Clearsign of growth

Clearsign Technologies (NASDAQ: CLIR) is the first pre-revenue company that I have invested in. The company is very close to commercializing a game-changing technology in combustion systems with ExxonMobil and other customers with significant scale. Clearsign’s technology costs much less, produces far less emissions, and presents as the sole viable update to a 65-year-old combustion treatment method that is as expensive as it is antiquated. Competent leadership, proper incentives, viable commercialization roadmaps, and environmental tailwinds support a $500m valuation (~3x current valuation).

Potential customers are industrial furnaces, boilers, refineries, and any facility that employs large-scale combustion systems. These facilities constantly balance between generating revenue and pollution: how long can combustion systems operate (ie revenue generation) while keeping NOx (nitrogen oxides) emissions low enough to comply with increasingly strict environmental regulations. The most common solution is a post-combustion treatment process. Run the combustion system; pass the resulting NOx into a catalyst chamber comprising porous ceramics, precious metals, and gases; circulate the mixture to reduce NOx before release to atmosphere. This process, known as SCR (selective catalytic reduction), is expensive because of the use of precious metals, cumbersome because of the need for gas circulation, and unstable because of the constant need to replace porous ceramics clogged by pollutants.

Clearsign provides an ingenious solution. Its key IP (known as Clearsign Core) allows the combustion system to produce less NOx in the first place, eliminating the need for post-combustion treatment.

Combustion begins with an ignition of a flame. This flame initially burns at very high temperatures that produces high levels of NOx. As fuel and air mix with the flame, temperatures decline and exponentially less NOx is produced. Clearsign Core reduces the duration and intensity of peak flame temperatures. The technology allows fuel and air to mix properly before starting ignition, resulting in shorter and lower peak flame temperatures and lower NOx emissions than standard combustion.

The technology has other means to reduce peak flame temperatures. It “separates” the source of a single, large flame into numerous, shorter flames that can be more easily mix with fuel to lower peak temperatures. Its mechanism also employs radiation cooling that cools the flame quickly.

Clearsign provides the aforementioned benefits at much lower prices compared to the current SCR solution. A third-party engineering consultant found that Clearsign Core (originally named Duplex) provided 80-90% lower startup and maintenance costs compared to SCR (see tables below). Clearsign Core is currently the most cost effective solution to meet strict NOx emission standards (under 2.5 parts per million, or 2.5ppm) set by California and Texas, which set the standard for the rest of the country to eventually follow.

In spite of valuable IP, Clearsign had a difficult start as a public company. Its IPO in 2012 was build on a ground-up, complex redesign of combustion systems. Its technology today started as an offshoot to the original technology. Clearsign invented a flame management system that significantly reduced NOx emissions as a complement. Clearsign did not sell any combustion systems, but found large refineries owned by Tesoro and Delek wanting its flame management system. However, all the company had to show, after 7 years and and 5 equity raises, was less than $2 million in cumulative sales. Its stock declined from $4 at initial offering to a low of $1 in 2018.

Poor execution capped the sales potential of its technology. Management failed to account for extensive pre-installation testing, after-sales support, and customization required by complex refinery operations. The high cost of failures in combustion systems made refinery operators extremely careful in adopting new technologies. They demand multiple demonstrations and field-testings, and require a comprehensive maintenance system. Clearsign, as a startup, had limited resources that were perceived as risky. The company required collaborations with other vendors to get to market, but was unable to find partners because of its insistence on standard licensing deals. A typical deal require partners to pay Clearsign large upfront cash as a licensing fee, but partners were unwilling to pay for startup technology without proven traction. Without partners, Clearsign could not commercialize its valuable IP.

The company’s fortunes began to turn when Robert Hoffman, a veteran investment manager, bought a 20% stake and joined the board in July 2018. He repealed the attempt of a ridiculous proxy battle by a sketchy investment banker. Anthony Digiandomenico was an owner of MDB Capital, focusing on micro-caps and purporting to be “Wall Street’s only intellectual property-focused investment bank” (not kidding), albeit with history of misrepresentation (see Red Flag #3 in link). Digiandomenico owned only 112,733 shares of CLIR (less than 1% of 1% shares outstanding) when he submitted a proposal to replace the board and management on November 9, 2018. Luckily for CLIR shareholders, Hoffman stood his ground, convinced Digiandomenico to back down, and prevented the escalation of the proxy battle to an all-out war.

I did not find information concerning Hoffman’s long-term track record. However, his written responses in the proxy contest showed him to have sound business logic, focus on fundamentals, a long-term outlook, and transparent communication (see highlights below from 14A filed on December 6, 2018):

The second most important decision that Hoffman made for Clearsign, after settling the proxy battle, was the hiring of Jim Deller as CEO in January 2019. Deller spent his entire career, all 28 years, in combustion systems and had a doctorate in flame chemistry. That Deller left a comfortable job as director at Honeywell to join Clearsign validated the company’s technology and sales potential. His deep understanding of technical underpinnings and commercialization processes made him the most invaluable addition to the company after Hoffman. Hoffman described Deller as such:

While younger investors might not grasp the analogy, Jim is the Bart Starr as opposed to the Joe Namath of the combustion industry. As investors get to know Jim, they will discover that he will never be like Broadway Joe and “guarantee victory”, nor will he ever be the flashiest guy in an investor conference. Conversely, like Bart Starr, he will be the understated field general getting the most out of his team while, at the same time, being the most important cog in driving that team to victory and making the clutch decisions when it is most important.” -Q119 earnings call

Clearsign has gained significant traction under Deller. He simplified the core technology for easier “plug-and-play” installation at customer sites. He found partners by abstaining from licensing deals in favor of revenue-share collaborations without upfront investments. He recruited leaders in sales and engineering. He steered the company through Covid-19 and secured a license to sell in China amidst complex US-China relations. Notable customers and partnerships include:

  • ExxonMobil and Zeeco. The oil major will be testing Clearsign Core in calendar Q3 2021, after completing a year-long exhaustive qualification of the technology. If successful, this would be the most vital validation of Clearsign Core in company history. Equipment required for Exxon’s testing would be fabricated by Zeeco, a household name in the global refining and petrochemical industry. The collaboration with Zeeco in manufacturing, sales, and research is a transformative partnership that solidified Clearsign’s commercialization efforts.
  • An unnamed European oil major, likely Shell or Total. This company placed an order for Clearsign Core on February 2, 2021.
  • California Boiler and a North American major energy infrastructure company who purchased Clearsign Core in October 2020. The technology has been installed, and revenues would be recognized in financial Q1 2021. This project was sold by California Boiler, which is Clearsign’s channel sales partner.
  • World Oil and the California South Coast Air Quality Management District (government emissions regulator in Southern California). Clearsign Core is so promising that the emissions regulator in California had agreed to fund 1/3 of a demonstration project with World Oil, a recycler of motor oil and antifreeze. The demonstration has been delayed for a year because of Covid, and would likely be completed by early 2022.
  • Beijing heating district and Jiangsu Shuang Liang Boiler Company (simplified as JSL Boiler). Clearsign cleared government testing of its technology in China, and obtained a license to sell in China. The northern districts in China received central heating during winters from numerous industrial boilers (combustion system for heating) operated by the government, unlike individual boilers in homes in the United States. Clearsign had also signed a collaborative agreement with JSL Boiler, the top boiler manufacturer in China. Sales efforts just began in May 2021.

In thinking about valuation, I suggest readers consider Buffett’s quip: “you don’t need to know a man’s weight to know that he is fat”. Precision matters less in this thesis than directional accuracy. The most obvious markets for Clearsign are the 14,000 industrial combustion systems in refineries and petrochemical plants in Texas and California, and 350,000 heating furnaces in the northern districts of China. In the table below, assuming modest market shares and retrofit prices (about 10% cost of SCR), I estimate a near-$5-billion opportunity.

Combustion systems in CA and TX [A]14,000
Initial addressable market [B]10%
Revenue per retrofit ($m) [C]$1.0
Maintenance revenue over 10 years ($m) [D] $0.5
Revenue potential ($m) [A*B*(C+D)]$2,100.0
  
Firetube boilers in northern China350,000
Initial addressable market2.5%
Revenue per retrofit ($m)$0.2
Maintenance revenue over 10 years ($m) $0.1
Revenue potential ($m) $2,625.0
  
Total revenue potential ($m)$4,725.0

Three tailwinds may further expand the opportunity. One, pollution emission standards are expected to become more stringent over time in the US, China, and other developed countries, regardless of political affiliations. The regulatory tailwind would become stronger over time as more resources are devoted to managing climate change. Two, the slow pace of innovation and performance improvements in SCR provides a clear opportunity for Clearsign to take share. Providers of SCR are mature companies that compete largely on price. While the maturity of SCR presents a high barrier to entry, Clearsign has shown sufficient traction to be a viable substitute. Three, Clearsign is currently testing adjacent products such as flares and sensors that complement its core technology.

Clearsign is valued at about $160m today, which appears to significantly undervalue its potential. Judging from the $5-billion revenue potential over the next decade, it should be reasonable to estimate a valuation at $500m (1x annual sales). Margins are likely to be high (~30-40% EBITDA margins) given management’s plan to create an asset-light technology provider that relies on a network of partners for fabrication, sales, and customer support. In any new technology, commercialization is often the biggest risk. A successful demonstration with ExxonMobil in calendar Q3 later this year would significantly mitigate the risk and place Clearsign on the path towards realizing its true valuation.

How to generate insights

“…when it came to real-world complexities, the elegant equations and the fancy mathematics he’d spent so much time on in school were no more than tools – and limited tools at that. The crucial skill was insight, the ability to see connections.”

Taken from Complexity by Waldrop Mitchell, the quote described W. Brian Arthur, a pioneer in complexity economics.

Similar to Arthur, investors want insights. They want to know something that others do not. The most competitive institutional investors pursue insights in the form of differentiated data. Gabriel Plotkin, the owner of Melvin Capital (of GameStop short-selling fame), was an early user of credit card data when picking consumer stocks. Many investors interview vendors, employees, and product experts to uncover unique data.

If the puzzle of above-market returns is a walled castle, pursuing differentiated data is similar to a head-on attack. Accumulate armies of analysts and differentiated data, and swarm the castle’s defenses with sheer intellectual brilliance.

Yet a head-on attack is not without risk. Casualties abound (high turnover in analysts and data). The castle walls have to be re-built to prevent others from attacking (differentiated data is discounted by the market, demanding even more differentiated data sets for returns).

A better tactic would be to understand the needs of the town within the walls, and negotiate with its leaders for a surrender. No casualties. Nothing destroyed.

I label the alternative tactic as differentiated understanding.

The path to differentiated understanding begins with a hypothesis, which is tested against real-world data. Even when data is supportive, the hypothesis is never fully accepted. Statisticians use the term “cannot be rejected”. This means that other data, not yet considered or available, may reject the hypothesis. The analysis leaves room for further exploration, which, when repeated, leads to correct and deep understanding.

Compare what I just describe to the opposite. Start with differentiated data (instead of a hypothesis), determine patterns, then attempt to understand. This is what investors commonly do, and it leaves plenty of room for error instead of exploration. The patterns are likely to reflect correlation. Mistaking correlation for causation results in erroneous understanding, which is often expensive to correct in investing.

Yet data is often used as the starting point because it is relatively easier to obtain than a hypothesis. A credible hypothesis is abstract and creative, because it hasn’t been fully proven by data, isn’t known by others, and isn’t discounted by the market (hence containing the potential for profit).

More difficult than coming up with a hypothesis is refining it. The process demands more creativity and abstraction, more grounding by real-world data, and sometimes long feedback cycles. This is diametrically different from the touch-and-go nature of the typical data analysis process, in which analysts move quickly from one data set to another without proper understanding (hence creating an industry for more and more differentiated data).

Differentiated understanding is wisdom, whereas differentiated data is still data, just with unique sources. Dee Hock (founder of Visa) describes it best:

“Data, on one end of the spectrum, is separable, objective, linear, mechanistic, and abundant. Wisdom, on the other end
of the spectrum, is holistic, subjective, spiritual, conceptual, creative, and scarce.”

That statement is as profound as it is useful. The modern scientific pursuit is reductionistic. It distills the world to the few factors that mostly explains the observed phenomena. The resulting theory is thus indisputable and mechanical. Yet the theory only works in the defined environment, and can hardly be extended to the real-world containing more variables and complexity.

What wisdom attempts to achieve is results in the real-world. It discards the elegance and simplicity of reductionistic theories for utility. It sees the world not as linear cause-and-effect, but as a system comprising myriad interrelated nodes. Changes in a single node would reverberate through countless others.

Perhaps an example would aid in solidifying concepts. Say theory and experience have proven that mean reversion works in investing. Buy cyclical companies at trough valuations, sell them at peak valuations, repeat. Wisdom would require asking “and then what”. What happens when more and more investors realize and practice this strategy? Trough valuations would no longer be low when more investors buy. Peak valuations would no longer be high when more investors sell. What happens when more and more cyclical companies realize what investors are doing? They might adjust their business models to maintain stable valuations, which would aid as a currency for M&A and employee stock incentives. The entire game would evolve.

In another example, research has consistently proven that rising EPS is the key determinant of rising stock prices. Linear cause-and-effect logic would dictate one look for stocks with rising EPS and stagnant or declining prices.

This simple dynamic leads to complex behaviors. Consider that one should buy the stock before EPS rises. How early should investors be? Perhaps wait for indicators such as rising operating earnings that precedes rising EPS? If the market knows about rising operating earnings, the wise investor would have to look for earlier indicators yet to be discounted.

Here’s the kicker. What happens to the original linear cause-and-effect logic? The market has evolved such that actual rising EPS forms a weak (but not impossible) case for rising stock prices. When the market is looking for earlier and earlier indicators for rising EPS and yet ignores actual rising EPS (when it shouldn’t be), the market is strongly signalling that the rising EPS won’t last.

To achieve wisdom is to understand how the world really works. The real world evolves, but an experimental one does not. Little changes result in enormous shifts in the real-world, but are unlikely to be demonstrated as such in a controlled setting (it would make the setting uncontrollable).

This is not to say scientific inquiry is useless. If anything, wisdom recognizes both the utility and limits of scientific inquiry.

Every investor begins with a scientific mindset. There is no starting if you cannot count and theorize. But achieving great returns requires leaps beyond, and many non-scientific subjects would aid that effort. Dee Hock said it best again:

“Science has traditionally operated in the provinces of data … where measurement, particularity, specialization and rationality are most useful. It has often blithely ignored the provinces of understanding and wisdom.

Theology, philosophy, literature, and art have traditionally operated in the provinces of understanding and wisdom, where subjectivity, spirituality, and values are most useful. It has often blindly opposed the scientific way of knowing.

Data moves at the speed of light today (quite literally). Data, no matter how differentiated, is quickly discounted. Wisdom is difficult to gain but also hard for the market to discount quickly, and should henceforth be the insight upon which investors rely.

2020 letter to partners

Topics: a lesson from Napoleon; the unprecedented risk in stocks; our investments; trust as leverage; gratitude shout-outs

Dear partners and friends,

YearFRC ReturnS&P 500 Total Return
201766.1%21.8%
2018-7.9%-4.4%
201919.9%31.5%
2020139.5%18.4%
   
CAGR since inception44.8%16.0%
Cumulative since inception339.4%81.3%

Farm Road Capital gained 139.5% in 2020, while the S&P 500 gained 18.4%. From inception in 2017, FRC gained 339.4%, while the S&P 500 gained 81.3%.

Your manager assures you that the S&P is not an unduly short yardstick. 68% of my peers under-perform benchmarks over the most recent 3-year period (2016-2019). Over 5 years, 82% under-perform. Over 10 years, a staggering 89%. The numbers suggest that excess return is rare. Your manager faces long odds, but hopes you are assured by the alignment of our interests. 99% of your manager’s net-worth is invested in the same securities in which you are invested. Your manager eats his own cooking.

Your manager is also the first to assure you that the success in 2020 is unlikely to be repeated. Unprecedented conditions created once-in-a-lifetime (maybe twice) opportunities that are unlikely to repeat in the foreseeable future. Your manager prefers to avoid throwing cold water on your returns, but would do so only to adjust your expectations to reality. There is nothing like cold water in wintry January that wakes one up to the real world. It is your manager’s goal to perform in excess of benchmarks by 10% annually over the long-run.

A lesson from Napoleon

Some pilots describe their jobs as “hours and hours of boredom punctuated by moments of sheer terror.” It is almost the same with investing (replace ‘boredom’ with ‘uneventful reading’).

What pilots do during terrifying moments determine the fates of many. The few decisions made in those moments have impacts larger than many decisions combined in routine flights. The same principle applies to investing. What investors decide during extreme market turmoil have outsized impacts on performance.

Napoleon perhaps has the best response, that is “the average thing when everyone else is losing their minds”. An experiment first conducted in 1951 showed why this is difficult. Psychologist Solomon Asch showed eight college students two diagrams:

He asked them: “Which line – A, B, or C – is similar to the line in the other diagram?” Each participant answered sequentially, such that the next participant could hear the previous answer.

The correct answer was simple. Yet when the first seven participants (who were actors with predetermined responses) gave the wrong answer, the eighth (the sole real subject) almost always gave the wrong answer as well.

The researcher viewed the results “as a striking example of people publicly endorsing the group response despite knowing full well that they were endorsing an incorrect response.” The experiment demonstrated the tendency to conform with the crowd.

When everyone else is losing their minds, the easy choice is to lose your mind as well. The difficult choice is to “do the average thing”. In investing terms, the difficult and “average thing” to do is to take action when the market goes crazy.

I wrote a short article to buy stocks on March 23 2020. With hindsight, the day coincided with the market reaching a bottom after plunging at the fastest pace since the Great Depression in 1929. On March 23, there was no way to tell whether the market would decline further, but there was a reason to buy:

“How do we know that stocks have bottomed? The truth is that there is no way to tell, despite what you hear from Goldman Sachs and CNBC. Steve Jobs said that “you can never connect the dots forward. You can only connect the dots backwards”, which means that we will only know the bottom with hindsight. Because you can’t be certain to buy at the bottom, you should only buy when you expect reasonable returns. Using 90-year averages for the S&P 500 as a benchmark, the investor can expect roughly 7% returns per year in the next 10 years (see Appendix at end of article for calculation), a reasonable return.”

Farm Road Capital on March 23 2020

Buying during turmoil entails a few overlooked yet important nuances:

  1. You must not have excessive leverage: You cannot buy when a significant downturn obliterates your equity. You can only buy if you survive the downturn. This sounds obvious, but it is difficult to expect a downturn when the going is good. Because there is no way of knowing when downturns occur, your manager operates with limited to no leverage.
  2. You know what to buy: The most opportune windows do not stay open for long. Your manager keeps a wish-list of the best businesses to own should opportunities arise.
  3. You know what to abandon: This is perhaps the most difficult yet important. Significant downturns tend to alter the economics of some businesses. The investor must be aware of the paradigm shift. The endeavor requires an unique and rather contradictory blend of skills: sound logic backed by commonsense and financial history, the foresight in imagining what hasn’t happened before (that may defy current logic), and the sturdy independence of thought when commonsense is on shaky ground.

An old saying in Chinese opera goes: every minute of performance on-stage is backed by ten years of hard work off-stage. Investors toil in the shadows for years, ready to perform at a moment’s notice when opportunity arises.

The unprecedented risk in stocks

The exuberance in some technology stocks – electric vehicles, autonomous driving, software – is hard to miss, but the mania is nowhere near the previous sector bubble. These stocks may still decline, just not because of a bubble burst.

I am old enough to remember Yahoo listing on the Nasdaq in 1996. The then-quintessential search engine was valued at $850 million, implying a massive revenue multiple of 607. China.com (a Chinese clone of Yahoo) was listed in 1999 with a $5 billion valuation and $2.4 million revenue, an astonishing 2083x revenue. Even mega-caps with slower growth rates were valued highly. AOL was valued at $200 billion at its peak, even when it generated slightly more than $4 billion revenue. These bubble conditions are absent today. There is only a handful of technology stocks (about 40) trading at 30x revenue or more, and only 4 at 100x revenues or more. Recent popular IPOs such as Snowflake and C3.ai trade at 245x and 125x respectively, which are very expensive but not bubble-expensive. Mega-cap technology companies (Facebook, Apple, Netflix, Google) trade at 8x revenues or less, a far cry from AOL’s 50x.

The general market does not seem expensive either. The S&P trades at 22 forward PE, near its 2000-tech-bubble peak of about 24, raising alarms for a probable (and painful) mean reversion. But it is likely wrong to assume that 24 applies as the ceiling for valuation today. The economy was very different in year 2000. Quantitative easing and Facebook were not invented yet. Netflix only delivered DVDs. Amazon offered mainly books. Banks were over-levered and held little equity relative to risky assets. Overnight rates were 5-6%, compared to 0-0.25% now. The American economy is stronger and better supported today. The large technology companies are delivering consistent double-digit growth in revenue and profits with no end in sight. They make up 12% of the S&P, up from nothing in year 2000. Banks now hold plenty of capital relative to risk levels, and still make record profits even with lower leverage. The Federal Reserve is much more effective in responding to crises. A stronger economy, with unprecedented supportive monetary (and now fiscal) policies, should see higher multiples.

The question is how long the support would last. This is a complex topic, because the central bank does not have complete control. The Fed has a dual mandate of maintaining stable prices and maximum employment. It currently keeps rates low to promote job growth, and affords to do so because inflation has been below the 2% target in half of the past decade.

What happens if inflation is above 2%? The Fed would raise rates to control inflation, yet higher rates reduce job growth. Fulfilling the dual mandate would be trickier. What happens if longer-term rates increase? The Fed only has control over overnight rates, which influences, but does not determine, longer-term rates. Higher longer-term rates depresses growth. What can the Fed do when overnight rates are already at 0%?

What is certain is that the Fed is determined to expand its influence. It sends the message through a relentless stream of unprecedented and timely policies during the past 12 years, whenever the outlook is dire. An influential Fed amplifies the upsides of its supportive policies, but also intensifies the downsides of policy missteps. The global economy is fortunate that the Fed has not made major policy errors since 2008. The lack of errors is actually an aberration. The Fed made numerous errors in the 94 years between its birth in 1913 and 2007. Its inclination towards novel policies means that significant errors in the future may be difficult to reverse, because history would offer little guidance.

We should count ourselves lucky for a responsive and (almost) error-free Fed for the past 12 years, but should not count our chickens before they hatch.

Our investments

Your manager is invested in the same securities as you are. Our portfolio contains 6 US-listed equities in the technology, media, and healthcare industries, with market capitalizations ranging from $100 million to $100 billion.

Your manager favors businesses in sustainable, predictable, and non-traditional niches growing at above-market rates. History has shown that sustainable above-market growth is rare, because the required ingredients tend to exist separately. Each ingredient appears ordinary in isolation. When bundled together, each ingredient reinforces others, and, in a flywheel fashion, results in a multiplier effect and a whole that is greater than the sum of its parts. The ingredients include heavy commitments to innovation and people, clear visions for market leadership, industry tailwinds, excellent unit economics, and incentivized leadership, among others.

The value of our holdings in ecommerce and digital advertising have significantly increased. Social distancing and lockdowns caused by the pandemic accelerated the shift to online retail and advertising. Consumers are unlikely to favor physical retail after experiencing the seamless nature of ecommerce. Advertisers are also less likely to commit to non-digital formats after benefiting from the greater efficiency of digital advertising over alternative formats. More importantly, the management teams of our holdings delivered. No one expected the scale of the pandemic and disruptions. Yet our management teams recovered quickly from the economic shock, took advantage of disruptions, and led their businesses to new highs.

The key risk in technology and media is the unintended consequences of regulations. Not the regulations in and of itself, not even the immediate effects, but the second, third, and subsequent order effects of regulations. Ecologist Garrett Hardin advocates asking ‘and then what?’

Regulators start with good intentions in restricting the outsized, black-box influence of Facebook and Google (black-box because many elements of ad pricing are proprietary). The results should unfold like any worthy economics textbook says so. More players in digital advertising results in more competition and innovation. Advertisers can shop for the lowest cost amidst the plethora of choices. The lower cost of advertising would benefit the ultimate end-consumer.

Reality is more complex. Human behavior, unlike physical phenomena, unfold in unpredictable ways. Facebook and Google have commanding leads because of their copious collections of data. New and smaller players do not have an edge in data. What would they do to get an edge? Would they resort to new methods of data collection that threatens consumer privacy further? Would they refuse transparent reporting on collection methods to protect their edge? Would digital advertising then become less transparent than it was?

There is no one easy answer that balances the interests of all involved. Your manager does not dream to be a regulator, but would align our investments with the way the world works.

Trust as leverage

I have been told that the logo is an abstract image of houses or mountains. It really is simply a handshake, which is a universal representation of trust.

Any self-respecting investor knows that it is impossible to know everything about a company, though it is just as important to work as hard as possible to know as much as possible.

So how is it possible to be sure of a good deal? One has to trust that the other side would hold up its end of the deal. And that is what the handshake really represents. You trust that the other side would take care of things, that you would never have known, to your benefit.

In this perspective, trust functions like leverage. In physics, leverage allows for results that are multiplies of the effort exerted. When you trust the other side, the Pareto principle works (80% of results from 20% of effort).

How is trust established for an investor in practical terms? Your manager hardly has contact with holdings. There are no boots on the ground. Your manager favors publicly available information, and it is usually sufficient to know whether management is trustworthy. How does the CEO represent the business, industry, and competitors? Does the CEO fulfill promises? What excuses does the CEO or CFO have for missing targets? How do senior executives talk about other employees? The catch is to read enough to know what makes sense.

Gratitude shout-outs

Many have provided advice and support to your manager:

  • My wife and mother-in-law, who have believed and supported me in every way possible (nothing motivates an investor more than having the mother-in-law’s savings on the line)
  • My parents, who provided the opportunity for me to pursue my education in the United States
  • Peter Kaufman, who generously showed me how the world works by teaching me its timeless and unique principles
  • Scott Hendrickson and Mike Kimpel, who taught me their research framework and provided useful feedback on my ideas at Columbia
  • Alex, Ben, James, Roger, and many others who provided useful feedback

If you have any questions, contact me at farmroadcap@gmail.com.

Marcel Gozali

1/5/21

Unique and hidden flywheel powers growth at Elastic

Elastic NV (NYSE: ESTC) is the most interesting enterprise software company that I have encountered. Its strong and improving financials are obvious. What is hidden is its unique flywheel, which relies on open-source and powers rapid innovation and growth.

Its core competency is in Elasticsearch, an open-source and near-realtime search solution that supports many applications in analytics, monitoring, security, and likely many more in the future.

The keyword is open-source. Elastic’s steadfast commitment to open-source has fostered a large and dedicated community of developers (150,000 and counting). Users provide rapid feedback, remove bugs, and guide feature development. Elastic effectively crowd-sources innovation from its developer community, taking cues on what to expand into and what to acquire. Crowd-sourcing innovation is also faster and more cost-effective than the traditional, “closed” corporate R&D model.

Another benefit of open-source is shorter sales cycles. The large and loyal base of developers enables an effective bottom-up “grassroots” (as opposed to top-down CIO-first) approach to sales and marketing, shortening the infamously long sales cycle in enterprise software. Elastic counts about half of the Fortune 500 and a third of the Fortune 2000 as customers, which supports high recurring subscription revenue (92% FY20 revenue). Elastic is growing rapidly (57% FY20 rev growth yoy, 45-55% rev growth yoy during Covid qtrs), and is still early in its growth journey because of the innumerable innovations that its community would eventually discover in the future.

Critics often question developer loyalty. Why would a developer release self-developed improvements for free? Because the developer must use Elastic software for projects. The developer has to reports bugs and suggest improvements so that projects can be completed. Elastic has shined in strategic execution to achieve developer mind-share when there are many other open-source search and analytics solutions. There are two subtle but visionary nuances worth discussing.

The first is straightforward (but not simple to execute): a relentless focus on developers. Shay Banon, founder and CEO, discussed how Elastic differs from its open-source competitors:

“One common open source business model, is to sell support subscriptions. Sadly, support-only business models trend towards a conflict between what’s best for the user and what’s best for the company. In these situations, the company isn’t motivated to make their products easier to use, more reliable, scalable because that would eat into their support profits.”

This makes sense. Open-source providers are incentivized to make products difficult to use, so developers must pay for support, discouraging developers from improving the software.

“[Elastic] was never, and never will be, willing to bear the tension of not making our software better in order to ensure that the company stays in business. We want to continuously improve and our goal with support is to make your project successful so that you are the expert in your Elastic Stack deployment. We want you to be successful so that you choose the Elastic Stack for your next projects too.”

Elastic encourages developers to improve its software by promising it would do the same. The software would always improve, and would never be held back for commercial reasons. Wouldn’t this reduce support subscriptions? Elastic’s answer is the second nuance to its strategy. It only sells support to the most intensive users (think Fortune-ranked companies). It also sells them access to some features that are high-value to them but used little by the larger community, who mainly uses the free core software.

Elastic’s strategy serves the entire spectrum from the large corporations, from whom it charges and financially profits, right down to the individual developer, from whom it does not charge but gains technically and intellectually. The flywheel starts with Elastic relentlessly improving the software and helping the developer succeed, incentivizing the developer to turn only to Elastic and nothing else. As more developers use Elastic, they improve the core software more effectively than competing software. Increasing developer usage improves the standing of Elastic at large corporations, which pay for support subscriptions to Elastic, who in turn uses the revenue to reinforce the flywheel.

Competitors to Elastic are specialized solution providers (Datadog/Splunk in application monitoring, FireEye/Checkpoint in endpoint security), some of which are built atop Elasticsearch. The key advantage of Elastic is its large and loyal community, enabling the aforementioned flywheel and unbeatable economics. The community is also its key defense against Amazon. Amazon forked code from Elasticsearch to build AWS Elasticsearch (thought Amazon insisted it wasn’t), but was unable to build a community around it (see approximate community size for Elasticsearch and AWS Elasticsearch here), riddling the service with numerous bugs. AWS Elasticsearch should be effective in retaining unsophisticated users in the AWS ecosystem, but is unable to scale quickly without a dedicated developer community that takes years and resources to build. Because AWS Elasticsearch is only of many AWS managed services, Amazon is unlikely to dedicate sufficient resources to mount a challenge to Elastic.

Elastic’s leading competency in search, open-source distribution, unique flywheel, and shorter go-to-market cycles would sustain growth for years. A reasonable bull case can be made for 30-40% topline growth for current applications in the next 5 years, multiplying revenues by 4-5x (forward EV/sales about 5x). 50-60% growth in deferred revenue and $350 million cash provide adequate funding, further supported by expected positive FCF in FY21. Assuming 4.5x growth in revenue in 5 years, steady-state 35% EBITDA margin, and 36x terminal growth multiple (r=9%, g=6% ~ 3x global GDP), the projected EV is about $16 billion, and target price is $185/sh. (32% upside).

The projection excludes the upside optionality of additional applications which Elastic’s community has yet to discover. The community led Elastic to its current applications in monitoring and security, which were built with a three-pronged approach: community development, internal R&D, and M&A (executed brilliantly by current management). It is likely that Elastic would find future revenue growth from additional applications from its community, continue its excellent execution with improving internal funding, and launch new offerings in years to come.  

How to miss life-changing opportunities, again and again

How can anyone miss once-in-a-lifetime opportunities? Missing it once is unimaginable. Twice, ludicrous. Thrice, plain stupid.

How about missing it one thousand eight hundred times?

My dentist did exactly that.

His dental practice, built over three decades, is popular and profitable. The wait list to get an appointment is long. His success motivated his sons to be dentists as well. He makes enough for a million-dollar home, country club membership, and private schools for his kids.

These are nothing compared to what he missed.

20 years ago, he attended a farewell party. His cousin’s startup was acquired by Company X. As part of the acquisition, his cousin had to move to Nevada to work for X.

The cousin was a robotics engineer by trade. His startup owned two patents and had no revenue. He was thrilled that X appreciated the intellectual property enough to acquire it. What was better, in his mind, was that 98% of the acquisition price was paid in X’s stock, with more on the way as part of his employment agreement with X. Suffice to say, he was optimistic on X’s prospects.

X is the most amazing company. You all have to buy its stock, the cousin said at the party.

Out of 30 family members present, my dentist was the sole buyer of X’s stock. He had never owned stocks before. He had no idea what a stock is. But he trusted his cousin. X traded at $5 per share then.

As business at the dental practice picked up, my dentist almost forgot about his tiny stake in X. He checked his portfolio bi-annually, if at all.

To his surprise, X increased six-fold after two years. He was thrilled. He had never bought a stock before X, yet his first purchase was wildly profitable. He called the cousin to ask about X.

Everything is going great because of our proprietary advances in precision robotics and biomedical engineering. Do not worry about the litigation matters. You should continue to own X, the cousin said.

My dentist, perplexed by the conversation, promptly sold his stake at $30 per share.

For the next five years, my dentist had conversations about X with his cousin at least once annually, before they lost touch. At every conversation, his cousin touted the milestones at X. Every major regulatory approval, significant purchase order, and successful R&D outcome reinforced the cousin’s confidence in X (These were all public information). At their final meeting before losing touch, my dentist insisted that he was satisfied with the six-fold return, and could do without owning X’s stock.

Shortly after the final meeting, the United States faced its worst financial crisis since the Great Depression.

My dentist felt justified in not owning stocks at all, and perhaps even more justified in selling X’s stock five years earlier.

Fast-forward to 2020. It has been 12 years after the financial crisis, a little over 12 years since the final conversation my dentist had about X with his cousin, 17 years since my dentist sold X at $30 per share, and 19 years since the cousin began working for X and recommended X at $5 per share.

The cousin invited his entire family to a 4-day, all-expense-paid trip at an exclusive ranch in California. The occasion was his retirement. After 19 years of service at X, he retired as a senior executive. Adjusted for stock splits, X was trading near $700 per share.

The cousin never sold a single share of X in his 19 years of employment.

The cousin was wealthy. How wealthy? If he had sold his startup at $1.5 million (estimated, the price was never disclosed), of which 98% was paid in X’s stock, his stake in X would be worth $206 million, a staggering 140-fold return.

And $206 million is the minimum estimate of his stake. Because about 30% of his compensation was paid in stock annually.

My dentist invested $50,000 in X and sold his position for $300,000. Had he held until his cousin retired, his stake would have been worth almost $7 million.

My dentist missed the boat for a 23-fold return ($30 to $700 per share). Not once. Not twice. Arguably for five years (between selling X and losing touch with his cousin), my dentist had direct access to an executive with intimate knowledge of X and keen enthusiasm for the business. Every day in those five years was a chance to buy X again. He missed all of them.

All 1,800 days.

A few takeaways:

1 – Missing life-changing opportunities happens more often than you think.

Between 1952 and 1962, Warren Buffett taught investing at the Municipal University of Omaha (now the University of Nebraska Omaha, or UNO). It was a non-credit class because Buffett disliked giving bad grades.

A photo showed 16 students in Buffett’s class. In 10 years, Buffett perhaps taught 160 students. Guess how many invested with Buffett?

One.

Less than one percent of 160 students felt that the Oracle of Omaha was good enough for them to invest in. Every student listened to hours of insights, taught directly by the Oracle, yet almost every student thought he won’t be good enough.

You probably think that you won’t behave like those students or my dentist.

But you probably would.

We are inclined to perceive things as “normal” because that is the easy way to understand things. Perceiving things as extraordinary requires a leap of faith and more mental effort.

Ergo, the human instinct is unable to spot the outstanding early. The outstanding, by definition, is different. The human mind is inclined to dismiss the differentiating elements, and explains them as similar to everything else. The outstanding has to be prove itself, again and again and again, for the human mind to believe.

For the purposes of investing, this innate quality is not helpful. The cat is out of the bag by the time the outstanding has proven itself to be outstanding. As more investors learn about the outstanding, they would bid its price up to a point that curtails the potential for it to appreciate further.

The point is to spot the outstanding early. To buy and hold X at $5 per share before it becomes $700. To invest in a young Buffett before he becomes the Oracle.

I know one way to do so.

2 – All you need is context, a lot of it.

If you have $122 billion in a checking account, how would you look for an investment?

Most would rely on hordes of lawyers, consultants, bankers, accountants, analysts, and of course, endless meetings. This is the gold standard of corporate due diligence.

Berkshire Hathaway has exactly that in cash and equivalents. Its owners prefer this way:

We really can tell you in five minutes whether we’re interested in something.” – Warren Buffett

Five minutes is all he needs, because he has context. A lot of it.

Within 5 minutes, he analyzes the investment relative to others and knowledge accumulated over 70 years (Buffett started investing at 11 years old. He is 90 now), during which he spent 80% of his days reading.

In an average day, Buffett spends almost 13 out of 16 waking-hours reading (assuming 8 hours of sleep). How much does he read? 500 pages.

Per day.

Todd Combs did that and more. He is now the CEO of Geico, arguably Berkshire’s most valuable asset.

Knowledge provides context. You can identify the outstanding early with context.

To be clear, what you want is not just any knowledge. You want the right set of knowledge, which provides what I term the complete context.

To determine whether X is a good investment, you have to know not only about X, but also everything else that surrounds it and came before it. Knowing all three and seeing their connections provide complete context.

Think about the complete context as a three-dimensional perspective (x-y-z planes, if you recall high school geometry).

The first dimension is a single plane (x-axis). Your knowledge of X per se, no matter how much, is just one plane. It is a start, but not helpful in forming a complete picture.

The second dimension comprises two planes (x and y axes). The additional plane is akin to knowledge of X’s peers, industry, market size etc. In this dimension, you can draw lines to connect your knowledge of X to its comparable peers, industry, market size etc.. A better picture emerges.

So what else is missing? The time element.

The third dimension (x, y, and z axes) includes the historical perspective – the knowledge of X and its peers, industry, market size etc in their past.

Complete context is only possible when you combine knowledge of X (first dimension), knowledge of peers, industry, market size etc surrounding X (second dimension), and the corresponding historical knowledge (third dimension).

Combining all three creates unique knowledge when you see connections among the three. Therefore, the combination of the three is greater than the sum of its parts.

Think back to my dentist and his cousin. Who had the more complete context of X and significantly more success?

Recall that my dentist sold X after his cousin mentioned complex engineering terms and litigation matters. He feared about his lack of understanding, which, in all likelihood, is not because of the inherent complex nature of X.

It is because he did not work hard enough to attain the complete context of X.

And that is the problem with this method. To most people, it takes too much effort.

And the reason it works is because most people won’t do it.

“Read 500 pages every day. That’s how knowledge works. It builds up, like compound interest. All of you can do it, but I guarantee not many of you will do it.” – Warren Buffett

What will you do?

The investor who crashed but ended wealthy

I have a large extended family. My mother has ten siblings. My father, six. Out of eighteen people (including my parents), only two are financially well-off.

The first arrived in the United States without knowing a word of English. She was not a bright student. She struggled in a work-study program paying below-minimum wages. She eventually built a significant manufacturing operation employing near fifty.

Yet it is the second relative with the more interesting story. I’ll call him V.

V was expelled from school because he got into one too many fights. He then worked in his father’s (my grandfather’s) small hardware store, and later delivered eggs for a farm. His fortunes turned after his tiny startup capitalized on the explosive growth of jeans. He supplied the then-unique fabric to manufacturers. He even timed the sale of his controlling stake at peak valuations. By any measure, V was no longer poor.

This was when his problems began.

Armed with abundant capital for the first time, V started “investing”. He speculated in currencies, stocks, and derivatives, with a rudimentary understanding of finance. His frequent and enormous trades summed to hundreds of millions in turnover, attracting brokers of all sizes. The more shrewd and cunning suggested trades that he could never understand. They explained that the trades were exclusive to a select few, and that profits were virtually certain, but not when and how. V never stood a chance against Wall Street.

V took five years to build and sell his startup. His account took less than half that time to be down 120%.

V should have been in utter financial ruins. An otherwise tepid transaction, suggested by his wife, not only saved V from ruin, but also generated profits enough to make him a millionaire many times over.

His eldest son was born shortly before the sale of his startup. In planning for the family’s future, his wife suggested that they plant roots in the neighboring country of Singapore. This was the late 1970s, when the majority of Singapore was rural and unsewered (I urge readers to learn what unsewered is to appreciate the miracle that is the modern sewage sanitation system). V was flabbergasted. To leave the familiar for a wasteland (pun intended) was absurd. His wife was insistent, believing that the public school system in Singapore, however undeveloped, was better than what her country offered. V, with little formal education, did not understand her perspectives, but gave in out of respect. The young family eventually migrated to Singapore. V used some proceeds from the startup sale to purchase land and real estate in the new developing country, and allocated the balance to his trading account.

In 1978, Singapore’s GDP per capita was about $3,200, roughly a third of the United States’. In 2018, the same measure for Singapore had grown 20-fold and exceeded the USA by about 3% (The USA grew the measure by about 6-fold in the same period).

So how did real estate in Singapore performed? About a 22-fold increase. V’s returns are likely much higher because of leverage and favorable exchange rates. By rough calculations, his returns were at least 60-fold in USD terms, considering a 50% down-payment (interest rates exceeded 10% in the late 1970s, so buyers tend to place large down-payments to reduce interest payments) and a 70% appreciation of Singapore versus US dollars (meaning that one Singapore dollar today buys 70% more US dollars than it did).

A few takeaways:

1 – Success is not transferable. What made you successful in one game does not directly make you win in another.

V was a sharp operator before indulging in securities. He knew exactly how machines are running, and what his suppliers, customers, and employees expect. His skills did not translate directly to trading. Only his confidence and ego did.

As an operator, constant action was the norm. V was always on his feet, inspecting machinery and talking to people. He thought he had to do the same in trading. He could, but he didn’t have to. His skills may had worked if he were a tape-reader (ie the quintessential intuitive trader), but results certainly showed he wasn’t.

You have to understand your strengths at their core, in order to know what to work on and how to work in the unique way that caters to your strengths.

To work in a certain way, just because your peers do or some successful person says so, ensures disappointment.

2 – When you have capital, the right thing to do mostly is nothing.

Warren Buffett once quipped “I make mistakes when I get cash. Charlie tells me to go to a bar instead. Don’t hang around the office.”

Investing is akin to a baseball game with infinite pitches. You only have to swing at those which assure home-runs. You have to know your sweet spots. Every investor has unique backgrounds, knowledge, and by extension, sweet spots.

Home-runs are rare by definition, so the investor should behave similar to the batter who watches many pitches pass and does not swing. Yet the batter must always be prepared to swing at a moment’s notice.

Wall Street gets paid when the batter hits the baseball. Every hit on the bat is a commission, so WS throws as many pitches as possible in as many ways as possible to entice investors to swing their bats. WS does not care whether the batter hits a dud or a home-run.

WS makes it difficult to do nothing when you have capital, but nothing is exactly what you mostly have to do.

To be fair, real estate in Singapore is not V’s sweet spot. However, V is likely to understand where and how to buy a home for his family, more so than when to buy and sell complex securities. V’s home-run in real estate obscures an important but subtle point, which brings us to the next takeaway:

3 – Home-runs are not short-term large returns, but are really long-term moderate returns.

A 60-fold return is significant. Over 40 years, it equates to 10.8% per year. The annual returns do not appear significant relative to equity indices. The S&P 500 reached a bottom in March 2009, and returned 16.5% per year between then and now (with dividends reinvested).

The secret is not in the annual returns. It is in the duration.

Over a long-enough time frame, even moderate returns can achieve astonishing results. Committing to the investment, through large and small downturns, is essential but counter-intuitive. It is a characteristic that V had help on, which leads to the final takeaway.

4 – Marry the right person, and have reasonable expectations.

Investors take pride in sticking to cold, hard facts. But investing is predominantly a human activity. The cold, hard facts underpinning decisions are selected by bias, which contains an emotional element. Trading algorithms, designed to be emotionless, are designed by emotional humans.

This means that human psychology matters.

Just knowing about home-runs is insufficient. You have to know how to emotionally stick to the strategy producing the home-run.

Judging from V’s trading, I am certain that he wanted to sell the real-estate in Singapore after a certain level of appreciation. One who trades in minutes and hours does not have the patience to invest for months and years, let alone decades.

I am almost certain that every discussion V had with his wife about selling, he was persuaded otherwise. His wife loved the home that was built from scratch, the large yard, its polite neighbors, proximity to great schools and public transportation etc. On top of those, the home and land values appreciated almost every year.

In all fairness, V’s wife knew next to nothing about investing. Singapore real estate was certainly not her sweet spot. However, that she could stick to a home and its surroundings for more than 40 years is telling of her reasonable expectations (she does not want or need a bigger house) and stable sense of contentment. The average American, contrary to V’s family, moves 11.7 times in a lifetime (Singaporeans about a third as often).

Reasonable expectations allows one to stick to a strategy, through thick and thin, for the long-term. It is not the same as being ignorant. Quite the opposite. It is being aware of the facts, and the understanding of the futility of reaching for the very best returns when the present works fine.

Contentment, the act of not reaching for more, is the paradoxical key to get more, because it results in long-term duration that underpins astonishing returns.

Marrying the right person can do wonders for the psychology of contentment. After V saw his trading account wiped clean, he never traded again for short-term profit, likely at the behest of his wife. His wife constantly reminded him to appreciate the present, which offered a higher standard of living by any measure relative to their lives before moving to Singapore. V’s appreciation for the present grew gradually. As his brokers pay less attention to him, he eventually sold all his luxury jewelry and watches, and traded his Mercedes for a beat-up Honda van. The less he owned, the more contented he became.

The Oracle of Omaha said the following about reasonable expectations. It is no wonder that he achieved the rare combination of being wealthy, contented, and in all likelihood, happy.

I was going to do the same things when I had a little bit of money as when I had a lot of money. If you think of the difference between me and you, we wear the same clothes basically (SunTrust gives me mine), we eat similar food—we all go to McDonald’s or better yet, Dairy Queen, and we live in a house that is warm in winter and cool in summer. We watch the Nebraska (football) game on big screen TV. You see it the same way I see it. We do everything the same—our lives are not that different. The only thing we do is we travel differently. What can I do that you can’t do?” -Warren Buffett

Tech boom or bubble?

The strength of technology stocks in 2020 is indisputable. The five largest companies – Google, Amazon, Apple, Facebook, Microsoft – are all in technology. Their dominance is increasing. Their market values make up more of the S&P 500 than their counterparts did at the dotcom bubble peak 20 years ago.

Yet such strength often precedes bubbles that end with painful crashes. History contains many examples – conglomerates in the 70s, thrifts and semiconductors in the 80s, “tiger” economies in Southeast Asia in the 90s, TMT in early 2000s, and financials before the GFC in 2008.

All bubbles begin as booms, but not all booms become bubbles. Sometimes a boom is deflated by timely contractionary policies or gradual loss of investor interest. The boom to worry about is the one with a self-reinforcing character. It starts with good fundamentals, leading to rising valuations, high investor interest, and pressure on companies to show improving fundamentals. Companies venture into less-profitable activities that are disguised as sound decisions to support valuations. Rising stock prices discourage investors from prudent due diligence and encourage them to use impractical assumptions to justify increasing valuations, which further increases pressure on companies. The self-reinforcing frenzy results in a bubble that would inevitably end after companies meet the limits of expansion and show deteriorating fundamentals.

History shows that two factors are essential for a boom to become a bubble:

  1. Excessive valuations
  2. Abundance of overly simplistic elements as key drivers of value

Is the boom in tech stocks actually a bubble?

1. The largest tech companies do not appear to be excessively valued relative to past bubbles and long-term interest rates.

The conglomerate bubble in the 70s involved the then-largest companies, while the dotcom bubble in 2000 involved TMT companies. These bubbles are used as proxies because they relate to the current largest tech companies in size and sector respectively.

The reader should also understand the concept of equity yield and its relation to long-term interest rates. The equity yield is the underlying company’s earnings and dividends relative to its stock price. Another way to put the equity yield in context is to view a stock as a bond. Earnings and dividends of a stock are akin to coupons of a bond (except that earnings are not distributed like coupons are, but are left for management to allocate).

Compared to the yield on treasury bonds, the equity yield of a stock should be higher. This is because a stock’s risk is higher than treasuries’ (ie risk-free), so it should almost always offer higher yields than treasuries to entice investors. Higher risk, higher yields; lower risk, lower yields.

During the 1970s, the conglomerate bubble saw large-cap stocks yielding less than 2%, while 10-year treasuries yield more than 6%. This discrepancy was evident of a bubble. Equity investors accepted yields lower than treasuries!

Put in a different way, investors accepted low yields that implied excessively high valuations (PE ratios range 70-80), and could had accepted higher yields in treasuries with much lower risk.

The tech bubble in 2000 saw even lower equity yields. The 100-200x PE ratios of Nasdaq stocks implied yields of 0.5-1.0%, which were much lower than the 6.5% yield on 10-year treasuries.

Today, with the exception of Amazon, the four large tech companies trade near 32x forward PE, which translates to 3.1% yield (inverse of 32). Only Microsoft and Apple in the group pay dividends in the 0.7-1.0% range. Hence 4.0% can be used as an appropriate proxy for the equity yield of the group.

Relative to 10-year treasuries yielding 0.7%, the group isn’t expensive. Even if the 10-year yielded 1%, PE ratios of the group have to triple to match.

This method shows that only Amazon is expensive relative to long-term rates. Amazon sports 108x forward pe, which translates to 0.9% yield, just slightly above the yield on the 10-year.

2. There is little evidence of overly simplistic elements as key drivers of valuations.

During the late 1960s and early 1970s, conglomerates relied on M&A for high growth rates that were rewarded with increasing multiples. Their success attracted imitators, which went on acquisition sprees. M&A activity eventually involved the most mundane businesses with stagnant or even contracting revenues, such as metal scrapping. At the peak of the bubble, companies were rewarded with high multiples as long as they promised fervent M&A.

In the late 1990s and early 2000s, any business related to the Internet, whether it be laying fiber-optic cables or selling pet products online, had high multiples based on projected revenues. No attention was paid to earning, cash flows, and other traditional indicators of the sustainability of business models.

As shown by history, bubbles are formed by the lack of investor discrimination.

Current trends favor SaaS (software-as-a-service), IaaS (infrastructure-as-a-service), and similar business models with strong customer loyalty, platforms with network effects, and subscription components that support recurring revenues. However, investors are selective about the quality of business models. Most importantly, they discriminate to an extent that a company cannot market itself as “something-as-a-service” to achieve high multiples.

Take Grubhub as an example. The company was once a high-flyer, whose stock increased 22% in a single session after Yum Brands (the parent of Taco Bell, KFC, and Pizza Hut) bought a small stake. The stock crumbled after competition from Uber, Postmates, and Amazon intensified (Amazon has shut down its food delivery business) and revealed that high margins were unsustainable because of expensive customer acquisition cost. As with airlines and autos, consolidation to achieve scale is the solution to the problem of costs outpacing revenue growth. Uber is merging with Postmates, and Just Eat Takeaway is acquiring Grubhub.

Another example is lesser-known Alteryx. The company pioneered data analytics SaaS, increased revenue by 12x in 6 years, and oversaw near 13-fold returns from its IPO. Despite its excellent history, AYX was punished with a 53% decline in its stock when it provided earnings and an outlook that missed expectations. Investors were unforgiving despite the SaaS nature of the business and the company’s excellent track record.

Even Intel faced the wrath of the market when it failed to deliver. After announcing a delay in its next-generation chips, the stock of the technology stalwart fell 23% in 3 days.

Investors are (still) careful, so the current tech boom is not a bubble (yet).

Despite the lack of a tech bubble, technology stocks are still prone to large and small declines. It is hard to tell what the market would focus on. At one moment, it is revenue growth rates, no matter the cost. At another, it is liquidity and cash flows., no matter the business model. The investor must be ready for the market to do anything, and has to prepare accordingly. Investors, especially those in technology, would do well to heed Ben Graham’s advice:

“You can get in more trouble with a sound premise than an unsound premise because you’ll just throw out the unsound premise” -Ben Graham, father of value investing and mentor to Warren Buffett

How to invest during Covid-19

Anything can happen in the markets. Events that historically took many months or years to unfold happened in weeks in 2020. The S&P 500 declined by 30% in a record 22 days. The only event that surprised investors more was the recovery. The S&P recovered to its 2020 starting point within months.

Yet many households could not afford rent and mortgage payments in July, and millions are out of work. Consumer spending, the engine of growth in the United States, is likely to stall. How is the market not at lower levels?

These are confusing times. I attempt to offer a few guidelines, as a response to the barrage of questions received.

To start, nobody knows what the market is going to do.

Warren Buffett frequently says the same. So does Seth Klarman. Perhaps Matthew McConaughey says it best in the movie Wolf of Wall Street:

“Number 1 rule of Wall Street. Nobody … I don’t care if you’re Warren Buffett or Jimmy Buffett … Nobody knows if the stock is going to go up, down, sideways, or in f****** circles” (scroll to 1:48 in this clip)

How are you suppose to invest not knowing where the market is going?

There are 4 things to do:

1. Believe in the long-term potential of the American economic expansion

Earlier in March 2020, I wrote:

“Stocks reflect the long-term earnings potential of their underlying businesses. The American economy is contracting because of necessary measures to contain the virus, but the long-term potential of the American economic expansion is intact. In the past 100 years, America, backed by relentless dynamism, has survived a flu epidemic; the Great Depression; costly World Wars and numerous conflicts; an assassination and a resignation of her Presidents; hyperinflation and oil shock; Black Monday; savings-and-loans implosion; September 11; the dot-com crash; defaults of major economies; the Lehman and mortgage crisis; and the Eurozone sovereign crisis. Yet the S&P 500 increased from 18 to 3,231. There is little doubt that major corporations in the United States would set record earnings 10, 20 and 30 years from today.”

2. Invest in passive index funds

For the 99% of readers who believe in #1, you should invest in index funds designed to mirror the broad economic performance of the United States***.

3. Have a long-term orientation

Not a day, a month, or even a year. Years. Decades is even better.

Compounding takes time. Decent returns take time. Give your investing the time it needs.

4. Do not use excessive leverage

There are bold investors. There are old investors. There are no bold and old investors.

To have decent returns, the investor must first survive.

Leverage increases both returns and losses. What is the point of having 10 years of fantastic returns before losing it all in the 11th year? Some funds did exactly that.

The guidelines above are easy to follow, only if you live in a world without Wall Street and the media.

In recent memory, Morgan Housel perhaps summarized the challenge best:

“Investing isn’t an IQ test; it’s a test of character.”

The conviction to follow simple rules in investing for decades, without undue influence from family/friends/CNBC/Wall Street/that smart analyst, is extremely rare.

***The remaining 1% may attempt to invest in active managers such as myself. However, it is a wild ride for the uninitiated. Most of my peers under-perform passive benchmarks.

Bet your own money, not OPM, to learn and win

I was excited to attend my first class on investing principles in graduate school. I was finally with like-minded individuals who share my pursuit of investing excellence.

My first conversation with a classmate dulled my spirits.

We discussed everything about a stock that we both liked. Our knowledge on its business model, finances, history, management, and competitors overlapped yet complemented. I have a substantial portion of my net worth in the stock. Yet the classmate said:

Classmate: I have a little invested in the stock. I would never have much invested in it.

I was in disbelief. For heaven’s sake, why?

Classmate: I know a lot about this stock to prepare for a job interview at a [insert well-known company] fund. Why bet your own money when you can bet OPM (other people’s money), take a large salary, and avoid losing your own money if you’re wrong?

My classmate would be a highly-paid employee at a large fund. Yet it is unlikely that he would become an outstanding investor.

Investing one’s own hard-earned capital and investing OPM have subtle yet important differences. Capital fuels life in capitalistic societies. It pays for food, water, shelter, and education. It supports families, relationships, and self-esteem (to an extent, arguably). Investing one’s own capital is an undertaking of personal risk. Enormous stress is an understatement. Investing OPM reduces personal risk significantly.

Yet investing one’s own capital confers huge advantages to the bearer of risk. Because the drive to protect and profit from one’s capital is strong and tangible, one starts to learn the exacting means to do so. One inevitably begins to see the irrational practices of the market. Does the business value of the strong company fluctuates as much as its stock price volatility or market commentaries suggest? The investor of own capital would find the answer, struggle with it, lose money, choose to believe in it more, lose money again, believe in it enough to make it part of one’s being, practice it right, and finally survive price volatility. Repeat the grueling journey for other investing principles enough, and one finally profits.

By taking significant personal risk with own capital, the investor learns from a journey that the OPM investor would not. The OPM investor has a large salary and bonus (yes, bonus is due even in a losing year) which guarantees a high standard of living. There is little incentive to take personal risk by betting personal capital. Being a good employee would suffice.

Humans are conditioned to seek security now. Now, not in the long-run. The majority of “investors” would seek the stable paycheck of a large fund. What the majority is giving up in exchange is significant – the opportunity to go through near-term grueling self-doubt and learning to achieve long-term life-changing profits.

As an illustration, consider the stock of The Trade Desk (NASDAQ: TTD). As a provider of advertising technology, its IPO was unpopular. Predecessors have risen and fallen. The Rubicon Project (now Magnite MGNI after merging with Telaria) was a high-flying SSP (supply-side platform) before crashing because it missed out on the crucial transition to header-bidding technology. The pace of technology change was so fast that even a leader could not keep pace. Why would TTD do any different? To top it all off, large competitors include Google and Facebook, which owned more than half of the digital advertising market with their walled gardens. How likely would a newly IPO company win against the titans?

If the investors of OPM did not give up on TTD pre-IPO, they would have post-IPO. TTD stock popped 60% on day 1 and declined 20% in the next two months. In the next four months, the stock did nothing from its day-one pop (see chart below). To avoid explaining why the stock didn’t perform (while the S&P was up 6%) and how TTD could compete with Google and Facebook, investors of OPM would likely replace TTD with a stock that can be easily explained to clients and bosses.

If one had focused on business value, one would have understood the value of TTD as a leading DSP riding the CTV wave, armed with a technically-savvy founder-CEO with a 50% stake proven by a previous successful exit to Microsoft. I’ll leave the competition with Google/Facebook for another day (or for the reader to find out). Even with the knowledge of business value, one has to suffer multiple significant declines, ranging from 5% to 50%, to profit from TTD (see chart below for multiple rises and declines. Yet TTD returned about 15x from day-one close to now over about 4 years). Those who profit have taken to heart the lessons from multiple rounds of grating mistakes and missing out on other profitable opportunities, driven by their investing of personal capital and taking of great personal risk.

To AT&T and Comcast: take your head out of the sand

Fans of Roku and Amazon Fire might have been surprised when AT&T-owned HBO Max – the almighty channel featuring Game of Thrones and Southpark – would not be directly available to them (there is a workaround, albeit cumbersome). They might be shocked again upon realizing that Comcast-owned Peacock would likely be unavailable too. Roku and Amazon Fire reach 80 million households. Why would the media giants give up this reach if their objective is to reach as many subscribers as possible?

Their decisions appeared to be motivated by the fear of accelerating loss of cable subscribers. The rise of TV streaming and cord-cutting have caused the permanent loss of millions of cable subscribers. AT&T owns Time Warner Cable (now Spectrum) and Comcast owns cable assets under Xfinity. The cable business has historically been very profitable, producing north of 30% net margins. AT&T and Comcast are motivated to protect their cable business and stem the loss of subscribers by limiting streaming access to their most popular content.

However, the restrictions are akin to a shack in front of an enormous tidal wave. There is no way to prevent TV streaming from disrupting the cable industry (I argued in a previous article that cable was ripe for disruption). Hence little can be done to stop the outflow of cable subscribers. AT&T and Comcast are merely slowing down the inevitable with content restrictions and customer-hostile tactics, which would ironically accelerate the demise of the cable business in the long run by increasing the relative appeal of user-friendly streaming channels.

If it is impossible to stop customers from moving to streaming, it would be rational for AT&T and Comcast to position for and profit from streaming. Allowing Roku and Amazon Fire easy access to HBO Max and Peacock is a step in the right direction, yet is still insufficient to assure success in attracting and retaining subscribers. AT&T and Comcast are light-years behind streaming stalwarts like Netflix. Large content libraries, owned by the media giants, alone are not enough. The success of streaming champions (Netflix, Roku channel, Hulu etc) depend on both content and sophisticated technology. For example, Netflix has at least a decade lead in its recommendation engine technology that pairs users with content that they otherwise would not discover. The recommendation engine, named Cinematch, uses machine learning algorithms, so advanced and refined that it does not require users to rate content in order to recommend content (for more on this fascinating topic, read chapter 11 of this book). The Cinematch algorithm retains customers by facilitating discoveries of hidden gems. Therefore, it would be unwise for AT&T and Comcast to rely solely on large libraries of premium content for success in streaming. They would also have to consider matching the user experience and back-end technology of competitors. This is a formidable undertaking whose difficulty is compounded by constantly-improving competitors. Yet AT&T and Comcast are squandering precious time and resources over squabbles concerning content access.

Streaming is the future. Netflix is already worth more than AT&T and Comcast. Roku would eventually be recognized as the premier platform for streaming. The sooner that AT&T and Comcast position for streaming, the sooner they can start competing for a future that is increasingly likely to cast them aside.