Author: Scott Buchanan

Many Impressive AI Demos Were Fakes

Scott Buchanan1 Comment

I recently ran across an article on the Seeking Alpha investing site with the provocative title “ AI: Fakes, False Promises And Frauds “, published by LRT Capital Management. Obviously, they think the new generative AI is being oversold. They cite a number of examples where demos of artificial general intelligence were apparently staged or faked.  I followed up on a few of these examples, and it does seem like this article is accurate. I will quote some excerpts here to give the flavor of their remarks.

In 2023, Google found itself facing significant pressure to develop an impressive innovation in the AI race. In response, they released Google Gemini, their answer to OpenAI’s ChatGPT. The unveiling of Gemini in December 2023 was met with a video showcasing its capabilities, particularly impressive in its ability to handle interactions across multiple modalities. This included listening to people talk, responding to queries, and analyzing and describing images, demonstrating what is known as multimodal AI. This breakthrough was widely celebrated. However, it has since been revealed that the video was, in fact, staged and that it does not represent the real capabilities of Google’s Gemini.

… OpenAI, the company behind the groundbreaking ChatGPT, has a history marked by dubious demos and overhyped promises. Its latest release, Chat GPT-4-o, boasted claims that it could score in the 90th percentile on the Unified Bar Exam. However, when researchers delved into this assertion, they discovered that ChatGPT did not perform as well as advertised.[10] In fact, OpenAI had manipulated the study, and when the results were independently replicated, ChatGPT scored on the 15th percentile of the Unified Bar Exam.

… Amazon has also joined the fray. Some of you might recall Amazon Go, its AI-powered shopping initiative that promised to let you grab items from a store and simply walk out, with cameras, machine learning algorithms, and AI capable of detecting what items you placed in your bag and then charging your Amazon account. Unfortunately, we recently learned that Amazon Go was also a fraud. The so-called AI turned out to be nothing more than thousands of workers in India working remotely, observing what users were doing because the computer AI models were failing.

… Facebook introduced an assistant, M, which was touted as AI-powered. It was later discovered that 70% of the requests were actually fulfilled by remote human workers. The cost of maintaining this program was so high that the company had to discontinue its assistant.

… If the question asked doesn’t conform to a previously known example ChatGPT will still produce and confidently explain its answer – even a wrong one.

For instance, the answer to “how many rocks should I eat” was:

…Proponents of AI and large language models contend that while some of these demos may be fake, the overall quality of AI systems is continually improving. Unfortunately, I must share some disheartening news: the performance of large language models seems to be reaching a plateau. This is in stark contrast to the significant advancements made by OpenAI’s ChatGPT, between its second iteration (GPT-2), and the newer GPT-3 – that was a meaningful improvement. Today, larger, more complex, and more expensive models are being developed, yet the improvements they offer are minimal. Moreover, we are facing a significant challenge: the amount of data available for training these models is diminishing. The most advanced models are already being trained on all available internet data, necessitating an insatiable demand for even more data. There has been a proposal to generate synthetic data with AI models and use this data for training more robust models indefinitely. However, a recent study in Nature has revealed that such models trained on synthetic data often produce inaccurate and nonsensical responses, a phenomenon known as “Model Collapse.”

OK, enough of that. These authors have an interesting point of view, and the truth probably lies somewhere between their extreme skepticism and the breathless hype we have been hearing for the last two years. I would guess that the most practical near-term uses of AI may involve some more specific, behind the scenes data-mining for a business application, rather than exactly imitating the way a human would think.

Behind Last Week’s Stock Minicrash: Unwind of the Yen Carry Trade

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Last Monday, August 5, the S&P 500 crashed by 3.5% from the previous close. That is a huge daily move, which seems to have been a surprise to most market watchers. The VIX index, a measure of the cost of options and widely seen as a measure of fear in the markets, went off the charts that day. What happened?

The previous week, there was an employment report that showed higher than expected jobless claims. Although that led to angst over a recession, a genuine serious dent in employment would bring the Fed roaring in with interest rate cuts, and the stock market loves rate cuts. In addition, as we have highlighted in recent posts (here and here), there is increasing skepticism that the monster spends on AI will produce the profits that Big Tech hopes. However, the AI skepticism and the employment worries seemed already baked into stock prices by the Friday close.

What apparently happened over the weekend was the unwinding of a big part of the yen carry trade.

What is that, you ask? To frame this, imagine you have $100 to invest in something very safe, like short term Treasury securities. In the simplest case, you go buy a 1-year T-bill which yields 4.5%. You will make $ 4.50 in a year, from this transaction. If you had $100 million to invest, you would make $ 4.5 million.

Now suppose that you could use that $100 as collateral to borrow $1000 at 0.05%. You then take that $1000 and buy $1000 worth of 4.5% T-bills. Voila, instead of making a measly $ 4.50, you can now make  1000*(4.5% – 0.05%) = $44.5. This is nearly ten times as much, a 44.5% return on your $100. Financial alchemy at its finest!

Now, if instead of investing in boring 4.5% T-bills, you had been buying Microsoft and Apple shares (up 25% and 21%, respectively, in the past twelve months), just imagine the profits from this 10X leveraged trade. Especially if you started with a $100 million hedge fund instead of $100.

Where, you may ask, could you borrow money at 0.05%? The answer is Japan. The central bank there has kept rates essentially zero for many years, for reasons we will not canvass here. This scheme of borrowing in yen, and investing (mainly in the US) in dollars is termed the yen carry trade. Besides this borrowing/investing, simply betting that the Japanese yen would decline against the dollar has been profitable for the past 18 months.

What could possibly go wrong with such a scheme? Well, you have to do this borrowing in Japanese yen. So, if you borrow in yen and then convert it to dollars and invest in the dollar world, you can be in a world of hurt if the value of yen in dollars goes up by the time you need to close out this whole trade (i.e. cash in your T-Bills into dollars, convert back to yen, and pay off your yen borrowings.

What happened on Wednesday, July 31 was the Bank of Japan unexpectedly raised its key interest rate target from 0-0.1% to around 0.25%, and announced they would scale back their QE bond-buying, in an effort to address inflation. As may be expected, that raised the value of the yen on Thursday and Friday, though not by much. But the yen made a surge up at the end of Friday’s trading.

Apparently, that caused enough angst in the carry trade community that participants in the carry trade started running for the exits, selling dollar-denominated assets (including stocks) and scrambling to buy yen. Naturally, that shot the price of yen up even more, so on Monday, Aug 5, we had a disorderly market rout.

Bad news sells, and so all the finance headlines on Monday were blaring about the stock price collapse and start of an awful bear market. However, nothing substantive had really changed. By Friday, the S&P 500 had recovered from this big head-fake.

As usual, investors sold stocks (at a low price) on Monday, and presumably bought them back at a higher price later in the week. This is why the average investor’s returns fall well below a simple buy and hold. But that is another subject for another time.

Will the Huge Corporate Spending on AI Pay Off?

Scott Buchanan2 Comments

Last Tuesday I posted on the topic, “Tech Stocks Sag as Analysists Question How Much Money Firms Will Actually Make from AI”. Here I try to dig a little deeper into the question of whether there will be a reasonable return on the billions of dollars that tech firms are investing into this area.

Cloud providers like Microsoft, Amazon, and Google are building buying expensive GPU chips (mainly from Nvidia) and installing them in power-hungry data centers. This hardware is being cranked to train large language models on a world’s-worth of existing information. Will it pay off?

Obviously, we can dream up all sorts of applications for these large language models (LLMs), but the question is much potential downstream customers are willing to pay for these capabilities. I don’t have the capability for an expert appraisal, so I will just post some excerpts here.

Up until two months ago, it seemed there was little concern about the returns on this investment.  The only worry seemed to be not investing enough. This attitude was exemplified by Sundar Pichai of Alphabet (Google). During the Q2 earnings call, he was asked what the return on Gen AI investment capex would be. Instead of answering the question directly, he said:

I think the one way I think about it is when we go through a curve like this, the risk of under-investing is dramatically greater than the risk of over-investing for us here, even in scenarios where if it turns out that we are over investing. [my emphasis]

Part of the dynamic here is FOMO among the tech titans, as they compete for the internet search business:

The entire Gen AI capex boom started when Microsoft invested in OpenAI in late 2022 to directly challenge Google Search.

Naturally, Alphabet was forced to develop its own Gen AI LLM product to defend its core business – Search. Meta joined in the Gen AI capex race, together with Amazon, in fear of not being left out – which led to a massive Gen AI capex boom.

Nvidia has reportedly estimated that for every dollar spent on their GPU chips, “the big cloud service providers could generate $5 in GPU instant hosting over a span of four years. And API providers could generate seven bucks over that same timeframe.” Sounds like a great cornucopia for the big tech companies who are pouring tens of billions of dollars into this. What could possibly go wrong?

In late June, Goldman Sachs published a report titled, GEN AI: TOO MUCH SPEND,TOO LITTLE BENEFIT?.  This report included contributions from bulls and from bears. The leading Goldman skeptic is Jim Covello. He argues,

To earn an adequate return on the ~$1tn estimated cost of developing and running AI technology, it must be able to solve complex problems, which, he says, it isn’t built to do. He points out that truly life-changing inventions like the internet enabled low-cost solutions to disrupt high-cost solutions even in its infancy, unlike costly AI tech today. And he’s skeptical that AI’s costs will ever decline enough to make automating a large share of tasks affordable given the high starting point as well as the complexity of building critical inputs—like GPU chips—which may prevent competition. He’s also doubtful that AI will boost the valuation of companies that use the tech, as any efficiency gains would likely be competed away, and the path to actually boosting revenues is unclear.

MIT’s Daron Acemoglu is likewise skeptical:  He estimates that only a quarter of AI-exposed tasks will be cost-effective to automate within the next 10 years, implying that AI will impact less than 5% of all tasks. And he doesn’t take much comfort from history that shows technologies improving and becoming less costly over time, arguing that AI model advances likely won’t occur nearly as quickly—or be nearly as impressive—as many believe. He also questions whether AI adoption will create new tasks and products, saying these impacts are “not a law of nature.” So, he forecasts AI will increase US productivity by only 0.5% and GDP growth by only 0.9% cumulatively over the next decade.

Goldman economist Joseph Briggs is more optimistic:  He estimates that gen AI will ultimately automate 25% of all work tasks and raise US productivity by 9% and GDP growth by 6.1% cumulatively over the next decade. While Briggs acknowledges that automating many AI-exposed tasks isn’t cost-effective today, he argues that the large potential for cost savings and likelihood that costs will decline over the long run—as is often, if not always, the case with new technologies—should eventually lead to more AI automation. And, unlike Acemoglu, Briggs incorporates both the potential for labor reallocation and new task creation into his productivity estimates, consistent with the strong and long historical record of technological innovation driving new opportunities.

The Goldman report also cautioned that the U.S. and European power grids may not be prepared for the major extra power needed to run the new data centers.

Perhaps the earliest major cautionary voice was that of Sequoia’s David Cahn. Sequoia is a major venture capital firm. In September, 2023 Cahn offered a simple calculation estimating that for each dollar spent on (Nvidia) GPUs, and another dollar (mainly electricity) would need be spent by the cloud vendor in running the data center. To make this economical, the cloud vendor would need to pull in a total of about $4.00 in revenue. If vendors are installing roughly $50 billion in GPUs this year, then they need to pull in some $200 billion in revenues. But the projected AI revenues from Microsoft, Amazon, Google, etc., etc. were less than half that amount, leaving (as of Sept 2023) a $125 billion dollar shortfall.

As he put it, “During historical technology cycles, overbuilding of infrastructure has often incinerated capital, while at the same time unleashing future innovation by bringing down the marginal cost of new product development. We expect this pattern will repeat itself in AI.” This can be good for some of the end users, but not so good for the big tech firms rushing to spend here.

In his June, 2024 update, Cahn notes that now Nvidia yearly sales look to be more like $150 billion, which in turn requires the cloud vendors to pull in some  $600 billion in added revenues to make this spending worthwhile. Thus, the $125 billion shortfall is now more like a $500 billion (half a trillion!) shortfall. He notes further that the rapid improvement in chip power means that the value of those expensive chips being installed in 2024 will be a lot lower in 2025.

And here is a random cynical comment on a Seeking Alpha article: It was the perfect combination of years of Hollywood science fiction setting the table with regard to artificial intelligence and investors looking for something to replace the bitcoin and metaverse hype. So when ChatGPT put out answers that sounded human, people let their imaginations run wild. The fact that it consumes an incredible amount of processing power, that there is no actual artificial intelligence there, it cannot distinguish between truth and misinformation, and also no ROI other than the initial insane burst of chip sales – well, here we are and R2-D2 and C3PO are not reporting to work as promised.

All this makes a case that the huge spends by Microsoft, Amazon, Google, and the like may not pay off as hoped. Their share prices have steadily levitated since January 2023 due to the AI hype, and indeed have been almost entirely responsible for the rise in the overall S&P 500 index, but their prices have all cratered in the past month. Whether or not these tech titans make money here, it seems likely that Nvidia (selling picks and shovels to the gold miners) will continue to mint money. Also, some of the final end users of Gen AI will surely find lucrative applications. I wish I knew how to pick the winners from the losers here.

For instance, the software service company ServiceNow is finding value in Gen AI. According to Morgan Stanley analyst Keith Weiss, “Gen AI momentum is real and continues to build. Management noted that net-new ACV for the Pro Plus edition (the SKU that incorporates ServiceNow’s Gen AI capabilities) doubled [quarter-over-quarter] with Pro Plus delivering 11 deals over $1M including two deals over $5M. Furthermore, Pro Plus realized a 30% price uplift and average deal sizes are up over 3x versus comparable deals during the Pro adoption cycle.”

Tech Stocks Sag as Analysists Question How Much Money Firms Will Actually Make from AI

Scott Buchanan4 Comments

Tech stocks have been unstoppable for the past fifteen or so years. Here is a chart from Seeking Alpha for total return of the tech-heavy QQQ fund (orange line) over the past five years, compared to a value-oriented stock fund (VTV), a fund focused on dividend-paying stocks (SDY) and the Russel 2000 small cap fund IWM.

QQQ has left the others in the dust. There has been a reversal, however, in the past month. The tech stocks have sagged nearly 10% since July 11, while the left-for-dead small caps (IWM, green line) rose by 10%:

Some of this is just mean reversion, but there seems to be a deeper narrative shift going on. For the past 18 months, practically anything that could remotely be connected with AI, especially the Large Language Models (LLM) exemplified by ChatGPT, has been valued as though it would necessarily make every-growing gobs of money, for years to come.

In recent weeks, however, Wall Street analysts have started to question whether all that AI spending will pay off as expected. Here are some headlines and excerpts (some of the linked articles are behind paywalls):

““There are growing concerns that the return on investment from heavy AI spending is further out or not as lucrative as believed, and that is rippling through the whole semiconductor chain and all AI-related stocks,” said James Abate, chief investment officer at Centre Asset Management.”

www.bloomberg.com/…

““The overarching concern is, where is the ROI on all the AI infrastructure spending?” said Alec Young, chief investment strategist at Mapsignals. “There’s a pretty insane amount of money being spent.
Jim Covello, the head of equity research at Goldman Sachs Group Inc., is among a growing number of market professionals who are arguing that the commercial hopes for AI are overblown and questioning the vast expense required to build out infrastructure required for the computing to run and train large-language models.”

www.bloomberg.com/…

“It really feels like we are moving from a ‘tell me’ story on AI to a ‘show me’ story,” said Ohsung Kwon, equity and quantitative strategist at Bank of America Corp. “We are basically at a point where we’re not seeing much evidence of AI monetization yet.”

https://finance.yahoo.com/news/earnings-derail-stock-rally-over-130001940.html

Goldman’s Top Stock Analyst Is Waiting for AI Bubble to Burst

Covello casts doubt on hype behind an $16 trillion rally

He says costs, limited uses means it won’t revolutionize world

https://finance.yahoo.com/news/goldman-top-stock-analyst-waiting-111500948.html

Google stock got dinged last week for excessive capital spending, even though earnings were strong. Microsoft reports its Q4 earnings after the market closes today (Tuesday); we will see how investors parse these results.

How to (Almost) Double Your Investing Returns 3. “Stacked” Multi-Asset Funds

Scott Buchanan2 Comments

Two weeks ago we described a simple way to achieve roughly double investing returns on some asset class like an S&P 500 stock basket, or on some commodity like gold or oil, by buying shares in an exchange-traded fund (ETF) whose price moves up or down each day two times as much as the price of the underlying stocks or commodities. For instance, if the S&P 500 stocks go up (or down) by 2% on a given day, the price of the SSO ETF will move up (or down) by 4%.  And last week we noted that buying deep in the money call options can also result in an investment which can move up or down by twice the percentage of the underlying stock. These call options side-step the volatility drag implicit in the 2X funds, but require some housekeeping on the investors part to roll them over once or twice a year.

Today we present a third approach for multiplying the return on your investment dollars. This is to buy shares of a fund which holds two different asset classes, in a leveraged form. As an example: if you buy $100 worth of the fund PSLDX, you are buying the equivalent of $100 worth of S&P 500 stocks PLUS about $100 worth of long-dated US Treasury bonds. (PSLDX happens to be an old-fashioned mutual fund, not an ETF, but no matter). It works like this: The fund takes your $100 and buys a bucket of bonds. It then uses those bonds as collateral, and uses futures to get around $100 worth of exposure to the price movements of the S&P 500 stocks. There is not quite a free lunch here, since there is a “carry” cost on the futures, which is about equal to the LIBOR/SOFR short term interest rates (currently ~ 5%).

PSLDX does not promise exactly 100/100  stock/bond exposure, but it comes out pretty close much of the time. A similar product is NTSX which is leveraged x1.5. It gives 90/60 stocks/mixed-term bonds. NTSX has outperformed PLSDX in recent years, since the price of long-term (10-20 year) bonds has been crushed due to the rise in interest rates. RSSB is a recent entry into this space, offering 100/100 exposure to global stocks/laddered Treasuries.

Another reason these leveraged stock/bond products have done relatively poorly in the past two years is that the cost of leverage is actually higher than the bond coupons, due to the inverted yield curve.  This problem will go away if the Fed lowers short-term rates back down to near zero, as they were prior to 2022, but lingering inflation makes that prospect unlikely.

That said, if I have $200 to invest and want $100 stock and $100 bond coverage, I can put $100 into one of these 100/100 funds, and still have $100 left to collect interest on or to invest in some other, hopefully higher-yielding venue. So, these stock/bond funds have their place.

Where this so-called asset stacking shines even more is combining stocks or bonds with something like managed futures. Managed futures are an excellent diversifier for equities (see here). Moreover, since managed futures are typically held in both long and short positions, there will be less financing (carry) cost associated with them. When both stocks and bonds cratered in 2022, managed futures went up. Thus, funds like BLNDX (50 global stocks/100 managed futures) and MAFIX (stocks plus managed futures) went up in 2022, and then continued to rise as stocks recovered. Thus, the returns for these two funds have been steadier and higher than plain stocks (SP 500) over the past three years:

Total returns for past three years, for BLNDX (50 stocks/100 managed futures), SP500 stocks, BND broad US bonds, and MAFIX stacked multi-asset.

BLNDX and its sister fund REMIX are readily available at most brokerages (I hold some), while MAFIX may have daunting minimum investment requirements. RSST is a recent 100/100 stock/managed futures ETF that is easily invested in, and seems to be performing well.

Disclaimer: As usual, nothing here should be considered advice to buy or sell any investment.

How to (Almost) Double Your Investing Returns 2. Buy Deep in the Money Calls

Scott Buchanan6 Comments

Last week we described a simple way to achieve roughly double investing returns on some asset class like an S&P 500 stock basket, or a narrow class of stocks such as semiconductors, or on some commodity like gold or oil. That way is to buy one of the many exchange-traded funds (ETFs) which use sophisticated derivatives to achieve a 2X or even 3X daily movement in their share prices, relative to the underlying asset. For instance, if the S&P 500 stocks move up by 2% on a given day, the SSO ETF will rise by 4%.

Of course, these leveraged funds will also go down two or three times as much. They also have a more subtle disadvantage, which is that when the markets go up and down a lot, they tend to lose value due to their daily reset mechanism.

In this post we describe a different way to achieve roughly double returns, which does not suffer from this volatility drag issue. This way is to buy long-dated deep in-the-money call options on a stock or a fund.

Say what? We have described how stock options work here and here. The reader who is unfamiliar with options should consult those prior articles.

A stock option is a contract to buy (if it is a call option) or to sell (if it is a put option) a given stock at some particular price (“strike price”), by some particular expiration date. Investors generally buy calls when they believe that the price of some stock or fund will go up.  For a call option with a strike price far below the current market price of a stock, the market price of the option will move up and down essentially 1:1 with the market price of the stock.

For instance, as I write this the market price of Apple is about $230. Suppose I think Apple is going to go up by say $40 in the next six months. One way for me to capture this gain is to invest $230 in buying Apple stock. The alternative propose here is to instead of buying the stock itself, buy, say, a call option with a strike price of $115 and an expiration date of January 17, 2025. The current market price of this option is about $119.

Other things being equal, we expect that the market value of this call option will go up by $40 if Apple itself goes up by $40. But we have invested only $119, rather than $230, so our return on our investment is roughly double with the option than by buying the stock itself.

There is a subtle cost to this approach. At a stock price of $230 and a strike price of $115, the intrinsic value of this call option is $115. But we pay an extra $3 of extrinsic value when we buy the option for $118. This extrinsic value will gradually decay to zero over the next six months.

Thus, if Apple went up by $40 within the next month or so, we could turn around and sell this call option for nearly $40 more than our purchase price. But if we wait for six months before selling it, we would only net $37 (i.e., $40 minus $3). This is still fine, but it illustrates that there is a steady cost of holding such options. This annualized cost is about equal to or slightly higher than the prevailing short term interest rate (5% /year). This option pricing makes sense, since an alternative way to control this many shares would be to borrow money at current interest rates (5%) and use those borrowed funds to buy Apple shares. Options and futures pricing is generally rational, to make things like this equivalent, or else there would be easy arbitrage profits available.

As a side comment, the reason I am focusing on deep in the money calls here is that the extrinsic premium you pay in buying the call gets lower the further away the strike price is (i.e. deeper in the money) from the current stock price. A deeper in the money call does cost you more up front, but net net its dollar movements up and down more closely track (1:1) the movements of the underlying stock. So, if I am not trying to guess right on any market timing, but simply want to get the equivalent of holding the underlying stock but tying up less money to do so, I find buying a call that is about 50% in the money generally works well.

How I Use Deep in the Money Call Options

I consider the technology-oriented stock fund QQQ to be a core holding in my portfolio, so I would like to stay exposed to its movements. But I might as well do this on a 2X basis, to make better use of my funds. I do hold some of the 2X ETF QLD. But if we experience a lot of market volatility, the price of QLD will suffer, as explained in our previous post.

As a more conservative approach here, I recently bought a deep in the money call on the QQQ ETF. As usual, I went for a call option with a strike price roughly half of the market price, with an expiration date 6-12 months away. When this gets close to expiration (May-June next year), I will “roll” it forward, by selling my existing call option, and buying a new one dated yet another 6-12 months further out. This takes little work and little decision making. I will pay the equivalent of about 5% annualized cost on the decay of the extrinsic option premium, but I come ahead as long as QQQ goes up more than 5% per year.

This is a little more work than just holding the 2X QLD ETF, but it gives me a bit more peace of mind, knowing I have done what I can to smooth out some of the risk there. Of course, if QQQ plunges along with the markets in general, I will be looking at double the losses. For that reason, I am taking some of the money I am saving by using these leveraged approaches, and stashing it in safe money market funds. In theory that should give me “dry powder” for buying more stocks after they drop. In practice, I may be too frozen with fear to make such clever purchases. But at any rate, I should not be appreciably worse off for having used these leveraged investments (2X funds or deep in the money calls).

Disclaimer: As usual, nothing here should be considered advice to buy or sell any investment.

How to Roughly Double Your Investing Returns 1. 2X (or 3X) Leveraged Funds

Scott Buchanan7 Comments

Most years, stocks go up, by something like 9%. Wouldn’t it be nice to invest in a fund that went up double those amounts? Such funds exist. They use futures or other derivatives to move up (or down!) by double, or even triple, the percentage that the underlying stock or index moves, on a daily basis.

For instance, a common unleveraged fund (ETF) is SPY that roughly tracks the S&P 500 index of large U.S. stocks is SPY. SSO is a 2X fund, which gives double the returns of SPY, on a daily basis. UPRO is a 3X fund, giving triple the returns. 2X funds exist for many different asset classes, including semiconductor stocks, treasury bill, and crude oil – see here. And similarly for 3X funds.

Since all the action in stocks these days seems to be in large tech companies, I will focus on the NASDAQ 100 index universe. The leading unleveraged fund there is QQQ. The 2X version is QLD, and the 3X is TQQQ. Let’s look at how these three funds performed over the past twelve months:

QQQ is up a respectable 36%, but QLD is up by 70%, and TQQQ by a mouth-watering 106%. You could have doubled your money in the past twelve months simply by investing in a 3X fund instead of holding boring 1X QQQ. 

These leveraged funds can be utilized in more than one way. One approach is to just put the monies you have allocated for stocks into such funds, and hope for higher returns. Another approach is to put, say half of your speculative funds into a 2X fund (to get roughly the same stock exposure as putting all of it into a 1X fund), and then use the remaining half to put into other investments, or to keep as dry powder to give you the option to buy more equities if the market crashes.

What’s not to like about these funds? It turns out that a year of daily doubling of returns does not necessarily add up to doubling of yearly returns. There is “volatility drag” associated with all the exaggerated moves up and down. As an illustration of how this works, suppose you held a stock that went down by 50% one day, say from a price of $100 to $50. The next day, it went back up by 50%. But this would only get you back to $75, not $100.

It turns out that with these leveraged funds, as long as stocks are generally going up, the yearly returns can match or even exceed the 2X or 3X targets. But in a period with a lot of volatility, the yearly returns can fall far short. And in a down year, the combination of the leverage and the volatility drag lead to truly horrific losses. For instance, here is what 2022 looked like for these funds:

QQQ was down by 31%, which is bad enough. But imagine your $10,000 in TQQQ melting down to $3,300 that year.

And here is the chart from January 2022 to the present:

QQQ is up 27% in the past 2.5 years, 2X QLD is up only 16%, while 3X TQQQ is actually down by 6%, as it could not recovery from 2022.

This was a kind of a worst-case scenario, since 2022 was an exceptionally bad year for QQQ, coming off a fabulous 2021. A chart of the past five years, which includes the 2020 Covid crash and recovery, and the 2022 crash and subsequent recovery still shows the leveraged funds coming out ahead over the long term:

The net returns on QLD (321%) were about double QQQ (158%), while the more volatile TQQQ return (386%) was plenty high, but fell well short of three times QQQ.

In my personal investing, I hold some QLD as a means to free up funds for other investments I like. But if I smell major market trouble coming, I plan to swap back into plain QQQ until the storm clouds pass.

There are some other ways to get roughly double returns, which suffer less from volatility drag than these 2X funds. I will address those in subsequent posts.

Disclaimer: As usual, nothing here should be considered advice to buy or sell any investment.

How Repurposing Graphic Processing Chips Made Nvidia the Most Valuable Company on Earth

Scott Buchanan1 Comment

Folks who follow the stock market know that the average company in the S&P 500 has gone essentially nowhere in the last couple of years. What has pulled the averages higher and higher has been the outstanding performance of a handful of big tech stocks. Foremost among these is Nvidia. Its share price has tripled in the past year, after nearly tripling in the previously twelve months. Its market value climbed to $3.3 trillion last week, briefly surpassing tech behemoths Microsoft and Apple as the most valuable company in the world.

What just happened here?

It all began in 1993 when Taiwanese-American electrical engineer Jensen Huang and two other Silicon Valley techies met in a Denny’s in East San Jose and decided to start their own company. Their focus was making graphics acceleration boards for video games. Computing devices such as computers, game stations, and smart phones have at their core a central processing unit, CPU. A strength of CPUs is their versatility. They can do a lot of different tasks, but sequentially and thus at a limited speed.  To oversimplify, a CPU fetches an instruction (command), and then loads maybe two chunks of data, then performs the instructed calculations on those data, and then stores the result somewhere else, and then turns around and fetches the next instruction. With clever programming, some tasks can be broken up into multiple pieces that can be processed in parallel on several CPU cores at once, but that only goes so far.

Processing large amounts of graphics data, such as rendering a high-resolution active video game, requires an enormous amount of computing. However, these calculations are largely all the same type, so a versatile processing chip like a CPU is not required. Graphics processing units (GPUs), originally termed graphics accelerators, are designed to do enormous number of these simple calculations simultaneously. To offload the burden on the CPU, computers and game stations for decades have included on auxiliary GPU (“graphics card”) alongside the CPU.

This was the original target for Nvidia. Video gaming was expanding rapidly, and they saw a niche for innovative graphics processors. Unfortunately, they the processing architecture they choose to work on fell out of favor, and they skated right up to the edge of going bankrupt. In 1993 Nvidia was down to 30 days before closing their doors, but at the last moment they got a $5 million loan to keep them afloat. Nvidia clawed its way back from the brink and managed to make and sell a series of popular graphics processors.

However, management had a vision that the massively parallel processing power of their chips could be applied to more exulted uses than rendering blood spatters in Call of Duty.  The types of matrix calculations done in GPUs can be used in a wide variety of physical simulations such as seismology and molecular dynamics. In 2007, and video released its CUDA platform for using GPUs for accelerated general purpose processing. Since then, Nvidia has promoting the use of its GPUs as general hardware for scientific computing, in addition to the classic graphics applications.

This line of business exploded starting around 2019, with the bitcoin craze. Crypto currencies require enormous amount of computing power, and these types of calculations are amenable to being performed in massively parallel GPUs. Serious bitcoin mining companies set up racks of processors, built on NVIDIA GPUs. GPUs did have serious competition from other types of processors for the crypto mining applications, so they did not have the field to themselves. With people stuck at home in 2020-2021, demand for GPUs rose even further: more folks sitting on couches playing video games, and more cloud computing for remote work.

Nvidia Dominates AI Computing

Now the whole world cannot get enough of machine learning and generative AI. And Nvidia chips totally dominate that market. Nvidia supplies not only the hardware (chips) but also a software platform to allow programmers to make use of the chips. With so many programmers and applications standardized now on the Nvidia platform, its dominance and profitability should persist for many years.

Nearly all their chips are manufactured in Taiwan, so that provides a geopolitical risk, not only for Nvidia but for all enterprises that depend on high end AI processing.

Boardroom Backstabbing: The Rise of “Lender-on-Lender Violence”

Scott BuchananLeave a comment

When I first started reading of “Lender-on-Lender Violence” this year, images of bankers in three-piece suits brawling in the streets of Lower Manhattan came to mind. It turns out that this is a staid legal term for a practice which has been around for some time, but is becoming more common and consequential.

Consider a case where say three lenders (e.g. banks or more likely venture capital funds) have lent money to some startup or struggling company XYZ. Let’s call these lenders A, B, and C. Now XYZ needs even more funding, perhaps because they need to build another factory, or perhaps because things are not working out as they hoped and they cannot pay off the original loans and still stay in business.

Now Lenders A and B get together and cook up a scheme. They will lend some more money to company XYZ to largely replace the original loan, but they contrive to get legal terms for that new loan that give it a higher priority for payment than the original loan. This is called “up-tiering” the new loan.  This has the effect of reducing the market value of the original loan.

Lender C is now hosed. It faces murky prospects for repayment on that original loan. Lenders A and B offer to buy them out of the original loan for 40 cents on the dollar. Lender C proceeds to sue Lenders A and B.

Will Lender C prevail? Probably not, if the course of recent cases is any guide. Unless there is very specific language in the legal “covenant” regarding the first loan forbidding this practice, it seems to be legal.

A similar maneuver would be for a new Lender D to offer a replacement loan to Company XYZ, with legal language giving it priority over the original loan. This is called “priming.”

Yet another tactic by the aggressive lenders includes working with Company XYZ to move its more valuable assets into a subsidiary or shell company, and to get the new loan to hold that as collateral. This again hoses the “victim” lenders, since again the assurance that they will be repaid has gone down.

My Personal Experience with Lender-on-Lender Violence

Some years ago, I bought the bonds of a company called SeaDrill. I bought the bonds instead of the common or preferred stock, for an additional margin of safety. Unlike the stock, the bonds must be repaid in full, right? Both the bonds and the preferreds were paying about 9%, back when general interest rates were much lower than that are now. So, I was a lender to the company.  

Silly me. Times got tough in the oil patch, and the company would have had difficulty paying off its bonds AND paying its management their high salaries. So, they went for Chapter 11 bankruptcy. I had not realized the difference between Chapter 7 bankruptcy, where the company shuts down and liquidates and pays off its creditors in pecking order, and Chapter 11, which is largely a chance for the company to put the losses on its creditors and to keep on operating.

As with the example above, some big institution offered to refinance things with new secured bonds that had priority ahead of the old bonds (which I held). In the end I got about 44 cents on the dollar for my bonds. I was not happy about that, but I did make out better than the hapless preferred stockholders, who got just a tiny crumb to make them go away. It was a learning experience. I did feel, well, violated.

Implications for the Burgeoning Private Credit Market

I will be writing more on the booming “private credit” market. Many of the loans in this space are “covenant-lite.” Back before say 2008, a large fraction of loans to business were through banks, who would insist on strong legal protection for their money. But in recent years, private equity funds have competed for this lending, allowing the borrowers to borrow on terms that give much less protection to the lenders. Cov-lite is now the norm.

Traditionally, loans (as distinct from bonds) to businesses have enjoyed decent recoveries (e.g., around 70%) in case of defaults, thanks to strong collateral backing the loans. But if we face any sort of prolonged recession and elevated defaults, the recoveries on all these loans will be far less than in the past. These are uncharted waters.

A Reference for “Lender-on-Lender Violence”

A solid description  of these matters is found in “ Uptier Transactions and Other Lender-on-Lender Violence: The Potential for More Litigation and Disputes on the Horizon “ at dailydac.com.

How To Drive a Turbocharged Car, Such as a Honda CR-V

Scott Buchanan2 Comments

My old Honda Civic was a fairly small sedan. It had a 1.8 liter engine, that generated about 140 horsepower. It would not win any drag races, but had functional acceleration.

I recently got a Honda CR-V, a much larger, heavier vehicle. I was nonplussed to learn that it only had a 1.5 liter engine. Would I have to get out and push it up steep hills? As it turns out, this small engine can crank out some 190 horsepower. Given the size of this crossover SUV, this still does not make for a peppy drive, but at least I can actually pass another car as needed.

This high power with small engine displacement is made possible by the magic of turbocharging. As the (hot, expanded) exhaust gas leaves the engine, it goes through a turbine and makes it spin. A connected power shaft then spins a compressor, which takes outside air and jams it into the engine at higher pressure, i.e., higher density. With extra air stuffed into the engine cylinders, the engine can inject extra gasoline (keeping the air/fuel ratio roughly constant) – -and voila, high power output.

A schematic of this setup is shown below. I drew in a red arrow to mark the exhaust turbine, and a blue arrow for the intake air compressor. The rest should be fairly self-explanatory.

Source: Wikipedia

Also, here is a diagram of what the actual turbo hardware might look like:

Source: TurbochargersPlus

When the engine is turning at low-moderate speeds (say below 2000-3000 RPM), the turbine is doing relatively little, and so you are essentially driving around with a small (1.5 L) engine. This is good for gas mileage. When you floor it, the engine spins up and the turbo boost kicks in, giving considerably more power. [1]

What’s not to like? Apart from the potential maintenance headache of a rapidly spinning, complex chunk of precision machinery, there are a couple of issues with driving turbocharged engines that drivers should be aware of. There are articles  and videos (see good comments there) that address these and other issues in some detail.

( A ) Time Lag Before Turbo Boost Kicks In

With a normal non-turbo engine, you can feel the power kick in nearly immediately when you depress the pedal. The pedal opens the throttle, and instantly the engine is gulping more air (and fuel).

With a turbo, there can be a detectible time lag. The engine must rev up until the turbo effect starts to kick in, and then it spins faster, and there is more air shoved into the engine. As long as you know this, you can drive accordingly. This might be a life and death matter if as you are in the middle of passing a car on a two-lane highway, and suddenly an oncoming car appears in your passing lane. If you are not up to full power by that point, such that you can complete the passing quickly, you could become a statistic. I have only faced that situation maybe once every ten years in my driving, but it should be figured in.

The actual time lag varies from one model to another. I’d suggest just testing this out on your car. In some safe driving scenario, floor it and assess how much of a lag there is.

( B ) Don’t turn the engine off immediately if it has been running fast.

The thought here is to let the engine slow down to idle, and maybe even cool down a hair, before turning it off. The reason is that if the engine is revving at say 2000 rpm, and you suddenly turn the engine off, the oil pumping action stops, but the turbo is still spinning away in there. Having the turbine spinning away with no oil circulation can wreck the bushings.

There are articles  and videos (see good comments there) that address these and other issues in some detail.

Comment on Driving Honda CR-V Turbo Engine

Various engines have been used in CR-Vs. The 1.5 L turbo has been common in North America since 2017. It was designed to not have a very noticeable lag, in the sense that nothing happens for two seconds, and then the vehicle lurches forward. The turbo effect reportedly starts to kick in at 2000 rpm. However, this effect is progressive, so the power at 2000-3000 rpm is still modest. So, if you just push halfway down on the accelerator, the response is modest. If you floor it, the engine will within a second or two scream up to like 5000 rpm, and then start to really accelerate. That said, I have a visceral aversion to revving my engines that close to the red-line danger zone on the tachometer (my previous non-turbo cars I never took above about 3500 rpm, never needed to). Even with all that revving, the net acceleration is still modest.

Another factor with driving a CR-V is the “Econ” fuel-saving engine setting. When that is on, it seems to prevent the engine from revving over about 3500 rpm. So, if I plan to pass another car, or if I need power for some other reason, I need to remember to punch the leafy green Econ button to turn off this mode.

The bottom line is that I will think twice, maybe thrice, before passing another vehicle on a two-lane road in my CR-V.

ENDNOTE

[1] That is the theory anyway: great gas mileage most of the time, and bursts of power available for those rare times when you need it. The reality seems to be a little different. There may be reason to believe that turbocharged small engines give good idealized EPA test gas mileage numbers, but that in ordinary driving, the results are not so great. The turbo is never actually turned off, it just contributes more or less at various RPMs. The turbocharging forces the manufacturer to adjust the air/fuel mixture to be less efficient, in order to avoid knock. So, the manufacturer may be essentially manipulating things to look good on the EPA tests.  A larger engine, where some of the cylinders are shut off when not under load, may be more efficient. See video.