From a new working paper “The Price of Housing in the United States, 1890-2006” by Ronan C. Lyons, Allison Shertzer, Rowena Gray & David N. Agorastos (emphasis added):
“Zoning was adopted by almost every city in our sample during the 1920s. We see a slightly steeper gradient over the next two periods (coefficients of .48 and .29, respectively). In these periods it is possible both that the existing zoning regimes were causing higher price growth and that home price appreciation was incentivizing cities to adopt even more restrictive measures, particularly by the 1970s (Fischel, 2015; Molloy et al., 2020). The gradient in the final period (1980-2006) is even steeper, however (coefficient of .67), suggesting a closer relationship between zoning and home price appreciation towards the end of the 20th century.”
Most years, stocks go up, by something like 9%. Wouldn’t it be nice to invest in a fund that went up double those amounts? Such funds exist. They use futures or other derivatives to move up (or down!) by double, or even triple, the percentage that the underlying stock or index moves, on a daily basis.
For instance, a common unleveraged fund (ETF) is SPY that roughly tracks the S&P 500 index of large U.S. stocks is SPY. SSO is a 2X fund, which gives double the returns of SPY, on a daily basis. UPRO is a 3X fund, giving triple the returns. 2X funds exist for many different asset classes, including semiconductor stocks, treasury bill, and crude oil – see here. And similarly for 3X funds.
Since all the action in stocks these days seems to be in large tech companies, I will focus on the NASDAQ 100 index universe. The leading unleveraged fund there is QQQ. The 2X version is QLD, and the 3X is TQQQ. Let’s look at how these three funds performed over the past twelve months:
QQQ is up a respectable 36%, but QLD is up by 70%, and TQQQ by a mouth-watering 106%. You could have doubled your money in the past twelve months simply by investing in a 3X fund instead of holding boring 1X QQQ.
These leveraged funds can be utilized in more than one way. One approach is to just put the monies you have allocated for stocks into such funds, and hope for higher returns. Another approach is to put, say half of your speculative funds into a 2X fund (to get roughly the same stock exposure as putting all of it into a 1X fund), and then use the remaining half to put into other investments, or to keep as dry powder to give you the option to buy more equities if the market crashes.
What’s not to like about these funds? It turns out that a year of daily doubling of returns does not necessarily add up to doubling of yearly returns. There is “volatility drag” associated with all the exaggerated moves up and down. As an illustration of how this works, suppose you held a stock that went down by 50% one day, say from a price of $100 to $50. The next day, it went back up by 50%. But this would only get you back to $75, not $100.
It turns out that with these leveraged funds, as long as stocks are generally going up, the yearly returns can match or even exceed the 2X or 3X targets. But in a period with a lot of volatility, the yearly returns can fall far short. And in a down year, the combination of the leverage and the volatility drag lead to truly horrific losses. For instance, here is what 2022 looked like for these funds:
QQQ was down by 31%, which is bad enough. But imagine your $10,000 in TQQQ melting down to $3,300 that year.
And here is the chart from January 2022 to the present:
QQQ is up 27% in the past 2.5 years, 2X QLD is up only 16%, while 3X TQQQ is actually down by 6%, as it could not recovery from 2022.
This was a kind of a worst-case scenario, since 2022 was an exceptionally bad year for QQQ, coming off a fabulous 2021. A chart of the past five years, which includes the 2020 Covid crash and recovery, and the 2022 crash and subsequent recovery still shows the leveraged funds coming out ahead over the long term:
The net returns on QLD (321%) were about double QQQ (158%), while the more volatile TQQQ return (386%) was plenty high, but fell well short of three times QQQ.
In my personal investing, I hold some QLD as a means to free up funds for other investments I like. But if I smell major market trouble coming, I plan to swap back into plain QQQ until the storm clouds pass.
There are some other ways to get roughly double returns, which suffer less from volatility drag than these 2X funds. I will address those in subsequent posts.
Disclaimer: As usual, nothing here should be considered advice to buy or sell any investment.
Macron calls for a snap election, the gears of political bargaining begin turning after Marine le Pen wins the first round and the threat of a nationalist government becomes very real,a center-left coalition emerges, and et voila a surprisingly strategic median voter snatches victory from the jaws of xenophobic cruelty.
BREAKING FAR RIGHT REJECTED in France after tactical voting between Left-Green Alliance in 1st place and Macron’s party in 2nd place
Can such things happen in the US system? Yes and no. The US is neither a parliamentary system nor do we have a two-stage majority-rule electoral rule, but the same bargaining occurs beyond closed doors, yielding new and sometimes surprising coalitions. The political bargaining behind candidates, however, is beholden to the primary system, so it’s not always clear when bargaining plays out and what actually transpires. For example, as the prospects of President Biden winning re-election over former President Trump, there is increasing speculative expectation of an alternative Democratic candidate despite the party already nominating the President.
The process happening as we speak is a messy process, absent explicit institutional rules and, in the case of the Democratic candidate, a player with veto power, both effectively and literally. The gravity of the median voter is far weaker when the rules, or in this case the absence of specific rules, lead to large transaction costs and, in turn, enormous uncertainty. Whether the US median voter will hold in November’s election is unclear. All we can do for the moment is doff our caps to French voters, their (in my opinion superior) voting rules, and the political operators who bargained the country out of a potentially disastrous new administration.
I’m exploring whether the meme generator by Glif could be a way to introduce an econ paper. What if you identify a main character in your research project for GLIF to drag? (BTW, I have learned that the Wojack Meme Generator will re-write the name of the person you put in if your phrase is too long but that does not mean that the phrase is not used for content. So, you can put a longer phrase into the meme generator.)
I’m going to re-print here the prompt I actually used to get the Glif meme. As a warning, this approach is obviously not appropriate for more professional audiences. But sometimes you have a chance to quickly show your paper to a more informal audience either in a presentation or online. Having a way to wake up the audience in that situation could be helpful.
I’m not sharing all of these because I like them. I’m trying to give readers a chance to decide if they’d want to try it themselves. I think some of these prompts don’t work well and the cartoons either aren’t funny or are not true to life. However, I do find them interesting if the assignment is to scrape the internet for the maximally negative sentiment about a certain thing.
The prompt I used: “Pay Transparency Advocate” / “Effort Transparency and Fairness,” with Elif Demiral and Umit Saglam (under review)
Prompt: “Person Who Trusts ChatGPT” / “Do People Trust Humans More Than ChatGPT?” (2024) with William Hickman. Journal of Behavioral and Experimental Economics, 112: 102239.
Wojak Meme Generator from Glif will build you a funny meme from a short phrase or single word prompt. Note that it is built to be derogatory, cruel for sport, and may hallucinate up falsehoods. (see tweet announcement)
I am fascinated by this from the angle of modern anthropology. The AI has learned all of this by studying what we write online. Someone can build an AI to make jokes and call out hypocrisy.
Here are GLIFs of the different social media user stereotypes as of 2024. Most of our current readers probably don’t need any captions to these memes, but I’ll provide a bit of sincere explanation to help everyone understand the jokes.
Twitter user: Person who posts short messages and follows others on the microblogging platform.
Facebook user: Individual with a profile on the social network for connecting with friends and sharing content.
Bluesky user: Early adopter of a decentralized social media platform focused on user control.
One of my favorite economic journal articles is by Barry Weingast and has the short title “Market Preserving Federalism” (MPF). In this paper, Weingast lays out the conditions necessary for two tenuous equilibria: A) Federalism & B) Federalism that preserves a market economy. Given that we just celebrated Independence Day in the USA, it seems to me like a good opportunity to share some brief thoughts on this paper. I’ll speak in terms of the US for ease.
Weingast enumerates 5 features for MPF, starting with two that characterize a stable federalism:
F1) A hierarchy of governments, that is, at least “two levels of governments rule the same land and people,” each with a delineated scope of authority so that each level of government is autonomous in its own, well-defined sphere of political authority
F2) The autonomy of each government is institutionalized in a manner that makes federalism’s restrictions self-enforcing
Last Friday the Supreme Court overturned the doctrine of Chevron deference as part of its ruling in Loper Bright Enterprises v Raimondo. This might not have even been their most discussed ruling of the past week, but in my (non-lawyerly) opinion, there is a good chance it will be their most economically impactful ruling of the past decade. SCOTUSblog explains the basics:
the Supreme Court on Friday cut back sharply on the power of federal agencies to interpret the laws they administer and ruled that courts should rely on their own interpretation of ambiguous laws. The decision will likely have far-reaching effects across the country, from environmental regulation to healthcare costs.
By a vote of 6-3, the justices overruled their landmark 1984 decision in Chevron v. Natural Resources Defense Council, which gave rise to the doctrine known as the Chevron doctrine. Under that doctrine, if Congress has not directly addressed the question at the center of a dispute, a court was required to uphold the agency’s interpretation of the statute as long as it was reasonable. But in a 35-page ruling by Chief Justice John Roberts, the justices rejected that doctrine, calling it “fundamentally misguided.”
Justice Elena Kagan dissented, in an opinion joined by Justices Sonia Sotomayor and Ketanji Brown Jackson. Kagan predicted that Friday’s ruling “will cause a massive shock to the legal system.”
When the Supreme Court first issued its decision in the Chevron case more than 40 years ago, the decision was not necessarily regarded as a particularly consequential one. But in the years since then, it became one of the most important rulings on federal administrative law, cited by federal courts more than 18,000 times.
The most common reaction I’ve seen is that people expect this to reduce the power of executive branch agencies, both in general and relative to courts and businesses, likely resulting in deregulation. Thus those on the economic left have been mostly decrying the decisions, while free–marketers and businesspeoplehave mostly beencelebrating:
Back in January I encouraged you to follow the money in the Presidential race, by which I meant follow the betting markets. I suggested this was a good way to cut through the sometimes inaccuracy of polls, and the uncertainty of listening to any one expert or group of experts. Bettors in prediction markets can take all of these into account.
Lately of course the big question in the Presidential race is whether Biden will actually be the Democratic nominee. There is much uncertainty right now, and you will all kinds of predictions from experts, media quoting “inside sources,” and other such rumors. How are you, as a relatively uninformed outsider, supposed to know who to trust?
The answer again I will suggest is: watch the betting markets. And if you check the betting markets today (aggregated across multiple markets by EletionBettingOdds.com), you will see that Biden and Kamala Harris have roughly equal chances of becoming the next President (and Trump is about a 60% favorite):
Folks who follow the stock market know that the average company in the S&P 500 has gone essentially nowhere in the last couple of years. What has pulled the averages higher and higher has been the outstanding performance of a handful of big tech stocks. Foremost among these is Nvidia. Its share price has tripled in the past year, after nearly tripling in the previously twelve months. Its market value climbed to $3.3 trillion last week, briefly surpassing tech behemoths Microsoft and Apple as the most valuable company in the world.
What just happened here?
It all began in 1993 when Taiwanese-American electrical engineer Jensen Huang and two other Silicon Valley techies met in a Denny’s in East San Jose and decided to start their own company. Their focus was making graphics acceleration boards for video games. Computing devices such as computers, game stations, and smart phones have at their core a central processing unit, CPU. A strength of CPUs is their versatility. They can do a lot of different tasks, but sequentially and thus at a limited speed. To oversimplify, a CPU fetches an instruction (command), and then loads maybe two chunks of data, then performs the instructed calculations on those data, and then stores the result somewhere else, and then turns around and fetches the next instruction. With clever programming, some tasks can be broken up into multiple pieces that can be processed in parallel on several CPU cores at once, but that only goes so far.
Processing large amounts of graphics data, such as rendering a high-resolution active video game, requires an enormous amount of computing. However, these calculations are largely all the same type, so a versatile processing chip like a CPU is not required. Graphics processing units (GPUs), originally termed graphics accelerators, are designed to do enormous number of these simple calculations simultaneously. To offload the burden on the CPU, computers and game stations for decades have included on auxiliary GPU (“graphics card”) alongside the CPU.
This was the original target for Nvidia. Video gaming was expanding rapidly, and they saw a niche for innovative graphics processors. Unfortunately, they the processing architecture they choose to work on fell out of favor, and they skated right up to the edge of going bankrupt. In 1993 Nvidia was down to 30 days before closing their doors, but at the last moment they got a $5 million loan to keep them afloat. Nvidia clawed its way back from the brink and managed to make and sell a series of popular graphics processors.
However, management had a vision that the massively parallel processing power of their chips could be applied to more exulted uses than rendering blood spatters in Call of Duty. The types of matrix calculations done in GPUs can be used in a wide variety of physical simulations such as seismology and molecular dynamics. In 2007, and video released its CUDA platform for using GPUs for accelerated general purpose processing. Since then, Nvidia has promoting the use of its GPUs as general hardware for scientific computing, in addition to the classic graphics applications.
This line of business exploded starting around 2019, with the bitcoin craze. Crypto currencies require enormous amount of computing power, and these types of calculations are amenable to being performed in massively parallel GPUs. Serious bitcoin mining companies set up racks of processors, built on NVIDIA GPUs. GPUs did have serious competition from other types of processors for the crypto mining applications, so they did not have the field to themselves. With people stuck at home in 2020-2021, demand for GPUs rose even further: more folks sitting on couches playing video games, and more cloud computing for remote work.
Nvidia Dominates AI Computing
Now the whole world cannot get enough of machine learning and generative AI. And Nvidia chips totally dominate that market. Nvidia supplies not only the hardware (chips) but also a software platform to allow programmers to make use of the chips. With so many programmers and applications standardized now on the Nvidia platform, its dominance and profitability should persist for many years.
Nearly all their chips are manufactured in Taiwan, so that provides a geopolitical risk, not only for Nvidia but for all enterprises that depend on high end AI processing.
As maybe the least libertarian economist on this blog roll, its interesting that the timing of today’s Supreme Court Decision falls on my watch. The best thing to read is probably Sotamayor’s dissent which lays it out plainly: the President is, by today’s ruling, clear to use their power with almost complete immunity from criminal prosecution. It feels like hyperbole, but this is really dark stuff. The kind of thing I didn’t think I would ever see in my lifetime. I know many are framing this in terms of Trump and his current slate of legal cases, but those costs are comparatively trivial relative to the costs going forward.
Justice Sotomayor's dissent is one of the most terrified and terrifying pieces of judicial writing I've ever encountered—she lists the criminal acts that presidents can now take with immunity and says "the president is now a king above the law." https://t.co/ovLYlcsF4spic.twitter.com/6mx4nP0SHE
How did we get here? It’s tempting to trace back a conspiratorial timeline, but I think the answer is far more banal. It only takes the appointment of a few incompetent careerists to undermine the collective wisdom of a nine person voting body and here we are:
Economist Writing Every Day 