systematic investing
INVESTMENT STYLES

Systematic investing

BlackRock's Systematic investment capabilities span equity, fixed income and alternative asset classes to help provide solutions designed to target specific risk, reward and diversification characteristics.

Capital at risk. The value of investments and the income from them can fall as well as rise and are not guaranteed. Investors may not get back the amount originally invested.

Portfolios powered by technology

Systematic investing combines alternative data, data science and deep human expertise to help modernize the way we invest and construct portfolios. By leveraging data-driven insights, scientific testing of investment ideas, and advanced computer modelling techniques, we constantly innovate new approaches as we seek to improve investment outcomes on behalf of our clients.

Systematic solutions for wealth clients

There is no guarantee that a positive investment outcome will be achieved

When we think about our purpose of solving investor challenges in an increasingly complex world, we have developed a suite of systematic strategies to help clients build portfolios in three ways.

Systematic income solutions help clients target a particular yield, risk objective, or ESG outcome. Systematic active building blocks help clients construct portfolios. And systematic liquid alternatives help clients seek uncorrelated returns to stocks and bonds.

What's different about quant investing today?

Systematic investing has come a long way since the “quant crisis of 07”. Back then we had few datasets, lots of quants doing similar things, and investors were left disappointed by the systematic investing industry.

However, with advancements in technology over the past 15 years, systematic investing looks very different today. A quant now has an incredible amount of data at their fingertips - and through the digitisation of almost everything in the world today - there are a vast range of sources including search activity, electronic invoicing, jobs data and hiring data, foot traffic, and even social media, which we use today as part of our investment process.

What's different about BlackRock Systematic today - why us?

We also employ new techniques such as natural language processing or text analysis to extract information from news sources, transcripts of earnings calls and analyst reports on companies. And we can do this in many different languages, such as Chinese, Japanese and Korean, where we find that more information is available in local language than in English.

What solutions could be interesting in 2023? Why now?

Our liquid alternative capabilities are the result of decades spent pushing the boundaries in the fields of investment research and product innovation.

Systematic liquid alternative strategies seek to generate consistent returns in up and down markets regardless to market leadership, and provide diversification to client portfolios.

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Risk Warnings

Capital at risk. The value of investments and the income from them can fall as well as rise and are not guaranteed. Investors may not get back the amount originally invested.

Past performance is not a reliable indicator of current or future results and should not be the sole factor of consideration when selecting a product or strategy.

Changes in the rates of exchange between currencies may cause the value of investments to diminish or increase. Fluctuation may be particularly marked in the case of a higher volatility fund and the value of an investment may fall suddenly and substantially. Levels and basis of taxation may change from time to time.

The value of equities and equity-related securities can be affected by daily stock market movements. Other influential factors include political, economic news, company earnings and significant corporate events.

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Seeking to systematically solve investor challenges

An increasingly complex world demands an evolved approach to investing. To help clients solve for new solutions to generate income, active portfolio building blocks and uncorrelated return drivers in liquid alternatives, hear more about the next generation of quantitative investing at BlackRock.

Next generation portfolio construction

AI-enabled decision-making and alpha research is paired with market expertise seeking to maximize the economic soundness of investment ideas.

Next generation portfolio construction
Data-driven insights

Detailed quantitative attribution helps constantly refine portfolios and targets distinct sources of return to help deliver specific investment outcomes.

Scientific testing

Quantitative data-analysis techniques yield scaled insights across large sets of securities, enabling high-breadth portfolios.

Disciplined construction

Scientific testing helps validate return potential, while simultaneously mitigating behavioural bias and cognitive errors.

Continuous refinement

Portfolio positions sized by disciplined risk budgeting and optimization processes seeks to balance a complex set of trade-offs in portfolio construction.

Data-driven insights

Detailed quantitative attribution helps constantly refine portfolios and targets distinct sources of return to help deliver specific investment outcomes.

Scientific testing

Quantitative data-analysis techniques yield scaled insights across large sets of securities, enabling high-breadth portfolios.

Disciplined construction

Scientific testing helps validate return potential, while simultaneously mitigating behavioural bias and cognitive errors.

Continuous refinement

Portfolio positions sized by disciplined risk budgeting and optimization processes seeks to balance a complex set of trade-offs in portfolio construction.

Investment strategies

Our systematic approach to investing can be applied across a spectrum of strategies. We manage both highly diversified and specialized investment capabilities seeking to provide clients with solutions that compliment their portfolios.

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Equities

Our systematic equity strategies are designed with the aim of to delivering consistent and differentiated alpha to our clients. Our passion is combining insight and technology to generate compelling benchmark relative or absolute investment returns.
Long only
Our data-driven insights and technology offer cost-efficient, risk-managed equity solutions to help clients generate returns across an extensive opportunity set.
Partial long/short
Designed to deliver differentiated alpha by selecting stocks through optimized tilts, we seek to exploit market inefficiencies and minimize exposure to uncompensated risks.
Absolute return
Absolute return strategies use distinct sources of return to seek positive absolute returns in equity markets, irrespective of financial conditions.
Disclosure:

There is no guarantee that a positive investment outcome will be achieved.

Fixed income

Systematic fixed income strategies employ differentiated data-driven insights backed by disciplined risk management that seek to deliver differentiated portfolio outcomes to investors.
Enhanced
Enhanced building blocks seek to deliver consistent, high information ratio alpha by combining security selection insights with optimized portfolio construction.
Liquid alternatives
Our investment decisions use multiple, independent and risk management alpha models, seeking enhanced risk-adjusted returns with low correlations to broad asset classes.
Hedge funds
Seek to uncover alpha using relative value, directional and security selection strategies, across markets, with return profiles independent of traditional market betas.
Disclosure:

There is no guarantee that a positive investment outcome will be achieved.

Alternatives

Our liquid alternative strategies seek to generate idiosyncratic alpha, with low correlations to broad asset classes. Our alternative solutions are available across a full spectrum of broad and specialized investment capabilities including multi-strategies, global equity market neutral, and global macro.
Risk parity
Multi-asset strategies built by diversifying across sources of risk rather than by asset class.
Absolute return
Strategies that seek to deliver uncorrelated alpha through long/short investing.
Multi-alternatives
Strategies that employ our differentiated investment ideas using multiple, independent and risk-managed quantitative models, seeking uncorrelated returns across asset classes.
Disclosure:

There is no guarantee that a positive investment outcome will be achieved.

Factors

Factors are broad and persistent drivers of returns both in and across asset classes. BlackRock has been at the forefront of factor-based investing for decades1 and continues to innovate new strategies to help address clients’ investment challenges.
US Equity Factor Rotation
Dynamically allocates to US stocks based on their exposures to historically rewarded factors, using proprietary insights and a forecast of near-term alpha potential.
Market Advantage
Macro factor strategy seeking returns with resilience. Portfolio allocations based on risk, not capital, that aim to capture the economic drivers of return.
Style Advantage
Long/short style factor strategy seeking liquid and diversified absolute returns.
Disclosure:

There is no guarantee that a positive investment outcome will be achieved.
1 Our decades-long history reflects predecessor firms.

ESG integration

ESG integration is one of our top priorities in investment research and innovation. As systematic investors, our quantitatively-driven investment process can explicitly balance a number of complex portfolio considerations — incorporating estimates of risk, return and correlation alongside ESG-related metrics.

Systematic strategies can meaningfully integrate sustainable investment considerations in three distinct ways.

ESG integration
Align

Seek to align a portfolio with investor preferences by removing securities perceived to be controversial

Uplift

Seek a more sustainable portfolio relative to a benchmark by targeting improvements along ESG and carbon intensity dimensions

Enhance

Seek incremental returns or mitigate potential downside risks in a portfolio through ESG-related insights

Disclosure:

There is no guarantee that a positive investment outcome will be achieved. While the investment approach described herein seeks to control risk, risk cannot be eliminated.

Systematic Investing: Investing, Evolved

Discover how systematic investing is built for today’s investment challenges. By utilising distinct return sources returns, pursuing big data insights, and integrating ESG considerations, systematic investing is different by design.
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Decoding the Markets Webcasts

Join us for our quarterly Decoding the Markets Webcast where Blackrock Systematic experts apply a data-driven lens to help navigate the current market landscape.

Decoding the Markets: Data, Alt data, and Statistics

JEFF ROSENBERG: Hi, I'm Jeff Rosenberg, and welcome to the second quarter of Decoding the Markets. With me today here in New York is Jeff Shen, and in San Francisco, we're joined by Raffaele Savi and our special guest presenter, Taylor Dufour. Today's topics, we're going to cover the macro backdrop, our both alternative data, top-down views, our views on equity markets and fixed income markets, and then Taylor's going to present a special topic on how we're harnessing the power of AI specifically to build thematic baskets. So, I'm really excited about the lineup that we have here today. And I'm going to kick us off.

So, let's talk about the title, Data, Alt Data, and Statistics. Some of you may vaguely catch the reference here. There's a little bit of a Mark Twain theme going throughout today's presentation. So hopefully you'll see that.

I'm going to talk about kind of what I call traditional alternative data around the macro theme. Then I'm going to pass it over to Raff. He's going to talk about our normal systematic alternative data lens on looking at the macro and the investment outlook. By alternative data and traditional alternative data, really referring to one of the themes that we've seen roll through in the first quarter.

Three hotter than expected CPI reports have dramatically changed. the macro backdrop. And so, while we were in a period of soft landing, immaculate disinflation, supporting both stocks and bonds because the Fed could cut interest rates in a period of high growth where that growth wasn't pushing up inflation, those three in a row hotter than expected CPI prints have definitely changed the narrative.

Part of that narrative that had been supportive of the soft landing was this traditional alternative data viewpoint. On the lower left-hand chart you see GDP versus GDI, gross domestic income being the traditional alternative measure of the state of growth in the economy. And that had been under reporting or lower growth than what had been in the headline GDP numbers. And for some, that was a measure that was pointing to, aha, this economy is actually softer. The Fed can do more cutting. Well, you fast forward to the latest print on GDI, it caught up to GDP, kind of invalidated that narrative. And it was part of what contributed to the surge in interest rates, interest rate expectations.

Go to the chart next to it and it's about the payroll survey versus the household survey. So, kind of a stretch to call this alternative data, but a lot of focus on headline payrolls. A lot of the data has been also showing a difference, a gap. You see the orange line versus the yellow line. 'Hey, the household survey looks weaker here. Maybe this labor market isn't as strong as we think it is.' And then again, the Fed can cut more aggressively, more easily.

Well, fast forward into the first quarter of this year, you saw the immigration data that was put out by the CBO. You saw the Brookings Institute paper on the impact of undercounting immigration that basically moved the consensus to, no, the payroll survey is right, the household survey is undercounting, and this labor market is stronger than we think it is otherwise. And so that alternative data kind of pushing up interest rate expectations because it's pushing back against those two alternative narratives. So, some of the things we're seeing in the macro data, I want to turn it over to my colleague, Raff, in San Francisco, talk about more traditionally how we talk about alternative data to gauge the macro and equity perspective from our systematic alternative data perspective. Raff, over to you.

RAFFAELE SAVI: Thank you, Jeff. Well, if we look at the prior slide, one second, there's sort of our… summary of our sort of alternative data views on the biggest question of last year, people are not really asking that question that much anymore, which already in and of itself, I think, tells you something. But the probability of a recession based on the models we've estimated on historical NBR recession and movements of both markets and macroeconomic releases in the six months before those occurrences have a very, very low expectation that sort of this economy could get off the rails. It's interesting to me to see the trend here in the last few quarters. Of course, I do think we see it in the way that asset prices moved from the peak concern that was end of 2022 to a situation that has been improving constantly throughout 2023 and we're off to a strong start in 2024.

If I look at the next slide, and we're trying here to shed some light on this whole idea of where is monetary policy going. As Jeff said, we had a couple of very strong, stronger than expected releases on the inflation front. And so, the expectation of many rate cuts early in the year has been moved back. That, of course, had impacted the performance of fixed income markets. Equities all in all have kept doing well. So, the question that we're trying to ask, these are charts that we showed in the past, is if we look at the underlying driver of inflation, how are things going?

I'd say that my favorite one is the top right. Maybe for an 'alt-data' guy, I am a traditionalist, and I do think that ultimately, unless we see sustained wage inflation, it's hard to imagine that sort of this inflation shock we lived through and we're mostly out of can sustain for much longer. That data is still indicating a moderation on wage growth. I remind you, we're using millions of online job postings, matching like for like job openings and trying to understand entry level salaries or compensation packages through time. It does seem that, of course, we see very visually the COVID shock that I suspect we're going to see in a lot of macroeconomic series for many years to come. It's going to be something that will catch our eye when we look at the past in a few years. But it does seem that we're going back towards a more sustainable wage growth in our economy.

The minor signs of concerns on this front are on the bottom left. So, here we scrape web data again for like for like price of items. You can eyeball the seasonality in this data. And even so, we've seen some signs of increasing price inflation. And to a certain extent, this is also what people experience. So, when we look at things like CPI, of course, shelter has a large weight in it, and there's some sort of different dynamics. Here, if you want, is a little bit more what we all experience when we buy something from Amazon or at the store, right? And again... I don't think that this is breaking out of the seasonal pattern we've seen in the last year and a half, but it's definitely something that is worth keeping an eye on. When we put all this together and we run our models, that makes us neutral now on duration. If you look at the pattern again, we thought that rates were way too low in 2022, found an equilibrium in 2023, and actually our models indicated that we could expect also some earlier rate cuts and definitely the data moved in a different direction and we brought that position in somehow. Back to you, Jeff.

JEFF ROSENBERG: So, we're going to turn to inflation and its impact on the equity markets. It's been a big theme in the fixed income markets, as Rob was just saying, the shift in inflation expectations and the impact that it's had in terms of pricing out the number of cuts. At the beginning of the year, we had up to seven cuts priced into the bond market. Now we're pricing in two. We've had break-even inflation that looked relatively contained. Now it's starting to break out, so much so that we're starting to even price in a shift in the narrative from how many cuts are the Fed going to have to the potential for no cuts at all. And even at the extreme of that distribution, which is kind of a natural expectation, that you might even start to talk about the possibility that the next Fed move is a hike rather than a cut. So, we've seen a lot of changes in the bond markets pricing for and around this inflation narrative. This slide captures a bunch of our alternative data and approaches around equity markets. So, Raf, I'm going to turn it back to you, kind of walk through what the equity markets are saying about the inflation narrative.

RAFFAELE SAVI: Thank you, Jeff. Yes, here, what I think is interesting, again, we created these baskets that are trying to infer equity market pricing on sort of the economic expectation around the development of the cycle, right? So here, we have four stereotypes that we call hard landing, very hard landing, no landing, and soft landing. And what this shows is that equity markets have been predominantly pricing the soft landing scenario up until the very last few weeks. And since the beginning of March, we've actually seen an uptick in this idea of no landing here, meaning that both growth accelerates and inflation doesn't come down to the 2%. So, the pink scenario, the pink line was essentially betting on growth staying strong and inflation coming down towards the steady target of 2% in the US. And the green line is depicting a world where actually inflation might stay higher, 2.5%, 3%. And yet, even with higher rates, the economy continues to grow above potential.

And so, I think that someone might call it overheating. And as you can see, this has not been a concern for markets up until a few weeks ago. And then the combination of releases that Jeff was talking about and also the information we all gathered from different data sources, the cycle seems to be going strong.

JEFF ROSENBERG: So, there's another Mark Twain aphorism which says it's not what you know, it's not what you don't know that gets you in trouble, but what you know for certain that just ain't so. I use this in my quarterly piece around this inflation narrative, and I think the title was what the bond market knows for certain that just ain't so. And that was that inflation was going down to 2% steadily. And, again, it got the bond market into trouble. The whole narrative around bonds are back expectations for falling interest rates, long duration positioning. And we see some pretty significant negative returns, especially to that long end, but across the fixed income landscape. So, trouble, yes, not end of the world. But it's an example of this Mark Twain aphorism coming to fruition, where there was a lot of confidence that this was going to be so, and it wasn't.

Now, the title of this slide is sort of reflecting of that, you know, when the Fed starts cutting with in parentheses, the 'if the Fed starts cutting, what does it mean from a fixed income duration and duration positioning perspective?' So, the upper right-hand chart kind of highlights the relationship between the yield curve and direction of interest rates. What you can see here is most of the time, the bond market behaves what I call on diagonal. That is, you have bull steepening, you have bear flattening. You can see the two-way frequency table. Most of the time, that's what you would expect. And so, when the Fed is expected to cut interest rates, you also expect the curve to steepen. And the lower left-hand chart highlights this. In cutting cycles, what are the interest rate declines by maturity? The Fed controls the short end, and the maturities that are closest follow most closely. So, you get that bull steepening. On the opposite side, you get the bear flattening.

And so, curve changes propagate out from the front end. So the steepening is to be expected. When you think about how to position, you got to look on the lower right-hand chart. It's the combination of both yield change and duration that matters. Because here, we're looking for that price appreciation from the yield decline. But if you're all in the front end, there's not a lot of duration. So what you can see in the performance is it's really the three to five-year part of the curve. And that's the sweet spot for performance in a Fed cutting cycle. And what you see in most consensus bond market expectations is that this is what most people expect, the steepening. And so, you want a position in that part of the curve.

The tricky part to this is timing, that the timing of that outlook matters. And what, again, the bond market consensus, most economists' consensus have gotten wrong. We were a little bit more suspicious of this consensus at the beginning of the year was that that inflation narrative was going to be so easily met that immaculate disinflation was going to lead to that success and the cutting cycle to begin, remember at the beginning of the year was supposed to begin back in March, so you can't hang out in that three to five year part of the curve and outperform unless the Fed is actually cutting.

And if they're not, then you're actually underperforming because you're giving up yield relative to what else you could hold in your portfolio. So, the timing really matters. I've called this St. Augustine and the yield curve trade or the steepener trade. 'Give me the steepener,' meaning you want a position for that part in the curve, 'but just not yet.' And it's not yet because it's not yet clear that the Fed's gonna be able to deliver on that cutting cycle. And I think, as the charts that Raff were just highlighting from the equity perspective, validating the increase in the no-landing scenario. Remember, no-landing means you don't get the decline in the inflation that leads the Fed to an aggressive cutting cycle, that you got to kind of be patient on this fixed income theme.

Let's flip to the next chart. I'm going to use this as a bridge to talk about, it's titled 'Macro to Micro.' It's an observation that we're seeing this change in the macro narrative is pushed back expectations for cuts. So, one of the lines or the shaded area in that chart is basically showing you what are the number of cuts expected by June, right? So that was up to three cuts at the beginning of the year. Now it's basically at zero. So, we rapidly priced out in the fixed income markets expectations for the Fed. We've kind of beaten that horse a lot so far in the call. So, we're going to broaden the perspective, talk about equity markets. And another topic, I'm going to bring Jeff Shen into this conversation, and a lot of focus on momentum and the momentum factor. And there's a long-only momentum factor. There's a long-short momentum factor. Both are kind of looking like this chart. This chart, I think, is the long-short. But it's gotten a lot of people a little bit worried. The observation we're making here is there's a little bit of a macro orientation to momentum factor that you don't typically think of in a long, short market neutral, sector neutralized portfolio. So, well, with that as kind of an intro, I'll turn it over to you and some of your thoughts.

JEFF SHEN: Definitely. I think momentum is not market neutral is definitely sort of the big headline. And I think when we look at the momentum exposure, it is interesting in the sense that momentum has certainly got these characteristics that it tends to work. And it tends to work for a while until it doesn't. When it doesn't, you can give back quite a few number of years or quarters of gain in one go in a very sharp period. So, from that perspective, it's very important to think about. And also, I mean, the bad news for the people who have done well is also that momentum exposure tend to creep up on you as you're doing well. And then you really need to worry about what's next.

So, in terms of momentum exposure not being market neutral, I think we've actually done some interesting work here, which is essentially try to look at the current momentum exposure, try to figure out what's the macro content underneath it. What does the momentum exposure tell us from a macro positioning perspective? So, there are three interesting things come out of that. And if you want to follow this chart on the right. So essentially, I'll say that there is a 'pro risky asset' view for momentum. It's long equity, it's a long credit, it's overall that risk-seeking aspect of it. So, I think that's sort of one which is not too many people surprised given that risky assets over the last 12, 18 months have also been performing quite strongly.

I think the second aspect is, Jeff, to what you were talking about. There is a little bit of this kind of a higher for longer embedded view in there now in the sense that it's long dollar. It's actually short bonds. So, there is this aspect of that. It is actually positioning for interest rate could potentially be staying for longer, at least throughout the end of this year. So, I think there is a little bit of accumulation towards that. And the interesting thing in there is actually the curve steepener that you were just talking about. It's actually pretty neutral to that as we speak in the equity momentum exposure.

Then, I think this kind of momentum exposure, there's also a little bit of a hedge in the sense that it's long VIX slightly. There's also a little bit of a long oil exposure given some of the geopolitics that's actually unfolding in front of us. So, there is a bit of, I think, pro-risk higher for longer, but then there is a little bit of a hedge through long VIX and also long oil embedded in there.

Now, what’s our view on timing of this momentum? I'll say that overall, our view is actually on the positive side, but a bit less so. I think we're still... reasonably constructive on momentum exposure, but there are some signs of worry for sure. I'll say that the things that are actually going for momentum is really that our regime model which will look at some of the similarities in terms of where we are in the cycle in relation to the momentum exposure is actually showing a lot of positive sign for momentum.

The thing that's on the flip side of that's actually getting us to worry clearly is valuation on some of these high momentum names is actually getting to be quite stretched. So, there's a bit of a negative view from a valuation perspective. But from a technical perspective, the chart on the lower right here, I'll say that it's really digging a little bit deeper into the momentum exposure to see what this thing really is about. What about the tail risk for some of these names? What about order imbalance? Try to look at the concentration of the momentum exposure.

[00:20:00]

JEFF SHEN: And those things are actually all pointing towards a reasonably moderately positive. I mean, some of them actually have gone up, some of them have gone down, as you can see on the chart. But overall, it's still slightly constructive.

So, I think we're having our fingers on the pulse for the momentum exposure. We certainly are watching this very carefully, and this thing can change pretty quickly. So, I think still reasonably constructive, but I think there's definitely a bit of a risk management head on for the momentum exposure.

Now... when we talk about momentum, one surprising thing I'll say that there is actually momentum in Japan. I think quite a few of us have been around for a long time. And when we think about Japan, we think about reversal, we think about contrarian, we think about value. And momentum in Japan is a bit of a new, new thing. And if you look at the chart on the lower left, there's definitely a bit of a sense that over the last 12 months also, momentum exposure after really not performing for over a decade starting to see some signs of life.

And not only that, but also when you compare this relative to other developed countries, US, Europe, Japan is actually outperforming when we look at its momentum factor performance. So not only it's done well, a bit of a reversal of the longer term history, but it's also done better than a couple of other very important markets.

And I think then the big question is, is this a 'head fake?' Or could this be a bit more of a sustained pattern going forward? Obviously, I don't think we have the magic answer. Per se, that's maybe the bad news. The good news is maybe a couple of perspectives here that hopefully will be helpful.

One is actually when we look at the decomposition of the momentum exposure in Japan, just to get a sense of what is it actually betting on, what is it actually leaning on. And I think there's sort of two positive news, one slightly question mark. Positive news is actually one is actually certainly is betting on some of the larger, higher quality companies. There's quite a bit of a structural changes in Japan. Governance is improving, quality of the management is improving, and momentum exposure is betting on some of these names. So you would think that some of the structural changes are going to have a bit of a tailwind to it, given how long sustainable these things can be.

The second aspect is also that sentiment, especially from international investor sentiment towards Japan is also positive. So, sentiment is also driving momentum exposure. So, that's also another positive thing. I think the one that's a little bit question mark is actually higher volatility names are also seeing a bit of a support is that there is a bit of a 'risk-on' type of mindset for Japan. So higher volatility names actually have done well, and that's also part of the momentum exposure. That may be a bit of a slight worry sign when you look at that particular aspect of the momentum exposure.

So, all in all, I'll say that it's reasonably constructive. And the second aspect is I do think that as the money is starting to come out of China, I think that... There's actually still marginal dollars actually looking for destinations. And the top candidates from a country allocation perspective, Japan, India, Middle East, there are a few pockets, or EM x-China in general, certainly becoming pockets of destinations. I think from that perspective, there's probably also some support from an overall strategic asset allocation perspective that is actually going for Japan. So, I'll say that, overall, we're reasonably constructive on momentum for Japan. But of course, when you have something that hasn't worked for 10, 15 years suddenly start to work, you always want to look at it closely to make sure that you don't overstay your welcome as well.

So, with that, I think momentum, Japan, certainly hot topics, but I don't think any of these topics are as hot or as popular as AI. And with that, I'll turn it over to Raffaele.

RAFFAELE SAVI: Thank you, Jeff. Well, certainly a very popular topic. And here we're trying to figure out, in equity markets, is this a story just about NVIDIA, essentially, or is there more to it? And I think, in some sense, we are seeing already signs of broadening, both in terms of number of companies, sectors, and workflows that are being considered for increased efficiency or just better performance with AI. And I think that that is also leading to broadening of the beneficiaries from the GPU manufacturers to everybody that is participating in that chain and now also on the application side. The point of this slide is just showing that this is not just about semiconductor, that this theme is becoming a theme that is carrying many sectors and is having consequences for many companies. I really want to make sure we have enough time on this call for a special topic. And so, back to you, Jeff, to introduce our guest today.

JEFF ROSENBERG: Great. Thank you, Raff. So, we just heard a little bit on investing in AI and the broadening of the opportunity set. We wanted to also flip the perspective and talk about how we're using AI to invest. And with that, I'm going to introduce Taylor Dufour. She's a researcher at BlackRock Systematic.

I had the opportunity to see Taylor present some of her research, and it is really spectacular in terms of a very practical solution to a problem that we come on as investors often, especially in the equity long short world, and that is the creation of thematic baskets. You saw thematic baskets earlier when Raff presented the macroeconomic regime baskets, no landing, soft landing. So, Taylor, over to you to introduce your work.

TAYLOR DUFOUR: Thanks, Jeff. As Jeff said, I will be presenting on how we can harness AI in order to invest in themes. Because themes are very different in nature, it's hard to create a one-size-fits-all approach to systematically capture them.

In the past, we've either built bespoke equity baskets, which was very time-consuming, or we've relied on sell-side broker baskets, which lacked both transparency and breadth. Now, we can harness the power of large language models to build a framework that is fast, flexible, and automated. Using the combination of human insight, big data, and LLMs, we are able to now systematically create equity baskets at the click of a button.

So, we'll look at this case study to kind of demonstrate this framework. There's a new class of weight loss drugs called GLP-1s that have recently been approved by the FDA and quickly risen in popularity.

While the winners of this theme are quite clear, the drug manufacturers themselves, such as Eli Lilly and Novo Nordisk, the potential losers of this theme are less clear. And this is where the thematic robot can step in and assist us in finding the less obvious leg of a thematic trade.1

There are many second and third order effects at play. For example, as this Bloomberg headline highlights, Intuitive, which is a bariatric surgery provider, could be hurt as people substitute away from surgery and towards drugs to lose weight. So, if we continue to the next slide and see the video, we can see the robot in action.

First, we import the robot, then we set up the basket parameters. We specify the situation, which reads, the release of a drug that suppresses appetites and induces substantial weight loss. We specify the start and end date, the stock universe, the temperature, which controls how deterministic the output will be, and we have an optional parameter for seed companies. In this case, we specify Eli Lilly and Novo Nordisk.

So now we've initialized the robot and we're ready to run it. The average robot run takes about five to 10 minutes. So, I've already run it for this demonstration. You can see that the robot automatically saves all the intermediate output in CSV files so that one can easily load it back into memory and you don't have to wait for the robot to run from scratch again. Another benefit of this framework-driven approach is that we can create a standardized set of empirics to evaluate the basket.

Here we can see the typical equity-backed-test statistics, such as the full-sample IR, the cumulative returns plot, and we can see the key stock contributors. On the key stock contributors positively contributing, we see Eli Lilly and Novo Nordisk, of course, Top Longs, but we also see stocks that the robot has wanted to go short in, again highlighting how the robot can find the less obvious leg of a trade. We can also create robot-specific empirics such as the source attribution of the latest holdings. This shows for every stock that the robot has selected, where it comes from, the LLM, the conference calls, or the seed list.

We can also see how GPT enables us to be very flexible as it creates or defines categories of companies that are often more granular and differentiated than standard industries or sub-industries. For example, you can see that the robot wants to go long GLP-1 manufacturers and it wants to go short fast-food companies. Every time we interface with the LLM, we ask it for a rationale for its decisions, which you can see on the right-hand column. You can see, for example, for fast food companies, it explains the short saying that the GLP-1 drug could suppress appetites and reduce the amount of fast food that people eat. And indeed, we have seen fast food companies start to express concerns about the GLP-1 drugs.

Earlier, I mentioned Intuitive, which is that bariatric surgery company. So, let's see if it's in the basket. We can see that the robot creates a case study automatically, and the robot wants to go short this stock around late July, and that's indicative by the blue line, which is the holdings. Around the same time, the stock's price, the orange line, started to decline, and the green line is the P&L for this position.

Now why did the robot want to short Intuitive? We can see that it's because of the conference call source. So, if we continue on and we inspect the conference calls, we can see Exactly why the LLM came to this decision, ' the company's growth rate in bariatric surgery slowed due to increased patient interest in weight loss drugs.' And you can even see the conference call excerpt right there that the large language model read to come to that determination. This is just a very quick preview of the power of the thematic robot. We're so excited to leverage AI to capture themes ranging from micro-secular trends to emerging macro and mega forces and anything in between. Thanks.

JEFF ROSENBERG: Taylor, that is great. I love seeing that presentation. You and I had a chance to work through a couple of examples that were of interest to me. In that process, I learned a little bit about using AI. I'll ask you a couple of questions. This also gives me an opportunity to introduce the third of my Mark Twainism aphorisms here. It's relative to this topic of prompt engineering. Twain, in reference to this topic, writes, and certainly not about prompt engineering, but just about language, 'I didn't have time to write you a short letter, so I wrote you a long one instead.' And I think it's a very perfect application of something we talked about. Tell us a little bit of your experience around the differences in prompts, a long, detailed prompt versus a very short, pithy prompt.

TAYLOR DUFOUR: Yeah, that's a great question. So, there's kind of two different types of prompts that we can talk about when we talk about the thematic robot. First, there's the prompts that are within the thematic robot. And these kind of control, you know, the ebb and flow of the information that we give the robot and the information that the robot gives back to us. There's been a lot of research on prompt engineering to make sure that the robot has very reliable, consistent and intuitive results.

But what you're kind of getting at, in terms of, you know, creating this wildfire basket is the prompt or the situation that I've highlighted earlier in the demo that we pass into the robot. Of course, for this GLP-1 demo, the prompt or the situation was pretty short and concise, and we're still sort of figuring out the best practices for writing the situation because, of course, this technology is so new.

Some observations that we have found, though, is that if you write a shorter situation, that sort of gives the robot more room, more room for creativity and experimentation, versus if you provide a very lengthy prompt, maybe even a few paragraphs long, that really, you know, overloads the robot with a lot of information and can kind of constrain the robot into a corner solution. So, we're still trying to find the best balance between them, but it's ongoing research.

JEFF ROSENBERG: Thanks very much. I think it's wonderful that a bunch of computer scientists have given English majors new job prospect hopes. So, thank you for that. And thank you, Taylor and Raff, for joining. Jeff Shen next to me, thank you for joining. And thank you for joining us for this second quarter edition of Decoding the Markets.

You can find all of this information and more by reaching out to us directly. We're here to take your questions, show you more of what we're doing on the cutting edge of applying alternative data and alternative data to investing, as well as AI and artificial intelligence to our investment practice, as well as our investments in AI.

Join us next quarter for the Decoding the Markets. We'll be back here. We look forward to seeing you then again. Take care.

1This case study is shown for illustrative purposes only, and was selected to demonstrate BlackRock’s capabilities with respect to using large language models in thematic investing. There is no guarantee than an actual strategy will be executed or executed as shown above, or that if executed, will be profitable. This investment was selected as it represents the flexibility and power of large language models in parsing unstructured data. This case study does not predict future results, even if a similar strategy is used. Reference to the company names mentioned in this communication is illustrative purposes, and should not be construed as investment advice or investment recommendation of that company.

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Decoding the Markets: Data, Alt data, and Statistics

JEFF ROSENBERG: Hi, I'm Jeff Rosenberg, and welcome to the second quarter of Decoding the Markets. With me today here in New York is Jeff Shen, and in San Francisco, we're joined by Raffaele Savi and our special guest presenter, Taylor Dufour. Today's topics, we're going to cover the macro backdrop, our both alternative data, top-down views, our views on equity markets and fixed income markets, and then Taylor's going to present a special topic on how we're harnessing the power of AI specifically to build thematic baskets. So, I'm really excited about the lineup that we have here today. And I'm going to kick us off.

So, let's talk about the title, Data, Alt Data, and Statistics. Some of you may vaguely catch the reference here. There's a little bit of a Mark Twain theme going throughout today's presentation. So hopefully you'll see that.

I'm going to talk about kind of what I call traditional alternative data around the macro theme. Then I'm going to pass it over to Raff. He's going to talk about our normal systematic alternative data lens on looking at the macro and the investment outlook. By alternative data and traditional alternative data, really referring to one of the themes that we've seen roll through in the first quarter.

Three hotter than expected CPI reports have dramatically changed. the macro backdrop. And so, while we were in a period of soft landing, immaculate disinflation, supporting both stocks and bonds because the Fed could cut interest rates in a period of high growth where that growth wasn't pushing up inflation, those three in a row hotter than expected CPI prints have definitely changed the narrative.

Part of that narrative that had been supportive of the soft landing was this traditional alternative data viewpoint. On the lower left-hand chart you see GDP versus GDI, gross domestic income being the traditional alternative measure of the state of growth in the economy. And that had been under reporting or lower growth than what had been in the headline GDP numbers. And for some, that was a measure that was pointing to, aha, this economy is actually softer. The Fed can do more cutting. Well, you fast forward to the latest print on GDI, it caught up to GDP, kind of invalidated that narrative. And it was part of what contributed to the surge in interest rates, interest rate expectations.

Go to the chart next to it and it's about the payroll survey versus the household survey. So, kind of a stretch to call this alternative data, but a lot of focus on headline payrolls. A lot of the data has been also showing a difference, a gap. You see the orange line versus the yellow line. 'Hey, the household survey looks weaker here. Maybe this labor market isn't as strong as we think it is.' And then again, the Fed can cut more aggressively, more easily.

Well, fast forward into the first quarter of this year, you saw the immigration data that was put out by the CBO. You saw the Brookings Institute paper on the impact of undercounting immigration that basically moved the consensus to, no, the payroll survey is right, the household survey is undercounting, and this labor market is stronger than we think it is otherwise. And so that alternative data kind of pushing up interest rate expectations because it's pushing back against those two alternative narratives. So, some of the things we're seeing in the macro data, I want to turn it over to my colleague, Raff, in San Francisco, talk about more traditionally how we talk about alternative data to gauge the macro and equity perspective from our systematic alternative data perspective. Raff, over to you.

RAFFAELE SAVI: Thank you, Jeff. Well, if we look at the prior slide, one second, there's sort of our… summary of our sort of alternative data views on the biggest question of last year, people are not really asking that question that much anymore, which already in and of itself, I think, tells you something. But the probability of a recession based on the models we've estimated on historical NBR recession and movements of both markets and macroeconomic releases in the six months before those occurrences have a very, very low expectation that sort of this economy could get off the rails. It's interesting to me to see the trend here in the last few quarters. Of course, I do think we see it in the way that asset prices moved from the peak concern that was end of 2022 to a situation that has been improving constantly throughout 2023 and we're off to a strong start in 2024.

If I look at the next slide, and we're trying here to shed some light on this whole idea of where is monetary policy going. As Jeff said, we had a couple of very strong, stronger than expected releases on the inflation front. And so, the expectation of many rate cuts early in the year has been moved back. That, of course, had impacted the performance of fixed income markets. Equities all in all have kept doing well. So, the question that we're trying to ask, these are charts that we showed in the past, is if we look at the underlying driver of inflation, how are things going?

I'd say that my favorite one is the top right. Maybe for an 'alt-data' guy, I am a traditionalist, and I do think that ultimately, unless we see sustained wage inflation, it's hard to imagine that sort of this inflation shock we lived through and we're mostly out of can sustain for much longer. That data is still indicating a moderation on wage growth. I remind you, we're using millions of online job postings, matching like for like job openings and trying to understand entry level salaries or compensation packages through time. It does seem that, of course, we see very visually the COVID shock that I suspect we're going to see in a lot of macroeconomic series for many years to come. It's going to be something that will catch our eye when we look at the past in a few years. But it does seem that we're going back towards a more sustainable wage growth in our economy.

The minor signs of concerns on this front are on the bottom left. So, here we scrape web data again for like for like price of items. You can eyeball the seasonality in this data. And even so, we've seen some signs of increasing price inflation. And to a certain extent, this is also what people experience. So, when we look at things like CPI, of course, shelter has a large weight in it, and there's some sort of different dynamics. Here, if you want, is a little bit more what we all experience when we buy something from Amazon or at the store, right? And again... I don't think that this is breaking out of the seasonal pattern we've seen in the last year and a half, but it's definitely something that is worth keeping an eye on. When we put all this together and we run our models, that makes us neutral now on duration. If you look at the pattern again, we thought that rates were way too low in 2022, found an equilibrium in 2023, and actually our models indicated that we could expect also some earlier rate cuts and definitely the data moved in a different direction and we brought that position in somehow. Back to you, Jeff.

JEFF ROSENBERG: So, we're going to turn to inflation and its impact on the equity markets. It's been a big theme in the fixed income markets, as Rob was just saying, the shift in inflation expectations and the impact that it's had in terms of pricing out the number of cuts. At the beginning of the year, we had up to seven cuts priced into the bond market. Now we're pricing in two. We've had break-even inflation that looked relatively contained. Now it's starting to break out, so much so that we're starting to even price in a shift in the narrative from how many cuts are the Fed going to have to the potential for no cuts at all. And even at the extreme of that distribution, which is kind of a natural expectation, that you might even start to talk about the possibility that the next Fed move is a hike rather than a cut. So, we've seen a lot of changes in the bond markets pricing for and around this inflation narrative. This slide captures a bunch of our alternative data and approaches around equity markets. So, Raf, I'm going to turn it back to you, kind of walk through what the equity markets are saying about the inflation narrative.

RAFFAELE SAVI: Thank you, Jeff. Yes, here, what I think is interesting, again, we created these baskets that are trying to infer equity market pricing on sort of the economic expectation around the development of the cycle, right? So here, we have four stereotypes that we call hard landing, very hard landing, no landing, and soft landing. And what this shows is that equity markets have been predominantly pricing the soft landing scenario up until the very last few weeks. And since the beginning of March, we've actually seen an uptick in this idea of no landing here, meaning that both growth accelerates and inflation doesn't come down to the 2%. So, the pink scenario, the pink line was essentially betting on growth staying strong and inflation coming down towards the steady target of 2% in the US. And the green line is depicting a world where actually inflation might stay higher, 2.5%, 3%. And yet, even with higher rates, the economy continues to grow above potential.

And so, I think that someone might call it overheating. And as you can see, this has not been a concern for markets up until a few weeks ago. And then the combination of releases that Jeff was talking about and also the information we all gathered from different data sources, the cycle seems to be going strong.

JEFF ROSENBERG: So, there's another Mark Twain aphorism which says it's not what you know, it's not what you don't know that gets you in trouble, but what you know for certain that just ain't so. I use this in my quarterly piece around this inflation narrative, and I think the title was what the bond market knows for certain that just ain't so. And that was that inflation was going down to 2% steadily. And, again, it got the bond market into trouble. The whole narrative around bonds are back expectations for falling interest rates, long duration positioning. And we see some pretty significant negative returns, especially to that long end, but across the fixed income landscape. So, trouble, yes, not end of the world. But it's an example of this Mark Twain aphorism coming to fruition, where there was a lot of confidence that this was going to be so, and it wasn't.

Now, the title of this slide is sort of reflecting of that, you know, when the Fed starts cutting with in parentheses, the 'if the Fed starts cutting, what does it mean from a fixed income duration and duration positioning perspective?' So, the upper right-hand chart kind of highlights the relationship between the yield curve and direction of interest rates. What you can see here is most of the time, the bond market behaves what I call on diagonal. That is, you have bull steepening, you have bear flattening. You can see the two-way frequency table. Most of the time, that's what you would expect. And so, when the Fed is expected to cut interest rates, you also expect the curve to steepen. And the lower left-hand chart highlights this. In cutting cycles, what are the interest rate declines by maturity? The Fed controls the short end, and the maturities that are closest follow most closely. So, you get that bull steepening. On the opposite side, you get the bear flattening.

And so, curve changes propagate out from the front end. So the steepening is to be expected. When you think about how to position, you got to look on the lower right-hand chart. It's the combination of both yield change and duration that matters. Because here, we're looking for that price appreciation from the yield decline. But if you're all in the front end, there's not a lot of duration. So what you can see in the performance is it's really the three to five-year part of the curve. And that's the sweet spot for performance in a Fed cutting cycle. And what you see in most consensus bond market expectations is that this is what most people expect, the steepening. And so, you want a position in that part of the curve.

The tricky part to this is timing, that the timing of that outlook matters. And what, again, the bond market consensus, most economists' consensus have gotten wrong. We were a little bit more suspicious of this consensus at the beginning of the year was that that inflation narrative was going to be so easily met that immaculate disinflation was going to lead to that success and the cutting cycle to begin, remember at the beginning of the year was supposed to begin back in March, so you can't hang out in that three to five year part of the curve and outperform unless the Fed is actually cutting.

And if they're not, then you're actually underperforming because you're giving up yield relative to what else you could hold in your portfolio. So, the timing really matters. I've called this St. Augustine and the yield curve trade or the steepener trade. 'Give me the steepener,' meaning you want a position for that part in the curve, 'but just not yet.' And it's not yet because it's not yet clear that the Fed's gonna be able to deliver on that cutting cycle. And I think, as the charts that Raff were just highlighting from the equity perspective, validating the increase in the no-landing scenario. Remember, no-landing means you don't get the decline in the inflation that leads the Fed to an aggressive cutting cycle, that you got to kind of be patient on this fixed income theme.

Let's flip to the next chart. I'm going to use this as a bridge to talk about, it's titled 'Macro to Micro.' It's an observation that we're seeing this change in the macro narrative is pushed back expectations for cuts. So, one of the lines or the shaded area in that chart is basically showing you what are the number of cuts expected by June, right? So that was up to three cuts at the beginning of the year. Now it's basically at zero. So, we rapidly priced out in the fixed income markets expectations for the Fed. We've kind of beaten that horse a lot so far in the call. So, we're going to broaden the perspective, talk about equity markets. And another topic, I'm going to bring Jeff Shen into this conversation, and a lot of focus on momentum and the momentum factor. And there's a long-only momentum factor. There's a long-short momentum factor. Both are kind of looking like this chart. This chart, I think, is the long-short. But it's gotten a lot of people a little bit worried. The observation we're making here is there's a little bit of a macro orientation to momentum factor that you don't typically think of in a long, short market neutral, sector neutralized portfolio. So, well, with that as kind of an intro, I'll turn it over to you and some of your thoughts.

JEFF SHEN: Definitely. I think momentum is not market neutral is definitely sort of the big headline. And I think when we look at the momentum exposure, it is interesting in the sense that momentum has certainly got these characteristics that it tends to work. And it tends to work for a while until it doesn't. When it doesn't, you can give back quite a few number of years or quarters of gain in one go in a very sharp period. So, from that perspective, it's very important to think about. And also, I mean, the bad news for the people who have done well is also that momentum exposure tend to creep up on you as you're doing well. And then you really need to worry about what's next.

So, in terms of momentum exposure not being market neutral, I think we've actually done some interesting work here, which is essentially try to look at the current momentum exposure, try to figure out what's the macro content underneath it. What does the momentum exposure tell us from a macro positioning perspective? So, there are three interesting things come out of that. And if you want to follow this chart on the right. So essentially, I'll say that there is a 'pro risky asset' view for momentum. It's long equity, it's a long credit, it's overall that risk-seeking aspect of it. So, I think that's sort of one which is not too many people surprised given that risky assets over the last 12, 18 months have also been performing quite strongly.

I think the second aspect is, Jeff, to what you were talking about. There is a little bit of this kind of a higher for longer embedded view in there now in the sense that it's long dollar. It's actually short bonds. So, there is this aspect of that. It is actually positioning for interest rate could potentially be staying for longer, at least throughout the end of this year. So, I think there is a little bit of accumulation towards that. And the interesting thing in there is actually the curve steepener that you were just talking about. It's actually pretty neutral to that as we speak in the equity momentum exposure.

Then, I think this kind of momentum exposure, there's also a little bit of a hedge in the sense that it's long VIX slightly. There's also a little bit of a long oil exposure given some of the geopolitics that's actually unfolding in front of us. So, there is a bit of, I think, pro-risk higher for longer, but then there is a little bit of a hedge through long VIX and also long oil embedded in there.

Now, what’s our view on timing of this momentum? I'll say that overall, our view is actually on the positive side, but a bit less so. I think we're still... reasonably constructive on momentum exposure, but there are some signs of worry for sure. I'll say that the things that are actually going for momentum is really that our regime model which will look at some of the similarities in terms of where we are in the cycle in relation to the momentum exposure is actually showing a lot of positive sign for momentum.

The thing that's on the flip side of that's actually getting us to worry clearly is valuation on some of these high momentum names is actually getting to be quite stretched. So, there's a bit of a negative view from a valuation perspective. But from a technical perspective, the chart on the lower right here, I'll say that it's really digging a little bit deeper into the momentum exposure to see what this thing really is about. What about the tail risk for some of these names? What about order imbalance? Try to look at the concentration of the momentum exposure.

[00:20:00]

JEFF SHEN: And those things are actually all pointing towards a reasonably moderately positive. I mean, some of them actually have gone up, some of them have gone down, as you can see on the chart. But overall, it's still slightly constructive.

So, I think we're having our fingers on the pulse for the momentum exposure. We certainly are watching this very carefully, and this thing can change pretty quickly. So, I think still reasonably constructive, but I think there's definitely a bit of a risk management head on for the momentum exposure.

Now... when we talk about momentum, one surprising thing I'll say that there is actually momentum in Japan. I think quite a few of us have been around for a long time. And when we think about Japan, we think about reversal, we think about contrarian, we think about value. And momentum in Japan is a bit of a new, new thing. And if you look at the chart on the lower left, there's definitely a bit of a sense that over the last 12 months also, momentum exposure after really not performing for over a decade starting to see some signs of life.

And not only that, but also when you compare this relative to other developed countries, US, Europe, Japan is actually outperforming when we look at its momentum factor performance. So not only it's done well, a bit of a reversal of the longer term history, but it's also done better than a couple of other very important markets.

And I think then the big question is, is this a 'head fake?' Or could this be a bit more of a sustained pattern going forward? Obviously, I don't think we have the magic answer. Per se, that's maybe the bad news. The good news is maybe a couple of perspectives here that hopefully will be helpful.

One is actually when we look at the decomposition of the momentum exposure in Japan, just to get a sense of what is it actually betting on, what is it actually leaning on. And I think there's sort of two positive news, one slightly question mark. Positive news is actually one is actually certainly is betting on some of the larger, higher quality companies. There's quite a bit of a structural changes in Japan. Governance is improving, quality of the management is improving, and momentum exposure is betting on some of these names. So you would think that some of the structural changes are going to have a bit of a tailwind to it, given how long sustainable these things can be.

The second aspect is also that sentiment, especially from international investor sentiment towards Japan is also positive. So, sentiment is also driving momentum exposure. So, that's also another positive thing. I think the one that's a little bit question mark is actually higher volatility names are also seeing a bit of a support is that there is a bit of a 'risk-on' type of mindset for Japan. So higher volatility names actually have done well, and that's also part of the momentum exposure. That may be a bit of a slight worry sign when you look at that particular aspect of the momentum exposure.

So, all in all, I'll say that it's reasonably constructive. And the second aspect is I do think that as the money is starting to come out of China, I think that... There's actually still marginal dollars actually looking for destinations. And the top candidates from a country allocation perspective, Japan, India, Middle East, there are a few pockets, or EM x-China in general, certainly becoming pockets of destinations. I think from that perspective, there's probably also some support from an overall strategic asset allocation perspective that is actually going for Japan. So, I'll say that, overall, we're reasonably constructive on momentum for Japan. But of course, when you have something that hasn't worked for 10, 15 years suddenly start to work, you always want to look at it closely to make sure that you don't overstay your welcome as well.

So, with that, I think momentum, Japan, certainly hot topics, but I don't think any of these topics are as hot or as popular as AI. And with that, I'll turn it over to Raffaele.

RAFFAELE SAVI: Thank you, Jeff. Well, certainly a very popular topic. And here we're trying to figure out, in equity markets, is this a story just about NVIDIA, essentially, or is there more to it? And I think, in some sense, we are seeing already signs of broadening, both in terms of number of companies, sectors, and workflows that are being considered for increased efficiency or just better performance with AI. And I think that that is also leading to broadening of the beneficiaries from the GPU manufacturers to everybody that is participating in that chain and now also on the application side. The point of this slide is just showing that this is not just about semiconductor, that this theme is becoming a theme that is carrying many sectors and is having consequences for many companies. I really want to make sure we have enough time on this call for a special topic. And so, back to you, Jeff, to introduce our guest today.

JEFF ROSENBERG: Great. Thank you, Raff. So, we just heard a little bit on investing in AI and the broadening of the opportunity set. We wanted to also flip the perspective and talk about how we're using AI to invest. And with that, I'm going to introduce Taylor Dufour. She's a researcher at BlackRock Systematic.

I had the opportunity to see Taylor present some of her research, and it is really spectacular in terms of a very practical solution to a problem that we come on as investors often, especially in the equity long short world, and that is the creation of thematic baskets. You saw thematic baskets earlier when Raff presented the macroeconomic regime baskets, no landing, soft landing. So, Taylor, over to you to introduce your work.

TAYLOR DUFOUR: Thanks, Jeff. As Jeff said, I will be presenting on how we can harness AI in order to invest in themes. Because themes are very different in nature, it's hard to create a one-size-fits-all approach to systematically capture them.

In the past, we've either built bespoke equity baskets, which was very time-consuming, or we've relied on sell-side broker baskets, which lacked both transparency and breadth. Now, we can harness the power of large language models to build a framework that is fast, flexible, and automated. Using the combination of human insight, big data, and LLMs, we are able to now systematically create equity baskets at the click of a button.

So, we'll look at this case study to kind of demonstrate this framework. There's a new class of weight loss drugs called GLP-1s that have recently been approved by the FDA and quickly risen in popularity.

While the winners of this theme are quite clear, the drug manufacturers themselves, such as Eli Lilly and Novo Nordisk, the potential losers of this theme are less clear. And this is where the thematic robot can step in and assist us in finding the less obvious leg of a thematic trade.1

There are many second and third order effects at play. For example, as this Bloomberg headline highlights, Intuitive, which is a bariatric surgery provider, could be hurt as people substitute away from surgery and towards drugs to lose weight. So, if we continue to the next slide and see the video, we can see the robot in action.

First, we import the robot, then we set up the basket parameters. We specify the situation, which reads, the release of a drug that suppresses appetites and induces substantial weight loss. We specify the start and end date, the stock universe, the temperature, which controls how deterministic the output will be, and we have an optional parameter for seed companies. In this case, we specify Eli Lilly and Novo Nordisk.

So now we've initialized the robot and we're ready to run it. The average robot run takes about five to 10 minutes. So, I've already run it for this demonstration. You can see that the robot automatically saves all the intermediate output in CSV files so that one can easily load it back into memory and you don't have to wait for the robot to run from scratch again. Another benefit of this framework-driven approach is that we can create a standardized set of empirics to evaluate the basket.

Here we can see the typical equity-backed-test statistics, such as the full-sample IR, the cumulative returns plot, and we can see the key stock contributors. On the key stock contributors positively contributing, we see Eli Lilly and Novo Nordisk, of course, Top Longs, but we also see stocks that the robot has wanted to go short in, again highlighting how the robot can find the less obvious leg of a trade. We can also create robot-specific empirics such as the source attribution of the latest holdings. This shows for every stock that the robot has selected, where it comes from, the LLM, the conference calls, or the seed list.

We can also see how GPT enables us to be very flexible as it creates or defines categories of companies that are often more granular and differentiated than standard industries or sub-industries. For example, you can see that the robot wants to go long GLP-1 manufacturers and it wants to go short fast-food companies. Every time we interface with the LLM, we ask it for a rationale for its decisions, which you can see on the right-hand column. You can see, for example, for fast food companies, it explains the short saying that the GLP-1 drug could suppress appetites and reduce the amount of fast food that people eat. And indeed, we have seen fast food companies start to express concerns about the GLP-1 drugs.

Earlier, I mentioned Intuitive, which is that bariatric surgery company. So, let's see if it's in the basket. We can see that the robot creates a case study automatically, and the robot wants to go short this stock around late July, and that's indicative by the blue line, which is the holdings. Around the same time, the stock's price, the orange line, started to decline, and the green line is the P&L for this position.

Now why did the robot want to short Intuitive? We can see that it's because of the conference call source. So, if we continue on and we inspect the conference calls, we can see Exactly why the LLM came to this decision, ' the company's growth rate in bariatric surgery slowed due to increased patient interest in weight loss drugs.' And you can even see the conference call excerpt right there that the large language model read to come to that determination. This is just a very quick preview of the power of the thematic robot. We're so excited to leverage AI to capture themes ranging from micro-secular trends to emerging macro and mega forces and anything in between. Thanks.

JEFF ROSENBERG: Taylor, that is great. I love seeing that presentation. You and I had a chance to work through a couple of examples that were of interest to me. In that process, I learned a little bit about using AI. I'll ask you a couple of questions. This also gives me an opportunity to introduce the third of my Mark Twainism aphorisms here. It's relative to this topic of prompt engineering. Twain, in reference to this topic, writes, and certainly not about prompt engineering, but just about language, 'I didn't have time to write you a short letter, so I wrote you a long one instead.' And I think it's a very perfect application of something we talked about. Tell us a little bit of your experience around the differences in prompts, a long, detailed prompt versus a very short, pithy prompt.

TAYLOR DUFOUR: Yeah, that's a great question. So, there's kind of two different types of prompts that we can talk about when we talk about the thematic robot. First, there's the prompts that are within the thematic robot. And these kind of control, you know, the ebb and flow of the information that we give the robot and the information that the robot gives back to us. There's been a lot of research on prompt engineering to make sure that the robot has very reliable, consistent and intuitive results.

But what you're kind of getting at, in terms of, you know, creating this wildfire basket is the prompt or the situation that I've highlighted earlier in the demo that we pass into the robot. Of course, for this GLP-1 demo, the prompt or the situation was pretty short and concise, and we're still sort of figuring out the best practices for writing the situation because, of course, this technology is so new.

Some observations that we have found, though, is that if you write a shorter situation, that sort of gives the robot more room, more room for creativity and experimentation, versus if you provide a very lengthy prompt, maybe even a few paragraphs long, that really, you know, overloads the robot with a lot of information and can kind of constrain the robot into a corner solution. So, we're still trying to find the best balance between them, but it's ongoing research.

JEFF ROSENBERG: Thanks very much. I think it's wonderful that a bunch of computer scientists have given English majors new job prospect hopes. So, thank you for that. And thank you, Taylor and Raff, for joining. Jeff Shen next to me, thank you for joining. And thank you for joining us for this second quarter edition of Decoding the Markets.

You can find all of this information and more by reaching out to us directly. We're here to take your questions, show you more of what we're doing on the cutting edge of applying alternative data and alternative data to investing, as well as AI and artificial intelligence to our investment practice, as well as our investments in AI.

Join us next quarter for the Decoding the Markets. We'll be back here. We look forward to seeing you then again. Take care.

1This case study is shown for illustrative purposes only, and was selected to demonstrate BlackRock’s capabilities with respect to using large language models in thematic investing. There is no guarantee than an actual strategy will be executed or executed as shown above, or that if executed, will be profitable. This investment was selected as it represents the flexibility and power of large language models in parsing unstructured data. This case study does not predict future results, even if a similar strategy is used. Reference to the company names mentioned in this communication is illustrative purposes, and should not be construed as investment advice or investment recommendation of that company.

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The information contained in this document, may contain statements that are not purely historical in nature but are “forward-looking statements”. These include, amongst other things, projections, forecasts or estimates of income. These forward-looking statements are based upon certain assumptions, some of which are described in other relevant documents or materials. If you do not understand the contents of this document, you should consult an authorised financial adviser.

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For investors in Switzerland

This document shall be exclusively made available to, and directed at, qualified investors as defined in Article 10 (3) of the CISA of 23 June 2006, as amended, at the exclusion of qualified investors with an opting-out pursuant to Art. 5 (1) of the Swiss Federal Act on Financial Services ("FinSA").

For information on art. 8 / 9 Financial Services Act (FinSA) and on your client segmentation under art. 4 FinSA, please see the following website: www.blackrock.com/finsa.

Any research in this document has been procured and may have been acted on by BlackRock for its own purpose. The results of such research are being made available only incidentally. The views expressed do not constitute investment or any other advice and are subject to change. They do not necessarily reflect the views of any company in the BlackRock Group or any part thereof and no assurances are made as to their accuracy.

This document is for information purposes only and does not constitute an offer or invitation to anyone to invest in any BlackRock funds and has not been prepared in connection with any such offer.

© 2024 BlackRock, Inc. All Rights reserved. BLACKROCK, BLACKROCK SOLUTIONS and iSHARES are trademarks of BlackRock, Inc. or its affiliates. All other trademarks are those of their respective owners.