Taxes are not taken into account, nor are early withdrawal penalties.
- The analysis models assets classes, not investment products. As a result, the actual experience of an investor in a given investment product (e.g., a mutual fund) may differ from the range of projections generated by the simulation, even if the broad asset allocation of the investment product is similar to the one being modeled. Possible reasons for divergence include, but are not limited to, active management by the manager of the investment product or the costs, fees, and other expenses associated with the investment product. Active management for any particular investment product—the selection of a portfolio of individual securities that differs from the broad asset classes modeled in this analysis—can lead the investment product having higher or lower returns than the range of projections in this analysis.
- Results of the analysis are driven primarily by the assumed long–term, compound rates of return of each asset class in the scenarios.
- Investment expenses in the form of an expense ratio are subtracted from the return assumption of each asset class. These expenses represent what we believe to be a reasonable approximation of investing in these asset classes through a professionally managed mutual fund or other pooled investment product.
Our corresponding return assumptions, all presented in excess of 3% inflation, and expense assumptions are as follows:
|Sub–asset Class||Return Assumption in Excess of Inflation||Expense Ratio Assumption|
|High Yield Bonds||3.23||0.75|
- All securities are "bucketed" into the seven sub–asset classed listed above. The sub–asset class may or may not best represent each security. Any allocation that falls in the "specialty/sector" category is treated as mid–/small–cap stock for the purposes of the projection engine.
- Portfolio allocations remain the same in distribution and accumulation unless a preconstructed allocation based on retirement year or age is selected. In this case, the allocation shifts throughout the retirement horizon, which is illustrated in your Recommendation.
There are 10 model investment portfolios, designed by our investment professionals, for use in Advisory Planning Services. For purposes of the model portfolios, we selected seven sub–asset classes out of a universe of all investable asset classes. The following explains how we designed the model portfolios and selected the various asset and
The investment portfolios underlying the Recommendation were developed according to the principles of the Modern Portfolio Theory. Over the last few decades, this theory has proven to be useful by mathematically defining risk in order to achieve more effective diversification among different asset classes. The application of this theory to real–life investment problems is commonly known as asset allocation.
When we design portfolios, our goal is not to entirely remove risk but to limit the longer–term volatility that can result from a disproportionate concentration in one or two asset classes. The importance of the Modern Portfolio Theory has less to do with the performance of an individual asset class and more to do with how all the asset classes within a model portfolio interact to affect overall risk and return. Portfolios with limited volatility are more likely, over time, to produce an expected level of return for a given level of risk. Therein lies their efficiency.
In theory, an effectively diversified portfolio consists of all investable asset classes: equities, bonds, real estate, foreign investments, commodities, precious metals, currencies, and venture capital, among others. For Advisory Planning Services, since few investors can reasonably be expected to own such a comprehensive array of assets, we sought to take advantage of the Modern Portfolio Theory by limiting this investable universe.
In segmenting the investable universe, we first divided assets between foreign and domestic. Within the domestic component, we selected stocks, bonds, and short–term securities as potential investment options. We chose not to consider real estate because it is usually very illiquid and because many investors already have significant real estate exposure through homeownership. For other reasons, we also chose not to consider such asset classes as venture capital and commodities. Ultimately, we eliminated any explicit allocation to cash, believing that the individual investor is best positioned to determine his or her own allocation to this riskless asset based on personal, short–term requirements.
Among domestic investment opportunities, we segmented the equity market into large–cap and mid–/small–cap and the fixed income market into investment–grade, high yield, and short–term securities. We believe that these categories fairly represent the broad, domestic capital markets. For analytical purposes, we decided not to include municipal bonds because tax efficiency is not a primary goal for a typical Advisory Planning Services client who may have most of his or her retirement assets invested in tax–deferred vehicles. However, municipal bonds may be valid alternative choices when implementing a strategy based on account type and tax bracket. We selected short–term securities, which include lower volatility investments, such as money market securities and short–term, investment–grade bonds, to provide stability.
Among foreign investment opportunities, we segmented very simply into foreign stocks and foreign bonds. Given the relatively small percent of assets actually allocated internationally, we did not develop specific country–level allocations.
To summarize, the asset and sub–asset classes selected for our model portfolios are as follows:
|Asset Classes||Sub–asset Classes|
|Stocks||Large–Cap, Mid–/Small–Cap, International|
|Bonds||Investment–Grade, High Yield, International|
|Short–Term Securities||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.