Data

Sometime in the next month, I'm going to make a post about the true odds of humans creating a perfect NCAA bracket (misstated in the media), and another about how I believe people are lying about their age to get in the Boston Marathon.

Hindsight is 20/20

I recently invested in a stock (First Sensor, a European sensor producer) that went up a lot after I purchased it.  At the same time, I invested in a stock (ALJ Holdings, a conglomerate of various U.S. service businesses) that went down a lot after I purchased it.

The other day, I thought to myself, "If only I had put all my money into First Sensor and not bought ALJ, that would have been way smarter!"   Then I shook my head.  Hindsight is always 20/20.  Sometimes you make good decisions and get unlucky, and sometimes you make bad decisions and get lucky.  A lot of times you make average decisions and get lucky and think you're better than you actually are.

Several years ago I wrote a blog post on this blog declaring that I had purchased Netflix at $76 and sold it at $91, and that I thought the stock was slightly undervalued, but it would have low long-term profit margins.  In fact, I was right.  Netflix has consistently returned low profit margins.  Yet selling Netflix was one of the worst investment decisions I have ever made.  Its competitive advantage and stock price is stronger than ever. If I had just held onto my shares, I would have multiplied my investment by 20 or 30 or some embarrassing number like that.

The point of this post is, don't only judge yourself on outcomes.  Judge yourself on the process, the intentions, the methodology, and the outcomes.  Your null hypothesis should be that you are average; you are not smarter than the market.  Assume that is the case until you do enough due diligence and find a process that proves otherwise.  And recognize that many of your successes and failures are coincidence.

Chasing Returns

When the market goes up, everyone wants to try to call a top. It is as hard to call a top as it is to call a bottom when the market goes down. everyone thought the 2008 bottom was in November but it was actually in spring 2009.  Very few saw the top coming in 2008 and 2001.

That being said, one sign of an overbought market is "chasing returns" through leverage.  That occurs when people use real leverage (loans) or artificial leverage (options and other leveraged products) to try to increase their returns on a basic investment. Often those investors do not understand the risk they face if their investment declines.

Another sign of an overbought market is chasing returns through alternative investments.  That occurs when people turn to alternative currencies, emerging markets, legeraged ETFs, and other unusual investment products they are unfamiliar with to get the returns they had been getting in common stocks, real estate etc.

Often when leverage and alternative investments collide, you are ripe for a correction.

I'm not sure we're there yet, but of course, I can't call the top.

The bias of the null hypothesis

"An infinite number of monkeys sitting at an infinite number of typewriters would eventually reproduce the works of Shakespeare by chance."

Which is true, but an absolutely ridiculous statement, and practically meaningless, because humans cannot comprehend the value of infinity.  Everything is possible in infinity.  It is the same as saying that there is a remote possibility that the world was created and continues to be ruled by one snail.  The probability that a chorus of monkeys types anything noteworthy that is longer than a page in any finite amount of time is close to nil.  For an example of how improbable the monkey theory is within a time period that is comprehensible by humans, see this study: https://www.theguardian.com/uk/2003/may/09/science.arts and this funny clip from the Simpsons: https://www.youtube.com/watch?v=no_elVGGgW8.

This absurd statement demonstrates that infinity is incomprehensible, but usually the speaker misses the point, and says it to prove that if you try a random thing over and over again good things might end up happening.  Which is usually wrong.  If you try a random thing, good things, bad things, or nothing might end up happening.

In statistics and the scientific method, every study must affirmatively show that a certain result has significant probability within your data set - usually 95% or 99% - in order for such result to have statistical significance.  If there is no statistical significance, it is assumed that the result does not exist.  The original assumption is always that a certain result or correlation does not exist, and is called the null hypothesis, Why is this?  It is elegant, but it is not always right.  The null hypothesis makes sense when testing arbitrary ideas.  For example, the idea I wrote above that the world was created by a snail can only be described as arbitrary.  The null hypothesis is that such idea is wrong.  The evidence in favor of such idea is scant, so the null hypothesis wins.

But what about ideas that are not so arbitrary?  The idea that things are ordered rather than random, and the idea that progress is designed rather than lucky.  By default, statistics says you should not presume a correlation or pattern exists until you can prove it beyond 95% probability, so the ineffable becomes false.  This is the bias of the null hypothesis.  It has dramatic results in the real world.

I find it quite bizarre to argue that the evolution of millions of species of plants and animals, each of which has different and valuable skills which give them the greatest chance at survival using the least amount of evolutionary capital, was completely random.  Evolutionary capital the degree of variation from the previous species.  It takes energy and effort to move the mean of a set of data, and the farther you move that mean, the more energy it takes.  In animals, the mean is the current gene pool and each species must adapt by changing its gene pool in order to survive and thrive.  The genes of various animals might try different alterations to add genetic features to their offspring and see what gives them a better chance of survival.  Any genetic alteration must be present in a significant number of the offspring in order to take effect and change an animal population.  Therefore, the genetic alteration that is the smallest change from the parent's genes and gives the children the greatest chance of survival gives you the most bang for your buck and is most likely to occur.  This is random in the sense that it occurs through trial and error.  But it cannot really be RANDOM.  

I admit I am no expert on genetics; however I know there are a staggering number of combinations of genes and alleles that make up the DNA of an animal.  From what I understand, it is similar to a long computer program, and gene has a different function and impact on the physical body.  A random alteration of such a complex process would rarely be beneficial to the body as the whole.  It would be quite lucky to randomly change a gene and get a positive result.  But that is what many people believe occurs in evolution, because randomness is the null hypothesis.  We are not advanced enough scientifically to find the pattern; therefore we cannot prove it exists; therefore, statistically it does not exist.  This is a flaw in statistics.  On top of this first alleged randomness - the random gene alteration - is a second bit of randomness which is the randomness of life and survival of the species.  So out of billions of potential gene variations, you randomly find one that is beneficial to the species.  Say you have a fish and an (allegedly) random gene variation of a bigger dorsal fin allows it to swim 2% faster than its brothers.  How much does swimming 2% faster increase the fish's chance of survival?  The fast fish might be eaten by surprise, or catch a disease, or get caught in a net.  Whether the more theoretically fit fish survives is itself a product of chance.  (Again, it is not random, as the fast fish is more likely to survive than its brothers).  Combine these two layers of chance - the chance of having a good gene mutation, and the chance of such good gene mutation surviving.  If both of those layers of adaptation were truly random, evolution would be extremely inefficient.  But it is not.  Yet we must presume randomness because it is the null hypothesis and we cannot prove anything else.

I did not mean to go into such detail about evolution.  There are more practical aspects of the bias of null hypotheses.  An egregious one is the null hypothesis that the markets are efficient.  In order to prove the markets are inefficient, you must prove there is a strategy that exists to consistently beat the markets.  The null hypothesis is that each strategy does not beat the markets. The problem is once a strategy is known and studied, the market adapts to become more efficient and incorporate that strategy into its pricing of companies.  So the market is smart but not perfect and not efficient.  But the null hypothesis is they are efficient, and people fall for that.

Plenty of clever sports statisticians fall victim to the bias of the null hypothesis when they confidently proclaim that there is no such thing as choking under pressure in sports, there is no such thing as a clutch performer, and there are no hot or cold streaks.  For each of these tests, the null hypothesis is that the phenomenon does not exist, and that all variation in performance is a random deviation from each player's mean skill level.  But people are not robots, and the null hypotheses are wrong.

Costs of Capital

In business school, you learn about two costs of capital:  First there is the cost of debt, which is a function of interest rate and principal and time.  Additionally, there is the cost of equity, which is a function of the opportunity cost of capital, or what you could earn investing the capital somewhere else (side note, there is a theoretical problem with the concept of cost of equity being opportunity cost of capital from the investee's perspective - that only works from the investor's perspective. That is another article).  Next there is the weighted average cost of capital which is a way to average the two and create a hurdle rate for your projects, that you should exceed if you want to be profitable.

Capital in business is actually slightly more nuanced than that.

You can raise capital in other ways - for example, through revenues and profits.  Tesla raised about $400,000,000 by collecting $1,000 deposits from customers toward the yet-to-be-produced Model 3.  That is neither debt nor equity - instead Tesla is borrowing revenue from its customers with the promise to deliver them value in the future.  Tesla's cost of capital is high in this regard in terms of implied promises to its creditor-customers, but that won't appear in any WACC calculation.  If Tesla is able to deliver, the strategy will pay off.  If it is not it could be disastrous for its goodwill and economic moat.

Amazon borrows from its suppliers by delaying their payments.  Amazon is able to do this because it is a large and powerful platform.

There is also the marginal cost of increasing revenues, which is a test of the stickiness of your product.  How much marketing does it take to produce 1% revenue growth?  10%?  20%?  If little or no marketing spend is required to grow your revenues at a profitable level, and the business model is not easily copied, that is the sign of an excellent business worth investing in.  (Note - sometimes that is just a sign of a novel business or business model which is not necessarily worth investing in. Think Groupon.)

Cost of revenues can also be in the form of declining prices and declining margins.  Will lowering your price 10% result in a revenue increase of more than 10%?  If it will, you likely sell a product with elastic demand and you may not be in a good business with strong long-term economics.  For example, an airline could dramatically increase its revenues by dropping its prices, but not at a profitable level.

Finally, the worst way to raise capital is through borrowing from your loyal customers, but many businesses do this.  BlackBerry is an example.  Each of their last 7 or 8 product launches has been released at a very high price point to a dwindling number of core fans.  BlackBerry makes steep marginal profits on their original products sold to those core fans.  Unfortunately, there is little demand outside of the loyalists, so the phone or tablet must quickly see steep discounts in order to maintain revenues.  Essentially, what BlackBerry is doing with this strategy is borrowing from its customers, with their loyalty as security for the loan.  BlackBerry is saying, you have to pay us full price to get your phone when it comes out, but if you had waited two months you could have gotten it for $200-$300 less.  That extra $200-300 that BlackBerry is collecting from its loyalists is no different than a short-term loan, which the customers pay to BlackBerry in hopes that its product will be good quality and have high demand.  When BlackBerry was unable to repay those loans in the form of sustained product popularity in 2011-2015, it faced dire consequences because the loyalty that was "security" for those customer loans began to disappear.  It would have been better for the company to reward its loyal customers by starting prices lower rather than higher, even though that would have had an immediate negative effect on its bottom line.

Profit Margins and Deadlines to Buy or Sell

One of the important things that determines whether a company is consistently profitable and earns high returns is how much pressure it has to sell its product quickly.

Each transaction in business involves a buyer and a seller.  If the seller has to sell his items by a deadline or quickly, with low marginal cost to selling the items, the seller will be forced to cut prices toward the end of his selling deadline.  This kills profit margins and creates a bargain-hunting concept among buyers.  Two examples are (1) a hotel or airline, which must sell plane tickets before the cutoff time for the flight or the nightly stay; and (2) a produce company, which must sell its bananas before they turn brown.

If the buyer has to purchase items by a deadline, and the seller is under no obligation to sell by such a deadline, the seller will tend to have higher profit margins and higher return on investment, especially when the seller's products are unique or differentiated from the competing brands.  Examples include a potato chip company such as Lays:  The buyer has to purchase chips before he has people over to his house. He is under a deadline. The seller does not have to sell his chips by a specific time because he can control the supply and the items will last for weeks on the shelf without going bad.  The seller is under no obligation to lower prices to get a quick sale, while the buyer is obligated to pay whatever sellers are charging when he needs the chips.  So the seller has more power in this transaction and will command higher profits and return on capital.

Generally, whether the buyer or seller gets the better deal depends on the time that each has to buy or sell the item and the supply and demand for the specific item in question.  These factors determine the seller's pricing power.

The concept of supply and demand as it relates to products that spoil is a fascinating concept.  Take, for example, a cable company.  The cable company in theory has a product that is constantly spoiling.  Every day that a customer does not sign up for the cable company's service is a day of lost revenue for the cable company.  But in most areas, there are only two or three cable companies available for customers to choose from.  Therefore, demand exceeds supply and the cable companies have pricing power, which will bring them long-term profits.  The same holds true with cell phone companies, which need valuable spectrum to be able to offer products to customers.  Alternatively, could you imagine the low profit margins that would exist if there were 20 or 30 cable companies in each market offering the same channels?  You would see extreme price wars among the companies, with customers being the only winners.

Sellers can differentiate their brands enough to overcome the negative pressures associated with selling deadlines so long as demand for the seller's product exceeds supply within the deadline time period.  The key is the ability to limit supply.  For example, the Four Seasons Hotel (should be) largely profitable despite the constant spoliation of its product because it has such a good reputation that the demand to stay in its hotel exceeds the average supply for most of its hotels on a given night.  That means it has pricing power and therefore high returns on capital.

Operational Moat vs. Intrinsic Moat

A lot has been made of "moat" in the last decade and how it protects a company's return on invested capital.

Moat is the guard that stops other companies from stealing your profits.

Any company that consistently generates above-average returns on invested capital has some kind of moat.  If it did not have a moat, then others would jump into the same business and take the profits and the return on invested capital would decrease.

The idea is that a company or brand is so entrenched competitively that it cannot stop making a profit.

Of course this is not true absolutely and on an infinite scale because all companies may eventually lose their competitive edge because of society's changes or poor management, and profit margins are constantly under attack through competition from others.

However it is true that some companies are so competitively entrenched and fortified that it would take years of poor management and poor capital allocation to erode their profit margins.  These tend to be the companies with high brand recognition and brand loyalty.  Obvious examples being Coca Cola, Pepsi, Doritos, Cheez-Its, Charmin, Dawn, etc.

The idea is that customer goodwill and brand awareness has been built steadily over a number of years, so that the brand will continue to have a positive perception among its customers regardless of current lazy or incompetent management.  These brands have intrinsic moat.

On the other hand, companies may also generate high return on invested capital through operational moat.  This is the moat generated by the current managers of the company being more competent than the managers of the company's competitors.

Whether a company has operational or intrinsic moat depends on the economics of the business and customer's loyalty to the brand.  Businesses in industries with low fixed costs, uniform products, control over their supply, high profit margins and high customer loyalty tend to have high intrinsic moat.  It is hard for competitors to take their profits because the businesses have huge marketing budgets and the customers won't easily switch.

In capital-intensive industries with low switching costs, a company that is achieving consistent profits likely has operational moat because of the intelligence of its management.  This is a more risky investment, because the operational moat could evaporate if management leaves.

But a sustained period of operational moat can actually turn into intrinsic moat.  Some examples: Google - search engine industry has very low switching costs, but through years of operational excellence, Google has built a loyal customer base that only uses Google searches and is unlikely to switch search providers.  Apple - through operational excellence and innovation, has created millions of loyal customers who will buy iPhones for the rest of their lives.  Facebook - through constant innovation and implementation of new features, has created billions of users that use Facebook as their primary means of online social communication.  Wal Mart - a capital intensive retail business that created millions of loyal customers through its low-pricing strategy.  McDonald's - another capital-intensive and low-margin business, that created high return on invested capital through years of effective marketing and strategic real estate purchases.

I am only using large company examples here, but many small companies also have moats based on their operational excellence.

Operational moats can result in high return on investment if the management sticks around.  Intrinsic moats tend to last longer than operational moats, but not always.  No company is protected forever by its moat.