Kevin, we're at record highs right now in the stock market, and coming into the year there was this story of broadening out, but my understanding is that that has sort of dwindled and now we're getting back into this top-heavy situation. What's going on here?
Yes, you're right to point that out. I think that if you segment the market into really just sort of two phases so far this year, you could sort of take everything up until the war in Iran, obviously. But I would actually extend that to the low that we had on March 30th for the S&P 500. So since then, it has been this sort of tale of two markets, as I've been calling it, where you do have the really big companies and just a few of them at this point in the S&P that are, you know, continuing to rip higher. Of course, we've had a little bit of weakness in the past couple of days, but the rest of the market, the equal-weighted S&P 500, if you want to use that as the proxy, has failed to keep up. So yes, in some ways we're sort of back to almost regularly scheduled programming, you know, before the beginning of '26, and you sort of flipped that whole consensus call at the beginning of the year, which was international outperforming the US, you know, big tech ceding some leadership to small caps.
But what I do find interesting, and I think it sort of goes against the narrative that it is just a few big tech stocks that are leading, there are still meaningful pockets of outperformance in the market. It's just harder to find and sort of harder to come by, and maybe lost in the headlines. So just as an example, small-cap tech has outperformed large-cap tech significantly over the past year, doesn't get covered a lot. And you know, that's a little bit, admittedly, that's a little bit of a cherry-picking, I'll admit on my side, and just picking that one-year time horizon because you could extend that out maybe a couple more years, and of course large-cap tech has done really well and outperformed. But I think that is an instance where you do want to see that, you know, in performance terms, it's not just a couple of companies that are up.
But I will say, you know, moving forward, if it does sort of continue where you get this split, not just in terms of a couple of stocks lifting the S&P, but in terms of underlying breadth, you know, materially weakening, and if it does break down, historically that is consistent with more, you know, more corrective phases and corrective activity at the index level. So that's where you could potentially sort of open yourself up to, you know, maybe a bigger drop and a bigger correction.
Well, I think that gets so hidden beneath just like news coverage and the news cycle, because we see every day the AI trade pretty much accelerating record highs every day. But you have this stat here on a rolling three-month basis, the percentage of members that are outperforming the index has dropped to 23%.
And we've only seen that in '24, '23, and 1973.
So to me, there's this tension here where generally in a bull market you want that breadth, but then also we're in this generational change with AI, and like, how do you resolve that conflict? Because I think AI could fuel things on its own, but you also historically want this broader rally here.
Yeah, it's an interesting stat because I mean, when you point to the years that I mentioned, you know, you look at 2024 and 2023, of course not consistent with epic bear markets to any extent, but you had, you know, significant corrective phases in the market in 2023. The fall of '23 was sort of that classic example, a little bit like we're living now in terms of rates really spiking, and then, you know, big tech in particular driving a good portion of the correction at that point. And then you also had some corrective activity in 2024. So, and of course you bring it back to 1973, that was a very different scenario of a pretty epic bear market and then a pretty, you know, a pretty tough recession, but the economy looked so different back then. So I think that if you looked at in the instance of the past few years, when you did have that really low share of companies that were outperforming, we know now with the benefit of hindsight that it wasn't the end of the bull market, but it didn't mean that you couldn't get some of that corrective activity at the index level.
So I think it's more an instance of understanding that when you do have such a large concentration or a large portion of the market cap being explained by fewer names, that in and of itself is not a bad thing. We shouldn't point to that. I think we shouldn't point to that and say, 'Oh, that means that's a negative in and of itself.' I think it just means that you need to be more aware of how much weight and punch and power they have, both to the upside but to the downside. And you know, I think that to your point earlier about the beginning of this year when we were seeing that shift away from the largest names and towards the average stock, you know, we were getting a lot of questions at the time of, 'Well, the market's not really doing well, not doing much this year.' And we were saying, 'No, it is. You just have to go under the surface and look at that average stock that was, you know, meaningfully outperforming the indexes.' And again, that was almost purely driven by the weight that the biggest companies have in the S&P.
Well, I spoke with a lot of investors in December who were saying they were going overweight into the equal-weight index instead of the market-cap weight. And I think that's totally shifted in the last couple months. But because AI and technology in general is becoming more and more prevalent in the economy, to me it makes total sense that it would continue to like overwhelm the market essentially. Like I don't see that as necessarily a negative thing. I think it's kind of just inevitable, right? That tech is going to be the thing that drives the entire market.
I think that, you know, as a driver, again, in contribution terms, in terms of market cap, I think that that could certainly be the case. The one thing that I would say, and I think this does sort of speak to the benefits of being properly diversified across all sectors, to the extent, you know, whatever you're comfortable with as an investor. I think that the distinction between the contribution to returns for an index and performance is very important at this point. And just to give an example, you know, and I don't do any coverage on individual stocks, so this is not a recommendation. This is just using it as an example. If you look at Nvidia, which is the largest company, you know, in the S&P 500 by market cap, the contribution to the S&P 500's return this year so far is ranked number one because of its size and it's got, you know, positive performance. So therefore, you have, you know, a pretty significant contribution.
If you look at its performance, there are almost 90 names in the S&P 500 that are seeing stronger gains this year, you know, in front of Nvidia. So again, not to say that it's a good or bad thing, but I think it's to point out that, you know, there's often this conflating with the biggest stocks and the best performers, not necessarily the case. So I think two things can be true where you can have a significant contribution from those big AI names, but you can also have meaningful performance outside of those areas. So that's why I think, you know, it sounds boring sometimes to people to talk about diversification, but I think it's an important topic because it does sort of speak to how strong some of the gains have been across the market and across many sectors so far this year.
Well, I think that reiterates the call from before about equal-weight exposure. And I do think there's value there. To me, I have a very long time horizon as an investor. I'm going long tech right now, as I know a lot of my same-age-group peers are. But all right, let me ask you about this chart. You have this idea of a convergence trade between the Mag 7 and the S&P 493, essentially, when the rest of the market's earnings are going to catch up to big tech. Walk us through what's going on here.
Yeah, so this is, and admittedly we've been writing about this for a while, where, you know, we've sort of been waiting for not a convergence in terms of the growth rates have to surpass each other in terms of earnings growth. So you take the Mag 7 and then you take the 493, you split them in two. You look at their earnings growth trajectories. Actually coming into this year, they were expected to almost at this point have already crossed, or at least the estimates by, you know, by the middle of the year were supposed to have crossed. I don't look at it that way and saying that has to happen, you know, for the market to do well. But we've kind of been waiting for that convergence for a while to maybe, you know, provide some validity to the fact that the average stock could continue to do well, that it wasn't just a few companies holding up the S&P in price performance terms or in earnings terms.
So I think the good news is that, you know, so far in this reporting season for the first quarter, which we're essentially, you know, done with the exception of one big company, but, you know, so far the beat rate has been quite strong both on the revenue and the earnings side, and you've seen this pretty remarkable upward revision to S&P 500 earnings for the rest of the year. Some of that is concentrated in some sectors. But I think broadly when you do look at tech and communication services, you know, their weight in the S&P in an earnings sense, not just in a market cap sense, it does provide some, you know, validity to the idea and it does sort of underscore this idea that the earnings picture as of right now has been relatively strong and relatively resilient.
So when we think about that sort of convergence trade, yes, the growth rates may not be close to each other right now, but I think directionally it matters more for, you know, the 493 to continue to see growth estimates improve. That to me, I think the trajectory is more important than just, you know, looking at the two percentages and comparing them against each other.
Yeah, I totally agree with you, and everything in the stock market is about expectations, and as long as expectations don't break, I think we're on a pretty steady path here. If we think about how there's a small number of giant companies that are driving the AI trade, to me it also makes total sense that as they spend the most and they also allocate the most to different parts of the economy, they attract the most talent, right, in the employment sector. Traditional metrics of like breadth or even valuations seem to be getting less and less relevant because there's such a, you could call it top-heavy, but just like a power concentration across the whole economy and these few companies. What do you make of that?
Well, I think I would first say as an employment driver, it certainly has looked different this time because, you know, traditionally in a strong capital spending cycle, you would see these pretty powerful ripple effects where you would go through, you know, a strong business investment cycle that would eventually filter through to labor and you would see a pickup in hiring. And then the idea is that you create this virtuous cycle of more people spending money because they have, you know, more income growth or they actually obtain an income if they get hired. But we haven't seen it yet specifically in the tech sector, which is why productivity has looked so strong in that sector because output has been quite strong but employment growth has been lacking.
And really, the tech sector, it's named information in the BLS data, so, but it's essentially the tech sector. But that hiring has looked incredibly weak. It's actually one of the worst charts right now out of all of the industries in terms of employment levels. But I think a lot of that has to do with how strong productivity has been in that sector. Could be a cost story as well because a lot of that spending from companies is going towards, you know, just sort of that pure investment piece of it. But I think stepping back beyond that, we have seen some stronger ripple effects in the broader economy where, you know, hiring has largely stabilized so far this year. I'm not sure how much I would put that on sort of the AI story other than maybe companies just finding a little bit of relative stability, you know, after pretty significant disruption geopolitically over the past year. But regardless, you know, it's a good sign.
To the point on valuation, you know, I think that the difference over time and when you look at the construction of the market, at least in the US, how much it has changed over time, if you compare it, you know, in the '70s or the '80s or even the '90s, the early '90s to now, you look at the weight of areas like tech or communication services and they've grown significantly. But we've also grown in our economy, in economic terms we've grown significantly in terms of their weight in output. So to some extent I think it does justify why multiples have been more elevated, and not even in the past several years. This is taking it back to, to some extent, the early 2000s, but especially the late 2000s, and you look at a long-term chart of any price-to-earnings metric. It is elevated and we've sort of had this higher floor in the past several decades versus the couple of decades that preceded that.
So I do think to some extent it has been consistent with how the economy has evolved over time. We've become more services-oriented. We've become more tech-oriented. And at the end of the day, you know, to me, valuation is really just a gauge of investor sentiment. That's how I was sort of trained to look at it. You know, there is a P/E that somebody is willing to pay. Sometimes, I would argue today, they're willing to pay a lot for stocks. Sometimes in, you know, times like in portions of 2020 when we were deep in the sell-off, or 2009, or even 2022 to some extent in October, people are not willing to pay anything and they're waiting for multiples, they're willing to wait for multiples to even go lower. So I use it more as a gauge of sentiment than anything else, especially a market timing tool because it is not strong as a market timing tool.
So long-winded answer, but I think important context around why valuation metrics have changed, but also, you know, why sometimes investors get tripped up in thinking that they are these perfect signals for what the market is going to do in any given time frames, out six months, out one year. There's just not enough historical data to support that.
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Well, I know plenty of investors who only pick stocks based on P/E, which I think is a very one-dimensional way to operate as an investor.
Yes. By definition, one-dimensionally, as you're just picking one metric.
Yeah, it's, well, you'd be surprised. Some of these guys are very successful too. And, but let me ask you this. You said P/E is not a, you said it's not a good predictor of market timing.
It's not a good predictor of performance. So, if you take a sort of a standard price-to-earnings multiple for the S&P 500, you do a modern-era look back, so around, you know, to the 1950s as your starting point. And we often show this as a scattergram. So, just a bunch of dots, you know, on a chart. There's basically no statistical significance and essentially no relationship. So if you kind of looked at a linear relationship between the two, between where multiples were a year ago and what performance looks like today, you really don't find any relationship.
And there are times, you know, on that chart, there are times when you've got sky-high multiples and yet the market has done well a year later. There are times when multiples have been, you know, at the lows and the market has still done poorly a year later. So it's a little bit shorter-term, you know, thinking in terms of just a year out, but I think it's a good exercise because oftentimes, especially, you know, in the current time, you will see a lot of charts put up with P/Es being very high and a lot of warnings around, you know, there's an imminent correction or crash coming. And it's not that it's right or wrong. I just think that using only that variable leaves out a lot of context of, you know, what the macro environment looks like and what the earnings trajectory looks like of a particular company or a particular sector.
Well, there's the chart of the Shiller P/E ratio. Everyone uses that to make the dot-com comparison saying the stock market's more expensive or frothier than it was. But I think that's an amazing stat that there's no connection to performance. I haven't verified this, but that sounds, I mean, sounds about right.
And you know, I think dot-com is an interesting thought experiment or exercise because yes, you could look at that moment in March of 2000 when everything was essentially at its peak. It was sort of that perfect storm. I mean, price-to-sales, price-to-earnings, price-to-free-cash-flow, the Buffett indicator, you could, you know, market cap to GDP, I could literally take whatever metric you want and everything was sort of screaming this is ridiculously expensive.
But there are also several metrics that were saying that for several years in the late '90s leading up to that moment. So if you had, you know, sat out of that rally really early on, you would have lost out on a lot of those gains. So again, it's sort of a way, I think, to underscore that nobody could have perfectly seen that in the run-up and that kind of parabolic move. And then vice versa. Nobody could have necessarily seen that right at the peak and then perfectly predicted, okay, this is going to be a two-year-long, you know, bear market, almost two-and-a-half-year-long bear market process where the NASDAQ gets, you know, chopped significantly, you know, upwards of 70 to 80% depending on the sector.
Are you seeing anything in today's data that makes you think the comparisons are warranted?
