4% Inflation. Stretched Valuations. Why Is the Market Still Risk-On? | Tian Yang

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**Jack Forehand** (0:58)
Welcome to Excess Returns. I'm Jack Forehand and I'm excited to be joined today by Tian Yang. Tian is the head of research at Variant Perception and also the portfolio manager of the VPX ETF. And we're going to talk a little background today. So, Tian, thank you for joining us.

**Tian Yang** (1:11)
Thanks for having me. Looking forward to it.

**Jack Forehand** (1:14)
You guys do some awesome work and you blend quantitative and qualitative frameworks which is exactly what I like to do as well. So, I'm really interested to dig into, we're going to dig into the economy in general, but we're also going to dig into the framework behind it. And how you're getting to the conclusions you guys are getting to. But I want to start with a quote. You have a really great quote here that I think summarizes what you guys do, but I think is really, really relevant for what's going on in the market today. And the quote is, data is easier to access and more available than ever before. The key now is how creatively you use these inputs, and most importantly, what you choose to leave out. And that last part was really important, I think, to me in the world of noise is what you choose to leave out. So, can you talk about what you mean by that, Steve?

**Tian Yang** (1:51)
Yeah, so, I think what we're implicitly trying to say is that you have to think from first principles, what are like the cause or reasons that data is statistically meaningful. So, you know, we all want to build models, especially in this age of AI, you know, pretty soon we'll have like, you know, super powered AI to help us build models, right? So, I think a lot of times though, even when you're doing that and processing data, you know, it's easy to, you know, throw things to a black box, right? Something that looks back to us really well and want to use it. I think from where we're coming from, we think a lot about, is there something causal? Is there like a real world reason that this thing has a relationship and it persists? So, like a very simple example we give is, essentially, the intuition behind the idea of evenly dedicated in the first place, right? That there's a certain sequence in which things happen in real world economies. You have to get a building permit if we can build a house. So, you know, if you keep out building permits, it will give you a sense of when people want to build a house, right? And there's lots of these examples. And, yeah, so I think that's probably more what we're getting at, to think a lot about causal relationships, which actually just means sequencing. What moves first to then cause something else to move? Is that something that is first principles likely to persist through time? And let's build models around that, find data to proxy for those, and then from there, obviously, build up the analytical framework.

**Jack Forehand** (3:17)
You referenced leading indicators in your answer, and I know that's a big part of what you guys do. You know, many people tend to focus on sort of what the data is right now, and maybe not necessarily what is going to be in the future. So can you just explain to me, like, what do you think, what do you define a leading indicator as? Like, what's important in a leading indicator for you?

**Tian Yang** (3:33)
Yeah, so, you know, I think as investors will look a lot like, you know, GDP, right, or inflation, or, you know, Fed policy, Wall Street made a policy announcement. These are all what we would say is like real-time coincident things that happen. They may or may not move the market. But often, though, by the time these data points move, there's a sequence of things that have happened ahead of time that you can actually track and look at. And in a way, when we say leading indicators, it's almost like instead of trying to get a crystal ball and forecast something, you're just standing back and observing if the data is shifting.