Step 9 - History

9.2

Definitions

The premier source of historical data used by the academic and corporate community comes from the Center for Research in Security Prices (CRSP). CRSP, which is housed at the University of Chicago Graduate School of Business was established in 1960 with the goal of building and maintaining historical databases for stock (NASDAQ, AMEX, NYSE), indexes, bond, and mutual fund securities. Part of the goal of the center was to unite the common interests between the academic and financial communities by providing a better understanding of the operations of the market. Since computer technology was in its infancy, no machine-readable, historical stock data files were in existence at the time CRSP was launched. Initially, CRSP was formed to accurately measure the returns from investing in common stocks listed on the New York Stock Exchange for the period 1926 to 1960. It took the researchers at CRSP four years to complete this initial study. Since its inception CRSP has developed a host of new data resources. The data housed at CRSP is used extensively for financial, economic, and accounting research. Currently, Eugene Fama, a well respected professor of finance at the Graduate School of Business at the University of Chicago and director of research at DFA, is the chairman of CRSP. DFA bases several of its investment products on Fama’s findings from that database.
The prestigious University of Chicago publication, The Journal of Business, caused the academic equivalent of an earthquake in an article published in January 1964 titled “Rates of Return on Investments in Common Stocks.” In the article, James Lorie and Lawrence Fisher, two business professors at the school, made the first comprehensive measurement of the performance of all common stocks listed on the New York Stock Exchange from 1926 through 1960. They obtained and compiled their data from CRSP.

The study presented a mind-boggling accumulation of statistical calculations. Both academic researchers and investment professionals were astonished at Fisher and Lorie’s discoveries. For instance, the study showed that an investor who invested $1,000 in the stock market in 1926, reinvested all dividends, paid no taxes, and remained fully invested until the end of 1960 would have accumulated nearly $30,000 or a gain of about 9% a year. In light of the fact that many investors in 1964 still had vivid memories of the Great Depression and its stock market crash, 9% a year was a great deal of money. In addition, this return was far greater than the amount an investor would have earned from bonds or savings bank deposits during that time period. For the first time, investors had comprehensive historical investment data that gave them a sense of how common stocks performed compared to other investments.

Roger G. Ibbotson and Rex A. Sinquefield, two graduates of the business school at the University of Chicago, released a study that was published in The Journal of Business titled “Stocks, Bonds, Bills and Inflation.” The two researchers were the first to compile and present in an organized way historical investment data that covered not only stocks, but bonds as well. They even reported data on inflation. As was the case with Lorie and Fisher’s study, their data went back to 1926, and was obtained from CRSP. The Ibbotson-Sinquefield data, now updated annually in what has come to be known as the “Stocks, Bonds, Bills and Inflation (SBBI) Yearbook,” is widely used in the investment world.

In 1990, G. William Schwert of the University of Rochester published an article in The Journal of Business titled “Indexes of U.S. Stock Prices from 1802 to 1987.” Schwert pointed out that the data compiled by CRSP launched an explosion of research in finance in the 1960s to 1970s. However, notes Schwert, a major drawback of the CRSP database is that it starts in 1926, a time right before the Great Depression. Consequently, the behavior of the stock market and stock returns was unusual in the 1929 to 1939 decade. Therefore, an empirical study based on the data could be “suspect.” So, Schwert set his sights on pre-CRSP stock return data. His article compares and contrasts all of the major indexes of stock prices or returns that were available monthly from 1802 to 1925 or daily from 1885 to 1962. The outcome of the comparison is a series of monthly stock portfolio returns from 1802 to 1925, and daily returns from 1885 to 1962. This important study included many refinements of the concept of “stitching” together several different index data series to obtain a longer term prospective.

 

9.2.2

Time Series Construction

A time series construction is the stitching together of indexes through history so that researchers and investors can better characterize the risk and return of their investments. See Figure 9-1 for an example of the time series construction of indexes. As seen in the graph, indexes are stitched together to increase the sample size of the data. All indexes have been taken back to 1927 through this process. A substitution process is used to extend current indexes back in time. This process is far from perfect, but provides the best information available for extending current indexes and mixes of those indexes back in time. Table 9-1 is the annual returns of these stitched together indexes with corresponding color buttons, and a total market index for comparisons. This is an interesting assembly of the per-year annual returns for each index going all the way back to 1927! These indexes are described in further detail in Appendix B.

Figure 9-1

Table 9-1

9.4

Solutions

9.4.1

Long-term History Characterizes Risk and Return

Looking over the long term, David Booth reviews the history of the stock market and highlights the importance of time, not timing, in the achieving long term investment success.

The history of several U.S. stock markets are captured in Figure 9-2. In essence this chart captures the effectiveness of capitalism over the last 81 years. The numbered events in Figure 9-2 are taken from the historical events in Table 9-2 below it, titled “Market Turmoil and the Dow Jones Industrial Average.” Despite several set backs, capitalism continues to work. Also note that the value of a dollar scale is a log scale, so each unit increases by a factor of 10. These are indexes and therefore the growth of a dollar does not reflect any fees or transaction costs. This long-term history of quality data allows investors to create the best set of probabilistic estimates of future performances of these indexes.

Figure 9-2

Table 9-2

IFA Index Portfolios have also shown tremendous long-term despite the impact of short-term bear markets. Click here to view a pdf of the growth of a dollar invested for the last 69 years in Index Portfolios 5, 50 and 100 as well as for the S&P 500.   

(click here to view the pdf)

9.4.1a

Effects of Government Intervention on Equity Returns

History shows that after nearly every major economic downturn, questions arise as to whether the free market system remains an appropriate way to organize and direct the nation’s resources.

Many individuals may be surprised to learn that government intervention can play a key role in free market systems. Milton Friedman, widely known as the most vocal proponent of the free market system cited that the true cause of the Great Depression was the US government’s failure to act swiftly to inject capital into the failing banking system.

"The Federal Reserve system stood idly by when it had the power and the duty and the responsibility to provide the cash that would have enabled the banks to meet the insistent demands of their depositors without closing their doors," Friedman stated in “Free to Choose 3: Anatomy of a Crisis.”

The chart below shows the relationship between equity returns and economic freedom rank. Economic freedom rankings data from Heritage Foundation awards their rankings in consideration of 10 specific elements.

As the chart shows, the US ranks very high in the area of economic freedom, while France came in significantly lower. It would be widely determined then, that the equity returns of a more Socialist-leaning France would be lower than those of the US. The reality, however, is quite the opposite. The chart’s vertical axis measures the equity returns of the countries. It shows that higher returns over the 39-year period were not always delivered to the countries with the highest degrees of economic freedom. Notoriously socialist-leaning countries relative to the US include UK, Canada, Sweden, France, Norway, Belgium and Denmark. The 39-year annualized returns of each of these countries defy the presumption that increased returns come from increased economic freedom.


[ Click here to play ]

Figure 9-2A

The figure directly below depicts the annualized standard deviation, or the Risk, of each of the above countries, plotted against their annualized return, the Reward, over the last 39 years.

Figure 9-2B

The bar chart directly below depicts the 39-year returns shown in figure 9-2A..

Figure 9-2C

The bar chart below shows the 10-year returns for countries based on their economic freedom rankings, as well. As you can see, in both long-term and short-term data, economic freedom indicators dispute the commonly held belief that government intervention hampers returns.

Figure 9-2D

(Click to play the Weston Wellington's DFA video: "What Should Investor Do Now- Part 5: Is Nationalization a Threat to the Free Enterprise System?" )

While the data presented here may seem surprising, the explanation is very straightforward. Just as value investments demand a higher return relative to growth investments to compensate for the higher risk associated with them, so too should investments in countries with increased government intervention demand higher expected returns to compensate investors for the increased perceived risk of investing in them.

This research, once again points to the simple and profound truth that investment returns come from investment risk, proving once again that there is no free lunch — even for perceived free market economic systems. Ken French talks about this subject and more in this interview.

The global history of the size and value effect on stocks is made even more clear by reviewing Figure 9-3. Next, Table 9-4 provides a thorough analysis of many indexes over the 1927 to 2008 period. Both the chart and table indicate that over the 82-year period, small-value has outperformed the S&P 500 and large-cap growth. Also, it is clear that value has higher returns in international and emerging markets, even though available data only dates back to 1982 for international and 1989 for emerging markets.

Figure 9-3

Figure 9-4

Table 9-4

Table 9-3

To expand the range of asset classes to include art, farmland and gold, let’s take a look at Table 9-3.

It is interesting that over the 48-year period emerging market public equities outperformed venture capital, and at a lower risk level. In addition, the S&P 500 outperformed real estate by more than 50%, although the S&P 500 had about three times the risk. Figure 9-4 graphs the data from Table 9-3 on the Markowitz risk/return plot and adds in index portfolios 5, 50 and 100 for comparison. Note where venture capital and emerging markets sit on the plot. Gold and silver are also interesting, reinforcing the idea that they have lots of risk and returns pretty close to T-bills and bonds.

 

 

 

 

Table 9-5

Venture Economics, an information provider for equity professionals, compiled a 20-year data series of various types of private equity strategies for the period ending December 31, 2005. According to the survey, venture and private equity strategies generally performed well over the period. But, the premium relative to public securities appears rather small considering the higher risk, investment concentration, absence of liquidity, transparency and daily pricing. The results are shown in Table 9-5.

 

 

 

 

 

Table 9-5A

9.4.1b

Probability of Portfolio Recovery

Rare and severely punishing drops in the stock market can find investors wondering how long it might take for their portfolios to recover from a big loss.

The table below shows the percentage amount of loss for the S&P 500 Index as well as for IFA Index Portfolios 90, 70, 50, 30, and 10 during the 35-month time period from November 2007 through September 2010, as well as the percentage gain that is required to restore each portfolio to its end of October 2007 high.

The probabilities of achieving those post-drop recoveries are set forth in the line graph below the table which shows the probability of each portfolio recovering within a specified time period from 1-year through 20 years. The probability studies were created using 82+ years of historical rolling period returns data for each Index Portfolio and the S&P 500 Index. The y-axis in the line chart below expresses the probability that each portfolio’s recovery will occur in the number of years expressed along the x-axis. For example, the IFA Index Portfolio 70 has a 94% probability of a full recovery or better in less than 6 years from the first day of the end of the time period stated.  

Figure 9-4A

Figure 9-4B

Figure 9-4C

Figure 9-4D

Figure 9-4E

The 10 years ending in 2009 is often referred to as the "lost decade" due to the -9% to loss for a Simulated SP500 index fund investment over the period. However, with proper global and fixed income diversification and a small value tilt, the chart below shows how much better investors would have been. Please note that this gap is larger than what we have seen in other ten year periods.

Figure 9-4F

9.4.2

Cross Correlation among Indexes

In addition to the long-term risk and return of indexes, a third input used to create optimal portfolios is cross correlation. Cross correlation refers to the extent to which performances of different asset classes move in relation to each other. The lower the correlation among different indexes in a portfolio, the greater the diversification, which means lower volatility of returns.

If indexes are highly correlated, then their prices are responding to market news in the same direction at the same time. Market news that affects prices in all markets, include the overall strength of the U.S. economy, consumer confidence, the level of interest rates and expectations for inflation rates. A low correlation means that market prices of different indexes react in different directions to the same news. These indexes have market price movements that are not connected, showing a low similarity in movement to each other.

For example, stocks and fixed income historically have a low correlation. As seen in Figure 9-5, large company stocks and one-year fixed income have a very low correlation of 0.02, which means that there’s almost no correlation between the market price movements of these two asset classes.

The next best diversifier of risk is low positive correlation among asset classes in a portfolio. By designing the proper mix of low correlation index funds, it is possible to lower a portfolio’s risk and increase its risk-adjusted return at the same time. More historical data on the correlation among indexes found in the global financial markets appears in Figure 9-5.

Figure 9-5

Figure 9-6 (Chart Link)	The data in Figures 9-6, 9-7and 9-8 is attributable to the three risk factors documented by Eugene Fama, Kenneth French, and Jim Davis. These factors are used in a multiple regression analysis to risk adjust returns of other investments and to establish the cost of capital of firms that sell their equity. Remember that a firm’s cost of capital is equal to the investor’s expected return. The Fama/French data indicates that these three factors explain 95% of stock returns in diversified portfolios. In those calculations, average instead of annualized returns are used. The average annual returns of these risk factors are known as the risk premiums. A Comparative History of Several Indexes using Rolling Periods At times investors doubt whether the fundamentals of capitalism and the relationship between risk and return will hold up in the future. For example, the August 13, 1979 issue of BusinessWeek featured this question on the cover: “Are Equities Dead?” After 10 years of lousy performance, it really must have appeared that way. For the 11-year period of 1969 to 1979, the S&P 500 average annual compound return was only 4.5%. And, it was even worse, 3.2%, for the more than seven-year period of 1973 to 1979, just before the article. These kinds of returns made it seem as if stocks were no longer a viable investment. Thus, many investors decided to invest only in Treasury bills, which outperformed stocks for both periods, and avoid the risk of stocks. Of course, the concern that the fundamental relationship between risk and return wouldn’t hold up was as ridiculous then as it is now. An analysis of multiple year rolling periods offers an interesting way to sort out these kinds of concerns. For example, if you look at the flash chart below and select S&P vs Treasury Bills, you will see that we have 853 ten-year periods shifting one month at a time, over the 81 years from 1928 to 2008. Of those 853 periods the S&P 500 Index outperformed T-bills 85% of the time. In the 733 twenty-year periods it out performed T-bills 100% of the time. And in one-year periods, it outperformed only 67% of the time. This brings to mind Benjamin Graham's famous observation that, “In the short run, the market is a voting machine, but in the long run it is a weighing machine.”
Figure 9-7 (Chart Link) Figure 9-8 (Chart Link)
Figure 9-8B (Chart Link)
(also see this money chimp calculator )
In "The Little Book of Common Sense Investing", page 160, there is a mention of the difference between value stocks and growth stocks returns. Here is a table comparing these returns, growth of $1, and standard deviations of returns over the periods mentioned and also an 81 year period (all the data available for these dimensions). Click here for backtested data sources and disclosures. The links in the IFA Index column will take you to the IFA Risk Return Calculator, which was used to calculate the data. Table 9-A Table 9-C Table 9-B Table 9-D   The Flash chart below offer numerous comparisons of this kind of data and they are very helpful in understanding the comparisons of various indexes. This data shows that large value does not always outperform large growth stocks. In fact, the size and value risk factors come and go unpredictably. This is consistent with the Random Walk Theory of changes in stock prices. In addition, the cycle of good or bad returns for small company stocks compared to large company stocks can last for many years. Figure 9-9 A Comparative History of Market Cap Deciles Figure 9-10 clearly lays out the history of the size effect. The several charts breaks out a number of time periods in history to illustrate the diversifying power of small-cap stocks. This chart is created using CRSP market capitalization data broken down into one-tenth size buckets, referred to as deciles. All 10 deciles are then measured and charted in different time periods. It illustrates that especially in shorter periods, small company stocks don’t always outperform large company stocks, but as seen in the top left chart, over the whole time period of 1927 to 2006, there is a clear advantage to have some exposure to small companies. But, in shorter periods anything can happen. For example, during the five-year period of 2002 to 2006, small company stocks widely outperformed large company stocks, while during the seven-year period of 1984 to 1990, and six years from 1994 to 1999, large-cap stocks were the king of the hill. Figure 9-10 (Chart Link)   The Returns Matrix The use of a return matrix is yet another interesting way to look at long-term data. Figures 9-11 bring together annual and annualized returns covering every combination from 1974 to 2008 for an index portfolio 90 (see Appendix A). This big triangle identifies the years along all three borders. The intersection of any two years shows the annualized return over that period. The diagonal lines show one year returns on the first diagonal and rolling period returns can be found on each diagonal line below the first one. For example, the first gray diagonal shows five year rolling periods from 1974 to 2008. The very bottom left hand corner shows the annualized return over the entire 35 year period, which is 12.5% for index portfolio 90. Figure 9-11 (Chart Link) Click on the image to see the full matrix for all IFA Indexes, Portfolios and S&P500. *How to read the Annualized Returns Matrix: You can locate the annualized compounded rate of return for this simulated Index Portfolio for a designated time period by following these easy instructions: Locate the column for the beginning year of the period. Years are labeled at the top and the bottom of each column. Then, locate the ending year of the period on the left-most vertical column. The annualized return can be found where the first year's column intersects with the ending year's row. IFA advisory fees of 0.9% per year and DFA mutual fund expense ratios have been deducted from these results. The 10-Yr diagonal (highlighted, starting from far left column) represents the estimated average holding period for investors who score 90 on the Risk Capacity Survey at ifa.com. Sources, Updates, and Disclosures: ifabt.com.   9.5 Summary A good understanding of the long-term historical risk and return of various indexes enables an investor to know how to construct an efficient asset allocation according to risk capacity. Risk and return will work themselves out or revert to the mean over the long run. In the meantime, the best bet is to diversify among index funds that are structured for optimal exposure to risk factors that history has shown to be most rewarding.   9.6 Review Questions Please answer the following questions before moving on to the next Step: 1. Stock markets are best characterized when looking at: a. 1 year period b. 5 year periods c. 80 year periods d. 3 year periods     2. The long-term characteristics of indexes are important because: a. they better reflect the differences between capital and capitalization b. margin rules are the same throughout history c. favored industries change with time d. the law of large indexes is not applicable to market returns     3. Many high net worth investors try to get allotments of venture capital partnerships. According to Morgan Stanley, over a 48-year period venture capital had a 16% return and a 35.4 risk index. Emerging market equities over the same period had the following: a. 4.9% return, 26 risk b. 16% return, 29.6 risk c. 5.4% return, 6.2 risk d. 12.7% return, 8.2 risk     4. Many people look at 80-year risk and return data and say that it is not relevant to them because they don’t have 80 years to invest. This is faulty logic because: a. the basic concept of sampling error means short-term data is worse than long-term data b. three years of data contains a large sampling error c. one year of data has no predictive value on the following year's data d. five years of data have no predictive value on the following year's data e. all of the above     5. The index with the highest return since 1928 is: a. large growth index b. large value index c. small growth index d. small value index e. total market