How to Survive the Next 2008 By Ken Simon

How to Survive the Next 2008 By Ken Simon Copyright 2017 by XLWebData All Rights Reserved Article Highlights Strategies abound for how to protect against large market declines. Most strategies have a common goal diversify to provide ballast for your portfolio in a sea of falling stocks. Historical Correlation provides another alternative for achieving the common goal. Whether it s called a recession or a depression, one thing is certain about 2008 it will happen again. We don t know when or why it will occur, but it will. The question is not when the next crash will occur, how deep it will cut or how long it will last. The question is simply: Will you be ready for it? More to the point, what does it mean to be ready for a significant market decline and what should you do to prepare for it? Diversify To the surprise of no one, the key to surviving the next 2008 is to diversify your portfolio so that all your investments do not crash at the same time. This simple principle is well known. The implementation of it has many strategies. Some are simple. Others are complex. Here are a few: 60/40 Rule This is the simplest of all. Put 60% of your investments in the equity market and 40% in high grade bonds and treasuries. The problem with the 60/40 rule is that one size does not fit all. Young investors are better served by a larger equity position, while investors in their 80 s or 90 s are better served by a smaller equity position. 100 Minus Your Age Unlike the static 60/40 rule, this strategy reduces risk as you grow older by gradually reducing your exposure to the stock markets. Investing in equities with an allocation of 100 minus your age, you would have 70% in equities at age 30, 50% at age 50, and only 30% at age 70. Recently, this strategy has been challenged. Critics say that while it reduces the impact of market crashes as you age, it may increase the risk of outliving your savings. When people retired at 65 and lived another 12 years, they were unlikely to outlive their saving using this strategy. However, today many people will live well beyond that. Some will survive another 20 or even 30 years after retirement. This concern led to the next strategy. 120 Minus Your Age This is the same strategy, but boosts equity exposure to 50% at age 70 and so on. This reduces the risk of outliving your saving by increasing the risk of suffering significant losses during a major market decline. Target Date Funds In the early 1990 s, a more sophisticated strategy emerged, which addresses the concerns of the 120 Minus Your Age approach. The idea is to put your portfolio in a single Target Date Fund (TDF) and trust that its Glide Path will land you safely before you expire. The Glide Path is determined by the fund manager and is intended to reduce risk as you age, while capturing as much market upside as it can. The problem with this approach is that the intent of a fund manager is not always achieved and capturing market upside depends on the skill of the fund manager. They decide what to buy and sell and when to do it. This exposes TDF s to the same risk as every other strategy the next 2008! December, 2017 Page 1

Unfortunately, TDF s did not fare well in 2008, as shown in the table below: 1 Target Date Fidelity Vanguard T. Rowe Price 2010-25.3% -20.7% -30.8% 2015-27.2% -24.1% -34.4% 2020-32.1% -27.0% -37.4% 2025-33.7% -30.0% -39.8% 2030-36.9% -32.9% -41.4% Figure 1-Target Date Performance in 2008 In 2008, the 2030 TDF s lost between 33% and 41%. There is time for these funds to recover, before withdrawals begin. However, the 2010 TDF s lost between 21% and 31%. With only 2 years to recover, it is possible or even probable that these losses will not be recovered when withdrawals begin. Other Strategies Many other strategies strive to achieve a diversified portfolio. Some involve geographic area, such as domestic, North America, South America, Europe, Asia Pacific and Japan. Others focus on industry sector, such as Health Care, Financials, Information Technology, Telecommunication Services, Utilities, etc. Another division is by asset class, such as Bond, Large Cap Growth, Large Cap Value, Small Cap Growth, Small Cap Value, etc. Common Objective These strategies share a common objective: To reduce risk by diversifying across multiple investment types that complement rather than duplicate each other. These strategies try to balance the returns of funds so that they neither rise nor fall all at the same time. They strive to achieve the objective by embracing assumptions about market trends, such as: When stocks fall, bonds rise. When the U. S. economy flounders, foreign economies flourish. the board. For example, not all bonds rise when stocks fall. Bonds are far more influenced by interest rates and bond ratings. Also, when the domestic economy flounders, it is likely that markets in some country will flourish. However, if a global recession hits, how can anyone anticipate which countries may escape it? The problem with these strategies is that they depend heavily on the skill of the fund manager. Finding skilled fund managers is also a skill, and is one that most of us do not possess. There is another, more direct strategy that also shares the common objective. But before describing it, let s take a quick look at the impact of large market losses. Recovering from a Large Loss It may seem intuitive that if you lose 20%, then you will need to gain 20% to recover from your loss. However, intuition is not always accurate. The fact is that you need to gain 25% to recover from a 20% loss. This is easily seen by example: You invest $10,000 in a mutual fund that proceeds to lose 20%. You now have $8,000. To recover your investment, you need to gain $2,000, which is 25% of the $8,000 that remains after your loss. The return, R, needed to recover from a loss of L is: 1 R = ( 1 L ) 1 Using this formula, the following table shows the returns needed to recover from the indicated loss: Loss Return to Recover 10.00% 11.11% 20.00% 25.00% 30.00% 42.86% 40.00% 66.67% 50.00% 100.00% Figure 2- Return Needed to Recover from a Loss Of course, these are generalizations that are not always true. Even when they are true, they are not true across December, 2017 Page 2

Returning to the 21% - 31% losses of the 2010 TDF s in 2008, recovery will require a return of 27% - 45%. Now it seems even less likely that these losses would be recovered before withdrawals begin. The importance of avoiding large losses cannot be overemphasized for those who are retired or are about to retire. A New Strategy Historical Correlation As soon as you read historical, your mind whispers a fund's past performance is no guarantee of its future success. Of course, this is true, but only if taken literally. Guarantee is a very strong word. However, if a fund has a track record of consistent annual gains and few annual losses, it is more likely than not that it will have gains in the future. Basing future decisions on past events is something that we all do all the time. In 2008, not only did financial markets hit the skids, but so did the Detroit Lions. That was their 0-16 season. Going into that 16 th game, based on past performance, there was no guarantee that they would lose again. But, nearly everyone expected that they would. And they did! The past does not guarantee the future, but it certainly gives us a clue. Correlation is also a hot word, loaded with misconceptions. Now your mind whisperer says, Correlation does not imply causation. Again, this is a true statement. Causation requires an understanding of all variables involved and how they interact. Correlation is simply a mathematical relationship between two variables that have matching sets of values, such as Return on Investment over time. Two mutual funds are said to be highly correlated if they perform the same over time. Examples are the DOW and the S&P 500. At a given point in time, they may not move in the same direction. There are days when the DOW gains and the S&P 500 loses and vice versa. However, over long periods of time, they tend to rise and fall in unison. Mathematically, correlation is measured by the correlation coefficient, which indicates whether the objects being compared move in the same direction (coefficient of 1.0), in the opposite direction (correlation coefficient of -1.0) or somewhere in between. The Correlation Coefficient of the DOW and the S&P 500, based on Total Annual Returns from 2008 to 2016, is 0.9974. 2 That is a very high correlation. For long term investment returns, the DOW and the S&P 500 are essentially clones. As an example of funds with low correlation, consider: PRGTX - T. Rowe Price Glb Tech TLT - ishares 20+ Year Treasury Bond ETF The correlation coefficient between PRGTX and TLT from 2008 to 2016 is -0.8025. This means that these funds do not move synchronously, as shown by the Annual Total Returns in the table below: Year PRGTX TLT 2016 6.6 1.4 2015 21.0 (1.6) 2014 23.9 27.4 2013 39.9 (13.9) 2012 19.2 3.2 2011 (1.7) 33.6 2010 22.6 9.3 2009 80.2 (21.5) 2008 (44.1) 33.8 Figure 3 Total Return with Low Correlation The concept of the Historical Correlation Strategy is to develop a portfolio by pairing High Performance Funds with Low Correlation Funds. This approach is not dependent on rules of thumb or assumptions about market trends. Implementation While the concept of Historical Correlation is simple, its implementation is not. The issue becomes which High Performance and Low Correlation Funds to select from the thousands that are available. Implementing Historical Correlation is the objective of Fund Selection Assistant, a product that uses data provided by AAII for mutual funds and ETF s to create a portfolio based on user options. December, 2017 Page 3

Fund Selection Assistant 3 Data for FSA is imported from AAII s QMFU and ETF spreadsheets. The combination of this data produces a Funds sheet with approximately 3,600 entries. Before creating a portfolio with FSA, it s a good idea to use filtering criteria to eliminate funds that you do not want to consider. FSA allows filtering by any field. Example 4 Input Here is an example of creating a portfolio with FSA. It is based on QMFU data from 3Q2017 and ETF data dated June 30, 2017. After data was imported, it was filtered by these fields: Field Condition Value Bear (%) > (55) Assets (Mil) > 100 Expense Ratio < 1.2 Years with Returns > 9 Years Up % > 60 Figure 4 - FSA Filter Criteria After the filter was applied, the fund counts were: Total 3,578 Excluded 2,612 Remaining 966 Consider the portfolio below, which was created using the Fund Selection Assistant (FSA), based on the 966 remaining funds. Cash PSI EDV PRGTX TLT JAGTX WHOSX XSD BTTRX Cash PowerShares Dynamic Semiconductors ETF (PSI) Vanguard Extended Duration Trs ETF (EDV) T. Rowe Price Glb Tech (PRGTX) ishares 20+ Year Treasury Bond ETF (TLT) Janus Henderson Global Technology T (JAGTX) Wasatch-Hoisington US Tr (WHOSX) SPDR S&P Semiconductor ETF (XSD) AmCent Zero Cpn 2025 Inv (BTTRX) Figure 5 - FSA Generated Portfolio In Figure 5, funds with a Blue background were selected for High Performance, specifically for the best 3-Year Total Return after some funds were excluded by filtering. Those with a yellow background were selected for having a Low Correlation to the High-Performance fund that precedes it. Since there are multiple funds with low correlation, in each case, the one with the best performance along with low correlation was selected. The correlation matrix for these funds is shown in Figure 6. December, 2017 Page 4

Correlation Matrix The shading in each cell indicates the degree to which the funds are correlated. Funds with HIGH correlation are shaded ORANGE. Funds with LOW correlation are shaded YELLOW. Funds with VERY LOW or NEGATIVE correlation are shaded GREEN. PSI 1 PSI EDV PRGTX TLT JAGTX WHOSX XSD BTTRX EDV -0.64434 1 PRGTX 0.798117-0.80049 1 TLT -0.64944 0.998226-0.80247 1 JAGTX 0.855216-0.84647 0.960063-0.84407 1 WHOSX -0.63132 0.997011-0.76974 0.99796-0.82034 1 XSD 0.88926-0.77943 0.943851-0.78499 0.917872-0.75443 1 BTTRX -0.69612 0.962089-0.82418 0.972575-0.82999 0.965839-0.848 1 Figure 6- FSA Correlation Sheet Performance FSA shows a comparison between the Generated Portfolio and one of four benchmarks (DOW 30 Industrials, Russell 2000, Russel 3000 and S&P 500). For our example, the Performance Sheet is shown below. Notice the difference in 2008. In 2008, the Portfolio lost 3%, while the S&P 500 lost 37%. Despite lagging behind the S&P 500 in 2012 and 2013, the portfolio significantly outperformed the index from 2008 2016, owing to the avoidance of a large decline in 2008. Figure 7- FSA Performance Sheet December, 2017 Page 5

Allocation FSA shows the allocation of the Portfolio across different investment types. Based on the Allocation Sheet, you may want to adjust the Portfolio. For example, if you think that a Portfolio is weighted too heavily in Domestic Stock, either reduce weights for funds heavy in stock, increase weights for funds heavy in bonds, manually select additional funds or delete some that were generated. Foreign Bonds Other 0% 0% Cash 12% Domestic Stock 38% Domestic Bonds 44% Foreign Stock Preferred Conv Securities Stock 6% 0% Domestic Stock Foreign Stock Preferred Stock Conv Securities Domestic Bonds Foreign Bonds Other Cash Figure 8- FSA Allocation Sheet Notice the 12% Cash that appears in Figure 8. It s an important allocation for retirees. Since you ll be withdrawing 4-5% annually, a 12% Cash allocation provides enough income for 2-3 years, hopefully enough time to recover from any market downturn, even one as severe as 2008. Historical Correlation provides another alternative to consider for How to Survive the Next 2008. For more details about FSA or to download the free Demo version, visit the website: www.fundcorrelation.com/fsa 1 How Well Do Target-Date Funds Perform in a Downturn? Sound Mind Investing, Matt Bell, 07/29/15. https://soundmindinvesting.com/articles/view/how-well-do-target-date-funds-perform-in-a-downturn 2 Based on IVV (ishares Core S&P 500 ETF) and IYY (ishares Dow Jones U.S. ETF). 3 Copyright 2017 by XLWebData, All Rights Reserved. 4 The results shown here were produced by FSA Premium, which uses a proprietary algorithm to generate a portfolio, based on user specifications. Portfolio generation is not supported in FSA Basic. Similar results can be obtained using FSA Basic; however, the user must manually select funds to be included in the portfolio. December, 2017 Page 6