if we take parallels to the past seriously, there arises the question of whether a 40 year old with a 100% index fund portfolio should consider hedging/reallocating their portfolio a decade sooner than they planned on becuase, well, just look at that [CAPE] chart.
But what to do about it?
So, seriously. Are we hedging? And what are we hedging into? Asking for a friend. Who is an economist. And also me.
My answer: I’m diversifying into global stocks, which tend to be much lower-priced:
A simple and cheap way to get a highly diversified basket of global stocks is VEU (Vanguard ex-US). Over 3000 stocks from around the world, P/E of 19, rock-bottom fees. One disadvantage for diversification purposes is that its biggest 5 holdings make up 12% of the index and they are all AI-related.
If you’re willing to make more concentrated and specific bets in order to get even cheaper, here are some other foreign indices I like:
FRDM (Freedom 100 Emerging Markets ETF): Invests in emerging markets with the highest economic freedom ratings. I love the theory & it’s performed very well for me & its overall PE is a reasonable 17, though it’s largest holdings are in Taiwan and South Korea, so not a good way to get away from AI.
ILF (iShares Latin America 40): Invests in large Latin American companies. P/E 11 with low AI exposure.
FYLD (Cambria Foreign Shareholder Yield ETF): Invests in high-yielding companies in developed countries outside the US. Measures yield using stock buybacks in addition to dividends. This serves as a value screen that keeps PE low (currently 13) and avoids expensive sectors like AI.
EYLD (Cambria Emerging Shareholder Yield ETF): Same idea as FYLD but for emerging markets. Current PE 12
I also hold single-country indices for Japan (FLJP, PE 20), Poland (EPOL, PE 13), and India (EPI, PE 17). These are probably riskier than the broad indices above, but still fairly diversified across companies.
While foreign stocks are my preferred hedge, there are many others. For US stocks there are small-cap or value indices, or just buying individual stocks you like (not necessarily crazy). The 20- and 30-year US treasury bonds are intriguingly yielding over 5%, though between my age and my inflation concerns I don’t own any. TIPS are much more attractive, yielding 2-3% over inflation, I don’t own any yet but I’m considering it. Gold indices like IAUM are another good hedge.
Of course this isn’t necessarily a bubble that will pop any time soon, or at all- AI is a real technological advance- so between that & general efficient market principles I don’t think the answer is to sell everything & go to cash, much less short the market. But Mike is right to ask how to hedge, and I think anyone holding ~100% US stocks should consider diversifying.
What could be better than creating data so valuable that an institution is happy to host and update it forever, like the Sean Lahman baseball database?
Creating data that sells for $375 million, like the Center for Research in Security Prices. University of Chicago professors assembled this series of finance datasets over decades, starting in 1960 with an effort to track every transaction of every publicly traded security. U Chicago sold CRSP to Morningstar last year for $375 million.
Why could they sell it for so much? It helps to be working in finance, where the willingness to pay is the highest. It also represents 65 years of work from what became a large team that included Nobelists like Eugene Fama. The data was valuable enough to become widely used by key institutions even though CRSP charged for it:
Today, $3 trillion in fund assets are linked to CRSP Market Indexes, including U.S. equity ETFs run by Vanguard, and more than 600 subscribers across 35 countries use CRSP Research Data Products.
Did U Chicago sell CRSP at the right time? On the one hand, I wonder if this was a fire sale driven by federal grant cuts putting pressure on the U Chicago budget. On the other hand, assembling datasets like this is only going to get easier in the age of AI, so perhaps Chicago sold at the top.
For now though there is still an edge in having restricted datasets that AIs haven’t trained on and can’t access. When I ask myself what advantage my human research assistants have over AIs in 2026, the most obvious answer is that they can legally access restricted databases like CRSP or, in my current case, HeinOnline.
Imagine my surprise the other day when I noticed that $1000 had been transferred from my bank account to some nonprofit, without any knowledge or approval on my part. It turned out that an acquaintance, with no access to my banking credentials, had done this. They wanted to make a charitable donation, using a direct bank transfer rather than a credit card, to save the charity the 3% fee charged by Visa/Mastercard. I had written them a check some months back, and they accidently entered the routing number and account number from that check of mine, instead of their own account information.
Was this some unique, gross security lapse by the bank? Or can anyone drain money from your bank account from anywhere, simply knowing your routing and account numbers? After all, these numbers appear on every check you write.
In the old days, someone armed with those two numbers could print fake checks for your account and forge your signature. Sadly, the same principle holds true now in the age of electronic transfers.
The ACH network, which powers most bank-to-bank transfers, bill payments, and direct debits, was historically designed on the assumption that initiating a pull was authorization enough. Someone with your numbers can:
Set up a fake “bill pay” or utility payment pulling from your account
Add your account as a payment method on PayPal, Venmo, Amazon, or similar platforms
Set up recurring ACH debits through many merchant processors
Ouch. It seems there is really nothing you can do to prevent this, other than closing your existing account if you are worried, and opening a new account with different numbers.
The main mitigation method is to set your account to notify you every time there is an electronic transfer, and notify the bank immediately (call, and follow up in writing or bank’s message system). Start documenting all aspects of this dispute process, in case of litigation. Or at a minimum, look through every month’s statement soon after it appears, to check for shady transfers. Close or restrict the account if appropriate.
As I understand it, if you notify the bank within 60 days of when they sent the monthly statement, your max liability is $500. Beyond 60 days, you are toast – the money is just gone.
There is a slightly murky provision for limiting liability to $50 if you notify the bank within two days of you learning of the fraud (as long as that is within the 60-day window noted above). The application of this 2-day rule is clear if you are getting real-time notifications of transfers. But, say, if you slit open a printed statement the day after receiving it in the snail mail, perhaps three days after it was sent, notice a fraud and call the bank immediately, it is less clear whether you get dinged $50 or $500. If you are a good customer, I’m guessing the bank would make it $50.
All this is powerful motivation for using credit cards or Venmo/Zelle instead of paper checks. Bad guys can’t withdraw from your Venmo or Zelle unless they have your login information. With a credit card, you generally don’t get charged anything for a fraudulent transaction on your credit card (assuming you report it in reasonable time) – – plus you can usually get 2% or more cash back.
Here is a graphic that compares the size of the initial public offering (vertical axis) and the total company market cap (size of circle) of SpaceX to everything that has come before:
Elon Musk’s space launch/AI conglomerate spin-off SpaceX went public on Friday. Retail investors were all over it like a pack of starving dogs, driving up prices of SPCX from its opening $162 to $192 as of the close Monday. This has been grand theater, with Musk serving up signature visions of gargantuan total addressable markets, while investors are in fact getting crumbs of a money-loser. In the restaurant biz, this is known as selling the sizzle instead of the steak.
Let us pause for a reverent moment to savor the grand vision used to sell SpaceX: making humanity multiplanetary by dramatically lowering the cost of access to space. It extends beyond launch services into global communications (via Starlink), space infrastructure, in-space manufacturing, resource extraction, transportation, and ultimately a potential Mars economy—expanding from billions to trillions of dollars in theoretically addressable markets. Ooh, ahh, who would not want a piece of that?
Well, there are some problems here. It is hard not to splutter when trying to explain it, it is so bad for investors. I will just call out three issues I see:
( 1 ) Governance: You Own It But Can’t Influence It The IPO float represents roughly 4-5% of total shares, so we the people only get a sliver of the company. But it gets worse. Public shareholders receive Class A shares with one vote each, while Musk holds Class B shares carrying ten votes each, giving him approximately 85% voting control. More unusually, the company bylaws explicitly prohibit shareholder proposals — meaning investors cannot even put advisory resolutions to a vote. This is governance subordination beyond what even Zuckerberg imposed on his investors.
( 2 ) Valuation: Priced for Perfection Without Profits There is no price/earnings ratio because there are no earnings. At $2 trillion, SpaceX trades at approximately 20 times REVENUE. That price/sales is not unheard of for a small, fast-growing software company with almost no capital requirements (think: early-stage Amazon, Google, Palantir, etc.). But it makes no sense to apply it to a capital-intensive hardware and infrastructure business with negative GAAP earnings. Starlink is growing rapidly but requires continuous heavy capital expenditure to maintain and expand its satellite constellation. And SpaceX faces meaningful competition for orbital launches from Blue Origin, ULA (for military missions), maybe Rocket Labs, and the Chinese (for non-West payloads). And, if you dig into it, over 90% of their proposed addressable market is not space at all, but enterprise AI (!!). SpaceX pitches a total addressable market of $28.5 trillion, with AI opportunities alone accounting for $26.5 trillion. This is essentially the entire global GDP of the planet for a single year, and I guess they assume their pitiful Grok will claw back lots of market share from Claude, ChatGPT, and Gemini. As we said, priced for perfection.
( 3 ) Unbuilt Revenue Streams SpaceX has announced contracts to provide AI compute services to other companies — potentially a significant revenue source — but the data centers required don’t yet exist. Investors are therefore paying partially for infrastructure that is neither built nor generating revenue, on a timeline that remains speculative.
OK, but we have seen shares of Musk’s other baby, Tesla (TSLA), remain at uniquely high price/sales and price/earnings, seemingly indefinitely. So, investing in SpaceX is much like investing in that shiny yellow metal called gold: there will never be conventional earnings payback, but there might well be some greater fool out there who will pay more for my shares than I did. This really comes down to a psychological head game, not fundamentals. Gold has in fact done very well over the years, and the pros learned the hard way not to short TSLA, not matter how unsupportable its price is.
Final comments on index fund buying to drive up the share price – one of the bull drivers for SpaceX has been the prospect that the huge company market cap (around $2 Trillion) would force index funds like NASDAQ and S&P500 to buy boatloads of SPCX stock, driving up the price. But it turns out this will not be such a big factor. These indices only take into account the publicly traded shares, not locked-up, non-traded founder shares. So, we are looking at around $100 billion in traded SPCX shares, not the full $2 billion, which is mainly shares controlled by Musk and venture capital. $100 billion is only about 0.15% of the total S&P500 market cap of about $70 trillion. This means fund purchases of SPCX should not by itself drive down prices of other companies.
It is true that inclusion in Nasdaq-100 and Russell indexes will force automatic buying of around $25 billion in SPCX shares from funds tracking those indices. That seems like a significant driver, but (a) everyone knows this, so it is already factored into today’s prices, and (b) index fund purchases will be offset by billions in sales from VC’s as they sell shares when their lock-up periods expire in a few months.
Side comment: Historically, the major indices have had a little gravitas about what companies to include. The Nasdaq-100 typically requires at least a 3 month “seasoning” period for an IPO to trade, and then waiting till the next regularly-scheduled reconstitution. Thus, it might take around six months for an IPO to make it into the Nasdaq-100 index. For SpaceX (and presumably for Anthropic and OpenAI IPOs), NASDAQ changed the rules to allow REALLY big IPOs to be included within 15 days. (This means that some other company will get booted from the Nasdaq-100). Russell caved even further than NASDAQ, with almost immediate inclusion in the Russell 3000.
Staid Standard and Poor’s alone has maintained its dignity here, refusing to compromise on its principles. For inclusion in the S&P 500, a company must be publicly listed for at least one full year, must show positive GAAP earnings in the most recent quarter and positive cumulative earnings across the trailing four quarters (this is going to be tough for a cash-burner like SpaceX), and at least 10% (not 5%) of its shares must be publicly traded. So, no S&P listing for SpaceX in the near future.
“Boomers- live it up now at the expense of your kids, the government, charities, and your future selves.” That’s what I worried the popular book “Die With Zero” by Bill Perkins* might advocate based on its title and the brief descriptions I heard. After reading it, I’d say it’s at most 20% the book I worried about. A more accurate summary would be “planning ahead is great but it doesn’t always mean saving more” or even “here’s how to plan out your optimal consumption path like an economist”.
The core argument is that you’ll be happiest if you spend or dispose of all your money while you’re alive, then die right as you run out of money. He acknowledges that “dying with exactly zero is an impossible goal” because you don’t know when you’ll die, but he thinks most people could get much closer to zero than they do and would be better off for trying.
He then considers a variety of obvious objections.
Q: Isn’t the risk of running out of money early worse than the risk of not spending everything?
A: It’s a real risk, but one that can easily be eliminated with financial products like annuities and long-term care insurance.
R (My reaction): This is basically right. In fact, the best argument for his thesis he seems to miss is that there’s also always Social Security and Medicaid, so in America you’d never really hit zero; still less so in a country with a stronger welfare state.
Q: What about kids? Or charity?
A: Figure out how much you want them to have, then give it to them before they die. They’d rather have it sooner- right now the modal recipient of an inheritance is 60 years old, but money is more useful to people when they are younger, closer to 30.
R: True as far as it goes, but my guess is that most people would end up giving much less this way. Especially if they also listen to Perkins’ advice about working less. He mentions giving money away early but his heart doesn’t seem in it compared to planning out the optimal consumption path.
Highlights: Your ability to enjoy your wealth depends on your health, since many fun activities can’t be done when you are frail or sick. It seems obvious when you hear it, but the idea of measuring the marginal utility of wealth with respect to health is underrated even in health economics. The book does lots of good work with data on Americans’ finances; maybe the best argument for Perkins’ idea that many people over-save is that 1/3 of Americans end up increasing their wealth after retirement.
Lowlights: Graph of optimal net worth by age (page 166) contradicts graph of optimal spending by age (page 172). Arguing that John Arnold should have retired earlier than he did (age 38) because he already had more than enough money for himself, without considering how this would have made one of the world’s most innovative and effective charities much less effective. Arguing that Warren Buffett should have given his money away sooner because the charities would rather have it sooner- arguably this is true for most people, but definitely not for the one guy who really can beat the market and give much more later!
Do I recommend Die With Zero? It’s a quick and easy read that I enjoyed, but I don’t think it changes any of my financial plans. If we over-simplify its message to be “consume more now”, it’s a bad message for the typical American (who saves only 2.6% of their income), but perhaps a good message for the typical reader of personal finance books. As always it’s good to ask yourself “who is this for” and “should you reverse any advice you hear”.
“the people I’m writing for- people who are saving too much for their own good” -Die With Zero
“Objectivism might be a vicious cycle. The people who are already too selfish see an opportunity to be selfish with a halo. They join Objectivism, egg each other on, and become even more selfish still. Meanwhile, the people who could really have benefitted from Objectivism, the people who feel guilted into living for others all the time while ignoring their own needs, are off in some kind of effective charity group, egging each other on to be even more self-destructively altruistic….. Every piece of social commentary is most likely to go to the people who need it least.” – Scott Alexander
*Bill Perkins is the only name on the cover, but the Acknowledgements and the ending note that the book was co-written by Marina Krakovsky with some work done by economist Kay-Yut Chen.
Last week I discussed absolute measures of portfolio performance and management, specifically between two portfolios that are composed of different assets (utilities and tech). I began with comparing the basics of return, standard deviation, and Sharpe ratio to some other possible portfolio in the Markowitz cloud. But, simply comparing the difference between these possible portfolios can be sensitive to the spread of stats within a specific Markowitz cloud. In other words, it’s not scale independent. A larger spread of possible stats can make a portfolio look bad due to the spread return/standard deviation/Sharpe ratio alone.
In this post I introduce quasi-relative measures. Again, I lean on the Markowitz cloud. They’re pasted below (Utilities on the left, tech on the right).
If we can somehow express the returns, volatilities, and Sharpe ratios on a common scale that is independent of the level values, then we can make the realized portfolios more comparable. One thing that we can do is to express a stat as a weighted linear average between the maximum and minimum possible values. Conditional on the realized standard deviation, there exists a maximum and minimum of possible return. Something like the below. Rho is the weight on the maximum return. It’s also the proportion of possible conditional returns that are lower than the realized return.
The unconditional version is the same, but would be relative to the global maximum and minimum stats. We can represent the weigh on the maximum return and the percentile among possible returns as gamma.
A final quasi-relative measure of performance is the dissimilarity index between the realized portfolio weights and some reference portfolio weights. This provides a measure of how much the asset weights would need to change in order to adjust the portfolio. If changing portfolio weights is costly, then it’s also a measure of the transaction cost of reallocation. It’s quasi-relative because it is independent of the spread of possible performance stats.
Below are the quasi-relative measures for each the utility and tech company portfolios.
It’s feeling like the late ’90s, with an impressive new technology pushing tech stocks and the broader US market to all-time highs. Retail investors are using new platforms to get in on the action, tech companies are doing more IPOs to take advantage of the higher stock prices, and other companies are trying to boost their stocks by saying they are pivoting to the new technology (though often they aren’t really changing).
The excitement drives valuations to record levels:
Shiller CAPE Ratio
In the ’90s, the internet really was a transformational new technology that would enable lots of profitable new companies. But the market got ahead of itself, a bubble that led to a crash- the S&P fell by almost half, while the tech-heavy NASDAQ fell by over 3/4 and took 15 years to recover.
History rhymes, but it doesn’t repeat exactly. I don’t currently expect a big crash driven by AI stocks; it helps that unlike in the ’90s, many of the big players are currently profitable. But I also don’t expect the NASDAQ to keep posting 20+% returns every year.
If the AI bull market doesn’t end in a dramatic crash, how will it end? It’s already shrugged off a war. A US recession is unlikely this year, though plausible next year.
The end I see slowly approaching comes from crowding out. What Robert Solow said about computers in 1987 is true about AI today: you see the AI age everywhere except the productivity statistics. There’s only so much money to go around in markets when productivity growth is unexceptional and savings rates are falling.
We’re already seeing the war hit certain markets (if not US stocks). Iran’s gulf neighbors are now putting lots of money into missile defense, money they now won’t be spending on data centers or gold (down 16% from pre-war), and everyone else has to spend more on oil.
Interest rates have been rising- partly due to central bank attempts to fight inflation, partly due to ongoing high rates of government borrowing, and partly due to financing the AI buildout itself. Higher rates make it more expensive for companies to invest in the physical AI buildout, and make investors discount future AI revenues more while making bonds a more attractive substitute for stocks today. 10-year TIPS now yield 2% over the inflation rate, a sharp contrast to the 2021 stock boom when they yielded less than inflation. If I were older I’d be loading up on TIPS, and even at 38 I’m starting to get tempted.
Trying to call the top exactly is a fool’s errand, but if I were feeling foolish, I’d point to the big upcoming IPOs. SpaceX just filed for an IPO that would be the biggest ever both for the amount of money raised ($75 billion) and the total company valuation ($1.77 trillion). This shatters the previous records for the biggest overall raise ($29 billion raised by Saudi Aramco when it went public in 2019) and the biggest raise by an American company ($18 billion raised by Visa in 2008). OpenAI and Anthropic are likely to follow with IPOs that would also break the previous records- making 3 companies each trying to raise more than the $45 billion raised by the entire US IPO market in 2025. Even if the process of going public doesn’t reveal any flaws in the companies, that money has to come from somewhere- and it takes up a substantial proportion of all net inflows to US stocks in a typical year (IPOs plus new money into existing stocks).
In short- where will the money come from? What are investors going to sell in order to buy into these IPOs? Technically they could do it all with cash, but I think it’s at least plausible that they start selling other stocks. The selling pressure will continue after the IPOs as employees of the newly-public companies see their stocks vest and other early investors become able to sell off.
I’m not trying to time the market. Even if this is a ’90s re-run, we could easily still be in the 1998 buildup, not the 2000 peak and crash. But I am diversifying. US stocks are currently the world’s most expensive. Investors value US stocks that highly because there’s a real chance that US companies are profitably building the technologies that will drive the future. But there’s also a real chance they aren’t– and if that state of the world comes to pass, I’d prefer to own a significant chunk of bonds, foreign stocks, and real assets.
The basic idea is that we want to compare the performance of different portfolios or their managers. This is relatively easy as long as the portfolios contain the same assets. Then, the portfolios are simply characterized by the different weights among the different assets. But how do we compare the performance of portfolios whose assets are different? In finance, we usually assume that everyone can invest in everything. But there are plenty of cases in which that’s a bad assumption: when clients want exposure to particular industries, when there are statutory limitations on holding certain assets, or when an individual company is considering specific projects within the same company under conditions of scarce financing.
The most primitive step is to compare the return and standard deviation of two different portfolios. However, higher risk investments tend to have higher returns in dynamic equilibrium. So, if we were to compare the returns of a tech company to a utility company, then we’d often see the tech companies performing better. But, if we compare the volatilities, then the utility companies would tend to perform better. Sharpe stepped in with a ratio to express the excess return (benefit) per standard deviation (the cost). This way, we can compare the price of volatilities between two portfolios. We’ll stick with just these basic 3 measures: return, standard deviation, and Sharpe ratio. (Others do exist)
Let’s put some meat on this with an example. Say that we have two portfolios, each composed of different assets. There’s a utility portfolio that’s composed of NEE, DUK, and SO. There’s also a tech portfolio that’s composed of AMD, MSFT, and NVDA. Both portfolios have weights of (0.33, 0.33, 0.34). The results of the utility versus the tech portfolio are:
Returns: 14.2% vs 136.3%
Standard Deviation: 14.9% vs 32%
Sharpe: 0.684 vs 4.134
Goodness me! The tech portfolio returns much more in absolute terms and much more per unit of risk. It’s twice as volatile as the utility portfolio, but the returns are almost ten times as high. If you could, then many of us would choose the tech portfolio over the utility portfolio. But, what if, for one reason or another, you can only invest in one of the two industries? Or, what if you want to invest your money with a skilled manager, rather than a risky one?
One way to tackle this problem is to introduce the Markowitz cloud. Specifically, we can essentially list out all of the possible portfolios along with their return and standard deviations. Then, we can compare the actual performance to the entire menu of possible performances within each set of assets. Below are the possible performances for the utility (left) versus the tech (right) portfolio. The actual portfolios are marked with an X.
One way to evaluate the two portfolios is to compare their return, standard deviation, and Sharpe ratio to the other candidates that were achievable with the same assets. As we can see, conditional on the assets, neither portfolio minimized the volatility, maximized return, nor maximized the Sharpe ratio. Furthermore, assuming that the realized rate of return was the goal, neither portfolio minimized the conditional volatility. Assuming that the realized volatility was the goal, neither portfolio maximized the conditional return. Below are two tables that describe some candidate alternatives and how they differ from the realized portfolio.
The share prices of many semiconductor chip companies have gone nearly vertical in the past month. Here are five-year charts for Micron (MU) and AMD, as of the close Monday:
Micron (MU) 5-Year Stock Chart
Advanced Micro Devices (AMD) 5-Year Stock Chart
Many analysts have been taken by surprise by the magnitude of the recent surge and prices. There has been no sudden, truly new news to drive this shift. It has been known for over a year that there is a huge shortage of memory chips, allowing Micron to charge high prices for its products. But apparently the official quarterly announcement of earnings and projections substantiated that narrative. The bears have been claiming that memory chips are a cyclic business, where chip shortages are followed by building more manufacturing capacity, which inevitably leads to overcapacity and a crash in memory chip prices. It has happened repeatedly, and therefore the current Micron stock party would end in tears after a couple of years. But the bears have been beaten back to their caves for now. Micron was up another full 7% yesterday.
AMD, which specializes in central processing units (CPUs), also released good earnings and strong projections. But the real share price driver there seems to be the new narrative that the shift from the shift to agentic AI will require a higher ratio of CPUs to GPUs. GPUs (graphic processing units) are the engines that do the core large language model (LLM) AI calculations. But apparently an increasing number of CPUs will be required to coordinate the activities of the GPUs:
AI agents—or the Agentic Era, as called by analysts—need more CPUs per GPU because they are responsible for the orchestration of AI workloads and the required data processing in order for the agent to accomplish its task, or, more simply, CPUs organize the steps of the workflow for the agent. Traditional LLM models—not agents—required a CPU:GPU ratio of 1:4 to 1:8, but analysts anticipate this ratio to shift toward 1:2 or even 1:1 in the coming years.
All that to say demand for AMD‘s chips is projected to increase.
So far, so good. But apparently being swept up in the whirlwind of exhilaration is the share price for lowly Intel (INTC). Intel was the leading manufacturer of processor chips back in the day, but it missed the boat on GPUs and just cannot seem to execute at global standards. In recent years, Intel has mainly been famous for ever-slipping deadlines on producing high performing chips. Its earnings have been approximately zero for some time. The good news is it now has a foundry business. The bad news is that the foundry business loses around $2 billion a year. The foundry has pulled in a few large customers, and after their experience there, they all run screaming for the exits. But wait, there’s been an announcement that Apple may contract with Intel to produce some low-end chips. Whoopee!
Intel (INTC) Five-year stock chart
Folks who look at technical behavior of stocks rather than the fundamentals of the business seem somewhat skeptical about the current surge. Terms like overbought are thrown around. I read an article claiming that hedging activities in the options market is creating an artificial, temporary demand for these high-flying stocks:
It is also fairly clear what has been driving these overbought conditions at the index level: aggressive call buying is creating a gamma squeeze across several stocks, such as Micron (MU). This occurs when aggressive call buying forces dealer hedging flows, resulting in purchases of the underlying stock. The more the stock rises, the more call buying tends to increase, and the cycle builds on itself.
My take on this spectacle
I can get the fundamental bull case in general for Micron stock. I bought into it about six months ago. Even that far back, it was clear that the demand for memory chips far outstripped the supply, so Micron could not help minting money for the next year or two. It was one of my fairly rare successes in stock picking. Sadly, I only bought a little bit, because I was influenced by many negative articles claiming that memory chips are a cyclic business, so this boom would end like all the previous Micron booms, with a glut and a crash.
There seems to be a solid bull case for AMD as well. For pitiful Intel, however, I see its price chart as a sign of market FOMO.
Where these stock prices go from here, I have no idea. My observation over the years is that this level of enthusiasm is usually followed eventually by, “What was I thinking?”, and a return to earth. However, in the meantime, tech stock prices often run up longer and further than I would have thought possible.
Usual disclaimer: Nothing here should be taken as advice to buy or sell any security.
Next week the Fed will almost certainly hold interest rates steady. Stephen Miran will probably dissent saying the Fed should be cutting rates. Kevin Warsh, Trump’s nominee for Fed Chair, would also like to see cuts. But other prominent voices think that rising oil and gas prices mean we should be raising rates.
I still think that rate hikes make more sense than cuts- but not because of oil. The high oil and gas prices we’re seeing are obviously driven by supply shocks from the Iran war- not increasing demand. Raising rates to fight an oil shock would mean repeating a classic mistake.
But raising rates to fight core inflation that is at 3% makes perfect sense. Especially when inflation (overall or core) hasn’t been at or below the Fed’s supposed 2.0% target in over 5 years, and market forecasts predict it will stay well above 2.0% for the next 5 years.
Especially when real GDP is growing, and NGDP is still above trend, and the unemployment rate is 4.3%. Financial conditions are so loose that stock markets are hitting all time highs in the middle of a war.
Various Taylor Rules suggest that the Fed Funds rate should be between 4.25% and 6.25%, but the Fed currently has us at 3.75%.
I see so many good arguments to raise rates- there is no reason to bring up a bad one like oil prices. If we must latch on to a headline to find the argument to raise rates, let’s focus on a shoe company’s stock going up 600% because they announced they were pivoting AI.
Economist Writing Every Day 