Ted Aronson

"It takes between 20 and 800 years of monitoring performance to statistically prove that a money manager is skillful rather than lucky - which is a lot more than most people have in mind when they say 'long-term' [track record]."

— Ted Aronson, "Confessions of a Fund Pro", Money, pp. 73-75., February 1999

William Bernstein

"Those who are ignorant of investment history are bound to repeat it. Historical investment returns and risks of various asset classes should be studied. Investment results for an asset over a long enough period (greater than 20 years) are a good guide to the future returns and risks of that asset. Further, it should be possible to approximate the future long-term return and risk of a portfolio consisting of such assets."

— William Bernstein, 2001

James Davis

"While much has changed over the years, some things remain the same. There is still a strong relation between risk and expected return... Some things stand the test of time."

— James Davis, Digging the Panama Canal

Patrick Henry

"I know of no way of judging the future but by the past."

— Patrick Henry, Virginia Convention Speech, March 23, 1775


I think we can safely say that most investors don't make decisions based on the long-term history of the stock market. They generally look at the most recent 1, 3 and 5-year returns and assume that recent past performance will persist. Unfortunately, they don't understand that short-term returns are based on random news and that investment decisions based on 50 years of data are more likely to enhance wealth than decisions based on 5 years of data.

Historical stock market data provide investors with a powerful set of tools for constructing portfolios that can maximize expected returns at given levels of risk. By analyzing the historical returns for various asset classes, including stocks, bonds, private equity, real estate, and even precious metals, an investor can see the difference between compensated and uncompensated risk over time. Statisticians require data from periods of at least 30 years to minimize the sampling error of short-term data and to provide a more reliable estimate of expected returns. Very few managers are able to provide 30 years of data to their clients.

Historical data serves as a testament to the enduring nature of capitalism. By considering and understanding long-term data, investors can use long-term risk and return data for various indexes to construct an asset allocation based on history and the science of investing, not on speculation.


Investors Focus on Short-Term Data

The first problem investors face is that the long-term history of stock market returns is not provided to them. Secondly, investors are not aware that long-term data has more value to them than does short-term data. When presented with 85 years of data, many investors deem the data irrelevant, because they do not have 85 years to live. This perspective overlooks the value of a large sample size. Investors who make decisions based on short-term data often later regret it.

When describing the risk and return of an index, significant errors are likely to occur when using a subset of the available data. For example, in the 5-year period from 2008 to 2012, the S&P 500 Index had an annualized return of only 1.66%. Based on that low return, many investors would conclude that the S&P 500 was not a good investment. However, for the 20-year period ending 2012, the annualized return was 8.22%, comparable to the annualized return of 9.53% for the 85-year period ending 2012 and to the 50-year return of 9.80%. The S&P 500 consists of 500 of the most economically important U.S. companies, and it comprises between 70% and 80% of the total market capitalization of the U.S. equity market. Therefore, an S&P index fund is still an important building block for a diversified index portfolio. When gathering information to identify the risk and return characteristics of the many asset class indexes that belong in a diversified portfolio, the more quality long-term data you have, the more accurate your conclusions.


History Characterizes Risk and Return

The most complete historical database for stocks, bonds and mutual funds can be found at the Center for Research in Security Prices (CRSP) at the University of Chicago’s Booth School of Business. Figure 9-1 shows the annualized rates of return for 24 different indexes. This table provides an interesting review of various indexes over several different time periods. Note the pattern of higher annualized returns of small cap and value stocks over large cap and growth stocks over time.

Figure 9-1

The time series construction in Figure 9-2 enables index funds investors to make investment decisions based on a statistically substantial and significant 85-year time frame. This time series construction simulates a fund’s composition prior to its inception and allows for estimates of past performance data. The style purity of index funds investing allows for this exercise, providing an abundance of data. This time series construction carefully stitches together 85 years of risk and return data for the indexes referenced in this book, with the black-dotted outlined section representing the simulated indexes and the solid black lines representing live mutual fund data for investable asset class investments. While not a perfect representation, the data produced by the time series construction is a very useful tool. Statisticians who consider 30 years of risk and return data to be statistically significant would consider this collection of 85 years of data a feast!

Figure 9-2


The Resilience of Capitalism

Capitalism has proven to be resilient. Figure 9-3 shows the growth of a dollar in various indexes over the course of 86 years, marked with 15 major news events listed in Figure 9-4. While the major events had large short-term impacts on market prices, they proved to be largely inconsequential in the long term as the market marched ahead. Despite several setbacks, capitalism has not only persevered, but thrived. This long-term history of quality data is the most useful tool for investors to construct risk appropriate portfolios.

Figure 9-3

Figure 9-4


Monthly Rolling Periods

Despite the historic advance of equities and the proven resilience of capitalism, many investors still get nervous during extended or sharp down periods such as the one we endured in 2008. When market-moving news appears, many investors may question if the fundamental relationship between risk and return is still valid. However, when a larger data set is considered, the situation looks better for long-term investors.

Rolling period analysis enables investors to examine large sets of performance data by dividing returns into monthly rolling periods, instead of traditional calendar year periods with a January beginning and a December ending. This method provides Simulated Passive Investor Experiences (SPIEs) which begin at the 1st of each month throughout the designated period. Figure 9-5 shows 12 consecutive 12-year rolling periods beginning on January 1, 1959. Each rolling period can be thought of as an outcome representing the experience of a unique investor who started and ended on the dates specified in the period. Hence, the name Simulated Passive Investor Experiences.

Figure 9-5

The primary advantage of rolling periods is the large number of simulated investors who can be observed in a given time period. For example, in a 50-year period, there are 589 rolling 12-month periods as opposed to 50 consecutive, non-overlapping 12-month periods. One disadvantage of rolling periods is the heavier weight given to returns that occur in the middle of the period and the lighter weight given to returns that occur at the beginning and end of the period.

Figure 9-6 charts the comparison of the performance of various equity indexes for 3 different time periods through 2013 using this SPIE analysis. First click on your desired time period in the bottom left corner of the chart. Then click on any two buttons to compare performance between different asset classes. For example, click on the 86+ year time period. Then click on the LV (large value) and LG (large growth) buttons. (Note: you may need to click off a button in order to click onto another button). The chart then illustrates that over 1,021 12-month holding periods (monthly rolling time periods), a simulated passive investor in a large growth index beat a simulated passive investor in a large value index 44% of the time, causing investors to think it might be a toss-up between large growth and large value. Compounded by the financial media touting the benefits of large growth companies, investors tend to believe that large growth can perhaps be a good investment. But in 793 20-year monthly rolling periods, the large value index beat the large growth index 88% of the time. Over short periods, volatility and price swings confuse investors as to which indexes are better long-term investments, but the picture becomes much clearer when longer periods and more rolling periods are considered.

Figure 9-6

Figure 9-7 tracks large, small, value, blend, and growth indexes from around the world. For U.S. markets, more than 85 years of data are shown. For non-U.S. developed markets, 38 years of data is available, and there are 24 years of data for emerging markets. In each case, it is worthwhile to note the lackluster annualized returns of both large and growth indexes, relative to the strong annualized returns delivered by all of the indexes labeled small or value.

Figure 9-7


The Importance of History

Although all disclosure statements from investment advisory firms are required to state that “past performance does not guarantee future results,” studying the very long-term past can empower individuals to make better choices for the future. Market history demonstrates the enduring nature of capitalism and exposes the benefits of investing based on long-term risk and return data. The use of historical data enables investors to build an asset allocation that meets their own particular risk capacity and equips them with the knowledge to withstand short-term volatility. For these important reasons, investors would serve themselves well by relying on large sets of historical data when building an investment portfolio aimed at capturing higher expected returns.

step 9introductionhistoryshort-term returnslong-term returnssampling errorcapitalismshort-term datasample sizes&p 500center for research in security pricescrspuniversity of chicagotime series constructionstyle puritygrowth of a dollarlong-term historymonthly rolling periodssimulated passive investor experiencesspiesrolling periodsdisclosurepast performancehistorical datarisk capacityvolatility

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