I’ve been investing since about 1999, and I’ve seen all sorts of crazy market antics including:

• The late 1990s to 2000 – Everyone knew that raging tech stocks were in a bubble, but somehow, it still came as a shock when the bubble finally burst.
• 2000 to 2010 – Ten years of a sideways stock market with two huge stock crashes (the lost decade)
• 2008 – A Great Financial Crisis that surprised everyone¹.
• 2000 to 2018 – Nearly twenty years of mid and long-term Treasury Bonds outperforming stocks, which is not “supposed” to happen.
• 2010 to 2020 – Another raging stock market that kept (keeps)² lasting longer than almost anyone expected.

However, the last 42 months (from January 2018 through June 2021) have been some of the craziest that I’ve ever experienced as an investor.  But you won’t see many headlines calling out this period as particularly unusual, so let me explain.

What Was So Unusual About It?

Let’s take a chronological look at the investing insanity from the last 42 months.

Everything Down in 2018 – After 8 years of a historically relentless upward stock market, every major asset class lost money in 2018, as shown in this graph from the Visual Capitalist.

Even “safe” bonds had either negative or barely positive nominal returns.  And even though U.S. stock and Treasury bond returns have been negatively correlated for the last 20 years, that relationship waned in 2018.

At the time, it seemed like the meteoric bull market was finally leveling off.  Everyone could see the top of the hill and expected a long overdue downward leg in 2019.  The Fed even paused interest rate hikes for fear that they would stifle the flagging economy and markets.

Everything Up in 2019 – Of course 2019 delivered the exact opposite of what everyone expected with every major asset class posting robust positive nominal returns, as shown in this graph.

Large-cap U.S. stocks led the pack with a whopping 31% return, which puts 2019 way up in the 90th percentile of annual stock returns going back to 1928.

And various types of bonds also performed exceedingly well by historical standards with corporate bonds returning 13% nominal (89th percentile since 1928) and the 10-tear Treasury bond returning 10% (80th percentile).  And although stocks and Treasury bonds reverted to a fairly strong negative correlation based on monthly returns, 2019 was the second year in a row where every major asset went in the same direction.

Even gold, which often performs poorly when stocks and bonds are doing well, posted an 18% nominal return for 2019, which is in the 65th percentile of annual gold returns since 1972.

Pandemic Dip in 2020 – At this point, most investors seemed pretty sure that 2019 had been the last sprint of a flagging bull market.  After all, we were now officially into the longest-running rising stock market in U.S. history.  How much longer could this last?

But when the crash finally did come, the reason was a total surprise.  The crash had nothing to do with the most popular worries about overpriced stocks, an aging bull market, or a cyclical turn in the economy.  As shown in this graph, the stock market crashed by -35% in 2020, but it rebounded so quickly that stocks managed a handsome 18% nominal return for the calendar year.

I’ve never seen anything like that before.  It turned out that the “crash” of 2020 was really nothing more than a brief dip on the road to stock nirvana.

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Some of the most popular content on Mindfully Investing continues to be historical returns data for various asset classes.  In a continuing effort to expand the returns datasets for different asset classes at Mindfully Investing, today’s post explores the historical returns of global central government/treasury bonds.  (If you’re looking for corporate bond returns click here.)

Beyond The United States

Most American investors are familiar with U.S. Treasury bonds and bills, which are debt instruments issued by the U.S. government.  I’ve summarized historical returns for U.S. government bonds here.  Many other central governments around the world have also issued government bonds for more than a century.  This graph from Stevens Sweet shows the relative sizes of the world’s government (dark green) and corporate (light green) bond markets as of 2015.

While the U.S. is the biggest issuer of government bonds, it still represents less than a third of the value of all government bonds issued around the world.  So, if you want to diversify the government bonds in your investment portfolio, it’s reasonable to consider countries beyond the U.S.

And it’s easy to invest in government bonds from many different countries by using low-cost index funds that track indices for:

• Global government bonds
• Developed market government bonds¹
• Emerging market government bonds¹
• Short, intermediate, and long-term global government bonds
• Global inflation-protected government bonds.

Further, many of these funds come in both “hedged” and “unhedged” forms.  In its simplest form, hedged means that the investments are tracked in U.S. dollars to eliminate currency variations from the returns.  Unhedged means the local currency is used, so that total returns are a product of both bond returns and currency fluctuations relative to your home currency.

Bond Returns Around The Globe

Although there are many types of global government bond funds, they have only existed since the mid-1990s, and many of them also include U.S. bonds as a major constituent.  So, data from these funds don’t cast much light on the long-term history of bond returns outside the U.S.

However, Credit Suisse publishes an annual report that summarizes historical government bond annualized returns (Compound Annual Growth Rate; CAGR) going back to 1900 for several groupings of 23 countries as shown in this graph.²

In inflation-adjusted (real) terms, government bonds from various developed market countries have returned 1% to 2% annualized.  The track record for global bonds is very similar to the real returns for U.S. bonds (10-Year T-Bond), which have also been around 2% annualized since 1928.   However, emerging market bonds have produced negative real returns over a comparable period.

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In my last post, I introduced the Permanent Loss Index, which I proposed is a better measure of investment risk than the conventional measure of annual volatility (standard deviation).  Among other uses, the Permanent Loss Index can provide more nuanced insights into asset allocation when building investment portfolios.

In today’s post, I provide examples of asset allocation using the Permanent Loss Index as a primary risk measure.  And perhaps more importantly, I examine why permanent loss allocations may be more useful than the typical portfolio advice that’s based on annual volatility.

If you’re new to the topic of permanent loss versus routine annual volatility, I suggest you read the first part of my last post.  I won’t repeat those basic explanations here.

Refining the Permanent Loss Index

Before getting into the asset allocation examples, I should note that I decided to simplify the calculation of the Permanent Loss Index since my last post.  However, the concept behind the Permanent Loss Index remains the same, and the results I present today are pretty similar to the results in my last post.

Basic Risk Analysis – To understand the simplified Permanent Loss Index, let’s consider a risk analysis for the hypothetical town of Podunk, U.S.A.  Unfortunately, Podunk is located in an area where both tornados and severe earthquakes tend to occur.  To help prioritize disaster preparedness spending, the mayor of Podunk commissioned a risk analysis to estimate which of the two natural disasters pose the highest risk of fatalities.  The hypothetical risk analysis for Podunk is summarized in this table.

Natural Disaster	Probability of Occurrence in Any One Year	Typical Magnitude of Fatalities	Probable Risk of Fatalities (Probability x Magnitude)
Tornadoes	2%	100	2
Earthquakes (>Scale 5)¹	1%	1000	10

This procedure multiplies the probability of disaster occurrence by the magnitude of expected damage in terms of fatalities.  So, the mayor can now say that earthquakes are “riskier” than tornadoes, and Podunk should spend more disaster preparedness money on earthquakes.  Some people would argue that Podunk should spend exactly five times more money on earthquakes than tornados, but that’s a debate for another day.

Applying It To Investing – We can apply the same basic procedure to determine the risk of permanent loss when investing in stocks (S&P 500) versus bonds (U.S. 10-Year Treasury Bond) using actual historical return data from 1970 to 2000 as shown in this table.  All statistics are calculated on an inflation-adjusted (real) basis.

Asset	Probability of a Loss in Any One Year	Greatest Magnitude of Loss in Any One Year	Probable Risk of Permanent Loss (Probability x Magnitude)
Stocks	27.45%	-36.57%	-10.04%
Bonds	43.14%	-13.39%	-5.78%

So, we can say that in any given year stocks are “riskier” than bonds.  And this should be no surprise to most investors.  However, these statistics are all based on a 1-year timeframe, while the timeframe of the average investor is more like 3 years.  And many investors (like me) tend to invest for 10 years or longer.

Based on the same return data, this table provides inflation-adjusted risk estimates for a wider range of investor timeframes.

Asset and Timeframe	Probability of a Loss	Greatest Magnitude of Loss	Probable Risk of Permanent Loss
Stocks: 1-Year	27.45%	-36.57%	-10.04%
Stocks: 5-Year	29.79%	-8.34%	-2.48%
Stocks 10-Year	14.29%	-3.80%	-0.54%
Bonds: 1-Year	43.14%	-13.39%	-5.78%
Bonds: 5-Year	25.53%	-8.01%	-2.04%
Bonds: 10-Year	14.29%	-4.91%	-0.70%

If the last 50 years of data are predictive, stocks are only slightly riskier than bonds if you hold them for 5 years.  And stocks are actually a little less risky than bonds if you hang on to them for 10 years.  Given that bonds are commonly considered one of the “safest” assets, this result is a bit more surprising.

The Refinement – It seems to me that the probable risk estimates in the right-most column of these tables are the simplest and most intuitive way to define Permanent Loss Risk for investors with different timeframes.  But there are a couple of additional details I should note.

The first detail is that I’m using the worst-case loss from 1970 to 2020 for the magnitude estimate.  You could argue that I should instead be using an average loss or something else less pessimistic.  But I like using the worst-case because the next 50 years could periodically generate worse losses than the last 50 years.  For example, almost no investor (including me) that suffered through the long slide in stocks from 2000 through 2002 would ever have guessed that an even worse Great Financial Crisis lay just six years ahead.

The second detail is about how to present risk estimates for those who don’t read all the fine print in this post.  For example, looking at the 1-year timeframe for stocks in the above tables, a “-10.04% probable loss” kind of sounds like the worst result you could ever get is a 10% loss of your initial investment value, which would be a dangerous misunderstanding.  Consequently, I like to express these results as a Permanent Loss Index, where -10.04% is simply presented as a unitless index value of 10.04.  The higher the index value, the proportionally greater the relative risk.

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In January of 2019 and 2020, I published year-in-review posts on the returns performance of various asset classes.  But I have to say, I found those posts distinctly unsatisfying.  For example, in 2020, U.S. large-cap growth stocks were the best performer of any asset class with a remarkable total annual return of 40%.  The next best performers, were U.S. small-cap growth stocks (35%), mid-cap growth stocks (34%), U.S. micro-cap stocks (25.5%), gold (25%), and the total U.S. stock market (21%).

But is this information useful?  Because mindful investors are long-term investors, I certainly wouldn’t replace my moderately diversified all-stock portfolio with concentrated bets on U.S. growth stocks or gold because of last year’s results.  On the contrary, mindful investors know that the relative performance of asset classes will almost always ebb and flow over time.  And history has shown that chasing the performance of the recent “hot asset” is pretty much doomed to failure.

The Ranks Come to Attention

One of the most common ways to depict the ebb and flow of annual returns is with a so-called “periodic table” of assets.  The Callan Institute provides this example table that ranks the annual returns for several asset classes from 2001 through 2020.

The rank order of each asset class jumps around from year to year, which you can kind of see by scanning vertically for the colors assigned to each asset class.  If you instead step back and view the table as a whole, the erratic color patterns don’t convey much more meaning than your typical modern art painting.  These sorts of depictions support simple platitudes about the benefits of a diversified portfolio, such as “You never know which asset will perform best”.  But because mindful investors already know that diversification is no guarantee of anything, periodic tables of asset ranks offer few surprises.

Another Look at Asset Ranks

I figured there had to be a better way to find some signals buried in all the noise generated from the annual shuffling of ranked asset returns.  So, I took historical annual return data from all my usual sources¹ and ranked each asset’s return performance for every year going back to 1995.  Readily available data are missing for some important asset classes (particularly emerging market stocks) prior to 1995.  So, limiting my window to 1995 through 2020 gave me the most diverse set of 13 asset classes to compare.  I ranked nominal (not inflation-adjusted) total returns for the asset classes for each year.

Instead of a periodic table, I thought this graph shows the long-term changes in asset ranks much better.

In this type of graph, shorter bars indicate better returns because, in any given year, the top-ranked asset gets a value of 1 and the bottom-ranked asset gets a value of 13.  Higher ranks are shown by proportionally thinner blocks within each bar, and lower ranks are shown by proportionally thicker blocks.

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There’s something about Mindfully Investing that’s been bothering me for a long time.  I’ve often written about how a portfolio of 100% stocks is better than a portfolio with a mix of stocks and bonds, assuming that you’re mindful enough to handle the sometimes wild ups and downs involved with an all-stock portfolio.

Stocks make the most sense for long-term investors because historical data indicate you have an 85% chance of making money after 10 years of stock investing.  Selling stocks before that “time horizon” increases your chances of a permanent loss of your original investment.

That all sounds good, but many investment regulators, professionals, and bloggers point out that our investing time horizons may be shorter than we assume.  For example, most informed sources say you should have an emergency fund covering 3 to 6 months of living expenses.  But what if you lose your job and can’t quickly find a new one?  Once your emergency fund is drained, it won’t be very long before you start measuring your stock portfolio in terms of how many mortgage payments it could cover.

The Game of Life

The question of investing time horizons reminds me of the Game of Life.  I used to play this board game with my daughter when she was younger.

For those who aren’t familiar with the game, players take turns going down a tortuous path with the number of spaces moved determined randomly by a small Roulette wheel; sometimes you land on certain spaces and sometimes you pass them over.  Each space represents different life events like going to college, getting a job, getting paid, having children, buying a house, and even investing!  The most relevant spaces for today’s post are the ones with big financial setbacks, such as getting sued or losing a job.  The winner is the player who has the most money at the end of the path where players “retire”.  I guess the game creators decided it was a bit too morbid for the kiddies to drop dead at the end of the game.

Whether we’re talking about the Game of Life or real life, how do we mindfully invest for the long term when random financial setbacks can force us to divest at almost any time?  The conventional answer is to assume our time horizons are shorter than we might expect and allocate more of our portfolios to safer (less volatile) investments, the prime example being government bonds.

Bonds typically suffer fewer and less precipitous declines, than stocks.  For example, according to 92 years of Shiller and Damodaran annual return data, 10-year Treasury bonds lost value much less frequently than stocks, and the biggest single-year declines were:

• -11% for bonds.
• -44% for stocks

This implies that if you need the money back in an unexpectedly short timeframe, you’re much less likely to have lost money with bonds than with stocks.

It’s this Game of Life problem that has been bothering me about the mindful all-stock investing approach for several years now.  And today’s post is going to finally lay my concern to rest, one way or the other.

Risk Assessment

The problem of unexpected financial setbacks can be addressed with a tool known as “risk assessment”, which is a science that attempts to quantify (always with some uncertainty) the magnitude and likelihood of almost any negative outcome.  Fortunately, I’ve conducted quite a few risk assessments over my 30 years as an aquatic toxicologist, and I applied these methods to the Investing Game of Life.  A complete description of all my methods would be too detailed for a blog post.  So, if you’re interested in more information than I provide here, you can inquire via my contact page.

The primary question I wanted to answer was:

• Does a mixed 50% stock/50% bond portfolio provide a better outcome in the face of life’s unexpected financial setbacks as compared to a mindful portfolio of 100% stocks¹, assuming the investor has a goal to retire at age 65?

Typical Investor – To answer this question, I tried to imagine a typical investor who is reasonably savvy with her finances.  So, I won’t be addressing issues like low saving rates, flagrant spending, huge credit card debts, etc.  Specifically, I assumed that our typical investor:

1. Went to four years of undergraduate college
2. Graduated with the average U.S. student loan debt ($30,000), which she paid back before starting to invest
3. Got a job at age 23 with an average U.S. salary ($50,000)
4. Saved 10% of her gross salary each year for investing, which may seem ambitious but is consistent with standard recommendations
5. Received annual salary increases at the average historical rate of inflation of 3% per year
6. Bought an average-priced housed in the U.S. at age 35 (which inflation-adjusted to 13 years from now gives a price of $322,000)
7. Put an average downpayment of 12% on this first home
8. Was able to pay the 30-year mortgage without reducing her 10% saving rate.

Bad Events in the Game of Life – Now comes the fun part.  I gathered statistics on the probabilities and the dollar magnitudes of some common financial setbacks that are pretty unexpected.  I included the potential occurrence of the following “bad events”:

1. An under-insured home disaster (like a flood or fire)
2. Long-term medical disability costs and lost wages
3. Obtaining a 2-year post-graduate degree
4. Long-term unemployment (like being laid off in a global recession)
5. A legal, tax, and/or liability situation involving legal fees and a big check to settle the matter
6. Marrying into a large debt
7. One or more divorces (50% of all U.S. marriages end in divorce, but no one plans for it)
8. A small business or similar commercial bankruptcy (not personal bankruptcy given our investor is supposed to be savvy)
9. Paying for 4-year college educations for 1 or 2 children
10. Criminal fines or imprisonment including lost wages.

The dollar amounts associated with each bad event were inflation-adjusted to the future year when they randomly occurred.  Also, you might argue that some of these events seem more planned than unexpected.  However, I know quite a few people who never expected their careers to push them into more schooling or saved enough to fully cover the sky-rocketing costs of their kids’ higher education.

Putting It All Together – The final step in the process was to run randomized trials, where some (but rarely all) of these bad events could happen throughout a lifetime based on relatively realistic levels of probability.  The trial results were applied to the growth of both the 100% stock portfolio as well as the 50/50 portfolio.  I ran one hundred² such trials with the computer picking random pairs of stock and bond annual returns from the Shiller and Damodaran datasets.  That is, I used actual historical data to generate random and unique “future” sequences of stock and bond returns for each trial.

And The Winner Is…

This exercise produced mountains of data, but I’ll try to stay focused on our main question about which of the two portfolios produced a better outcome for retirement at age 65.  I defined “better outcome” as a larger final account value.  Even if the 50/50 portfolio had fewer ups and downs along the way, the final value seems most relevant to the goal of a comfortable retirement.

This graph shows the final value at age 65 for the all-stock portfolio minus the 50/50 portfolio with the 100 trials sorted in descending order.  So, positive columns mean the all-stock portfolio “won” the trial, and negative columns mean the 50/50 portfolio won.

I cut off the extremely high values for the first eight trials on the left side of the graph to show the rest of the results more clearly.  Moving our attention to the right side of the graph, we can see the 50/50 portfolio produced a better final value in only 11 trials, as indicated by the negative values.  It turns out that the all-stock portfolio provided a better outcome 89% of the time!

But some of the 100 trials had few if any bad events randomly occurring.  So, let’s take a closer look at the 25 trials with the highest magnitude of bad events (the worst quartile).  The average unexpected expenses for these 25 worst trials was over $660,000!  The total dollar amounts of the bad events for each trial are shown by the thin black line on this graph.

In every case, the all-stock portfolio final value (blue line) was similar or better than the 50/50 portfolio final value (orange line).  So, even during a very hard life, which is what we all worry about, an all-stock portfolio performed better.

It’s also noteworthy that $1 million in today’s dollars equates to more than $3 million in inflation-adjusted dollars at the assumed retirement date 43 years from now.  And both portfolios frequently failed to reach a final value north of $3 million in the face of substantial financial setbacks.  The all-stock portfolio produced final values greater than $3 million in only 21 of the 100 trials.  So, we can say the all-stock portfolio “wins the contest”, but in most cases, it was a feeble victory.

This may suggest that the probabilities I assigned to bad events happening might be unrealistically pessimistic in aggregate.  On the other hand, I realistically assumed that divorce, which is pretty common in the U.S., usually halves the existing account value of each person in the marriage.  Splitting assets can result in huge dollar losses, particularly if the divorce occurs later in life.

The results may also suggest that it’s important to maximize your income and savings rate as much as possible throughout life to increase your chances of retiring comfortably.  Either way, the all-stock portfolio provides a better chance of a larger retirement nest egg if you do happen to suffer an unexpectedly hard life.

Conclusions

So, what’s going on here?  The standard advice is to use bonds to protect against permanent losses.  But if we follow that advice throughout our lives this risk assessment shows it will mostly exacerbate our problems.

The apparent disconnect here is that most of the time when people talk about the risk of a “permanent loss”, they’re thinking about a one-time investment.  The most common example is an investor who sporadically scrapes together enough cash to buy 100 shares of fund X and then reverts to not saving and investing for a long period.  (A less common example is an investor who gets a windfall like an inheritance or winning the lottery.)  I’m not casting aspersions here.  Sometimes sporadic investing is all people can manage, and that understandably puts them in the mindset of tracking the gains/losses of each one-time investment.

But this whole exercise shows that it’s much more effective to save regularly and invest regularly.  The only exception would be when you’re in debt due to unexpected financial setbacks, in which case it’s often best to pay off the debts before starting to invest again.  This graph of account values over time for one trial provides a great example of what I mean.

Again, I cut off the vertical axis to show the results better, but the all-stock portfolio ended up at almost $4.5 million.  Despite three distinct financial setbacks totaling $670,000 of unexpected expenses, the all-stock portfolio manages to recover each time, while the 50/50 portfolio keeps getting knocked back down into debt.

If you tracked every annual investment, you’d undoubtedly be able to identify some instances of a permanent loss in both portfolios.  But the key point is that those permanent losses mean less with an all-stock portfolio and a consistent regular approach to saving and investing.

This is not to say that every trial looks this good for the all-stock portfolio or this bad for the 50/50 portfolio.  There were some pretty stellar returns for the stocks in this chart, but they all reflect actual annual returns from past years.  So, this sort of trajectory is certainly well within the realm of possibility.

My concern about Mindfully Investing has been laid to rest.  Clearly, a steady stream of savings that are steadily pumped into a mindful all-stock portfolio (or paying down unavoidable debts when necessary) is the best defense against the unexpected financial setbacks that are an integral part of the Game of Life.

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1 – Specifically, a mindful portfolio holds a moderately diversified set of low-cost stock index funds and no bonds (at least while bond yields hover around 5000-year historic lows).  The reason for this stock configuration is described more here and some example portfolios are provided here.  Also, I chose the 50/50 portfolio to represent a fairly aggressive application of the already conservative 60% stock/40% bond portfolio often recommended by financial advisers.  Stocks are represented in both portfolios by the S&P 500 returns history from Shiller, and bonds are represented by the 10-year U.S. Treasury bond returns history from Damodaran.

2 – It’s not unusual for thousands of trials to be conducted for this type of “Monte Carlo” simulation.  But in my experience, with these levels of event probabilities, I never learned much more from 1000, or even 10,000 trials, than was pretty apparent after just 100 trials.