Investments Unlocked ep3: Economic data and Asset Allocation

Chris, welcome back Episode 3.

We've been renewed for new season investments unlocked.

How you feeling?

It's great to be picked up by the network for a full season.

Let's see where we can take this.

Absolutely.

Let's talk about, get straight into it, asset allocation, markets, news, noise, data.

And we just had a big pop in the market on Friday¹, the back of Powell's speech.

How you feeling about the state of data and, information markets today?

Sure.

And I would say, you know, a high level take away is, you know, as investors, we are awash in data.

There's data everywhere.

You could argue that investors are drowning in data.

So we really want to focus on what drives asset class returns over a short and long period of time.

That's really what our clients are focused on.

It's return generation, risk management and effectively deploying asset classes to be able to do so.

So when we focus on data and data sets, it's always going to flow through into our investment process which is focused on identifying drivers of asset class returns across equities, fixed income as well as alternatives.

And we think about that over multiple time periods.

So we have data that we think is important and relevant that's going to drive long term asset class returns.

Think about 7 to 10 year periods.

There's data we look at where we think there are effective multi year opportunities to harvest premium or harvest returns.

And then there's more short term tactical data that we look at.

As you know, it's really looking at what's called like 6 month to three-year positioning, a bit shorter term, a bit going to be focused on data that's going to drive markets over a shorter term time frame.

But that's how we think about that.

Is it going to drive returns and over what time period is it going to do so?

So the data we see out there that we're hit with in the news every day, some of it's interesting and maybe informative to a degree, but a lot of it isn't going to effectively

drive asset class returns over our set time frame and we maintain consistent exposure and a consistent focus on the data that does that.

Now, can you find, you know, analogs and correlations between the data sets we use in some of the data you see release on a daily basis?

Yes, But we remain focused on the data we think is consistent and relevant to us, Right, OK.

So, you know, we've got earnings reports, other economic reports, you know, inflation, PCE, for example, the Fed likes to look at employment or unemployment rather.

So what are some of the data sources you think can trip up asset allocators or aren't necessarily super informative when it comes to asset allocation?

Absolutely.

And I would say a high level take away.

Well, I won't pick on any source of data individually.

Typically data that is lagged and highly subject to revision is more challenging for us to use in our research ecosystem.

Most GDP forecasts are as such.

It's lagged information.

 It's telling you what's already happened.

And by the way, that can be revised upward or downward in subsequent quarters.

It'd be very similar with jobs reporting.

So you can have a report that comes out on a Friday, the market reacts to it, but then a couple of months later that data can be revised up or down for it.

We want data that's real time, consistent and is not subject to revision, particularly with respect to tactical asset allocation.

We have a shorter term investment horizon.

We're trying to navigate within a market cycle to generate our performance over let's say like six months to three years.

So understanding what data is that driver and what's going to affect our position is very, very important for us.

So those are kind of a guiding principle we have.

We think about a lot of survey based data particularly for our tactical asset allocation.

So if you think about consumer surveys, if you think about business and manufacturing surveys, if you look at financial conditions, if you look at industrial output, I can kind of talk about why that's relevant and interesting.

Even in the US as it's become less of an industrial economy, that's still very important information.

And then construction and housing data and that data is typically forward-looking.

It's not as subject to revision.

And we found putting that data together creates a very good set of leading economic indicators that we think can meaningfully and positively impact our portfolio positioning, whether we want to take more risk or less risk in our portfolio.

We complement that with market sentiment oriented data, which is simply just risk adjusted returns of asset classes over short to intermediate term time periods.

So we were having this conversation offline before we started the podcast.

The market actually is a great piece of forward-looking data, forward-looking information.

The market actually leads our leading economic indicators.

So when you pair those two together, you can really get a good clear, well, OK, is the economy accelerating or decelerating and is it above or below its long-term steady state?

And that's really, as you know what drives our tactical asset allocation positioning.

And from there it's simply just identifying the asset classes that are most effective in those macroeconomic regimes, which we've already done.

We published papers on that.

So that that's how we think about data that's not subject to revision, scalable, reliable, typically prospective in nature.

So those are the type of things we're going to focus on.

OK, that sounds like some of the challenges of that backwards looking data that can be adjusted later on.

It is backwards looking sounds like a lot of people use that data to inform their portfolio construction process as well, which maybe not be super robust.

So how does this approach allow you to capture opportunities in markets?

So for instance, in Friday we did see a pop in markets.

That's not necessarily something that I imagine you're looking to capture.

It's more of a sentiment type of markets.

But how does this process allow you to capture opportunities in markets whilst keeping an eye on risk, for example?

Sure.

It's, it's a great question And at least you know from a tactical perspective we would look at something like a kind of a consistent increase in risk assets.

So we actually think that market improvement you saw on Friday is simply just a reflection of more positive views on the macro economy, thinking about the market leading the economy and not the other way around, right?

You don't want to be adjusting US allocation just based on some sentiment.

Exactly right.

You're going to want to look at consistent data over a short term, immediate term period of time, not based on one source or piece of information.

I mean, it is we've kind of talked about in prior podcast, you've actually seen the data coming out of the US continuing to decelerate since the back half of 2024.

The market is kind of continued to plug along regardless.

But those are the things we're typically focused on.

If you saw a meaningful improvement in sentiment and then you saw that coupled with improving leading economic indicators, which I haven't really seen yet, you would pair those two together.

And then that would compel us to re risk our portfolio.

Because remember we're thinking about our tactical asset allocation not over one day time frames, but over six months to three-year time frames.

So since we have time to effectuate that decision, that's how we're going to think about deploying asset classes.

That's how we're going to think about relevant data and therefore

OK, it makes sense.

And you know I spoke about Friday market popped up because people think there's potentially an upcoming rate cut.

How does this approach allow you to position your portfolios to take advantage or to navigate potential rate cuts or rate hikes?

Absolutely.

So a piece of data we really like to look at is financial conditions.

So when the market reacts favorably to a perception that rates are going to be cut more aggressively, what they're saying is financial conditions are now going to be easier, borrowing costs are going to be lower.

That's going to compel the creation of credit, it's going to compel spending, and that's going to be good for the macro economy.

That's generally how that thought process works.

So we would look at financial conditions data, which we do.

It's actually publicly available.

Most of it is for what it's worth.

So that's how we would think about that.

Like, you know, policy information is reflected in that number.

There's other kind of private sector data that's reflected in that number as well.

I mean, obviously from, you know, 2020 to 2022 to 2023, there was a massive degradation in financial conditions because conditions were just tightened and tightened and tightened.

And that's where you saw in 2022 where you had both negative returns from equities and bonds in the same year.

So now we're starting to see financial conditions improve early innings, we'll need more data to support that as well as some of those other data pieces I talked about, but probably headed in the right direction.

So that's how we would think about that information that came across the board on Friday.

OK, great.

And would that how would your strategic asset allocation change and your tactical asset allocation change, would it be both adjusting or just one?

How would you adjust your strategic asset allocation and tactical asset allocation?

And then the SAA maybe later on, it's a great question.

So I'll break out how we think about are return drivers over short and long periods of time, what data goes into those.

For the most part we've been focused on tax class allocation in this conversation.

You bring up a good point around SAA (strategic asset allocation).

So SAA or strategic asset allocation has a very different set of return drivers associated with it because SAA is typically a 5 to 10 year process.

So for that we're going to look at long term Capital Market Assumptions (CMAs) and we're going to look at we call strategic return drivers or strategic premia for CMAs.

For CMAs, that's simply just what are the best drivers of asset class returns over long periods of time, which for equities is going to be nominal earnings growth, dividends plus buybacks as well as evaluation.

Component to that, fixed income is mainly going to be driven by starting yields, shape and slope of the yield curve, expectations for forward rates as well as expected credit loss.

For both, let's call it investment grade as well as non investment grade (IG) bonds, I can kind of determine like how we think about the concept of credit loss.

Those are very different than you know a piece of short term data or macroeconomic survey or financial conditions or housing starts or anything like that.

So that's what drives Strategic Asset Allocation (SAA) because what drives asset class returns is fairly consistent and academically studied.

It's kind of ironic because I think most things in life it gets harder to predict the further you weigh, the further away you are from the present asset classes.

It's almost like it works in reverse where you can think about what drives returns over long periods of time.

It's harder when you actually get shorter to the present moment.

Most asset allocators will tell you I have much more confidence in my return forecast over the next 10 years than over the next 24 hours.

Most things in life work the other way.

So that's how we think about SAA particularly those Capital Market Assumptions (CMAs).

OK.

I know that for instance, you have as part of your, I think this is your SAA, you'd prefer to target systematic drivers of risk.

So for instance, rather than high yield, vanilla U.S. dollar high yield, you like fallen angels, you like emerging market debt currency currently.

So is that coming to your SAA? And what kind of data do you use as well in those situations?

Exactly.

So as you know, with SAA, there's a CMA component that is very asset class focused.

The challenge with CMAs is they're very effective over 7 to 10 year time periods.

Most clients don't exhibit the level of patience to stick with a singular idea for seven straight years if they don't see it working.

So yeah, yeah.

So CMAs are helpful as a guidepost and as a component of our asset allocation process, but having I'd say more effective drivers that can be deployed and effective over multi year periods.

And that's where you get into these we'll call strategic return drivers and we've talked about some of these on subsequent calls.

Where can we harvest consistent premia over a multiyear period in the fixed income space?

It's much more intuitive, right?

Because you're harvesting spreads. Where can you harvest spread in an asset class adjusted for risk over a multi year period.

Like collateralized loan obligations is something I think we've talked about every single call we've had so far.

Because that is a strategic return driver, you can be compensated more for owning that asset class versus a commensurately rated investment grade corporate bonds.

You're getting paid that spread.

We actually consider that an SAA driver.

You mentioned high yield fallen angels, so high yield fallen angels, that's another strategic risk premium.

We think about improve premium over basic high yield instruments over a multi year time period.

So they fit somewhere kind of in between Strategic Allocation (SA) and Tactical Asset Allocation (TAA).

We technically allocate them to the SAA basket.

We'd also put style factors in that basket as well.

So think about quality, value, momentum.

So where can we harvest some of this consistent premium over let's say a three-year time period and get paid to do it.

It's very hard to generate a CMAs for something like that that's not incredibly intuitive with the CMAs that are more longer dated, that are more basic asset classes and then the strategic return drivers.

We marry those two together and we think what is a pretty effective SAA process over a / a three plus year time period.

Again, I haven't talked about any of the data that came up at the beginning of the call.

So, so I think that's something I want to want to emphasize as, as well as what drives long term returns, what feeds into that.

And that's how we think about data sourcing from there.

Excellent.

We'll come to the end.

What do you like in markets today before we wrap up, what are you looking at right now?

Sure.

So complex question.

I, I would say, you know, we remain relatively defensive in our tactical asset allocation because like I said, there's decelerating data coming out of the US looking at those leading economic indicators, you've seen sentiment start to improve.

Let's see if that's improvement in sentiment works its way over to leading economic indicators that would cause us to re risk our portfolio in our tactical sleeve.

I would say in the SAA category that remains pretty consistent.

I think one big aspect investors might have to get used to over the next 10 years vis-à-vis what you saw over the last 10 years is in a basic portfolio of stocks and bonds.

Over the next 10 years, a much greater proportion of your return is going to come from fixed income than we saw over the last 10 years.

So if you think about fixed income yields, you know, on the corporate side being let's call it like 4 1/2, five plus percent, that's much different than what we saw ten years ago.

So that's being I think particular in fixed income and really thinking a lot about your fixed income or your income allocation, being very intelligent there, being very thoughtful there because that's going to be a huge driver of your returns.

So I'd say right now in our fixed income portfolio we're a bit more quality IG focused, but like I mentioned earlier, you can still harvest meaningful spread in that space without taking a lot of risk.

So that's really where we're thinking about tilting in the fixed income space right now.

If you are going to go into sub investment grade an asset class that's very meaningful and our asset allocation as you know is probably syndicated bonds.

So it's some investment grade, but typically has much higher quality prospects than a commensurate high yield bond.

And you actually get paid more to own it right.

You're getting 100 basis points spread almost over there.

So you're getting you know 100 basis points in spread over traditional high yield and actually about half the expected credit loss.

So even in that non IG space, we're going to be very thoughtful about how we harvest spread, how we manage risk and, and therefore, so that's really I think how we want to think about fixed income going forward is how do we get, you know, effectively paid to own that risk.

But we want something that's going to be a big return driver for us going forward.

We don't just want to go completely into risk off because we do think of the next 10 years, equity returns might not look like what we saw over the last 10 years.

So we really want to make sure the fixed income piece is on point.

You know, what do you think of the tax-based US equities right now?

Yeah, so that's a question we get asked I think just about just about every day.

Yeah.

So there's a couple of schools of thought there.

I think as asset allocators, we are naturally always thinking about diversification and how do we diversify our portfolios globally, how do we diversify our portfolios within markets.

And obviously, as you've seen, US equities continue to concentrate more and more at the top end.

We've seen a lot of institutional investors, in particular more sophisticated asset owners look towards diversifying it to, let's say, systematic strategies that have a kind of natural aversion to high levels of concentration that you might see.

And I mean, tech companies now are much different than the tech companies of 25 years ago.

So they have much higher quality than what we would have seen in that index a couple of decades back.

And if you actually pair that building block with another building block that's a bit more systematic in nature, as we've seen, can actually be a fairly interesting combination.

I think what gets more challenging is when investors want to put all their eggs in one basket, there's definitely a place for that investor portfolios.

It's just effectively squaring and impairing it off.

So the sum of the parts is essentially greater than the individual components.

Fantastic.

Well, Chris, thank you very much for your time today.

And we'll hopefully see everyone in the next episode.

Hopefully we'll have to fight another day.

Thanks, Chris.

That was really good.

Yeah.