BlackRock American Income Trust Plc

00:00:00:01 - 00:00:16:08

Unknown

So the plan is to do a bit of a recap of the investment process. We know that it's different from a lot of the trusts out there, and perhaps different from things that you might be more familiar with. So before we talk about how we're generating outperformance, we're going to talk a bit a little bit about how we do it.

00:00:16:10 - 00:00:40:11

Unknown

And the process so y is a systematic, approach to investment becoming more common actually not just through the UK and through or through the world. Y more and more people adopting it might y more and more people getting comfortable with it. But I think everybody recognises that data and technology is changing the world. You know, artificial intelligence, we now carry that in our pockets.

00:00:40:11 - 00:01:02:14

Unknown

We can't be carrying supercomputers around in our pockets for a very long time. And ultimately, what is investing? It's about gathering information and then blending that information together to make an investment decision there now exists tools that enable you to do this in a very, sophisticated way. 20, 30 years ago, we had company accounts and we had prices.

00:01:02:17 - 00:01:24:05

Unknown

Now we have much more information about what people are buying online, about what's being spoken about in the news or even in podcasts. We can take that data and form investment views from that. And then also, maybe you've got lots of trusts in your portfolio and lots of funds in your portfolio that follow the same investing approach, a sort of long term, fundamental, quality biased approach.

00:01:24:05 - 00:01:44:04

Unknown

Perhaps if you then blend that with systematic, if you blend that with a manager that has a different philosophy, there's a good chance that the different managers, instead of underperforming at the same time, some of them will perform well when others are struggling. And so you get a little bit of diversification. Can you know the team, our team is going to be quite, quite new to you, potentially,

00:01:44:05 - 00:02:06:07

Unknown

but we've been around a really long time. We've been managing systematic equity strategies and actually started or started it with a US strategy over 40 years ago. So we've been managing money systematically in equity markets for over 40 years. We manage over $300 billion, and we're a team across systematic equities and fixed income by over 200 people. Why is that important?

00:02:06:09 - 00:02:29:19

Unknown

Well, in the same way that some of the the big AI hyperscalers, as they call them, are spending billions of dollars on computing power and infrastructure. We also think we need to do that in investing. We're not spending quite billions of dollars, but it still costs money to buy data to buy computing power, and to hire the smart people who know what to do with these tools.

00:02:29:20 - 00:02:55:20

Unknown

So we think that that's really important. But it's also important to recognise that this isn't physics or just computer science. This is investing in markets. Markets change. Things happen. Things are happening right now, between between the US and Iran, for example. So we've also would emphasise that we've got an experienced team who've been running these types of systematic strategies through different market environments and monitor what the models are doing.

00:02:55:20 - 00:03:28:20

Unknown

And we step in and intervene and manage risk when necessary. So yes, there are some lots of pictures here. There are lots of human beings in the team. Lots of us have been around a while. I've only actually been in the team 16 years I think. Travis, you've been here 20, 20 plus years. He's been coaching that. But, you know, it's an experienced team, who've been through different market cycles, who've been through different environments and have overseen the evolution of these models to now use more sophisticated tools.

00:03:28:20 - 00:03:57:19

Unknown

And it's also a broad range of people. We've still got an accountant, we've got economists, we've got people from engineering backgrounds, but we've also got people from computer science and AI backgrounds. The key thing is you bring them all together. You need collectively that understanding of how markets work as well as how technology works. And so, you know, there are a couple of charts here that go from bottom left to top right, as all good charts do.

00:03:57:19 - 00:04:18:11

Unknown

But what we're really highlighting here is just the explosion in data and computing power. So the, you know, the first one, the amount of data in the world, as I mentioned many years ago, we had company accounts and we had prices. Now there are thousands of podcasts every day. We have algorithms that can read those podcasts and detect emerging themes.

00:04:18:13 - 00:04:35:18

Unknown

There is, you know, a huge amount of broker research out there. We have algorithms that can read that when something happens in the news. And then we've got, you know, someone today, hopefully then not right now. They're going to listen to what we've got to say, maybe later on or on the train up here. You bought something online with an app.

00:04:35:20 - 00:04:54:02

Unknown

That's data that will show up in company earnings in the future, but we can detect it right now and make sure our models are trading into it. The other key thing is just computing power. Some of the data that we have today is so big, or we have so many different data sets that 20 years ago we wouldn't have been able to handle them.

00:04:54:02 - 00:05:10:24

Unknown

We didn't have the computing power. I used to know Travis and I used to run back tests overnight because it would take, you know, 8 or 10 hours to, to, to to test the impact of a signal on our models. Now we can do that in two points or under two seconds because we have so much more computing power.

00:05:11:04 - 00:05:41:00

Unknown

And so finally, what does that mean? It means we've just got many, many more investment signals. We are effectively on a mission to capture all of the things that drive stock prices, whether it's what's going on top down in the economy, whether that's what's going on bottom up with a company's products or the quality of their management. There are lots and lots of different investment ideas that you can capture with data and it's our mission to evaluate those and to blend them all into all into our models.

00:05:41:02 - 00:05:59:20

Unknown

So I think with that, I'm going to invite Travis up to join me. He's just going to recap a little bit more in terms of like what our process really looks like, what our models think about and look for right at the time.

00:05:59:22 - 00:06:26:20

Unknown

Perfect. So this is an overview of how we think about, forecasting returns, in the strategy. So we're looking primarily at three different dimensions that we're analysing companies again. So we're looking at their fundamentals. We're looking at the sentiment. We're looking at the macro environment. So fundamentals or things like traditional financial statement or ratio analysis to understand the profitability of the company.

00:06:26:22 - 00:06:49:17

Unknown

Things like the free cash flow yield that they're generating. And we also look at non-traditional things. So things like text analysis of the company's financial statements, the different filings that they're doing. We pay very close attention to things like, what the executives are saying on their conference calls and in investor meetings, market sentiment.

00:06:49:17 - 00:07:17:02

Unknown

So this is important to understand the trends that the company is exposed to. We use a lot of alternative data, things like activity, online looking, searching for a company's products. We also look at the analyst community and understand of all the broker research reports that are getting generated, on a daily basis for this particular firm, are they more, bullish or bearish?

00:07:17:02 - 00:07:45:13

Unknown

And we're analysing the text. And then lastly, the macro component certainly been very important in recent years. Just understanding, is this company facing a headwind or a tailwind based on their exposure? In terms of what industry they're in, what sector they're in, and what their geographic footprint in is like, are they selling into areas into different industries and sectors where it's more conducive for them to be earnings, for example?

00:07:45:19 - 00:08:10:24

Unknown

Or is that preventing or presenting more of a headwind as a result? So this is how it all kind of comes together just visually. This is, how we analyse a single company. So each, model that we build has a weight that's assigned to it. You can think of a company getting scored on a bell curve like distribution.

00:08:11:01 - 00:08:39:24

Unknown

And this is following one company through that process. So we're analysing the company's fundamentals, the market sentiment and the macro environment. Taking that bell curve distribution, multiplying the score of the particular firm times that signals importance in the overall structure of the model. And then evaluating the company, with respect to risk and transaction costs. That's the middle panel that you see there.

00:08:40:01 - 00:08:57:11

Unknown

So the riskier the stock, the smaller the position that will take, the more costly it is, the more impact that we would think we would have by buying into it or selling out of it. Then also, the smaller the position we would also take, the end result is what you see on the right hand side.

00:08:57:11 - 00:09:26:00

Unknown

So a big position for us relative to the index would be about 100 basis points overweight. And underweight. And because we have a view on every single name in the index will tend to have an active weight on every single name. This is the portfolio positioning, as it stands as of the end of February. So pretty tight, in terms of risk characteristics and sector weightings overall.

00:09:26:02 - 00:09:54:19

Unknown

Again similar to and at the individual stock level. So a big position for us at the industry and sector level would be about 100 basis points. So currently as it stands a small overweight to financials in it with underweight coming from areas like materials and real estate from a characteristics perspective. So about 150 names in the portfolio relative to about 860 in the index.

00:09:54:21 - 00:10:12:14

Unknown

And then the characteristics of the fund are going to look very similar to the index. We're trying to match those characteristics. And run that portfolio with an expected beta of one relative to its benchmark.

00:10:12:16 - 00:10:36:08

Unknown

So then it's going to be helpful. Just take you a little bit through the performance. We'll start in terms of the backdrop with just what was going on in markets last year. So this chart is the MSCI Acwi index. So overall global equities were up about 18% for the year. The bottom left just shows the US relative to the rest of the world.

00:10:36:08 - 00:11:03:21

Unknown

So the US after outperforming global markets for several years in a row lagged a little bit. So up just 15%. With countries like Europe overall as a region outperforming, up 29% em up, just over 28%. And then from a sector perspective, on the bottom right there, I was very much, growth oriented sectors outperformed last year.

00:11:03:21 - 00:11:28:12

Unknown

So things like communication services industrials and it outperforming that AI trend that's certainly been talked a lot about certainly driving a lot of those sector returns. If we were to look at this for this year, it would almost be the exact opposite. So you'd see the underperformers. So things like healthcare staples and energy underperformed last year in 2026.

00:11:28:14 - 00:11:51:18

Unknown

Even if we were to update this through, March of this year, those were the outperformer. So big change in market environment, which is certainly, been interesting for the strategy. The next page here. So this is just looking at the basket returns. So we're kind of digging in a little bit further overall. On what was happening underneath the hood.

00:11:51:18 - 00:12:16:05

Unknown

And I'd say in general, last year was kind of the, the measures of kind of riskier assets did the best. So the top left here just shows the performance of different baskets by debt rating. So h y is high-yield and IG is investment grade. So IG one three is the highest end of investment grade. So the highest quality companies underperformed.

00:12:16:05 - 00:12:44:07

Unknown

Last year. It was the lower quality names that outperformed Similarly on the top right there. This just shows the difference between loss making companies and profitable companies. So similarly, the more, heavier loss making firms, those that were unprofitable actually outperformed last year. Again, big switch from what we're seeing this year where, profitable companies are vastly outperforming unprofitable companies.

00:12:44:07 - 00:13:11:08

Unknown

We've seen a big change in the risk dynamics. And then the last two on the bottom here is just looking at, the I theme overall, which has certainly been a huge trend in the US and globally. These are some baskets that we track, just looking at different cohorts within that AI vertical and how they're performing, relative to the broader market index.

00:13:11:10 - 00:13:43:03

Unknown

Last one on the right, there were many executive orders that were announced last year in the US, causing quite a big difference in cross-sectional returns as we look at different components within the index and how they did. So things like rare earths, things like drone manufacturers, things like nuclear energy focused companies, quite significantly outperformed, particularly around that July period where we did see, a rash of, of different, executive orders announced.

00:13:43:05 - 00:14:09:05

Unknown

And then just, overall for, the kind of the since inception period to the end of the year. So, the top line there is the Russell two that outperformed by about 27%. The Russell 1000 value underperformed the S&P. So the S&P was up 25. The Russell one value was up 17%.

00:14:09:07 - 00:14:35:19

Unknown

So here we get into the net performance of the strategy. So happy to report a very strong start to the mandate, for our team. So, on the trailing one year basis, actually since inception, we were up 423 basis points, since the inception of the fund, up 47 basis points on a year to date basis through February.

00:14:35:21 - 00:15:09:04

Unknown

The full year numbers, for last year were up 480 basis points relative to the index. This just shows that by horizon again, highlighting that, probably that, rightmost column is our since inception, since the April inception for our team and running the mandate up 423 basis points of outperformance. And then the last couple slides here, I just wanted to share a little bit of information on what drove the returns, what drove the performance.

00:15:09:04 - 00:15:30:05

Unknown

So on the left hand side we break down the attribution from a sector perspective. So the best performing sectors for the strategies, for the strategy was within health care, IT and financials. So those each added about 115 to 130 basis points. The one industry where we lagged a little bit relative to the index was within energy.

00:15:30:05 - 00:16:00:12

Unknown

And energy was a net underweight to the strategy. And that detracted mainly driven from more recent returns in 2026. But good broad diversified positive contribution across most sectors. On the right hand side here, this just shows the performance attribution. From our model perspective. So going into the attribution as to those return drivers that I talked about before, so fundamental sentiment and macro, what drove the performance.

00:16:00:12 - 00:16:26:08

Unknown

And happy to report that was positive contribution across all three of those components, the strongest of which was sentiment that added about 235 basis points of the total 423 basis points, and then about an equal contribution of around 100 basis points coming from both macro and fundamentals.

00:16:26:10 - 00:16:27:06

Unknown

And I think that's it.

RET0326-5346851-EXP0327