Investing Through A Recession

The Risk of Investing Through a Recession

Today’s Insight is a five-minute read. You can scroll right to the bottom to get the summary, but we may be stating the obvious. We encourage you to scroll through the full article before reading the summary, because some of the data is actually quite interesting!

The level of recession risk here in the United States remains rather muted today, but the global slowdown is starting to bleed into a number of US economic data points. The conversation may begin to shift from a potential slowdown in growth to the probability of a hard or soft landing.
At Tamco, we do not attempt to predict what will happen in the future. We see this as a fool’s errand. Monitoring the state of the economy however, should be relevant to anyone who owns financial assets.

Understanding the potential downside of investing through a recession is an important part of risk management.

 The common definition of a recession is two consecutive quarters of negative GDP growth, but the National Bureau of Economic Research defines recession as: “A significant decline in economic activity spread across the economy, lasting more than a few months, normally visible in real GDP, real income, employment, industrial production, and wholesale-retail sales.”

 Going through 90 years of data, we looked back at 14 recessionary periods here in America to determine the historical risk of investing in the stock market throughout past recessions.

We also looked at a couple additional factors to see if there were any relevant observations we could observe from the data.

To look at the impact of investing through a recession, we modeled the historical 1-year, 3-year and 5-year returns assuming that the initial investment was made at the start of each recession. We also looked at the Duration of each recession along with the Price to Earnings Ratio and the Risk-Free Rate at the beginning of each recession.

Source: NYU Stern, Telos Asset Management Company, Federal Reserve Bank of St. Louis,, Capital IQ

Being careful to not make any sweeping conclusions based on 14 data points, we do think a few observations are worth noting:

  1. When risk appetite was high at the beginning of a recession, the short-term downside was more severe.
  2. Longer recessions had more severe impact on short-term performance.
  3. Patient investors were rewarded for their patience.

We assessed risk appetite by looking at the Risk-Free Rate and the Price to Earnings Ratio that investors were willing to accept when they unknowingly put their capital to work at the start of past recessions.  

These two pieces of data theoretically work together by the fact that investors should demand a higher rate of return from risky assets when risk-free yields are higher (ie. investing in the S&P 500 at 20 times earnings would not be very attractive if the 10-Year Treasury bond was paying a 12% yield). This sounds great in theory, but it often breaks down in the real world. 

The reason we looked at the Risk-Free Rate separately from the Price to Earnings Ratio was to see if there was any evidence that higher rates equip the Fed to assist in a softer landing. 

We have all read commentary about today’s risks being higher because the Fed is out of tools to manufacture a soft landing.

We saw very little evidence that the risk-free rate at the beginning of the recession had any impact on short-term outcomes.

  • When the risk-free rate was above 5%, one-year returns were still negative in four out of six scenarios
  • When the risk-free rate was below 5% at the start of the recession there was a 50/50 chance of having a positive one-year return in the next year with the average return of -3.5%.
Our conclusion: the risk-free rate at the start of a recession had very little impact on the short-term outcome. 
Source: NYU Stern, Telos Asset Management Company, Federal Reserve Bank of St. Louis, Capital IQ

Additional observations:

  • There were times when the stock market was trading at attractive levels with high risk-free rates, as one would expect (1980, 1981).
  • And there were times when the stock market was trading at expensive levels in spite of higher risk-free rates (ie. 1969).
  • And there were times when bonds were apparently expensive and stocks were cheap (1948, 1953).
A fairly clear pattern with the Price to Earnings Ratio: 

Source: Telos Asset Management Company,, Federal Reserve Bank of St. Louis, Capital IQ

In 75% of the recessions that started with equities trading at high valuations (above 15 times earnings), the first-year returns were negative and the average of all 1-Year returns for the “expensive” cohort was -16%. However, when stocks were trading at less expensive levels (less than 15 times earnings), four out of six recessions had positive 1-Year returns with an average return of +15%.

We also noticed another pattern when we looked at the impact that the duration of the recession had on one-year returns:

Source: Telos Asset Management Company, Federal Reserve Bank of St. Louis, Capital IQ

Five out of five recessions had negative one-year returns when the recession lasted longer than one year, and the average one-year return for this cohort was -32%. In the cohort of short-duration recessions, 75% of the one-year returns were positive with an average return of 14%.
Our overall conclusion:
  1. Given the current price of today’s stock market (trading at 21 times earnings), the short-term risks are higher if we were to enter into a recession within the next twelve to eighteen months.
  2. The length and depth of the next recession are likely to impact the short-term returns to investors, and neither of these factors can be predicted with any level of accuracy.
  3. Investors who stay the course and remain disciplined through future recessions have a higher probability of achieving their long-term goals. The 6% return earned by investors who remained invested over the three to five-year periods was not outside the bounds of the long-term rate of return for equities.

Flip Flops Are Not Just For Politicians

Flip Flops Are Not Just For Politicians

Sometimes interest rates do them too!

If you happen to have read the Wall Street Journal the weekend of April 6th, you may have concluded that we are in the middle of a nice smooth rising market “with stocks climbing for the seventh trading session in a row, capping a quiet week of trading.”
This article was an interesting contrast to the alarm bells that the Journal was ringing just two weeks earlier. In a radically different tone: “fresh data suggesting the global slowdown…. U.S. manufacturing activity sliding to its lowest level in almost two years

…. the drumbeat of unsettling news Friday drove the yield on 10-year Treasury notes below that of three-month bills for the first time since 2007.”

 … an inverted yield curve has preceded every U.S. recession since 1975 and is viewed as a reliable predictor of downturns.”

So which one is it? Are we heading into a recession, with choppy volatile markets ahead? Or are we poised for continued growth with steadily rising markets ahead?

The inverted yield curve and why it matters:

The “yield curve” refers to the relationship between short term and long-term interest rates. Interest rate risk is the concept that investors lose money in a rising interest rate environment when they have their money locked up for longer periods of time. To compensate investors for taking this risk, longer maturity instruments generally earn higher yields than shorter maturity instruments, hence a normal yield curve has a positive slope to it.

An inverted yield curve happens when investors are no longer concerned about rising interest rates, but are more concerned about falling interest rates and falling asset prices.

Yield curve inversions oftentimes happen when the economic cycle shifts from growth to recession.


How accurate is an inverted yield curve at predicting recessions? After all – “an inverted yield curve has preceded every U.S. recession since 1975!”

We looked at the data from the last ten yield curve inversions here in the United States (excluding the most recent) to see if it was accurate in predicting future recessions. It turns out that there were three false positives and seven accurate predictions. 

Source: Capital IQ, Telos Asset Management Company

Of the ten times that the yield curve inverted over the last 55 years, seven of them were proceeded by a recession, and the average return of the S&P 500 from the one-year anniversary of inversion was 3.2%. The average three-year return from the time of inversion was 5.3% and the average five-year return was 7.3%.

Missing the Mark

Duke University Professor Campbell Harvey has done extensive research on this subject and his general conclusion is that the yield curve needs to remain inverted for three months to have any real predictive accuracy. In other words, a week or two of inversion is more likely random noise than a leading indicator.

Where the financial press may be missing the mark is in their extrapolation of ten points of data. To make sweeping conclusions on such a limited data set seems a little ridiculous.
With that being said, we are not implying that an inverted yield curve is something to be ignored. On the contrary, the relevance of an inverted yield curve is in what it tells us about investor sentiment:

Knowing that investors are willing to accept a lower yield to take on more interest rate risk tells us that we may be coming into an environment where investors demand a higher risk premium to put their capital to work.

An inverted yield curve is one of a number of data points that can be used to assess where we are in the economic cycle. It may or may not be relevant with regards to its accuracy in predicting a coming recession. The real relevancy of an inverted yield curve is in what it is telling us about investor sentiment.

The yield curve gets inverted when investors fear the future.

When investors fear the future, financial assets are more susceptible to short-term price depreciation, requiring disciplined investors to prepare for heightened levels of volatility.

These are also the environments that can create the setup for opportunistic investing – environments when the odds significantly improve for those who remain disciplined (How Great Investors Use Fear To Their Advantage).

Extreme Valuation Gaps

The Cyclicality Of Growth

If you had observed fashion trends over the past four decades you would have seen bell bottom jeans, tapered jeans, boot-cut jeans along with those awful skinny jeans. Fashion trends go in and out style without much rhyme or reason, but what about investment trends?

Do investment styles work for a period of time and then go out of style? Is there any cyclicality to these trends?

For the past 12 years, growth stocks have outperformed value stocks by a wide margin, yet this is the opposite of what transpired between 2000 and 2007 – when value dramatically outperformed growth.

And 2000 to 2007 is the opposite of what transpired between 1994 and 2000 when the tech bubble caused growth stocks to dramatically outperform value stocks.

This sounds like a trend – a longer period of time when one style is in favor, until something new comes along, or something happens to disrupt the trend that was formerly in place. 

The disrupter in 2000 was the implosion of the tech bubble. NASDAQ peaked in March of 2000 and proceeded to drop nearly 80% of its value over the next thirty months.   

There is no clear explanation on what caused the trend to shift in 2007, but so many investors jumped onto the value band-wagon as an over-reaction to the that “value” was no longer very valuable. 

The competing relationship between growth and value is captured in the graph above comparing the Russell 1000® Value Total Market Index to the Russell 1000® Total Market Index.

The competition between growth and value is actually a relatively new phenomenon. Apart from the last twenty-five years, value stocks have consistently dominated the performance of growth stocks going all the way back to the Great Depression.   

Source: Ken French Data Library, Telos Asset Management Company 

Looking back at 90 years of data, there have only been three periods when growth stocks were a strong competitor to value stocks: the tail end of the Great Depression, the Technology Bubble of the 1990s and the past twelve years. The length and depth of the most recent run is the most extreme on record.
Over that 90 year window, growth (the most expensive quartile of stocks) has historically traded around 2.1 times book value while value (the cheapest quartile of stocks) has historically traded around 0.7 times book value.

Today’s valuation gap between growth and value is one of the widest on record. With today’s wide valuation gap, the odds of valuations reverting back to their mean are high.

Source: Ken French Data Library, Telos Asset Management Company 


Could growth stocks continue to outperform value stocks over the next few years? Yes, of course they could. But the trend will shift at some point.

That shift might come from investor fears over the high valuations of the stock market, concerns about the economy, or something that is not even on investors’ minds today. That shift will likely drive a change in investor sentiment, and the change in investor sentiment is likely to cause valuations to revert back to their mean with value stocks regaining a place of prominence.
It’s impossible to predict when that shift will take place, but the good news for value investors is that the odds increase as the valuation gap between growth and value widens. With the valuation gap as wide as it is today, the odds are fairly high.

The essence of value investing can be summarized by maintaining a strong conviction in three simple concepts:

1) Buy assets that are cheap relative to historical levels
2) Avoid assets that have run up in price
3) Be patient while waiting for prices to revert back to their mean over time

Patientia – The Not-So-Secret Sauce


”Repetitio est mater studiorum” is a Latin proverb that says “Repetition is the mother of learning.” We are going to repeat a theme from the past because one of the biggest mistakes made by investors has the simplest of fixes.
If one were to study the traits that John Templeton, Warren Buffet, Benjamin Graham or Ray Dalio shared (or continue to share) in common, they would find that each of them employed (or continue to employ) a disciplined process for identifying market opportunities. Each of them put their capital to work in areas where they believed they had an edge or in areas where they had a reasonable level of conviction that the market was mispricing assets. And each of them were patient with their capital, knowing that the monetization of market mispricing can take time (see Three Marks of Great Investors).
Warren Buffet’s comment that “the stock market is a device for transferring money from the impatient to the patient” sums up his perspective on the value of being disciplined when seeking to harvest superior returns.
The challenge with patient investing is that it’s easier said than done. That’s because it’s a perfectly normal response for people to avoid pain. If you have a headache, you take an aspirin, or drink some water. You respond with an action to reduce the pain.
The simplest way for investors to avoid short-term pain is to exit the investment strategies that are underperforming, but that is the type of behavior that ultimately leads to underperformance.
In looking at Callan’s Periodic Table of Investment Returns, we can observe the bottom to top movements of both low-risk and high-risk asset classes from 1998 to 2017.

Click Here for a full-scale view of Callan’s Periodic Table of Investment Returns from 1998 to 2017
In 1998 and 1999 the Russell 2000 Value Index (the light blue box in the bottom left corner) was at the bottom of the pack for two years in a row and then moved to the top of the pack in 2000 and 2001. But how many investors had the discipline to stay in small-cap-value-land when it underperformed the S&P 500 by a cumulative 63% in 1998 and 1999?
Or which investors had the discipline to remain in “low-risk” bonds (green boxes at the bottom, left of center) from 2003 to 2007 (when they were the worst performing asset class in four of those five years) to hold onto the only asset class that had a positive return in 2008?
Which investors pulled out of “high-risk” emerging market equities (orange boxes) after any one of the six bottom-of-the-pack years, causing them to miss out any one of the nine years that EM was the top performing asset class? (see How Intelligent Investors Use Fear To Their Advantage)
We are not saying that the Barclay’s Aggregate, or the Russell 2000 Value or Emerging Markets are the path to outperformance. We are simply saying that the only investors who benefited from exposure to these asset classes were the ones who had the conviction to remain after periods of significant underperformance.
Investment strategies that deliver superior long-term returns require investors to be incredibly patient, disciplined, and indifferent to short-term performance. That’s because the seasons of underperformance drive away demand by pushing away the impatient investors, making things more attractive on a relative basis, and act as the build-up to the seasons of outperformance.
While this is easy to comprehend, it is much more challenging to execute. Without strict discipline, and a deep understanding of how and why alpha-producing strategies generate their returns, even seasoned investors will want to pull out of a strategy after two or three years of under-performance.

It is these seasons of under-performance however, that effectively create the risk premium that patient investors capture when they keep their eyes fixed long-term. 
As long as investors continue to chase short-term performance, there will be opportunities for disciplined, process-driven investors to harvest superior long-term returns.

If you are still wondering about the title, “Patienta” is Latin for “Patience!”

Thinking About How We Think

Decisions…. Decisions….

The world is complex, and it can tax our mental resources to process all the information that is coming our way.

To keep up with all the data that our minds are processing, we come up with time-saving (and energy-saving) rules of thumb, called heuristics. We may be applying these heuristics unconsciously, and they don’t even need to be rational. We simply need to believe them.

In Thinking, Fast and Slow, Nobel Prize winner and author Daniel Kahneman breaks down our decision-making process into two systems. To keep things simple he describes them as “System 1” and “System 2.” 

System 1 is intuitive and emotional. It is fast and easy. “There was a shark attack last week, I am never going to the beach again.”

System 2 is deliberative and logical. It is also slow and requires effort“What are the chances of getting attacked by a shark? Are they higher today than they were last week? Is swimming in the ocean more dangerous than swimming in a community pool?”

If I were to ask you “What is 7 times 3?” You could access System 1 and respond immediately with “21.” 

If I were to ask you “What is 277 times 53?” You could respond with the correct answer, but it would likely require you to tap into System 2 before you answered correctly.

System 2 requires a deeper level of thinking, and it requires a near-exclusive devotion to the problem-solving effort.

It would be nearly impossible for someone to multiply 277 by 53 in their head while writing out instructions on how to make banana bread – even if they had a great recipe for banana bread stored in their memory. That’s because System 2 thinking requires concentration to solve the problem.

The interesting thing about System 2 is that it can morph into System 1 when we spend hundreds or thousands of hours training our mind. Here are two examples:

My younger son got his license back in February. When he first started driving, it took all of his concentration to remember where to put his hands on the steering wheel, the rules of the road, and the process of looking ahead, behind and the sides of the road to identify threats. When we first start driving, we go into “System 2” to concentrate on the task at hand.

As time goes by, those basic driving and awareness skills become second-nature to us, and driving can move from a System 2 process to a System 1 process. I often listen to podcasts while driving on the interstate. I can do this because System 1 takes over, and driving is second-nature .

Another example of this would be Garry Kasparov, who retired from professional chess after being ranked as the world’s top chess player for 20 years. If Gary were to come and play 10 amateur chess players, he could play them all at the same time. We could line them up and Gary could move from player to player, knowing immediately what his next move would be.

The next move on a chess board is second nature for Gary because he has played thousands of games. He knows the implications of moving the rook or moving the queen. He doesn’t need to access System 2 to beat an inexperienced player.

The challenge with our System 1 and System 2 thinking is when we “think” we are an expert, or we have a life experience that impacts us.

This perception of “expertise” or the impact of life experience can shape our decision-making process in an equally profound way – to our benefit and to our detriment.

That’s because these experiences build the heuristics that we use as short-cuts to make decisions, and some of these heuristics are helpful, but some are not. So how do we distinguish between the two?

Understanding how System 1 and System 2 work together is critical because we cannot always trust our System 1 intuitions. And a big part of our challenge is that System 1 is so much easier to operate from, because we have all the data to reinforce our personal biases. 

How do we determine which of these short-cuts lead to better outcomes?

Over the next few weeks, we will explore how to limit the downside of our System 1/System 2 thinking and how to use these two systems to make better investment decisions

Better Thinking

Better Thinking…

 This Insight is the second part of our series on decision making. Applying some of the information from Thinking, Fast and Slow, we are diving into the research from Nobel Prize winner Daniel Kahneman and how he breaks down our decision-making process into two systems. 

Notes from our last Insights:

  1. To keep things simple Kahneman breaks down our thinking process into two systems that he describes as “System 1” and “System 2.” 
  2. System 1 is intuitive and emotional, fast and easy. It is reactive. “There was a shark attack last week, I am never going to the beach again.”
  3. System 2 is deliberative and logical. It is also slow and requires effort. “What are the chances of getting attacked by a shark? Is swimming in the ocean more dangerous than swimming in a community pool?”
  4. The challenge with our System 1 and System 2 thinking is when we “think” we are an expert, or we have a life experience that impacts us. This perception of “expertise” or the impact of life experience can shape our decision-making process in a profound way.
  5. That’s because life experiences build the heuristics that we use as short-cuts to make System 1 decisions. Some of these heuristics are helpful, and some are not.

If I were to ask you what is more probable: Dying in a train accident or getting struck by lightning?…Most people would say train accident.

That’s because System 1 kicks in, pulls up memories of train accidents in the news and assumes that there is a higher probability of dying from a train accident than getting struck by lightning.

This System 1 action is referred to as the availability or familiarity heuristic. 

But according to the National Center for Health Statistics, we have a higher chance of dying from a lightning strike than we do from a railway accident.

According to that same study, Americans are two and a half times more likely to die from a bee sting than from a dog attack.

Like bee stings, average market gains over long periods of time aren’t as headline grabbing as train crashes or market crashes, so they are not as prominent in our minds.

Investors are quick to succumb to System 1 thinking when we avoid “riskier” asset classes, especially when we have the impact of the great recession burned into the back of our minds.

By focusing all our attention on the potential for short-term fluctuations in performance, we ignore the fact that these “riskier” asset classes can be the ones that have the greatest long-term impact on portfolio growth.

The familiarity heuristic may not only cause us to avoid high-performing asset classes, it can also work against us by biasing us toward things we are familiar with. According to JP Morgan, people living on the West Coast tend to overweight the technology sector, while people living in Texas tend to overweight energy; and people in the Midwest tend to overweight industrials.

While it is wise to invest in asset classes where we have an edge, it is a statistical improbability that the entire universe of Texans has an edge in energy investments. And it’s equally improbable that the entire universe of Midwesterners has an edge in industrials. While System 1 might convince us all that we are “experts,” we can’t all have an edge. Not everyone is the “smartest person in the room.”

The irony of the familiarity heuristic is that it can cause us to avoid “riskier” asset classes on one side and cause us to overweight our portfolio on the other side, ultimately creating more potential hazard from a lack of diversification and concentration risk.

We are all swayed by our personal biases and deceptive thinking from time to time. The key to managing our thinking is to simply recognize that we are inclined to be biased. Rather than reacting to System 1 and our biases, we need to access System 2 and ask ourselves deeper questions.

  • System 1 thinking looks at the high performing mutual fund and says “this fund has significantly outperformed my other mutual funds in the last two years. Let’s sell my underperforming funds and buy more of this fund.”
  • System 2 thinking looks at the high performing mutual fund and asks: “Why is this fund outperforming? Did the manager tactically recognize the hot sectors?  Or was it always invested in this sector, and this sector happened to outperform the last two years? How likely is it that this sector will continue to appreciate when it is extremely expensive today? Let’s sell half of this holding and move into something that has more potential to grow.”
  • System 1 thinking looks at a marginal company in a stagnant industry and says: “Wow this company has not grown earnings in the last three years, there is no way I would own this stock.”
  • System 2 thinking says: “This company has not grown earnings in the last three years, but the balance sheet is stronger today and the market cap is one-third of what it was three years ago. At the extremely depressed valuation, I’m willing to bet that this company will converge back to a more normal valuation when investors begin to recognize the safer balance sheet.”

Boiling it Down:
System 1 can do a pretty good job of keeping us alive in the jungle, but may not serve us as well when seeking to maximize our long-term wealth.
That’s where we need to recognize that we are inclined to be biased and our short-cutting heuristics may be hurting us. Tapping into the benefits of System 2’s slow thinking can help prevent us from making costly mistakes.

The Fallacy of the Formula

The Fallacy of the Formula

What do Circuit City, Fannie Mae and Pitney Bowes all have in common?

If you answered that they have all been remarkable failures in the past eighteen years, you would be half-right. If you answered that they have all been remarkable failures AND were three of the eleven “Great Companies” identified in Jim Collins’ Good to Great book, you would be more-right.

Good to Great: Why Some Companies Make the Leap… and Others Don’t took the business management community by storm when it was published in October of 2001. Like the latest Hallmark movie, the book follows the same pattern of past business books: create a list of great companies based on some “quantifiable” measure and then identify the formula that brought these companies to greatness.

And imply that your readers can apply the same magic formula to achieve greatness within their own organizations!

Ignore luck, randomness and good fortune, and over-attribute all the greatness to the formula and the management team.

Oh, and one more thing – make sure you have a BHAG! (a big, hairy, audacious goal) It doesn’t matter if the BHAG is a good one, it matters that it’s HAIRY and AUDACIOUS!

The core message of Built to Last is that good management practices can be identified and implemented, and once implemented good results will follow. The fallacy of this logic is that Collins was essentially comparing successful firms with less successful firms and attributing the success to something other than luck and randomness. Of the 5,000+ publicly traded companies that existed from 1986 to 2001, there was a reasonable probability that eleven of them would outperform over a 15-year period!

“Knowing the importance of luck, you should be particularly suspicious when highly consistent patterns emerge from the comparison of successful and less successful firms. In the presence of randomness, regular patterns can only be mirages.” Daniel Kahneman.

Nassim Taleb touched on this human tendency when he introduced the narrative fallacy in The Black Swan.

The narrative fallacy describes how flawed stories of our past shape our views and expectations of our universe.  

Driven by our need to make sense of our world, narrative fallacies are the simple, compelling stories that create meaning and assign larger roles to things like talent and intelligence rather than luck or randomness. 

Focusing on the few significant events that actually happened, rather than the countless number of events that could have happened, we wrap our view around a nice clean narrative and fail to account for the randomness that exists in our world.

People have a deep need to be reassured that actions have consequences. And we all want to believe that success will be the rewards of courage and good decision-making. Books like Good to Great provide a nice clean message about the determinants of success and failure by offering a sense of understanding. But their logic is faulty and misleading.

The reality is that the world (and especially stock prices!) operates in a more-random fashion than most people care to recognize. The recognition that our world has a randomness to it does not however, leave us powerless. It can actually empower us to make better decisions when we recognize that fact.

We will address how to make better decisions in light of a random world in next week’s Insight, but before we wrap up this week, let’s look at how Collins’ eleven “great” companies have performed since Good to Great was published 18 years ago:

Abbott Laboratories
Circuit City         
Fannie Mae
Philip Morris
Pitney Bowes
Wells Fargo

On a cursory look, the end results show three remarkable losers in Circuit City, Fannie Mae and Pitney Bowes; four weak performers in Kimberly Clark, Kroger, Wells Fargo and Walgreens; two market performers in Gilette and Wells Fargo and three winners in NuCor, Philip Morris and Abbot Labs:

3 Remarkable Losers
4 Underperformers
2 Market Performers
3 Winners

That doesn’t sound like “greatness” to me. Some underperformed, some outperformed and some were right in the middle. It sounds more like randomness and a reversion to the mean from former greatness

Better Decisions By Overcoming Our Cognitive Bias

Overcoming Our Cognitive Biases

We started a series on decision-making back in June when we introduced the concept of Level 1 and Level 2 thinking from Daniel Kahneman’s book “Thinking Fast and Slow.” 

The main goal of the series (Thinking Fast and SlowFast and Slow 2Good to Great to Bad) was to explore how cognitive biases are formed and how they influence our decision-making.

The challenge with our cognitive biases is that they tend to influence us most at the extreme ends of the spectrum. And it’s at these extreme ends of the spectrum where we may need to ignore them the most, because all risky asset classes will experience long periods of underperformance.

The S&P 500 Index has experienced three separate periods where it underperformed riskless one-month Treasury bills for more than a dozen years (1929-1943, 1966-1982, and 2000-2012).

Any student of the market knows that longer periods of underperformance by risky assets are a necessity. If these periods never occurred, there would be no risk, and the risk premium would disappear.

The periods of underperformance essentially create the equity risk premium that investors capture when they choose to take on the random and unpredictable risk of the equity markets.

If The Markets Are Random and Unpredictable, How Should That Impact Our Decision-Making?

Mean reversion is the theory that security prices return to their long-term averages over time.

In every asset class, from bonds to stock to commodities, buying what is cheap leads to better outcomes because expensive stocks revert down to their mean over time while cheap stocks revert up to their mean over time. Unfortunately, that truth only holds up over longer periods of time.  Expensive stocks can get more expensive in the short-term while cheap stocks can get even cheaper.

Using the CAPE Ratio (the Cyclically Adjusted PE ratio from Robert Shiller) for the S&P 500, we can look back at periods of time when assets were expensive and times when assets were cheap.

Source: Macrotrends, and Telos Asset Management Company

The CAPE ratio is a valuation measure that uses real earnings per share (EPS) over a 10-year period to smooth out fluctuations in corporate profits that occur over different periods of a business cycle. The ratio is generally applied to broad equity indices to assess whether the market is undervalued or overvalued.

Source: Macrotrends, and Telos Asset Management Company

By inverting the CAPE ratio chart we can observe the direct relationship between price and future returns.  The following chart lines up the annualized 10-year forward returns of the S&P 500 with the CAPE ratio at the start of the period.

When the blue line is high, stocks are theoretically undervalued and their future return potential is high. When the blue line is low, stocks are theoretically expensive, and the potential for future returns is muted. 

Source: Macrotrends, and Telos Asset Management Company

While these charts clearly prove that price matters, they do not address the value premium (the advantages of buying cheap stocks over expensive stocks). And unfortunately, there is little evidence that investors can accurately time the value premium or when the mean reversion will take place. That’s where patience and discipline come in.

And How Do We Overcome Our Cognitive Biases?

The key to overcoming our cognitive biases is to override them with a process that systematically allocates based on math and sound logic rather than human judgement.

Process-driven investing is nothing more than a long-term approach to putting capital at risk by owning a broad variety of asset classes, making periodic contributions and regularly rebalancing. The challenge with process-driven investing is that it requires an investor to focus on the investment process and not the short-term results.

That can be extremely difficult when the short-term results don’t coincide with the long-range return objectives. Over the long term, however, overcoming our cognitive biases with a good process should deliver more reliable outcomes with better results.

Where Are All The 400 Hitters?

Where Are All The 400 Hitters?

Between 1876 and 1941 there were 28 Major League Baseball players who batted +400 in a season (according to Wikipedia!).

From 1942 to 2018 there have been zero. In the seventy-six years that have passed since Ted Williams achieved that feat, not one single major league baseball player has hit +400 in a season. And only three players have even come within striking distance.

It would be a real stretch to say that I am a baseball fan. But I don’t need to be a baseball fan to know that something must have fundamentally changed within the game of baseball. So how does that relate to investing?

The parallel is the shrinking amount of outstanding active managers and the zero-sum game theory. 

The Zero-sum game theory is the central concept that underlies the case for index investing. The theory states that the market consists of the cumulative holdings of all investors.

Since the market return represents the average return of all investors, for each manager that outperforms the market, there must be another manager that underperforms the market by the same amount.

Because it’s a zero-sum game, the aggregate excess return of all managers equals zero (it’s actually less than zero when you add in the management fees). If the theory is true, the wise investor would logically invest in a simple basket of securities and use the lowest cost option to do it – as there is no theoretical alpha to be captured.

We would argue that the theory is true, but it fails to address the reality that dumb money does exist in the marketplace. There are investors who consistently make poor investment decisions (see Growth Bias). And good active managers should be able to capitalize on the poor decisions of weak investors.

Regardless of your view on the Efficient Market Theory, the case for passive investing is growing.

This is essentially a self-fulfilling prophecy!

Without a large number of investors making bad investment decisions, it is very difficult for active managers to outperform.

“If you ever sit down at a poker table and don’t know where the dumb money is sitting, you’d better get up, because it’s probably you!”

Warren Buffet recognized this concept over twenty-five years ago when he said: “By periodically investing in an index fund… the know-nothing investor can actually outperform most investment professionals. Paradoxically, when ‘dumb’ money acknowledges its limitations, it ceases to be dumb.”

Even the FINRA website states: “Simply put, as a group, actively-managed funds, after fees have been taken into account, tend to underperform their passive peers.”

Source: Source: Capital IQ, BAM Alliance, Telos Asset Management

And it’s not just actively managed mutual funds that are struggling, the entire hedge fund universe is struggling. Between 2009 and 2018 the HFRX Global Hedge Fund Index (an index designed to represent the overall composition of the hedge fund universe) delivered a whopping 1.5% annualized net return to investors.

Actively managed funds are underperforming and shrinking while money is flowing into passive funds, and that is not a coincidence!

On 9/11/19 Bloomberg published an article that stated passive equity funds have finally eclipsed “old fashioned” actively managed funds. Over the same period that passive funds outperformed the actively managed universe, low-cost ETFs have doubled their market share and now represent the lion’s share of the fund universe.

As the passive funds have grown to dominate the investment landscape, the available alpha has been getting squeezed out of the market because there are less weak investors for the smart money to prey upon.

The dumb money has essentially responded to Warren Buffet’s challenge twenty-five years ago and ceased to be dumb money.

Where investors may be missing the mark is in what should be painfully obvious: If the lion’s share of the fund flows is going into passive funds that are investing in the same assets, it will naturally drive up the price of the underlying assets in those funds.


This is also a self-fulfilling prophecy. It should be no surprise that passive investing will outperform active investing when all the asset flows are going into passive funds. Higher demand naturally drives higher prices.

In the same respect, the current momentum that is being captured on the upside when all the money is flowing into passive strategies will be captured on the downside when all the investors investing in passive funds (that own the same underlying assets) head for the exit sign at the same time.

Passive investing works well when everyone is piling into it, and it’s likely to work just as poorly when everyone heads for the exit sign in the next major market downturn.

To be clear – we are not saying the investors should avoid using low-cost ETFs. We think they should, because the market is becoming more efficient.  

But investors who recognize the advantages of low-cost ETFs should also recognize their limitations – because the investors who avoid the underlying assets in the passive strategies in the next market downturn are likely to be the ones who outperform.

Risk Parity – “It’s about balance!”

Risk Parity – “It’s about balance!”

The Risk Parity approach to investment management is changing the way some of the largest pools of capital manage their money. Originally developed by Ray Dalio and his team at Bridgewater Associates back in the 1990’s, this approach attempts to build portfolios that can handle a variety of economic environments.

The beauty of the approach stems from its simplicity. The simplicity of the approach is driven by the theory that asset classes react in understandable ways based on the relationship between their cash flows and the economic environment. 

The theory behind the Risk Parity movement can be broken down into two main points:

  1. Changes in market prices are driven by economic surprises (or changes relative to the expectations that are already priced in)
  2. The impact of economic surprises can be minimized through a combination of asset classes that perform well in different environments

The critical element of the asset mix is that the assets produce cashflows that react differently to four potential environments:

  1. Rising Inflation
  2. Falling Inflation
  3. Rising Economic Growth
  4. Falling Economic Growth

If an economic surprise is simply an unexpected event, then the key to building insulated portfolios is to have the right combination of assets (or asset classes) that offset one another during unexpected events.

One of the challenges with this approach is the unknown unknowns (ie. the Black Swan). A more realistic approach will accept the fact that the future is unpredictable, and choose to invest for the long-run by investing in a combination of asset classes that produce positive returns over a full credit cycle.

While no individual asset (or asset class) will perform in every environment, the effectiveness of the strategy relies on two things:

  1. Each holding must contribute to a positive return in one of the four environments.
  2. Each holding must also generate a positive return over the full credit cycle

At it’s core, the Risk Parity approach is basically four separate portfolios that react independently to rising inflation, falling inflation, rising economic growth and falling economic growth:

Filling In The Boxes…..
It would be difficult for the average individual investor to match off the risk of the different environments with a couple of ETFs or mutual funds. But I am reminded of a comment from a former colleague who said “If it’s worth doing, it’s worth doing poorly!”

Considering that my friend was a form Naval officer, the comment seemed out of character, until I realized that he was essentially saying that some movement in the right direction is better than no movement at all.

So here are a few broad things to consider when thinking about tilting your portfolio to toward the Risk Parity approach:

The key is to have a rising star for each of the four scenarios and to have a combination of assets that collectively hit your target return.

As we close out the year, I want to wish you all a happy holiday season and a very Merry Christmas! I look forward to the opportunity to connect with you in 2020.


Isaiah 9:6 “For to us a child is born, to us a son is given, and the government will be on his shoulders. And he will be called Wonderful Counselor, Mighty God, Everlasting Father, Prince of Peace.  Of the greatness of his kingdom and peace, there will be no end.”