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.

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 modeling techniques, we constantly innovate new approaches as we seek to improve investment outcomes on behalf of our clients.

What’s different about this moment for AI & machine learning for investors?

What the advent of large data sets in the investment process has been about for over 20 years, has been this idea of moving things that were considered qualitative to quantitative. You probably heard this, this, this saying that investing is both art, and science. And I think the art part is this idea that some things are hard to quantify, and science part is the part that we all agree with.

Risk models, transaction cost model, advanced portfolio construction techniques. We can we can treat in analytical fashion. The explosion of machine learning and artificial intelligence has made so that a lot of what we consider only approachable with qualitative analysis is now lending itself to quantitative analysis and the scientific approach to take advantage of of these trends, of course, you need you need a team that can do both the art and the science of of investing.

And so the type of question we can ask and the type of answer we can get requires very strong analytical skills computer science, machine learning and, and large language models.

What market trends are prompting investors to take a more active approach?

The first one is of course, interest rates are not at zero anymore. and this creates a lot of opportunities to invest both in equity markets, and in, in fixed income assets.

And then I think the other the other thing that is happening also is that we're seeing less synchronization across the major economies. Partly is a slowdown in globalization, partly is different inflation regimes and monetary policies regime. But dispersion across global markets has gone up. And this is this is very good for, for active investors.

Where does the influence of AI create risks for investors?

We always had machine learning and data at the heart of investment processes. But, but it was very much something that only experts could access. Now I think what's happening is these models are accessible to people that are might not be machine learning expert. And that's drawing in a lot more insight into, into, the investment process. While there might be some risks, I think the opportunities are much more plentiful and is what’s getting us exciting.

One of the things that is really special about this generation of artificial intelligence model is how conversational they are, and the breadth of use cases that you can apply to. The idea that a lot of what was qualitative in an investment process, and important, can also now be brought in and quantified and integrated more fully in an approach that is using systematic tools like optimization.

As these models become more and more powerful and more prevalent in any investment process, of course, asking the right question and judging the quality of an answer becomes even more important.

What should clients be asking their active managers?

The first one, how are you using technology in your investment process that's going to be a stronger, stronger driver of success in the future, I believe. And the second one is how do you make sure that what you're doing is different and stays ahead of, of the competition. Whether it's public or private, whether it's on, on equities or fixed income, whether it's a fundamental style or a systematic style.

I think that an efficient use of technology, and the sort of promise of AI, is going to be a bigger part of, of investment success.

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Unlocking innovation through systematic investing

Raffaele Savi, Global Head of BlackRock Systematic, examines what sets this moment apart for AI in the eyes of investors, why investors are considering a more active approach, where the influence of AI creates risks and the questions clients should be asking their active managers.

Next generation portfolio construction

AI-enabled decision-making and alpha research is paired with market expertise 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 behavioral basis 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 behavioral basis 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 to provide clients with solutions that best compliment their portfolios.

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Equities

Our systematic equity strategies are designed to deliver 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.

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.

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.

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 decades and continues to innovate new strategies to help address clients’ investment challenges.
U.S. Equity Factor Rotation
Dynamically allocates to U.S. 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.

How does BlackRock’s Systematic team seek an edge in markets?

We believe that an investment process underpinned by continual innovation is vital to our goal of delivering sustainable alpha to our clients.
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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: Global Easing, Elections and Emerging Markets
[JEFF ROSENBERG]
Hi, I'm Jeff Rosenberg and I'm hosting this quarter's Decoding the Markets alongside Jeff Shen, our CIO and Head of Systematic Active Equities, Chris Sinclair from our Thematics Group will also be joining us on a special topic on using artificial intelligence in large language models in the application of managing some of the election risk. I'm both your host and content provider, a part of our BSYS fixed income group and Co lead Portfolio manager for the systematic multi strategy fund. So let's get into our themes. On the macro side, we're going to cover global easing, some of the elections and emerging markets. On the global easing cycle, I'll start here. You know, we're kicking this off as we've seen some large developed market banks begin what seems to be expectations for the global easing cycle. In the taxonomy that we introduced earlier this year on how to think about this easing cycle, we've really laid out three different types of easing to distinguish what's really driving policy cuts in an environment where global growth is really holding up. And the kind of, we'll talk about this later, the recession fears have given way. So what we're really focused on here is the global policy rate declines expected. You can see that in the lower left hand chart is really a function of what we call maintenance cuts. And maintenance cuts are policymakers maintaining the degree of policy accommodation and avoiding an increase in the real interest rate. So here you see the orange area. The path for real interest rates under maintenance cuts is flat. That's keeping policy maintained at its current level of restrictiveness. As inflation goes down, you've got to cut nominal interest rates. This is following a period of falling global inflation. Now more recently, what we've seen is that the path that's suggested here in the lower left chart may not be as smooth as inflation surprises in Canada and Australia have kind of highlighted that the path towards this, this maintenance cuts as I'm describing may not be as smooth as markets are anticipating. Final point here is when we look at this from a global perspective, this chart here on the lower right is highlighting the evolution of global growth forecasts. The orange line for the US, the yellow line for Eurozone in 2024. And you can see heavy revisions upward in the U.S. growth expectations relative to Europe. This is partly due to why we've seen the first cuts here from the ECB and the delay in terms of relative to earlier year expectations for cuts in the US. And a significant reduction from seven earlier this year to somewhere between 1-2 today, really on the back of kind of pushing back on really how much cuts the Fed will need to do to maintain policy restrictiveness, really because the strength of that economic growth is kind of pushed back on the fears that policy is overly restrictive. OK. So let's move on to the next kind of macro theme and a little bit more of a focus here in in in the US the labor market and its support for arguing for or against that that policy easing expectations. We highlighted this a little bit in the last DTM last quarter, the debate over kind of traditional, what I'm calling traditional alternative measures of Labor market. On the upper right hand chart, we're highlighting our SAE alternative wage growth measures as a way of highlighting what we've highlighted for some time. And this data has highlighted for some time, again, using job postings to scrape out what our wage is doing. And what you see there is that normalization that Powell and others have been talking about, the normalization kind of flatlining. More recently, when we look not at the wage picture, but at the payroll or the establishment survey data, we've seen this break between the orange line, which establishment survey data, and the yellow line, which is the household data. Earlier this year, we had an immigration argument kind of arguing that that household data may be understated, households not under immigration, households under reporting relative to establishment survey. On the right hand chart, we highlight the kind of the counter argument that is suggesting that it's the establishment data that's overstated. We have some benchmark revisions that will be coming through some alternative measures, kind of more traditional alternative measures for that payroll establishment data, the QC data, the BID data, talking about the potential that we're overstating payroll growth. Some of this is being pointed to in terms of the birth death adjustment, technical factors. This may be overstating the degree of payroll growth. And this is an argument for supportive of, you know, more Fed cuts rather than less Fed cuts in that the payroll data, which has really been rather strong and pushing back against expectations for cuts by the Fed may be overstated, something that's important in the data evolution. We'll see that later this summer in terms of the benchmark revision, something we in the market will be paying a lot of attention to as it feeds into those expectations. Currently, market pricing between 1-2 cuts for the Fed this year. Let's move on to the next perspective here on inflation, on this Immaculate and otherwise. You know some of the important things to keep an eye on. You know when we look at some of our alternative data on web scrapes, inflation, you can see those year on year figures. This is a global perspective somewhat ticking upwards. This is important. It's highlighting something that we're going to see on the right hand chart. When you look at year over year measures, this orange line is highlighting the PCE measure, kind of the most important measure for the Fed and for the markets. The year over year figures are going to start to get into a pattern where the year over year seasonal fact, not the seasonal factors, but the comparison from a year ago are going to become kind of challenging. So when you're looking at the year over year figures, they may actually start to rise. Those are going to be less important than the annualization of the shorter run figures. For example, think about 3 month annualized figures and what you see in kind of the yellow line and the green line in terms of oil price stability since the spike in April coming down, hips break even, inflation is relatively stable. This is pointing to a much more benign outlook here for inflation. You see that in the upper chart here on wage inflation, the more traditional wage inflation measures, those are coming down. And that's all kind of supportive of a much more reasonable level of policy expectations for the Fed one to two cuts by the end of this year. That's a September cut, That's a December cut. We think September is still in play here. This inflation picture is still very supportive here of the Fed being able to deliver one to two cuts this year. Let's move on to the next slide on the economic growth picture. This is really just a continuation of moving away from last year's kind of recession consensus much more towards the soft landing and, and, and why is that the case? Upper right hand chart. You know, the Fed has really adopted an asymmetric response function to the inflation data that we just discussed. Meaning if the inflation data comes in a bit hotter, a little bit stronger, the feds going to hold policy higher for longer.
If the inflation data delivers on the expectations of normalization, then the Fed will be in a position to cut. So asymmetric response to the economic inflation data, willing to cut on that validation of that inflation success, not talking about hikes, but just holding policy higher. That's a very kind of easy policy stance, if you will, a very accommodative policy stance, and it's factored into a degree of policy accommodation, if you will, through financial conditions. That's what the upper right and the right hand, lower right hand chart are highlighting, that financial conditions are actually much easier than the policy rate would otherwise suggest. Why is that the case? Because the Fed has been very supportive of easy financial conditions. By adopting an asymmetric policy response function to inflation, the impact in the economy of easing financial conditions transmits through the wealth effect.
That's what the lower right hand chart, lower left hand chart is highlighting that the restoration of wealth, which is what financial conditions here is mostly measuring the restoration of housing wealth, the strength in housing prices, not necessarily activity, but prices, asset inflation, stock market wealth and the inverse relationship that has with savings, the savings rate going down, the adverse of spending going up. And yes, there is a distributional impact here, a very important one that these policies are disproportionately benefiting the high end consumer because that's where the wealth is disproportionately held. And so we have a bit of a K shaped recovery where inflation is hurting the lower end consumer. But the response to inflation and the asymmetric response that I've just mentioned, it's actually benefiting consumption. And because that consumption is disproportionately driven by the high end consumer, what you're getting is in aggregate the economy really kind of supported by this easier financial conditions supporting consumption. And if we go on to the next slide, we could see this. One of the things we do in the SAE Group building baskets of economic sensitivity of individual stocks in long short baskets relative to various economic outcomes.
We've tracked these for some time and you can see that soft landing portfolio really still dominant in terms of the equity market pricing. The update since last quarter is that we did see the no landing scenario gain on the soft landing scenario and that followed the three consecutive quarters of faster than expected inflation kind of pushing up the no landing scenario, pushing down the soft landing.
Since that May CPI report in June, we've seen that kind of reverse back to soft landing consensus in economic sorry in equity performance, reduced pricing of the no landing. That comports with our views in terms of soft landing as the base case scenario. So let's flip the the script here. Talk about the second main theme here from global easing policy and the economic outlook to elections induced volatility. You know we have seen a lot of elections, royal financial markets of the upper right hand chart, you know post the European parliamentary elections, the French snap election, surprise markets, big spread widening here in French bonds, relative French oats relative to German bonds. The lower left chart. Earlier this year we had a surprise results in the Mexican elections, the strength of the Mexican elections with some impact on Mexican financial assets here. And on the lower right hand chart, of course, the focus is very rapidly moved even beyond the kind of traditional calendar of post the, the, the national conventions into a focus on the US election. In all of these cases, the market impact, you know, focuses on the policy impact and that policy impact on fiscal sustainability. The lower right hand chart highlighting CBOs, more recent projections of of really unsustainable debt and deficits in the US and, and how much this election outcome may or may not affect that outcome. So clearly we're seeing a lot of elections. I want to bring in Jeff Shen, take a little bit more of a global perspective. We've had other elections here that you can highlight and turn our focus on some of the key countries that I know you're focused on in with the systematic team.
[JEFF SHEN]Thanks very much, Jeff. So when you look at India clearly just gone through a major election and the most populous democracy in the world, and it's actually going through some pretty exciting changes.
So I think we want to highlight a few interesting things here. One is certainly the chart on the top right, You can see that India is getting digitalized very quickly. I mean, it's a large country with large population. We already have about 900 million people that have broadband access and that trend is actually accelerating and continuing. So this digital nature of the economy is becoming more and more important. The other lens that we look at, you may have heard us talking about job posting data that we've actually looked at job posting data like Glassdoor type of data in the United States for a long while. We actually found a few interesting online job posting data in India. And the lower left chart essentially shows you a bit of the, the, the, the number of the job postings across, you know, different online portals that cover different jobs. And, you know, the, the quite a few sort of lines are that really give us a bit of a sense of a unique lens of looking at the composition of the growth of the Indian economy through job posting. And you can see that. So the yellow line here is highlighting the IT software software service, which is certainly a bit of a main state for the Indian economy and that's actually continue to continue to go up quite a bit. The green line on other side of it is actually looking at a bit of a more of a traditional business professional service. Those actually have been slowing down quite a bit. And for all for any of us who's actually been playing around with ChatGPT or other chat bot, I think they're probably going to be a little bit of structural headwind for that type of jobs on a forward-looking basis. And I think the historical trend is already showing a bit of a slowing down in the sector over the last last year or so. And at the same time, real estate activities, property related sector job posting continue to continue to have a pretty healthy, healthy growth. So this is certainly an economy that is growing. And if you look at the lower right chart from a domestic investor position perspective, there's also a pro growth oriented position. Lots of people are buying industrials and selling financials and the really align themselves with the government policy, especially given the latest election, election outcomes. So zooming out slightly, I'll say that lots of exciting things going on in India despite some of the headwinds against some of the traditional business professional service operations, given the potential development of AI, it's the economy very much vibrant and and then we're pretty excited about this. Talking about exciting things, we're going to switch gear a little bit looking at going to look at Japan next.
Lots of enthusiasm for Japan. Japan has also gone up quite a bit, not only on its own, but also relative to other developed market. What we want to hear, you know, take a close look here is really some of the longer term structural support for Japan. So, you know, when you look at the top right chart, what we're seeing is essentially that Tokyo Stock Exchange has actually measure, you know, how many topics firms are actually adopting the reform plan that's been that's been put out. And you can see that that number year over year from 2023 to 2024 is already gone from, you know, below 40% to over 60% in one year. So there's lots of reform plan that's being articulated, but essentially really give a bit of a sense of capital efficiency for some of these Japanese companies. And the, the lower right chart is also showing that when we look at, you know, disclosure on capital improvement, Japan is actually certainly a little bit lower compared to other developed market, whether it's large cap or small cap. But at the same time, if you look at the red bar on the lower right chart, that red part is steadily increasing. So about half of the company now are actually in the disclosed status. So they are disclosing the capital improvement plan, which is also providing a bit of a structural tailwind for the Japanese companies. And last but not the least is again, using alternative data, taking a look at it from the bottom up perspective on just how, how happy the, the, the Japanese employees are at these different, different companies. And you can see that it's actually the, the, the lower right, the lower left chart essentially shows you the red line is actually trending upward, you know, pretty steadily, certainly some wiggle, especially in 2022, but overall the trend is actually being trending upward. So employees are also finding more satisfaction in their jobs and that's always a good sign both from a micro perspective and also from a macro perspective. So I think you know, summing up a little bit over here, I'll say the corporate governance employee satisfaction alongside with the more capital efficiency are giving a bit of a longer term structural tailwind for Japan and Japan, Japanese companies. Now we talked about two happy cases and then I want to talk a little bit about the China. Clearly, you know, every day you open up a newspaper, there's a, you know, potentially a negative article on China and it's been ongoing for the last 18 to 24 months. So when we look at little bit details over here, I'll say there's a couple of things that actually worth highlighting that may not be out there that actually has been talking about being talked about #1 is actually when we think about the the share buyback, so the top right chart essentially looking at the increasing share buybacks by the Chinese companies. So this is actually important, you know, from a structural reform from a sense of making sure that shareholder returns and shareholder values are top priorities for the corporate management. This is actually we're certainly see some welcoming trend given the tough economic environment and also the the equity market downturn in China. So the share by playback is a is certainly trending in the right direction. When we look at you know some of our data timing views and in details when we especially when we look at the momentum and also sentiment. If you look at the the bottom panel of the right lower right chart, it is interesting in the sense that the analysts are becoming more positive even though at a bit of a moderated fashion while the corporate management, that sentiment we extract from the conference transcript continue to be reasonably muted. So there is a bit of a tug of war if you will. The end is up getting to become a bit more bullish.
And even though you know, sort of more flat and not necessarily dramatic going up, the corporate is actually still remain to be a a bit muted in terms of in terms of reaction. And when we look at the the top part of the lower right chart, when we look at some of the momentum in the news news articles where there's the China news or Bloomberg News. It is actually also interesting to see a bit of a diverging trend in the sense of the the big part of the 2024. We saw negative momentum, or news momentum if you will, both from the US press represented by Bloomberg, alongside with local press represented by the China Times time series. And now we're actually seeing a bit of a divergent in China. The sentiment continue to be reasonably muted, but we see a bit of a pickup of the sentiment from the Bloomberg News. So when I look at this contrast on the lower right chart, my interpretation of the data here is really that I think the corporate management and the domestic audience are still reasonably negative or muted in terms of sentiment. But we are seeing a bit of a green shoots both from analyst community alongside with the US oriented investment community. So sum it up all together, I'll say that that the lower left chart is actually showing that our beta timing model is showing some some positive signs in terms of beta. So I think this is certainly something that I think it's important to keep keep track of just given it's still the second largest equity market in the world and it's been unloved for a long time. And I think if that were to return, that can certainly change the overall market dynamics in a pretty dramatic fashion. So let's zoom out on the next page a bit. So what I want to take a look at here is that I'm going to invite my dear friend Jeff from from New York into this conversation as well. Is that, you know, on the top right chart that I'm going to comment that I'm going to have Jeff to talk about the rest of the page here on the top right. It's really zooming out a little bit. Just look at year to date from a factor perspective, how things are shaping up. And this is the line from Jeff Rosenberg. It's which is actually from beta to bifurcation. So what we're seeing is certainly a bifurcation in the equity market in the sense that when you look at the Magnificent 7 index or whether you look at the momentum index, those have actually handily outperform the overall S&P 500. When you look at you know small cap or value oriented factor returns, they've actually underperformed significantly on a year to date basis. So underneath the overall pretty healthy equity market in the US, we've actually seen a pretty significant bifurcation between tech, higher quality names compared with small cap and also value. So Jeff, I mean, what's your perspective on this whole point on bifurcation?
[JEFF ROSENBERG]
Yeah, I, I think the bifurcation point is really important because it's what we do mostly in our alpha strategies, right? And it's, it's taking advantage of what's underneath the surface. So this movement from beta which is talking about what you know direction of markets, looking underneath markets, this has been one of the best market environments for alpha investing because of what you're seeing in that bifurcation. So we're seeing this on the credit side, that's the lower right hand chart. And and again if you look on the surface, not much is happening. The credit market spreads are at mid cycle tight levels, it's relatively low volatility. But underneath there is some divergence that is being highlighted and it's echoing the divergence that Jeff, was just describing in terms of the equity markets. And it echoes a broader macro theme that I was hinting at at the beginning, the kind of winners and losers from post COVID policy intervention. I talked about the consumer side in terms of inflation is hurting the bottom end, but wealth effects are benefiting the upper end. We see this on the business side as well. You're seeing it in that credit segment that companies that didn't have the size and capability to term out debt to reduce loan based debt and get access to bond based fixed rate debt have much more interest rate sensitivity. And that's what you're seeing in the credit market bifurcation. It's a mirror of what you see in the bifurcation between kind of large cap and small cap equity performance. And the final kind of really interesting slide that that captures this bifurcation and this long short equity market neutral alpha opportunity that so many of our alpha strategies across systematic are taking advantage of is this historic gap between implied correlations and the level of overall volatility. The level of overall volatility, it's, it's low, but the level of implied correlations is even lower. And it's highlighting that you have a big gap between winners and losers, not just the MAG 7 and everybody else, but even the winners and losers inside of every sub sector that we invest in. So it's highlighting a tremendous opportunity environment for alpha investing. 
[JEFF SHEN]
So let me bring this back a little bit. I mean, we talked a little bit about Magnificent 7 as Jeff just highlighted. So we certainly cannot go on talking about market without really talking about AI, AI does seem to be everywhere. And I think from a market perspective, it is actually interesting in the sense that a, clearly we've seen NVIDIA going through a bit of volatility, USD $500 billion seem to have been lost over the over a couple of days. So this is certainly almost a from on the point of bifurcation, it almost feels like that there's NVIDIA and then there's everybody else. What we want to highlight over here is really that I think they're actually at least a couple of interesting ways of looking at this above and beyond just a narrow lens on NVIDIA or Microsoft. So the top right chart here is actually an interesting analysis and study that's Taylor and who actually presented about tactical robot have actually done and quite a few team members have actually been involved on this. So what we're doing over here is essentially the red line here are the the normal what we call AI enabled. So in there you actually have Microsoft, you have for NVIDIA, etcetera, and that's sort of what people are typically talking about. And and that's actually on a year to date basis, it's certainly done quite well. Then we ask the the the over a tactical robot and basically say, you know, what are the other interesting opportunities that actually can potentially associated with this over AI trend and between a bit of a human intelligence plus the machine intelligence, it's actually come up with three additional ideas. One is actually when we look at this pinkish line AI jobs, this one is actually the team has actually done some interesting work. Essentially it's actually looking at the company that are actually more exposed to more modern, advanced innovative AI rated skills and long those and then short companies that actually have a more traditional outdated skills. So it's a bit of, if you will, long AI skills or AI jobs and then short ones that actually going to be potentially threatened by the advancement in AI. And you've actually seen that, that it's a very broad basket and that actually have done also quite well on a year day basis. And then AI itself, our tactical robot itself also came up with a basket called AI non tech beneficiary. And so this one is essentially try to locate above and beyond the technology sector as we think about how AI can be beneficial for different companies in the world, What are the other potential beneficiaries for this big AI trend? So that basket, the AI came up by itself and so far hasn't really done as well. So I'll say that maybe it's human 1, AI 0 so far, but I, I wouldn't really, you know, sort of bet against AI for too long. So let me bring back the last point over here, which is AI power here. So AI power here is actually really focusing on the, the data center and its potential power needs and looking at the utility companies, power generating companies, how they will actually potentially benefit given essentially the supply constraint that we actually face. So think about data center as a shock to the power generation business in, in the US in particular. And the the the utility and power company that actually are facing the supply constraint are actually benefiting from the from the demand shock. So that basket is also have done quite well. You can see the the, the up trending what of the of the green line. And that also brings specifically, you know, I've been doing investment for a long time, but the first time actually, at least for me to learn this PJM spark spread. So it turned out to be that the most of the data center that's been built in this country in the US is actually on the Pennsylvania, New Jersey, Maryland grid. And when we look at the term structure of the PGM spark spread, you can see that as of 2022 December, the term structure in the yellow line on the lower left chart is essentially flat. Fast forward today to May of this year, the term structure is extraordinary steep, really give us a bit of a sense of this demand shock to the power generation is essentially steepening this PGM spark spread in a significant way. And it's essentially implying a 4% growth in the AI electrical, you know, power generation over the next four years. So think about the AI opportunity, even though there's NVIDIA, there's everybody else, but underneath the hood, there are actually quite a few different ways to think about how do you play around this, think about the jobs, think about the beneficiaries, also think about the energy demand. So with that I want to turn to the next page really to zoom out slightly and they really give a broader view on over a country views, sector views and also style views. So let me start with the style. I think on the style one I'll see that's given all the things that we look at, it will actually continue to be reasonably constructive on momentum, on size, on growth. And so there is a still a little bit of a continuation of what you actually think from a factor trend on a on a year day basis. And some of these major theme whether we what we talked about AI GLP1 and some of the energy infrastructure, we continue to be reasonably positive on. So that's so if you will, long momentum, long quality, long growth is really the overall style view.
And on other side of it is clearly betting against some of the low quality names, betting against some of the value names that also have actually suffered on a year to day basis is on the other side of this trade on a sector basis, you know, pretty constructive. On industrial, just give him over a macro cycle that Jeff Rosenberg has just talked about is actually giving us a bit of a sense that there may be a reasonable macro regime for this industrial oriented companies and some of the sentimental metrics that we track. Also pointing a bit of a more positive type of view. And on other side of it, consumer discretionary is actually slightly on the negative side, given some of the demographic dynamics that Jeff was highlighting a little bit earlier as well. That's also part of the reason on the country, you can see that the lots of nuance over here in Europe. There's certainly some of the specific longs and shorts. I think on Europe overall we're actually more bullish on some of the cyclical and service oriented countries and Japan as we have actually highlighted but also continue to be a net long from over a portfolio perspective. So I'll say that's if I zoom it out, I'll say that's overall I think we're pretty much overall have a reasonably pro risk, pro momentum, pro quality and pro growth oriented bias and cross-sectional dispersion as Jeff highlighted earlier is very large. So the alpha opportunity continue to be quite abundant. So we're we're you know, getting close to wrapping up the the first half of the year and very much looking for an exciting second-half. So with that, I'm going to turn it back to Jeff.
[JEFF ROSENBERG]
Thanks Jeff for those great insights. We're going to pivot to the next topic of this quarter's DTM. There's been a lot of action on the political side. Clearly, we are looking at a number of different elections. Some have been passed, some are in front of us. I'm going to invite Chris Sinclair, Chris is coming to us from our London office and and Chris over there you guys have a lot of elections coming up in the UK. You got elections across the Channel in France. Investors ourselves, we're, we're spending a lot of time thinking about that. We're going to pivot a little bit and talk about the thing we're talking a lot about over here, which is the US elections. And you guys have done some really interesting work around elections in general and specifically around the US election. So Chris, want to share with us some of your work that you guys are working on.
CHRIS SINCLAIRYeah, sure thing. Thanks, Jeff. So, so as you rightly mentioned, investor attention has started to shift quite dramatically from the current macro environment towards elections, particularly the US election. Inevitably, these discussions tend to relate to the likelihood of any given candidate winning and the associated policy ramifications of each possible outcome on listed equities. Now a systematic investors, we caution on reading too much international polling or traditional betting markets. These can provide a general sense of the public's preference for one candidate over another, but they're not always accurate in predicting the winner of a US presidential election due to the Electoral College system. On top of this, national polls may not accurately represent the population of each state. They may not capture late swings in voter opinion or may fail to account for factors like voter turn out. Focusing on real time alternative data, we've built a probabilistic model of the electoral votes each candidate is expected to win in the forthcoming election. The model incorporates state level insights to gauge the likelihood of each candidate winning the state, ultimately merging the state level probabilities to calculate a probabilistic distribution of electoral votes expected to be won by each candidate. So, for example, a candidate spending on online advertisements could lead to heightened voter enthusiasm or intention to vote, thereby improving the chances of that given candidate winning the state. The impressions that these advertisements receive on social media can be indicative of how well a campaign in any given state is progressing. The exposure time that a given candidate is receiving on news media can also potentially influence the ultimate outcome, and that's something that we're tracking systematically. And by tracking the number of news articles pertaining to specific policy issues in any given state, and then mapping those back to relative voter derived importance, we can get a sense for whether a candidate is focusing on the issues that are most likely to appease local voters. We also understand that forecasting of events in general is a task that is prone to unconscious biases, which is why we include the views of an elite set of super forecasters in our model expressions. Super forecasters tend to make more forecasts with more incremental revisions to their core view than generalists, who instead update their core view less often but with a higher propensity to make a large amendments each time. In having a wider breadth of knowledge, super forecasters tend to be more accurate in predicting the event itself, with that edge increasing as complexity increases. And the US election is certainly a a complex issue. We know in particular that this group of individuals are less prone to overconfidence bias when assigning probabilities to outcomes that are perceived as relatively unlikely. So in short, by systematically incorporating alternative data with the power of super forecasters, we believe that we're actually pretty well placed to exploit any mispricing of the ultimate outcome in equity markets.
In terms of policy ramifications, clearly these have an impact on equity markets and beyond. We can see from pulling in a range of sell side election baskets. The impact on exposed stocks is often aligned with the outcome of the election. So for example the Republican baskets outperformed on and following the Election Day in 2016. In 2020 we saw our performance from the Democrat baskets in the run up, but much of this was given back on the day of the election result after it proved to be much closer than initially expected and the result was uncertain for a prolonged period of time after voting had occurred. The construct of these baskets is simply a pair's trade of a long versus a short basket. Some of the pervasive themes that come through from broker baskets exposed to Republican policies include beneficiaries of infrastructure spending and tax cuts on the long side with clean energy companies on the short side. Whilst in the case of the Democrat exposed baskets, they contain for example, clean energy on the long side with some fossil fuel companies and financials on the short side.
[JEFF ROSENBERG]
Thanks Chris. Those baskets make a lot of sense. We've spent a lot of time looking at those baskets as well. I think the details get really interesting when you look at the construction and the debate over which companies go into which baskets. I'm interested in in what you guys have been doing on the systematic approach to constructing the baskets. Tell us a little bit more about that.
[CHRIS SINCLAIR]
Sure. Yeah. So in in order to capture these trends more systematically, we employ our proprietary thematic robot to generate baskets that are exposed to each of the presidential election outcomes. This robot, as we like to call it, is a large language model based tool that determines the types of business models that are likely to be impacted by any user supplied scenario. So in this case, both candidates stated policy intentions around international trade, defense, migration, financial regulation, taxes and and everything else. For example, if we refer to former President Trump's election issues on his campaign website, we can see that he's provided detail on a range of policies, from outright economic pledges to more specific issues such as veteran care and parents' rights. Similarly, we know that President Biden wants to expand expand the Inflation Reduction Act and has spoken previously about his views on guns and climate change. While some of these have pretty clear and obvious impacts on equities, others do not. But that is not to say that they don't have any impact. So, having determined the likely impact on different categories of stocks under each scenario, we then map these impacted business models to a basket of listed equities based on how closely their business activities match the business models articulated by the LLM. We do this by feeding prosaic descriptions of the company's activities into the LLM, and we supplement this information by using earnings calls to tease out mentions of the election and in turn infer the likely direction of exposure based on the sentiment expressed in the call. We can also help to steer the robot in the right direction, both by seeding it with companies that we know are exposed and by adding a human in the loop whereby the researcher running the tool can adjust it in real time. This systematic approach to building baskets of exposed companies allows us to efficiently manage the election risk that we're running in our models as the event draws closer. And it's actually a process that we're now using across many of our more tactical macro signals.
[JEFF ROSENBERG]
Chris, thank you so much. I love this theme because we're really combining a couple of the key themes market participants are focused on. Obviously, we're talking about the elections, but what you just went through was an application of AI to this election theme. I know we've talked about some other examples. Could you just quickly tell us other examples of how you guys have been using this? LLM, AI approach in your thematic work?
[CHRIS SINCLAIR]
Yeah, for sure. So this is an approach that can be scaled to tackle many different themes in markets, for example, GLP one drugs or or AI beneficiaries. It's scalable and it's pretty simple to use and therefore something that we're very excited about.
[JEFF ROSENBERG]
Excellent. Well, thanks Chris for joining us and let me thank you our viewers and our investors for joining us for this quarter's Decoding the Markets. Please reach out to us if anything that you've seen in today's presentation you'd like some follow up on and join us next quarter for the next quarterly edition of Decoding the Markets. Thanks very much.
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BSYSH0724U/M-3680534

Decoding the Markets: Global Easing, Elections and Emerging Markets
[JEFF ROSENBERG]
Hi, I'm Jeff Rosenberg and I'm hosting this quarter's Decoding the Markets alongside Jeff Shen, our CIO and Head of Systematic Active Equities, Chris Sinclair from our Thematics Group will also be joining us on a special topic on using artificial intelligence in large language models in the application of managing some of the election risk. I'm both your host and content provider, a part of our BSYS fixed income group and Co lead Portfolio manager for the systematic multi strategy fund. So let's get into our themes. On the macro side, we're going to cover global easing, some of the elections and emerging markets. On the global easing cycle, I'll start here. You know, we're kicking this off as we've seen some large developed market banks begin what seems to be expectations for the global easing cycle. In the taxonomy that we introduced earlier this year on how to think about this easing cycle, we've really laid out three different types of easing to distinguish what's really driving policy cuts in an environment where global growth is really holding up. And the kind of, we'll talk about this later, the recession fears have given way. So what we're really focused on here is the global policy rate declines expected. You can see that in the lower left hand chart is really a function of what we call maintenance cuts. And maintenance cuts are policymakers maintaining the degree of policy accommodation and avoiding an increase in the real interest rate. So here you see the orange area. The path for real interest rates under maintenance cuts is flat. That's keeping policy maintained at its current level of restrictiveness. As inflation goes down, you've got to cut nominal interest rates. This is following a period of falling global inflation. Now more recently, what we've seen is that the path that's suggested here in the lower left chart may not be as smooth as inflation surprises in Canada and Australia have kind of highlighted that the path towards this, this maintenance cuts as I'm describing may not be as smooth as markets are anticipating. Final point here is when we look at this from a global perspective, this chart here on the lower right is highlighting the evolution of global growth forecasts. The orange line for the US, the yellow line for Eurozone in 2024. And you can see heavy revisions upward in the U.S. growth expectations relative to Europe. This is partly due to why we've seen the first cuts here from the ECB and the delay in terms of relative to earlier year expectations for cuts in the US. And a significant reduction from seven earlier this year to somewhere between 1-2 today, really on the back of kind of pushing back on really how much cuts the Fed will need to do to maintain policy restrictiveness, really because the strength of that economic growth is kind of pushed back on the fears that policy is overly restrictive. OK. So let's move on to the next kind of macro theme and a little bit more of a focus here in in in the US the labor market and its support for arguing for or against that that policy easing expectations. We highlighted this a little bit in the last DTM last quarter, the debate over kind of traditional, what I'm calling traditional alternative measures of Labor market. On the upper right hand chart, we're highlighting our SAE alternative wage growth measures as a way of highlighting what we've highlighted for some time. And this data has highlighted for some time, again, using job postings to scrape out what our wage is doing. And what you see there is that normalization that Powell and others have been talking about, the normalization kind of flatlining. More recently, when we look not at the wage picture, but at the payroll or the establishment survey data, we've seen this break between the orange line, which establishment survey data, and the yellow line, which is the household data. Earlier this year, we had an immigration argument kind of arguing that that household data may be understated, households not under immigration, households under reporting relative to establishment survey. On the right hand chart, we highlight the kind of the counter argument that is suggesting that it's the establishment data that's overstated. We have some benchmark revisions that will be coming through some alternative measures, kind of more traditional alternative measures for that payroll establishment data, the QC data, the BID data, talking about the potential that we're overstating payroll growth. Some of this is being pointed to in terms of the birth death adjustment, technical factors. This may be overstating the degree of payroll growth. And this is an argument for supportive of, you know, more Fed cuts rather than less Fed cuts in that the payroll data, which has really been rather strong and pushing back against expectations for cuts by the Fed may be overstated, something that's important in the data evolution. We'll see that later this summer in terms of the benchmark revision, something we in the market will be paying a lot of attention to as it feeds into those expectations. Currently, market pricing between 1-2 cuts for the Fed this year. Let's move on to the next perspective here on inflation, on this Immaculate and otherwise. You know some of the important things to keep an eye on. You know when we look at some of our alternative data on web scrapes, inflation, you can see those year on year figures. This is a global perspective somewhat ticking upwards. This is important. It's highlighting something that we're going to see on the right hand chart. When you look at year over year measures, this orange line is highlighting the PCE measure, kind of the most important measure for the Fed and for the markets. The year over year figures are going to start to get into a pattern where the year over year seasonal fact, not the seasonal factors, but the comparison from a year ago are going to become kind of challenging. So when you're looking at the year over year figures, they may actually start to rise. Those are going to be less important than the annualization of the shorter run figures. For example, think about 3 month annualized figures and what you see in kind of the yellow line and the green line in terms of oil price stability since the spike in April coming down, hips break even, inflation is relatively stable. This is pointing to a much more benign outlook here for inflation. You see that in the upper chart here on wage inflation, the more traditional wage inflation measures, those are coming down. And that's all kind of supportive of a much more reasonable level of policy expectations for the Fed one to two cuts by the end of this year. That's a September cut, That's a December cut. We think September is still in play here. This inflation picture is still very supportive here of the Fed being able to deliver one to two cuts this year. Let's move on to the next slide on the economic growth picture. This is really just a continuation of moving away from last year's kind of recession consensus much more towards the soft landing and, and, and why is that the case? Upper right hand chart. You know, the Fed has really adopted an asymmetric response function to the inflation data that we just discussed. Meaning if the inflation data comes in a bit hotter, a little bit stronger, the feds going to hold policy higher for longer.
If the inflation data delivers on the expectations of normalization, then the Fed will be in a position to cut. So asymmetric response to the economic inflation data, willing to cut on that validation of that inflation success, not talking about hikes, but just holding policy higher. That's a very kind of easy policy stance, if you will, a very accommodative policy stance, and it's factored into a degree of policy accommodation, if you will, through financial conditions. That's what the upper right and the right hand, lower right hand chart are highlighting, that financial conditions are actually much easier than the policy rate would otherwise suggest. Why is that the case? Because the Fed has been very supportive of easy financial conditions. By adopting an asymmetric policy response function to inflation, the impact in the economy of easing financial conditions transmits through the wealth effect.
That's what the lower right hand chart, lower left hand chart is highlighting that the restoration of wealth, which is what financial conditions here is mostly measuring the restoration of housing wealth, the strength in housing prices, not necessarily activity, but prices, asset inflation, stock market wealth and the inverse relationship that has with savings, the savings rate going down, the adverse of spending going up. And yes, there is a distributional impact here, a very important one that these policies are disproportionately benefiting the high end consumer because that's where the wealth is disproportionately held. And so we have a bit of a K shaped recovery where inflation is hurting the lower end consumer. But the response to inflation and the asymmetric response that I've just mentioned, it's actually benefiting consumption. And because that consumption is disproportionately driven by the high end consumer, what you're getting is in aggregate the economy really kind of supported by this easier financial conditions supporting consumption. And if we go on to the next slide, we could see this. One of the things we do in the SAE Group building baskets of economic sensitivity of individual stocks in long short baskets relative to various economic outcomes.
We've tracked these for some time and you can see that soft landing portfolio really still dominant in terms of the equity market pricing. The update since last quarter is that we did see the no landing scenario gain on the soft landing scenario and that followed the three consecutive quarters of faster than expected inflation kind of pushing up the no landing scenario, pushing down the soft landing.
Since that May CPI report in June, we've seen that kind of reverse back to soft landing consensus in economic sorry in equity performance, reduced pricing of the no landing. That comports with our views in terms of soft landing as the base case scenario. So let's flip the the script here. Talk about the second main theme here from global easing policy and the economic outlook to elections induced volatility. You know we have seen a lot of elections, royal financial markets of the upper right hand chart, you know post the European parliamentary elections, the French snap election, surprise markets, big spread widening here in French bonds, relative French oats relative to German bonds. The lower left chart. Earlier this year we had a surprise results in the Mexican elections, the strength of the Mexican elections with some impact on Mexican financial assets here. And on the lower right hand chart, of course, the focus is very rapidly moved even beyond the kind of traditional calendar of post the, the, the national conventions into a focus on the US election. In all of these cases, the market impact, you know, focuses on the policy impact and that policy impact on fiscal sustainability. The lower right hand chart highlighting CBOs, more recent projections of of really unsustainable debt and deficits in the US and, and how much this election outcome may or may not affect that outcome. So clearly we're seeing a lot of elections. I want to bring in Jeff Shen, take a little bit more of a global perspective. We've had other elections here that you can highlight and turn our focus on some of the key countries that I know you're focused on in with the systematic team.
[JEFF SHEN]Thanks very much, Jeff. So when you look at India clearly just gone through a major election and the most populous democracy in the world, and it's actually going through some pretty exciting changes.
So I think we want to highlight a few interesting things here. One is certainly the chart on the top right, You can see that India is getting digitalized very quickly. I mean, it's a large country with large population. We already have about 900 million people that have broadband access and that trend is actually accelerating and continuing. So this digital nature of the economy is becoming more and more important. The other lens that we look at, you may have heard us talking about job posting data that we've actually looked at job posting data like Glassdoor type of data in the United States for a long while. We actually found a few interesting online job posting data in India. And the lower left chart essentially shows you a bit of the, the, the, the number of the job postings across, you know, different online portals that cover different jobs. And, you know, the, the quite a few sort of lines are that really give us a bit of a sense of a unique lens of looking at the composition of the growth of the Indian economy through job posting. And you can see that. So the yellow line here is highlighting the IT software software service, which is certainly a bit of a main state for the Indian economy and that's actually continue to continue to go up quite a bit. The green line on other side of it is actually looking at a bit of a more of a traditional business professional service. Those actually have been slowing down quite a bit. And for all for any of us who's actually been playing around with ChatGPT or other chat bot, I think they're probably going to be a little bit of structural headwind for that type of jobs on a forward-looking basis. And I think the historical trend is already showing a bit of a slowing down in the sector over the last last year or so. And at the same time, real estate activities, property related sector job posting continue to continue to have a pretty healthy, healthy growth. So this is certainly an economy that is growing. And if you look at the lower right chart from a domestic investor position perspective, there's also a pro growth oriented position. Lots of people are buying industrials and selling financials and the really align themselves with the government policy, especially given the latest election, election outcomes. So zooming out slightly, I'll say that lots of exciting things going on in India despite some of the headwinds against some of the traditional business professional service operations, given the potential development of AI, it's the economy very much vibrant and and then we're pretty excited about this. Talking about exciting things, we're going to switch gear a little bit looking at going to look at Japan next.
Lots of enthusiasm for Japan. Japan has also gone up quite a bit, not only on its own, but also relative to other developed market. What we want to hear, you know, take a close look here is really some of the longer term structural support for Japan. So, you know, when you look at the top right chart, what we're seeing is essentially that Tokyo Stock Exchange has actually measure, you know, how many topics firms are actually adopting the reform plan that's been that's been put out. And you can see that that number year over year from 2023 to 2024 is already gone from, you know, below 40% to over 60% in one year. So there's lots of reform plan that's being articulated, but essentially really give a bit of a sense of capital efficiency for some of these Japanese companies. And the, the lower right chart is also showing that when we look at, you know, disclosure on capital improvement, Japan is actually certainly a little bit lower compared to other developed market, whether it's large cap or small cap. But at the same time, if you look at the red bar on the lower right chart, that red part is steadily increasing. So about half of the company now are actually in the disclosed status. So they are disclosing the capital improvement plan, which is also providing a bit of a structural tailwind for the Japanese companies. And last but not the least is again, using alternative data, taking a look at it from the bottom up perspective on just how, how happy the, the, the Japanese employees are at these different, different companies. And you can see that it's actually the, the, the lower right, the lower left chart essentially shows you the red line is actually trending upward, you know, pretty steadily, certainly some wiggle, especially in 2022, but overall the trend is actually being trending upward. So employees are also finding more satisfaction in their jobs and that's always a good sign both from a micro perspective and also from a macro perspective. So I think you know, summing up a little bit over here, I'll say the corporate governance employee satisfaction alongside with the more capital efficiency are giving a bit of a longer term structural tailwind for Japan and Japan, Japanese companies. Now we talked about two happy cases and then I want to talk a little bit about the China. Clearly, you know, every day you open up a newspaper, there's a, you know, potentially a negative article on China and it's been ongoing for the last 18 to 24 months. So when we look at little bit details over here, I'll say there's a couple of things that actually worth highlighting that may not be out there that actually has been talking about being talked about #1 is actually when we think about the the share buyback, so the top right chart essentially looking at the increasing share buybacks by the Chinese companies. So this is actually important, you know, from a structural reform from a sense of making sure that shareholder returns and shareholder values are top priorities for the corporate management. This is actually we're certainly see some welcoming trend given the tough economic environment and also the the equity market downturn in China. So the share by playback is a is certainly trending in the right direction. When we look at you know some of our data timing views and in details when we especially when we look at the momentum and also sentiment. If you look at the the bottom panel of the right lower right chart, it is interesting in the sense that the analysts are becoming more positive even though at a bit of a moderated fashion while the corporate management, that sentiment we extract from the conference transcript continue to be reasonably muted. So there is a bit of a tug of war if you will. The end is up getting to become a bit more bullish.
And even though you know, sort of more flat and not necessarily dramatic going up, the corporate is actually still remain to be a a bit muted in terms of in terms of reaction. And when we look at the the top part of the lower right chart, when we look at some of the momentum in the news news articles where there's the China news or Bloomberg News. It is actually also interesting to see a bit of a diverging trend in the sense of the the big part of the 2024. We saw negative momentum, or news momentum if you will, both from the US press represented by Bloomberg, alongside with local press represented by the China Times time series. And now we're actually seeing a bit of a divergent in China. The sentiment continue to be reasonably muted, but we see a bit of a pickup of the sentiment from the Bloomberg News. So when I look at this contrast on the lower right chart, my interpretation of the data here is really that I think the corporate management and the domestic audience are still reasonably negative or muted in terms of sentiment. But we are seeing a bit of a green shoots both from analyst community alongside with the US oriented investment community. So sum it up all together, I'll say that that the lower left chart is actually showing that our beta timing model is showing some some positive signs in terms of beta. So I think this is certainly something that I think it's important to keep keep track of just given it's still the second largest equity market in the world and it's been unloved for a long time. And I think if that were to return, that can certainly change the overall market dynamics in a pretty dramatic fashion. So let's zoom out on the next page a bit. So what I want to take a look at here is that I'm going to invite my dear friend Jeff from from New York into this conversation as well. Is that, you know, on the top right chart that I'm going to comment that I'm going to have Jeff to talk about the rest of the page here on the top right. It's really zooming out a little bit. Just look at year to date from a factor perspective, how things are shaping up. And this is the line from Jeff Rosenberg. It's which is actually from beta to bifurcation. So what we're seeing is certainly a bifurcation in the equity market in the sense that when you look at the Magnificent 7 index or whether you look at the momentum index, those have actually handily outperform the overall S&P 500. When you look at you know small cap or value oriented factor returns, they've actually underperformed significantly on a year to date basis. So underneath the overall pretty healthy equity market in the US, we've actually seen a pretty significant bifurcation between tech, higher quality names compared with small cap and also value. So Jeff, I mean, what's your perspective on this whole point on bifurcation?
[JEFF ROSENBERG]
Yeah, I, I think the bifurcation point is really important because it's what we do mostly in our alpha strategies, right? And it's, it's taking advantage of what's underneath the surface. So this movement from beta which is talking about what you know direction of markets, looking underneath markets, this has been one of the best market environments for alpha investing because of what you're seeing in that bifurcation. So we're seeing this on the credit side, that's the lower right hand chart. And and again if you look on the surface, not much is happening. The credit market spreads are at mid cycle tight levels, it's relatively low volatility. But underneath there is some divergence that is being highlighted and it's echoing the divergence that Jeff, was just describing in terms of the equity markets. And it echoes a broader macro theme that I was hinting at at the beginning, the kind of winners and losers from post COVID policy intervention. I talked about the consumer side in terms of inflation is hurting the bottom end, but wealth effects are benefiting the upper end. We see this on the business side as well. You're seeing it in that credit segment that companies that didn't have the size and capability to term out debt to reduce loan based debt and get access to bond based fixed rate debt have much more interest rate sensitivity. And that's what you're seeing in the credit market bifurcation. It's a mirror of what you see in the bifurcation between kind of large cap and small cap equity performance. And the final kind of really interesting slide that that captures this bifurcation and this long short equity market neutral alpha opportunity that so many of our alpha strategies across systematic are taking advantage of is this historic gap between implied correlations and the level of overall volatility. The level of overall volatility, it's, it's low, but the level of implied correlations is even lower. And it's highlighting that you have a big gap between winners and losers, not just the MAG 7 and everybody else, but even the winners and losers inside of every sub sector that we invest in. So it's highlighting a tremendous opportunity environment for alpha investing. 
[JEFF SHEN]
So let me bring this back a little bit. I mean, we talked a little bit about Magnificent 7 as Jeff just highlighted. So we certainly cannot go on talking about market without really talking about AI, AI does seem to be everywhere. And I think from a market perspective, it is actually interesting in the sense that a, clearly we've seen NVIDIA going through a bit of volatility, USD $500 billion seem to have been lost over the over a couple of days. So this is certainly almost a from on the point of bifurcation, it almost feels like that there's NVIDIA and then there's everybody else. What we want to highlight over here is really that I think they're actually at least a couple of interesting ways of looking at this above and beyond just a narrow lens on NVIDIA or Microsoft. So the top right chart here is actually an interesting analysis and study that's Taylor and who actually presented about tactical robot have actually done and quite a few team members have actually been involved on this. So what we're doing over here is essentially the red line here are the the normal what we call AI enabled. So in there you actually have Microsoft, you have for NVIDIA, etcetera, and that's sort of what people are typically talking about. And and that's actually on a year to date basis, it's certainly done quite well. Then we ask the the the over a tactical robot and basically say, you know, what are the other interesting opportunities that actually can potentially associated with this over AI trend and between a bit of a human intelligence plus the machine intelligence, it's actually come up with three additional ideas. One is actually when we look at this pinkish line AI jobs, this one is actually the team has actually done some interesting work. Essentially it's actually looking at the company that are actually more exposed to more modern, advanced innovative AI rated skills and long those and then short companies that actually have a more traditional outdated skills. So it's a bit of, if you will, long AI skills or AI jobs and then short ones that actually going to be potentially threatened by the advancement in AI. And you've actually seen that, that it's a very broad basket and that actually have done also quite well on a year day basis. And then AI itself, our tactical robot itself also came up with a basket called AI non tech beneficiary. And so this one is essentially try to locate above and beyond the technology sector as we think about how AI can be beneficial for different companies in the world, What are the other potential beneficiaries for this big AI trend? So that basket, the AI came up by itself and so far hasn't really done as well. So I'll say that maybe it's human 1, AI 0 so far, but I, I wouldn't really, you know, sort of bet against AI for too long. So let me bring back the last point over here, which is AI power here. So AI power here is actually really focusing on the, the data center and its potential power needs and looking at the utility companies, power generating companies, how they will actually potentially benefit given essentially the supply constraint that we actually face. So think about data center as a shock to the power generation business in, in the US in particular. And the the the utility and power company that actually are facing the supply constraint are actually benefiting from the from the demand shock. So that basket is also have done quite well. You can see the the, the up trending what of the of the green line. And that also brings specifically, you know, I've been doing investment for a long time, but the first time actually, at least for me to learn this PJM spark spread. So it turned out to be that the most of the data center that's been built in this country in the US is actually on the Pennsylvania, New Jersey, Maryland grid. And when we look at the term structure of the PGM spark spread, you can see that as of 2022 December, the term structure in the yellow line on the lower left chart is essentially flat. Fast forward today to May of this year, the term structure is extraordinary steep, really give us a bit of a sense of this demand shock to the power generation is essentially steepening this PGM spark spread in a significant way. And it's essentially implying a 4% growth in the AI electrical, you know, power generation over the next four years. So think about the AI opportunity, even though there's NVIDIA, there's everybody else, but underneath the hood, there are actually quite a few different ways to think about how do you play around this, think about the jobs, think about the beneficiaries, also think about the energy demand. So with that I want to turn to the next page really to zoom out slightly and they really give a broader view on over a country views, sector views and also style views. So let me start with the style. I think on the style one I'll see that's given all the things that we look at, it will actually continue to be reasonably constructive on momentum, on size, on growth. And so there is a still a little bit of a continuation of what you actually think from a factor trend on a on a year day basis. And some of these major theme whether we what we talked about AI GLP1 and some of the energy infrastructure, we continue to be reasonably positive on. So that's so if you will, long momentum, long quality, long growth is really the overall style view.
And on other side of it is clearly betting against some of the low quality names, betting against some of the value names that also have actually suffered on a year to day basis is on the other side of this trade on a sector basis, you know, pretty constructive. On industrial, just give him over a macro cycle that Jeff Rosenberg has just talked about is actually giving us a bit of a sense that there may be a reasonable macro regime for this industrial oriented companies and some of the sentimental metrics that we track. Also pointing a bit of a more positive type of view. And on other side of it, consumer discretionary is actually slightly on the negative side, given some of the demographic dynamics that Jeff was highlighting a little bit earlier as well. That's also part of the reason on the country, you can see that the lots of nuance over here in Europe. There's certainly some of the specific longs and shorts. I think on Europe overall we're actually more bullish on some of the cyclical and service oriented countries and Japan as we have actually highlighted but also continue to be a net long from over a portfolio perspective. So I'll say that's if I zoom it out, I'll say that's overall I think we're pretty much overall have a reasonably pro risk, pro momentum, pro quality and pro growth oriented bias and cross-sectional dispersion as Jeff highlighted earlier is very large. So the alpha opportunity continue to be quite abundant. So we're we're you know, getting close to wrapping up the the first half of the year and very much looking for an exciting second-half. So with that, I'm going to turn it back to Jeff.
[JEFF ROSENBERG]
Thanks Jeff for those great insights. We're going to pivot to the next topic of this quarter's DTM. There's been a lot of action on the political side. Clearly, we are looking at a number of different elections. Some have been passed, some are in front of us. I'm going to invite Chris Sinclair, Chris is coming to us from our London office and and Chris over there you guys have a lot of elections coming up in the UK. You got elections across the Channel in France. Investors ourselves, we're, we're spending a lot of time thinking about that. We're going to pivot a little bit and talk about the thing we're talking a lot about over here, which is the US elections. And you guys have done some really interesting work around elections in general and specifically around the US election. So Chris, want to share with us some of your work that you guys are working on.
CHRIS SINCLAIRYeah, sure thing. Thanks, Jeff. So, so as you rightly mentioned, investor attention has started to shift quite dramatically from the current macro environment towards elections, particularly the US election. Inevitably, these discussions tend to relate to the likelihood of any given candidate winning and the associated policy ramifications of each possible outcome on listed equities. Now a systematic investors, we caution on reading too much international polling or traditional betting markets. These can provide a general sense of the public's preference for one candidate over another, but they're not always accurate in predicting the winner of a US presidential election due to the Electoral College system. On top of this, national polls may not accurately represent the population of each state. They may not capture late swings in voter opinion or may fail to account for factors like voter turn out. Focusing on real time alternative data, we've built a probabilistic model of the electoral votes each candidate is expected to win in the forthcoming election. The model incorporates state level insights to gauge the likelihood of each candidate winning the state, ultimately merging the state level probabilities to calculate a probabilistic distribution of electoral votes expected to be won by each candidate. So, for example, a candidate spending on online advertisements could lead to heightened voter enthusiasm or intention to vote, thereby improving the chances of that given candidate winning the state. The impressions that these advertisements receive on social media can be indicative of how well a campaign in any given state is progressing. The exposure time that a given candidate is receiving on news media can also potentially influence the ultimate outcome, and that's something that we're tracking systematically. And by tracking the number of news articles pertaining to specific policy issues in any given state, and then mapping those back to relative voter derived importance, we can get a sense for whether a candidate is focusing on the issues that are most likely to appease local voters. We also understand that forecasting of events in general is a task that is prone to unconscious biases, which is why we include the views of an elite set of super forecasters in our model expressions. Super forecasters tend to make more forecasts with more incremental revisions to their core view than generalists, who instead update their core view less often but with a higher propensity to make a large amendments each time. In having a wider breadth of knowledge, super forecasters tend to be more accurate in predicting the event itself, with that edge increasing as complexity increases. And the US election is certainly a a complex issue. We know in particular that this group of individuals are less prone to overconfidence bias when assigning probabilities to outcomes that are perceived as relatively unlikely. So in short, by systematically incorporating alternative data with the power of super forecasters, we believe that we're actually pretty well placed to exploit any mispricing of the ultimate outcome in equity markets.
In terms of policy ramifications, clearly these have an impact on equity markets and beyond. We can see from pulling in a range of sell side election baskets. The impact on exposed stocks is often aligned with the outcome of the election. So for example the Republican baskets outperformed on and following the Election Day in 2016. In 2020 we saw our performance from the Democrat baskets in the run up, but much of this was given back on the day of the election result after it proved to be much closer than initially expected and the result was uncertain for a prolonged period of time after voting had occurred. The construct of these baskets is simply a pair's trade of a long versus a short basket. Some of the pervasive themes that come through from broker baskets exposed to Republican policies include beneficiaries of infrastructure spending and tax cuts on the long side with clean energy companies on the short side. Whilst in the case of the Democrat exposed baskets, they contain for example, clean energy on the long side with some fossil fuel companies and financials on the short side.
[JEFF ROSENBERG]
Thanks Chris. Those baskets make a lot of sense. We've spent a lot of time looking at those baskets as well. I think the details get really interesting when you look at the construction and the debate over which companies go into which baskets. I'm interested in in what you guys have been doing on the systematic approach to constructing the baskets. Tell us a little bit more about that.
[CHRIS SINCLAIR]
Sure. Yeah. So in in order to capture these trends more systematically, we employ our proprietary thematic robot to generate baskets that are exposed to each of the presidential election outcomes. This robot, as we like to call it, is a large language model based tool that determines the types of business models that are likely to be impacted by any user supplied scenario. So in this case, both candidates stated policy intentions around international trade, defense, migration, financial regulation, taxes and and everything else. For example, if we refer to former President Trump's election issues on his campaign website, we can see that he's provided detail on a range of policies, from outright economic pledges to more specific issues such as veteran care and parents' rights. Similarly, we know that President Biden wants to expand expand the Inflation Reduction Act and has spoken previously about his views on guns and climate change. While some of these have pretty clear and obvious impacts on equities, others do not. But that is not to say that they don't have any impact. So, having determined the likely impact on different categories of stocks under each scenario, we then map these impacted business models to a basket of listed equities based on how closely their business activities match the business models articulated by the LLM. We do this by feeding prosaic descriptions of the company's activities into the LLM, and we supplement this information by using earnings calls to tease out mentions of the election and in turn infer the likely direction of exposure based on the sentiment expressed in the call. We can also help to steer the robot in the right direction, both by seeding it with companies that we know are exposed and by adding a human in the loop whereby the researcher running the tool can adjust it in real time. This systematic approach to building baskets of exposed companies allows us to efficiently manage the election risk that we're running in our models as the event draws closer. And it's actually a process that we're now using across many of our more tactical macro signals.
[JEFF ROSENBERG]
Chris, thank you so much. I love this theme because we're really combining a couple of the key themes market participants are focused on. Obviously, we're talking about the elections, but what you just went through was an application of AI to this election theme. I know we've talked about some other examples. Could you just quickly tell us other examples of how you guys have been using this? LLM, AI approach in your thematic work?
[CHRIS SINCLAIR]
Yeah, for sure. So this is an approach that can be scaled to tackle many different themes in markets, for example, GLP one drugs or or AI beneficiaries. It's scalable and it's pretty simple to use and therefore something that we're very excited about.
[JEFF ROSENBERG]
Excellent. Well, thanks Chris for joining us and let me thank you our viewers and our investors for joining us for this quarter's Decoding the Markets. Please reach out to us if anything that you've seen in today's presentation you'd like some follow up on and join us next quarter for the next quarterly edition of Decoding the Markets. Thanks very much.
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