Tag Archives: Stocks

Technical analysis and fundamental analysis cannot both be true

I’ve said before about how I’m not sold on technical analysis being viable, but I’ve seen counter-arguments floating around that say “it doesn’t matter if TA doesn’t make sense, if people believe it and act on it then it will still move the markets.” In this case, knowing TA yourself lets you read the minds of all the other TA-knowers and join in their pumps and their dumps, making money by being part of the crowd. Yet fundamental analysis says that there is an underlying “fair value” for an asset, and good investing is about going long on undervalued assets and short on overvalued ones. Fundamental analysis accepts the possibility of hype and speculation, “the market can stay irrational longer than you can stay solvent” etc, but it requires that at SOME point things fall back to earth and assets reach their fair value.

Technical analysis on the other hand implies that the future price of a stock is most strongly connected to its previous prices, not to the value of the underlying asset. This means that previous highs, lows, and averages give a kind of momentum that can be predicted and traded on.

My question is, how can these both be true? Technical analysis assumes that the price reflects all available information, otherwise past trends cannot predict future prices. Fundamental analysis assumes that the price does not reflect all available information, otherwise there are no over-valued or under-valued stocks and all stocks are at their fair value.

How can the price of a stock be dependent both on the analysis of the underlying asset AND on the previous prices of the stock, since those can easily move in opposite directions? If there is a conflict between the TA and the FA, who wins? Well if you believe the Efficient Market Hypothesis, neither win because the winning move is to buy once and hold forever. But if you believe FA then FA wins and the price must go up, if you believe TA then TA wins, but I don’t think both can be true in all or most cases.

I’d like to know if anyone believes both TA and FA can be true and if so why.

As an aside, Wikipedia explicitly labels Technical Analysis as a pseudoscience.

So who’s still sitting on cash?

A couple of months ago people were clambering that anyone holding stocks was a moron and it was better to be sitting on cash. Where are those people now?

This time December, the S&P 500 was hovering around 3800 and we had just emerged from the S&P’s worst performing year since the Great Recession. With the Federal Reserve continuing to tighten plenty of folks were scrambling to say that the worst was yet to come and everyone needed to get out of stocks NOW.

Since then, the market has recovered, the FTSE in particular has hit all-time-highs, and sentiment is strengthening. Is there still a case to be made that the market will drop another 20% from here? If there is, I’m not seeing it get made. If you pulled everything out of the market in December, well you missed the upswing. Cash isn’t without its downsides.

This was supposed to be a bigger post, but frankly I just don’t have much else to say. Don’t be like this guy, just because stocks can go up as well as down doesn’t mean your best bet is to sell everything and put it under a mattress. On average, the people who make the least amount of trades have the best portfolios, and that means buying once and never selling.

10x Genomics: what happened to all the DNA stocks?

2020 was the year of COVID, but it was also the year of DNA. Thousands of DNA companies and researchers got in on the pandemic since there was a sudden surge in demand for COVID testing, contact tracing, and virus studying. Now, COVID is an RNA virus, but RNA and DNA work so similarly that most organizations that do one can do both. Even the Universities got into it, I know the Genomics Research Core Facility at the university I used to work for got money from city, state, and federal governments to process COVID tests, which was way more profitable for them than sequencing my plasmids once a month.

So 10x Genomics was a DNA sequencing company that, like so many others, skyrocketed in valuation during 2020. It hit its absolute peak in early 2021 and then fell precipitously through 2022. That describes a lot of companies but it especially seems to describe DNA companies. Still, I wanted to know if 10x Genomics was a buy, and considering my LinkedIn account got spammed all last year with recruiters and ads for the company, I figured they were at least worth a look. The result? A resounding “eh.”

One of 10x Genomics’ big claims to fame is their ability to perform reads of long segments of DNA as opposed to the shorter segments read by rival Illumina. Sequencing DNA gets less accurate the longer the DNA is, so Illumina and others use a technique of chopping the DNA into pieces, sequencing each piece, and putting them all back together. This usually works fine because there are enough overlapping pieces to make the puzzle fit, if you know read 3 different pieces with letters “AATT”, “TTGG” and “GGCC” then you can hazard a guess that the full sequence reads “AATTGGCC” and that the 3 pieces simply overlapped each other. This doesn’t work with long sequences of a single letter or repeating patters. If you simply have “AAAA” “AAAA” “AAAA” then you actually don’t know how long that string of As is. Those 3 reads could overlap on 2 letter and the result could be “AAAAAAAA” or they could overlap completely and the result is simply “AAAA.” About 8% of the human genome is these sorts of repeating patterns that Illumina and others are ill-equipped to read, which gives 10x an exploitable niche.

Now this is a genuinely interesting piece of equipment and their barcoding of DNA segments to read them in the correct order is a nice bit of chemistry, but was this a company that was ever worth 21 billion dollars? In my opinion *no*. The COVID era was a bubble in a number of ways, the easy-money policies of the post Financial Crisis era led to the super-duper-easy-money policies of the COVID era. Operation Warp Speed and other pandemic-focused funding sources meant that money was flowing into the biotech sector, and with most of the world still coming out of lockdown investors were desperate to park their money in companies that were still able to operate. It seemed like the moment for Biotech had come, and companies like 10x Genomics rode the wave to the very top. But from where I’m standing this was always an obvious bubble, and people were making claims about DNA companies that were woefully unfounded.

I’ve just written a lot about 10x Genomics, but all this could apply to most any DNA/RNA startup around. They all had neat ideas, got woefully overvalued during the pandemic, and have since crashed to earth taking shareholder value along with them. My question today then is why, why does it seem like Wall Street Investors saw something in DNA/RNA companies that I, a biology researcher, never did? Now part of that is that I’m just curmudgeony by nature, but part of that is that I think a lot of investors have this Sci-Fi idea of genetics in their head that isn’t really reflected in the field. I could just point to Cathie Wood and say “she doesn’t know Jack” but I want to dig a little deeper into some of the strange narratives that surround nucleic acids.

First, “DNA as a coding language.” I wonder if computer scientists just latch onto the word “code” or something, because the genetic code is no where near to being something that we can manipulate like computer code, and probably won’t be for decades. We’ve been inserting novel DNA into organisms since the 70s, but recently there’s been a spate of investors and analysts who believe that we’re on the cusp of truly programming cells, being able to manipulate them into doing everything we want as cleanly and as easily as a computer. This would definitely unlock a whole host of industries, it’s also not going to happen for a long while yet. DNA codes for proteins, and proteins are the functional units of most biological processes. Nothing you change in the DNA matters until it shows up in the proteins, and we are far, FAR from being able to understand how to manipulate proteins as easily as we do computers. You cannot simply say “let’s add a gene to make this wheat crop use less water,” you have to find a protein from another plant that causes it to use less water, insert that gene into wheat, do tests to make sure the wheat crop tolerates the new protein, alter the protein to account for unexpected cross reactions, and then finally test your finished wheat product out to make sure it works as designed. Any one of these steps could require an entire company to do, and each step could prove impossible and bankrupt the company working on it. We can’t just make de novo proteins to do our bidding because we don’t even know yet how to predict what a protein will look like when we code for it. Folding at home and other machine learning projects have helped us get partway there, but it will still take many Nobel Prizes before we can make de novo proteins as easily as we make de novo programs. So while there is a genomics revolution going on, it’s still an expensive and time consuming one, it’s not going to solve all our problems in a single go.

Second, “DNA as a storage medium.” I’ve said before that while DNA does store and transmit information, that information cannot be well integrated with our modern technology. The readout of DNA information is in RNA and proteins, while the readout of the circuits in your computer is photons on a screen or electrons in a modem. RNA and proteins do not easily produce or absorb electrons and photons, so having DNA communicate with our current technology is not currently doable. In addition, the time lag between reading DNA and making RNA/proteins is astronomical compared to the speed of information retrieval in semiconductors. I sometimes get an annoyed by the seconds-long delay it takes to load a webpage, but I’d be tearing my hair out if I had to wait on the minutes-long delay for DNA to be transcribed into RNA! At this time I really don’t see any reason to use DNA as a storage medium and I certainly don’t see a path to profit for any company trying to use it as such.

Third, curing genetic diseases. There is definitely a market for curing genetic diseases, let me just say that first, but many of the hyped-up corporate solutions are not feasible and rely more on sci-fi than actual science. I’ve discussed how even though CRISPR can change a cell’s DNA, bringing the CRISPR and cell together is much more challenging. The human body has a lot of defenses to protect itself from exogenous DNA and proteins, and getting around those defenses is a challenge. But in addition I don’t think investors realize that DNA is not the end-all be-all of genetic diseases, and so they tack things on to the Total Addressable Market (TAM) of DNA companies that shouldn’t really be there. Then they get flummoxed when the company has no path to addressing its TAM. Valuing companies based on what they can’t do is a bad investment strategy that I see over and over again with DNA companies. As to genetic disease themselves: there’s a truism in biology that “you are what your proteins are”. Although DNA codes for those proteins, once they’re coded they act all on their own, and some of their actions cannot readily be undone. When a body is growing and developing, its proteins can act up in ways that cause permanent alterations, and after they’ve done so changing the DNA won’t change things back. There are a number of genetic-linked diseases which are not amenable to CRISPR treatment because by the time the disease is discovered the damage has been done and changing the DNA won’t undo the damage.

Finally, “move fast and break things” doesn’t work with DNA the way it does with computers. I’ve worked on both coding projects and wet-lab projects. When something goes wrong in my computer code, fixing it is a long and arduous process but I have tools available that let me know what exactly the code is doing every step of the way. I can step through the code line by line and find out exactly what went wrong, and use that knowledge to fix things. Nothing is so straightforward when working with DNA, your ability to bugfix is only as good as your ability to read the code and reading DNA is a difficult and time-consuming process. Not only that, remember how I said above that we don’t know the exact relation between the DNA we put in and the DNA product we get out? If I’m trying to make a novel protein using novel DNA and it doesn’t get made, what went wrong? I can’t step through the code on this one because there’s no way to read out the activity of every RNA polymerase, every ribosome, or every post-transcriptional enzyme in the cell. I can make hypotheses and do experiments to try to guess at what is going on, but I can’t bugfix by stepping through the code, even using Green Fluorescent Protein as a print(“here”) crutch is difficult and time consuming. Even if I try to bugfix, the time lag between making a change and seeing the results can be weeks, months, or years depending on what system I’m working in, a far cry from how long it takes to compile code! A DNA-based R&D pipeline just doesn’t have the speed necessary to scale the way a coding house does, once you’ve got a program working the cost of sharing it is basically zero and the cost of starting a new project isn’t that great. That speed isn’t’ available to DNA companies yet.

This was a lot of words not just on 10x Genomics but on DNA-based companies in general. The pandemic-era highs may never be seen again for many of these companies, much like how some companies never again saw the highs of the Dotcom bubble. I think it’s important for investors to take a level-headed approach to DNA-based companies and not get caught up in the sci-fi hype. Anyone can sell you an idea, it takes a lot more work to make a product.

A series of proposals for testing the validity of technical analysis (TA)

I’ve said before that I think TA is astrology, and I still haven’t seen any evidence to rid me of that belief. I’ve thought about genuine scientific experiments that could be done to see if it’s true and I’m wondering if people have already done them.

See if TA-knowers all move the same way by giving a bunch of them a chart and have them predict the forward movement of the stock based on that chart. Two key ways you know astrology/fortune telling is fake are 1. that it uses weasel-words and vagueness to make predictions, and 2. because the same data can cause its practitioners to make wildly different predictions. In actual science however, any two scientists should be able to take the same data and make the same prediction: if two bowling balls of different weights are dropped from the Eiffel Tower, which hits the ground first? Any physicist can tell you the answer. Now to be clear, second opinions in medicine do exist, but these occur because we often work with incomplete information and have to use priors and estimations for the rest. But TA claims that the chart is the information, so if the information is complete than the prediction should always be the same. So if 100 TA-knowers all make the same prediction using the same chart, then perhaps we can start treating this as a complete and testable theory. If they all draw different lines on it then it becomes more clear we’re dealing with astrology.

Find out if the TA of ETFs follow the TA of their underlying assets. The mechanisms of ETFs ensures that their price never deviates far from the price of their underlying assets, and if both ETFs and the securities they contain obey TA then the movement of the two should correlate. Essentially you should be able to make predictions of the movement of an ETF by performing TA on the stocks that compose it, and I’d like to know if this is true.

Correlation analysis. Most theories of the stock market claim that the movement of a stock price is uncorrelated with any of it’s previous prices. Just because a stock is down 50% doesn’t mean it’s dead or a bargain. If I’m going to believe TA I’d like a TA-believer to prove to me that price movement is correlated with previous prices.

A working, mathematical definition of “resistance” and “support.” I understand that these are TA terms, but I’ve asked 5 different TA people for a true definition of them and have gotten 5 different answers. If TA really is based on math then these terms need to be mathematically defined, not emotionally defined based on how someone wants to analyze a chart at that time.

These are just a few of the things I’d like to be demonstrated before I start believing in TA.

What was the best 10-year period to invest in the S&P 500?

I’m doing a small project right now looking at whether stop losses are actually useful in investing. When FTX blew up, it was noted that the traders there didn’t believe in stop losses, for which they were ridiculed on social media. Of course, do stop losses actually help? Or are they more likely to kick you out of a volatile-but-profitable investment than save you from an unprofitable one? Well I can’t answer that yet, but I can answer a different question.

To start my project, I downloaded 30ish years of S&P 500 data starting September 1990 and asked a quick question: what 10-year period gave the best return if you had invested in the S&P? Once I get the baseline return down, I can add in things like stop-losses and momentum strategies to see if a savvy investor could have improved their return with simple rules. Anyway, here’s the data:

I make a small program to estimate the return if you have bought $10,000 of S&P 500 stocks and simply held them for 10 years, selling them at the end of the 10th year. From this we can see that 1990 would have by far been the best years to start as you would have been able to sell at the peak of the Dotcom Bubble. Just a couple of years later however and you would have sold into the Dotcom Crash instead, drastically lowering your returns. The worst years for a 10-year buy-and-hold were 1998-2000 as you would have sold into the teeth of the Financial Crisis. These are only years where your 10-year return would have been negative. Then we can see 2008-2009 themselves as some of the best years to start investing, since you would have bought right at the bottom and ridden strong returns into 2018-2019.

I hope to update the program soon to see if momentum strategies beat buy-and-hold, but for now this gives a good picture of the historical returns for the S&P 500. The average 10-year-return was 100%, but with an 80% standard deviation. The absolute worst return would have been to start investing March 30th 1999, you would have bought into the Dotcom Bubble and sold into the Financial Crisis with a net return of -48%. The best 10-year-return was to start October 11, 1990, which would have had you buy very low and sell near the tippy top of the Dotcom Bubble for a 510% return. There are some wild swings with the buy-and-hold strategy, but the average is still very positive, we’ll see later if stop-losses can beat that.

Beam Therapeutics: what’s so special about prime editing?

Beam Therapeutics is another biotech company often mentioned in the same vein as Ginkgo Bioworks, Amyris, and Twist Bioscience, and since I’ve blogged about all three of those I might as well blog about Beam. Unlike Ginkgo and Twist, Beam isn’t a shovel salesman in a gold rush, they’re actually trying to create drugs and sell them, in this case they’re trying to break into or perhaps even create the cutting edge industry of medical genetics, changing people’s genes for the better. I’ll briefly discuss the science of their technology, but I feel like the science surrounding their technology deserves the most focus.

Beam has a novel form of CRISPR/Cas gene editing called prime editing. In both normal CRISPR/Cas and prime editing, genetic information is inserted into a living organism by way of novel DNA, guide-nucleotides and a DNA cutting enzyme. The guide-nucleotides direct the information to the specific part of the genome where it is needed, the DNA cutting enzyme excises a specific segment of host DNA, and hopefully DNA repair mechanisms allow the novel DNA to be inserted in its place. These techniques always rely in part of the host’s own DNA repair mechanisms, you have to cut DNA to insert novel DNA and that cut must then be stitched back up. Most CRISPR/Cas systems create double-stranded breaks while prime editing creates just single stranded breaks, and this greatly eases the burden of the host DNA repair mechanisms allowing inserts to go in smoothly and with far less likelihood of catastrophic effects. Double stranded breaks can introduce mutations, cancers, or cause a cell to commit cell-suicide to save the rest of the body from its own mutations and cancers. Because Beam is using prime editing, their DNA editing should have less off-target effects and far less chances to go wrong.

So the upside for Beam is that they’re doing gene editing in what could be the safest, most effective way possible. The downside is that gene editing itself is still just half the battle.

When I look at a lot of gene editing companies, I quickly find all kinds of data on the safety of their edits, the amount of DNA they can insert or delete, and impressive diagrams about how their editing molecules work. I rarely see much info about delivery systems, and that’s because delivering an edit is still somewhat of an Achilles’s heel of this technology. In a lab setting you can grow any cell you want in any conditions you want, so delivering the editing machinery (the DNA, the guide-nucleotides, the enzymes) is child’s play. But actual humans are not so easy, our cells are not readily accessible and our body has a number of defense mechanisms that have evolved to keep things out and that includes gene editors. To give you an idea of what these defenses are like, biology has its own gene editors in the form of retroviruses which insert their DNA into organisms like us in order to force our body to produce more viral progeny, a process which often kills the host. Retroviruses package their edit machinery in a protein capsid which sometimes sits inside a lipid (aka fatty) envelope, and so the human body has a lot of tools to recognize foreign capsids and envelopes and destroy them on sight. These same processes can be used to recognize and destroy a lot of the delivery systems that could otherwise be harnessed for gene editing.

Some companies side-step delivery entirely, if it’s hard to bring gene editing to cells why not just bring the cells to gene editing. This was the approach Vertex Pharmaceuticals used in its sickle cell anemia drug, blood stems cells were extracted from patients and edited in a test tube, before being reinserted into the patients in order to grow, divide, and start producing non-sickled red blood cells. This approach works great if you’re working on blood-based illnesses, since blood cells and blood stem cells are by far the easiest to extract and reinsert into the human body. But for other illnesses you need a delivery method which, like a virus, is able to enter the organism and change its cells’ DNA from within.

So if Beam Therapeutics wants to deliver a genetic payload using their prime editing technology, they’re going to need a delivery system which obeys the following rules

  • It must be able to evade the immune system and any other systems which would degrade it before it finds its target cells
  • It must be able to be targeted towards certain cells so that it doesn’t have off target effects
  • It must be able to enter targeted cells and deliver its genetic package

So let’s look at the options.

Viruses have already been mentioned, and they can be engineered in such a way as to deliver a genetic package without causing any disease. However as mentioned they are quickly recognized and dispatched by the immune system whenever their are found, their protein shells being easy targets for our bodies’ adaptive immune system. Normal viruses get around this by reproducing enough to outcompete the immune system that is targeting them, but we don’t want to infect patients we just want to cure them, so using viruses that reproduce is off the table for gene editing.

A variety of purely lipid-based structures exist which can ferry a genetic package through the body. Our cell membranes are made of phospholipids, and phospholipids will naturally form compartments whenever they are immersed in water. Phospholipids also have the propensity to fuse with each other, allowing their internal compartments to be shared and anything inside them to move from one to the other. Packaging a gene editor inside phospholipids would be less likely to trigger the immune system, and they can be created in such a way that they target a particular cell type to deliver their genetic package. However random phospholipids can be easily degraded by the body, limiting how long they can circulate to find their target cell. Furthermore their propensity to fuse is both a blessing and a curse, allowing them to easily deliver their genetic package to targets but also making them just as likely to deliver it to any random cell they bump into instead. This means a lot of off-target delivery and the possibility for plenty of off-target effects

At the other end of the scale are nanoparticles made of metals or other compounds. Many methods exist to attach drugs to the outside of a nanoparticle and target that nanoparticle to a cell, however this in turn leaves the drug free to be interacted with and targeted by the immune system. For many drugs this is fine, but prime editing uses foreign proteins, DNA and free nucleotides and the body is downright paranoid about finding those things hanging around since that usually means the body has either a cancer or an infection. To that end, the body destroys them on site and triggers an immune response, which would severely curtain any use of nanoparticles to deliver a genetic package. Nanoparticles can also be designed hollow to allow for the prime editing machinery to fit snugly inside them, but this can lead to the machinery just falling out of the nanoparticle in transit and being destroyed anyway. You might say “well not a hollow sphere that fully surrounds the machinery so it can’t fall out?” But it does need to get out eventually if it wants to edit the cell, and if it’s encased in a solid sphere of metal it can’t do that. Enzymes to breach the metal would be cool but are impractical in this case.

Between these two extremes we have a number of structures made of lipids, proteins, polymers or metals, and they all struggle with one of these points. They can’t encase the machinery, or they can’t easily deliver the machinery, or they trigger an immune response, or they degrade easily, or they often cause off-target delivery. Delivery to the target is Step 0 of both prime editing and gene editing in general, and for the most part this step is still unsolved. I’ve visited several seminars where viral packages for delivering CRISPR/Cas systems were discussed, and while these seem some of the most promising vectors for gene editing they still have the problem of triggering the body’s immune system and being destroyed by it. The seminars I’ve watched all discussed mitigating that problem, but none could sidestep it entirely.

I do believe that Beam therapeutics has technology that works, their prime editing is clearly a thing of beauty. Beam is currently working on treatments for sickle cell anemia, as is Vertex Pharmaceutical, and as are most gene editing companies because it’s a blood-based disease that is amenable to bringing the cells to the gene editing machinery instead of having to go vice versa. But for anything where you can’t bring the cells to the editing, Beam isn’t quite master of it’s own fate because for prime editing to reach the cells of the body it will need to be delivered in some way and currently that’s an unsolved problem. Even a system that works to deliver some packages won’t necessarily work for all of them as size and immunity considerations change with the specific nature of the genetic package you’re delivering. I would also be worried about Beam’s cash burn, they are essentially pre-revenue and will need to do a lot of research before any of their drugs get to market or can be sold to a bigger player. I think they can survive for a long while by selling stock since their price has held up a lot better than other biotechs I’ve blogged about, but that’s good for them and not for a shareholder. As long as interest rates keep going up, I’ll treat pre-revenue companies with a wary eye.

People buy stocks instead of ETFs because their values are different

I enjoy talking stocks, and whenever you hang around on the finance parts of the internet, you’ll inevitably run into the following sentiment:

Why are you even buying individual stocks? You should just buy a broad-market ETF. You’ll never beat the market so ETFs are the best and most reliable way to grow your money.

Bogleheads et al

I’ve written about the Efficient Market Hypothesis before and about the difficulties of stock picking. I understand and to an extent agree with the arguments that people in general cannot beat the market reliably over any significant length of time. Any good runs are transitory, purely luck based, and eventually fall back to earth (see $ARKK 2016-2021 and then 2021-today). But that isn’t the primary value most stick pickers are going for, they’re going for potential return not expected return.

When you buy a broad market ETF, what is your expected return? Well the ETF tracks the whole market and the market goes up 5-10% every year, so that’s the return you can expect. Some years you’re down 20% (like 2021), some years you’re up 30% (like 2019), but on average you get a 5-10% yearly return that will slowly grow your money. Slowly is the key word: investing in the stock market probably won’t make you rich, for the average American it won’t even make you a millionaire over the course of your entirely life, but it will give you a small leg up in the long run with very little risk to yourself.

So what’s the expected return for stock picking instead? Well, definitely less than 5-10%. The efficient market hypothesis and significant amounts of experimental data show that stock pickers broadly lose to the market over any significant timescale. They might be up 100% one year but are equally likely to lose it all the next. But the key here is that the expected return is not everyone’s return. The expected return is just the average of everyone’s return, and while on average people lose to the market there are always a lucky few that beat the market and some of them win big. There is at least one person out there who went all in on Tesla stock in 2013, sold in 2021 when Musk started acting weird, and made a truly life changing amount of money, and everyone who stock picks hopes to be like that person. Is it likely? Of course not, but it’s possible and that’s what keeps people going.

This may sound illogical to a bogglehead, and they may scoff and say the stock picker is no different that the casino gambler, but let’s try another example. What is the expected return of starting a small restaurant? Well, it takes a lot of capital investment to start a restaurant and 80% of them fail within the first 5 years of operation, so it’s safe to say that the expected return of a restaurants is actually negative. On average a person starting a restaurant will end up losing money, so are an restauranteurs as illogical as stock pickers? I’d argue no, the expected return isn’t as important to them as the potential return. A restaurant is an opportunity to make a life-changing amount of money, and while it’s clearly very uncommon, it happens often enough to continue enticing people to try it. The bogglehead could just as easily state that it’s more efficient for restauranteurs to not open up restaurants at all and they should instead invest in broad market ETFs, but if no one ever took risks like that then we’d never have new businesses at all.

Big gains require big risk, and I’d argue being content with your lot and investing like a bogglehead is no more “logical” than going all in on smart but high-risk plays, it’s simply a questions of values.

Amazon and PE

Conventional business indicators such as the price-earnings ratio, the price-to-book ratio, and discounted cash flows belong in the Bronze Age – so say the new economists. But if the old metrics don’t capture the potential of today’s fast-growth companies, some new formulas can.

PERManent Upside, WIRED Staff, February 2000

I think about the above quote a lot these day. At about the absolute peak of the dotcom bubble, there were writers and (supposedly) economists claiming that the foundation of the stock market had changed, and that what appeared to be overvalued tech stocks driven by computer-illiterate investors FOMO-ing into anything with a website were in fact some of the greatest stocks to own since sliced bread. PE, PB, DCF were useless in evaluating these stocks, they stood on their own through a new metric created just for them, PERM. No one knows, cares, or remembers what PERM stood for (you can read the linked article if you really want to), but it was supposed to prove that earnings weren’t important and that high PE stocks were still good deals. I think about this a lot because this is the same argument many have used on me regarding Amazon.

Amazon had a bad 2022, over the year it’s stock price cratered around 50% and it lost 1 trillion dollars in market cap. The old adage that “Amazon’s PE doesn’t matter” has seemed less and less true as it’s PE has gotten closer and closer to “normal.” Sure it’s still well above value stocks, even well above most tech stocks, but it’s not to far off from Walmart these days which would have been unthinkable just a few years ago. It may be that economic gravity is catching up to Amazon, and if so I’d like to share my theory as to why. Full disclosure, I did buy 10 shares of Amazon right after their latest stock split and have held them ever since. I’m down rather a lot on the investment and if what I’m about to say is accurate I’m soon to be down even more, so you can consider that data point as a hedge against my thesis and read on.

The conventional theory for why Amazon’s PE never mattered was that it invested almost every dollar of profit back into the business. By re-investing their profits rather than claiming them as earnings, Amazon avoided a lot of corporate taxes. And if Amazon’s reinvestments were wise, then the stockholders gained value tax-free rather than through taxable dividends. There’s also an argument that Amazon’s reinvestments were more efficient even outside of tax implications. Every dollar Amazon reinvested could create so much growth that it was better for an investor to let Amazon keep their money and grow than for an investor to demand Amazon hand money back to shareholders. When you look at what Amazon was investing in: cloud computing, content delivery, and an every increasing share of online shopping; this certainly seems to have been the case for the last decade or so, an investor gained more value by parking their money with Amazon than they would have parking their money with a company that handed earnings back to investors.

But perhaps something has changed, and changed drastically enough that Amazon’s PE lows won’t be temporary. Amazon’s revenue and earnings continue to grow year after year, but if its stock price continues to sink it’s PE may eventually reach downright normal levels. If that is the case then I think the reason why would be clear: investors no longer believe that a dollar re-invested by Amazon is worth quite so much more as it used to be. Amazon may be approaching the limits of its momentous growth, and may now start evolving into a “mature” company like Microsoft and Apple before it. In those cases a moderately high PE is still justified, I mean these are trillion-dollar tech companies, but they can’t be expected to continue their meteoric growth and so PEs in the 100s are no longer sensible. Amazon is famous for how much it re-invests, but the dollar amount of investment is less important that the future dollars that investment generates. In the past, Amazon’s future returns of ever re-invested dollar were great enough to justify a sky-high PE but that won’t last forever. Many companies that aren’t valued like Amazon re-invest a lot of their profits, the Red Queen Hypothesis makes as much sense in biology as it does in Economics “you have to run as fast as you can just to stand still.” Companies which re-invest a lot to maintain their dominance don’t necessarily get a premium over those that hand money back to shareholders but maintain dominance. And if Amazon reinvests a greater percent of its earnings vs Apple or Microsoft but doesn’t grow significantly faster than them, then it’s stock price shouldn’t command a premium either.

I think it’s possible that Amazon is indeed maturing into a company that will be valued by it’s PE just like all the other tech companies. That doesn’t mean it’s time to dump the stock, the revenue and earnings continue to grow and will probably catch up to the PE, or at least that’s just as likely as the PE falling to meet the revenue. Regardless of the mechanics, economic gravity will eventually catch up to Amazon just like it caught up to Tech stocks of the 2000s. Nothing is ever truly new.

“Market Capitulation” is a circular argument

Will the market recover in the new year? Or do we still have a ways to go? Bears online have been going on and on about “capitulation” as in “nothing will change until we finally have capitulation.” Capitulation in normal terms means surrender, so in financial terms it means the point where investors finally give up holding and sell their shares at a loss. According to Investopedia capitulation is also the point where the investment hits its bottom. Prima facia this is a circular argument, “we won’t hit the bottom until we’ve reached the bottom” is another way to phrase it. But even dumber, this is a backwards looking argument that cannot be used for predictions. Over the year of 2022, $SPY (a popular ETF that tracks the performance of the S&P 500) hit it’s 52 week low in November at 348$ per share (it currently trades at 382$). Who’s to say that that wasn’t the capitulation, and it won’t go below that? When the S&P500 hit 666 in 2009, that was the bottom of the bear market, yet many people still didn’t believe it, expecting that there was still more pain to endure. It wasn’t until a while later that we realized no, that really was the bottom, there’s no more “capitulation” after that. So I don’t put any stock in people talking about “market capitulation.”

The stock market doesn’t care about your cost basis

When someone is down 50% or more in a stock, they’ll often take to social media to complain and casually ask “what should I do next”? No one wants to sell for a loss, people almost act like it’s admitting failure. And people’s perceptions are often colored by the price at which they bought the stock. “Oh I bought 10 shares at 100$ and now they’re each worth 50$, when can I expect to break even again?” I can’t predict the market but I can say one thing: the price you paid for the stock DOES NOT MATTER. It doesn’t matter if you’re up or down, you should look at any stock you own and as yourself “do I think this stock will perform as well or better than the market in the near future?” A lot of people get stuck in a mental narrative, they start to think trends will either continue indefinitely or definitely reverse soon, depending on what would make them feel better. But a stock that is way down could still be overvalued just like a stock that is way up. A few months ago Carvana ($CVNA) stock was down 50% year to date. What did it do after that? It dropped another 50%, and another 50% from there, and just for good measure another 50% from there. Dropping 50% 4 times in a row meant it had lost about 94% of its starting value from January 1st. And Carvana still had a ways to go as it’s currently down 98%. If you had bought $CVNA on January 1st, then by April 1st you would have seen it lose 50% of it’s value. Your friend may have been tempted to think “it can’t go much lower, can it?” and bought the dip while you held your shares. You would then see your shares go on to lose 98% of their value while your friend’s shares lost 97% of their value. Your friend lost relatively less than you did, but still lost nearly everything.

Your cost basis on a stock is only relevant for tax purposes, it should have no bearing on your investment decisions. The only thing you should care about is the current price and the expected future price.