Thoughts on the Market

Original Release Date: April 28, 2026
Tom Wigg and Stephen Byrd discuss the accelerating pace of AI breakthroughs, the forces driving them and why the next phase of development may look very different from anything we’ve seen so far.
Read more insights from Morgan Stanley.

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Tom Wigg: Welcome to Thoughts on the Market. I’m Tom Wigg, Head of Specialty Sales in the Americas at Morgan Stanley, and a sector specialist in Technology, Media and Telecom.
We wake up every day to new AI product releases, so it’s easy to lose sight of the unprecedented non-linear improvement in AI capabilities. But things are about to get weird.
It’s Tuesday, April 28th at 8am in New York.
The market has been thinking about AI in linear terms. But we need to reframe that assumption of only incremental improvement and think about exponential improvement.
That was my takeaway from a conversation with Stephen Byrd, Global Head of Thematic and Sustainability Research at Morgan Stanley. In our conversation, we zeroed in on Stephen’s bull case for broader AI model improvements.
Tom Wigg: First, I want to talk about one obsession that you’ve been writing about for the last several months – is this idea that we’re going to see nonlinear improvements in the frontier models coming out this spring.
Stephen Byrd: Yes.
Tom Wigg: There’s been, you know, some big headlines around new models, benchmarks coming out publicly. Is this, you know, your bull case playing out on these models? And what are the implications?
Stephen Byrd: Yes! Absolutely, Tom. So we have, to your point, we are obsessed. And I know I’m not shy about that – with the nonlinear rate of AI improvement. It is the most important impact to so many stocks that I can think of in the sense that it can impact all industries, all business models. So, what we’ve been saying for some time is, if you look back over the last couple of years at the relationship between the amount of compute used to train these LLMs and the capabilities, we have a very clear scaling law.
And approximately the law is, if you increase the training compute by 10x, the capabilities of the models go up by 2x. Now, as you and I’ve talked about this a lot; just meditate on that for a moment. I think things are about to get weird in the sense that on the positive side, we’re going to see all kinds of underappreciated capabilities across many industries. So this disruption discussion, I think, is going to spread, but it’s also going to require investors to, kind of, be more thoughtful about what they do with that concept. Meaning you can’t sell everything. In the sense that AI will disrupt some businesses.
I actually think this is healthy in some ways because now it forces investors to really look at each business model and assess which is going to get disrupted, which can get supported and enabled by AI, which are immune. Because there are some business models that actually are immune.
But essentially from here, Tom, I’d say we are expecting through the spring and summer to see multiple models that are able to perform a much greater percentage of the economy at better levels of accuracy at incredibly low cost. Which I know you and I have talked a lot about the cost of actually doing this work from the LLMs.
This is massive. This is going to impact so many industries. I think this is all to the good for the AI infrastructure plays because it shows the importance of getting more intelligence out into the world.
Tom Wigg: So, you mentioned the constraints we’re seeing across compute, memory and power. It seems like most of the CEOs of the labs and hyperscalers are talking about this. Investors are bullish in terms of the ownership in, you know, memory, optical, semi-cap, et cetera. But the question I’m getting more recently is around what’s the ROI on all this spending. And does the market action in these hyperscalers, which have been pretty bearish year-to-date, force a cut on CapEx? So, maybe if you can marry that with what you’re picking up on the ground in terms of compute spend and whether the frenzy still continues, you know, versus the ROI? And, like, what could happen?
Stephen Byrd: Yeah. The short answer – I’m going to go through detail – is I think the bullishness is going to get more bullish over the coming months. And let me walk you through a couple of the mathematics and then just what I’m seeing on the ground to your point, Tom.
So the mathematics. We have a token economics model that looks from the perspective of a hyperscaler or an LLM developer in terms of – if they sell their token at a certain price and you fully load the cost of a data center and all associated costs, financing, you name it – in what are the returns? And the bottom line is the returns are excellent.
The other element we spend a lot of work on, and you and I talk a lot about, is the demand for compute. In this world where the LLMs are increasing in capability and the token usage goes way up with agentic AI, video world models, all that stuff, we think that there is a massive shortage of compute. So, if you’re lucky enough to be a hyperscaler with the compute, with the power, we think that they will have a lot of pricing power on the tokens.
Let me explain why we see price power on the tokens. Now I’m going to flip to the perspective of an adopter. Let me give you just rough mathematics. There was a study last year from one of the big labs showing that on average, an enterprise user using an LLM might be able to replace work that would take about one and a half hours from a human. That would save about $55 of cost. A million tokens, depends on whether you’re looking at input or output – but let’s just call it $5 for a million tokens.
The average usage case today for a fairly complex agentic task in an enterprise setting is in the tens of thousands of tokens. Okay? So let’s just do that math again. $55 of savings. A million tokens cost $5, and a typical agentic usage is far less than the million tokens today, though that will accelerate. The economics are a home run for adopters.
So, we’re in a situation where compute is very scarce. I see pricing power all over the place for those who have the compute and have the power.
Tom Wigg: So, when you put it like that, Stephen, it seems so inevitable and obvious. But I wonder why the hyperscalers are trading the way they are? And when do they see the revenue inflection you’re talking about? Is this like a stay tuned kinda 2026 event? Is this something we have to wait for for 2027-2028?
Like, how do you think this flows through to the extent that the market will get more comfortable that all this free cash flow pressure is worth it on the other side?
Stephen Byrd: Yeah. This is, in short, I think this is a 2026 event. But let me dive into that because what you just asked is so important for so many stocks.
So, let’s talk through this. The capabilities of the models are advancing so fast that the average corporate user is not yet keeping up. There is this gap. But that will happen quickly, and we’re seeing signs from these labs of revenue at the lab level that is accelerating. So that’s a good sign.
What we’re seeing, though, among fast adopters is those adopters who really understand the capabilities are quickly realizing just how economically beneficial there is. An example, one of my best friends founded a software company many years ago. Last month was – that was the last month in which his programmers wrote code. They’re done with writing code.
The efficiency benefits for his business are absolutely massive. But he feels like he’s just scratching the surface, and he’s about as technically capable as anyone I know. He has two PhDs in the subject matter. He’s very, very good.
So long way to say that we’re living in almost two worlds where the fast adopters will show what’s possible. The average utilization for enterprises will still take some time. But I do think that the market will react to what they see from the fast adopters in the sense of – the tangible economic benefits are so big.
Now, on the ground, what I’m seeing on the infrastructure side, my friends in power tell me that a couple months ago is when they saw the sense of urgency from the AI community go up a couple of notches for them to get the infrastructure they need. So they saw this explosion in compute coming. In the last two months, the weekly usage of tokens according to OpenRadar is up a couple hundred percent in a couple months.
So, I do think we’re seeing this. So, this is; it’s happening quickly. What I would say is the market will have these signposts in every industry of early adopters showing this benefit. I think that’s enough for us to start to get bullish. We also… I just think when you look at the demand for compute, the compute numbers need to go up. And with that, you know, everything in the AI value chain, infrastructure value chain, the volumes need to go up.
Tom Wigg: One bear case that I wanted to interrogate was – there’s one view that, yes, there’s a token explosion right now. But it’s because the first use case is coding. Which is inherently, you know, very developer-friendly and token-intensive relative to other knowledge work.
Can you talk about, you know, whether you subscribe to that? Or whether the token intensity will be as high or lower as this expands to other areas of knowledge work in the next several years?
Stephen Byrd: Yeah, it’s a great question. The short version is that, yes, it’s true that software usage is more token intensive. However, what we’re going to be seeing – we’re starting to see it – is in almost every knowledge-based job, we’re going to move to agentic AI. And when we do that, you tend to see an explosion in compute.
Let me walk you through the numbers. There are a couple studies that show essentially when you go from a query-based usage of LLMs to an agentic use for any occupation, you see about a 10x increase in token usage per use of those models. And you can see why.
I’ve anecdotes of some of my friends who are newer to this – who set their agents loose overnight to do non-coding work. And in the morning they get some pretty amazing results. But they also used a lot more tokens than they’d expected … (laughs)
Tom Wigg: And a five grand credit card bill?
Stephen Byrd: Exactly. It’s like maybe next time you put a few parameters around that. But long way to say, it’s agentic across every workflow that I can think of that will still result in an explosion in token demand.
Tom Wigg: It’s definitely a good idea to put some parameters around your agentic workflow.
My thanks to Stephen for that conversation. And thank you for listening. Let us know what you think of the show by leaving us a review where you listen. And if you find Thoughts on the Market worthwhile, tell a friend or a colleague about us today.

Despite the historical energy disruption from the Iran conflict, stocks are back to record highs. Our Global Head of Fixed Income Research Andrew Sheets and our Head of Commodity Research Martijn Rats discuss different views and fundamentals driving markets.
Read more insights from Morgan Stanley.

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Andrew Sheets: Welcome to Thoughts on the Market. I'm Andrew Sheets, Global Head of Fixed Income Research at Morgan Stanley.
Martijn Rats: I'm Martijn Rats, Head of Commodity Research at Morgan Stanley.
Andrew Sheets: Today: oil, oil inventories, and the price at the pump.
It's Wednesday, May 6th, at 2pm in London.
Martijn, it's great to talk to you. We remain in this very unique market where on the one hand, the energy market is severely disrupted. On the other hand, we're making new all-time highs in the stock market. And part of this debate is a creeping sense that maybe the energy market is just a lot more resilient than many people initially thought.
So, let's just jump right into it. As you look at the current state of the world, the state of things, how are you seeing the energy market at the moment?
Martijn Rats: There are definitely two views in the market. I would say commodity specialists, oil traders, people that trade oil and gas equities for a living, tend to focus on the size of the supply shock. And it is neither hyperbole nor disputed that the size of the supply shock is the largest in the history of the oil market. We have the statistical data to back that up. That is not a controversial statement.
But at the same time, the other view in the market, generally held by your generalist investors who invest across many markets. They tend to focus on the likelihood or possibility that this supply shock might also be uniquely short. It was there all of a sudden, from one day to the next, the strait was closed. It felt a bit man-made, so to say. It was an outcome of a political decision, and that can also be undecided. And so, this is – the to-ing and fro-ing in the market is; on the one hand, this shock is very, very large. But the other hand it may also be very, very short.
Now we went into this supply shock, arguably well-prepared. In the sense that during the course of like late 2024, all of 2025, and the very early part of 2026, we were telling a story of oversupply surplus. And on top of that, given the military buildup was going on in January and February, a lot of countries in the Arabian Gulf – Saudi Arabia, the UAE, Kuwait – visibly put out a lot of oil at sea.
So, in the oversupply of 2025, we put oil in storage in lots of places that we can't always see. But that seems very likely. Oil in the water was very, very high. So, we have been living off these buffers, and that has helped. And then, yeah, at any point in time, there were good enough reasons to assume that on a timeframe of a couple of weeks, this would largely be resolved. We would eat into these buffers, draw some inventory.
And it has been hard for the market then to really capitalize the size of the supply shock and say, "Yeah, really oil prices need to spike very, very high." And in that sense, we’re left with this significant supply shock, but we haven't taken out the highs that we saw in 2022, for example.
Andrew Sheets: So maybe a way to think about this, right, is that if we imagined all of that oil as sitting in a big tank. We've kind of stopped a lot of the flow into the top of the tank as the Strait of Hormuz has remained closed. But oil's still able to drain out of the bottom, kind of, like normal because that tank is being drained. Those inventories have been drawn down. Maybe that's a quite a crude analogy, to forgive the pun.
But how long can that last? I mean, if we think about these inventories, if we think about the speed of which they're being drawn down; and I think that's an important point that you mentioned, that these inventories were unusually high going in. But they're obviously not unlimited.
Where does that stand? And I guess, you know, what is the limit of that? How long can those inventory draws last?
Martijn Rats: Yeah, yeah. To say that this is the billion-dollar question would be understating it, Andrew. It's also a unusually complicated question to answer in the sense that it depends very heavily on the region, on the product that you're looking at. Jet fuel in Europe, NAFTA in Asia, you might see something sooner. But other products in other regions, you know, might take longer.
We often don't really know where the operational limitations of inventories are. Globally, we see something like 8 billion barrels of oil in some form of storage. That is an enormous amount. We can't draw that down to zero because a lot of that is there for operational, like working capital type reasons. Just to facilitate the operations of the industry. Is the floor seven? Is the floor six? These things are hard to answer.
Andrew Sheets: You’ve got to have some oil in the pipeline to make the pipeline flow…
Martijn Rats: Exactly, exactly. You can't operate a refinery if you don't have at least some storage right next to it. It just doesn't work. So, these things are hard to know. But I would say that we are eating through these buffers very, very re-rapidly now. Oil on water has largely normalized and is no longer elevated.
We are seeing very large inventory draws across every data point that we have on refined products. Refined products are universally drawing. On crude, the data is more patchy. But we are seeing large inventory draws now coming through in the United States. I would say – and this is partly having worked with this data for a long time and sort of developing some market feel rather than very analytical spreadsheets, so to say. But I would say that if the flow of oil through the Strait of Hormuz does not resume on the sort of next four to six weeks, we will get very, very tight by June, early summer.
And, well, look, I mean, from there, it's simply… You know, if you then were to forecast. You know, project forward from there on. It would be getting tight by August, September. But of course, that's done under the assumption that the flow remains impaired over that period, which I would say most market participants would not assume at the moment.
Andrew Sheets: And another point that comes up sometimes, at least in my conversations, is, ‘Oh, but, you know, maybe Venezuelan oil is going to be coming online.’ There's more investment. The U.S. seems very focused on increasing oil output in Venezuela. You know, can that match in any sense the scale of what we've had disrupted here?
Martijn Rats: No, that is a complicated issue in the sense that, you know, growing oil production takes time. It takes capital, it takes equipment, it takes a lot of people. Venezuela at the moment, produces a bit more than a million barrels a day. I'd have to say, like, relative to the size of Venezuela's production, the last two monthly data points have actually come in better than expected. But you're talking about 100,000 barrels a day, 200,000 barrels a day, that sort of thing. Relative to a supply shock that is 13-14 million barrels a day.
The fastest ever single amount of production growth of any country in any year was 2018. U.S. shale with natural gas liquids included grew 2 million barrels a day in a single year. But yeah, even that…
Andrew Sheets: So, 2 million barrels relative to 14 million barrels lost is…
Martijn Rats: Yeah, exactly.
Andrew Sheets A drop in the bucket.

Martijn Rats: And that had a huge run-up of several years of putting the infrastructure in place to do that. I mean, it…. You don't turn it on a dime either. So no, that remains difficult.
Andrew Sheets: So, you know, maybe a dynamic to close with is actually another way that I think people care about the oil price, you know, besides their portfolio – which is they drive.
And, you know, you had a great stat in your report that one out of every 11 barrels of oil that's produced ends up in an American car. And the U.S. is a big producer. Its inventories have been drawing down. There are clear signs that the U.S. is exporting a lot of energy, and as a result, gas prices are also going up in the U.S.
So, you know, what… If you could just talk a little bit about the move in gasoline and maybe, you know, I think this could be a good segue into this idea of distillates into, kind of, parts of refined product. And how those prices can deviate or not from the barrel of oil we often talk about. And then even just more generally, kind of what is the price at the pump that people might need to think about as you head into the summer – assuming, you know, this conflict is still somewhat uncertain.
Martijn Rats: Yeah. So, the United States is very interesting at the moment. In the sense that the regular discourse about the United States is that the United States is energy independent because it is a net oil producer. And at the most aggregate level, that is correct. But that doesn't mean that the United States is not connected to the rest of the world from an oil market perspective. I would say actually it's the opposite.
The U.S. oil market is deeply connected to the rest of the world. It is a net exporter because there are very large imports, and there are very large exports, and it just happens so that the exports are a little bit bigger than the imports. So, it's a net exporter.
But flows in both directions exist for every product – for crude, for diesel, for gasoline. So, the U.S. should be the last place to have physical disruptions because the supply is close to home. But in the end, it's so connected; that in the end, there's only one global oil price – and we all pay it, including in the United States.
Now, because of the deficits at the moment, in Asia, to [an] extent in Europe, there is a very large pool on oil from the United States, and we're seeing that across the board. Crude oil exports were 4 million barrels a day, at the start of the year. They're now running sort of 5.5, even 6 million barrels a day. So, there's a lot of crude being pulled out of the United States. That is partly also the SBR release, the release from the Strategic Petroleum Reserve. But the export's very, very large.
Another product where that is also happening is in gasoline. Now, the gasoline market in the United States has a degree of complexity to it in the sense that the U.S. is a big importer of gasoline in the East Coast and the West Coast, but then a big exporter from the Gulf Coast.
Andrew Sheets: Hunh! Okay. Yeah.
Martijn Rats: Net-net, it's an exporter, but in the East Coast and the West Coast, big, big importer. Now, in Europe, for example, we are normally long gasoline, short diesel. We export our surplus to the U.S. East Coast. But, at the moment, it's tight in Europe, so we're not exporting that much gasoline. So, imports in the United States have dropped a lot.
At the same time, Asian customers, Brazilian customers, Mexican customers [are] pulling a lot of gasoline out of the Gulf Coast. And as a result, the net exports are unusually high for this time of the year. On top of that, the Strait of Hormuz issue has tightened the diesel market so much relative to the gasoline market that it is favorable for refineries to maximize their diesel output over their gasoline output.
Andrew Sheets: Hmm. And these are decisions you can make in terms of how you crack that barrel in a refinery and split it up.
Martijn Rats: Yeah, exactly. Within a relatively narrow window, but you can make tweaks that are significant. Now, normally, we're going into this summer driving season, refineries switch from what we call max diesel to max gasoline. At the moment, they are not doing that.
Andrew Sheets: Mm.
Martijn Rats: So, you have low gasoline production, and you have large net exports of gasoline. Over the last 11 weeks already, we have seen a very significant, very significant decline in gasoline inventories in the United States. And prices have risen at the pump. The nation's average is now $4.50 per barrel, as of reports this morning.
The summer driving season has yet to start. That can become $4.70, $4.80. That can become $5. Above $5 is historically a point where people get, yeah, worried about demand destruction. And it has a real impact.
Andrew Sheets: Well, Martijn, I think this remains such an important and interesting story. And even if, you know, it can seem sometimes like the market has moved on to other things, clearly there are a lot of other factors driving the equity market. It remains pretty historic, pretty significant, and pretty complicated. Also, something that I think, you know, affects the day-to-day spending and lives of a lot of people out there.
So, Martijn, again, thank you for taking the time to talk.
Martijn Rats: Thank you.
Andrew Sheets: And thank you, as always, for your time. If you find Thoughts on the Market useful, let us know by leaving a review wherever you listen. And also tell a friend or colleague about us today.

Our Head of Europe and Asia Technology Research Shawn Kim discusses AI’s move from passive chatbots to active agents—and how this influences tech supply chains.
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Welcome to Thoughts on the Market. I’m Shawn Kim, Head of Morgan Stanley’s Europe and Asia Technology Team.
Today: A foundational shift in the development of AI and its broad market implications.
It’s Tuesday, May 5th, at 3pm in London.
Think about the last time you asked a chatbot to write a summary or a draft. Or maybe answer a query. It was probably useful. But you were also still driving the interaction: asking, refining, copying, checking, and moving the work forward.
Now imagine a system that does not just respond, but acts. It remembers what you asked last week, understands your preferences, works across digital tools, plans a workflow, and adapts as circumstances change.
That is the shift from GenAI to agentic AI: from AI that helps with thinking to AI that helps with doing. GenAI is mostly passive. It takes a prompt and produces an answer. Agentic AI is active – less a copilot for one task but an autopilot for multi-step workflows.
The distinction is key because computing requirements are changing. In GenAI, large language models and GPUs handle much of the thinking. GPUs, or graphics processing units, process many calculations in parallel, making them central to modern AI models. In agentic AI, CPU becomes more important. CPUs, or central processing units, coordinate tasks and connect systems to the broader digital infrastructure.
Agentic AI also depends on three stacks: the brain, or the large language model; orchestration, where the CPU manages the doing; and knowledge, which is memory.
Memory may be the most important layer. An agent that knows your preferences, documents, tone, and task history becomes more useful over time. That creates a context flywheel. The more context it collects, the more personalized it becomes, and the harder it is to leave.
Typically, in computing, we think of memory as storage, mainly. We need to rethink this. Memory is also continuity. When an AI system can use past experiences, memory becomes a long-term state, shared knowledge, and behavioral grounding.
And that matters because LLMs have fixed context windows. Once a conversation exceeds that window, older content falls off. For simple questions, that may be fine. But for a coding agent working across a large codebase over days or weeks, it is a major limitation. Serious work requires persistent memory, short-term orientation, and active retrieval – remembering prior decisions, understanding changed files, and finding relevant codes without the user pointing to every dependency.
For investors, the implication is clear – agentic AI changes the bottlenecks. We see CPUs as the new bottleneck, with memory seeing the highest content increase. We estimate as much as 60 percent, or $60 billion of incremental CPU total addressable market by 2030, within a total CPU market of more than $100 billion. We also estimate up to 70 percent of incremental DRAM bit shipment tied to this theme.
That makes us more positive on supply chains including memory, foundry, substrates, CPU and memory interface, and capacitors and CPU sockets. These areas benefit from content growth, pricing power, and capacity constraints into 2027.
As AI moves from answering questions to taking actions, investors should watch the infrastructure behind the shift. Because in the agentic era, the next big AI leap may be less about the prompt, but more about the processor.
Thanks for listening. If you enjoy the show, please leave us a review wherever you listen and share Thoughts on the Market with a friend or colleague today.

Introducing a recent episode of Hard Lessons, featuring Rick Rieder, BlackRock’s CIO for Global Fixed Income and Head of the Global Allocation Investment Team, in conversation with Seth Carpenter, Global Chief Economist and Head of Macro Research at Morgan Stanley.

Watch and listen on your favorite podcast platform.

Our CIO and Chief U.S. Equity Strategist Mike Wilson explains the factors behind stock gains across sectors.
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Welcome to Thoughts on the Market. I'm Mike Wilson, Morgan Stanley’s CIO and Chief U.S. Equity Strategist.
Today on the podcast I’ll be discussing why earnings remain the most important variable for equity markets.
It's Monday, May 4th at 2pm in New York.
So, let’s get after it.
The more I think about what’s been driving this market, and the more time I spend with the data, the more I keep coming back to the same conclusion: it’s earnings. Not the headlines, not even the Fed. Earnings are doing the heavy lifting right now.
When I look at this reporting season, what stands out isn’t just resilience, it’s strength that’s broader than most people appreciate. The typical company in the S&P 500 is growing earnings at about 16 percent, and the median earnings surprise is running around 6 percent. That’s the strongest we’ve seen in four years.
What’s really interesting to me is that this strength is no longer confined to just the biggest tech names. Yes, hyper scalers and semiconductors are still playing a leading role, but the story is expanding. We’re seeing earnings revisions move higher across Financials, Industrials, and Consumer Cyclicals, in particular. That kind of breadth tells me this isn’t just a narrow leadership story; it’s something more sustainable.
At the same time, many investors are focused on the geopolitical backdrop, particularly the Iran conflict and what it means for oil, inflation, and supply chains. To be fair, companies are feeling some of that pressure. When you listen to earnings calls, you hear about rising freight costs, tighter supply chains, and higher input prices across industries like chemicals and machinery.
But here’s the nuance: those impacts are uneven. They’re not hitting the entire market in the same way. In fact, at the index level, they’re being offset. Energy has become a positive contributor to earnings growth, and the higher-end consumer remains relatively strong. Even with higher fuel costs, we’re not seeing a meaningful pullback in overall consumption – at least not yet.
That tells me that we’re not dealing with a classic demand shock. We’re dealing with a redistribution of pressure, and companies are adapting. In many cases, they’re passing through higher costs. Revenue surprises are running above historical norms, which suggests pricing power is improving.
Now, of course, earnings aren’t the only piece of the puzzle. Policy still matters, and the shift in rate expectations this year has been meaningful. The Fed has clearly become more concerned about inflation, and the market has repriced expectations to fewer cuts, and maybe even a higher probability of hikes. That repricing is a big reason why valuations corrected so sharply over the past six months.
It’s notable that even with that headwind, equities have managed to stabilize, thanks to earnings. When earnings are growing at an above-trend pace, equities can deliver solid returns regardless of whether the Fed is cutting or not.
That said, I do think that there’s one area of risk that deserves further attention, and that’s liquidity. We’ve seen periods of funding stress over the past six months, and those moments have coincided with pressure on valuations. The Fed and the Treasury have stepped in at times to stabilize these conditions, helping to reduce bond volatility and support equity multiples.
Bottom line, we have already had a meaningful correction in valuations this year with price earnings multiples falling 18 percent from their peak last fall. That adjustment occurred as the market digested the many risks that we have been highlighting. Meanwhile, earnings are not only holding up, they’re accelerating and broadening across sectors. The risks that we’ve all all focused on – geopolitics, oil, supply chains – are real. But they’re being absorbed at the company level. As a result, the price declines were much more modest than the compression in valuations.
Meanwhile, monetary policy is providing some headwinds, but it’s not overwhelming the earnings story. Equity markets move on two things: earnings and liquidity. Right now, earnings are more than offsetting the lingering liquidity concerns. In short, earnings growth is greater than the valuation reset. This is classic bull market behavior and as long as that continues, I think the U.S. equity market will grind higher for the rest of the year with intermittent bouts of volatility.
Thanks for tuning in; I hope you found it informative and useful. Let us know what you think by leaving us a review. And if you find Thoughts on the Market worthwhile, tell a friend or colleague to try it out!