Category Archives: Finance

Joel Kurtzman is the opposite of Richard Heinberg

I just wanted to start by saying I’ve become much more lackadaisical about these posts recently. My work is getting interesting, so I’m not putting as much time and effort into my research prior to posting. I’m mostly shooting from the hip based on whatever comes to mind. I still enjoy this though so I’ll keep doing it, and I hope my couple of readers don’t mind the decline in quality.

With that said, it’s so interesting that Joel Kurtzman detects the exact opposite problem as Richard Heinberg. For those who remember, Richard Heinberg wrote “The End of Growth” in which he posited that there would be no more economic growth after 2010 (lol, lmao even). He claimed that this was because the world had entered an inextricable supply crunch, there just wasn’t enough stuff to go around (especially oil!) and our economy was already well past the carrying capacity of the planet. This meant that we couldn’t keep growing, because without more stuff to put in our factories we couldn’t make products to sell to people. We would all have to get by with less.

Hilariously, Joel Kurtzman detects the opposite problem from his vantage point in 1987. He detects a severe overproduction of commodities and finished goods caused by the industrialization of the global south and its competition with America, Europe and Japan. In Kurtzman’s thesis, we are entering an inescapable race to the bottom where wages will continue to fall further and further as companies try to make money while the prices of goods fall. Not only that but the nations of the world have financed their overproduction through the accumulation of debt, which they won’t be able to pay off as prices fall meaning there will be a debt collapse and further unemployment.

I’m sure both authors would think me uncharitable towards their theses, but that was my reading from their books.

The point is, I think both of them are suffering from extreme recency bias. Heinberg was writing after a decade of constricted oil supply had caused a rise in prices and had been followed by an economy crash. He thought the constricted supply would continue forever and the low-growth era following the crash was permanent.

Kurtzman was writing after a supply crunch had turned into a supply glut. OPEC’s oil embargo of the 70s had forced the world’s economies to become more efficient and induced many companies to step up their own oil production. In the late 80s, rising oil investment turned into an oil boom, and to maintain market share OPEC countries increased production without the consent of the entire group. This, alongside new technologies to make oil use more efficient, led to an oil glut and depressed prices. Add to this that prices were falling in other sectors, and Kurtzman thought this trend would continue forever.

Both Kurtzman and Heinberg astutely identified trends in their immediate present, and then extrapolated those trends infinitely into the future to arrive at their desired policy goals. For Heinberg: it was degrowth. For Kurtzman: it was protectionism. Both of them failed to understand that actions change with changing conditions. Heinberg didn’t realize that a rise in oil prices would spur investment into new extraction methods (fracking) and more efficient usage of oil (hybrid/electric cars). Kurtzman didn’t understand that falling commodity prices allows companies to produce more for less, nor did he understand that the American economy didn’t need manufacturing jobs to stay highly paid. If more stuff is being produced while still profitable, then consumers win because prices go down. And American consumers won most of all because tech jobs were replacing laborious manufacturing jobs.

I know pontificating is a hard job, I think all the pontifications I’ve made on this blog have been off the mark (though I don’t ask for money). But I find it fascinating that these two authors erred in exactly the same way to arrive at completely divergent answers. I’d love to have Kurtzman from 1987 debate Heinberg from 2010. Don’t let them use historical data, just explain to each other why will commodity prices have to remain high/low for the foreseeable future? I wonder whose head would explode first.

Follow up: what did Joel Kurtzman think of the 90s and 2000s?

I wrote a post last week about Joel Kurtzman’s “The Decline and Crash of the American Economy,” a book from the 80s that posited that America’s best days were behind it. Kurtzman’s central thesis appears to be:

  • Manufacturing is moving overseas, causing America to run a trade deficit
  • To buy foreign goods, America and Americans are becoming indebted to the rest of the world
  • Foreign investment is flooding into American stocks and American debt, causing us to lose control of our own economy
  • The much touted “service jobs” and “information age economy” are a mirage
  • As a result of the above four facts, the American economy is entering a period of decline and crash which can only be solved by strong protectionism and government control of the economy

This was all written in the 80s, and to an old-school leftists I guess it all seemed very sensible. I could imagine Jeremy Corbyn or Bernie Sanders making these exact arguments in 1980, while adding a few more worker-centric chapters of their own. The problem is that this thinking has largely been supplanted by modern economics.

Manufacturing is not the only thing an economy does. The knowledge economy, which Kurtzman scoffed at as the “information age economy,” has rapidly eclipsed all the manufacturing that came before it and continues to propel American forward. Likewise foreign investment flooding into America is by no means bad, as it allowed American companies and the Government to finance themselves with debt or equity. If foreign investment was fleeing America, that would be cause for concern. Being in debt is not a biblical sin for an economy. We all take on debt all the time because the value of having a car or a house now is greater than the value of the money we will use to pay off that debt over 5 to 20 years. The same is true for companies expanding, and foreign investment flooding into America means companies can issue debt much more cheaply than they could otherwise.

Furthermore Kurtzman’s prescription was largely abandoned in the 90s. Both Republicans and Democrats largely made peace with free trade (although the 2 most recent presidents have bucked this trend). There is a strong argument to be made that tariffs on foreign goods hurt the American economy as much as they do the foreign economy for a number of reasons. Tariffs create a walled garden for certain goods, allowing noncompetitive industries to remain in business for longer than they should. In turn these noncompetitive industries suck up investment and compete for resources, making it harder for actually competitive companies to expand as they should be able to. There is only so much supply of money, parts, and workers, if Ford was heavily subsidized by tariffs, would Tesla have been able to take off? Finally tariffs alter the incentive calculus for a company because once tariffs are part of the political equation, companies can increase their profits more by demanding higher and higher tariffs from the government than they can by actually improving production. This caused some Latin American countries to enter a tariff spiral where goods became more and more expensive because rather than compete with the rest of the world, companies put their effort into demanding higher and higher tariffs.

In the 90s and the 2000s America largely abandoned Kurtzman’s thesis and his prescriptions. Angst and newsrooms aside, the trade deficit kept expanding, NAFTA remained in place, the service and information sector were seen as avenues of growth, and debt kept piling up. If Kurtzman then thought the Financial Crisis was proof of his theory, he would have been rather sad that America came out of the crisis much better than most of the nations he said it was indebted to, such as Japan, Latin America, and Europe.

Reading Kurtzman’s book is like reading politics from a bygone age. I once read a book about “the Crime of ’73,” a much maligned bill which removed the right of silver-bullion-holders to have their silver minted into dollars. Pro-silver advocates despised this bill so utterly that it eventually launched William Jennings Bryan as a presidential candidate, a candidacy he might not have gained had the silver movement not been so motivated and powerful. Yet reading it today, it’s hard to understand why this economic debate was filled with such hatred and vitriol. It’s hard to understand the motivations behind the players, and how for them this was the defining issue of their age. Because honestly, America has moved past that debate long ago: silver isn’t money and neither is gold, dollars are. I almost feel the same way with Kurtzman’s book. The last 2 presidents notwithstanding, most of my adult life has been shaped by a bipartisan agreement on free trade and the importance of the information economy over traditional manufacturing. I just wonder what Kurtzman would think now.

Was the Crash of 1987 all that important?

America has had a lot of recessions, depressions, and financial crises. Every country has of course, but since America has been the world’s largest economy for well over 100 years, ours get more press and reverberate more strongly throughout the world. But the crash of 1987 is one that I rarely see talked about, and I thought that was with good reason. On October 19th 1987, stock prices worldwide crashed by double digits in a single day. But the effects on the wider economy were not so severe, and the US economy still grew by 3.5% that year.

The Crash of 1987 is a good reminder that the stock market is not identical to the “real” economy. Now, they are not wholly diverged either, and if stock prices crash companies will find it harder to use their stock to finance expansion. But they aren’t tightly coupled and the Crash of 1987 is one of the many events that proves it. However I’m reading a book now called “The Decline and Crash of the American Economy” that appears to posit more from 1987 than was warranted.

The author, Joel Kurtzman, tells you his thesis on the cover of the book, and the inside jacket makes special note of how 1987 heralded deep problems that would not be fixed without his preferred policies being implemented. But Kurtzman is basically a left-protectionist who blames Nixon and Reagan for ending the gold standard/Bretton Woods and liberalizing American trade. Kurtzman’s policies were by no means implemented, but the 90s were hardly a decline and crash by anyone’s definition. It feels to me like Kurtzman had a thesis already in place, and simply used the crash of 1987 as ex post facto proof of what he already believed.

I’ll try to write more about this book in the coming days, but I don’t think 1987 was an important as Kurtzman thinks.

Do momentum strategies beat buy-and-hold?

This post has been a LONG time coming, but a while ago I wrote about the rate of return for investing in the S&P 500. In that article, I compared the returns of someone executing a buy-and-hold strategy starting in a certain year and ending 10 years later. Unsurprisingly, the best time to start a 10-year investment was in 1990 or early 1991, as the peak of the DotCom bubble happened 10 years later and you could sell out at the top.

Figure 1: Return over 10 years of a $10,000 investment, assuming buy-and-hold strategy

But what about someone who wants a more sophisticated strategy than simple buy-and-hold? The reason people day-trade is that they hope to beat the market, not just match it. One strategy that I have seen genuine, peer-reviewed literature discussing is the so-called “momentum” strategy of buying while the market is going up and selling while it’s going down. In this way you should avoid big loses (like the DotCom bust) but still have big gains (like the DotCom bubble).

Now, a momentum strategy can be done in different ways. It can look at specific time periods, it can include shorting, it can include sector rotation, etc. But the simplest momentum strategy I found was to simply sell out whenever the market dropped by 20%, and then buy back in when it recovered 20% from the bottom. This is intended to stop loses on the way down and avoid FOMO-ing back in during a bull trap, only buying stocks during a true bull market.

I wrote a program to calculate the return on a $10,000, 10-year investment using that strategy.

Figure 2: Return over 10 years of a $10,000 investment, assuming 20% momentum strategy

The results are fairly discontinuous because of the rigidity of the 20% cutoff, but some patterns do emerge. The return is almost identical for people who invested in 1990, because for that 10-year period the market never dropped 20%. Once you get into 1991 however, this strategy would have allowed some people to avoid the worst of the DotCom crash, as they would have sold out when the market dropped hard. In that case they would have done better than a buy-and-hold strategy.

However that’s just an example of the strategy working at it’s best. I decided to compare the two strategies. I simply subtracted the two graphs from each other, creating the below figure as a result. Any dot that is on the zero line is a point in which buy-and-hold performed identically to momentum. Any dot below is where momentum performed worse, and the few dots above are where it performed better.

Here, we see some interesting patterns, the momentum strategy actually performed pretty poorly for anyone who started a 10-year investment in the 2000s. The peaks in the early 90s are people who sold out during the DotCom bust and missed the worst of the loses. The peak around 1999 is people who sold out during the Financial Crisis and missed the worst of the loses. But the declining valley during the 2000s is the result of people who would have sold out during the Financial Crisis, but then waited for the market to get above where they had sold before buying back in.

Remember that the momentum strategy involves selling when the market has lost 20% and only re-buying when it’s regained 20% off the bottom. Less than 20% off the bottom and you can argue (as some have this year) that it’s just a “bull trap” and the market still has “another leg down” ie much further to fall. This can result in standing on the sidelines with your cash while the market makes money without you. And using this momentum strategy, that’s exactly what can happen.

I use this to illustrate a point I’ve talked about before, it’s not usually smart to just sit on cash waiting for the market to fall further. Sure the market can fall further, but it can also rise and leave you behind. Time in the market beats timing the market. Furthermore, this experiment is as generous as possible to the momentum strategy: there are no transaction costs (the bid-ask spread is an unavoidable real-world cost) and we ignore dividends (which further rewards time in the market at the expense of timing the marker). If total returns were taken into account along with transaction costs, it’s debatable as to whether any 10-year momentum investment would have beaten buy-and-hold. Even as it stands now, only a very few lucky investment windows would have benefited from momentum strategies, most would do best with buy-and-hold.

Just for kicks, I reran this data with a 10% momentum strategy instead of 20%, and the results were even worse for momentum. Selling out at the first sign of trouble, FOMO’ing back in to the first recovery, and then losing all over again makes for a terrible strategy and that can basically be what momentum trading is.

I can go forward and look at more exotic momentum strategies some other time (for example short stocks that are falling and long stocks that are rising), but for now I think I’ve proven my point.

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.

Energy Return on Energy Investment, a very silly concept

Today I’d like to address one concept that I read about in Richard Heinberg’s The End of Growth, Energy Return on Energy Investment or EROEI. The concept is an attempt to quantify the efficiency of a given energy source, and in the hands of Heinberg and other degrowthers it is a way to “prove” that we are running out of usable energy.

EROEI is a simple and intuitive concept, taking the amount of energy produced by a given source and dividing by the amount of energy it costs to set up and use that source. Oil is a prime example. In the beginning of the 20th century oil extract was easy since it just seeped out of the ground in many places. Drilling a small oil well won’t cost you that much, hell you can probably do it with manpower alone. In that case the oil gushing forth will easily give you a good energy return.

In the 21st century however, things have become harder. Oil wells require powerful machines to drill (which costs energy), and the amount and quality of the oi you get out is often lower. Add to that the fact that modern wells require huge amounts of metal and plastics, all of which cost energy to produce and even more energy to transport to their location, then add the energy it took to find the oil wells in the first place using complex geographical surveys and seismographic data, and taken together some people claim that the EROEI for a modern oil well is already less than 1, meaning that more energy is being put in than the energy we get out.

And oil isn’t the only fuel source heading towards and EROEI of less than 1. Modern mining techniques for coal require bigger and bigger machines, natural gas requires more and more expansive facilities, even solar panels require minerals that are more and more difficult to acquire. It seems everything but hydro power and (perhaps) nuclear power are becoming harder and harder to produce, sending energy returns down further and further.

This phenomenon, where the EROEI for our energy sources is less than 1, is supposed to presage an acute energy crisis and the economic cataclysm that degrowth advocates have been warning us about. If we’re getting out less energy than we’re putting in, then we’re really not even gaining, aren’t we? The problem is, I’m struggling to see how EROEI is even a meaningful way to look at this.

First let me note that not all energy is created equal. Energy in certain forms is more usable to us than in others. A hydroelectric dam holds water which (due to its being elevated above its natural resting place) acts as a store of potential energy. The release of that water drives a turbine to produce electricity. But you can’t fly a plane using water power nor keep it plugged in during flight. Jet fuel is another source of potential energy, and it has a number of advantages versus elevated water. Jet fuel is very easy to use and transport, you can fill a tank with it and move it to wherever your plane is, then fill the plane’s tanks from there.

If the only two energy sources in the world were jet fuel and hydroelectric power, we would still find it beneficial to somehow produce jet fuel using hydroelectric power even though that would necessity an EROEI of less than one. Because although this conversion would have less total energy, the energy would be in a more useful form. People would happily extract oil using hydroelectric power, then run refineries using hydroelectric power, because jet fuel has so much utility. This utility means that (supply being equal), jet fuel would command a higher price than hydroelectric power per unit of energy. And so the economic advantages would make the EROEI disadvantages meaningless.

This is the fatal flaw of EROEI in my mind. The fact that some forms of energy are more useful than others means we can’t directly compare energy out and energy in. The energy that is used to run a modern oil well comes to it from the grid, which is usually powered by coal, solar, wind, or nuclear, none of which can be used to fuel a plane. Converting these forms of energy into oil is an economic gain even if it is an energy loss. Furthermore EROEI estimates are generally overly complex and try to account for every joule of energy used in extraction, even when those calculations don’t really make sense. Let me give you an example:

A neolithic farmer has to plow his own fields, sow his own seeds, reap his own corns. Not only that, but the sun’s rays must shine upon his fields enough to let them grow. Billions of kilocalories of energy are hitting his plants every second, and most of then are lost during the plants’ growth process because photosynthesis is actually not all that efficient to begin with. The plant will have used billions of kilocalories of energy, and from them the farmer gets a few thousands of kilocalories of energy. Most of the energy is lost.

This is the kind of counting EROEI tries to do, applied to farming. When you count up every joule of energy that went into the farmer’s food, you find his food will necessarily provide him with an EROEI of less than one thanks to the first law of thermodynamics. But this isn’t a problem because Earth isn’t a closed system, nor are our oil wells. We are blasted by sunlight every minute, our core produces energy from decaying nucleotides, our tides are driven in part by the moon’s gravity, there is so much energy hitting us that we could fuel the entire world for a thousand years and never run out. The problem is that there are some scenarios where that energy isn’t useful. You can’t fly a plane with solar or geothermal or gravitational energy, but you can power an oil well. So we happily use the energies we have lots of (including our use of solar power to grow useful plants and animals!) and use that energy to help us extract the energies with greater utility.

I think EROEI failed from the very beginning for this very reason. It ignores economic realities and the massive amount of energy that surrounds us, and instead argues from the first law of thermodynamics. Yes in any closed system energy eventually runs out, but it isn’t even clear that our universe is a closed system, and the earth definitely is not, so we need to face up to economic reality on this.

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.