Back in 1992, a shipping container hauling more than 28,000 various bath toys sunk in the Pacific Ocean. Fast-forward to 2007—little yellow ducks, blue turtles, and green frogs began washing up along coasts all over the world, some as far as the U.S. eastern seaboard and the British and Irish coasts. Because of this accident, oceanographers were able to improve their understanding of ocean currents. Now, in 2020, I don’t need to dump thousands of rubber duckies off the coast of New Jersey to find out where the ocean currents will take them. I can visit a variety of oceanography websites to get a good idea of how the ocean currents and trade winds will influence the direction of the bath toys. How do I know I can trust these websites to give me accurate information? They’re using years of historical data as well as real-time information about current conditions to chart ocean currents accurately.
Economic outlooks and forecasts provide an understanding of future market and economic conditions. Forecasting is a way of using historical data and experiences, as well as current conditions, to estimate what will happen in the future. For example, businesses can forecast the next quarter of business activity to determine budgetary needs, and economists can chart the next year or more of economic activity to predict the behavior of portfolio returns.
What if other bath toys—in a variety of shapes, sizes, weights, and materials—were stored inside the shipping container that sank in the Atlantic? The variations would add a few complex layers to the toys’ transatlantic adventure. Similarly, in the finance world, various securities behave individually during various economic cycles and events. Part of financial forecasting includes assessing securities, such as stocks, based on the issuing company’s assets, earnings, and liabilities. Through this valuation analysis, we attempt to find the “true” value of an investment, which can help us gain insight into the company’s value relative to other companies in a similar sector or field. Taking this a step further, we can use our understanding of many different securities, such as domestic and international stocks and bonds, to understand how they’re likely to behave together under certain market and economic conditions. For instance, if I toss thousands of assorted bath toys into the ocean off the New Jersey coast, it’s unlikely that every bath toy will follow the same path across the Atlantic. Some may meander slowly eastward, while some may move quickly. Still others may end up going another direction. But thanks to our knowledge of currents and winds, we can confidently predict the most probable landing spot for the majority of the bath toys over the long term.
Economic forecasting involves several layers of data and statistical analysis. Different individuals, companies, and organizations have different techniques for forecasting. We use the Vanguard Capital Markets Model® (VCMM), our financial simulation engine, to analyze historical relationships among certain data. These data drive asset returns, such as inflation, interest rates, and equity valuations. When you see an economic forecast from Vanguard, you’ll most likely see an estimate of the most probable scenario over a long period, such as 10 years. Using a longer time frame as well as an expected range of performance increases our chance of accuracy. An economic forecast—from Vanguard or another company—isn’t a crystal ball. Forecasts don’t aim to calculate the exact outcome of financial markets or economies; rather, they focus on the most probable scenarios over a set period and provide a range of outcomes for those scenarios. (This article contains more information about Vanguard’s approach to forecasting.)
According to Vanguard research, almost 90% of your investment portfolio’s performance—in other words, if (and how much) your portfolio gains or loses—is the result of your asset mix.* It’s important to know how forecasting models expect various asset classes to behave because they help us better understand how a balanced portfolio of stocks and bonds will perform under similar conditions. Past performance can’t predict future returns, but knowing what’s likely (versus unlikely) to happen can help you prepare for the future. Reading market and economic forecasts isn’t required for investing success, but choosing the right asset mix is. The “right” asset mix aligns you with your goals, risk tolerance, and time frame. Advised clients benefit from getting a custom asset allocation based on their specific goals, including retirement, buying a home, or paying for college. Individual investors often rely on online tools and resources to choose their asset allocations. If you’re feeling uneasy about how your portfolio is behaving, take our investor questionnaire and compare your results (and suggested target asset mix) with your current mix. You can also review Vanguard’s portfolio allocation models to learn how different asset allocations have performed historically.
Nobody has a crystal ball, and nobody knows precisely what the future holds. Anything is possible, but experience tells us what’s most probable and allows us to make more informed and appropriate decisions by weeding out less likely outcomes.
*Source: Vanguard, The Global Case for Strategic Asset Allocation (Daniel W. Wallick, et al., 2012).
The VCMM projections are based on a statistical analysis of historical data. Future returns may behave differently from the historical patterns captured in the VCMM. More important, the VCMM may be underestimating extreme negative scenarios unobserved in the historical period on which the model estimation is based.
The VCMM is a proprietary financial simulation tool developed and maintained by Vanguard’s primary investment research and advice teams. The model forecasts distributions of future returns for a wide array of broad asset classes. Those asset classes include U.S. and international equity markets, several maturities of the U.S. Treasury and corporate fixed income markets, international fixed income markets, U.S. money markets, commodities, and certain alternative investment strategies. The theoretical and empirical foundation for the VCMM is that the returns of various asset classes reflect the compensation investors require for bearing different types of systematic risk (beta). At the core of the model are estimates of the dynamic statistical relationship between risk factors and asset returns, obtained from statistical analysis based on available monthly financial and economic data from as early as 1960. Using a system of estimated equations, the model then applies a Monte Carlo simulation method to project the estimated interrelationships among risk factors and asset classes as well as uncertainty and randomness over time. The model generates a large set of simulated outcomes for each asset class over several time horizons. Forecasts are obtained by computing measures of central tendency in these simulations. Results produced by the tool will vary with each use and over time.
Please remember that all investments involve some risk. Be aware that fluctuations in the financial markets and other factors may cause declines in the value of your account. There is no guarantee that any particular asset allocation or mix of funds will meet your investment objectives or provide you with a given level of income.