Thoughts on Artificial Intelligence

Artificial intelligence has the potential to reshape the world.

The way Jeremy and I see it, artificial intelligence (AI) really leapt into the zeitgeist in late-2022 or early-2023 with the public introduction of DALL-E2 and ChatGPT. Both are provided by OpenAI and are known as generative AI products – they are software that use AI to generate art and text, respectively (and often at astounding quality), hence the term “generative”. Since then, developments in AI have progressed at a breathtaking pace. One striking observation I’ve found with AI is the much higher level of enthusiasm that company-leaders have for the technology compared to the two other recent “hot things”, namely, blockchain/cryptocurrencies and the metaverse. Put another way, AI could be a real game changer for societies and economies.

I thought it would be useful to write down some of my current thoughts on AI and its potential impact. Putting pen to paper (or fingers to the keyboard) helps me make sense of what’s in my mind. Do note that my thoughts are fragile because the field of AI is developing rapidly and there are many unknowns at the moment. In no order of merit:

While companies such as OpenAI and Alphabet have released generative AI products, they have yet to release open-source versions of their foundational AI models that power the products. Meta Platforms, meanwhile, has been open sourcing its foundational AI models in earnest. During Meta’s latest earnings conference call in April this year, management explained that open sourcing allows Meta to benefit from improvements to its foundational models that are made by software developers, outside of Meta, all over the world. Around the same time, there was a purportedly leaked document from an Alphabet employee that discussed the advantages in the development of AI that Meta has over both Alphabet and OpenAI by virtue of it open sourcing its foundational models. There’s a tug-of-war now between what’s better – proprietary or open-sourced foundational AI models – but it remains to be seen which will prevail or if there will even be a clear winner.
During Amazon’s latest earnings conference call (in April 2023), the company’s management team shared their observation that most companies that want to utilise AI have no interest in building their own foundational AI models because it takes tremendous amounts of time and capital. Instead, they merely want to customise foundational models with their own proprietary data. On the other hand, Tencent’s leaders commented in the company’s May 2023 earnings conference call that they see a proliferation of foundational AI models from both established companies as well as startups. I’m watching to find out which point of view is closer to the truth. I also want to point out that the frenzy to develop foundational AI models may be specific to China. Rui Ma, an astute observer of and writer on China’s technology sector, mentioned in a recent tweet that “everyone in China is building their own foundational model.” Meanwhile, the management of online travel platform Airbnb (which is based in the US, works deeply with technology, and is clearly a large company) shared in May 2023 that they have no interest in building foundational AI models – they’re only interested in designing the interface and tuning the models.
A database is a platform to store data. Each piece of software requires a database to store, organize, and process data. The database has a direct impact on the software’s performance, scalability, flexibility, and reliability, so its selection is a highly strategic decision for companies. In the 1970s, relational databases were first developed and they used a programming language known as Structured Query Language (SQL). Relational databases store and organise data points that are related to one another in table form (picture an Excel spreadsheet) and were useful from the 1980s to the late 1990s. But because they were used to store structured data, they began to lose relevance with the rise of the internet. Relational databases were too rigid for the internet era and were not built to support the volume, velocity, and variety of data in the internet era. This is where non-relational databases – also known as NoSQL, which stands for either “non SQL” or “not only SQL” – come into play. NoSQL databases are not constrained to relational databases’ tabular format of data storage and can work with unstructured data such as audio, video, and photos. As a result, they are more flexible and better suited for the internet age. AI appears to require different database architectures. The management of MongoDB, a company that specialises in NoSQL databases, talked about the need for a vector database to store the training results of large language models during the company’s June 2023 earnings conference call. Simply put, a vector database stores data in a way that allows users to easily find data, say, an image (or text), that is related to a given image (or text) – this feature is very useful for generative AI products. This said, MongoDB’s management also commented in the same earnings conference call that NoSQL databases will still be very useful in the AI era. I’m aware that MongoDB’s management could be biased, but I do agree with their point of view. Vector databases appear to be well-suited (to my untrained technical eye!) for a narrow AI-related use case, whereas NoSQL databases are useful in much broader ways. Moreover, AI is likely to increase the volume of software developed for all kinds of software – not just AI software – and they need modern databases. MongoDB’s management also explained in a separate June 2023 conference that a typical generative AI workflow will include both vector databases and other kinds of databases (during the conference, management also revealed MongoDB’s own vector database service). I’m keeping a keen eye on how the landscape of database architectures evolve over time as AI technologies develop.
Keeping up with the theme of new architectures, the AI age could also usher in a new architecture for data centres. This new architecture is named accelerated computing by Nvidia. In the traditional architecture of data centres, CPUs (central processing units) are the main source of computing power. In accelerated computing, the entire data centre – consisting of GPUs (graphic processing units), CPUs, DPUs (data processing units), data switches, networking hardware, and more – provides the computing power. Put another way, instead of thinking about the chip as the computer, the data centre becomes the computer under the accelerated computing framework. During Nvidia’s May 2023 earnings conference call, management shared that the company had been working on accelerated computing for many years but it was the introduction of generative AI – with its massive computing requirements – that “triggered a killer app” for this new data centre architecture. The economic opportunity could be immense. Nvidia’s management estimated that US$1 trillion of data centre infrastructure was installed over the last four years and nearly all of it was based on the traditional CPU-focused architecture. But as generative AI gains importance in society, data centre infrastructure would need to shift heavily towards the accelerated computing variety, according to Nvidia’s management.
And keeping with the theme of something new, AI could also bring about novel and better consumer experiences. Airbnb’s co-founder and CEO, Brian Chesky, laid out a tantalising view on this potential future during the company’s latest May 2023 earnings conference call. Chesky mentioned that search queries in the travel context are matching questions and the answers depend on who the questioner is and what his/her preferences are. With the help of AI, Airbnb could build “the ultimate AI concierge that could understand you,” thereby providing a highly personalised travel experience. Meanwhile, in a recent interview with Wired, Microsoft’s CEO Satya Nadella shared his dream that “every one of Earth’s 8 billion people can have an AI tutor, an AI doctor, a programmer, maybe a consultant!”
Embedded AI is the concept of AI software that is built into a device itself. This device can be a robot. And if robots with embedded AI can be mass-produced, the economic implications could be tremendous, beyond the impact that AI could have as just software. Tesla is perhaps the most high profile company in the world today that is developing robots with embedded AI. The company’s goal for the Tesla Bot (also known as Optimus) is for it to be “a general purpose, bi-pedal, autonomous humanoid robot capable of performing unsafe, repetitive or boring tasks.” There are other important companies that are working on embedded AI. For example, earlier this year, Nvidia acquired OmniML, a startup whose software shrinks AI models, making it easier for the models to be run on devices rather than on the cloud.
Currently, humans are behind the content trained on by foundational AI models underpinning the likes of ChatGPT and other generative AI products. But according to a recently-published paper from UK and Canadian researchers titled The Curse of Recursion: Training on Generated Data Makes Models Forget, the quality of foundational AI models degrades significantly as the proportion of content they are trained on shifts toward an AI-generated corpus. This could be a serious problem in the future if there’s an explosion in the volume of generative AI content, which seems likely; for context, Adobe’s management shared in mid-June this year that the company’s generative AI feature, Firefly, had already powered 500 million content-generations since its launch in March 2023. The degradation, termed “model collapse” by the researchers, happens because content created by humans are a more accurate reflection of the world since they would contain improbable data. Even after training on man-made data, AI models tend to generate content that understates the improbable data. If subsequent AI models train primarily on AI-generated content, the end result is that the improbable data become even less represented. The researchers describe model collapse as “a degenerative process whereby, over time, models forget the true underlying data distribution, even in the absence of a shift in the distribution over time.” Model collapse could have serious societal consequences; one of the researchers, Ilia Shumailov, told Venture Beat that “there are many other aspects that will lead to more serious implications, such as discrimination based on gender, ethnicity or other sensitive attributes.” Ross Anderson, another author of the paper, wrote in a blog post that with model collapse, advantages could accrue to companies that “control access to human interfaces at scale” or that have already trained AI models by scraping the web when human-generated content was still overwhelmingly dominant.

There’s one other fragile thought I have about AI that we think is more important than what I’ve shared above, and it is related to the concept of emergence. Emergence is a natural phenomenon where sophisticated outcomes spontaneously “emerge” from the interactions of agents in a system, even when these agents were not instructed to produce these outcomes. The following passages from the book, Complexity: The Emerging Science at the Edge of Order and Chaos by Mitch Waldrop, help shed some light on emergence:

“These agents might be molecules or neurons or species or consumers or even corporations. But whatever their nature, the agents were constantly organizing and reorganizing themselves into larger structures through the clash of mutual accommodation and mutual rivalry. Thus, molecules would form cells, neurons would form brains, species would form ecosystems, consumers and corporations would form economies, and so on. At each level, new emergent structures would form and engage in new emergent behaviors. Complexity, in other words, was really a science of emergence…

…Cells make tissues, tissues make organs, organs make organisms, organisms make ecosystems – on and on. Indeed, thought Holland, that’s what this business of “emergence” was all about: building blocks at one level combining into new building blocks at a higher level. It seemed to be one of the fundamental organizing principles of the world. It certainly seemed to appear in every complex, adaptive system that you looked at…

…Arthur was fascinated by the thing. Reynolds had billed the program as an attempt to capture the essence of flocking behavior in birds, or herding behavior in sheep, or schooling behavior in fish. And as far as Arthur could tell, he had succeeded beautifully. Reynolds’ basic idea was to place a large collection of autonomous, birdlike agents—“boids”—into an onscreen environment full of walls and obstacles. Each boid followed three simple rules of behavior:

1. It tried to maintain a minimum distance from other objects in the environment, including other boids.

2. It tried to match velocities with boids in its neighborhood.

3. It tried to move toward the perceived center of mass of boids in its neighborhood.

What was striking about these rules was that none of them said, “Form a flock.” Quite the opposite: the rules were entirely local, referring only to what an individual boid could see and do in its own vicinity. If a flock was going to form at all, it would have to do so from the bottom up, as an emergent phenomenon. And yet flocks did form, every time. Reynolds could start his simulation with boids scattered around the computer screen completely at random, and they would spontaneously collect themselves into a flock that could fly around obstacles in a very fluid and natural manner. Sometimes the flock would even break into subflocks that flowed around both sides of an obstacle, rejoining on the other side as if the boids had planned it all along. In one of the runs, in fact, a boid accidentally hit a pole, fluttered around for a moment as though stunned and lost—then darted forward to rejoin the flock as it moved on.”

In our view, the concept of emergence is important in AI because at least some of the capabilities of ChatGPT seen today were not explicitly programmed for – they emerged. Satya Nadella said in his aforementioned interview with Wired that “when we went from GPT 2.5 to 3, we all started seeing these emergent capabilities.” Nadella was referring to the foundational AI models built by OpenAI in his Wired interview. One of the key differences between GPT 2.5 and GPT 3 is that the former contains 1.5 billion parameters, whereas the latter contains 175 billion, more than 100 times more. The basic computational unit within an AI model is known as a node, and parameters are a measure of the strength of a connection between two nodes. The number of parameters can thus be loosely associated with the number of nodes, as well as the number of connections between nodes, in an AI model. With GPT 3’s much higher number of parameters compared to GPT 2.5, the number of nodes and number of connections (or interactions) between nodes in GPT 3 thus far outweigh those of GPT 2.5. Nadella’s observation matches those of David Ha, an expert on AI whose most recent role was the head of research at Stability AI. During a February 2023 podcast hosted by investor Jim O’Shaughnessy, Ha shared the following (emphasis is mine):

“Then the interesting thing is, sure, you can train things on prediction or even things like translation. If you have paired English to French samples, you can do that. But what if you train a model to predict itself without any labels? So that’s really interesting because one of the limitations we have is labeling data is a daunting task and it requires a lot of thought, but self-labeling is free. Like anything on the internet, the label is itself, right? So what you can do is there’s two broad types of models that are popular now. There’s language models that generate sequences of data and there’s things like image models, Stable Diffusion you generate an image. These operate on a very similar principle, but for things like language model, you can have a large corpus of text on the internet. And the interesting thing here is all you need to do is train the model to simply predict what the next character is going to be or what the next word is going to be, predict the probability distribution of the next word.

And such a very simple objective as you scale the model, as you scale the size and the number of neurons, you get interesting emerging capabilities as well. So before, maybe back in 2015, ’16, when I was playing around with language models, you can feed it, auto Shakespeare, and it will blab out something that sounds like Shakespeare.

But in the next few years, once people scaled up the number of parameters from 5 million, to a hundred million, to a billion parameters, to a hundred billion parameters, this simple objective, you can now interact with the model. You can actually feed in, “This is what I’m going to say,” and the model takes that as an input as if it said that and predict the next character and give you some feedback on that. And I think this is very interesting, because this is an emergent phenomenon. We didn’t design the model to have these chat functions. It’s just like this capability has emerged from scale.

And the same for image side as well. I think for images, there are data sets that will map the description of that image to that image itself and text to image models can do things like go from a text input into some representation of that text input and its objective is to generate an image that encapsulates what the text prompt is. And once we have enough images, I remember when I started, everyone was just generating tiny images of 10 classes of cats, dogs, airplanes, cars, digits and so on. And they’re not very general. You can only generate so much.

But once you have a large enough data distribution, you can start generating novel things like for example, a Formula 1 race car that looks like a strawberry and it’ll do that. This understanding of concepts are emergent. So I think that’s what I want to get at. You start off with very simple statistical models, but as you increase the scale of the model and you keep the objectives quite simple, you get these emergent capabilities that were not planned but simply emerge from training on that objective.”

Emergence occurred in AI models as their number of parameters (i.e. the number of interactions between nodes) grew. This is a crucial point because emergence requires a certain amount of complexity in the interactions between agents, which can only happen if there are large numbers of agents as well as interactions between agents. It’s highly likely, in my view, that more emergent phenomena could develop as AI models become even more powerful over time via an increase in their parameters. It’s also difficult – perhaps impossible – to predict what these emergent phenomena could be, as specific emergent phenomena in any particular complex system are inherently unpredictable. So, any new emergent phenomena from AI that springs up in the future could be anywhere on the spectrum of being wildly positive to destructive for society. Let’s see!

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in Alphabet, Amazon, Meta Platforms, Microsoft, MongoDB, Tencent, and Tesla. Holdings are subject to change at any time.

What Is The Monetary Cost of Stock-Based Compensation?

Confused by stock-based compensation? Here is how investors can account for SBC when calculating intrinsiic value.

It is common today for companies to exclude stock-based compensation (SBC) when reporting “adjusted” earnings.

In management’s eyes, SBC expense is not a cash outflow and is excluded when reporting adjusted earnings. But don’t let that fool you. SBC is a real expense for shareholders. It increases a company’s outstanding share count and reduces future dividends per share.

I’ve thought about SBC quite a bit in the last few months. One thing I noticed is that investors often do not properly account for it. There are a couple of different scenarios that I believe should lead to investors using different methods to account for SBC.

Scenario 1: Offsetting dilution with buybacks

The first scenario is when a company is both buying back shares and issuing shares to employees as SBC. The easiest and most appropriate way to account for SBC in this situation is by calculating how much the company spent to buy back the stock that vested in the year.

Take the credit card company Visa (NYSE: V) for example. In its FY2022 (fiscal year ended 30 September 2022), 2.2 million restricted stock units (RSUs) were vested and given to Visa employees. At the same time, Visa bought back 56 million shares at an average price of US$206 per share. In other words, Visa managed to buy back all the shares that were vested, and more.

We can calculate the cash outlay that Visa spent to offset the dilution from the grants of RSUs by multiplying the number of grants by the average price it paid to buy back its shares. In Visa’s case, the true cost of the SBC was around US$453 million (2.2 million RSUs multiplied by average price of US$206).

We can then calculate how much free cash flow (FCF) was left over that could be returned to shareholders by deducting US$453 million from Visa’s FCF. In FY2022, this FCF was US$17.4 billion.

Scenario 2: No buybacks!

On the other hand, when a company is not offsetting dilution with buybacks, it becomes trickier to account for SBC.

Under GAAP accounting, SBC is reported based on the company’s stock price at the time of the grant. But in my view, this is a severely flawed form of accounting. Firstly, unless the company is buying back shares, the stock price does not translate into the true cost of SBC. Second, even if the stock price was a true reflection of intrinsic value, the grants may have been made years ago and the underlying value of each share could have changed significantly since then.

In my view, I think the best way to account for SBC is by calculating how SBC is going to impact future dividend payouts to shareholders. This is the true cost of SBC.

Let’s use Okta Inc (NASDAQ: OKTA) as an example. In Okta’s FY2023 (fiscal year ended 31 January 2023), 2.6 million RSUs were vested and the company had 161 million shares outstanding at the end of the year (after dilution). This means that the RSUs vested led to a 1.7% rate of dilution. Put another way, all future dividends per share for Okta will be reduced by around 1.7%. Although the company is not paying a dividend yet, RSUs vested should lead to a reduction in the intrinsic value per share by 1.7%.

More granularly, I did a simple dividend discount model. I made certain assumptions around free cash flow growth and future dividend payout ratios. Using those assumptions and a 12% discount rate, I found that Okta’s intrinsic value was around US$12.5 billion.

With an outstanding share count of 161 million, Okta’s stock was worth US$77.63 each. Before dilution, Okta had 158.4 million shares and each share was worth US$78.91. The cost of dilution was around US$1.28 per share or US$201 million dollars.

Scenario 3: How about options?

In the two scenarios above, I only accounted for the RSU portion of the SBC. Both Okta and Visa also offer employees another form of SBC: Options.

Options give employees the ability to buy stock in a company in the future at a predetermined price. Unlike RSUs, the company receives cash when an option is exercised.

In this scenario, there is a cash inflow but an increase in share count. The best way to account for this is by calculating the drop in intrinsic value due to the dilution but offsetting it by the amount of cash the company receives.

For instance, Okta employees exercised 1.4 million options in FY2023 at a weighted average share price of US$11.92. Recall that we calculated our intrinsic value of shares after dilution to be US$78.91. Given the same assumptions, the cost of these options was US$66.99 per option, for a total cost of US$93.7 million.

Key takeaways

SBC can be tricky for investors to account for. Different scenarios demand different analysis methods.

When a company is buying back shares, the amount spent on offsetting dilution is the amount that can not be used as dividends. This is the cost to shareholders. On the other hand, when no buybacks are done, a company’s future dividends per share is reduced as the number of shares grows.

Ultimately, the key thing to take note of is how SBC impacts a company’s future dividends per share. By sticking to this simple principle, we can deduce the best way to account for SBC.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I have a vested interest in Okta and Visa. Holdings are subject to change at any time.

An Investing Paradox: Stability Is Destabilising

The epicenters of past periods of economic stress happened in sectors that were strong and robust. Why is that the case?

One of my favourite frameworks for thinking about investing and the economy is the simple but profound concept of stability being destabilising. This comes from the ideas of the late economist Hyman Minsky.

When he was alive, Minsky wasn’t well known. His views on why an economy goes through boom-bust cycles only gained prominence after the 2008-2009 financial crisis. In essence, Minsky theorised that for an economy, stability itself is destabilising. I first learnt about him – and how his ideas can be extended to investing – years ago after coming across a Motley Fool article written by Morgan Housel. Here’s how Housel describes Minsky’s framework:

“Whether it’s stocks not crashing or the economy going a long time without a recession, stability makes people feel safe. And when people feel safe, they take more risk, like going into debt or buying more stocks.

It pretty much has to be this way. If there was no volatility, and we knew stocks went up 8% every year [the long-run average annual return for the U.S. stock market], the only rational response would be to pay more for them, until they were expensive enough to return less than 8%. It would be crazy for this not to happen, because no rational person would hold cash in the bank if they were guaranteed a higher return in stocks. If we had a 100% guarantee that stocks would return 8% a year, people would bid prices up until they returned the same amount as FDIC-insured savings accounts, which is about 0%.

But there are no guarantees—only the perception of guarantees. Bad stuff happens, and when stocks are priced for perfection, a mere sniff of bad news will send them plunging.”

In other words, great fundamentals in business (stability) can cause investors to take risky actions, such as pushing valuations toward the sky or using plenty of leverage. This plants the seeds for a future downturn to come (the creation of instability).

I recently came across a wonderful July 2010 blog post, titled A Batesian Mimicry Explanation of Business Cycles, from economist Eric Falkenstein that shared historical real-life examples of how instability was created in the economy because of stability. Here are the relevant passages from Falkenstein’s blog post (emphases are mine):

“…the housing bubble of 2008 was based on the idea that the borrower’s credit was irrelevant because the underlying collateral, nationwide, had never fallen significantly in nominal terms. This was undoubtedly true. The economics profession, based on what got published in top-tier journals, suggested that uneconomical racial discrimination in mortgage lending was rampant, lending criteria was excessively prudent (underwriting criteria explicitly do not note borrowers race, so presumably lenders were picking up correlated signals). Well-known economists Joe Stiglitz and Peter Orzag wrote a paper for Fannie Mae arguing the expected loss on its $2 trillion in mortgage guarantees of only $2 million dollars, 0.0001%. Moody’s did not consider it important to analyze the collateral within mortgage CDOs, as if the borrower or collateral characteristics were irrelevant. In short, lots of smart people thought housing was an area with extremely low risk.

Each major bust has its peculiar excesses centered on previously prudent and successful sectors. After the Panic of 1837, many American states defaulted quite to the surprise of European investors, who were mistakenly comforted by their strong performance in the Panic of 1819 (perhaps the first world-wide recession). The Panic of 1893 centered on railroads, which had for a half century experienced solid growth, and seemed tested by their performance in the short-lived Panic of 1873.”

It turns out that it were the “prudent and successful sectors” – the stable ones – that were the epicenters of the panics of old. It was their stability that led to investor excesses, exemplifying Minsky’s idea of how stability is destabilising.

The world of investing is full of paradoxes. Minsky’s valuable contribution to the world of economic and investment thinking is one such example.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I currently have no vested interest in any companies mentioned. Holdings are subject to change at any time.

Takeaways From Silicon Valley Bank’s Collapse

The collapse of Silicon Valley Bank, or SVB, is a great reminder for investors to always be prepared for the unexpected.

March 2023 was a tumultuous month in the world of finance. On 8 March, Silicon Valley Bank, the 16th largest bank in the USA with US$209 billion in assets at the end of 2022, reported that it would incur a US$1.8 billion loss after it sold some of its assets to meet deposit withdrawals. Just two days later, on 10 March, banking regulators seized control of the bank, marking its effective collapse. It turned out that Silicon Valley Bank, or SVB, had faced US$42 billion in deposit withdrawals, representing nearly a quarter of its deposit base at the end of 2022, in just one day on 9 March.

SVB had failed because of a classic bank run. At a simplified level, banking involves taking in deposits and distributing the capital as loans to borrowers. A bank’s assets (what it owns) are the loans it has doled out, and its liabilities (what it owes) are deposits from depositors. When depositors withdraw their deposits, a bank has to return cash to them. Often, depositors can withdraw their deposits at short notice, whereas a bank can’t easily convert its loans into ready cash quickly. So when a large group of depositors ask for their money back, it’s difficult for a bank to meet the withdrawals – that’s when a bank run happens.

When SVB was initially taken over by regulators, there was no guarantee that the bank’s depositors would be made whole. Official confirmation that the money of SVB’s depositors would be fully protected was only given a few days later. In the leadup to and in the aftermath of SVB’s fall, there was a palpable fear among stock market participants that a systemic bank run could happen within the US banking sector. The Invesco KBW Regional Banking ETF, an exchange-traded fund tracking the KBW Nasdaq Regional Banking Index, which comprises public-listed US regional banks and thrifts, fell by 21% in March 2023. The stock price of First Republic Bank, ranked 14th in America with US$212 billion in assets at the end of 2022, cratered by 89% in the same month. For context, the S&P 500 was up by 3.5%.

SVB was not the only US bank that failed in March 2023. Two other US banks, Silvergate Bank and Signature Bank, did too. There was also contagion beyond the USA. On 19 March, Credit Suisse, a Switzerland-based bank with CHF 531 billion in assets (around US$575 billion) at the end of 2022, was forced by its country’s regulators to agree to be acquired by its national peer, UBS, for just over US$3 billion; two days prior, on 17 March, Credit Suisse had a market capitalization of US$8.6 billion. Going back to the start of 2023, I don’t think it was in anyone’s predictions for the year that banks of significant size in the USA would fail (Signature Bank had US$110 billion in assets at the end of 2022) or that the 167 year-old Credit Suisse would be absorbed by another bank for a relative pittance. These are a sound reminder of a belief I have about investing: Bad scenarios inevitably happen from time to time, but I just don’t know when. To cope with this uncertainty, I choose to invest in companies that I think have both bright growth prospects in peaceful conditions and a high likelihood of making it through a crisis either relatively unscathed or in even better shape than before.

The SVB bank run is also an example of an important aspect of how I invest: Why I shun forecasts. SVB’s run was different from past bank runs. Jerome Powell, chair of the Federal Reserve, said in a 22 March speech (emphasis is mine):

“The speed of the run [on SVB], it’s very different from what we’ve seen in the past and it does kind of suggest that there’s a need for possible regulatory and supervisory changes just because supervision and regulation need to keep up with what’s happening in the world.”

There are suggestions from observers of financial markets that the run on SVB could happen at such breakneck speed – US$42 billion of deposits, which is nearly a quarter of the bank’s deposit base, withdrawn in one day – because of the existence of mobile devices and internet banking. I agree. Bank runs of old would have involved people physically waiting in line at bank branches to withdraw their money. Outflow of deposits would thus take a relatively longer time. Now it can happen in the time it takes to tap a smartphone. In 2014, author James Surowiecki reviewed Walter Friedman’s book on the folly of economic forecasting titled Fortune Tellers. In his review, Surowiecki wrote (emphasis is mine):

“The failure of forecasting is also due to the limits of learning from history. The models forecasters use are all built, to one degree or another, on the notion that historical patterns recur, and that the past can be a guide to the future. The problem is that some of the most economically consequential events are precisely those that haven’t happened before. Think of the oil crisis of the 1970s, or the fall of the Soviet Union, or, most important, China’s decision to embrace (in its way) capitalism and open itself to the West. Or think of the housing bubble. Many of the forecasting models that the banks relied on assumed that housing prices could never fall, on a national basis, as steeply as they did, because they had never fallen so steeply before. But of course they had also never risen so steeply before, which made the models effectively useless.”

There is great truth in something writer Kelly Hayes once said: “Everything feels unprecedented when you haven’t engaged with history.” SVB’s failure can easily feel epochal to some investors, since it was one of the largest banks in America when it fell. But it was actually just 15 years ago, in 2008, when the largest bank failure in the USA – a record that still holds – happened. The culprit, Washington Mutual, had US$307 billion in assets at the time. In fact, bank failures are not even a rare occurrence in the USA. From 2001 to the end of March 2023, there have been 563 such incidents. But Hayes’ wise quote misses an important fact about life: Things that have never happened before do happen. Such is the case when it came to the speed of SVB’s bank run. For context, Washington Mutual crumbled after a total of US$16.7 billion in deposits – less than 10% of its total deposit base – fled over 10 days.

I have also seen that unprecedented things do happen with alarming regularity. It was just three years ago, in April 2020, when the price of oil went negative for the first time in history. When investing, I have – and always will – keep this in mind. I also know that I am unable to predict what these unprecedented events could look like, but I am sure that they are bound to happen. To deal with these, I fall back to what I shared earlier:

“To cope with this uncertainty, I choose to invest in companies that I think have both bright growth prospects in peaceful conditions and a high likelihood of making it through a crisis either relatively unscathed or in even better shape than before.”

I think such companies carry the following traits that I have been looking for for a long time in my investing activities:

Revenues that are small in relation to a large and/or growing market, or revenues that are large in a fast-growing market
Strong balance sheets with minimal or reasonable levels of debt
Management teams with integrity, capability, and an innovative mindset
Revenue streams that are recurring in nature, either through contracts or customer-behaviour
A proven ability to grow
A high likelihood of generating a strong and growing stream of free cash flow in the future

These traits interplay with each other to produce companies I believe to be antifragile. I first came across the concept of antifragility – referring to something that strengthens when exposed to non-lethal stress – from Nassim Nicholas Taleb’s book, Antifragile. Antifragility is an important concept for the way I invest. As I mentioned earlier, I operate on the basis that bad things will happen from time to time – to economies, industries, and companies – but I just don’t know how and when. As such, I am keen to own shares in antifragile companies, the ones which can thrive during chaos. This is why the strength of a company’s balance sheet is an important investment criteria for us – having a strong balance sheet increases the chance that a company can survive or even thrive in rough seas. But a company’s antifragility goes beyond its financial numbers. It can also be found in how the company is run, which in turn stems from the mindset of its leader.

It’s crucial to learn from history, as Hayes’s quote suggests. But it’s also important to recognise that the future will not fully resemble the past. Forecasts tend to fail because there are limits to learning from history and this is why I shun forecasts. In a world where unprecedented things can and do happen, I am prepared for the unexpected.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I do not have a vested interest in any companies mentioned. Holdings are subject to change at any time.

How Bad is Zoom’s Stock-Based Compensation?

On the surface, the rising stock based compensation for Zoom looks bad. But looking under the hood, the situation is not as bad as it looks.

There seems to be a lot of concern surrounding Zoom’s rising stock-based compensation (SBC).

In its financial years 2021, 2022 and 2023, Zoom recorded SBC of US$275 million, US$477 million and US$1,285 million, respectively. FY2023 was perhaps the most worrying for investors as Zoom’s revenue essentially flat-lined while its SBC increased by more than two-fold.

But as mentioned in an earlier article, GAAP accounting is not very informative when it comes to SBC. When companies report SBC using GAAP accounting, they record the amount on the financial statements based on the share price at the time of the grant. A more informative way to look at SBC would be from the perspective of the actual number of shares given out during the year.

In FY2021, 2022 and 2023, Zoom issued 0.6 million, 1.8 million and 4 million restricted stock units (RSUs), respectively. From that point of view, it seems the dilution is not too bad. Zoom had 293 million shares outstanding as of 31 January 2023, so the 4 million RSUs issued resulted in only 1.4% more shares.

What about down the road?

The number of RSUs granted in FY2023 was 22.1 million, up from just 3.1 million a year before. The big jump in FY2023 was because the company decided to give a one-time boost to existing employees.

However, this does not mean that Zoom’s dilution is going to be 22 million shares every year from now. The number of RSUs granted in FY2023 was probably a one-off grant that will likely not recur and these grants will vest over a period of three to four years.

If we divide the extra RSUs given in FY2023 by their 4-year vesting schedule, we can assume that around 8 million RSUs will vest each year. This will result in an annual dilution rate of 2.7% based on Zoom’s 293 million shares outstanding as of 31 January 2023.

Bear in mind: Zoom guided for a weighted diluted share count of 308 million for FY2024. This diluted number includes 4.8 million in unexercised options that were granted a number of years ago. Excluding this, the number of RSUs that vest will be around 10 million and I believe this is because of an accelerated vesting schedule this year.

Cashflow impact

Although SBC does not result in a cash outflow for companies, it does result in a larger outstanding share base and consequently, lower free cash flow per share.

But Zoom can offset that by buying back its shares. At its current share price of US$69, Zoom can buy back 8 million of its shares using US$550 million. Zoom generated US$1.5B in free cash flow if you exclude working capital changes in FY2023. If it can sustain cash generation at this level, it can buy back all its stock that is issued each year and still have around US$1 billion in annual free cash flow left over for shareholders.

And we also should factor in the fact that in most companies, due to employee turnover, the RSU forfeiture rate is around 20% or more, which will mean my estimate of 8 million RSUs vesting per year for Zoom could be an overestimate. In addition, Zoom reduced its headcount by 15% in February this year, which should lead to more RSU forfeitures and hopefully fewer grants in the future.

Not as bad as it looks

GAAP accounting does not always give a complete picture of the financial health of a business. In my view, SBC is one of the most significant flaws of GAAP accounting and investors need to look into the financial notes to better grasp the true impact of SBC.

Zoom’s SBC numbers seem high. But when zooming in (pun intended), the SBC is not as bad as it looks. In addition, with share prices so low, it is easy for management to offset dilution with repurchases at very good prices. However, investors should continue to monitor share dilution over time to ensure that management is fair to shareholders.

What Causes Stock Prices To Rise?

A company can be valued based on its future cash flows. Dividends, as cashflows to shareholders, should therefore drive stock valuations.

I recently wrote about why dividends are the ultimate driver of stock valuations. Legendary investor Warren Buffett once said: “Intrinsic value can be defined simply as the discounted value of cash that can be taken out of business during its remaining life.”

And dividends are ultimately the cash that is taken out from a business over time. As such, I consider the prospect of dividends as the true driver of stock valuations.

But what if a company will not pay out a dividend in my lifetime?

Dividends in the future

Even though we may never receive a dividend from a stock, we should still be able to make a gain through stock price appreciation.

Let’s say a company will only start paying out $100 a share in dividends 100 years from now and that its dividend per share will remain stable from then. An investor who wants to earn a 10% return will be willing to pay $1000 a share at that time.

But it is unlikely that anyone reading this will be alive 100 years from now. That doesn’t mean we can’t still make money from this stock.

In Year 99, an investor who wants to make a 10% return will be willing to pay $909 a share as they can sell it to another investor for $1000 in Year 100. That’s a 10% gain.

Similarly, an investor knowing this, will be willing to pay $826 in Year 98, knowing that another buyer will likely be willing to pay $909 to buy it from him in a year. And on and on it goes.

Coming back to the present, an investor who wants to make a 10% annual return should be willing to pay $0.07 a share. Even though this investor will likely never hold the shares for 100 years, in a well-oiled financial system, the investor should be able to sell the stock at a higher price over time.

But be warned

In the above example, I assumed that the financial markets are working smoothly and investors’ required rate of return remained constant at 10%. I also assumed that the dividend trajectory of the company is known. But reality is seldom like this.

The required rate of return may change depending on the risk-free rate, impacting what people will pay for the stock at different periods of time. In addition, uncertainty about the business may also lead to stock price fluctuations. Furthermore, there may even be mispricings because of misinformation or simply irrational behaviour of buyers and sellers of the stock. All of these things can lead to wildly fluctuating stock prices.

So even if you do end up being correct on the future dividend per share of the company, the valuation trajectory you thought that the company will follow may end up well off-course for long periods. The market may also demand different rates of return from you leading to the market’s “intrinsic value” of the stock differing from yours.

The picture below is a sketch by me (sorry I’m not an artist) that illustrates what may happen:

The smooth line is what your “intrinsic value” of the company looks like over time. But the zig-zag line is what may actually happen.

Bottom line

To recap, capital gains can be made even if a company doesn’t pay a dividend during our lifetime. But we have to be wary that capital gains may not happen smoothly.

Shareholders, even if they are right about a stock’s future dividend profile, must be able to hold the stock through volatile periods until the stock price eventually reaches above or at least on par with our intrinsic value to make our required rate of return.

You may also have noticed from the chart that occasionally stocks can go above your “intrinsic value” line (whatever rate of return you are using). If you bought in at these times, you are unlikely to make a return that meets your required rate.

To avoid this, we need to buy in at the right valuation and be patient enough to wait for market sentiment to converge to our intrinsic value over time to make a profit that meets our expectations. Patience and discipline are, hence, key to investment success. And of course, we also need to predict the dividend trajectory of the company somewhat accurately.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I do not have a vested interest in any stocks mentioned. Holdings are subject to change at any time.

How To Find The Intrinsic Value of a Stock At Different Points in Time

intrinsic value is the sum of all future cash flows discounted to the present, but it can also change over the course of time.

A company’s intrinsic value is the value of the sum of future cash flows to the shareholder discounted to the present day.

But the intrinsic value of a company is not static. It moves with time. The closer we get to the future cash flows, the more an investor should be willing to pay for the company.

In this article, I will run through (1) how to compute the intrinsic value of a company today, (2) how to plot the graph of the intrinsic value, and (3) what to do with intrinsic value charts.

How to calculate intrinsic value

Simply put, intrinsic value is the sum of all future cash flows discounted to the present.

As shareholders of a company, the future cash flow is all future dividends and the proceeds we can collect when we eventually sell our shares in the company.

To keep things simple, we should assume that we are holding a company to perpetuity or till the business closes down. This will ensure we are not beholden to market conditions that influence our future cash flows through a sale. We, hence, only need to concern ourselves with future dividends.

To calculate intrinsic value, we need to predict the amount of dividends we will collect and the timing of that dividend.

Once we figure that out, we can discount the dividends to the present day.

Let’s take a simple company that will pay $1 a share for 10 years before closing down. Upon closing, the company pays a $5 dividend on liquidation. Let’s assume we want a 10% return. The table below shows the dividend schedule, the value of each dividend when discounted to the present day and the total intrinsic value of the company now.

Year	Dividend	Net present value
Now	$0.00	$0.00
Year 1	$1.00	$0.91
Year 2	$1.00	$0.83
Year 3	$1.00	$0.75
Year 4	$1.00	$0.68
Year 5	$1.00	$0.62
Year 6	$1.00	$0.56
Year 7	$1.00	$0.51
Year 8	$1.00	$0.47
Year 9	$1.00	$0.42
Year 10	$6.00	$2.31
Sum	$15.00	$8.07

As you can see, we have calculated the net present value of each dividend based on how far in the future we will receive them. The equation for the net present value is: (Dividend/(1+10%)^(Years away).

The intrinsic value is the sum of the net present value of all the dividends. The company in this situation has an intrinsic value of $8.07.

Intrinsic value moves

In the above example, we have calculated the intrinsic value of the stock today. But the intrinsic value moves with time. In a year, we will have collected $1 in dividends which will lower our intrinsic value. But at the same time, we will be closer to receiving subsequent dividends.

The table below shows the intrinsic value immediately after collecting our first dividend in year 1.

Year	Dividend	Net present value
Now	$0.00	$0.00
Year 1	$1.00	$0.91
Year 2	$1.00	$0.83
Year 3	$1.00	$0.75
Year 4	$1.00	$0.68
Year 5	$1.00	$0.62
Year 6	$1.00	$0.56
Year 7	$1.00	$0.51
Year 8	$1.00	$0.47
Year 9	$6.00	$2.54
Sum	$14.00	$7.88

There are a few things to take note of.

First, the sum of the remaining dividends left to be paid has dropped to $14 (from $15) as we have already collected $1 worth of dividends.

Second, the intrinsic value has now dropped to $7.88.

We see that there are two main effects of time.

It allowed us to collect our first dividend payment of $1, reducing future dividends. That has a net negative impact on the remaining intrinsic value of the stock. But we are also now closer to receiving future dividends. For instance, the big payout after year 10 previously is now just 9 years away.

The net effect is that the intrinsic value dropped to $7.88. We can do the same exercise over and over to see the intrinsic value of the stock over time. We can also plot the intrinsic values of the company over time.

Notice that while intrinsic value has dropped, investors still manage to get a rate of return of 10% due to the dividends collected.

When a stock doesn’t pay a dividend for years

Often times a company may not pay a dividend for years. Think of Berkshire Hathaway, which has not paid a dividend in decades.

The intrinsic value of Berkshire is still moving with time as we get closer to the dividend payment. In this scenario, the intrinsic value simply rises as we get closer to our dividend collection and there is no net reduction in intrinsic values through any payment of dividends yet.

Take for example a company that will not pay a dividend for 10 years. After which, it begins to distribute a $1 per share dividend for the next 10 years before closing down and pays $5 a share in liquidation value.

Year	Dividend	Net present value
Now	0	$0.00
Year 1	0	$0.00
Year 2	0	$0.00
Year 3	0	$0.00
Year 4	0	$0.00
Year 5	0	$0.00
Year 6	0	$0.00
Year 7	0	$0.00
Year 8	0	$0.00
Year 9	0	$0.00
Year 10	$0.00	$0.00
Year 11	$1.00	$0.35
Year 12	$1.00	$0.32
Year 13	$1.00	$0.29
Year 14	$1.00	$0.26
Year 15	$1.00	$0.24
Year 16	$1.00	$0.22
Year 17	$1.00	$0.20
Year 18	$1.00	$0.18
Year 19	$1.00	$0.16
Year 20	$6.00	$0.89
Sum	$15.00	$3.11

The intrinsic value of such a stock is around $3.11 at present. But in a year’s time, as we get closer to future dividend payouts, the intrinsic value will rise.

A simple way of thinking about it is that in a year’s time, the intrinsic value will have risen 10% to meet our 10% discount rate or required rate of return. As such, the intrinsic value will be $3.42 in one year. The intrinsic value will continue to rise 10% each year until we receive our first dividend payment in year 10.

The intrinsic value curve will look like this for the first 10 years:

The intrinsic value is a smooth curve for stocks that do not yet pay a dividend.

Using intrinsic value charts

Intrinsic value charts can be useful in helping investors know whether a stock is under or overvalued based on your required rate of return.

Andrew Brenton, CEO of Turtle Creek Asset Management whose main fund has produced a 20% annualised return since 1998 (as of December 2022), uses his estimate of intrinsic values to make portfolio adjustments.

If a stock goes above his intrinsic value, it means that it will not be able to earn his required rate of return. In that case, he lowers his portfolio weighting of the stock and vice versa.

While active management of the portfolio using this method can be rewarding as in the case of Turtle Creek, it is also fairly time-consuming.

Another way to use intrinsic value charts is to use it to ensure you are getting a good entry price for your stock. If a stock trades at a price above your intrinsic value calculations, it may not be able to achieve your desired rate of return.

Final thoughts

Calculating the intrinsic value of a company can help investors achieve their return goals and ensure that they maintain discipline when investing in a company.

However, there are limitations.

For one, intrinsic value calculations require an accurate projection of future payments to the shareholder. In many cases, it is hard for investors to predict with accuracy and confidence. We have to simply rely on our best judgement.

We are also often limited by the fact that we may not hold stock to perpetuity or its natural end of life and liquidation. In the case that we need to sell the stock prematurely, we may be beholden to market conditions at the time of our sale of the stock.

It is also important to note that intrinsic value is not the same for everyone. I may be willing to attribute a higher intrinsic value to a company if my required rate of return is lower than yours. So each individual investor has to set his own target return to calculate intrinsic value.

What’s Your Investing Edge?

Whats your investing edge? That’s the question many investors find themselves asking when building a personal portfolio. Here are some ways to gain an edge.

Warren Buffett probably has the most concise yet the best explanation of how to value a stock. He said: “Intrinsic value can be defined simply: it is the discounted value of the cash that can be taken out of a business during its remaining life.”

This is how all stocks should theoretically be valued. In a perfect market where cash flows are certain and discount rates remain constant, all stocks should provide the same rate of return.

But this is not the case in the real world. Stocks produce varying returns, allowing investors to earn above-average returns.

Active stock pickers have developed multiple techniques to try to obtain these above-average returns to beat the indexes. In this article, I’ll go through some investing styles, why they can produce above-average returns, and the pros and cons of each style.

Long-term growth investing

One of the more common approaches today is long-term growth investing. But why does long-term investing outperform the market?

The market underestimates the growth potential

One reason is that market participants may underestimate the pace or durability of the growth of a company.

Investors may not be comfortable projecting that far in the future and often are only willing to underwrite growth over the next few years and may assume high growth fades away beyond a few years.

While true for most companies, there are high-quality companies that are exceptions. if investors can find these companies that beat the market’s expectations, they can achieve better-than-average returns when the growth materialises. The chart below illustrates how investors can potentially make market-beating returns.

Let’s say the average market’s required rate of return is 10%. The line at the bottom is what the market thinks the intrinsic value is based on a 10% required return. But the company exceeds the market’s expectations, resulting in the stock price following the middle line instead and a 15% annual return.

The market underwrites a larger discount rate

Even if the market has high expectations for a company’s growth, the market may want a higher rate of return as the market is uncertain of the growth playing out. The market is only willing to pay a lower price for the business, thus creating an opportunity to earn higher returns.

The line below is what investors can earn which is more than the 10% return if the market was more confident about the company.

Deep value stocks

Alternatively, another group of investors may prefer to invest in companies whose share prices are below their intrinsic values now.

Rather than looking at future intrinsic values and waiting for the growth to play out, some investors simply opt to buy stocks trading below their intrinsic values and hoping that the company’s stock closes the gap. The chart below illustrates how this will work.

The black line is the intrinsic value of the company based on a 10% required return. The beginning of the red line is where the stock price is at. The red line is what investors hope will happen over time as the stock price closes the gap with its intrinsic value. Once the gap closes, investors then exit the position and hop on the next opportunity to repeat the process.

Pros and cons

All investing styles have their own pros and cons.

Underappreciated growth
For long-term investing in companies with underappreciated growth prospects, investors need to be right about the future growth of the company. To do so, investors must have a keen understanding of the business background, growth potential, competition, potential that the growth plays out and why the market may be underestimating the growth of the company.

This requires in-depth knowledge of the company and requires conviction in the management team being able to execute better than the market expects of them.

Underwriting larger discount rates
For companies that the market has high hopes for but is only willing to underwrite a larger discount rate due to the uncertainty around the business, investors need to also have in-depth knowledge of the company and have more certainty than the market that the growth will eventually play out.
Again, this may require a good grasp of the business fundamentals and the probability of the growth playing out.
Undervalued companies
Thirdly, investors who invest in companies based on valuations being too low now, also need a keen understanding of the business. Opportunities can arise due to short-term misconceptions of a company but investors must have a differentiated view of the company from the rest of the market.
A near-term catalyst is often required for the market to realise the discrepancy. A catalyst can be in the form of dividend increases or management unlocking shareholder value through spin-offs etc. This style of investing often requires more hard work as investors need to identify where the catalyst will come from. Absent a catalyst, the stock may remain undervalued for long periods, resulting in less-than-optimal returns. In addition, new opportunities need to be found after each exit.

What’s your edge?

Active fundamental investors who want to beat the market can use many different styles to beat the market. While each style has its own limitations, if done correctly, all of these techniques can achieve market-beating returns over time.

When Shouldn’t You Pay a Premium For a Growing Company?

Return on retained capital and the reinvestment opportunity are two factors that impact valuation and returns for an investor.

You may assume that a faster-growing business always deserves a premium valuation but that’s not always the case. Growth is not the only criterion that determines valuation. The cost of growth matters just as much.

In this article, I will explore four things:

(I) Why growth is not the only factor that determines value
(II) Why companies with high returns on retained capital deserve a higher valuation
(III) How much we should pay for a business by looking at its reinvestment opportunities and returns on retained capital
(IV) Two real-life companies that have generated tremendous returns for shareholders based on high returns on retained capital

Growth is not the only factor

To explain why returns on retained capital matter, let’s examine a simple example.

Companies A and B both earn $1 per share in the upcoming year. Company A doesn’t reinvest its earnings. Instead, it gives its profits back to shareholders in the form of dividends. Company B, on the other hand, is able to reinvest all of its profits back into its business for an 8% return each year. The table below illustrates the earnings per share of the two companies over the next 5 years:

Company B is clearly growing its earnings per share much quicker than Company A. But that does not mean we should pay a premium valuation. We need to remember that Company B does not pay a dividend, whereas Company A pays $1 per share in dividends each year. Shareholders can reinvest that dividend to generate additional returns.

Let’s assume that an investor can make 10% a year from reinvesting the dividend collected from Company A. Here is how much the investor “earns” from being a shareholder of Company A compared to Company B after reinvesting the dividends earned each year:

The table just above shows that investors can earn more from investing in Company A and reinvesting the dividends than from investing in Company B. Company B’s return on retained capital is lower than the return we can get from reinvesting our dividends. In this case, we should pay less for Company B than Company A.

Retaining earnings to grow a company can be a powerful tool. But using that retained earnings effectively is what drives real value to the shareholder.

High-return companies

Conversely, investors should pay a premium for a company that generates a higher return on retained capital. Let’s look at another example.

Companies C and D both will generate $1 per share in earnings this year. Company C reinvests all of its earnings to generate a 10% return on retained capital. Company D, on the other hand, is able to generate a 20% return on retained capital. However, Company D only reinvests 50% of its profits and returns the rest to shareholders as dividends. The table below shows the earnings per share of both companies in the next 5 years:

As you may have figured, both companies are growing at exactly the same rate. This is because while Company D is generating double the returns on retained capital, it only reinvests 50% of its profit. The other 50% is returned to shareholders as dividends.

But don’t forget that investors can reinvest Company D’s dividends for more returns. The table below shows what shareholders can “earn” if they are able to generate 10% returns on reinvested dividends:

So while Companies C and D are growing at exactly the same rates, investors should be willing to pay a premium for Company D because it is generating higher returns on retained capital.

How much of a premium should we pay?

What the above examples show is that growth is not the only thing that matters. The cost of that growth matters more. Investors should be willing to pay a premium for a company that is able to generate high returns on retained capital.

But how much of a premium should an investor be willing to pay? We can calculate that premium using a discounted cash flow (DCF) model.

Let’s use Companies A, B, C, and D as examples again. But this time, let’s also add Company E into the mix. Company E reinvests 100% of its earnings at a 20% return on retained capital. The table below shows the earnings per share to each company’s shareholders, with dividends reinvested:

Let’s assume that the reinvestment opportunity for each company lasts for 10 years before it is exhausted. All the companies above then start returning 100% of their earnings back to shareholders each year. From then on, earnings remain flat. As the dividend reinvestment opportunity above is 10%, we should use a 10% discount rate to calculate how much an investor should pay for each company. The table below shows the price per share and price-to-earnings (P/E) multiples that one can pay:

We can see that companies with higher returns on retained capital invested deserve a higher P/E multiple. In addition, if a company has the potential to redeploy more of its earnings at high rates of return, it deserves an even higher valuation. This is why Company E deserves a higher multiple than Company D even though both deploy their retained capital at similar rates of return.

If a company is generating relatively low returns on capital, it is better for the company to return cash to shareholders in the form of dividends as shareholders can generate more returns from redeploying that cash elsewhere. This is why Company B deserves the lowest valuation. In this case, poor capital allocation decisions by the management team are destroying shareholder returns even though the company is growing. This is because the return on retained capital is below the “hurdle rate” of 10%.

Real-life example #1

Let’s look at two real-life examples. Both companies are exceptional businesses that have generated exceptional returns for shareholders.

The first company is Constellation Software Inc (TSE: CSU), a holding company that acquires vertical market software (VMS) businesses to grow. Constellation has a remarkable track record of acquiring VMS businesses at very low valuations, thus enabling it to generate double-digit returns on incremental capital invested.

From 2011 to 2021, Constellation generated a total of US$5.8 billion in free cash flow. It was able to redeploy US$4.1 billion of that free cash flow to acquire new businesses and it paid out US$1.3 billion in dividends. Over that time, the annual free cash flow of the company grew steadily and materially from US$146 million in 2011 to US$1.2 billion in 2021.

In other words, Constellation retained around 78% of its free cash flow and returned 22% of it to shareholders. The 78% of free cash flow retained was able to drive a 23% annualised growth in free cash flow. The return on retained capital was a whopping 30% per year (23/78). It is, hence, not surprising to see that Constellation’s stock price is up by around 33 times since 2011.

Today, Constellation sports a market cap of around US$37 billion and generated around US$1.3 billion in free cash flow on a trailing basis after accounting for one-off working capital headwinds. This translates to around 38 times its trailing free cash flow. Is that expensive?

Let’s assume that Constellation can continue to reinvest/retain the same amount of free cash flow at similar rates of return for the next 10 years before reinvestment opportunities dry out. In this scenario, we can pay around 34 times its free cash flow to generate a 10% annualised return. Given these assumptions, Constellation may be slightly expensive for an investor who wishes to earn an annual return of at least 10%.

Real-life example #2

Simulations Plus (NASDAQ: SLP) is a company that provides modelling and simulation software for drug discovery and development. From FY2011 to FY2022 (its financial year ends in August), Simulations Plus generated a total of US$100 million in free cash flow. It paid out US$47 million in dividends during that time, retaining 53% of its free cash flow.

In that time period, Simulations Plus’s free cash flow per share also grew from US$0.15 in FY2011 to US$0.82 in FY2022. This translates to 14% annualised growth while retaining/reinvesting just 53% of its free cash flow. The company’s return on retained capital was thus 26%.

Simulations Plus’s stock price has skyrocketed from US$3 at the end of 2011 to US$42 today. At the current price, the company trades at around 47 times trailing free cash flow per share. Is this expensive?

Since Simulations Plus is still a small company in a fragmented but growing industry, its reinvestment opportunity can potentially last 20 years. Let’s assume that it maintains a return on retained capital of 26% and we can reinvest our dividends at a 10% rate of return. After 20 years, the company’s reinvestment opportunity dries up. In this scenario, we should be willing to pay around 44 times its annual free cash flow for the business. Again, today’s share price may be slightly expensive if we want to achieve a 10% rate of return.

The bottom line

Investors often assume that we should pay up for a faster-growing business. However, the cost of growth matters. When looking at a business, we need to analyse the company’s growth profile and its cost of growth.

The reinvestment opportunity matters too. If a company has a high return on retained capital but only retains a small per cent of annual profits to reinvest, then growth will be slow.

Thirdly, the duration of the reinvestment opportunity needs to be taken into account too. A company that can redeploy 100% of its earnings at high rates of returns for 20 years deserves a higher multiple than one that can only redeploy that earnings over 10 years.

What Makes Some Serial Acquirers So Successful

What makes serial acquirers such as Berkshire Hathaway so successful?

Serial acquirers are companies that acquire smaller companies to grow and they can make for excellent investments. They use the cash flow produced by each acquisition to buy even more companies, repeating the process and compounding shareholder value.

There are many serial acquirers that have been hugely successful. The best-known of them is Warren Buffett’s Berkshire Hathaway. But there are others who have been tremendous successes in their own right.

Markel Corp, for example, is like a mini Berkshire. It is an insurance company at its core, but has used its profit and insurance float to acquire numerous companies and build a large public stock portfolio. Over the last 18 years, Markel’s share price has risen by 286%, or 7.8% compounded.

In the software space, Constellation Software has made a name for itself by acquiring vertical market software (VMS) companies. Its targets are usually small but have fairly predictable and recurring streams of cash flow. Constellation Software’s stock price has compounded at 33% over the last 16 years. The total return for shareholders is even higher, as Constellation Software started paying a quarterly dividend a decade ago and has given out three bumper special dividends.

Another great example of a niche serial acquirer is Brown & Brown Inc. Founded way back in 1939, Brown & Brown is an insurance brokerage company that packages and sells insurance products. The industry is highly fragmented but Brown & Brown has grown to become a company that generates billions in revenue each year. The company has done it by acquiring smaller insurance brokerage firms across the USA to build a large presence in the country. In the last 18 years, Brown & Brown’s stock price has grown by 439%, or 9.8% per year. In addition, Brown & Brown’s shareholders have also been receiving a growing dividend each year.

After reading through the success stories, here are some things I noticed that many of these successful serial acquirers have in common.

Buying companies at good valuations

Good returns on capital can be achieved if acquisitions are made at a reasonable valuation. Constellation Software is a great example of a company that makes acquisitions at really reasonable valuations.

The companies acquired by Constellation Software are often not fast-growing. This can be seen in Constellation Software’s single-digit organic growth in revenue; the low organic growth shows that Constellation Software does not really buy fast-growing businesses. But Constellation Software has still managed to generate high returns for its shareholders as it has historically been paying very low valuations for its acquisitions, which makes the returns on investment very attractive. It helps too that the companies acquired by Constellation Software tend to have businesses that are predictable and consistent.

Focusing on a niche

Constellation Software and Brown & Brown are two serial acquiries I mentioned above that focus on acquisitions within a particular field.

Judges Scientific is another company with a similarly focused acquisition strategy – it plays in the scientific instrument space. Specifically, Judges Scientific acquires companies that manufacture and sell specialised scientific instruments.

Since its IPO in 2004, Judges Scientific has acquired 20 companies and its share price has compounded at 27.9% per year. Its free cash flow has also grown from £0.3 million in 2005 to £14.7 million in 2021.

Serial acquirers that focus on a special niche have a key advantage over other acquirers as they could become the buyer of choice for sellers. This means they have a higher chance of successfully negotiating for good acquisition terms.

Letting acquired companies run autonomously

Berkshire Hathaway is probably the best known serial acquirer for letting its acquired companies run independently. The trust that Buffett places in the management teams of the companies he buys creates a mutually beneficial relationship.

This reputation as a good acquirer also means Berkshire is one of the companies that sellers want to sell to. Often times sellers will approach Berkshire themselves to see if a deal is possible.

Other than Berkshire, companies such as Constellation Software and Judges Scientific also have a reputation for allowing companies to run independently. Judges Scientific’s top leaders, for instance, may only have two meetings a year with the management teams of its acquired companies and they let them run almost completely autonomously.

Returning excess capital to shareholders

One of the common traits among all successful companies – be it a serial acquirer or not – is that their management teams emphasise shareholder value creation. This means effective use of capital.

When successful serial acquirers are unable to find suitable uses for capital, they are happy to return excess cash to shareholders. They do not let cash sit idly in the company’s bank accounts. Companies like Brown & Brown, Judges Scientific, and Constellation Software all pay dividends and rarely let excess capital build up unnecessarily on their balance sheets.

Final thoughts

Serial acquirers can be great investments. Those that are successful are usually great stalwarts of capital. While no single acquisition is the same, the thought process behind the acquisitions is repeatable. With a structured approach to acquisitions, these serial acquirers are able to repeatedly make good acquisitions to grow shareholder value. And when there are insufficient acquisition targets available, successful companies are not afraid to put their hands up and return excess capital to shareholders.

When you invest in a serial acquirer, you are not merely investing in a great business but in great managers and great processes that can keep compounding capital at extremely high rates of return for years to come.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I have a vested interest in Markel Corp. Holdings are subject to change at any time.