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Michael Cembalest, a man in glasses, appears in a small window in the top right. Text, Podcast: Five Easy Pieces. Michael Cembalest, Chairman of Market and Investment Strategy, J.P. Morgan Asset and Wealth Management. February 2024. Disclaimer, JPMorgan Chase Bank, N.A. and its affiliates (collectively "JPMCB") offer investment products, which may include bank-managed accounts and custody, as part of its trust and fiduciary services. Other investment products and services, such as brokerage and advisory accounts, are offered through J.P. Morgan Securities LLC ("JPMS"), a member of FINRA and SIPC. JPMCB and JPMS are affiliated companies under the common control of JPMorgan Chase & Co. The views expressed herein may differ from other JPMorgan Chase & Co. affiliates and employees. This constitutes our judgment based on current market conditions and is subject to change without notice. This has not been prepared with any particular investor in mind, and it may not be suitable for all investors. Investors should speak to their financial representatives before engaging in any investment product or strategy. This material should not be regarded as research or as a J.P. Morgan Research Report. Outlooks and past performance are not reliable indicators of future results. Copyright 2023 JPMorgan Chase and Co. All rights reserved. Investment products: not FDIC insured, no bank guarantee, may lose value.

(SPEECH)

Good morning, everybody. Welcome to the February 2024 Eye on the Market Podcast. This one's called Five Easy Pieces. It was a movie from 1970 with Jack Nicholson where he plays an oil rig worker who was also a former classical pianist. So you know, in Hollywood, anything is possible.

There's five topics I wanted to talk briefly about that we wrote about in the Eye on the Market this week. One of them has to do with the unending dominance of the Magnificent 7 stocks. I would like to talk a little bit about a related topic on open-source large language models, given that Nvidia is such a huge part of the Mag 7 right now.

A quick follow up on the No Labels movement, where I got into some debates with some clients at some conference recently, An Armageddonist update, which I'll explain, and then some comments on bottom fishing and Chinese equities.

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Text, Magnificent 7. Two infographic charts compare Mag 7 and S&P 493 percentages related to equity markets, including quarter 4 sales growth, margin percent, returns, share and S&P market cap, and price to earning ratios. Mag 7 percentages are overall higher than S&P 493. Source: G.S., J.P.M.A.M., February 2, 2024.

(SPEECH)

So let's start with a discussion of the Mag 7 stocks, which of course, you all know at this point, are completely dominating equity markets.

Last year, they returned 76%. The rest of the market returned 14%. But think it's important to keep in mind a couple of things. First, unlike 2000, 2001, these stocks are making a lot of money. Their sales growth is 7-times higher than the rest of the market.

Their margins were expanding at the end of last year instead of contracting. Their margins are almost three times higher than the rest of the market. So it's not a profitless boom. The bigger concern is that it's a profit-oriented boom and that these companies are increasingly dominating, not just market capitalization, but income as well.

So that's why we spent as much time as we did in the outlook thinking about antitrust issues, because that's really the only thing on the horizon that think can seriously dent the overall Mag 7 story. Tesla has run into a buzzsaw that's very specific to it recently, mostly related to pricing competition in Europe, the US, and in China. And also note that their fourth quarter earnings were flattered by a one time non-cash tax allowance adjustment that I explained in the Eye of the Market.

But in any case, the Mag 7 story just keeps rolling.

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Text, Highest stock market concentration since the early 1970s. A line chart labelled HHI concentration measure (sum of squared stock weights). The x-axis lists a range of years from 1964 to 2024. The y-axis denotes the HHI concentration measure from 60 at the bottom to 240 at the top. The line has several spikes but overall falls from 1964, hitting ultimate lows between 1984 and 1994. After a larger peak, the line falls again, but then starts to continuously rise back up from 2014 to 2024. Source: J.P. Morgan Equity Macro Research, January 30, 2024.

(SPEECH)

And we have a chart in here from some of my colleagues in the investment bank who do excellent research on how the market concentration has now reached the highest level since 1972. They don't think that's a great thing. They note that, historically, surges in market concentration have either preceded or coincided prior recessions. So they're not agnostic about this.

One thing I know for sure is that market concentration makes life a lot more difficult for active equity managers. Last year was one of the worst alpha years on record in terms of excess returns. Only 23% of managers managing against the Russell 1000 type index outperformed, and that compares to almost triple that, level 66% in 2022. So this kind of stock market concentration stuff is very tough, generally, on the active management industry.

Again, this is quite different than the bubble that took place in 2000, 2001. And the big risks here are whether it's the Biden administration, or a future Trump administration, or whoever, a political reaction to this kind of earnings concentration in addition to market concentration.

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Text, LLM performance.

(SPEECH)

Now, with the AI revolution being at the core of Mag 7 out-performance, I wanted to just talk a little bit about this report that came out quietly from Microsoft, of all people, last year.

So Microsoft, as everybody knows, is heavily invested in the success of OpenAI. They're also doing their own work with open-source language models. And this raises a lot of questions about the monetization of large-language models, the big closed ones, whether it's OpenAI, or Google's version, or anything else. So what Microsoft did-- And this is not a typo. --they took an open source model from Meta, that meta had released called Llama.

They adapted it, in ways we describe in the piece, and then they went to see how it would do on questions related to biomedicine, finance, and law, compared to some of the big, highly-trained private closed models. So one example in finance, they compared it to Bloomberg GPT, which took, like, a billion computing hours to create, a $1 million to create. I think Microsoft spent less than $100, reportedly, on their version of an open-source model.

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A bar chart labelled LLM performance on domain-specific multiple choice exams. Two colored bars are denoted as private closed source model, MedAlpaca-13B, and adapted open source model, AdaptLLM. The x-axis depicts the three categories of biomedicine, finance, and law. The y-axis depicts level of performance from 0 to 70. In all categories, performance level between the two are comparable. Source: "Adapting large language models via reading comprehension", Huang et al, Microsoft, September 2023.

(SPEECH)

And as you can see here in the chart, which was discussed in the piece, the performance was roughly the same in biomedicine, finance, and law, as the big, expensive, complicated, closed-source models.

The open-source models require work to integrate. You need a lot of programmers to kind of figure out what to do with them. But once you have that in-source talent, you have greater transparency. You have more version control. You can use whatever servers and cloud providers you want instead of the ones required by the closed models.

You have less exposure to the business issues, like the shenanigans that took place at OpenAI this year. You don't have to share your private data with people who own that model, who might censor what you do with it. And you could even run some of these open-source models on a single GPU or even a MacBook, instead of a huge GPU cluster. So this was a quiet paper.

Microsoft didn't make a lot of fanfare about it, but to me, it does raise questions about the profitability of some of these large-language model efforts. And also, there's a guy that I talk to a lot about this kind of stuff who will remain nameless, but he does tell me, the golden age of large-language models, in some ways, is already over. And I said, What do you mean by that? And he said, for the last few years, language models have been able to surreptitiously just scrape all the data without the people who own it knowing, repackaging it, and selling it and calling it research. And he said that golden age is over.

So there are a lot of interesting use cases for large-language models, but I'm very curious to see the follow through in terms of monetization and then what impact that would have on some of the premiums priced into some of these stocks.

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Text, No Labels and Twelfth Amendment Contingent Elections.

(SPEECH)

OK, so another thing, interestingly that happened, was I was speaking at a conference in Utah about this weird, triple-witching hour scenario, where the No Labels party in the United States runs a slate, which they say they may do. And let's say they win 4 of 5 states and they win enough delegates to prevent both Trump and Biden, presuming they win their respective nominations, from reaching 270. Right?

So I started to talk about what would happen if no candidate passed 270 and the rules around contingent elections that go into the House of Representatives to pick the President. And a hand was raised in the front of the audience from a CEO who is actually so active in the No Labels movement that their email handle is, it's @NoLabels.com. And this person objected to my line of thinking by saying, well, we would never let it get to a contentious election. We would do some horse trading to form a unity government before a contingent election took place.

In other words, we would throw our electoral support to either Trump or Biden in some kind of negotiated, coherent way. Maybe. This is real complicated. The system is not set up for that kind of horse trading. You can do it at a convention, but you can't do it after the general election, at least I don't think so, and there's four major reasons why.

First, No Labels can't force their electors to switch votes. They can decide whatever it is they want to do. Now, they can suggest they do it, they can try to compel them to do it, but they cannot control them. And those electors, should they win any, would be able to do whatever they want.

Number 2, around 2/3 of states have actual elector-binding laws in place that expressly prohibit electors from switching their votes. And even in the states that don't have them, I could imagine an avalanche of constitutional challenges from voters saying they were disenfranchised. So unless somehow the states that No Labels win happened to be states that have absolutely no elector-binding laws and that all constitutional challenges fail, I see that as a huge hurdle.

Two other things, even if they get past that point, on January 6, there's an Electoral Count Reform Act that requires electors to be faithfully given. And this is a term that's long existed in the constitutional law community for a couple hundred years, but the bottom line is, a faithless elector is somebody that votes for a candidate that's different than the one that they were allocated to based on the general election results. And you could imagine that Congress on January 6 would basically reject some of those No Labels electors that switched parties.

And then the last thing is, the No Labels people have been very kind of vocal about, well, we would throw our support depending upon a unity government where we'd get certain cabinet posts and this and that. There are laws, federal and criminal laws against that they'd have to avoid violating by horse trading their support for their electoral votes. So I think this is a huge gauntlet here.

And in the weird, triple-witching hour scenario where No Labels wins enough electors to prevent Trump and Biden from reaching 270, I think the higher probability is that you would end up with a 12th Amendment contingent election in the House. So you can read more about that if you're interested. I enjoy that kind of stuff.

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Text, Armageddonist update. The consequences of Armageddonsim, 2010-2019.

(SPEECH)

But one of the other things I enjoy and one of my guilty pleasures-- Everybody has guilty pleasures. Mine is rather benign. --in addition to fishing is I like to look at the consequences of Armageddonism, which is the media tends to flock to people who have the most horrifyingly, terrifyingly calamitous things to say. Now, there's been a lot of books written on behavioral human instinct towards bad news rather than good news, and a bunch of newspapers and magazines have historically conducted experiments where all they do is put good news on the cover and their newsstand sales go down by 2/3.

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A bar chart labelled performance impact of shifting $1 from S&P 500 to the Barclay's Aggregate Bond Index on date of Armageddonist comment. The x-axis bars are labelled with the names of economists, a month, and a year. The y-axis denotes percentages from 0 to negative 60. The bars are high toward negative 60% between 2010 and 2012 but slowly begin to decrease toward negative 30% in 2016. Source: J.P.M.A.M., Bloomberg. October 23, 2020.

(SPEECH)

So the media loves quoting a bunch of these people, and we show the names and the chart, and you'll probably recognize a bunch of these doomsayers. And so we looked at the end of 2019, we looked at their forecasts and we said, well what if on the day of their prediction of disaster we switched $1 from stocks into bonds, by taking the dollar from the S&P 500 and moving it to the Barclay's Aggregate? And by the end of 2019, you would have lost somewhere between 30 and 60% by listening to these statements, which were made between, let's say, 2010 and 2016.

So COVID hits. This is amazing. COVID hits, the market collapses, and now the Armageddonists think, OK, I've been bailed out by a global pandemic, which didn't predict.

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Text, Timing is everything. A list of bulleted quotes. Text, "The best economic outcome that anyone can hope for is a recession deeper than that following the 2008 financial crisis." "The depression we're entering is going to be extremely brutal in the inflation that is going to ravage the economy." "We're going to have the worst bear market in my lifetime." "We are in a depression, not a recession. It's a depression." "I continue to expect the S&P 500 to lose about two-thirds of its value over the coming years."

(SPEECH)

And so then after the markets had already declined by 20, 30%, they doubled down with some of these hilarious quotes about, this is going to be the worst bear market in my lifetime.

This is a deep depression. I expect the S&P to lose 2/3 of its value over the coming years.

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A line chart labelled S&P 500 index, Jan 1 2020 to present with date of latest comment. The x-axis lists the years between 2020 and 2024. The y-axis depicts a range between 2,000 and 5,000. The line of the graph dips sharply in 2020 to below 2,500 with several red points on the dip labelled with economists' names before generally rising back toward 5,000 as the years pass. Source: Bloomberg, J.P.M.A.M., Feb 2, 2024. See quote table for full sources.

(SPEECH)

And then so we have a chart in here where we plot the timing of these statements against what happened in the market, which of course, has almost doubled since all of these statements were made.

This is all fun and games. But it's a reminder that the timing of investing is important. There can be deep corrections in markets for different reasons, but usually the worst time to double down on them is after the market's already gone down. Now, one place where Armageddon is happening is if you're an investor in Chinese equities, which has, over the last couple of years, been quite the train wreck.

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Text, China. A line graph labelled China's CSI 1000 and 300 Index. Index (100 equals Jan 2014) depicting the rises and falls of the CSI 1000: Small-cap A shares and the CSI 300: Large-cap A shares. The dates range from 2014 to 2014 on the x-axis, and the index ranges from 75 to 325. There is a large spike in the CSI 1000 and 300 A shares between the years 2014 and 2016. Source: Bloomberg, J.P.M.A.M., February 5, 2024.

(SPEECH)

And there's lots of different equity indices in China, most of them are doing roughly the same kind of thing.

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A line chart labelled Total CSI 300 ETF trading volume. Units, billions. The x-axis depicts a range of years from 2012 to 2024. The y-axis depicts units from 0 to 16. The line generally stays between 0 and 2 units with a large spike in 2024 reaching between 14 and 16 units. Source: Bloomberg, J.P.M.A.M., February 4, 2024.

(SPEECH)

There was a spike in trading volumes recently and a lot of times a spike in trading volumes tends to coincide with the bottom in a market, because you get seller capitulation. So we're looking at that, but there's some bigger picture issues that I just want to talk about for a minute.

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A line chart labelled China equity valuations. MSCI China Index, forward price-to-earnings ratio. The x-axis lists a range of dates between 2014 and 2024. The y-axis depicts ratios from 8x up to 22x. There are large spikes in 2018 and between 2020 and 2022. Source: Bloomberg, J.P.M.A.M., February 5, 2024.

(SPEECH)

So we have a chart showing that the PE multiple on China has gone down to around 10 times, which is pretty low, particularly compared to valuations in the developed world.

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Text, Cheapest valuations. A bar chart labelled Cheapest valuations, Forward price-to-earnings ratio. The x-axis columns list a variety of different countries' stock exchanges. The y-axis depicts the forward price-to-earnings ratio from 0x to 12x. China's markets are red while other countries are blue. Source: Bloomberg, J.P.M.A.M., February 5, 2024.

(SPEECH)

But I just want to close with two things. First, China at a 10 PE looks cheap, but there's a lot of things that trade at below a 10 PE. And we have this giant bar chart in here that looks at them.

So if your value hunting, European energy, US energy, S&P 500 banks, Asia-Pacific energy, S&P 500 telecom, small-cap telecom, Asia-Pacific utilities, European financials, Brazil, Italy, Poland, Austria, Hungary, Turkey, all of these things trade below a 10 PE. So if you're interested in bottom fishing, China's not the only place to look.

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Text, China and US TARP bill of 2008. A chart labelled China slash US housing comparison. The chart compares the peak value of housing stock and real estate related rates of China to the US market. In all categories, China passes the US. Source: Empirical Research, CEIC, NIH, Numbeo, Fed, Rogoff & Yang, 2022.

(SPEECH)

And then the other thing I want to close with is just some comments on China, bottom fishing, and then and then what to watch for and the TARP Bill in 2008.

So 2008, I wrote a piece on October 14 talking about how we had become very bullish on US equities, at that point in time, why. And up until then, the government was dealing with a crisis of solvency perceptions on the banks. And their first plan was to buy all the bad loans from the banks, put them in a bad bank, work them out over time, and the losses would basically accrue to taxpayers who would be funding the acquisition.

The markets didn't respond to that and I talked to some people at the IMF who had done this study, showing that in prior decades, if you have a banking crisis of some kind or a solvency crisis of some kind, buying bad loans doesn't work well in terms of boosting GDP and the equity market. But when the government steps in and buys the liabilities and equity positions of the banks, you get a kind of a durable recovery and confidence growth in the stock market. So the TARP Bill, which failed the first time it went up for a vote but then passed, when TARP passed, we became more confident that we had hit a bottom.

We had a we had another bottom test in March 2009, but buying in October of 2008 would have made you a lot of money over the next you know 2, 3, 5 years. So my conclusion on China is, they've had this monstrous real estate bubble. The value of the housing stock to personal spending, as one example, was three times higher than the US at its at its peak. Home price to income ratio is four times higher.

This was a crazy bubble for China. And so if we got to the point where China did what the US government did in 2008, which is to say, OK, we're going to attack the heart of this problem and we're going to recapitalize, not just the regular banks, but all those shadow banks as well, a lot of the larger ones have been failing recently, that would give me more confidence that China was an investment rather than a trade. Try to bounce 10% or 15%, go up and down based on some monetary policy announcements, maybe.

But to really get bullish on China for the longer term, I would need to see an aggressive recapitalization of the banking system, both the regular one and the shadow one, and certainly not strong arming domestic mutual funds into buying shares and going after short sellers. So that's my take on Chinese bottom fishing and a bunch of other things. Thank you for listening.

Our next Eye on the Market paper we'll be in early March and will be our annual energy piece. And this year, it's called Electro-vision, for reasons you might be able to imagine. So, see you next time.

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