Will the Risk On Party Continue? | Chris Carrano on The Quantitative Factors Driving Markets
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The ECB is ready to do whatever it takes to preserve the euro. And believe me, it will be enough. Thank you. Let's close this f***ing door.

Very pleased to be joined by Chris Carano, VP of Strategic Research at Venn by Two Sigma. Chris is on the quantitative side, so we're going to be talking about factor performances, what has been working in the stock market, in the markets, what hasn't been working. Chris, great to see you. We're recording in the middle of January.

I want to get into what has been working in the markets with regards to factors, but perhaps we can start by just explaining what is a factor. Value, momentum, what are we talking about here? Let's define our terms and then we'll get into it.

Yeah, Jack, super happy to be here. I do have to say, before we get started, I do want to make clear that my commentary today is not necessarily an endorsement by Two Sigma, and the views expressed are not intended to be relied upon as investment advice. Please refer to the Venn by Two Sigma website for important disclaimers and disclosures. And I want to be clear, I said that on my own accord.

Not necessarily being told to say that. Anyway, with that being said, so I think a good question before we get into like what a factor is,

It might be just a little bit of setup on what Venn is and why are factors used in Venn and how do institutional clients leverage them? So just very quickly, Venn is a risk analytics platform. So we deal with holistic multi-asset portfolios. This includes public and private assets, and it also includes multi-assets. So fixed income, commodities, hedge funds, all of those things, et cetera.

And originally it was only offered to the Two Sigma hedge fund clients, but in 2019 we opened it up to the broader market. So now anyone with a multi-asset portfolio can use Venn. And one of the reasons why factors, which is what we're going to talk about today, are so important for multi-asset portfolio analysis is

is because data is very hard to come by. The definitions of how we define risk across multi-asset, you know, different asset classes is very different. And factors try to put that all into one common language where you can analyze risk across the whole portfolio, but you can, you know, do it in a really intuitive way. So I actually want to do a quick example. This is like a real world example of like, what do I actually mean by that?

So it's I know you just introduced me as being somewhat on the quantitative side. That's a dangerous term at two sigma. But I think relative to most, I do do a lot more quantitative type of analysis. But I'm actually going to start with a burrito.

I know that that might be unexpected, but if you think about food groups and you think about a burrito, if you tried to analyze the dietary benefits of, let's say, a California burrito, which has French fries, by the way, it really hits every food group. So it's got French fries, which are the fat. It's got sour cream, which is dairy. It's got salsa, which is fruits. You can kind of break it up into the food groups. But when you look at the nutritional label,

It isn't really as balanced of a meal as the food groups would tell you. It's got 88% of your daily sodium. It's got, you know, 60% of your daily fat. So the food groups can be a little misleading in terms of like how it really affects your health, which is ultimately what you're trying to do.

So where you turn is the nutritional label. And you can imagine you're at a grocery store, right? Like the first thing you do when you're trying to say, okay, well, is this chocolate really bad for me? Is this hamburger really bad for me? Doesn't matter the food. What you do is you look at the nutritional label.

So it turns out factors are kind of like the nutritional label for the investment world. So these are the fundamental drivers of risk and return that are really true across all asset classes, the same way the nutritional label is true across all food groups. And the same way individuals in the grocery store might look at the nutritional label to understand what's really good for them,

An institutional allocator might look at factors to understand what's the true drivers of risk and return for their portfolio at a more fundamental level.

So that's really the idea of factor analysis, is to put everything in one common language. And an example of what a factor is, is something like interest rates, or an equity factor, which represents exposure to economic growth. So if you think about

economic growth as a fundamental factor, like carbohydrates might be a fundamental nutrient, economic growth really affects real estate. It affects commodities. It affects emerging markets. It affects all of these different asset classes. And what part of the factor analysis is being able to analyze that multi-asset portfolio, see where exposure to economic growth is coming from, and then doing your best to quantify it. And the last thing I'll say about that

is when you actually look at what could be in a nutritional label or could be in a factor lens. Actually, a nutritional label could be thousands of items. But what is actually put on that label is only the most important components for public health.

So they really try to make it the most digestible because if you put all thousand, you know, no one's going to pay attention to that. That's not going to really help anyone eat better. So when it comes to factors, you can have,

hundreds of factors. And that might be used by an actual hedge fund to choose between stock A or stock B or things of that nature, very sophisticated asset managers, the same way a food scientist might use hundreds of components of a nutritional label. But then we really try to break it down to only 18 fundamental factors. So could it be more granular? Absolutely.

But we try to do a less is more approach. So it's the most explanatory, systematic risk factors that can be found across all asset classes. And that's really the fundamental concept here. So when we talk about factor performance, which we'll get into and all of those things of that nature,

It's really through the two sigma factor lens, which you can think of as our market nutritional label. And that's specific to our methodology and how we're encouraging institutional clients to kind of view the world. So that's what factors are.

Tell us, use some of these factors to describe, let's say, performance for the entire year of 2024. When I think of 2024, it was a banner year for stocks, probably about 25% for stocks. I think credit performed well. I think on the interest rate side, it didn't perform as well. So probably, you know, if you hold a bond, which has interest rate risk as well as credit risk, it's kind of a more mixed bag.

Um, but then there's so many other factors. I think value probably did not perform well. The cheaper stocks did not perform the, you know, investment funds who on January 1st, 2024 said, Oh, I'm going to buy, um, you know, and I'll name this company, you know, cheap, cheap companies with a PE of like 10 that have some problems. They struggled. It was the people who were along the companies with PEs of 25 that grew their earnings a ton. And then their, uh,

PEs were re-rated to 35 or 40. Growth did a lot better. That's my perception. But what does the data say? 2024, tell us about what worked, what didn't work. I liked where you started, which is with equity styles. So these are factors like small cap, value, momentum, quality, low risk, crowding.

And the point of equity styles, just for your audience in terms of why does an institutional investor care, is they're really designed for alpha, right? So when you think about like a foreign currency factor, the point of that is to understand your risk. You're not necessarily positioning, maybe in a short term for alpha, but there's no reason to think a foreign currency factor should be positive over time. But with equity,

And small cap value, you expect it to be positive over time. So, institutional investors generally want that equity style exposure. With that being said, I think one of the most interesting equity styles to talk about in terms of 2024 performance and actually some of the long-term historical philosophy behind it, which I want to get into, is small cap.

Something that's interesting is our small-cap factor had its worst year in its entire history. And that's going back to about 1998. It was down almost 8.5%, so negative. And that may be surprising for some, because even though small caps, as we all know, have pretty much struggled for quite a long time, there's been a lot of academic questions on whether the small-cap premium actually exists.

It actually did okay from a yearly performance perspective if you think about it in an asset class framework. So IWM, ETF, Russell 2000, for example, I only say, I'm only using this because this is what markets tend to use. This is like headline, how did small caps do in 2024? You go straight to the Russell 2. So Russell 2000 was up, I think around 11%, something of that nature. And again, this is when our small cap factor had its worst year in its history.

So when you put its performance through that factor lens I'm talking about, you have to make sure all your factors are independent with each other. Otherwise, the correlations start to mix and it becomes really hard to understand, well, what is actually due to the equity factor and what's just due to the fact that it's actually small? Because the Russell 2000 has equity factor exposure and it has small cap exposure.

So when you put it through that two sigma factor lens, you're trying to really break down, decompose and understand what different fundamental nutrients of the Russell 2000 are actually the drivers of performance in 2024.

And I want to talk about that. And I'll actually get into something we talked about, Jack, too, because you made me look at some data based on our last conversation. And I found some really interesting data based on it. But before I get off track. So if you look at 2024, the equity factor for the Russell 2000 contributed about 18 percent positive return. OK, so it was a good thing that the Russell 2000 had equity risk premium exposure.

But when you look at small cap, so the actual unique return attributable to the fact that the Russell 2000 is literally smaller than large cap, it actually contributed negative 16% to the Russell 2000. That's because our small cap factor was down around 8%. It's got around a 1.75, 2 beta. So the exposure is about double. So return was down about 16%.

But again, the Russell 2000 was still positive, up 11. So there was other factors that were contributing to that. So for example, our factor lens is a global model. It's a global nutritional label. So the Russell 2000 is a U.S. exposure. Now, you had lots of reasons why the U.S. did well in 2024, especially towards the end of the year, November. You had the Republican unified government. President Trump was elected.

Small caps tend to be more domestic exposure, protection from tariffs. There's a whole deregulation. There's a whole swath of things of why small caps would have done well this year. But it turns out that some of that was just the fact that they were U.S., right? So local equity exposure contributed about 2% to the positive return of the Russell II this year. So basically, the idea, if you compared it with other small caps—

the fact that it was U.S. and all of those things that are U.S. specific, not small cap specific, that added some positive return. So another aspect that I want to talk about is the risk-free rate. So the risk-free rate is, you know, five and change in 2024. So when you're trying to understand things like the equity risk premium, that is the

equity exposure above and beyond the risk-free rate, how that contributed to return. So you have to cut 5% off the Russell 2000's return in the factor world right away because 5% of that is just the risk-free rate, right? So once you start decomposing it that way, you still end up with the 11.3%, but you actually realize it was a very bad year for small caps. And

Just if I were to try to translate that spirit to the broad market, there's some extremely obvious indications that that's true. So I think IVV, you know, S&P 500 up around 25%. Equal weight S&P 500 up 8.6%, right? So it's like...

Now, that's not a one-to-one translation with our global small cap factor, but it's pretty clear that 2024 large caps growth, mega caps for that matter, it was really a year for them. What I think is most interesting is if you look at November,

performance reports. If you look at 2024, it's all like kind of positive for small caps, right? It's like, well, maybe they underperform large caps, but they're still up 10%. Like you got to be happy with that.

And the risk there is imagine you're an institution and imagine you think that way and you go, okay, well, maybe small caps are coming. I'm going to allocate some more. There's still some life there when really the unique aspect of the small cap factor was terrible in 2024, at least through the two sigma factor lens. So just to be clear, our methodology, our perspective on the world.

it was not good. And actually, all that benefit you got from the equity risk premium, local equity, you could have got that benefit from investing in something with a higher beta to the equity market. Or you could have got that benefit from maybe upping your US allocation from a 70-30 to a 75-25, right? There was other ways to benefit from those factor exposures that helped the Russell 2000 without necessarily getting that negative tailwind from

from the small cap exposure. So for me, I think that's one of the most interesting factors. I'd love to speak about some other interesting data regarding small caps, but that's one of the most interesting factors because it's the worst it's ever done in the history of our factor lens. And it's also a bit

counter to the narrative. Now, I know many people will talk about large caps outperformed and, you know, there's some acknowledgement there, but it's a little bit counter to the narrative that you've been hearing throughout 2024 where, you know, just because long only small caps are positive, it's almost like they're doing great. Yeah.

I was surprised that small caps, uh, the small cap factor was so bad and was, was the worst ever, uh, going back, you know, over, over 20 years. So basically, and you know, this is a podcaster math, but basically, so 11% in the Russell 5% of that is the risk-free rate. So the return is 6%. Uh, that 6% comes from an,

18% return contribution from the equity factor for the Russell and then minus basically 16% or 15.78% for the small cap factor. So that's a,

After that, you got 2%. And then the other 4% that goes into 6% is from other factors. So basically, is the Russell just a very high beta index? It has a very high exposure to the equity factor. And then the fact that they were small actually just really, really dragged them down. And I think I pay more attention probably to financial small caps. And the financial small caps and financial sector did quite well in 2024. So maybe that's why I was so surprised that the small cap factor did so poorly. Yeah.

Yeah, financials did do well in 2024. And actually, low risk, that's another equity style. That's basically going long low-risk stocks and going short high-risk stocks. That actually did very well in 2024. And a lot of that was part of its net long exposure to financials and going that long short within the financial sector. So, yeah, that's absolutely true. But I can see how that's surprising for

for small cap in terms of, you know, that's the difference between an asset class and a factor view is trying to get a little more fundamental and trying to understand those risks at a little bit of a more nutritional label type way in a way you can apply those factors across, you know, all asset classes. So something I did want to mention about small cap that I thought was interesting, it's two things.

One is, if you actually look, going back, I'm just pulling it up right now on my other screen, all of our equity styles have positive returns. So I mentioned at the beginning, the point of institutional investors really trying to have a beta or exposure to these is to drive alpha over time within the equity universe of their allocation.

And we have small caps in there because it helps explain the risk of institutional investors, because institutional investors do seek small cap exposure, again, under the assumption they can drive alpha. But if you actually look at our factor long-term, five out of six of our equity styles have a positive return over 20 years. Small cap is actually flat and even very slightly negative.

And this itself is extremely contrary to the idea that small cap is a positive exposure that you want over full business cycles. Now, there's tons of academic research. You know, obviously, if I'm in French three factor model, they started really early.

getting the small cap as part of the model and adding to the capital asset pricing model in that three-factor regression model. But the truth is, since then, there's been a lot of debate as whether the small cap premium is gone. Cliff Asness wrote a very famous piece

Size matters if you control for junk, basically saying if you control for quality, the small cap factor is still positive. But in Venn's factor lens, the way we build our equity styles, which is long, short, global, and beta neutral, that's a really important aspect, the beta neutrality. Small cap is actually negative. So we still use it. Institutions still value it to explain risk. But

So far in our 20-year history, it's not something that's actually rewarded investors over long periods of time. And my guess is the main reason for that is we're beta neutral. So

Imagine if the long, which is small, has like a, I'm just, these are random numbers, but imagine it has like a 1.3 beta to global equities. And imagine the short, which is large caps, has like a 1.0 beta to global equities. You would essentially allocate less dollars to the long and more dollars to the short. That way they have an equal global equity beta.

And what that does is you eliminate some of the extra equity risk premium that the small cap has been harvesting, and you really make it more about just the fact that it's small. So I think by eliminating the fact that it's had a one, I'm just, again, over 1.0 beta, whether it's 1.1, 1.2, 1.3, you could look at rolling betas over the years. But our factor tries to control for that. And as a result, there's not really been a small cap premium over the history of our Leds.

What about small caps after the Republican sweep, the election of November? Surely small caps did really well. I mean, they surged and that was the narrative, right? What does the data say? So they did do well. So it's not always this contrary belief. Like the model, you know, sometimes it can spit out something that's unintuitive. Sometimes it's intuitive.

So in November, small caps did very well. I think they were up around 10%, 11% in November alone. Our small cap factor was up. It was up, I think, about 1.4%, just looking at the data. But there's a big difference there, right? Now, they're completely different. Ours is global. That's US. Ours is long, short, incredibly different than being long only. But the basic idea here is

measuring small caps going up 11% after the Trump rally and saying, oh, that's great for small caps, right? Clearly, that's great for small caps. That's a very blunt

tool to look at something like a long only equity exposure to try and correlate how much that news actually benefited small caps. Clearly, it wasn't 11% because equities were up 4% after the Trump election, and they have a higher beta to equities. So clearly, a lot of that is attributable to just being the equity risk premium. So the basic idea is that when you try to do our

our methodology, the way we do global long short, the magnitude of that was actually much smaller. So it was our small cap factor up around 1, 2%. So clearly

Unified Republican government, Trump talking about deregulation, tax cuts, America first mindset, all of these things. Clearly, it was a benefit for small caps. The question is just, what's the magnitude? Just quickly relate that back, the magnitude is important because now you imagine you're an institutional investor. You're running a multi-asset portfolio through a factor lens.

And you're trying to attribute how much did you really benefit from small cap exposure?

And that could be coming from your biotech fund, that could be coming from your small cap fund, that could be coming from your long short equity fund, it could be coming from an absolute return fund. But you're really trying to be clear how did you benefit based on your small cap exposure. And if you take your small cap exposure and you keep multiplying it by 11% for the month of November, you're going to get some really big attribution numbers.

when reality is we think it's better to start attributing small cap factor around 2% return for that month, 1%, you know, one and a half, and then your exposure to that. And that's how it's a good reminder to put the entire performance through the lens of analyzing an asset.

or a portfolio, because then it puts into perspective a little more, I think, like, what do these numbers actually mean in terms of decomposing the risk and return of an opaque, you know, multi-asset portfolio? So spoiler alert, the small cap factor didn't actually do so well in November, like the 11% return or whatever from the Russell in November was mostly due to the equity factor, i.e. it's high beta. It goes up when stocks go up and stocks went up a lot. And the local equity factor, the fact that US stocks rallied way more than the rest of the world.

Yeah, exactly. And that's intuitive, right? Like basically every U.S. asset, you know, that's a gross oversimplification. But the idea is like there's tons of assets that are U.S. specific that benefited from all of the narrative of U.S. growth and, you know, all of these things.

And when you try to break down their risk in return, it doesn't matter what asset class they're in. The question is, are they correlating to the return stream of the U.S. outperforming global equities, at least in the two sigma factor lens? So yeah, exactly that. Let's move forward a month to December 2024, a tough year, a tough month for equities. What happened in December? What worked? What didn't?

Yeah. So, I'd love your macro thoughts generally. I mean, for me, just as a sidebar, I look at markets about once a month in terms of digesting factor performance. The general themes I was seeing are pretty obvious, I think, where the Fed gave a hawkish projection, only seeing two more interest rate cuts in 2025, equity and interest rates

Both of those equity interest rate factors, excuse me. So like our factor lens, like both of those went meaningfully lower. U.S. dollar strengthened. So it was kind of that, you know, that classic like less rate cuts than we were expecting type of environment. Small cap factor completely reversed from November was down 15%.

It was incredibly over that month. I think it was like somewhere around 20% gains for 2024, somewhere in that ballpark. And then it was down, not the small cap back, but small caps in general, like the Russell 2 or something like that. I think it gave up like 8, 9% return in December.

So, when you look at December performance, our small cap factor was down about negative 1%. Some other things that were interesting in December, so like local equity factor, again, that US home bias, that actually underperformed in December. So, where in November, it was a good thing to have, in December, it was actually a bad thing to have, right? And that makes sense, right? Because

Just the most simple interpretation of December, which is the Fed hawkish projections, less rate cuts for 2025. That's mostly US specific to our equity market. And even though global equities might have gone down that month, it hurt a little more to be in the local equity market for the US because of that connection. So US home bias or the local equity factor that was down in December.

Something interesting, our quality factor actually had an extremely positive month in December. So you think about what is quality, it's...

long, high-quality companies, so high profitability, low leverage, short, junky companies, which is basically the opposite, unprofitable and higher leverage. That actually had one of its best months. It's in the top 20th percentile of its monthly history. And basically, that's a flight to quality, essentially, is people started to prefer those higher-quality stocks, and they relatively preferred less those junky stocks in December. So that was another high performer for that month.

So yeah, very bad month for the equity factor down 2.17%. Bad for the small cap factor down 1.15%. Those two things didn't surprise me. What surprised me is that the low risk factor is down 2.53%. So in my mind, I kind of conflate low risk and quality. I think of a company that's expensive, but everyone knows it's a good company. And

But what is the difference between quality and low risk? And what does that say that the quality factor was the best performing equity factor and low risk was the second worst performing equity factor of the A team? Yeah, so I think it's good that idea of low risk and quality being correlated. That's typically something that makes itself known through whether it's academic research or other things. One thing that's important about how we build low risk, it's extremely different than the

the typical understanding, which is low beta, low volatility, you're long that thing. We do that. But remember, I talked about beta neutrality, right? So that idea that

we want to take out the equity risk premium from small caps and just give it a 1.0 beta to global equities. What does that mean with low risk when you have low beta securities, right? So you have a 0.8 beta to the US market or whatever, you're probably going to make it into SPLV or something of that nature. But what you're basically admitting upfront is that you have less exposure to the equity risk premium, right?

And now, again, let's take it back to the factor lens. You're an institution and you're analyzing your multi-asset portfolio through our factor lens. Well, how do you interpret that? If you just have a bunch of low beta securities, isn't that going to show up as a lower beta to the equity risk premium? Absolutely, it will. So something we have to do in our low risk factor to make sure that it doesn't show up in the equity risk premium when

you have a lower high beta is we have to do beta neutrality and it's the same idea as small cap so you basically you it's safe to say that the long is always going to have a lower beta than the short for low risk so basically you have to allocate more dollars to the long of the low risk factor that way it has a more or less beta of one to global equities so

Just back to your question, I think it's important to understand that our low risk factor is actually one of our highest vol factors. It's not just a low volatility play because you try to equal out those betas and you're basically saying, what are the low risk securities after I control for the fact that if I took a 0.8 beta security and I pretended like it was one, is it still low risk? What does low risk actually measure, Chris?

So it's again, it's measuring the long, which is low risk securities and the short, which is high risk securities. So it's trying to capture the premium between those two. But how do you determine what's low risk and high risk?

So you do do low beta and low volatility. But the idea is that just because a stock is low beta and it's in your long portfolio, doesn't mean that long portfolio is allocated the same amount of dollars as the short. And because you can allocate different amounts of dollars because you're long short, you're still able to get a portfolio that has a beta of one to the market. So

Fundamentally, still, I don't mean to confuse, it's still low beta, low risk securities, but the portfolio itself is not meant just to move opposite to equity markets, because we want to make it as independent as possible with the equity risk premium. This is all a very long way to say, well, how do you interpret that December performance

Well, basically, it was not good to be in low-risk stocks, right? So that was basically the way to interpret that, like independent of the equity market. And when you look at what actually drove some of that underperformance, it mainly came from consumer staples and utilities and not

To be honest, no stocks really stood out to me. So I did look at it just quickly beforehand. And generally, no individual stocks stand out because our global equity factors have like 8,000, 9,000 securities. So for the most part, idiosyncratic risk tends to be pretty minimal.

So, yeah, low risk is low volatility stocks, but low volatility stocks tend to have a low beta. So when you're isolating for the factor, you go long, more low beta stocks than you're short the high beta ones. Okay. So another way of saying that is that on a relative basis, high risk, more volatile stocks did better than the less volatile stocks. Yes. That's interesting. And that's really the key where you might, sometimes it can be a little unintuitive where you're saying, well, yeah,

you know, why like being beta neutral with low risk, but that's really the concept of this performance report. You always have to put it in the perspective of analyzing through the entire factor lens. And it's not that you're losing that low beta effect. It's just when you attribute return risk and exposure, it would be better to take that low beta piece and put it in the equity factor.

than it would be to put it in the low risk factor. Because the more all of these factors are correlated with each other, the harder it is to look at those correlations and say, you know, this part of the return goes here, this part of the return goes here, this part of the return goes here. So you really have to design each factor to be as independent as possible.

Chris, one thing that surprised me is going back to the entire year performance, I would have thought the value factor did not perform well. In my mind, the stocks that did well were the PEs that had 25 and they were re-read to 35. Yet this quantitative analysis shows actually the value factor contributed 5.84% for the contributors.

2024 calendar year. And that is actually in the 60th percentile for going back in history. So actually the value factor did work. There's so many aspects that go into trying to make sure that the factor is representative of value, right? So again, beta neutrality is a really big component of it. So I

I don't know what it looks like right now. There was a time when value was like the long of value was typically much higher beta. That might not be true today. I haven't looked at it. Growth is probably higher beta at this moment. And it's probably been that way for quite a while. So when you do that beta neutrality and you equalize that, that starts to put things on a different playing field. I think we typically look at things from a U.S. perspective. So our factors are, in fact, global.

And we do things to kind of control for biases. So like when we are trying to judge how value a Japanese stock is, it's not really fair to judge that valuation versus the U.S. equity market and NVIDIA or, you know, Berkshire Hathaway, you know, negative or positive. So when we do that, we try to look at value characteristics relative to each individual region. And that puts things on a little bit of a more fair playing ground.

So I would just say that for us, it's a little less about, you know, I actually don't even know, I would assume, but whether the Russell 1000 value or the Russell 1000 growth outperformed. And it's more that even playing field, like adjust the betas, right?

make sure everything is being compared relative to its peers in like a region, and then try to understand whether that value premium outperformed or not. And it actually, it did, it was up, you know, 5.84% in 2024. So we can probably say, it's safe to say that outside of the United States,

the cheaper stocks tend to do better than the more expensive stocks. Even though in the United States, that maybe wasn't true, but a lot of that can be explained by, I guess, the local equity factor. Yeah, I think there's some truth to that statement. I would still be curious to look at value versus growth in the US in a beta neutral way. But you'd have to look into that data. But I think certainly at a surface level, what you just said makes sense to me.

We're recording Friday, January 17th, two days after the release of the December inflation data. How have stocks reacted? What factors worked and what didn't after the lower than expected inflation reading that was quite good for stocks?

I'm not a macro economist. I don't want to present, but just as a quick refresher for everyone. So headline CPI did tick up a little bit, but core CPI, which has been pretty sticky, I think since December, it's kind of been stuck in place. That actually ticked downward a little bit. So you basically had a risk on, you know, equity factor was up. You know, everyone was basically happy in this scenario. So, yeah.

There was a few things that were interesting. So our equity factor was up. I mentioned that equity risk premium. So our momentum factor, which is going basically long stocks that have been performing relatively better and short stocks that are performing relatively worse. So capturing equity trends in the market, that was up 80 bps. So that's a good sign that the momentum factor was basically positioned well

for that news. And if this trend would continue, which is this less rate cuts or excuse me, rate cuts back on the table and cooling inflation and all of that, that momentum is potentially positioned well for that. Something that's interesting is low risk actually was negative as well. Low risk has actually been, it had a great 2024, but it's actually been sliding a bit since December. Something

a little bit interesting between momentum and low risk is they're actually, they've been negatively correlated with each other, extremely so through November. There's a bunch of reasons why you can talk about why that might be occurring. But one that I think is interesting and topical, and I can talk about

Bitcoin, crypto at a high level, but they tend to have completely opposite positioning in crypto stocks, which are basically like think like a coin base, a micro strategy. So, for example, MicroStrategy was one of the biggest headwinds for low risk in 2024. So with we have like a crypto rally pretty recently.

then obviously, Bitcoin went back down to like 90. So, MicroStrategy is one of the most volatile stocks out there. So, by definition, it's not a low-risk stock. So, when it rallies like crazy, the low-risk factor does not do well. That was definitely a driver. And that opposite with momentum is kind of interesting, just because momentum is long Coinbase. It's long MicroStrategy because those have been doing well. And now, again, I don't want to...

oversimplify the effects that individual stocks have on these equity styles. But over single days, especially the amount of volatility crypto has, it's just insane to see some of the return moves where even very low weights can start to impact a very diversified portfolio in a meaningful way. So some of that was interesting to see where after the CPI announcement, you saw momentum continue to do well and low risk continue to do poorly.

fixed income carry emerging markets these were other factors that we saw were very negative after cpi so fixed income carry goes basically long uh high yielding 10-year bond futures and short the the low yielding ones and you're basically trying to capture that carry premium in between but you do have exposure to the movements of those bonds definitely on a short-term basis as well

you know, it's not just about the difference in yield. Is that just the basis trade where you go, I guess, maybe short the 10-year futures and you go long the cash bonds that have a slightly higher yield? So it's actually, yes, but it's a little bit the opposite where you go long the 10-year bonds and you short the cash bonds. So you want to go typically. Now, you bring up a great point, which is like, well, what about when the yield curve is inverted?

But typically, you're going to be long the 10-year bonds. So for example, our fixed income carry factor has six government bonds in it. So it's got the UK, Australia, Japanese government bonds, Germany, Canadian, and the US. Basically, after CPI, every bond yield went down pretty much.

pretty much among the ones I just talked about. And our fixed income carry factor is actually short most of these bonds. And you say, why is it short most of these bonds? It's the exact dynamic you just talked about, which is the term spread is typically the signal for a fixed income carry factor. So you say to yourself, where can I get a lot of, for lack of a better term, just for now, where can I get a lot of term risk premium, right? And you say, okay, well, in the US, a

a lower cash is yielding more than a long duration bond, there's not a lot of term premium to be had there, right? It had been negative for a while. I think it's now technically positive. Exactly. So you have fixed income carry basically short these major developed countries because all of their term spreads were extremely negative or close to being so.

So basically, when all of these bonds, their yields go down after CPI, that turned out to be a really negative thing for fixed income carry, since it was short most of these things. Now, what's interesting about, this is a general comment, not just about fixed income carry, but

The Japanese yield curve is super interesting because again, not a macro economist here, but they had yield curve control for a while. Their yield curve looks completely different than the rest of the world. They actually maintained a very attractive term spread.

Even when the rest of the world had inverted yield curves. So this is actually an extremely long position in fixed income carry because this is a place where the 10 year still yields relative to cash a lot more because they had been controlling their yield curve for so long. But the JPY didn't really move that much.

So like the yields on the 10-year JPY didn't really go down that much. I think they went down a little bit, but it wasn't enough to counterbalance the negative return of all the shorts that people would have in fixed income carry, you know, again, using our definition. So that was another just interesting one. And that whole JPY dynamic, their yield curve and how that affects fixed income carry, you know, even USD versus JPY, how that affects FX carry. But that, you know,

Again, not too educated to speak about the nuances of Japanese monetary policy, but I can tell you in terms of factor positioning, FX carry, fixed income carry, foreign currency, that has definitely been an interesting dynamic to see how factors position themselves as an institutional investor, understanding that, okay,

My fixed income carry exposure might be short basically everything except the JPY. That is meaningful when it comes to understanding risk, performance, and things of that nature. So definitely after CPI, we saw fixed income carry get hit pretty hard.

Going back to 2024, one of the best performing factors compared on the percentile basis compared to history was momentum. Momentum factor contributed 20%, which is huge, and that's in the 88th percentile. So only 12% of the time has that momentum factor done better. Equity short volatility contributed 7% in 2024. That's the 80th percentile. So only 20% of the time has that performed better.

what are these factors? What does that mean? Momentum is basically like stocks that have been working, continue to work. And also what is equity short volatility? Let's start with momentum. So we talked about this a little bit. Stocks that are performing relatively well are typically in the long basket and stocks that are performing relatively worse are in the short basket. A lot of what I talked about in 2024 was actually comparing it to low risk because I found it so interesting that they had basically a negative

0.5 correlation in 2024, but low risk was up 16% and momentum was up 20%. So I spent a lot of time discussing, they have a completely opposite sector positioning for the year, but they were still able to harvest positive return premiums within each of their sectors for the most part. But not getting too sidetracked there with comparing those two, specifically for momentum, it's actually not much of

a mystery it's pretty much all of the narratives you're you're thinking about which is like those mega cap tech names Nvidia is one of the largest holdings in momentum for example um did well so you had uh crypto markets also did well which you know it's so weird to say but it's true that that crypto risk is in the equity styles right micro strategy coinbase

Like those things did well. So like those really the biggest themes, AI, those are all things that Momentum was capitalizing on. And if you think about them just from a practical perspective, you're hearing about them for over a year, right? AI, tech, crypto, it's been going on for a year, year and a half. And when Momentum measures price trends, it's typically looking at

over a year. It's actually a year minus the most recent month, which I can tell you why it's minus the most recent month if you want to know. But it's basically over the last 12 months when it's measuring those price trends. So just this is the most oversimplified interpretation. But if you're hearing those headlines about tech keeps going, tech keeps going, crypto keeps moving, it's like chances are momentum is starting to capture those things as they become more and more robust.

And I think, you know, without getting too much into data and things of that nature, but just anecdotally, those headlines have been around for a while. They've been correlated with positive performance. And as a result, Momentum was benefiting from a lot of those things. And, you know, the mega cap names, the opposite of small cap. In many ways, Momentum is actually referred to as the chameleon factor sometimes among the academic community because it does have the opportunity to take really any shape or form.

It can look more like value at times. It can look less like low risk and can look more like quality. It really depends on wherever the trends are in the market, momentum is able to go. I imagine by late 2022, the highest momentum stocks are probably energy names and the lowest momentum stocks are probably tech names. And now, of course, it's the exact opposite.

Right. And if those things move quickly, it can be a momentum crash. It can be bad. So imagine like, I don't know, like a COVID or something like that, where you have trends that just reverse themselves instantly. But for trends that are robust and persistent, especially I think we've seen a lot of that, more so in equity markets than I would say interest rate markets,

But we've definitely seen some robust trends over the last year or two years. And that's where momentum can really start to pick up steam. So momentum worked beautifully in 2024. Why then did trend following strategies, which do equity trend following, commodity trend following, fixed income trend following, currency trend following, why did that strategy perform so poorly? In 2024, trend following was...

down only factor was down only 15 basis points, but that normally the median return is 7.45% for trend following. So that is in the 16th percentile. So 84% of the time, trend following does better than it did in 2024. Why is that? Especially considering that momentum did so well. You think that when momentum is good, trend following does good. We have a blog that we wrote many years ago and it's called, what's the difference between momentum and trend following? And

It continues to get lots of engagement years later because it is such a...

common and important question. So the basic idea is that trend following is looking for trends in, you know, it depends on how you do it. We do it with four asset classes, currencies, commodities, equities, and fixed income. And that's looking at trends in those asset classes. And the way it technically does it is by looking at underlying future contracts. So it's actually looking at, you know, 30 equity future contracts and looking at the trends within each of those contracts. But

For our sake, it's looking at the asset class. But it's actually looking at performance relative to itself. So imagine you're a fixed income trend following and all of your fixed income future contracts are doing really well, really well, really well. You're going to start to go long more and more those future contracts as they do well.

Now, let's say those future contracts start to do poorly. They go negative, negative, negative, negative. Then you'll start to go short those. And what that means is trend following could be short all four asset classes. It could be long all four asset classes. It's looking at the trends in performance relative to their own history.

So, it could be long, could be short, depends. Momentum is not looking at the return of Apple and saying, has Apple been doing well or has Apple been doing poorly? I'm going to go long or short based on that decision. What it's doing is saying, is Apple outperforming other stocks? And if Apple is outperforming other stocks, I'll go long. And if Apple is outperforming other stocks, I'll go short. So, even if all stocks are negative,

momentum equity styles will still be long some stocks because it's trying to harvest the difference, the premium between the stocks that are doing well and the stocks that are doing poorly. So it can isolate that momentum factor.

So, it's really about the measurement of the price trends. So, trend following, looking at relative to each asset class' own history can be long or short based on that absolute measurement, whereas momentum is really a relative measurement. Momentum can always be harvested, even if all stocks are negative in the global equity universe, even if every single stock is negative. If your shorts are down more, you're still an opportunity to harvest a momentum premium, the difference between the two.

Trend falling, astutely, you noted that it was only down a little, but it's actually one of the worst years for trend falling, calendar year returns in its history. And that's really a testament that, I mean, the

The strategy has been unbelievably consistent. Now it's had some bad years, similar to momentum. If trends change quickly, you know, if you're long fixed income and then there's a big change in fixed income markets, you don't, that trend falling factor, it's not going to be able to adjust to that quickly. It can, it can definitely get hurt, but,

But among the years we've had it, our methodology and our factor lens, it's been remarkably consistent. So even when it's a little negative, that's actually one of the worst returns it's ever had. And specifically, when you look at the four sleeves, again, equities, commodities, fixed income currencies, fixed income trend falling was the only negative sleeve.

in 2024. And again, you could look at the underlying futures and say, where exactly is that coming from? But I think, again, that general feeling of just bringing it top level of like,

The amount of volatility that we hear about in interest rate markets, you have diverging global monetary policy. It's a little more in line now, but at one point the US was raising when it seemed like the rest of the world was cutting. All of these things contributed to less robust trends, I think, for trend following, especially in fixed income, so much so that it basically washed away the return of the other three asset classes.

So in 2022, I think, trend following did really well. And that was because bonds went down, bond yields went up, and trend followers said this trend is going to continue. And it did. Many pundits on TV said, there's no way the Fed can hike to 2.0. The 10-year at 3% is a buy. Of course, it wasn't. Trend followers were short. They won. 2023, I know there was a horrible, horrible, when Silicon Valley Bank collapsed, you

yields just surged downwards and all the trend followers were short. So they had a really very bad week there. But I think the performance was like, okay. So in 2024, you're saying the fixed income trend following strategy was quite bad. And that makes sense because the Fed kind of

it was at a moment of transition. It was a turning point between stopping hiking to pausing, to starting cutting, to debates about how much we should cut. And so there was a lot of uncertainty, whereas in 2022 and 2023, the, the,

If you just followed the Fed or followed the bond, it was pretty clear, they're hiking. So the trend in fixed income stopped. What about equity short volatility? As I said earlier, equity short volatility factor contributed 7%. That is in the 80th percentile. This is for 2024. What does that mean? What is equity short volatility?

Yeah. And I'll just quickly comment on one other thing I wanted to mention about trend following, because you brought up 2022. That's such a great example. Not only bond equity correlations and the benefit trend following can bring to a portfolio, but that year, which I'm sure we all want to try to block out of our minds, if you actually think about how many times you were surprised by the CPI report and how many times you thought

there was going to be a Fed decision that there wasn't, the negative trends there were actually very consistent, right? It made it very easy for trend following to kind of decide whether it's better to be net long or net short in asset class. So that's a great example where whether it's a positive or negative momentum that's happening in markets, trend following can still pick up on that.

So equity short volatility, this is a great question. So for those who don't know, equity short volatility is basically kind of like being an insurance provider for volatility. So you benefit, you get premiums when volatility is low, but then you have to pay out when volatility is high. Now, the thing that's interesting about our equity short volatility factor is let's take it back to you're an institutional investor, right? So maybe you have an equity short

vol exposure where you're expecting to do well if vol stays low. And you also have other asset classes. You've got equities, you've got bonds, you've got all these things. There might be some correlation between volatility going up and your equity exposures going down, right? And if you have an equity factor as part of your factor lens, that might capture that phenomenon.

So the challenge is with equity short volatility, even though equity short volatility is in the name, literally equity is in the name, our short volatility factor is really trying to analyze whether investors are being compensated for providing insurance on that volatility above and beyond the movements of our equity factor itself. So it's really about the short volatility exposure that you're benefiting from

you know, above and beyond the movements of equity markets. So basically in 2024, we saw that equity short volatility did well, meaning that people were compensated for providing that insurance basically for volatility. And what's interesting is we noticed it really pick up towards the second half of the year. So

My belief is that we did some research on the equity factor, so the equity risk premium right around the presidential election. We found that volatility tends to be very high before a presidential election, but as the election gets closer, even though the volatility is high, it goes down as the election gets closer. I think polls become more reliable, there's less uncertainty.

And then it basically continues to fall after that. So it's kind of like this diagonal downward motion for equities. So even after we remove that trend, we actually see equity volatility going straight up. So the idea here is, I think, even beyond movements of equity markets,

uncertainty has really diminished in the uncertainty of the presidential election. All of those things really diminished in markets. And I think even above and beyond equities, you were compensated for taking that risk because it ended up that the volatility never really manifested. In fact, it just kept going down and down.

It's really just I think the most important thing there is to remember as an institutional investor using this lens, you want to make sure that your short volatility exposure shows up in this factor. And then any of your equity exposure actually shows up in your equity factor. So the fancy way of saying that is our equity short volatility factor is orthogonal to the movements of equities pretty much.

And equity short volatility factor, it tries to capture the volatility risk premium of basically selling options. And you make money when realized volatility is lower than implied volatility. And when realized volatility is higher than implied volatility, you lose money. So the underlying component is actually systematic put writing.

So that's how that's actually being manipulated. It's just like, imagine you had systematic put writing. There's some exposure that has to equity markets, especially, I mean, it's obvious on the downside, right? You have exposure to the asset plus the premium. But it's like, what if you could take out

the part of that that is due to exposure to equity markets and only capture kind of like the actual volatility function, I'll call it, which is like, you know, the premium and how that responds to volatility and things of that nature. So that's, that's the basic idea. And when you're selling puts,

It's not necessarily a VIX at nine that you want. What you want is a VIX at 20, but actual realized volatility is 11. So you're selling overpriced volatility. Yeah, exactly. So interest rates as a factor was one of the worst performing factors in December. That makes sense. Interest rates went up a lot. With bonds, it's mathematical. When interest rates go up, the price of bonds go down.

When it comes to equities, how do you have a mathematical relationship? Because in 2022, rising bond yields were bad for the big tech stocks, and now they're bad for the Russell. The narrative changes because the price accident changes. So how do you try and withdraw and extract something that's actually real in equities? Because to me, it's just narratives.

I'll take a quick step back. So our equity and interest rate factors, basically, you can think of them as long equities, long bonds. That's like essentially what it is. And we have a tier system in our factor lens. I don't want to get into it, but they're the top tier system.

factors. So when you're trying to consolidate risk, so you want a pure EM factor, you want a pure small cap factor, you want a pure fixed income factor, you want to take any equity interest rate exposure that they may have, and you want to force it into those factors when you're doing risk analysis, when you're doing return analysis.

things of that nature. So those two are definitely our highest tier factors. They can be correlated over short periods like 2022, as we saw, but over long periods of time, they're typically not correlated or slightly negatively correlated. So they make for a good fundamental foundation in a place to consolidate risk because they're generally independent with each other.

So when it comes to interest rates and our interest rate factor, what I think is most interesting, and we talked about this a little earlier, but it's really the risk-free rate. So I think sometimes in the factor world, we put way more importance on the risk-free rate because ultimately when you're trying to see like, what's the actual benefit of equities, you're really measuring the correlation, the movements above and beyond the risk-free rate. That's what's important.

And that's actually true of bonds too. Like the bond risk premium is really what are you getting above and beyond the risk free rate when it's inverted, you're not getting anything. But as we saw it un-inverted pretty recently, but

But what's interesting is our interest rate factor in 2024, which is global seven to 10 year sovereign bonds, currency hedge. Currency hedge is very important in the factor world because otherwise that's a perfect example of overlapping risks. If you're not currency hedge and you're a US investor, you're taking foreign currency risk when you invest globally or internationally. But with that being said, it's seven to 10 year government bonds. Those returned about three, 4% in 2024. So despite the fact that, um,

you know, there could be reasons why you'd think they would be negative. It had a high yield to start off with. So even when, you know, it was faced with some of the interest rate move headwinds, it was still able to produce a positive return, again, around 3%, 4%. But

our interest rate factor was actually negative. And that's because it wasn't enough to overcome the 5% of the risk-free rate. So, you know, as if curves continue to steep in and like, you know, that narrative may end up changing.

But for 2024, like even though bonds were positive, you were really not being compensated for taking that interest rate risk. You would have been better off just plugging, taking your seven to 10 year allocation and just putting it into short term cash. So it's really, you know, that's incredibly important because without subtracting out that risk free rate, you might come to the conclusion that your interest rate exposure was beneficial for the year when actually, you know,

It really has to beat the risk-free rate for that to be true. So the interest rate factor, it's just duration. It's not really that much to do with equities at all. And even though bonds had a total positive return, that return was less than if you just held on to cash. It's interesting that it was...

worse than 88% of years, even though it was only down the factor 1.9%. I guess that's because in the backtest, going back to probably the early 2000s or late 1990s, almost all the years are years where bond yields rally. But that can't be expected to normalize. Oh yeah, every year, the 10-year goes down 50 basis points. That can't be normal, right? Yeah, I think so. And I think another part of it is for much of this 20-year period, the risk-free rate was basically zero. So like,

I don't know exactly to the day when that starts to change, but it's just the hurdle of being compensated for interest rate risk was so much lower historically, whereas today it's quite high. So there really needs to be strong performance from the bond market in order to say that that interest rate risk you're being compensated for. And I just say in general,

Obviously, interest rates is a lower volatility factor. So even though it could have some really bad years, similar to trend falling, when you're looking at things relatively and you're trying to understand historical performance, sometimes a down year of only 5%, 6% is pretty meaningful for basically 7 to 10-year bonds, essentially, and typically don't have those bad of years outside of a 2022. Yeah.

Chris, what does the data say historically, the environment that we're in now where momentum performed really well, value performed kind of okay, equity short volatility performed really well. Are there any historical analogs to that? And then what happened next? Basically, what does everything we were talking about, predictions are tough, no one knows, but is there any clues that is hidden in the data?

Yeah, it's a good question. I would just say in general, just hearing you say those words, like momentum doing well, equity short volatility doing well, like this is all indicative of risk on sentiment, right? And, you know,

If you really look, all of the equity styles except small cap were positive in 2024. So basically, that's indicative that institutional investors were harvesting alpha within their equity sleeve for the most part throughout 2024. So obviously, I'm certainly not the right person to ask in terms of a crystal ball and what could happen in 2025. I know there's a lot of reasons to believe that there could be some pretty significant volatility in 2025 and some volatility

potential big moves downward. But if you look at 2024 as your starting place and the factors that are working, it definitely, you know, it feels like it was a risk environment, feels like volatility is low. The presidential election has sort of the uncertainty there has sort of waned. And something that I

this is a kind of a little sidebar but you know we talk about the us a lot something that's important to understand is in global markets the us is you know 60 65 if you go by market cap gdp whatever so a lot of the times when big things happen in the us like those are big implications for global equity markets even our global factor lens so um

You know, we'll see what happens when it comes to, you know, tariffs and things like that potentially leading to higher inflation, higher rates, the volatility that, you know, could be associated with all of that. We'll see how that ends up playing out, how many, you know,

Republican policies are able to be enacted. But I will say that I do feel strongly that whatever happens in the US is going to be important globally. And definitely you see that in the data. Now, our local equity factor tries to isolate that, but there's still, you know, our global equity factor itself is 60% US, right? 65% US, whatever. So it's going to be important to keep an eye on US markets, even when you're taking a global lens.

Yeah, the U.S., the stock market globally is dominated by the U.S., as you said. I was surprised the local equity factor, quote unquote, only contributed 2%.

of uh in 2024 given just how much the s p performed every other stock market i mean i think it's literally is you know argentina and israel outperformed um the the s p but as the us market just dominated every everyone else and also i feel like the foreign currency factor uh was down six percent

In 2024, I think that the dollar was so strong that that was why S&P did it. But still, I'm surprised the local equity factor wasn't bigger. So the way we compare it is a U.S. market versus the global equity market, essentially. And it's the difference between that. So you can think about it going like long the U.S. market and short the global equity market.

It's not exactly that because when you do that process, you need to measure exactly what is the exposure that the US market has. And that tells you basically how to short it, kind of, is a good way to put it simply. So it's really more like the best way to get a proxy for that would be like the S&P 500 versus a global equity market like Acqui or something like that. But with that being said,

There's some things we do on the back end that can make it even a little more unintuitive in terms of factor performance, but makes it more intuitive in terms of interpreting the analysis when you're analyzing multi-asset portfolio. So I'll give you an example. In some cases, we'll do things like volatility scaling.

so what volatility scaling will allow you to do is when you're looking at all of your betas that you have to 18 different factors it allows you to interpret them more on a one-to-one basis so like a 0.2 beta in one thing essentially is is more or less trying to be equivalent to a 0.2 beta in another thing right so this is where we do volatility scaling in some of our factors which can also i don't know if that contributed to some of uh maybe like a softer intuition or not but

But that's an example where there's some things that go on statistically in the back end that when you use these factors for multi-asset portfolio analysis, the betas make sense alongside each other. They're all independent with each other or they try to be as independent with the possible with each other so the correlations don't overlap. So it's easier to isolate.

the return risk and exposure that belongs to each factor. So I'll just say that sometimes you have to take the model at face value and understand that like, like,

It can break down some of the linkage that is normally in an asset class perspective of like, well, this went up and this went down. So the difference should be X. Sometimes you have to take it at face value, understand that the model is working together so that when an institutional investor conducts multi-asset portfolio analysis, they're getting a true fair interpretation of their portfolio with the most important factors, right?

Chris, I know you and Venn and parent company Two Sigma done some work on applying factors to private assets. The S&P, five days a week it trades. Ten year, five days a week it trades. A private equity portfolio, I mean, what? You got to return every month, every quarter, four times a year. Talk about periodicity. I mean, if you wanted a 60-day, 60-sample thing, what? You have to go back five years. How the heck do you do any of this on the...

Private assets, which, you know, I mean, if you wanted to be a little bit cynical about it, they, you know, the valuations are kind of made up. They're not, you know, it's not a true market. What signal are you trying to draw? Why do you do this? Tell us about your factor, your methodology. Yeah, so that is a fantastic question. And it is fascinating.

One of the most valid questions a person can ask, especially now when private assets are becoming so popular. I'll give a brief take on this. I'll say for anyone who's interested in an institutional investor, I wrote a piece called The Alternative Truth of Private Equity and What It Means for Asset Allocation. That can be a good piece to check out that we'll talk more about what I'm about to say. But in...

In Venn, you imagine a private asset return stream. So let's just take like a broad pre-quin private equity index. Okay. So this is going to be everything you just said. It's going to be marked evaluation. You're going to get that number once every quarter.

And it's going to have insanely low volatility relative to public equity markets, right? It's not going to be realistic, to be perfectly frank. So there's three things that Venn can do, desmoothing, interpolation, and extrapolation. All three of these things are obviously very complicated to say, which is evident by the mouthful that I just gave. But they all have the same spirit to what they do.

They look at a public proxy, so say like the S&P 500 in this case, they establish a statistical relationship, and then they try to maintain the integrity of the private asset, but use the statistical relationship of the public proxy as a reference point. So think like the S&P 500 went down this quarter, my private equity probably should have went down too. The magnitude may be up for debate, but

Chances are economic growth, interest rates, these fundamental nutritional label factors that we talked about probably affect private equity and public equity in a similar fashion, not the same, but in a similar fashion.

So what you can do is when you de-smooth, you mark a private asset to the market. So you basically are modeling what the S&P 500 did. You're modeling the marked evaluation and you're kind of reverse engineering and saying, this is my best guess at if I marked my private asset to the market, what would it look like? That tends to raise the volatility considerably.

right? Because now you're marking it to the market at the bottom of 2008. You're marking it to the market during the COVID crisis. You're not waiting a month later to market devaluation, which could be a big range and you choose the highest range, you know, so you're marking it to the market. Now, interpolation is turning it from quarterly to daily. This is basically the idea that if I took the public proxies volatility,

there's some constant number to it. Let me say daily return. There's some constant number I can add to the daily return of the S&P 500 such that I get the private asset return.

So the private asset returns 6% and a quarter, the S&P maybe returned 5% and a quarter. But if I look at the daily marks of the S&P 500, I add some constant value that's going to get me to that 6% of the private asset. So what that effectively does is it maintains the private assets integrity, its return. But now you have the daily volatility of the S&P 500, right?

And then extrapolation is bringing that up to date. So basically most private, like frequent private equity index, for example, is probably three, six months lag in terms of when it's up to date. Well, you can say, well, where's the S&P 500 today? What's the typical relationship between these two things? I can account for idiosyncratic risk of the private equity investment. Where do I think if I brought those returns up to date today, where do I estimate they would be?

So do smooth market to the market, interpolation, turn it into daily, and then extrapolation is bring it up to date. And what those three things together really are, it's a public lens. So imagine you're an asset allocator and you want to do volatility analysis, or you want to do factor analysis. These are often some of the things that are most important to make sure it's accurate.

Marking it to the market, it's daily. So you can analyze risk in a more robust way. And then it has to be up to date. Imagine you're trying to communicate to an investment committee where your public and private portfolio is today, but you only have data as six months ago. You've got to find a way to come up with a best guess estimate to where that private asset could be today.

So on that returns-based side, it's really statistical analysis to try and turn a time-weighted return into something that has the same value and robustness and quality of a public market time-weighted return. And so what did the private equity factor do in 2024?

So it's not really a factor per se. So we don't have a private equity factor. It's more so like putting private equity into a public lens such that you can analyze it with our factor lens. So it's less about like a private equity factor and it's more about let me put my private equity portfolio into then and then see how did interest rates affect it? How did the equity factor affect it?

Was there emerging market exposure? And just being able to conduct that in a meaningful way. Because if you think about it, something unique about Venn, we didn't really talk about this, but it only uses returns. So imagine you're an institutional investor and you have private equity, you have a hedge fund. You have no idea what those holdings are, right? For the most part. So...

Then actually only looks at correlations and return streams. So even if you have zero holdings for private equity or hedge funds, we're still able to correlate that to our two sigma factor lens. We're still able to break down how much return came from one factor, how much risk came from another factor. So that's really the value add here.

is that for a private equity investment, being able to turn it into a robust quality return stream. And now that it's marked to the market, it should start to correlate more with public-based factors, right? Because when it's marked to valuation, the correlations are artificially low, the volatility is artificially low. And as a result, any factor analysis is not going to be as high quality as it could be.

And what are the exposures of private equity? Imagine they're exposed to the equity factor, probably exposed to the credit factor. Maybe they do good when interest rates go down because they're levered. Maybe the small cap factor because a lot of the companies are small cap. They're not taking private Apple or Microsoft.

Yeah, so I think you're absolutely right. I would have to go check to see like what are, you know, a broad private equity exposure today. But especially when you're talking about broad private equity, it tends to be things like the equity factor, right? Because you have all the private equity managers that are diversifying with each other and things of that nature. If you're analyzing individual managers or

smaller sleeves or things like that, you can start to see more dramatic exposures. But it's kind of the same way. Like you analyze the S&P 500 in our factor lens. It's not very exciting. I can tell you that. It's a lot of local equity factor exposure and a lot of equity factor exposure. So it's pretty much the same with a lot of these broad exposures.

private investments, but again, only in the cases where you're measuring like broad equities and things of that nature, but certainly institutional clients who can use it a little more in a nuanced way, like you can start to get some really interesting insights that are very actionable.

And Chris, when I think of Two Sigma, or when I say when I thought of Two Sigma, I thought it was a hedge fund. Going to the website, you have six different businesses, one of which is listed as investment management, Venture is real estate, and then one of them is Venn. So, Venn is owned by Two Sigma. Does Two Sigma use Venn? Tell us about the relationship there. So, Two Sigma itself runs, manages hedge funds. They have a lot of the businesses you just mentioned.

Hedge fund clients, so think about pensions, endowments, whomever it may be, they can have access to Venn. They use Venn. They're actually the reason Venn exists. So we never actually planned to launch a platform ever. It was Two Sigma hedge fund clients coming to Two Sigma and saying, you know what, like

I appreciate our relationship, but I've got a very complicated multi-asset portfolio and I need to better understand it. And I don't really have the right tools to do that. And it also helps them understand, you know, the two sigma portfolio in context of their multi-asset portfolio. So then really started as a way to help those clients understand their own multi-asset portfolio was never meant to be a platform.

And that started in like Python notebooks, you know, like working with them on an ad hoc basis. You eventually got client feedback and eventually things were so positive. It was like, OK, well, we might as well launch a platform on this. And like it rather than operating in Python notebooks, like let's build a UI. Let's let's systematize this and all this client feedback we're getting. So that was basically up until 2019.

it was a platform just for hedge fund clients to understand their complicated multi-asset portfolios. And again, I mentioned this, we're returns based. So you can imagine, it can be hard to get that data. And if you can just get a quality time series returns, that's all you need to start to conduct meaningful analysis and vent. So that was really the idea. And then in 2019, we opened it up to the broader investment community. Now people outside of the Two Sigma

can use Venn if they want to. But that's really the genesis of Venn and that's the relationship. So we, you know, the Two Sigma Factor lens was created by Two Sigma many, many years ago. They're still our parent company. We're in the same office. There's a glass wall that separates us, but we're in the same office. We work together, talk together. You know, I go to the company holiday party. So we're very much intertwined, but we really just service the environment

the hedge fund clients in terms of their portfolio risk and analysis. And then we serve as the direct market community. We actually don't have much, we have nothing to do with the hedge fund business, frankly, outside of really just conducting top tier portfolio risk analysis.

We'll leave it there. Chris, thanks so much. Where can people find you? Where can people find out more about Venn and maybe some of the blog articles that we referenced throughout this conversation? Yeah. So Venn's website, Venn by Two Sigma, it's a great place. We have an insight and resource section. We produce a monthly factor performance report, annual factor performance report, some interesting updates.

We're working on a lot of private assets and things of that nature. So whether they want to subscribe on LinkedIn, where we post all of our content, that could be a good way to do it. You could subscribe on the blog. But really, the website, social media, all the typical places can be a great way to track us down. I'm also going to be doing a webinar next week, which is something people can register for if they'd like to.

A reminder to everyone watching that Monetary Matters is available not just on YouTube, but also Apple Podcasts, Spotify, wherever else people get their podcasts. Thanks again. Until next time. Thanks, Jack. Thank you. Just close this door.