Investing

Random Sample Vs Sobol Sequence

Zachary Bartsch1 Comment

Say that you have a bachelor’s in finance, but that you’re not exactly a ‘math’ person. You know that diversification is good for reducing the volatility of returns, but the calculations are a bit mysterious to you. You know what the efficient frontier is, but its calculation is elusive. All is not lost! There are alternatives to the math: Sampling methods. By testing a few possible portfolios, we can get an idea of which one is the best fit for you. Here I’ll provide an example using 1) a simple random sample, 2) stratified random sample, 3) Sobol sequence, & 4) Scrambled Sobol.

I’ll use a two-asset portfolio here for ease. I assume that the portfolio includes no short or leveraged positions and that the asset weights sum to one. This is known as ‘long only’. The challenge is that we want a certain return-volatility profile for our portfolio, but it’s a bit hard to figure out what our options are. Luckily, both return and standard deviation (SD) of a portfolio are determined by the weights on the individual assets.

With a long only portfolio, all possible weights are uniformly distributed between one asset having zero weight and the same asset having 100% of the weight. Since the weights sum to one, whatever weight remains after the first asset is attributable to the 2nd asset. See the figure below. Unlike many distributions, we know exactly what’s possible for the weights. The portfolio expected return is simply the linearly weighted average and the portfolio variance is quadratically weighted. Return and SD equations are below.

The portfolio return is mathematically easy, since it’s just a linear average: more weight on asset A implies a rate of return that is closer to that of asset A. But the same is not true for SD, due to the covariance component. We can graph how both portfolio returns and SD are related to the asset weights below.

We already know that the possible weights are distributed uniformly (and so are the returns), but the possible SD are not distributed uniformly. Without the math, we want to know what our possible portfolio SD are so that we can determine what mix of assets is right for us. This example is a bit trite, but the gains occur when generalizing to many-asset portfolios. We can choose from a few methods for describing the correspondence between weights and the SD.

Method 1: Random Sample

The easiest thing to do is take a random draw of a few weights and calculate their associated returns and SDs. However, randomness also implies that we may get a set of points which are clustered together or that don’t sample the various nooks and crannies very well.  An example of a random sample is below, along with a line that connects the sampled options (I assume that we know the extrema weights: 0% & 100% values). Random sampling does an ‘ok’ job of choosing relevant portfolio allocations. But due to the random nature, substantial pieces of the actual SD menu options may be omitted. Sometimes the omitted portions are no big deal and other times they are – and which occurs entirely at random.  In the image below, the random portfolios left out the possible allocations that yielded the lowest volatility.

Method 2: Stratified Random Sample

The fact that the possible weights are distributed uniformly in weight-space is a big advantage. One way to constrain our sample of weights is to use a stratified random sample. In this case, we split the uniform weights into several equal intervals and then take a random sample from within each interval. This ensures that each interval’s characteristics are reflected, even partially, in the overall sample. The various shaded regions below reflect the regions from which a sample was drawn. While the regions are uniformly distributed, the random sample(s) within each region can occur anywhere within the region.

The green line implied by the sample is closer to the minimum SD, and the portions that have less weight on asset A are now less accurate (further from the dotted line). So, we gained some precision in some parts of the SD correspondence, but lost some in others. Loss is especially likely to occur near more jagged or irregular possible values. Since we don’t necessarily want the minimum SD allocation, using a stratified random sample is not always better than a simple random sample. But at least the maximum gaps between the observations are constrained. A simple random sample can detect local irregular shape sometimes, whereas a stratified random sample might always lack adequate resolution in a narrow interval.

Method 3: Sobol Sequence

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“You Can See Everything” and the Confidence Trap

mdmakowsky2 Comments

“You Can See Everything” is one of the great film-making experiences of my lifetime. That’s not even a particilularly hot take these days. If you need a recommendation, yes, four stars, you should see it. The singular dominant theme, for my money, was the grand telephone game that “truth” is distilled through as it skips from source to source, medium to medium. By the end of the film you may find that, like me, you have updated your priors a half dozen times, left with your hands up in the air as the crushing weight of subjective reality threatens to abandon you in a fog of nihilism. Then you get home, rewatch the documentary (“The Inventor: Out for Blood”), scroll through a few bullet points of the actual case and remind yourself that no, this person is very much a crook.

But that’s not what I want to talk about. What I want to talk about is confidence.

We are told, repeatedly and with hardly any caveat, that confidence is a good thing. An admirable thing. A sexy thing. But confidence can very much be a trap, particularly if confidence becomes your comparative advantage.

The parable of the young genius has become rooted in the tale of frustrated and constrained greatness. They are precocious and unruly, possibly with a history of mild behavioral issues or sullen solitude. They follow the “normal” track for a few years, often to a prestigious college, where they catch a glimpse of great possibility and simply can’t be held back any longer. They drop out and become an entrepreneur. They pursue investment and promise unblinking greatness and unfathomable innovation with the kind of a committed sureness that only youth can provide.

They are not tradditional frauds, at least not yet, because they believe it everything they say. And why wouldn’t they? They’ve never been wrong. The first time they stepped into an environment where they might be challenged they left before any incontrovertible truths might be controverted. Let’s stop the story here because it rarely actually starts in such a way and, if it does, it usually ends quickly. Which is for the best. It’s not crazy to suggest that the best thing that can happen at this stage is failure. Whether it’s an expert in your field explaining the fallacy underlying your grand promises or venture capitalists simply sniffing out a bad investment, the train stops and you can return to building yourself. You had a good life experience, a new perspective on your own education and skills, and the a greater appreciation for what it means to have something to contribute. No harm, no foul.

But that’s not what happens to a lot of people. Not just undergraduate drop outs either. It’s what can happy to anyone that a market anoints as special. That hype can fuel you until you’ve been running on it for so long you realize, perhaps too late, that you’re no longer selling a product or even yourself. Your selling the story of you. You change your title on LinkedIn to “thought leader”, “visionary”, or “futurist”. You think you are selling yourself, but you’re not because there’s nothing actually there to sell.

And this where the trap is. What you are selling is your confidence in the words coming out of your mouth. The problem is that the words themselves don’t have any value. You don’t know how things work. You don’t know, actually know, biochemistry. Or stochastic modeling. Or tokenization and neural network modeling. You know of them and how to reference them in a manner that obscures your own lack of understanding. The only thing you know, to your creeping dread, is your continued existence in this market is dependent on continuing to dazzle others with the kind of confidence they can only dream of posessing. You’re a carnival barker without a carnival. And eventually someone’s going to walk into an empty tent and they’re going to be pissed.

And that is terrifying.

When you watch “You Can See Everything”, pay attention to how hard they are working to maintain the image that they know what they are talking about. The 3X speed videos and obsession with rapid vocabularly adoption. They know of chemical reagents. And mechanism design. And blood. And lasers. And whatever it is they desperately need to convince people they know about. And notice to whom they scrupulously avoid ever interacting with: anyone with actual scientific or technical expertise. They are always cramming for a exam they didn’t study for. I’d call them dilettantes, but dilettante implies non-trivial, if thin, knowledge about a lot of things. Genuine knowledge. They aren’t dilletantes, they’re frauds.

Imagine Elizabeth Holmes was never sent to prison. What would she be doing right now? It’s pretty obvious to me that she’d be doing exactly the same thing. Selling grand visions wrapped unwavering confidence because that is the only thing she knows how to do. The film trope of the villain confessing to the hero (and the audience) “I’ve come too far” comes to mind.

I’m not saying being confident is going to turn you into a fraud. What I’m saying is that you need to have the skills, knowledge, and other forms of human capital first so that you can offer those skills to the world confidently. Confidence is a useful social lubricant in a market full of imperfect information. It’s useful as a complement, often necessary but never sufficient. To sell naked, untethered confidence itself is to be a confidence person.

You know. A con man.

Some Ways for Investors to Hedge Against Higher Interest Rates

Scott BuchananLeave a comment

Coming into 2026, all the chatter was about cutting short term rates (which the Fed does directly by fiat). Long term rates, which are generally set by the broader financial markets, were more or less steady, despite the angst over ongoing gigantic federal deficits; the world remained ready to absorb T-bonds, since they are regarded as the most liquid and secure yield-bearing instruments for global finance.

The Iran war has changed all that. The administration, like other administrations in other conflicts over the past half-century, apparently underestimated the adversary’s resilience in the face of bombing. Without further comment on the geopolitics, it suffices for our purpose as investors to assume that there is a good chance that the conflict will continue for some time, and thus oil prices will remain elevated. This works through the system as persistent inflation, even if the immediate effects of consumer gasoline prices are stripped out of the inflation measure. Bond buyers naturally must take into account expected inflation in pricing what rates they are willing to pay for. Also, the unprecedented boom in data center construction is competing for investment dollars. And so, the ten-year T-bond yield has surged from 4% in late February to over 5% now.

As everyone knows, the price of existing long-term fixed debt (e.g., T-bond, corporate bond, etc.) goes down as rate go up, since the existing bond now has to compete in the market with new, higher yielding bonds. Thus, bondholders are crying in their soup, as the price of IEF (ETF that holds 7–10-year T-bonds) has dropped 7% year to date, and the longer-termed (20+ year) TLT is down 10%. Those are big hits to what are thought to be safe, secure holdings.

What can investors do to protect themselves against further rate increases? One approach is simply to avoid holding long-term bonds or bond equivalents (fixed-rate debt). You can hold very short term (e.g. 3-month) T-bills, as in the TBIL fund.  Or you can hold instruments with floating rather than fixed rate. For instance, FLOT holds floating-rate government debt, PAAA is a complex but AAA-rated ETF, and many preferred stocks (e.g., NLY-F) also pay some fixed increment above the current market short-term rate. The values of these instruments are relatively insensitive to overall interest rates.

For a more direct hedge, that goes up when long-term bonds values go down (i.e., when rates go up), there are several funds that use derivatives that essentially short T-bond values. TBT is a straightforward, plain vanilla -2X short of the 20+ year T-bond index. With TLT down 10%, TBT is (unsurprisingly) up a full 21% YTD (total return).  PFIX is an actively-managed fund that does something similar, but with wilder swings. It is up a sizzling 30% in 2026, but it was down by 12% in late June. Most advisors seem to recommend TBT or PFIX for tactical trading only, to hold only when you have conviction that rates are going up soon. If the Fed come out swinging tomorrow with a QE bazooka to drive down rates, these stocks will likely crater. That said, if you hold long-term bonds (or fixed-yield preferred stocks, which are like super-long term bonds) in your portfolio, folding in a little PFIX will smooth out the overall portfolio ups and downs. If rates drop and PFIX crashes, your broader bond portfolio should be up.

RISR takes a middle ground, it holds interest-only strips of mortgage-backed securities, so it brings in a decent current yield (5.8% now), and its value rises somewhat as rates go up. Its total return YTD is 6.2% – – maybe not something to brag about at a cocktail party, but it beats CDs.

Here is a 3-year chart of some of these stocks (total return):

Another strategy is to buy individual bonds and hold them to maturity, so you know exactly what the final payout will be.

Saving the best for last: it turns out that if you are a homeowner with a fixed-rate mortgage, you are effective “short” long-term debt, so you “own” a primo hedge against rate increases. Congratulations!

Disclaimer: Nothing here should be considered advice to buy or sell any security.

“Whinging” or Merely Whining?

Scott BuchananLeave a comment

I have heard the term “whinging” at least twice so far in 2026, both times from fairly sophisticated speakers. I don’t recall ever hearing it before this year. Is this a new trend?

“Whinging” is a British/Commonwealth term that means much the same as “whining,” although (surprisingly) they derive from completely different Old English roots. However, it feels a bit sharper and more dismissive than mere complaining. Thus, whinging is a more intellectual-sounding put-down than whining. And goodness knows how much we need more effective put-downs around here.

ChatGPT informs me that in the realm of finance, the word fills a useful niche. Calling someone a whinger often implies not just that they’re wrong, but that they’re engaging in repetitive, unproductive complaining:

In investing discussions, you’ll often see phrases like: “perma-bear whinging”, “shareholder whinging”, “whinging about management”, “stop whinging and buy the index”

where an American 30 years ago would more likely have written: whining, griping, bellyaching, complaining.

In economics, practitioners often want to distinguish between a legitimate complaint or critique, versus repetitive, emotionally-driven complaining. Thus:

Calling an argument “whinging” implies that the speaker is expressing dissatisfaction without offering analysis or solutions. That rhetorical function is useful in debates over inflation, trade, housing, inequality, central banking, or academic economics itself.

I suppose the first one who unleashes the term “whinging” wins; it is tough to dig out from being buried by a term like that. Knowing all this hopefully leaves you better armed for your next testy exchange over trends or policies.

The Shifting Fortunes of Market Neutral Funds QMNNX and BDMCX

Scott Buchanan1 Comment

A market neutral equity fund holds a large number of stocks in both long and short positions, so the net asset value is near zero. In theory, this means that the value of the fund should be fairly insensitive to overall market fluctuations. The fund’s performance depends entirely on the skill of the fund manager in buying (going “long”) stocks with a better chance of going up, while selling short a suite of stocks that are likely to perform more poorly.

Fund managers use criteria (“factors“) such as value, momentum, quality, and mean-reversion to select which stocks to go long versus short. Investors see market neutral funds as a means of producing maybe 4 to 8% return (alpha), with relatively low volatility and low exposure to overall market movements (i.e., low beta).  Institutional investors love funds like these for adding diversification to their portfolios, so they hold many tens of billions of dollars of market neutral funds available only to them.

We lowly retail investors have access to some such funds. I will just focus here on two of the larger and more successful retail market neutral funds, offered by AQR Capital Management, and by BlackRock, respectively. These appear in a mutual fund wrapper, instead of an ETF. As with many mutual funds, there are different investor classes for a given fund family. If you invest a huge amount of money, you get the lowest annual fees (e.g., for QMNIX and BDMIX “Institutional” classes), but all the classes for given fund are invested in the same underlying strategy and assets.

To try to put these on the same basis for comparison for us ordinary folks, I will look at versions of each of these funds that do not require over $1 million additional investment, and which do not have an obnoxious (5.25%) upfront sales load. QMNNX is offered by AQR. It holds some international along with US stocks, and is constantly tweaking its long and short portfolio based on a proprietary set of factors, which are based on an enormous amount of data and calculations. The strategy behind BlackRock’s BDMCX is somewhat different. It probably uses similar types of factors (value, momentum, mean-reversion, etc.), but it tries to specifically match long and short companies within industry categories. Thus, in theory, it should be relatively insulated from a crash of say software stocks, since it is long and short in equal amount in that category.


OK, let’s look at actual performance. Here is a 10-year chart, with QMNNX in orange, BDMCX purple, compared to the S&P 500 in blue and BND (total bond market) in light green.

This chart might make you run screaming out the door – both funds did so poorly in the 2016-2021 timeframe that their ten-year total returns are far less than the broad market. But this is not really a fair comparison. These vehicles are not intended to compete with a 100%-long portfolio. A better comparison is to bonds, and it can be seen that both market neutral funds beat BND (green line) handily over this time period. It is worth noting that the AQR fund QMNNX got decimated in the 2018-2022 – – that was a time when the market rewarded ONLY growth, even for stocks which scored badly on traditional “value”. Thus, QMNNX was long the value stocks which were shunned by the market, and short the tech stocks that roared upward. So, while the overall market was ripping upward, QMNNX went down and down and down, losing some 30% of its value while its investors lost faith and sold out.  The within-industry matching for BlackRock’s BDMCX protected it from such gross losses, though it showed only modest gains in that 2016-2021 time period.

The picture changes entirely when we look at a five-year timeframe (below). QMNNX’s value tilt was finally vindicated in 2022, as the fund soared while tech stocks crashed. Three cheers for diversification! The fund kept up an absolute positive edge over the mighty S&P 500 for each of the years 2023, 2024, and 2025. It seemed like AQR had cracked the code for superior equity returns. Its five-year return is more than double that of the S&P, which is stunning. Meanwhile, BDMCX kept up a steady performance, roughly matching the S&P, but with much lower volatility. That is pretty good. Meanwhile, the rises in interest rates trashed the returns of bonds (green line).

The picture shifts again when we look at one-year total returns (below). BDMCX continues to roughly match the S&P. This is a significant achievement when stocks are in a big bull run, showing that the BlackRock fund managers continue to make very good calls on prospective weak vs strong stocks. QMNNX had a poor first half 2026, with an absolute decline. For some reason, its factors did not correctly forecast relative stock performances. It has started to recover some mojo in the past few months, enough to beat out the long-suffering bond market.

My personal takeaways are:

  • We cannot expect this type of fund to routinely beat or even match overall stocks. However, as diversifiers, they might be compared to say bonds or REITs, and they hold up well in that comparison. The overall U.S. market (dominated by big tech) has been going up so much, for so long, that it may seem like any diversification away from stocks is a waste. Time will tell.
  • The stellar (market-matching or even market-beating) performances seen for QMNNX and BDMCX over the past five years are probably largely flukes, and should not be relied on going forward.
  • Due to its rigorous within-sector net neutral construction, BDMCX is unlikely to soar, but it is also unlikely to crash. I think of it as a “Steady Eddie”.  The looser construction of QMNNX gives it the freedom for great outperformance (especially in a tech crash), but makes it more vulnerable to extended losses if the market moves against its view of reality.

Disclaimer: Nothing here should be taken as advice to buy or sell any security.

What If AI Earnings Fall Far Short of Expectations?

Scott BuchananLeave a comment

Trillions of dollars are being plowed into building U.S. data centers to handle future AI compute demand. These are being paid for by rich people and organizations, anticipating juicy returns on their investments. Those juicy returns depend on consumers (individuals or businesses) being willing to pay enormous amounts for access to AI.  Commentators are so enamored with the glorious prospects of how AI will end poverty and maybe even death, that it is hard to find a clear statement of who exactly will pay how much for all this. Lots of folks are happy to pay $20/month for AI. $200/month? Not so much.

I predict that those earnings will fall far short of expectations. We observe that AI consumption is bifurcating into two main markets, a commodity tier and a premium tier (with, of course, sub-tiers within each of those broad categories).  Chinese models are readily available over the internet, and they have proven capable of performing well enough to handle most AI tasks. The Chinese models are priced far lower (on the order of 10X lower) than the big U.S. “frontier” models (ChatGPT from Open AI, and Claude suite from Anthropic). By far the most U.S. compute usage depends on these two labs. Western users are starting to use the Chinese models more and more. This keeps prices so low that the frontier labs are losing money on their AI sales. Sophisticated users automatically route their AI workload to the cheapest feasible provider.

Their will always be a subset of AI usage in the West that requires the highest level of performance, or freedom from Chinese government spying or manipulation, that will be directed to a premium tier. But if that premium tier ends up being only, say, 20% of AI usage in 2028, there is a real question as to whether the financial bases of the data centers being built now can be sustained. There are huge bear and bull arguments on both sides here, which are tough to balance. I got only equivocal “it depends” answers from AI on this.

If the data centers don’t make expected profits, what then? It all depends on how they were financed. Most of the build-out to date has been the big 4 hyperscalers, Google, Amazon, Microsoft, and Meta spending their free cash flow from their other business lines. If it turns out they simply flushed that money down the toilet, no big deal. Just a trillion-dollar whoopsie. The CEOs will still get their bonuses, don’t worry.

But now as more debt financing enters in, the stakes get higher. Analysis seems to show that the debt loads that the big 4 hyperscalers have incurred is manageable – -their base cash flows are so huge that they can manage their own debt. But in the past year we have seen the emergence of monstrous “Special Purpose Vehicles” (SPVs) with a mixture of equity, debt, and guarantees, to finance practically all the upcoming trillion dollars of data centers. This pushes the financing of the balance sheets of the hyperscalers.

If those newer data centers flop, their equity investors will take a hit, leaving their creditors in the hole and in control. One really needs to analyze exactly who those equity and debt holders are for the SPVs.  If the creditors decide to recoup some of their investment by selling the data center for say 60 cents on the dollar, the most likely buyers would be…Google and Amazon. There is a school of thought that this (let the SPVs fail, scoop up their assets at discount) has been their plan all along. World domination in AI compute!  Just like they have achieved world domination in online search and video and shopping. Maybe.

The resulting slowdown in data center investing would likely throw the U.S. economy into slowdown or recession, considering that it’s estimated that fully half of our recent GDP growth has been from circularly-financed AI buildout. Chipmakers’ (Nvidia, Micron, AMD, etc.) profits depend on continued acceleration in AI build-out. If that build-out stalls, or even slows down, chipmaker profits will crater. Whether this risk is already priced into their share prices is debated.

Boilerplate disclaimer: Nothing here should be considered advice to buy or sell any security.

SPMO: One Momentum Stock Fund to Rule Them All

Scott BuchananLeave a comment

Academic studies have found that there is a momentum effect with stock prices: a stock which has done well over the past 6-12 months is likely to continue to do better than average over the next six months or so.  A number of funds (ETFs) have been devised which try to take advantage of this factor. This is a relatively effortless exercise: running calcs on stock price movements is way easier than doing a deep fundamental dive into a company’s future earnings potential.

Here we will compare several momentum ETFs against the S&P 500 index. In order to make it an apples-to-apples comparison, I am looking mainly at major momentum funds that primarily draw from the S&P 500 large cap universe of stocks, excluding small-cap or tech only funds. These large cap momentum funds are MTUM, JMOM, and SPMO, plus the newer FMTM. These funds all select stocks according to various rules. Besides trying to identify stocks with raw price momentum, these rules typically aim to minimize risk or volatility.  

I excluded the momentum fund GMOM, since it draws from a different universe of holdings. That fund does not hold individual stocks. Rather, it draws on some 50 different ETFs, including funds that focus on fixed income, commodities, or international or small cap as well as large cap US stocks, seeking to hold funds that show good relative momentum. (In a previous look at momentum funds, we found that GMOM did well in 2022, suffering less of a drawdown than the other funds, but it has lagged ever since; diversifying away from U.S. large caps was a big drag).

A plot of total returns over the past five years (which includes the 2022 correction) is shown below. The returns at the halfway mark (2.5-year mark, 4/1/2024) are shown on the chart.  FMTM only started 18 months ago, so it has no five-year returns. SPMO is orange, and the reference S&P500 line is blue. JMOM (purple) tracks fairly closely to S&P500 most of the time.  MTUM (green line) fell well behind during 2023, though it caught up by August, 2026. SPMO was close to S&P500 for the first two years, then steadily roared ahead over the next three years.

Plain SPY (blue line) held its own against most of these momentum funds in this timeframe. This is partly explained by the fact that SPY itself is a sort of momentum fund: the more a given stock’s price goes up, the bigger its representation in this capital-weighted fund. Also, over the past ten years or so, simply the biggest companies (the big tech quasi-monopolies like Google, Microsoft, etc.) have been generating more and more earnings, leaving the traditional auto and oil and consumer product companies, etc., in the dust – – and the S&P index incorporates this effect.

Key aspects of the funds’ strategies are listed in the table (thanks, Claude) below. JMOM includes the whole Russell 1000 universe of stocks, while SPMO is limited to the top 500 stocks. We note that JMOM is sector neutral, so it cannot be hugely overweight on sector, such as tech. On the other hand, SPMO has no such constraint, and so its major holdings for some time have consisted almost entirely of the giant AI darlings Nvidia, Micron, Broadcom, AMD, Google, etc. That has been the right bet over the past several years (although you pay for it in high volatility). SPMO also has perhaps the longest effective look-back period for calculating momentum. It uses a 12-month period, but excludes the most recent month (to avoid paying too much for a quick, temporary stock price run-up). SPMO is clearly doing something right, since its five-year total return (144 %) is nearly double that of the S&P 500; this is a stunning achievement.

The new entry FMTM has its own quirks: it holds mid-caps as well as large-caps, and it has a significantly shorter time frame for calculating momentum (six months, vs the usual 12 months), AND it reconstitutes every month (instead of six months). So, it reacts very quickly to a rising stock. This sounds great, but it might end up acting on false, short-lived surges in prices. Time will tell.

Turning now to the 1-year results, where FMTM gets a chance to strut its stuff, we find that the new upstart has soundly beaten them all, even SPMO:

 But this FMTM outperformance was very inconsistent. The one-year performance of FMTM was driven by a huge surge in late 2025/early 2026. But a six-month plot shows FMTM dead last among all these funds, while SPMO comes out on top (again). FMTM looks interesting as a smaller “satellite” holding, that may outperform in some regimes, but SPMO seems to be the more solid performer.

Boilerplate disclaimer: Nothing here should be taken as advice to buy or sell any security.

Bubble Worries Mean Diversify…. But How?

James Bailey1 Comment

Mike is concerned that US stocks could be a bubble, as are many others:

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: 

Source: The Idea Farm, Global Valuations Update July 2026

But how do you practically do this?

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.

Disclaimers: Not investment advice, I hold some of the indices named.

The Academic Data Project That Turned Into $375 Million

James BaileyLeave a comment

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.