The period from 2000 to 2010 is often referred to as “the lost decade” for stocks in the U.S.  Here’s a chart of the nominal total return (including reinvested dividends) of $1,000 invested in the S&P 500 at the end of 1999.  The blue box shows the 10 years over which this broad market investment failed to make money.

I happened to start seriously investing in stocks at the beginning of 2000.  So, this graph approximates my experience.  I was starting to make some money around 2007 when it all collapsed again.  I had to wait another 3 years before I could safely say I had made any money on most of my original stock investments.¹

One way to illustrate the value of reinvesting dividends is to examine only the price changes of the S&P 500 over this same period as shown in this graph.

An investor who spent their dividends along the way had to wait an additional three years to make gains on stocks.

So, Stocks Are Risky?

These graphs may give you the impression that stock investing is a risky business that only sometimes works out.  However, longtime readers know that “the lost decade” was more the exception than the rule.  Long losing streaks for stocks are relatively uncommon historically, and that’s why this unusual period earned a name.

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I usually don’t post about current events because the daily news cycle has very little relevance to mindful investing, which is practiced and measured in terms of decades.  Events that appear ominous as they unfold almost always fade into insignificance in a few weeks or months as this chart from JP Morgan nicely illustrates.

With that sort of lead-in, you’re probably thinking this post should be about Russia’s ongoing invasion of Ukraine.  Indeed, a hot war in Europe involving nuclear saber-rattling is potentially far more consequential than any of the events in the above graph.  A possible exception is when COVID started and huge uncertainties raised the specter of a coming plague-like dystopia.

But I think it’s far too early to even attempt a rational assessment of how the war in Ukraine might impact our investing plans, although that doesn’t stop financial news pundits from breathlessly speculating as each new event unfolds.  If you want to know my thoughts about unlikely but catastrophic investing risks in general you can read my post on Investing Disasters and Deep Risk.  Almost everything I say in there applies pretty well to the worst-case scenarios for Ukraine and Europe right now.

Instead, I’m posting today about the ongoing efforts to pass a new law banning members of Congress from trading individual stocks.  Although the debate on Congressional stock trading has almost no bearing on your and my long-term investing plans, it nicely highlights some key concepts of mindful investing.

What’s The Issue?

Members of Congress are routinely inundated with potentially useful business information that’s not available to most investors.  And they routinely make decisions that directly impact myriad business and financial interests in the U.S. and abroad, whether that’s through sponsoring new legislation, setting committee-level priorities, voting to pass bills out of committee, or voting on new laws.

So, members of Congress face the inevitable temptation to misuse their legislative power and privileged information when trading stocks, which is one form of “insider trading“.  Although the Stop Trading on Congressional Knowledge (STOCK) Act of 2012 prohibits members from insider trading, it’s extremely difficult to prove a link between a specific trade and specific privileged information.  Although no official record of punishments under the STOCK Act exists¹, it’s been widely reported that no member has ever received any substantial punishment for Act violations beyond paltry late reporting fines of $200.

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Last year I wrote two intentionally alarming posts about the stock market.  First, I pointed out that the stock markets have been seething with optimism since recovering from the pandemic dip in March 2020.  And exuberant markets have often preceded past stock crashes.  Second, I pointed out that almost every stock valuation measure in existence indicates that stocks are crazy expensive.  Although these valuation measures can’t predict the next crash, expensive stocks have often led to poor returns for the next five to ten years.

I’ve written these pessimistic posts as a warning to anyone following a mindful investing approach, which involves holding a portfolio of mostly low-cost stock index funds.  Specifically, stock-heavy investing is easy when the markets skyrocket as they have over the last decade, but it’s very hard when stocks crash or go sideways for years on end.

I’ve written tomes about why mindfulness is the perfect mindset for the emotional roller coaster of stock investing.  But mindfulness is a tricky business.  Some days are more mindful than others.  Some meditation sessions are blissful, while others are frustrating.  It would be nice if we could all stick to our long-term investing plans just through the sheer will to be mindful.  But when mindfulness falters, other tools like rational thinking and empiricism, which also happen to be the foundations of mindfulness, can help too.

As of the date of this post, the S&P 500 is down about -10% since the start of 2022.  And in typical fashion, the finance media is buzzing with unhelpful warnings of “superbubbles” and an imminent crash.  So, in today’s post, I’m writing a more optimistic take on stocks using the tools of rational assessment and hard evidence.  And perhaps, focusing on the more optimistic evidence about stocks can reduce some worries that you and I can’t quite quell just through mindfulness alone.

Stock Valuations Don’t Have To Revert

Let’s start with one of the most popular stock valuation measures, the Cyclically Adjusted Price Earnings (CAPE) Ratio.  The CAPE divides the inflation-adjusted current price of the S&P 500 by the average of the last 10 years of inflation-adjusted earnings for those companies.  The higher the CAPE ratio, the more pricey stocks are relative to the earnings they generate.  Last year I emphasized the expensiveness of stocks by showing a CAPE graph similar to this one using data from Robert Shiller, the inventor of the CAPE.

The CAPE is currently at its second-highest reading ever, which seems scary.  But I’ve added a couple of dashed horizontal lines to this graph showing the average of CAPE values over the last 30 years (26.95) versus the average for the 121 years before that (14.63).  So, if stocks are expensive, they’ve been that way for the last 30 years with no sign of falling back to their long-term historical average.  For example, the graph shows that even the huge crash of 2008 just barely managed to sink the CAPE briefly below the prior long-term average of about 15.

Will the CAPE Ratio ever revert to its prior lower averages?  If the CAPE can levitate for 30 years, why can’t it stay aloft for 30 years more?  Beyond such wishful thinking, there’s plenty of valid economic and market evidence suggesting that reversion to the lower long-term CAPE average is not guaranteed.  I and others don’t necessarily agree with all of those arguments, but they can’t be simply dismissed as wild conjecture either.

Valuations Have Recently Declined

Another older method of stock valuation is the simple PE Ratio, which is the current price divided by the most recent earnings¹.  This graph shows the simple PE for the S&P 500 using the same Shiller data.  The graph also shows the inflation-adjusted averages of the simple PE for the last 30 years as well as the 121 years before that.

Like the CAPE Ratio, the simple PE Ratio has been consistently elevated for the last 30 years.

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Over the years, I’ve devoted many posts to the idea that stocks aren’t as “scary” as typically assumed.  And sometimes I’ve approached it from the flipside and argued that government bonds don’t entirely deserve their reputation as a “safe” asset.

So, I’m constantly on the lookout for new arguments or research that emphasizes the scary aspects of stock investing.  Most of what I find leans too heavily on rhetoric and perfunctory analysis to be worth discussing.  But occasionally, something more insightful shows up like the 2021 paper, Stocks for The Long Run? Evidence from A Broad Sample of Developed Markets by Aizhan Anarkulova, Scott Cederburg, and Michael S. O’Doherty (ACO Study for short).

The ACO Study makes three major conclusions, which I’ll paraphrase as follows:

1. The long-term outcomes from diversified stock investments are highly uncertain.
2. Catastrophic stock investment outcomes are common even with a 30-year horizon.
3. Stock market performance across dozens of developed markets is notably worse than historical U.S. experience.

The title of the paper seems a bit cheeky because the authors clearly think that stocks are pretty scary, even for the long run.

I conducted a similar but less robust analysis for developed market stocks using MSCI developed market data going back to 1970 earlier this year.  And I reached a nearly opposite conclusion that “the risks from global¹ stocks are pretty similar to the risks from U.S. stock investing”.  So, at first glance, I found the ACO Study as a bit of mystery, and I love a good mystery.

Examining the Evidence

A good way to start solving a mystery that comes from historical returns is to examine the data that are included and excluded from any given analysis.  Here’s a graph from the ACO Study showing the timespans of return data from each Developed Market country that they used.

It’s an impressively large data collection.  And the ACO Study used specific economic criteria to expand their dataset to include some countries that have only qualified for developed market status in the last 5 to 30 years (see the bottom of the graph).

The ACO Study randomly sampled from this entire dataset to generate statistics on long-term stock performance.  One of the most interesting statistics they developed was the chance of losing inflation-adjusted money over different investing timeframes.  This statistic is essentially identical to the “permanent loss” stats that I’ve generated in the past based on other datasets.

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