Investing

Are Your Portfolio Weights Right?

Zachary BartschLeave a comment

What do portfolio managers even get paid for? The claim that they don’t beat the market is usually qualified by “once you deduct the cost of management fees”. So, managers are doing something and you pay them for it. One thing that a manager does is determine the value-weights of the assets in your portfolio. They’re deciding whether you should carry a bit more or less exposure to this or that. This post doesn’t help you predict the future. But it does help you to evaluate your portfolio’s past performance (whether due to your decisions or the portfolio manager).

Imagine that you had access to all of the same assets in your portfolio, but that you had changed your value-weights or exposures differently. Maybe you killed it in the market – but what was the alternative? That’s what this post measures. It identifies how your portfolio could have performed better and by how much.

I’ve posted several times recently about portfolio efficient frontiers (here, here, & here). It’s a bit complicated, but we’d like to compare our portfolio to a similar portfolio that we could have adopted instead. Specifically, we want to maximize our return given a constant variance, minimize our variance given a constant return or, if there are reallocation frictions, we’d like to identify the smallest change in our asset weights that would have improved our portfolio’s risk-to-variance mix.

I’ll use a python function from github to help. Below is the command and the result of analyzing a 3-asset portfolio and comparing it to what ‘could have been’.

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Michael Burry’s New Venture Is Substack “Cassandra Unchained”: Set Free to Prophesy All-Out Doom on AI Investing

Scott BuchananLeave a comment

This is a quick follow-up to last week’s post on “Big Short” Michael Burry closing down his Scion Asset Management hedge fund. Burry had teased on X that he would announce his next big thing on Nov 25. It seems he is now a day or two early: Sunday night he launched a paid-subscription “Cassandra Unchained” Substack. There he claims that:

Cassandra Unchained is now Dr. Michael Burry’s sole focus as he gives you a front row seat to his analytical efforts and projections for stocks, markets, and bubbles, often with an eye to history and its remarkably timeless patterns.

Reportedly the subscription cost is $39 a month, or $379 annually, and there are 26,000 subscribers already. Click the abacus and…that comes to a cool $ 9.9 million a year in subscription fees. Not bad compensation for sharing your musings on line.

Michael Burry was dubbed “Cassandra” by Warren Buffett in recognition of his prescient warnings about the 2008 housing market collapse, a prophecy that was initially ignored, much like the mythological Cassandra who was fated to deliver true prophecies that were never believed. Burry embraced this nickname, adopting “Cassandra” as his online moniker on social media platforms, symbolizing his role as a lone voice warning of impending financial disaster. On the About page of his new Substack, he wrote that managing clients’ money in a hedge fund like Scion came with restrictions that “muzzled” him, such that he could only share “cryptic fragments” publicly, whereas now he is “unchained.”

Of his first two posts on the new Substack, one was a retrospective on his days as a practicing doctor (resident in neurology at Stanford Hospital) in 1999-2000. He had done a lot of on-line posting on investing topics, focusing on valuations, and finally left medicine to start a hedge fund. As he tells it, he called the dot.com bubble before it popped.

The Business Insider summarizes Burry’s second post, which attacks the central premise of those who claim the current AI boom is fundamentally different from the 1990s dot.com boom:

The second post aims straight at the heart of the AI boom, which he calls a “glorious folly” that will require investigation over several posts to break down.

Burry goes on to address a common argument about the difference between the dot-com bubble and AI boom — that the tech companies leading the charge 25 years ago were largely unprofitable, while the current crop are money-printing machines.

At the turn of this century, Burry writes, the Nasdaq was driven by “highly profitable large caps, among which were the so-called ‘Four Horsemen’ of the era — Microsoft, Intel, Dell, and Cisco.”

He writes that a key issue with the dot-com bubble was “catastrophically overbuilt supply and nowhere near enough demand,” before adding that it’s “just not so different this time, try as so many might do to make it so.”

Burry calls out the “five public horsemen of today’s AI boom — Microsoft, Google, Meta, Amazon and Oracle” along with “several adolescent startups” including Sam Altman’s OpenAI.

Those companies have pledged to invest well over $1 trillion into microchips, data centers, and other infrastructure over the next few years to power an AI revolution. They’ve forecasted enormous growth, exciting investors and igniting their stock prices.

Shares of Nvidia, a key supplier of AI microchips, have surged 12-fold since the start of 2023, making it the world’s most valuable public company with a $4.4 trillion market capitalization.

“And once again there is a Cisco at the center of it all, with the picks and shovels for all and the expansive vision to go with it,” Burry writes, after noting the internet-networking giant’s stock plunged by over 75% during the dot-com crash. “Its name is Nvidia.”

Tell us how you really feel, Michael. Cassandra, indeed.

My amateur opinion here: I think there is a modest but significant chance that the hyperscalers will not all be able to make enough fresh money to cover their ginormous investments in AI capabilities 2024-2028. What happens then? For Google and Meta and Amazon, they may need to write down hundreds of millions of dollars on their balance sheets, which would show as ginormous hits to GAAP earnings for a number of quarters. But then life would go on just fine for these cash machines, and the market may soon forgive and forget this massive misallocation of old cash, as long as operating cash keeps rolling in as usual. Stocks are, after all, priced on forward earnings. If the AI boom busts, all tech stock prices would sag, but I think the biggest operating impact would be on suppliers of chips (like Nvidia) and of data centers (like Oracle). So, Burry’s comparison of 2025 Nvidia to 1999 Cisco seems apt.

What Tariffs Mean For Your Finances

James BaileyLeave a comment

That’s the title of a talk I’ll be giving Saturday at the Financial Capability Conference at Rhode Island College. Registration for the conference, which also features personal finance speakers and top Rhode Island politicians, is free here.

A preview: after many changes, the average tariff on the goods Americans import has settled in the 15-20% range:

If the tariffs stay in place, which is far from certain, this will represent roughly a 2% increase in overall costs for Americans (a ~17% tax on imports which are ~14% of the economy predicts a 2.4% increase, but a bit of that will be paid by foreign producers lowering prices).

This is bad for US consumers, but not as bad as the Covid-era inflation, and likely not as bad as our upcoming problems with debt and plans to weaken the dollar. It is more valuable for most people to make sure they are getting the personal finance basics right than to think about how to avoid tariffs, though they may want to consider investments that hold their value with a weakening dollar.

“Big Short” Michael Burry Closes Scion Hedge Fund: “Value” Approach Ceased to Add Value?

Scott Buchanan1 Comment

Michael Burry is famed for being among the first to both discern and heavily trade on the ridiculousness of subprime mortgages circa 2007.  He is a quirky guy: brilliant, but probably Asperger‘s. That comes through in his portrayal in the 2015 movie based on the book, The Big Short.

He called it right with mortgages in 2007, but was early on his call, and for many months lost money on the bold trading positions he had put on in his hedge fund, Scion Capital. Investors in his fund rebelled, though he eventually prevailed. Reportedly he made $100 million himself, and another 700 million for his investors, but in the wake of this turmoil, he shut down Scion Capital.

In 2013 he reopened his hedge fund under the name Scion Asset Management. He has generated headlines in the past several years, criticizing high valuations of big tech companies. Disclosure of his short positions on Nvidia and Palantir may have contributed to a short-term decline in those stocks. He has called out big tech companies in general for stretching out the schedule of depreciation of their AI data center investments, to make their earnings look bigger than they really are.

Burry is something of an investing legend, but people always like to take pot shots at such legends. Burry has been rather a permabear, and of course they are right on occasion. For instance, I ran across the following OP at Reddit:

Michael burry is a clown who got lucky once

I am getting sick and tired of seeing a new headline or YouTube video about Michael burry betting against the market or shorting this or that.

First of all the guy is been betting against the market all his career and happened to get lucky once. Even a broken clock is right twice in a day. He is one of these goons who reads and understands academia economics and tries to apply them to real world which is they don’t work %99 of the time. In fact guys like him with heavy focus on academia economic approach don’t make it to far in this industry and if burry didn’t get so lucky with his CDS trade he would be most likely ended up teaching some bs economic class in some mid level university.

Teaching econ at some mid-level university, ouch.  (But a reader fired back at this OP: OP eating hot pockets in his moms basement criticizing a dude who has made hundreds of millions of dollars and started from scratch.)

Anyway, Burry raised eyebrows at the end of October, when he announced that he was shutting down his Scion Asset Management hedge fund. This Oct 27 announcement was accompanied by verbiage to the effect that he has not read the markets correctly in recent years:

With a heavy heart, I will liquidate the funds and return capital—minus a small audit and tax holdback—by year’s end. My estimation of value in securities is not now, and has not been for some time, in sync with the markets.

Photo

To me, all this suggested that Burry’s traditional Graham-Dodd value-oriented approach had gotten run over by the raging tech bull market of the past eight years. I am sensitive to this, because I, too, have a gut bias towards value, which has not served me well in recent years. (A year ago I finally saw the light and publicly recanted value investing and embraced the bull, here on EWED).

Out of curiosity, therefore, I did some very shallow digging to try to find out how his Scion fund has performed in the last several years. I did not find the actual returns that investors would have seen. There are several sites that analyze the public filings of various hedge funds, and then calculate the returns on those stocks in those portfolio percentages. This is an imperfect process, since it will miss out on the actual buying and selling prices for the fund during the quarter, and may totally miss the effects of shorting and options and convertible warrants, etc., etc. But it suggests that Scion’s performance has not been amazing recently. Funds are nearly always shut down because of underperformance, not overperformance.

Pawing through sites like HedgeFollow (here and here) , Stockcircle, and Tipranks, my takeaway is that Burry probably beat the S&P 500 over the past three years, but roughly tied the NASDAQ (e.g. fund QQQ). This performance would naturally have his fund investors asking why they should be paying huge fees to someone who can’t beat QQQ.

What’s next for Burry? In a couple of tweets on X, Burry has teased that he will reveal some plans on November 25. The speculation is that he will refocus on some personal asset management fund, where he will not be bothered by whiny outside investors. We shall see.

Portfolio Efficient Frontier Parabolics

Zachary Bartsch5 Comments

Previously, I plotted the possible portfolio variances and returns that can result from different asset weights. I also plotted the efficient frontier, which is the set of possible portfolios that minimize the variance for each portfolio return.* In this post, I elaborate more on the efficient frontier (EF).

To begin, recall from the previous post the possible portfolio returns and variances.

From the above the definitions we can see that the portfolio return depends on the asset weights linearly and that the variance depends on the asset weights quadratically because the two w terms are multiplied. Since the portfolio return can be expressed as a function of the weights, this implies that the variance is also a quadratic function of returns. Therefore, every possible portfolio return-variance pair lies on a parabola. So, it follows that every pair along the efficient frontier also lies on a parabola. Not every pair lies on the same parabola, however – the efficient frontier can be composed on multiple parabolas!

I’ll use the same 3 possible assets from the previous post, below is the image denoting the possible pairs, the EF set, and the variance-minimizing point.

One way to find the EF is to calculate every possible portfolio variance-return pair and then note the greatest return at each variance. That’s a discrete iterative process and it definitely works. One drawback is that as the number of assets can increase the number of possible weight combinations to an intractable number that makes iterative calculations too time consuming. So, we can instead just calculate the frontier parabolas directly. Below is the equation for a frontier parabola and the corresponding graph.

Notice that the above efficient frontier doesn’t appear quite right. First, most obviously, the portion below the variance-minimizing return is inapplicable – I’ve left it to better illustrate the parabola. Near the variance-minimizing point, the frontier fits very nicely. But once the return increases beyond a certain level, the frontier departs from the set of possible portfolio pairs. What gives? The answer is that the parabola is unconstrained by the weights summing to zero. After all, a parabola exists at the entire domain, not just the ones that are feasible for a portfolio. The implication is that the blue curve that extends beyond the possible set includes negative weights for one or more of the assets. What to do?

As we deduced earlier, each pair corresponds to a parabola. So, we just need to find the other parabolas on the frontier. The parabola that we found above includes the covariance matrix of all three assets, even when their weights are negative. The remaining possible parabolas include the covariance matrices of each pair of assets, exhausting the non-singular asset portfolios. The result is a total of four parabolas, pictured below.

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Do Required Personal Finance Classes Work?

James Bailey1 Comment

41 states now require students to take a course in economics or personal finance in order to graduate high school:

Source: Council for Economic Education

12 states representing 21% of US high schoolers passed mandates for personal finance classes just since 2022. This sounds like a good idea that will enable students to navigate the modern economy. But does it work in practice?

A 2023 working paper “Does State-mandated Financial Education Affect Financial Well-being?” by Jeremy Burke, J. Michael Collins, and Carly Urban argues that it does, at least for men:

We find that the overall effects of high school financial education graduation requirements on subjective financial well-being are positive, between 0.75 and 0.80 points, or roughly 1.5 percent of mean levels. These overall effects are driven almost entirely by males, for whom financial education increases financial well-being by 1.86 points, or 3.8 percent of mean financial well-being.

The paper has nice figures on financial wellbeing beyond the mandate question:

As soon as I heard about the rapid growth in these mandates from Meb Faber and Tim Ranzetta, I knew there was a paper to be written here. I was glad to see at least one has already tackled this, but there are more papers to be written: use post-2018 data to evaluate the new wave of mandates, evaluate the economics mandates in addition to the personal finance ones, and use a more detailed objective measure like the Survey of Consumer Finances.

There’s also more to be done in practice, hiring and training the teachers to offer these new classes:

our estimates are likely attenuated due to poor compliance by schools subject to new financial education curriculum mandates. Urban (2020) finds evidence that less than half of affected schools may have complied. As a result, our estimated overall and differential effects may be less than half the true effects

META Stock Slides as Investors Question Payout for Huge AI Spend

Scott BuchananLeave a comment

How’s this for a “battleground” stock:

Meta stock has dropped about 13% when its latest quarterly earnings were released, then continued to slide until today’s market exuberance over a potential end to the government shutdown. What is the problem?

Meta has invested enormous sums in AI development already, and committed to invest even more in the future. It is currently plowing some 65% (!!) of its cash flow into AI, with no near-term prospects of making big profits there. CEO Mark Zuckerberg has a history of spending big on the Next Big Thing, which eventually fizzles. Meta’s earnings have historically been so high that he can throw away a few billion here and there and nobody cared. But now (up to $800 billion capex spend through 2028) we are talking real money.

Up till now Big Tech has been able to finance their investments entirely out of cash flow, but (like its peers), Meta started issuing debt to pay for some of the AI spend. Leverage is a two-edged sword – – if you can borrow a ton of money (up to $30 billion here) at say 5%, and invest it in something that returns 10%, that is glorious. Rah, capitalism! But if the payout is not there, you are hosed.

Another ugly issue lurking in the shadows is Meta’s dependence on scam ads for some 10% of its ad revenues. Reuters released a horrifying report last week detailing how Meta deliberately slow-walks or ignores legitimate complaints about false advertising and even more nefarious mis-uses of Facebook. Chilling specific anecdotes abound, but they seem to be part of a pattern of Meta choosing to not aggressively curtail known fraud, because doing so would cut into their revenue. They focus their enforcement efforts in regions where their hands are likely to be slapped hardest by regulators, while continuing to let advertisers defraud users wherever they can get away with it:

…Meta has internally acknowledged that regulatory fines for scam ads are certain, and anticipates penalties of up to $1 billion, according to one internal document.

But those fines would be much smaller than Meta’s revenue from scam ads, a separate document from November 2024 states. Every six months, Meta earns $3.5 billion from just the portion of scam ads that “present higher legal risk,” the document says, such as those falsely claiming to represent a consumer brand or public figure or demonstrating other signs of deceit. That figure almost certainly exceeds “the cost of any regulatory settlement involving scam ads.”

Rather than voluntarily agreeing to do more to vet advertisers, the same document states, the company’s leadership decided to act only in response to impending regulatory action.

Thus, the seamy underside of capitalism. And this:

…The company only bans advertisers if its automated systems predict the marketers are at least 95% certain to be committing fraud, the documents show. If the company is less certain – but still believes the advertiser is a likely scammer – Meta charges higher ad rates as a penalty, according to the documents. 

So…if Meta is 94% (but not 95%) sure that an ad is a fraud, they will still let it run, but just charge more for it.  Sweet. Guess that sort of thinking is why Zuck is worth $250 million, and I’m not.

But never fear, Meta’s P/E is the lowest of the Mag 7 group, so maybe it is a buy after all:

Source

As usual, nothing here should be considered advice to buy or sell any security.

Optimal Portfolio Weights

Zachary Bartsch6 Comments

All of us have assets. Together, they experience some average rate of return and the value of our assets changes over time. Maybe you have an idea of what assets you want to hold. But how much of your portfolio should be composed of each? As a matter of finance, we know that not only do the asset returns and volatilities differ, but that diversification can allow us to choose from a menu of risk & reward combinations. This post exemplifies the point.

1) Describe the Assets

I analyze 3 stocks from August 1, 2024 through August 1, 2025: SCHG (Schwab Growth ETF), XLU (Utility ETF), and BRK.B (Berkshire Hathaway). Over this period, each asset has an average return, a variance, and  co-variances of daily returns. The returns can be listed in their own matrix. The covariances are in a matrix with the variances on the diagonal.

The return of the portfolio that is composed of these three stocks is merely the weighted average of the returns. In particular, each return is weighted by the proportion of value that it initially composes in the portfolio. Since daily returns are somewhat correlated, the variance of the daily portfolio returns is not merely equal to the average weighted variances. Stock prices sometimes increase and decrease together, rather than independently.

Since the covariance matrix of returns and the covariance matrix are given, it’s just our job to determine the optimal weights. What does “optimal” mean? This is where financiers fall back onto the language of risk appetite. That’s hard to express in a vacuum. It’s easier, however, if we have a menu of options. Humans are pretty bad at identifying objective details about things. But we are really good at identifying differences between things. So, if we can create a menu of risk-reward combinations, then we’re better able to see how much a bit of reward costs us.

2) Create the Menu

In our simple example of three assets, we have three weights to determine. The weights must sum to one and we’ll limit ourselves to 1% increments. It turns out that this is a finite list. If our portfolio includes 0% SCHG, then the remaining two weights sum to 100%. There are 101 possible pairs that achieve that: (0%, 100%), (1%,99%), (2%,98%), etc. Then, we can increase the weight on SCHG to 1% for which there are 100 possible pairs of the remaining weights: (0%,99%), (1%, 98%), (2%, 97%), etc. We can iterate this process until the SCHG weight reaches 100%. The total number of weight combinations is 5,151. That means that there are 5,151 different possible portfolio returns and variances. The below figure plots each resulting variance-return pair in red.

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Is Tesla Stock Grossly Overpriced?

Scott Buchanan1 Comment

One of the more polarizing topics in investing is the valuation of Tesla stock. Its peers among the Magnificent 7 big tech leaders sport price/earnings ratios mainly in the 30s. Those are high numbers, but growth stocks deserve high P/Es. A way to normalize for expected growth of earnings is to look at the Price/Earnings/Growth (PEG) ratio. This number is usually 1.5-2.0 for a well-regarded company. Anything much over 2 is considered overvalued.

Tesla’s forward P/E of about 270 is nearly ten times higher than peers. Its anticipated growth rate does not seem to justify this astronomical valuation, since its PEG of around 4-10 (depending on assumptions) is way higher than normal. This seems to be a case of the CEO’s personal charisma dazzling shareholders. There is always a new “story” coming out to keep the momentum going.

Tesla’s main actual business is selling cars, electric cars. It has done a pretty good job at this over the past decade, supported by massive government subsidies. With the phasing out of these subsidies by the U.S. and some other governments, and increasing competition from other electric carmakers, it seems unlikely that this business will grow exponentially. Ditto for its smallish ($10 billion revenue) business line of supplying large batteries for electric power storage. But to Tesla fans, that doesn’t really matter. Tesla is valued, not as a car company, but as an AI startup venture. Just over the horizon are driverless robo-taxis (whose full deployment keeps getting pushed back), and humanoid Optimus robots. The total addressable market numbers being bandied about for the robots are in the trillions of dollars.

Source: Wikipedia

From Musk’s latest conference call:

Optimus is Tesla’s bipedal humanoid robot that’s in development but not yet commercially deployed. Musk has previously said the robots will be so sophisticated that they can serve as factory workers or babysitters….“Optimus will be an incredible surgeon,” Musk said on Wednesday. He said that with Optimus and self driving, “you can actually create a world where there is no poverty, where everyone has access to the finest medical care.”

Given the state of Artificial General Intelligence, I remain skeptical that such a robot will be deployed in large numbers within the next five years. It is of course a mind-bending exercise to imagine a world where $50,000 robots could do anything humans can do. Would that be a world where there is “no poverty”, or a world where there is no wealth (apart from the robot owners)? Would there be a populist groundswell to nationalize the robots in order to socialize the android bounty? But I digress.

On the Seeking Alpha website, one can find various bearish articles with the self-explanatory titles of, for instance, Tesla: The Dream Factory On Wall Street, Tesla: Rallying On Robotaxi Hopium, and Tesla: Paying Software Multiples For A Car Business – Strong Sell . There are also bullish pieces, e.g. here,  here, and here.

Musk’s personal interaction with shares has propped up their value. He purchased about $1 billion in TSLA shares in September. This is chicken feed relative to its market cap and his net worth, but it apparently wowed TSLA fans, and popped the share price. What seems even more inexplicable is the favorable response to a proposed $1 trillion (!!) pay package for Elon. For him to be awarded this amount, Tesla under his watch would have to achieve hefty boosts both in physical production and in stock market capitalization. But… said package would be highly dilutive (like 12%) to existing shareholders, so, rationally they should give it thumbs down. However, it seems likely that said shareholders are so convinced of Musk’s value that they will approve this pay package on Nov 6, since he has hinted he might leave if he doesn’t get it.

Such is the Musk mystique that shareholders seem to feel that giving him an even greater stake in Tesla than he already has  will cause hundreds of billions of dollars of earnings appear from thin air. From the chatter I read from Wall Street professionals, they view all this as ridiculous magical thinking, yet they do not dare place bets against the Musk fanbase: the short interest in TSLA stock is only a modest 2.2%. Tesla is grossly overvalued, but it will likely remain that way as long as Elon remains and keeps spinning grand visions of the future.

Looking Ahead: Post-Powell Interest Rates

Zachary BartschLeave a comment

Jerome Powell’s term as Fed Chair ends in late May 2026. President Trump has said that he will nominate a new chair and the US senate will confirm them. It may take multiple nominations, but that’s the process. The new chair doesn’t govern monetary and interest rate policy all by their lonesome, however. They have to get most of the FOMC on board in order to make interest rate decisions. We all know that the president wants lower interest rates and there is uncertainty about the political independence of the next chair. What will actually happen once Jerome is out and his replacement is in?

The treasury markets can give us a hint. The yields on government debt tend to follow the federal funds rate closely (see below). So, we can use some simple logic to forecast the currently expected rates during the new Fed Chair’s first several months.

https://fred.stlouisfed.org/graph/?g=1NwVQ

Here’s the logic. As of October 16, the yield on the 6-month treasury was 3.79% and the yield on the 1-year treasury was 3.54%. If the market expectations are accurate, then holding the 1-year treasury to maturity should yield the same as the 6-month treasury purchased today and then another one purchased six months from now. The below diagram and equation provide the intuition and math.

Since the federal funds rate and US treasury yields closely track one another, we can deduce that the interest rates are expected to fall after 6 months. Specifically, rates will fall by the difference in the 6-month rates, or about 49.9 basis points (0.499%).  This cut is an expected value of course. Given that the cut is between a half and a zero percent, we can back out the market expectation of for a 0.5% vs 0.0% cut where α is the probability of the half-point cut.* Formally:

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