What do Galileo's inquisition, the Capitol attack, and the GameStop frenzy have in common?

What is reality?  While this question sounds arcane, I believe it delivers valuable insights into recent events.

We can answer the question, “What is reality?” from two approaches.  The first approach is objective reality: reality is what I, as an impartial observer, can actually, empirically, or factually verify, regardless of how I feel or think about those facts.  The second approach is consensus reality: reality is what my “tribe” believes to be, decides is, or treats as real.  Consensus reality replaces facts that are objectively true, with facts that feel true.  In consensus reality, if facts are inconvenient or antagonistic to the tribe’s beliefs, feelings, or opinions, then the tribe ignores, discards, or discounts them as “fake,” lies, or propaganda. 

Consider the conflict between objective vs. consensus reality in the critical scientific question of the early 1600s: Which was the center of the solar system: the Earth or the sun? 

Galileo embraced objective reality.  After Galileo used his telescope to see the moons orbiting around Jupiter, he concluded that, in a similar way, the Earth must revolve around the sun. 

By contrast, the Catholic Church favored consensus reality.  Since the early 100s, the “tribe” -- in this case, the Catholic Church -- felt that the Earth stood still in the universe, and all heavenly bodies revolved around it. 

Objective reality collided with consensus reality in 1610 when Galileo published The Starry Messenger.  The Catholic Church attacked Galileo for spreading lies and heresy. In response, Galileo invited the church’s astronomers to look through his telescope, then decide for themselves.  What happened next?  As Galileo wrote to German astronomer, Johannes Kepler:

My dear Kepler, I wish that we might laugh at the remarkable stupidity of the common herd. What do you have to say about the principal philosophers of this academy who are filled with the stubbornness of an asp and do not want to look at either the planets, the moon or the telescope, even though I have freely and deliberately offered them the opportunity a thousand times? Truly, just as the asp stops its ears, so do these philosophers shut their eyes to the light of truth.

Sadly, the Catholic Church found Galileo “vehemently suspect of heresy” in 1633, and kept Galileo under house arrest until he died in 1642.  In a sign of how durable the “group think” of consensus reality can be, the Catholic Church did not accept that the Earth revolved around the sun until 1822, 180 years after Galileo died.

How does objective vs. consensus reality relate to recent events?  Let’s consider two examples: the January 6th attack on the US Capitol; and the recent frenzy in GameStop trading.

First, let’s examine the January 6th attack on the US Capitol.  As I read about the people involved in the attack, I struggled to understand what brought together such disparate groups -- ranging from a) politicians; b) white supremacists; c) evangelical Christians; and d) conspiracy theorists who believe that Democrats are Satan-worshiping cannibalistic pedophiles (yes, really). 

Why did such different groups band together in an effort to overthrow the results of the US election?  Perhaps the best answer is consensus reality. As I discussed in last quarter’s letter, Rise Of The Machines, artificial intelligence is being used to target different media messages to each individual.  These individually-targeted messages reinforced each “tribe’s” consensus reality that the US election had been “stolen” from Donald Trump, despite all objective evidence to the contrary.  The desire to “right” this wrong spread through the groups like a social contagion, and ultimately, it erupted into actual violence.  (Of course, violence has not been limited to the far right; last summer, violence erupted from the far-left as well.)

Second, let’s consider the recent frenzy in GameStop trading.  In 2019 and 2020, GameStop was one of the most “shorted” stocks, meaning that investors who “shorted” its shares -- including well-known short-sellers like Andrew Left of Citroen Research -- believed that GameStop’s price per share would decrease.  In a nutshell, their “short” investment thesis matched objective reality: GameStop’s brick-and-mortar business model is outdated, and over time, GameStop’s revenue and profits will decline.

But what about consensus reality?  Thousands of “investors” belong to the WallStreetBets “tribe” on Reddit, an online message board.  This “tribe” decided to launch a coordinated attack on highly shorted stocks, like GameStop. 

The “tribe’s” investment thesis aligned with their consensus reality: let’s “stick it to the man” by buying a heavily shorted stock, driving its price higher, and triggering a short squeeze.  (When a shorted stock stages a dramatic rally, it can be painful enough that short sellers are forced to exit from their positions.  To exit from their positions, short sellers have to purchase back the shares at a loss.  This pushes the stock sharply higher, in what’s known as a “short squeeze.”)  As the WSJ wrote:

By early January, GameStop had moved from a stock recommendation to a phenomenon. GameStop was no longer only an opportunity for a big payday or a way to back a struggling company. Buying GameStop for some users had turned into a way to confront institutional money. Users encouraged others to hold the line: “Do not sell.”

This consensus reality, and the resulting short squeeze, drove GameStop’s price from $17 to $483 in less than a month.  In this pump-and-dump scheme, the first in will do well -- if they sell early enough -- but the last in will get slaughtered.  The “tribe” believes in the consensus reality of buying GameStop as a way of “sticking it to the man.”  (As one “investor” told the WSJ, “Please tell the wolf of Wall Street that the pigeon of San Francisco is gonna eat your lunch.”) However, they can not overcome objective reality: GameStop’s business fundamentals can not support today’s price, and eventually, its stock price will collapse back to its intrinsic value.

What can we take away from the conflict between objective vs. consensus reality?  First, without a common, objective reality, it will be difficult for our country to achieve long-term stability.  This investment risk is real, and it will be with us for the long-term.  Second, the pump-and-dump mob might have permanently eliminated the ability to short-sell obviously failing businesses.  If this has happened, it will remove one of the checks and balances built into the market.  Third, as your investment manager, I will continue to focus on objective reality: facts that I can impartially, accurately, and observably verify. As Howard Marks said:

Emotion is one of the investor’s greatest enemies. Fear makes it hard to remain optimistic about holdings whose prices are plummeting, just as envy makes it hard to refrain from buying the appreciating assets that everyone else is enjoying owning. Superior investors may not be insulated, but they manage to act as if they are.

Now that you know about it, where else do you see the conflict between objective vs. consensus reality?

How does the "rise of the machines" affect you?

As I write, we are on the precipice of the 2020 US presidential election.  The most careful polling meta-analysis I have seen is done by Nate Silver’s fivethirtyeight.com.  As of October 28, 2020, Silver gives Trump an 11% chance of winning and Biden an 88% chance of winning.  Nate Silver’s analysis does not say that Biden will win.  Instead, it says that Biden is very likely to win.

Each of us likely feels differently about both this election and its outcome -- and that brings me to an important topic: the rise of individually-targeted media. In the last 15 years, our world has changed drastically in ways that are hard to see. However, the end result is that we have progressively handed control over to “the machines.”

What do I mean by “the rise of the machines?” Until the mid-1990s, mass-media truly was tailored to the masses. The left and the right, the short and the tall, the rich and the poor, and the white and the black all largely consumed the same newspapers and television news programs. Typically, these were local newspapers and TV stations, and to avoid alienating half their audience, these media outlets had to produce balanced, centrist content.

In 1996, the first cracks in mass-media appeared. In that year, cable news channels like Fox News and MSNBC launched. Instead of providing balanced, centrist content to a defined geographical area, these channels differentiated themselves by providing biased content to a narrow ideological group.

About 15 years ago, major internet media companies like Facebook, Twitter, and Google began. Instead of simply targeting content to an ideological group -- like the cable news channels did -- these internet media companies used artificial intelligence (AI) to target content to a particular individual. In essence, AI killed mass-media and replaced it with individual-media.

How does AI target content to you, individually and specifically? It is very simple. The companies use clever mathematics and the gobs of personal data they have collected on you, to tune exactly what you see. Their aim is to keep you on their platform as long as possible. The longer you are on their platform, the more ads you will view; and the more ads you view, the more money they will make. Simply put, tuning your content optimizes their profits. 

Media companies -- whether old-generation cable news channels or new-generation internet companies -- want to grab and retain your attention. That may be great for their bottom lines, but it is very negative for society. Why is that? First, it is easiest to capture users’ attention through negative emotions, like fear, anger, distrust, or disgust. Second, targeted content creates an echo chamber or an information silo, which gives the false impression that everyone else agrees with your views. Both of these factors combine to create a bifurcated, polarized us-vs.-them view of the world.

In their article, “The dark side of technology: An experimental investigation of the influence of customizability technology on online political selective exposure,” Ivan Dylko, Igor Dolgov, and others studied the effects of such customizability technology. They concluded: 

1. “Due to its automatic and unobtrusive operation, customizability technology may be particularly effective at reducing cognitive dissonance associated with avoidance of challenging information.”

2. “Customizability technology … encouraged ideologically moderate individuals to behave similarly to ideologically extreme individuals in terms of their [lack of] exposure to counter-attitudinal political information.”

3. “If ideologically moderate individuals are strongly affected by customizability technology (and we show that in our study they were), their levels of selective exposure become increased, and selective exposure is known to promote political polarization.”

4. “Taken together, these findings show that customizability technology can undermine important foundations of deliberative democracy. If this technology becomes even more popular … we can expect these detrimental effects to increase.”

If the current trend continues unchecked, it may very well produce a destabilized society, where every human loses, and only the machines win. This is a very real existential threat. For instance, in the last few election cycles, AI has spawned ads targeted to individuals. Imagine a world in which just a few thousand votes can sway an election. (For instance, in the 2018 election, just 1 vote determined the winner of the Kentucky House of Representatives 18th District and the Alaska House of Representatives 1st District, while 2 votes determined the winner of the New Hampshire House of Representatives 3rd District.) With personalized ads, candidates could make separate, inconsistent promises to just enough voters to win election -- or a foreign power could easily sway an election’s outcome. Like Aldous Huxley’s dystopian novel, this is a brave new world.

Now that you are aware of this, I hope you will notice when the machines feed you stories in order to capture your attention. In order to escape from our echo chambers, we must each consume media from sources that do not align with our natural political leanings. This is the only way to both fill in the knowledge holes that the machines create, as well as to train the machines to provide us with more balanced content. We must rise to the occasion, in order to counteract the rise of the machines. 

David R. “Chip” Kent IV, PhD
Portfolio Manager / General Partner
Cecropia Capital
Twitter: @chip_kent

Nothing contained in this article constitutes tax, legal or investment advice, nor does it constitute a solicitation or an offer to buy or sell any security or other financial instrument.  Such offer may be made only by private placement memorandum or prospectus.

What do poker, pandemics, and powder have in common with your portfolio?

What was your worst decision from the last five years?  At the other extreme, what was your best?

When most of us describe our worst decision from the last five years, we almost always talk about a situation that had a terrible outcome.  Perhaps money was lost, someone got hurt, or a life changed for the worse, forever. 

At the other extreme, when most of us describe our best decision from the last five years, we almost always talk about a situation that had an amazing outcome.  For instance, you might say that your best decision in the last five years was climbing K2, the world’s second-highest peak, because the view from the summit filled you with awe and wonder.

The responses above are examples of what professional poker players call “resulting.”  Resulting means that we equate the quality of a decision with the quality of its outcome.  Resulting can happen in both the positive and negative directions.  For example, 29% of climbers die on K2.  Was it really such a good decision to make the climb?  In this case, perhaps climbing K2 was your worst decision of the year, but it resulted in a good outcome because you got lucky.  At the other extreme, perhaps you decided that it was too risky to climb K2 (your best decision of the year), but while walking back to the hotel, a freak lightning bolt hit you and paralyzed you.  In this scenario, your best decision resulted in a poor outcome, because you got unlucky.

In professional poker, it is clear that poker is a mathematical game, and that to win, you must unemotionally compute how to play the odds of each hand.  In poker, your opponent’s cards are hidden, so you must make decisions without having a complete picture.  Additionally, the odds change as cards are flipped from the deck.  Due to the element of chance, you can calculate the probability of outcomes for each hand, but you will never know with 100% certainty how each hand will go.

Professional poker players understand that, in the long run, deviating from the mathematically-optimal decision-making strategy will have bad outcomes, even if it pays off for a hand or two.  That is why poker players have named the psychological biases, such as resulting, which lead to non-optimal play.  Biased professional poker players do not end up at the top.

Now that I have pointed out resulting, you will see it everywhere.  Everyday life is a sequence of calculated bets, but most of us are far less diligent than a professional poker player is at assessing the thinking and calculating the risks that go into our decision-making.  For instance, was it a good decision to drive while drunk or sleepy, even though you made it home safely?  Is a diet of highly processed foods OK, since we haven’t died yet?  Is it a good decision to eat indoors right now at a restaurant, where the odds of disease transmission are 19x higher than outdoors -- just because we haven’t yet been infected by COVID-19?

Let me share a personal example.  Our family likes to ski.  (In fact, as of this letter, Bear and I have skied for 21 straight calendar months, Amber has skied for 13 straight, Jade has skied for 10 straight, and Brooke divides her time between skiing and snowboarding.)  When we decided to start skiing in the backcountry -- that is, climbing publicly-accessible mountains where there is no avalanche mitigation in order to do a single run -- I knew that I needed to better understand the risks.  I took a 3-day avalanche hazard management course, and in that course, we learned that resulting is a key reason why skiers die in an avalanche.  For instance, just because you see ski tracks down a slope, does not mean that it is a safe decision for you to ski down too.

The Red Lady Bowl avalanche of November 25, 2018 (pictured below) is a perfect example. Five of the best skiers in Colorado, including a champion ski mountaineering racer, climbed Mt. Emmons near sunrise.  They tried to kick blocks of snow from the cornice in order to test the slope.  However, they could only get small crumbles loose, so they recognized that their avalanche test was not very robust.  Nevertheless, since they had skied this slope safely for the last 20 winters (resulting), they decided to proceed.  As they later said, “they recognized they were rolling the dice” based on their past experience (resulting).  After the first skier laid down 15 turns, an avalanche released.  The avalanche overran the skier’s tracks, and a smaller avalanche released to his left.  The skier ended up between 2 avalanches in motion, but fortunately, he was able to ski out.

Did that skier make a good decision, just because he survived the avalanche?  Before that, should the 5 skiers have “rolled the dice” and proceeded with their run, since they weren’t able to dislodge enough snow to do a robust avalanche test?  Was it a good decision for them to assume that, since they’d skied this slope without incident for 20 years, they could ski it again that day?  Was it wise for them to think that the existing ski tracks down the bowl indicated a green light to ski?

As interesting as this discussion is, does it apply to investing?  Absolutely.  Just like poker, investing is a mathematical game.  As your portfolio manager, my job is to carefully estimate the odds of good vs. bad outcomes when I decide whether or not to invest in a business.  However, once I make that decision, the outside world generally uses resulting to assess the quality of my decision.  Suppose someone made millions investing in bitcoin.  Was that a good decision, given bitcoin’s risks and speculative nature?  Or do we just say that decision was good, because of the good outcome?  At the other extreme, say that you invested in a good-quality business that had a high probability of an attractive return -- but then COVID-19 torpedoed that business.  Was that decision a bad one, just because of a bad outcome?

Two of my favorite books on this subject are Thinking in Bets by Annie Duke, a World Series of Poker champion, and A Spy’s Guide to Taking Risks by John Braddock, a retired CIA case officer.  In Thinking in Bets, Annie Duke writes:

“Thinking in bets starts with recognizing that there are exactly two things that determine how our lives turn out: the quality of our decisions and luck. Learning to recognize the difference between the two is what thinking in bets is all about.”

— Annie Duke

Just like poker, successful investing hinges on making the best decisions possible when you do not have all of the facts, and when the future is uncertain.  Superior investors may not be unemotional, but they force themselves to act -- and to decide -- as if they are.  Good luck may cause a bad decision to result in a favorable outcome, and bad luck may cause a good decision to result in a poor outcome.  The best investors learn to separate the effects of luck from the quality of their decisions so that they can improve their decision-making ability.  As Warren Buffett said:

“We don’t have to be smarter than the rest. We have to be more disciplined than the rest.”

— Warren Buffett

Now that you see resulting, where else does it impact your life? 

David R. “Chip” Kent IV, PhD
Portfolio Manager / General Partner
Cecropia Capital
Twitter: @chip_kent

Nothing contained in this article constitutes tax, legal or investment advice, nor does it constitute a solicitation or an offer to buy or sell any security or other financial instrument.  Such offer may be made only by private placement memorandum or prospectus.

How certain is the way out of COVID-19?

This quarter, the COVID-19 pandemic continued.  Poor leadership and weak social discipline have squandered the gains from our initial quarantine.  While there has been some progress on the therapeutic drug front, these drugs present only moderate gains in treating ill patients.  As a result, it looks like our only ways out of this quagmire are herd immunity, a vaccine, or a chance mutation that renders this virus less infectious, less deadly, or both.

The first way out is herd immunity.  To achieve herd immunity, roughly 70% of the population needs to be immune to COVID-19.  Antibody tests indicate that roughly 7% of Americans have already had COVID-19, so we are not close to the 70% threshold.  Additionally, given the current best estimates of COVID-19’s fatality rate (0.5%-1.0%), achieving herd immunity without a vaccine means that 1.1-2.3 million Americans and 27-55 million people globally would die.  The enormous human cost makes herd immunity a tragic way out of this quagmire.  Hopefully, we do not go down this path.

The second way out is a vaccine.  Right now, there are over 165 vaccines in various stages of trials.  Most of these are preclinical, so they will not be of help anytime soon.  Fortunately, five vaccines are in Phase III trials, the final trial before approval.  (Besides these five, there is one vaccine that was rush-approved for use by the Chinese military.)  

The leading candidates are:

• AstraZeneca / Oxford (UK/Sweden)

• Moderna (US)

• Murdoch Children’s Research Institute (Australia)

• Sinopharm / Wuhan Institute of Biological Products (China)

• Sinovac (China)

• CanSinoBIO (China, rush-approved for the Chinese military)

In Phase III trials, the vaccines are given to tens of thousands of volunteers to determine how the vaccine’s effectiveness and safety compare to placebos.  As much hope as these vaccine trials generate, there is no guarantee that any of the leading candidates will be viable and approved.

Given current US-China relations, it is not obvious that any of the Chinese vaccines would be available here, and even if they are available here, the FDA may require them to undergo US trials.  That leaves only three vaccines from ally nations.  

Moderna hopes to have doses available in “early” 2021, and AstraZeneca/Oxford hopes to begin delivering emergency doses in October.  Even in the most optimistic case of doses appearing in October, it will take some time to create 8 billion doses to cover the planet.  Like most things recently, the cure will create dissension.  Will the vaccine go to the most vulnerable?  The old?  The medical frontlines?  The rich?  Will everyone be forced to vaccinate? 

Clearly COVID-19 and the related economic uncertainty are far from over.  While we have more clarity than a few months ago, the future trajectory is still uncertain.

David R. “Chip” Kent IV, PhD
Portfolio Manager / General Partner
Cecropia Capital
Twitter: @chip_kent

Nothing contained in this article constitutes tax, legal or investment advice, nor does it constitute a solicitation or an offer to buy or sell any security or other financial instrument.  Such offer may be made only by private placement memorandum or prospectus.

Can a 4-quadrant table help you understand uncertain situations like COVID-19?

The last nine weeks have been unique and challenging for all of us, and have been tragic for many.  I hope that you and your loved ones have avoided the worst of COVID-19.  I also wish you the best going forward, as we struggle with COVID-19 and its repercussions.

What a difference a month can make.  Between February 20 and March 20, we experienced:

• A flu pandemic

• A global quarantine

• A 32% drop in the S&P 500 large-cap market index

• A 40% drop in the Russell 2000 small-cap market index

• A 53% drop in spot oil prices, in an attempt to bankrupt US frackers

• An all-time record for market volatility

• Multiple records for fastest market sell-off

As we contemplate all of the uncertainty related to COVID-19, I have been thinking about United States Secretary of Defense Donald Rumsfeld’s news briefing on February 12, 2002:

“Reports that say that something hasn’t happened are always interesting to me, because as we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns—the ones we don’t know we don’t know. And if one looks throughout the history of our country and other free countries, it is the latter category that tend to be the difficult ones.”

— Donald Rumsfeld

Rumsfeld’s thinking provides a valuable framework for examining uncertain situations.  Using this framework, we can examine what led the US to be the world leader in COVID-19 deaths: 

• Viruses have plagued humans from our beginning (known known).  

• Viruses occasionally pop up and cause great destruction (known known).  

• Scientists are monitoring a constant stream of new viruses that threaten humans (known known).  

• We observed a new disease spreading in China, but we have seen many similar diseases spread and then die out.  Will this new disease likewise fizzle, or will it be the one that causes major problems (known unknown)?  

• Moe, Larry, and Curly were put in charge of conceiving and executing the US pandemic response (unknown unknown).

As we consider how to move forward, two schools of thought predominate:  “safety at all costs” vs. “we can’t destroy the economy.”  The “safety at all costs” school-of-thought assumes that every possible human life should be saved, no matter what the cost.  By contrast, the “we can’t destroy the economy” school-of-thought assumes that each life is not infinitely valuable, so the lives of 97% can’t be ruined to save 3%.  

In the coming months, conflict is inevitable between these two camps.  At the moment, “safety at all costs” is winning, but as time passes and as Americans run low on cash, “we can’t destroy the economy” will become more prominent.

If we apply Rumsfeld’s framework to these two schools-of-thought, some important points emerge:

• Putting the economy on hold and minimizing social interactions reduces the spread of the disease.  Thus far, the pause appears to have been effective in slowing the disease progression (known known).  

• Pausing the world economy for months and then restarting it is the largest financial experiment in the history of the world (known known).  

• Will the economy quickly restart without grave problems (known unknown)?  

• Will restarting the economy and social interactions reinvigorate the disease (known unknown)?

• What other major variables and/or consequences will be obvious only in hindsight (unknown unknown)?  

Clarity will only come with time, and the ultimate outcome will come down to a complex mixture of science, economics, politics, and compliance.  Out of these variables, science is the easiest to understand.  We could arrive at a normal world through:

• Vaccine:  Best case estimates are 18 months for a vaccine, but it is possible there may not be a vaccine (known unknown).

• Drug:  As of the time of this writing, there is no scientific evidence of an effective drug, but there are many drugs in clinical trials (known unknown).

• Herd Immunity:  Many COVID-19 cases have had mild symptoms.  In a number of cases, antibody testing has shown that large portions of the population have already had COVID-19 (e.g. ⅕ of residents in New York City).  Some studies indicate that the number of actual cases may be 100x the number of confirmed cases.  Currently, the possibility of attaining herd immunity without a vaccine is a known unknown, but extensive antibody testing could rapidly transition our knowledge to a known known.   

How does this four-quadrant table help you understand uncertain situations like COVID-19? 

David R. “Chip” Kent IV, PhD
Portfolio Manager / General Partner
Cecropia Capital
Twitter: @chip_kent

Nothing contained in this article constitutes tax, legal or investment advice, nor does it constitute a solicitation or an offer to buy or sell any security or other financial instrument.  Such offer may be made only by private placement memorandum or prospectus.

What is the root of a good year?

We had a good return last year.  This return resulted from decisions made many years before -- but, only now did the decisions finally bear fruit.  This reminds me of the proverb:

“The day you plant the seed is not the day you eat the fruit.”

David R. “Chip” Kent IV, PhD
Portfolio Manager / General Partner
Cecropia Capital
Twitter: @chip_kent

Nothing contained in this article constitutes tax, legal or investment advice, nor does it constitute a solicitation or an offer to buy or sell any security or other financial instrument.  Such offer may be made only by private placement memorandum or prospectus.

Should a top-down view affect how you invest?

When I assess investments, I spend my time looking at low-level details like financial statements and risk of product obsolescence.  These low-level details are what paint my high-level view of the economy. Nevertheless, it is occasionally worth a few moments to step back and look at the forest, instead of at the trees.  

Donald Trump’s administration has created a number of economic curveballs.  The most obvious of these curveballs are the multiple global trade wars, most notably with China.  There are quite reasonable arguments that other nations have benefited economically from asymmetrical trade relationships with the US.  In addition, nations, especially China, have stolen every piece of American intellectual property they can get their hands on. These are real problems, and they were not addressed by previous administrations.  

Resolving such nation-vs-nation problems inevitably requires some sort of power struggle.  Trump has chosen a trade war as his method of action. In this standoff, Trump has made crazy and changing demands while China drags its feet, waiting for the problem to go away.  Is Trump negotiating the best deal for all of us? That remains to be seen. We will not know for sure until we look back years from now.  

Game theory is a branch of mathematics devoted to analyzing strategies that deal with competitive situations.  In many negotiating situations, game theory shows that the optimal negotiation strategy is to make crazy demands.  After hearing the really crazy demands, the counterparty eventually caves in to a demand that is only moderately crazy.  For instance, we all know of divorces where one individual made crazy demands and eventually came out with more than his or her fair share of the assets.  At this point, there is not enough evidence to convince me if Trump is acting in a game-theory optimal way, or whether he is just acting crazy. Time will tell. 

The trade wars are having real impacts.  Companies large and small have created both supply chains and businesses based upon a set of rules and expectations.  These rules and expectations included low trade barriers and cheap labor overseas. Suddenly, the future of these rules and expectations is in doubt.  US businesses are hesitant to build factories overseas because they have no idea what future taxes and political climates will look like. Similarly, the Chinese question their future access to American technology, so they now face the dire problem of creating technology that does not rely on the US.  This uncertainty will likely persist for many years, and it will destroy businesses on both sides.

The trade wars’ suppressed investment and demand exacerbates what appear to be recessions in many parts of the world.  Even before the trade war, China was struggling with a mountain of debt, and Europe never seemed to recover from the Great Recession.  These recessions may make other nations more likely to negotiate on favorable terms, or they may just worsen the results of the trade war.

To cope with the economic slowing described above, the US Federal Reserve has returned to a stimulative policy of lowering interest rates.  In fact, current short-term interest rates are 1.5%-1.75% while inflation is currently about 1.9%. That means that, on an inflation-adjusted basis, investors are losing about 0.3% per year by investing in bonds -- or in other words, negative real interest rates.  In fact, 10-year inflation-adjusted interest rates are roughly 0%.

This stimulation is designed to avoid a US recession triggered by the trade war.  While this sounds like a good idea, the piper must eventually be paid. The consequence of stimulative interest rates is that they drive risky financial behavior.  This is very apparent in Colorado. I recently looked into real estate in Colorado ski towns. The entry price for a condo is about $400,000 -- the same price as a trailer house in those ski towns.  Even calculating the economics with very optimistic rental assumptions, at these prices, the properties are cash-flow negative by about $20,000/year. That is not much of an investment, unless you assume that housing prices will continue charging higher.  Similarly, businesses burning money by the trainload, such as WeWork, have investors willing to continue sending the next carload of cash.      

We are late in the economic cycle, but the US economy still appears strong.  A recession will eventually come here. There are certainly excesses within the system, but they do not appear as egregious or as focused on the banking system as in the previous recession.  As a result, I expect that the next recession will be less catastrophic than the last.

When you look at the forest instead of the trees, remember what John D. Rockefeller told Henry Poor when Poor asked what was going to happen in the market.  Rockefeller replied: “It will fluctuate.” In the last 20 years, we have seen Y2K, the Dotcom bust, Enron, September 11th, the Iraq War, the Afghanistan War, the Great Recession, a banking crisis, a real estate bubble, massive wildfires, and even some major hurricanes.  All the while, the market has roughly doubled.

At Cecropia, regardless of economic curveballs like trade wars, disrupted global supply chains, or negative real interest rates, our focus is always to buy above-average businesses at below-average prices.  The market is our servant, not our master. We must remain patient and rational to profit from the market’s folly.

David R. “Chip” Kent IV, PhD
Portfolio Manager / General Partner
Cecropia Capital
Twitter: @chip_kent

Nothing contained in this article constitutes tax, legal or investment advice, nor does it constitute a solicitation or an offer to buy or sell any security or other financial instrument.  Such offer may be made only by private placement memorandum or prospectus.

What do mobsters, movie stars, and Microsoft have in common?

We have all been in a class, either in college or in high school, where students who performed poorly on a test were overjoyed when the instructor announced he was “grading on a curve.”  Somehow, through the magic of mathematics, the instructor performed an incantation that rocketed poor test performance to a passing grade. The magical incantation resulted in a few A’s (2.1%), many B’s (13.6%), a lot of C’s (68%), many D’s (13.6%), and a few F’s (2.1%) -- or in other words, a bell-shaped curve. 

Hidden in the instructor’s incantation was a belief that a bell-shaped curve represented the proper distribution of grades, and, more generally, the world.  While the bell curve does frequently appear in statistics and physics -- often associated with measurements -- it is not the only game in town.  

Let’s imagine that there is a school for criminals, where each criminal gets a grade.  We will call these grades “charges.” You might imagine that grades in the school of crime would resemble the bell-shaped distribution of grades in English Literature, but you would be wrong.  Instead, in crime, there are a very small number of individuals who generate massive numbers of criminal charges (“A’s”). These A-students are the Tony Sopranos or the Pablo Escobars of the underworld.  On the other hand, the vast majority of criminals only ever have one charge. These F-students commit one crime and then return to a law-abiding life.  

(Data from The Power Distribution as a Model for Criminal Careers)

As the graph above demonstrates, there is no bell curve in the school of crime.  A few criminals commit most crimes, while most criminals commit few crimes. In mathematics, such a distribution is known as a power law.

When I lived in Los Angeles, the power law was very evident.  It seemed like half of LA was trying to be the next big star. Even my hair salon -- I did have hair at the time -- was a haunt for would-be movie stars.  These aspiring actors and actresses auditioned for any and every part, made no money, and were supported by minimum-wage jobs or their families. On the other hand, a handful of big stars, like Brad Pitt, raked in tens of millions of dollars per year.  This is the power law. A few A-list stars live like kings, while millions of F-list stars -- if there is such a thing -- barely get by. The same power-law distribution occurs with models: look at how much Gisele Bundchen makes compared with everyone else.

(Figure from Power Law in Popular Media)

(Data from Forbes Releases 2014 Highest-Paid Models List)

As with mob bosses, movie stars, and models, power laws pop up in many other areas.  Power laws have been discovered in astronomy, criminology, physics, biology, meteorology, mathematics, the internet, and of course, economics.  Indeed, power laws seem to be a fundamental part of the fabric of our world. While the bell curve typically occurs when things are being measured, by contrast, power laws typically occur in situations driven by network effects, positive feedback loops, and popularity.   

The internet and artificial intelligence have made power laws more and more important to finance.  Consider Google. Back in the 1990’s, there were dozens of search engines to choose from: AltaVista, Ask Jeeves, Excite, Infoseek, Lycos, Yahoo, WebCrawler, etc.  These engines were very basic, but they could generally find what you wanted. In 1998, Google appeared. While the old engines used simple algorithms to perform a search, Google used intelligent algorithms.  As people used Google to search, Google used the user’s behavior to train its search algorithms, which made its search better, which caused more people to use Google’s search, which provided more user data, which made its search better, and so on.  After a few years of this virtuous cycle, Google was effectively the only game in town for search. You can see similar feedback cycles in other internet businesses, like Facebook or Airbnb.

Some forward-looking companies are very aware of our new winner-take-all power-law economics.  Uber and Lyft are in the midst of a money-burning bonanza to be the big winner in the ride-sharing business.  It is simple. If you need a ride, you will use the platform with the most drivers, and if you are a driver, you will use the platform with the most riders.  The reality of network effects and the power law makes it tough for more than one ride-sharing business to excel.

Similarly, Amazon, Google, and Microsoft are all desperate to win at cloud computing.  The more developers a cloud platform has, the more money the platform can spend to provide new features, which brings more developers to its platform, which pays for new features, and so forth.  Once again, a virtuous cycle is at work.

(Data from Top Cloud Vendors Will Crush $100 Billion In 2018 Revenue; Microsoft, Amazon, IBM Hit $75 Billion?)

As interesting as this discussion is, does it apply to investing?  Absolutely. The next time you consider investing in a company, ask yourself:  does this business exist in an industry that grades on a bell curve, where a “C” is just fine?  Or, does this business compete in an area that grades on a power-law curve, where the winner takes all, and only the best mobster or movie star thrives?

David R. “Chip” Kent IV, PhD
Portfolio Manager / General Partner
Cecropia Capital
Twitter: @chip_kent

Nothing contained in this article constitutes tax, legal or investment advice, nor does it constitute a solicitation or an offer to buy or sell any security or other financial instrument.  Such offer may be made only by private placement memorandum or prospectus.

Is value vs. growth a false dichotomy?

One of the major questions of the investing world is: “Value or growth?”  I hear this question when I talk to people on the street and even when I read books by accomplished investors.  In fact, Morningstar has codified this question into a popular infographic:

While this either/or question is popular, the dichotomy -- and the related logic it rests on -- always flummoxes me.  At the core of the either/or question are two specific, orthogonal views of the world:

1. Value investing is the practice of investing in businesses which are selling at low price-to-book, price-to-sales, or price-to-cash-flow ratios or which have high dividend yields.

2. Growth investing is the practice of investing in businesses which have high book-value, sales, cash-flow, or earnings growth rates.

The definitions above are common, and the investment strategies they produce are convenient and widely practiced.  Yet, if you look closely, this either/or thinking produces a number of paradoxes. For example, if you are a value investor, would you invest in a company, such as Sears, which spent many years on the path to bankruptcy, even though it was selling at a low price-to-sales ratio?  Similarly, if you are a growth investor, would you invest in a company, such as Tesla, which is growing rapidly but is losing $1B/year?

Like the paradoxes of physics, the financial paradoxes above result from a flawed underlying theory.  In this case, the flaw is in how you define value and growth investing. Simply stepping back and thinking about what your grandmother -- or Warren Buffet -- would tell you resolves the paradoxes. 

“Price is what you pay.  Value is what you get.  ”

— Warren Buffett

When purchasing a business, “what you get” are assets, debt, employees, and potential future business outcomes.  Whether or not you got a good value depends on if the future value of the business ends up being more than what you paid for it.  As such, good values can be had by businesses with negative growth (such as Warren Buffett’s purchase of Berkshire Hathaway), and terrible values can be had by businesses with great growth (such as the DotCom investor’s purchase of Microsoft).  Peter Lynch elaborated on the latter scenario when he wrote:

“It’s a real tragedy when you buy a stock that’s overpriced, the company is a big success, and still you don’t make any money.  ”

— Peter Lynch

In the early days, while managing small sums of money, Buffett tended to purchase marginal businesses selling at favorable financial ratios, just as his mentor Benjamin Graham did.  In time, Buffett’s partner, Charlie Munger, convinced Buffett to purchase higher-quality, growing businesses at less favorable financial ratios. In both cases, Buffett paid less than the businesses were worth.  However, once Buffett began buying higher-quality, growing businesses at less favorable financial ratios, he could produce above-average returns on a portfolio much bigger than would have been possible using his old strategy.  As you read how Buffett defines “value” and “growth” below, note that he thinks in terms of both/and, rather than in terms of a false dichotomy of either/or:

“But how, you will ask, does one decide what’s “attractive”?  In answering this question, most analysts feel they must choose between two approaches customarily thought to be in opposition:  “value” and “growth.” Indeed, many investment professionals see any mixing of the two terms as a form of intellectual cross-dressing.

We view that as fuzzy thinking (in which, it must be confessed, I myself engaged some years ago).  In our opinion, the two approaches are joined at the hip: Growth is always a component in the calculation of value, constituting a variable whose importance can range from negligible to enormous and whose impact can be negative as well as positive.  ”

— Warren Buffett

Next time you hear a discussion that hinges on a dichotomy of value vs. growth, remember this:   all real investing is determining if what you are buying is worth more than what you are paying. Trying to divide the question (and your resulting investment decision) into value vs. growth simply leads to nonsense.

David R. “Chip” Kent IV, PhD
Portfolio Manager / General Partner
Cecropia Capital
Twitter: @chip_kent

Nothing contained in this article constitutes tax, legal or investment advice, nor does it constitute a solicitation or an offer to buy or sell any security or other financial instrument.  Such offer may be made only by private placement memorandum or prospectus.

Do you take the long view?

During this most recent market panic, a few investors asked me questions along the lines of, “What should an investor do at a time like this?”  This is an interesting and important question.During trying times, like the last quarter, the media -- and many of our friends -- provide very emotional arguments for taking action.  “Something must be done!” they say. What action do they have in mind? Perhaps it is hedging, perhaps it is buying gold, or perhaps it is liquidating the entire portfolio and burying the proceeds in the backyard.  Their focus is on doing something, and not necessarily on the right thing to do.

This common, and emotionally attractive, view stands at complete odds with the view of people who create massive amounts of wealth.  Take a look at any of the lists of richest people. Perhaps they are the world’s richest people, America’s richest people, your state’s richest people, or even your city’s richest people.  Whoever they are, these people made their money by owning very high-quality assets for a very, very long time. They do not dart in and out of investments based upon how they feel. They do not make their money by hedging.  Instead, they let their businesses grow for very long periods of time, and they ignore what others will pay for their business from one microsecond to the next.

Consider how the average investor treats a house versus a stock.  In real estate, the average investor is happy to sit on his investment for decades.  By contrast, in the stock market, the ease of buying and selling combines with a constant stream of manic-depressive prices from “Mr. Market” to create a recipe for stupidity.  The temptation to make bad decisions is enormous. As a result, most people would be better off if they treated their stock portfolio like they treated their home. Investors should ignore short-term fluctuations and focus instead on growth over very long periods of time.

Does it make sense to sell anything when the market is down 1,000 points?  Obviously not. Quite the opposite. If any action is taken, it should be to buy.  As Baron Rothschild said: “The time to buy is when there is blood in the streets.” This logic is obvious, but it is rarely followed.  Paying too much attention to the market triggers negative emotions, and ultimately, illogical actions. During scary or upsetting times, investors are better off if they unplug and avoid the market for a while.  This emotional distance will provide the calm and rationality that investors need to weather Mr. Market’s temporary depressive period.

To illustrate this principle, let us look at the world’s three richest men:  Jeff Bezos, Bill Gates, and Warren Buffett.   

The vast bulk of Jeff Bezos’s wealth stems from his ownership in Amazon.  Since 1997, Jeff Bezos’s wealth has grown by about 956x. Bezos recognized Amazon as a high-quality asset, and he held it through thick and thin, including through a 93% drawdown which took 10 years to recover from. 

Similarly, the vast bulk of Bill Gates’s wealth stems from his ownership in Microsoft.  Since 1986, Bill Gates’s wealth has grown by about 1,100x. To achieve this return, Gates held onto Microsoft while suffering through a 72% drawdown that lasted 17 years.

Warren Buffett’s wealth comes from his ownership of Berkshire-Hathaway.  Since 1990, Buffett’s wealth has grown by “only” 42x. As with Bezos and Gates, Buffett’s road to such wealth was not smooth.  Achieving this growth required weathering two drawdowns of 45% or more.

All three of these men chose to own high-quality assets for decades.  They remained patient through thick and thin, and they stayed the course through significant drawdowns, some of which took more than a decade to recover from.  Clearly, they took the long view, and ultimately, they were rewarded for it.

I would like to close with some key points by John Maynard Keynes, the most influential British economist of the 20th century, and a value-investor himself.  Keynes managed a 9.12% annualized long-term return from 1927 to 1945. This period included the Great Depression and World War II, and the overall British market was down.  In chapter 12 of “The General Theory of Employment, Interest, and Money,” which Buffett ranks as one of the three chapters that every investor should read, Keynes contrasts short-term panic with long-term profits.

“Day-to-day fluctuations in the profits of existing investments, which are obviously of an ephemeral and non-significant character, tend to have an altogether excessive, and even an absurd, influence on the market ... [They are] the outcome of the mass psychology of a large number of ignorant individuals [and are] liable to change violently as the result of a sudden fluctuation of opinion due to factors which do not really make much difference to the prospective yield; since there will be no strong roots of conviction to hold it steady ... The market will be subject to waves of optimistic and pessimistic sentiment, which are unreasoning …

The Stock Exchange revalues many investments every day and the revaluations give a frequent opportunity to the individual ... to revise his commitments. It is as though a farmer, having tapped his barometer after breakfast, could decide to remove his capital from the farming business between 10 and 11 in the morning and to reconsider whether he should return to it later in the week.

The spectacle of modern investment markets has sometimes moved me towards the conclusion that to make the purchase of an investment permanent and indissoluble, like marriage, except by reason of death or other grave cause, might be a useful remedy for our contemporary evils. For this would force the investor to direct his mind to the long-term prospects and to those only.”

— John Maynard Keynes

We -- like Keynes, Buffett, Bezos, and Gates -- buy our investments “for keeps.”  Emotion is one of the investor’s greatest enemies, and we recognize that. We force ourselves to take the long view, however unconventional and rare that might be in an industry that focuses almost exclusively on activity and on the short-term.  Superior investors may not feel insulated from panic and from the urge to do something, but they manage to act as if they are.  

Do you take the long view?

David R. “Chip” Kent IV, PhD
Portfolio Manager / General Partner
Cecropia Capital
Twitter: @chip_kent

Nothing contained in this article constitutes tax, legal or investment advice, nor does it constitute a solicitation or an offer to buy or sell any security or other financial instrument.  Such offer may be made only by private placement memorandum or prospectus.

Are technologies of the future a wise investment in the present?

In last quarter’s letter (2018Q2 “Technology Of The Future”), I explored four new technologies -- CRISPR gene editing, self-driving cars, nuclear fusion, and quantum cryptography -- with the potential to change our world.  In the future, these technologies will make our health better, our commutes safer, our energy cheaper, and our computers more powerful.

If the future looks so bright, and if we know what technologies might change the world, then why not invest in them?  Unfortunately, it is far easier to predict which technologies might change the future than it is to predict which particular early-stage company might get lucky and make it big.

To illustrate this principle, let us consider a world-changing technology of the past: cars.  In the late 1800’s, cars were the hot new technology, and Detroit was the Silicon Valley of its day.  The auto industry went from one-off, expensive, hand-built, wooden buggies, to technological wonders produced by the millions.  Today, Americans buy almost 20 million new cars each year.

Many of the cars that U.S. consumers buy are produced by the “American” brands of GM, Ford, and Chrysler.  What we forget are all of the dead American car companies. Have you ever heard of DeLorean, Duesenberg, Packard, Studebaker, Auburn, Edsel, or Tucker?  How about Bacon and Beggs? In total, there have been about 3,000 U.S. car companies whose names range from ABC to Zip. Almost all of the 3,000 companies had died by 1920.  When car technology was new, automobile innovation exploded. In turn, this innovation led to an explosion in the number of car manufacturers, but eventually, this multitude of options collapsed into just 3 major players.  Would you have been able to pick the 3 winners out of 3,000 choices? Only 0.1% of U.S. auto businesses became winners.

Similar dynamics occur during other technological revolutions, and in broad terms, we can divide these dynamics into the three stages of an industry’s life cycle.  In the first stage, the new technology creates a proliferation of both ideas and businesses trying to profit from those ideas. Many of these businesses never turn a profit.  In the second stage, the dust settles from the explosion of new businesses, and a few winners begin to dominate the industry. These winners typically enjoy a period of steady growth and solid profits.  In the third stage, the once-hot technology becomes commonplace and easily reproduced. At this point, new businesses appear, which compete to produce the good at the lowest possible price (think cheap Chinese knock-off).  During this second proliferation of producers, margins significantly decrease.

It is possible to profit by investing in each of these three stages, but each stage requires a different investing strategy.  

Most first-stage investments go to zero, but there is an occasional huge winner.  This is where Venture Capital (VC) funds invest. A VC fund may invest in 10 businesses.  When the VC fund managers select investments, they must make sure that each of the investments has the potential to increase by at least 10x.  That is because, in a typical fund made up of 10 businesses, 7 will die, 2 will return about zero, and 1 will succeed. If the fund ends up with 2 big winners, then investors are very happy.  First-stage investing is a game of low odds and high variability.

As an industry transitions from the first stage to the second stage, the weak businesses fail, leaving a few winners.  With less competition, these winners have higher odds of success. Second-stage companies can potentially grow 10-30% per year for decades.  This is a game of higher odds and lower variability. These are my favorite investments.

Eventually, industries transition to stage three.  In stage three, the technology becomes common, competition increases, and margins decrease.  The winners of stage three are the businesses that can produce the technology at the lowest cost.  For example, at one point, knitting textiles was high tech. The US and the UK were the global leaders, and New England was the Silicon Valley of its age.  However, eventually textile technology proliferated globally. Now, the US and UK cannot compete with third-world sweatshops. Textile technology is cheap, common, and available.  Today, winning textile businesses are low-cost producers that have the lowest labor costs.

It is tough to invest in stage-three companies.  Competition abounds, and a stage-three winner must sustain low production costs, or its razor-thin profit margins, and its profits, will evaporate.  This is a game of low odds and high variability.

Occasionally, a stage-three business has built-in advantages which give it an edge in the marketplace.  Saudi Aramco is one such example. Everyone has the technology to pull oil from the ground, but very few can produce a barrel of oil for less than $10, like Aramco does.  Aramco won the geological lottery, so it is able to profit no matter what happens to the rest of the industry. This illustrates what makes a good stage-three investment.   

It is possible to make good investments in emerging technologies, but most of these opportunities look more like lottery tickets than wise investments.  In the first stage, the vast majority of investments lose, while only a few win. Instead, I prefer to concentrate on the second stage -- a game of higher odds and lower variability.  Warren Buffett explained it this way in his 2000 annual letter:

“At Berkshire, we make no attempt to pick the few winners that will emerge from an ocean of unproven enterprises. We’re not smart enough to do that, and we know it. Instead, we try to apply Aesop’s 2600-year-old equation to determine opportunities in which we have reasonable confidence as to how many birds are in the bush and when they will emerge ...”

— Warren Buffett

David R. “Chip” Kent IV, PhD
Portfolio Manager / General Partner
Cecropia Capital
Twitter: @chip_kent

Nothing contained in this article constitutes tax, legal or investment advice, nor does it constitute a solicitation or an offer to buy or sell any security or other financial instrument.  Such offer may be made only by private placement memorandum or prospectus.

Which four technologies might change your world?

As a technology nerd, I’m always looking to the future.  For the last 16,000 years, humans have produced a steady stream of new technologies that have drastically improved our lives.  Early inventions such as agriculture or copper tools took thousands of years to spread around the world. Newer inventions such as the internet or smartphones become global in a decade.  

What’s next?  A few technologies that I see as potentially world-changing are: CRISPR, quantum computers, self-driving cars, and nuclear fusion.

1. CRISPR

CRISPR (pronounced “crisper”) is a class of DNA sequences found in bacteria and archaea which constitute the immune system of the cells.  CRISPRs are found in ~50% of bacteria and ~90% of archaea. Each CRISPR contains a snippet of DNA which matches a virus seen at some point in the cell’s evolutionary past.  These snippets allow CRISPR to detect and destroy similar viruses during subsequent attacks.

In 2013, scientists figured out how to hijack CRISPR to perform highly targeted genome edits.  As such, CRISPR provides us with an unparalleled tool to change life as we know it. Just one year later, in 2014, over 1000 research papers discussed how to use CRISPR to change human cells, modify yeasts to make biofuels, alter crop strains, and change mosquitos to eliminate malaria.  By the start of 2018, 86 people in China have already had their genes edited by CRISPR. It is a brave new world.

2) Self-Driving Cars

In the not too distant future, taxi drivers and bus drivers will be a thing of the past, and you will be able to take a nap or check your email while commuting to the office.  

The 2004 DARPA Grand Challenge was a battle between autonomous vehicles to navigate the Mojave Desert.  The “winning” vehicle only completed 7 miles of the course. Today, 14 years later, the two leading autonomous driving companies -- Waymo (Google) and Cruise (GM) -- have logged millions of autonomous miles, and both companies average 5,000 or more miles between any human interventions.

Next year, GM will begin production of a car without a steering wheel or pedals.  The future is coming quickly.

This rapid surge in autonomous vehicle technology has been made possible by new artificial intelligence algorithms as well as massive performance leaps in GPU (Graphics Processing Unit) and TPU (Tensor Processing Unit) hardware to run such algorithms.

Traditional computer algorithms execute a set of instructions devised by their human creators. These creators can then devise tests to ensure that the programs are performing the prescribed rules.  However, the new AI algorithms that power autonomous vehicles are very different. The AI algorithm creators lay out how the AI “brain” is wired up.  Then, the human creators feed the AI algorithms massive amounts of data so that the AI algorithms can “learn” what to do. As a result, the machines are now programming themselves.  We can no longer understand what the program is doing, examine how it is making decisions, or even test that the machine is doing what we want. Because of this, using an autonomous vehicle is effectively a leap of faith that the machine has learned to drive better than a human.

3) Nuclear Fusion

The media typically describes nuclear fusion as a technology that is always 30 years away.  Performing nuclear fusion is not a challenge. Right now, it is possible to perform nuclear fusion in your garage.  Instead, the challenge is performing fusion in such a way that it produces more energy than it consumes.    

With little media fanfare, fusion technology has steadily improved at a massive rate.  Compared to the fusion of the 1960s, today’s nuclear fusion has a 100,000+x improvement in the triple product (temperature x density x confinement time).  In the coming decades, better numerical simulations and new, innovative reactor designs may finally push nuclear fusion over the break-even threshold as a commercially viable energy source.  

4) Quantum Computers

Quantum computers were first proposed in the 1950s by Nobel Prize winning Caltech physicist Richard Feynman.  Standard computers store information as and compute upon bits -- zeros and ones. By contrast, quantum computers store information as and compute upon qubits (quantum bits) -- a fuzzy blur somewhere between zero and one.  

Quantum computers have proven extremely difficult to build.  Tiny atomic vibrations create enough noise to destroy a quantum state and to ruin a computation.  Physicists have been developing ways to avoid these errors by creating computers that run at temperatures near absolute zero and by coupling multiple qubits in special ways to correct for errors.  

Quantum hardware has progressed at a rapid rate.  In 1998, the first working 2-qubit computer was created.  Today, Google has created a 72-qubit computer named Bristlecone.  Within the next year or so, quantum computers will have enough qubits to be more powerful than any classical computers for certain types of problems.

One important problem that quantum computers excel at is factoring integers.  While this may seem unimportant, integer factorization underlies essentially all of the cryptography which keeps our computers and our communications secure.  Right now, there is an arms race between the speed of new quantum computers and the technology that keeps our computers and communications secure. Let’s hope that cryptography makes rapid progress before quantum computers allow anyone to decrypt and read all internet traffic.

David R. “Chip” Kent IV, PhD
Portfolio Manager / General Partner
Cecropia Capital
Twitter: @chip_kent

Nothing contained in this article constitutes tax, legal or investment advice, nor does it constitute a solicitation or an offer to buy or sell any security or other financial instrument.  Such offer may be made only by private placement memorandum or prospectus.

What is a fair interest rate?

What is the most important variable in investing?  The answer is simple: future interest rates. Interest rates drive all other investor behavior.  For example, if interest rates are high, then home buyers are unwilling to pay as much for a house, because they will have large interest payments. Conversely, if interest rates are low, then home buyers are willing to pay more for a house, because they are not devoting as much cash flow to interest payments.  The same rules apply to all financial assets. If interest rates are high, it pushes asset prices down, and if interest rates are low, then it pulls asset prices up.  (For more details, see my previous article, “What is a baseball team, a bond, or a business worth?”)

Current interest rates are computed from bond prices.  These current interest rates fluctuate up and down based on several factors such as how investors are feeling, how the central bank (Federal Reserve) has decided to manipulate markets, etc.  These dynamic fluctuations mean that current interest rates may or may not reflect reasonable interest rates, given a long-term view.

Since interest rates influence the prices of all other financial assets, it raises the question of: “What should interest rates be?”  I think the most thoughtful answer to this question came from a 1993 paper by John Taylor of Stanford.  Taylor looked at the interest-rate problem through the lens of Optimal Control Theory.  While Optimal Control Theory sounds complex, it is conceptually very simple. As an example, take a self-driving car.  If the car drifts too far to the left, it steers right. If the car drifts too far to the right, it steers left. Small, incremental steers to the left or right keep the car between the lines.  

Taylor’s model applies the same reasoning to the economy.  Instead of steering a car with a wheel, the central bank steers the economy with interest rates.  If the economy is growing too quickly or if current productivity is unsustainable, then the central bank should raise interest rates to reign things in.  On the other hand, if the economy is weak or if productivity is languishing, then the central bank should lower interest rates to stimulate activity. By manipulating interest rates, the central bank can attempt to steer the economy and keep it between the lines.

Subsequent research has built upon Taylor’s work.  These papers primarily (1) tweak constants so that interest rate adjustments are more or less aggressive and (2) switch which factors are used to steer the economy (e.g. unemployment rate instead of productivity).  While these modifications produce slightly different numbers, the results are typically quite similar to Taylor’s original model.

The chart above shows one version of a Taylor Rule, computed with Federal Reserve data.  The red line is the actual market interest rate, and the blue line is the Taylor Rule target interest rate.  From this chart, we can make two interesting observations. First, during the 1970s, the Taylor Rule suggested that rates needed to be higher than they actually were in order to control inflation.  However, it was not until the 1980s that the Federal Reserve raised rates enough to finally moderate inflation. Second, from 2000-2008, the Taylor Rule once again suggested that interest rates needed to be higher than they actually were.  If the Federal Reserve had raised interest rates, it might have softened or avoided the housing bubble entirely.

The chart above focuses on the last decade, and like the previous chart, it is extremely interesting.  Fallout from the Great Recession left the US with weak growth and a very poor employment market. Many Taylor-variant models suggested that the Federal Reserve should impose negative interest rates in order to force the economy forward.  Some models suggested negative rates for a year or two, while other models suggested that we should have had negative rates all the way until 2016. While Europe ventured into negative interest rates, the US did not. It is possible that the sluggish economic growth and weak inflation we experienced over the last decade resulted simply from limiting interest rates to zero, rather than letting interest rates go negative as some Taylor models suggested.

Currently, the economy is beginning to heat up, unemployment is extremely low, and inflation is beginning to appear.  The various Taylor-like models now say that the economy is either between the lines or possibly is getting overheated.  As a result, almost all models prescribe interest rates of 4% or possibly more.

Most people would be stunned to see interest rates quickly rise to 4%.  Yet if the Federal Reserve continues to run interest rates significantly below 4%, it may negatively impact the economy.  As I said before, future interest rates are the most important variable in investing. Right now, a gap exists between what interest rates should be -- according to Taylor -- and what they are.  As mindful investors, we should be aware of the unintended outcomes that may result from this discrepancy.

David R. “Chip” Kent IV, PhD
Portfolio Manager / General Partner
Cecropia Capital
Twitter: @chip_kent

Nothing contained in this article constitutes tax, legal or investment advice, nor does it constitute a solicitation or an offer to buy or sell any security or other financial instrument.  Such offer may be made only by private placement memorandum or prospectus.

When should you tell a CEO, "You're fired"?

Out of all the jobs on the planet, being CEO is possibly the hardest job to be fired from for poor performance.  This sounds counterintuitive, but in reality: 1) the exact specifications on what constitutes good vs bad CEO performance are rarely laid out; 2) the CEO can always blame a subordinate for negative outcomes; and 3) the boss who can fire the CEO (the board of directors) more often than not offers no governance, and instead, simply rubber stamps anything set before it.

Of the CEO’s functions, the most important is allocating the business’s cash flow.  While the subject sounds complex, a CEO can really only allocate cash flow to a few purposes:  (1) paying taxes, (2) servicing debt, (3) reinvesting in the business, (4) repurchasing shares, (5) acquiring other businesses, and (6) disbursing cash to equity holders (dividends).

Options 1 and 2 -- taxes and debt -- are fairly straightforward.  Equity holders are in partnership with debtholders and the government.  Death and taxes are inevitable.  Not only does the government always get its cut of the profits, but the government gets to decide how big that cut is.  Over the last few decades, the government’s cut has been high.  Over the next few years, it appears that the government has voluntarily given up some of its cut, which leaves more profits for equity holders.  As for debt, the CEO decides how much debt the business should have and how much risk versus opportunity such debt poses to the business.  More debt may mean more money for short-term growth, but it also makes the business more susceptibility to bankruptcy during a downturn.

Options 3-6 are the most interesting and the most commonly misunderstood.  In the financial media, it is common to hear statements like “a company should pay a dividend” (Option 6) or “repurchasing shares is good” (Option 4).  Unfortunately, neither statement is always true (sometimes these might be good choices, and sometimes they might be poor choices), and the truth can not be distilled down to a simple sound bite.  

In reality, only one factor determines whether a CEO’s decision is a good capital allocation or a poor one, and that factor is the expected return of this choice versus other alternatives.  Unfortunately, a CEO may struggle to determine how to allocate capital, because he or she likely ascended to the CEO role from a position that did not require financial literacy.  Without the base knowledge of how to determine what is or is not a good capital allocation, how can the CEO make good decisions?  Often, CEOs who are financially ignorant rely on management consultants, and management consultants are incentivized to allocate capital in ways that maximize the consultant’s fees, rather than in ways that maximize shareholder profits.  Additionally, a CEO’s compensation plan may be structured such that what is best for the CEO is not what is best for the shareholder.  In a recent egregious instance, a new CEO sold a business, which we owned, at a massive discount to its intrinsic value with zero premium to the market price.  Shameful!  The CEO will receive a multi-million dollar payout for a few months of “work” while the shareholders get the shaft.

Back to Option 3: When should a CEO reinvest in a business?  When the return is higher than other alternatives.  Amazon and Berkshire Hathaway are fantastic examples of businesses that have effectively reinvested almost all of their profits over decades.  With Amazon, Jeff Bezos has grown sales at greater than 20% per year while establishing multiple monopolies.  With Berkshire, Warren Buffett has compounded the business’s book value at greater than 20% per year since 1965.  Both Bezos and Buffett have an investment hurdle for any investment.  If a project is unlikely to return 20% per annum, they will not fund it.

By contrast, more ordinary CEOs do not have such investment hurdles, and as a result, they succumb to two common problems.  The first problem happens when a CEO increases earnings simply by increasing the amount of capital used to generate the earnings.  These new earnings are produced with a very poor return on capital.  Buffett described the situation like this:

“When returns on capital are ordinary, an earn-more-by-putting-up-more record is no great managerial achievement. You can get the same result personally while operating from your rocking chair. Just quadruple the capital you commit to a savings account and you will quadruple your earnings. You would hardly expect hosannas for that particular accomplishment. Yet, retirement announcements regularly sing the praises of CEOs who have, say, quadrupled earnings of their widget company during their reign — with no one examining whether this gain was attributable simply to many years of retained earnings and the workings of compound interest.”

— Warren Buffett

The second problem happens when a CEO reinvests in a slowly dying business with high capital requirements.  Imagine a manufacturing business that is trying to compete against a cheaper foreign competitor.  In this scenario, the rational path forward is to limit reinvestment and to return as much capital as possible to investors.  Unfortunately, in most circumstances, the CEO continues buying the latest gadgets, with hopes that such gadgets will somehow permanently fend off the cheaper competitors.  They almost never do.

Option 4: When should a CEO repurchase shares?  When they are cheap!  A quality CEO should have a very good idea what his business is worth.  When the business trades significantly below that value, it is a good time to purchase shares.  When the business trades significantly above that value, it is a bad time to purchase shares.  For instance, Buffett has indicated that Berkshire will consider repurchasing shares if they ever fall to 120% of book value.  Any price above this does not clear Buffett’s investment hurdle for Berkshire to repurchase shares.  John Malone of Liberty Media takes this option one step further.  Malone not only repurchases (buys) shares when they are cheap, but he issues (sells) new shares when they are expensive.  He then uses the proceeds to buy back shares when the price finally falls.  What a brilliant insight!  Using this strategy, Malone has returned in excess of 20% per year since 1973.  

Unfortunately, most CEOs have no idea what their business is worth.  Instead, like most investors, they choose to buy high and to sell low.  The figure below clearly demonstrates the problem.  The higher the market (green line), the higher the amount of share repurchases (dark blue bars).  If a CEO were rational and understood what his business was worth, then he would do the opposite: purchase large numbers of shares when the market is down and taper off buying as the market goes up.  So much for rationality.

Share repurchases can also mask how executives pillage a company.  Many executive compensation plans pay out an outrageous fraction of a company’s earnings to executives.  These payouts typically happen via stock options.  To mask the dilution caused by such stock grants, many companies purchase offsetting amounts of stock, at any price -- but many investors fail to notice this sleight of hand.

Option 5: When should a CEO purchase another business?  When it is cheap!  Such a transaction should only happen when the CEO can acquire -- at a very good price -- the future cash flows or assets of the purchased business.  (Note that combining the businesses may reduce some overhead, leading to cash flows greater than the separate businesses.)  

Unfortunately, the average CEO shows the same skill at acquiring other businesses as he does at repurchasing his own company’s shares.  The chart below shows merger and acquisition activity over time.  You can see large activity peaks in 2000, 2007, and 2015, with troughs in 1991, 2002, and 2009.  Peak buying coincides with market tops (2000 & 2007), and inactivity coincides with market bottoms (2002 & 2009).  Once again, so much for buying low and selling high.

Buffett summarized the situation this way:

“The sad fact is that most major acquisitions display an egregious imbalance: They are a bonanza for the shareholders of the acquiree; they increase the income and status of the acquirer’s management; and they are a honey pot for the investment bankers and other professionals on both sides. But, alas, they usually reduce the wealth of the acquirer’s shareholders, often to a substantial extent. That happens because the acquirer typically gives up more intrinsic value than it receives. Do that enough, says John Medlin, the retired head of Wachovia Corp., and “you are running a chain letter in reverse.” ... The acquisition problem is often compounded by a biological bias: Many CEOs attain their position in part because they possess an abundance of animal spirits and ego.”

— Warren Buffett

Option 6: When should a CEO pay a dividend to shareholders?  The answer depends on what other opportunities are available.  A dividend should not be paid if reinvesting in the business, repurchasing shares, or purchasing other businesses can be done with returns higher than an investor is likely to get with cash.  On the other hand, if the business is in a long-term decline, if the stock price is high, and if there are no cheap businesses to purchase, then paying a dividend may make sense.  However, there is a catch.  Uncle Sam has decided to take two cuts out of the dividend pie.  The first cut is via the corporate tax, and the second cut is via the individual income tax.  Up until recently, this could mean a 35% corporate tax followed by a 35% dividend tax, so only 42% of the business profit may make it to the shareholder.  This double taxation makes dividends very tax inefficient, and it skews the optimal strategy towards other avenues for capital deployment.

As an investor, why should you care what makes a good CEO versus a poor one?  You should care because, over time, a CEO’s capital deployment decisions either increase or decrease the intrinsic value of a business.  Good decisions can lead to rapid, exponential increases in value, while bad decisions are the equivalent of setting fire to millions of dollars.  Unfortunately, most CEOs do not make good capital allocation decisions, yet they try to sell their decisions to investors as if they were wise choices.  Frequently, the only way to tell a CEO, “You’re fired,” is by cutting his business from your portfolio.

David R. “Chip” Kent IV, PhD
Portfolio Manager / General Partner
Cecropia Capital
Twitter: @chip_kent

Nothing contained in this article constitutes tax, legal or investment advice, nor does it constitute a solicitation or an offer to buy or sell any security or other financial instrument.  Such offer may be made only by private placement memorandum or prospectus.

Did Harvey cause Houston's worst flood since glyptodons roamed the Earth?

This August, Hurricane Harvey dumped 33 trillion gallons of water on the Houston area, flooding 50 counties, 100,000 homes, and 1 million cars.  Along with a few Cecropia investors, my brother and his family were among those affected.

In the days immediately after the water began to recede, Brooke and I put in long hours gutting my brother’s house, so that it could begin drying out.  One evening, exhausted, we collapsed on my parents’ couch.  As we sat, we heard the news station describe the flood as a once-in-40,000 year event.  

40,000 years, really?  40,000 years ago North America was filled with mastodons, mammoths, cheetahs, giant sloths, camels, and even glyptodons (think 2-ton armadillo).  Calling the flood a once-in-40,000 year event seems especially strange, when in a 1994 flood, I canoed through the same neighborhoods that flooded again in 2017.  Was Harvey really a once-in-40,000 year flood, or a once-in-23 year flood?

I don’t believe for a minute that the Harvey flooding was a once-in-40,000 year event.  What I do believe is that the news station’s 40,000-year estimate resulted from atrocious mathematical reasoning -- the same atrocious mathematical reasoning that regularly appears in finance.

Flawed mathematical reasoning -- be it in finance or in flood prediction -- commonly results from three errors:  (1) the new situation not being like the past; (2) simple models not reflecting reality; and (3) humans drastically misestimating rare events.  Let us look at a few illustrative cases.

First, let us consider Hurricane Harvey.  The first question to ask is: does reliable information exist for major floods over the past 40,000 years?   I assume not.  Reliable data from a USGS analysis of the 1994 flood showed that only 44% of measurement stations exceeded a 100-year flood -- meaning that 56% of measurement stations recorded flood levels that would occur more often than once per century.  This data suggests that the 2017 flood falls far short of the once-per-40,000 years estimate (which takes us to flaw #3 -- humans drastically misestimate rare events).  Additionally, it is worth noting that much of the flooding happened near waterways that have been dammed.  The dams altered the water’s natural flow, but above and beyond this, some allege that improper release of water from the dams made the flooding worse than it would have been.  Clearly the dams complicated Harvey’s effects, and that brings us to flaw #2 -- simple models may not reflect reality.

From Harvey, let us turn to the world of finance.  Long Term Capital Management (LTCM) was a multi-billion dollar hedge fund founded in 1994 by a group of elite bond traders and two Nobel prize winners.  For the first four years, LTCM returned 21%, 43%, 41%, and 27% with relatively little volatility.  Under the covers, LTCM executed a conceptually simple strategy.  LTCM looked for cases where one bond was cheap relative to another bond.  When LTCM found such cases, it would sell the expensive bond and buy the cheap bond.  Frequently, this meant that LTCM had bought less liquid bonds and had sold short highly liquid bonds.  Furthermore, in order to juice its returns, LTCM leveraged this trade by more than 25-to-1.  

While the world behaved “normally”, LTCM reaped enormous profits.  However, 1998 was not a normal year.  Solomon Brothers, one of LTCM’s competitors, decided to exit its arbitrage business in July 1998.  Solomon’s portfolio was similar to LTCM’s, so when Solomon liquidated its positions, that drove down the prices of LTCM’s long positions, and it drove up the prices of LTCM’s short positions, leading to significant losses.  Just afterwards in August and September of 1998, Russia decided to default on its ruble-denominated bonds.  At the time, it was unthinkable that a sovereign government would default on locally denominated bonds, since conventional wisdom assumed that the government would simply print money to pay for the bonds.  (That brings us to flaw #1: the new situation not being like the past.)

Faced with such an anomaly, investors panicked, driving up the prices of liquid bonds (which LTCM had sold short) and driving down the prices of liquid bonds (which LTCM had bought).  This adverse change in the bond spreads, combined with LTCM’s massive leverage, lead to crushing losses.  In the end, 1998 was not like the years preceding it:  Solomon Brothers, a major bond-trading player, exited the market, and the odds of Russia defaulting on its bonds were far higher than LTCM anticipated (see flaw #3:  humans drastically misestimate rare events).  These logical errors not only wiped out LTCM’s investors, but they required the Federal Reserve to bail out LTCM’s creditors to avoid a widespread failure of the financial system.

For the third case, let us consider a contemporary example:  Bitcoin.  Bitcoin is a decentralized digital payment system based upon cryptographic algorithms.  Bitcoin allows users to transfer payment, more or less anonymously, without the need for an intermediary, such as a bank.

Two theories about Bitcoin prevail.  The first theory rests on the fact that Bitcoin is designed to have a maximum of 21 million Bitcoins.  These Bitcoins are “mined” by running computationally expensive calculations on a computer.  This fixed size leads some people to assert that Bitcoins are analogous to stores of value that have a fixed total size, such as gold.  By extension then, the fixed-size theory says that Bitcoins must have an intrinsic value, because there are only a finite number of them.  

The contrasting theory, proposed by others like Warren Buffett, is that Bitcoin resembles a checkbook -- a mechanism of transmitting value -- and therefore, it has no inherent value.  In both cases, the analogies draw on past beliefs, and as we saw with flaw #1 -- sometimes the new situation is not like the past.  

Which theory will be right?  Do Bitcoins have intrinsic value, or are they inherently worthless, like the pages in a checkbook?  Does a sane foundation underpin the Bitcoin craze (see the figure below for Bitcoin’s price history), or is Bitcoin the modern equivalent of buying tulip bulbs in Holland in the 1630s?  Whatever happens, it is unlikely that both theories will prove correct.

Let us think about Bitcoin in terms of flaw #3 -- how humans drastically misestimate the odds of low-probability events.  The first risk case is fairly straightforward.  Since Bitcoin offers a reasonable amount of anonymity, various types of criminals have popularized Bitcoin for transferring assets.  In the most notable instance, until the site was shut down in 2013, essentially anything, including heroine, driver’s licenses, stolen credit cards, and weapons, could be bought from the Silk Road website and paid for anonymously with Bitcoin.  In another notable case, a virus held Los Angeles Valley College computers hostage for $28,000 in Bitcoins.

The US and Chinese governments have begun scrutinizing this illicit money transfer and money laundering, imposing financial regulations, like Know-Your-Customer.  Such scrutiny may make transacting illegal activities more difficult with Bitcoin, and that may reduce demand.

A more interesting risk case is quantum computing.  Bitcoin’s cryptographic algorithms assume that certain calculations are fast to perform and certain calculations are slow to perform.  This computational asymmetry is at the root of Bitcoin’s security model.  However, quantum computers throw a wrench into this security model.  Unlike classical computers which use on-off switches (zeroes and ones), quantum computers use the physics of quantum mechanics to perform calculations.  Because quantum computers work via a completely different mechanism than classical computers, quantum computers can make quick work of many classes of problems that are very difficult on classical computers.  In the last decade, we have seen massive progress in the effort to develop a general-purpose quantum computer, and within a few years, quantum computers should be available which can crack Bitcoin’s cryptographic algorithms, assuming that some government entity does not already have such computers.  In this scenario, have Bitcoin’s proponents correctly estimated the threat from quantum computers?  After all, how would you like to have your net worth in a currency that can be hacked?  

In all three cases -- Harvey’s flood, LTCM’s collapse, and Bitcoin’s spike -- people fell prey to the same flaws in their mathematical reasoning.  Whatever decision you face (For instance, is flood insurance worth the cost?  Is Bitcoin a money maker or a mania?) remember: (1) the new situation may not be like the past; (2) simple models may not reflect reality; and (3) humans drastically misestimate the odds of rare events.  Two-ton armadillos roamed the earth 40,000 years ago, and however far-fetched, rare, or unimaginable your worst-case scenario seems, it may be more likely to happen than you imagine.

David R. “Chip” Kent IV, PhD
Portfolio Manager / General Partner
Cecropia Capital
Twitter: @chip_kent

Nothing contained in this article constitutes tax, legal or investment advice, nor does it constitute a solicitation or an offer to buy or sell any security or other financial instrument.  Such offer may be made only by private placement memorandum or prospectus.

What is a baseball team, a bond, or a business worth?

Over the years, I have read countless articles on the market and have discussed financial concepts with many people.  From these interactions, I have observed that the vast majority of investors (and authors) have no idea how to calculate what a business is worth.  

The preponderance of investors use what Howard Marks calls “first order thinking.”  In the first-order worldview, good news means that a stock should move up, and bad news means that a stock should move down.  At first glance, this thinking seems very logical.  Such thinking is very simple and not mentally taxing.  Unfortunately, such thinking leads to booms and busts.  Assume a stock has a sequence of positive news.  The first-order worldview would have the stock increase in price for each bit of news, without regard for its initial or final price.  

Reality is far more complex.  Higher-order thinking requires an investor to ask: “Even though the news is good, is this price too high?” or “Even though the news is bad, is the price too low?”  Understanding what a business is worth provides both an anchor to answer such questions and a rational basis to go against the crowd.

Let us begin by considering the most simple possible business.  This is a very steady business with no debt that makes $1M of free cash flow every year.  (Free cash flow is how much cash a business generates after it pays its operating expenses and capital expenditures.)  How much is this business worth?  To buy this business, would you pay $2M?  $10M?  $100M?  

It is actually impossible to answer this question with the information I have given you.  To determine what the business is worth, you need to compare it against a “guaranteed” investment, such as high-grade bonds.  To calculate what this business is worth, divide its free cash flow by the interest rate of the “guaranteed” investment -- because at the resulting price, the business will produce the same return as high-grade bonds.  If interest rates were 10%, then the business is worth $1M/10%=$10M.  If interest rates were 1%, then the business would be worth $1M/1%=$100M.  At the end of the day, interest rates drive what all financial assets are worth, be they bonds, businesses, or baseball teams.

Let us now make the business slightly more complex.  The business now has $1M of debt, but it still produces the same $1M of free cash flow, after interest payments.  What is a business with debt worth?  To understand this, consider an everyday analogy: owning a home with a mortgage.  Having a mortgage does not affect what price you could sell your home for (your home’s value) -- but having a mortgage does affect your equity as an owner (homeowner’s equity = value of home - value of mortgage).  Similarly, whether or not a business has debt, its overall value remains the same (just as having a mortage does not affect a home’s sale price).  But as with having a mortgage, having debt does decrease a business owner’s equity, which is equal to the value of the business minus the value of the debt.  In the above example, assuming free cash flow of $1M, debt of $1M, and interest rates of 10%, the debt-laden business would be worth ($1M/10%-$1M)=$9M.  

Clearly, reality is more nuanced than these simple cases.  Businesses may be growing or shrinking, interest rates may be changing, and legislators may be revising rules the business plays by.  The uncertainty of these factors inevitably leads to ranges for what a business is worth.  For example, if interest rates will be somewhere between 9% and 11%, the simple debt-free business would be worth between $9.1M and $11.1M.

Currently, long-term interest rates are around 2.5%, but historically, long-term interest rates have averaged closer to 5%.  This difference in rates corresponds to a 2x difference in what the stock market is worth.  If long-term interest rates stay put for the next 30 years, stocks will be worth much more than they currently are, but if rates increase to their historical average, stocks will be worth somewhat less than they currently are.

At the end of the day, there are two truths about what a business is worth.  First, interest rates dictate what all financial assets are worth, from bonds to businesses to baseball teams.  (Remember the 10x increase in the business’s value, when interest rates dropped from 10% to 1%.)  Second, a business can be worth only as much as the cash that can be extracted from it, adjusted for the fact that money in the future is worth less than money today.  In the short-term, emotions may drive a business’s stock price (witness the short-lived bubble for money-losing companies like Pets.com), but in the long-term, logic and numbers prevail.  The market eventually gets things right.

David R. “Chip” Kent IV, PhD
Portfolio Manager / General Partner
Cecropia Capital
Twitter: @chip_kent

Nothing contained in this article constitutes tax, legal or investment advice, nor does it constitute a solicitation or an offer to buy or sell any security or other financial instrument.  Such offer may be made only by private placement memorandum or prospectus.

Who wins: the sloth or the hare?

You have heard me mention Peter Lynch in previous articles.  Lynch, a value-investor who ran Fidelity’s Magellan Fund, averaged a 29% annual return between 1977-1990.  In other words, if you had invested $1k in Lynch’s fund in 1977, it would have grown to $43k by 1990.  Lynch’s return was stellar, but the real question is: “How did his investors do?” 

The answer is quite surprising.  Lynch calculated that the average investor in his fund made only 7% per year between 1977 and 1990.  For this average investor, $1k only grew to $2.5k!  How could this be?  Where did all their money go?

Lynch explained it this way:  When his fund had a setback, money would flow out of the fund through redemptions.  Once the fund’s performance improved, money would pile back into the fund, having missed the recovery.  Investors bought high and sold low, and it cost them dearly.

What Lynch’s investors did to themselves seems so strange that it must be a fluke.  But in reality, the evidence indicates that when an investor looks in the mirror, he sees the worst enemy of his investing success:  himself. 

My friend Eric Falkenstein examines the actual returns that investors achieve in his book, The Missing Risk Premium.  Over the long haul, the US stock market has had an inflation-adjusted return of about 6-7% per year.  Yet, through mistiming the market and paying transaction costs, the average investor drops that 6-7% per year return all the way down to a 2-3% per year return, or just 0-1% per year after paying taxes.  Ouch!

Which investors do better?  Once again, the answer may surprise you.  According to an internal Fidelity study of client accounts between 2003-2012, the best-performing accounts belonged to customers who forgot they had their Fidelity accounts.

Do institutional investors, who command huge research budgets and an army of analysts, perform better than individuals?  Unfortunately not.  A 2009 study in the Financial Analysts Journal analyzed 80,000 institutional investment decisions between 1984-2007.  The study concluded that the investment products receiving new contributions underperformed products experiencing withdrawals over the following one, three, and five years.

Joel Greenblatt, another well-known value investor with a 40% annual return, discussed a similar phenomenon in his book, The Big Secret.  Greenblatt studied which managers performed in the top 25% for 2000-2010.  For those managers with the best record over the decade:

• 97% spent at least 3 out of 10 years in the bottom 50% of performance.

• 79% spent at least 3 out of 10 years in the bottom 25% of performance.

• 47% spent at least 3 out of 10 years in the bottom 10% of performance.

When asked about the study, Greenblatt said:

“You’re pretty sure that none of their clients actually stuck with them to get the good returns. And to beat the market you have to do something [a] little different than the market.  You’ve gotta zig and zag a little differently.  But clients are not very patient.”

In the studies discussed above, investors shared two common errors:  first, being impatient; and second, buying high and selling low.  It is easy to identify whether or not we are patient investors -- just look at your turnover or your average holding period (see the graph above).  What is harder to understand is why investors so often buy high and sell low -- a behavioral phenomenon that is both irrational and counterproductive.  Perhaps the simplest way to explain this phenomenon is to consider the two ways that humans tend to make purchasing decisions, epitomized by how we buy chicken vs. how we buy perfume.

How do you buy chicken?  When my wife goes to the grocery store, she will purchase chicken if it is below an acceptable price -- in our area, $3.00/lb for chicken breast.  If the price is above this acceptable threshold, the Kent household will dine on turkey, beef, pork, or vegetables.  On the other hand, if chicken is on sale, Brooke backs up the Mazda and fills the garage freezer.  Brooke understands the economic value of chicken relative to the alternatives.  As the price goes down, the quantity we purchase goes up.

How you buy perfume is an entirely different beast.  Having been gifted with both an X and a Y chromosome, I have zero ability to differentiate between good and bad perfumes.  Lengthy discussions with a perfumer add nothing to my understanding.  As a result, if I’m buying perfume, I buy an expensive bottle, because if it costs more, it must be better, right?  Most consumers apply a similar rationale to watches:  a Cartier must be better than a Timex because it costs more, right?  (But does it tell time any better?)  With luxury goods, consumers invert their normal demand response.  As the price goes up, we assume the product is more desirable, and we purchase more.  Conversely, as the price goes down, we assume the product is less desirable, and we purchase less.  Strange but true.

Given the two ways that people tend to make buying decisions -- chicken vs. perfume -- which method do people tend to use when buying stocks?  Suppose a stock goes “on sale,” and its price decreases.  Do investors stampede to the exchanges to buy more?  Or, suppose a stock’s price increases.  Do investors rush to sell?  In my observation, the vast majority of investors purchase stocks -- or other investments -- exactly like they purchase perfume or luxury goods.  The more the price increases, the more they want to buy.  The more the price decreases, the more they want to sell.  However, as the studies above have shown, such behavior is quite expensive, and it costs investors a huge price in their lifetime returns.  In contrast, a value-investing approach, like ours, forces investors to view stocks the same way we view chicken.  This mindset gives value investors an advantage over the herd.

As food for thought, do you think of stocks as chicken or perfume?

David R. “Chip” Kent IV, PhD
Portfolio Manager / General Partner
Cecropia Capital
Twitter: @chip_kent

Nothing contained in this article constitutes tax, legal or investment advice, nor does it constitute a solicitation or an offer to buy or sell any security or other financial instrument.  Such offer may be made only by private placement memorandum or prospectus.

What do earthquakes, terrorists, and investments have in common?

Recently, a very good friend of mine asked me when the next recession would be.  I frequently receive such questions.  As a fund manager, I’m expected to know such things -- or at least, I’m expected to spout off a bunch of gibberish so that I sound as if God himself told me the answer. 

Quite simply, I don’t know when the next recession will be.  While such questions are intellectually interesting, they are not likely to have predictable answers.  In fact, as we will explore below, the odds of timing most financial events are similar to the odds of timing an earthquake or a terrorist attack.

Nate Silver’s excellent and very readable book, The Signal And The Noise: Why So Many Predictions Fail -- But Some Don’t, provides some very interesting data for understanding what is predictable.  Nate’s claim to fame is the website https://fivethirtyeight.com, which has used statistical models to very accurately predict US election outcomes.

If you are like me, your elementary-school teacher told you: “Scientists are working very hard to predict when earthquakes will occur, and they are on the verge of a major breakthrough.”  However, to date this breakthrough has not happened.  Various scientists have had interesting ideas, but none have panned out for predicting an earthquake’s timing. 

Does this mean that earthquakes are not predictable?  No -- in fact, earthquakes are very predictable, if we ask the right questions about them.

The figure below plots the size of an earthquake vs the number of times such an earthquake occurred between January 1964 and March 2012.  The plot is a nice straight line, which indicates a very predictable system.  As a result, if we ask the right question about earthquakes (“How often does a magnitude 7 earthquake occur?”), then their behavior is very predictable.  However, if we ask the wrong question about earthquakes (“When will the next earthquake hit Los Angeles?”), then their behavior is not predictable.

Terrorist events are very similar to earthquakes -- it is extremely hard to predict when they will occur.  In fact, terrorist events are so difficult to predict that the U.S. government resorts to spying on every person worldwide (including you) in hopes of timing the next terrorist attack.  Yet despite this intrusive spying, terrorist attacks still regularly occur, both in the U.S. and abroad.

Does this mean that terrorist events are not predictable?  Again, terrorist events are predictable if we ask the right questions about them.

The figure below plots the number of fatalities from a terrorist event vs the number of such events in NATO countries between 1979 and 2009.  The dot to the far right is September 11, 2001.  Again, the plot is a nice straight line, which indicates a very predictable system.  As a result, if we ask the right question about terrorist events (“On average, how often do we expect to see a terrorist attack that kills at least 100 people?”), then the answer is very predictable.  However, if we ask the wrong question about terrorist events (“When will New York City experience its next terrorist attack?”), then the answer is not predictable.

The frightening extrapolation of this plot is that an event killing 100,000 people will likely occur on average about once per 150 years.  Such devastation could easily result from a crude nuclear weapon detonating within a city.  Assuming an average lifetime, a 1 in 2 chance exists of such a tragedy occurring during your lifetime.

In investing, the same principles apply.  If we ask the right question, then there is predictability in the market.  However, if we ask the wrong question, then the market is totally random.

For example, consider predicting the average return of the S&P 500.  The figures below use a price-to-earnings ratio to predict the average return of the market over 1-year and 20-year periods.  Over 1 year, the market’s behavior is extremely random and unpredictable.  By contrast, over 20-year periods, the market’s behavior has been quite predictable.

This is the exact reason why we own the businesses in Cecropia’s portfolio for many years.  Over short periods, like a couple of years, randomness dominates.  However, over much longer periods, there is predictability.

Back to our original question.  When will the next recession occur? 

A recession is two consecutive quarters of decreasing GDP (Gross Domestic Product).  The following figure shows predicted GDP vs actual GDP between 1986 and 2006.  As you can see, there is no correlation between predicted GDP and actual GDP.  The result: GDP and recession predictions are all noise and no signal, and it is likely that no one can predict with accuracy when the next recession will be.

(As an aside, note how likely Trump’s plan for 5-6% GDP growth is.)

One of the most critical and rarely discussed questions in investing is:  “What is predictable, and what is just noise?”  Our fund concentrates on what has been predictable.  Every position we own is based on one or more statistical views of the world which we believe appear predictive.  In a short time-period, like one year, I can’t say if a security we own will increase or decrease in price.  However, I can say that statistically, owning securities such as we own has produced good returns over long periods of time.

I’ll conclude with the prescient wisdom of Peter Lynch, a value investor who averaged a 29.2% annual return while running Fidelity’s Magellan Fund from 1977-1990.  Speaking about the “wrong” question, Peter said:

“I spend about 15 minutes a year on economic analysis. The way you lose money in the stock market is to start off with an economic picture. I also spend 15 minutes a year on where the stock market is going.”

Peter understood what is predictable and what is just noise. 

David R. “Chip” Kent IV, PhD
Portfolio Manager / General Partner
Cecropia Capital
Twitter: @chip_kent

Nothing contained in this article constitutes tax, legal or investment advice, nor does it constitute a solicitation or an offer to buy or sell any security or other financial instrument.  Such offer may be made only by private placement memorandum or prospectus.

How does code breaking relate to investing?

Wars have always pushed technology in interesting directions.  For instance, since conflict began, soldiers have needed to communicate securely on the battlefield.  From the dawn of written communication through World War I, codes were made and codes were broken, but people had very little mathematical understanding of information.

During World War II, computer-based encryption took hold.  The most famous instance of WW2 cryptography is the German Enigma machine.  The Enigma is a keyboard connected to a series of rotors.  When you press a key on the keyboard, the series of rotors scramble the output.  To descramble the output, you have to know the original configuration of the rotors before the message was typed.

To combat the German Enigma, the Allies were forced to understand the basic mathematics of information.  Alan Turing and a team of Allied cryptographers levered seemingly insignificant details -- like Enigma’s inability to encode a letter as itself -- to break the mathematical armor protecting Enigma.  While this mathematical battle remained classified for many years, it was recently depicted in two great movies:  “Imitation Game” and “Enigma.

Claude Shannon, a U.S. wartime cryptographer who worked for AT&T’s Bell Labs, met Alan Turing when Turing was stationed in Washington, D.C., for two months in 1943.  Impressed with and influenced by Turing’s work, by 1944, Claude Shannon had single handedly created a complete theory of information.  Shannon’s theories show how to precisely quantify information, determine how much information can travel over a wire, quantify how to ensure that communication is reliable, demonstrate how to compress data, etc.  Shannon’s work is incredible, and it certainly has impacted your life more than Einstein’s more famous theories.  Without Shannon, for instance, there would be no internet.            

By now, you are wondering: “How in the world does this relate to investing?”  In 1956, John Kelly, a scientist at AT&T’s Bell Labs, sought to understand noise issues in AT&T’s long-distance telephone signal.  While thinking about improving the telephone system, Kelly began thinking about gambling -- a risque subject for a mathematician in the 1950s.  One question Kelly asked was: “What would happen if a gambler had a source of useful information, but the source was not always right?”

As an example, consider a gambler in Chicago betting on horse races taking place in New York.  This Chicago gambler has a contact in New York who will call him with the results of the race just before betting ends in Chicago.  Unfortunately, during an exciting race, the New York crowd can get loud and rowdy.  As a result, the Chicago gambler may not be able to understand what his source says, and therefore, he may bet on the wrong horse.

Kelly’s analysis of placing bets with unreliable information came to be known as “The Kelly Criterion.”  The Kelly Criterion says that the optimal strategy is to maximize your average return. 

To achieve this, you should bet more in cases where you are more likely to win more money, and additionally, you should bet more in cases where you are more certain of the outcomes.

In the professional gambling world, the Kelly Criterion is very important.  For example, in blackjack, a good card counter only has a 0.5% advantage over the casino.  With such a small edge, it is imperative that a card counter optimally use the available information.  By adjusting bet sizes using the Kelly Criterion, a good card counter can improve his edge to roughly 1%.

In his book “The Dhando Investor”, Monish Pabrai gives a clear and concise example from the investing world.  For the business Stewart Enterprises, Pabrai estimates the outcomes to be:

I’ll spare you the complicated mathematics involved in calculating the Kelly Criterion.  However, what the formula says is, given the above probabilities and expected returns, if an investor has the choice between allocating capital to Stewart Enterprises or allocating capital to cash, the Kelly Criterion indicates that investor should be 97.5% invested in Stewart. 

At this point, Pabrai had not heard of the Kelly Criterion, and he invested 10% of his fund’s assets in Stewart Enterprises.  If Pabrai had known of Kelly, would he have invested more?  Since I know about the Kelly Criterion, would I have invested more than 10%?  Maybe not.  Here is why.

There are five primary reasons not to bet the full amount suggested by the simple Kelly Criterion.  (We can account for these cases using a more complex extension of Kelly’s work.)

1) Opportunity costs.  Kelly’s theory says that you should never make investments where there is a probability of total loss for the portfolio.  Let’s assume that we can choose between allocating capital to cash, to Stewart Enterprises, and to Trawets Enterprises.  Trawets has exactly the same payoff odds as Stewart, and the outcomes of Stewart and Trawets are independent.  As a result, if we allocated based on the simple Kelly Criterion, we would invest 97.5% of the portfolio in Stewart and 97.5% of the portfolio in Trawets.  Because we have invested almost double our assets, there is a non-zero chance that the portfolio will experience a total loss.  As a result, when presented with multiple investment opportunities, the optimal amount to invest in each security is less than the amount that you would invest if the only options were cash and a single security.

2) Overbetting is more harmful than underbetting.  If you know the exact probabilities for all outcomes of an investment, then the Kelly Criterion tells you the optimal amount of capital to allocate to the investment.  If you allocate slightly less to the investment, you will get a little less return and less volatility.  On the other hand, if you invest more than the Kelly Criterion, you will get less return and more volatility -- not a good combination.  Furthermore, betting in excess of a certain threshold will guarantee negative expected returns, no matter how favorable each of your individual bets are.  In the case of Stewart Enterprises, a bet of 97.5% is optimal.  Reducing the bet by 2.5% to 95% will reduce the return and the volatility very slightly.  On the other hand, increasing the bet by 2.5% to 100% will almost certainly lead to ruin in the long run, because in 1% of cases, the investment is a total loss. 

Keep in mind that the optimal Kelly size is dictated by the actual odds of the problem, rather than what we estimate the odds to be.  In cases where there is uncertainty in what the actual odds are, investing a fraction of what Kelly suggests protects us from accidentally overbetting if we have estimated the odds too optimistically.

3) Investing with the full Kelly size results in drawdowns that are beyond the comfort of most investors.  By investing a fraction of the Kelly size, the portfolio volatility is easier to stomach, without losing too much return.  For instance, in the Stewart Enterprises case, investing full Kelly size results in an average return of 50%, but there always is a 1% chance of losing 97.5% of your portfolio’s value.  However, investing half Kelly size results in an average return of 31%, but 1% of the time the maximum loss is only 49%.

4) Infrequent extreme events happen much more frequently than we appreciate.  As much as we would prefer not to think about it, extreme events -- like a nuclear weapon detonating in a major city -- do happen, albeit rarely.  Because these events are rare and extreme, we inevitably underestimate the odds of them occurring when we compute possible investment outcomes.  Again, using a fractional Kelly strategy insulates us from such estimation errors. 

5) We may never reach the “long run.”  It can be mathematically proven that for fixed goals -- such as multiplying your capital by 100x or 1,000x -- the Kelly investor will reach the goal, on average, in less time than all other strategies.  To achieve these results, however, an investor or bettor must be able to make a sufficient number of investments or bets.  The catch is that an investor may be unwilling or unable to make enough bets to attain the desired goal with sufficient odds.  In such cases, it may be optimal to invest less than what the simple Kelly strategy would suggest.

While many people are familiar with Warren Buffett, very few are familiar with how he actually allocates capital.  His approach is clearly at odds with the bulk of the financial industry.  Buffett thinks like a Kelly investor.  He commonly bets between 25%-40% of his net worth on a single company, and he bets more than that in situations with higher certainty and higher payout.  Here is how Buffett described his allocation process:

“I have 2 views on diversification. If you are a professional and have confidence, then I would advocate lots of concentration. For everyone else, if it’s not your game, participate in total diversification. The economy will do fine over time. Make sure you don’t buy at the wrong price or the wrong time. That’s what most people should do, buy a cheap index fund and slowly dollar cost average into it. If you try to be just a little bit smart, spending an hour a week investing, you’re liable to be really dumb.

If it’s your game, diversification doesn’t make sense. It’s crazy to put money into your 20th choice rather than your 1st choice. “Lebron James” analogy. If you have Lebron James on your team, don’t take him out of the game just to make room for someone else. If you have a harem of 40 women, you never really get to know any of them well.

Charlie and I operated mostly with 5 positions. If I were running 50, 100, 200 million, I would have 80% in 5 positions, with 25% for the largest. In 1964 I found a position I was willing to go heavier into, up to 40%. I told investors they could pull their money out. None did. The position was American Express after the Salad Oil Scandal. In 1951 I put the bulk of my net worth into GEICO. Later in 1998, LTCM was in trouble. With the spread between the on-the-run versus off-the-run 30-year Treasury bonds, I would have been willing to put 75% of my portfolio into it. There were various times I would have gone up to 75%, even in the past few years. If it’s your game and you really know your business, you can load up. [...]

In stocks, it’s the only place where when things go on sale, people get unhappy. If I like a business, then it makes sense to buy more at 20 than at 30. If McDonalds reduces the price of hamburgers, I think it’s great.”

— Warren Buffett, 2008

The portfolio of Monish Pabrai (the well-known value investor mentioned earlier) likewise demonstrates Kelly investing.

As this discussion began with information theory, so it will end.  The great information theorist Claude Shannon was also a great investor.  His long-term return was reported to be 28% per year -- exceeding Buffett’s return for the same period.  From the limited information available on Shannon’s portfolio, we know that he would take positions in excess of 80% in a single security.  I’m certain that Shannon’s investment ride was extremely bumpy, but he made optimal decisions with the available, uncertain information.

David R. “Chip” Kent IV, PhD
Portfolio Manager / General Partner
Cecropia Capital
Twitter: @chip_kent

Nothing contained in this article constitutes tax, legal or investment advice, nor does it constitute a solicitation or an offer to buy or sell any security or other financial instrument.  Such offer may be made only by private placement memorandum or prospectus.

What would the Mesopotamians think of our interest rates?

For the last 35 years, interest rates have steadily declined from around 16% to around 1%.  Since 2008, our country has been in a near zero interest rate environment.  35 years is a long time.  The only market participants who experienced increasing interest rates are retirement age or approaching retirement age.  All other market participants have only experienced a slow and steady decrease in interest rates (as seen in the chart below) -- which corresponds to a slow and steady increase in bond prices.

Such extended, one-way price moves tend to produce beliefs like “bonds are safe” or “you can’t lose money in bonds,” etc.  While these beliefs may be accurate during a one-way decrease in interest rates with tame inflation, reality is quite a bit more complex. 

While the last 35 years have experienced a one-way decrease in interest rates, the preceding 35 years corresponded to a one-way increase in interest rates.  During such increases in interest rates, bonds can become worth less.  Furthermore, if a bond is held until maturity, the holders will get back their money, but the money they do get back will likely be worth far less than expected, since high interest rates typically correspond to high inflation.  (On the figure below, note how high interest rates typically correspond with high inflation, while low interest rates typically correspond with low inflation.)

Andy Haldane from the Bank of England has done an interesting analysis on interest rates.  From various sources, he has compiled a 5,000 year history of prevailing interest rates -- from Mesopotamia to today.  Over this 5,000 year history, interest rates spent most of their time between about 4% and 6%.  More remarkably, our current near zero interest rates are at a 5,000 year low.  I guess the Mesopotamians were unwilling to lend for zero return.

As time passes since our recent deflationary event (the Great Recession), I expect interest rates will eventually begin trending towards more historically common levels.  Over time, I expect governments will have a hard time restraining spending and money printing, which will result in increased inflation.  In the case of the Great Depression, roughly a decade after the depression began, interest rates started their upward march. 

Furthermore, over the last few years, a stronger and stronger chorus of investors have piled into lower and lower quality bonds.  The reasoning typically follows this rationale: “I used to be able to get a 5% return; now I have to get at least a 4% return”.  Instead of stopping to think about whether a 4% return is reasonable in the current 1% return environment, the investor purchases any investment making at least 4%.  To achieve these “must have” yields, investors end up grabbing any garbage that promises the required yield (e.g. Greek government bonds for 5%, Petrobras 100-year bonds for 6.85%, etc.).  I see such risk-agnostic behavior as concerning, and I see the grab for garbage as very concerning when combined with a 5-millenium low in interest rates.

So, what does all of this mean for stocks?

Since the 1870’s, the average earnings yield (earnings / price) has been about 6% -- which corresponds to a P/E ratio of 16.7.  This long-term average earnings yield is close to the 4%-6% long-term average of interest rates.  Therefore, a reasonable P/E of the market is something like 1/(interest rate).  If interest rates stay in the 1%-2% range for an extended period, stocks could reasonably trade at 50x-100x earnings (2x-4x current prices).  Similarly, interest rates could increase to 4% or more without leading to a lasting drop in stock prices.  This analysis is at odds with the common media perceptions that increasing interest rates must correspond with a decrease in stock prices and that stocks in general are currently overpriced.

There is one more peculiar implication of a zero interest rate world worth discussing.  In the last few years, stock prices of money-losing businesses, which can best be described as toxic sludge, have outperformed the stock prices of money-making businesses. 

A business is worth the sum of all future cash flows adjusted for the the time value of money.  A dollar tomorrow should be worth less than a dollar today.

Things become very strange as interest rates approach zero.  With zero interest rates, cash flow in 30 years is worth the same as cash flow tomorrow.  Assuming zero interest rates will last forever allows investors to use this bizarre calculus to justify the price of a money-losing, highly-speculative biotech or social media company because the company “will eventually make a lot of money”. 

Because the hypothetical cash flows of a “make money someday business” exist far in the future, the value of the business is very sensitive to interest rate assumptions.  Furthermore, with a rise in interest rates, lenders will begin demanding more to loan the money to keep the business operational long enough to reach the hypothetical payday.  A small change in rates could rapidly bring the party to an end.

David R. “Chip” Kent IV, PhD
Portfolio Manager / General Partner
Cecropia Capital
Twitter: @chip_kent

Nothing contained in this article constitutes tax, legal or investment advice, nor does it constitute a solicitation or an offer to buy or sell any security or other financial instrument.  Such offer may be made only by private placement memorandum or prospectus.