Moody's Talks - Inside Economics - Bonus Episode: The Yield Curve Whisperer Weighs In

Episode Date: February 28, 2023

Duke finance professor Cam Harvey, the father of the yield curve as a prescient predictor of future recession, weighs in on what the curve is saying about recession in the coming year.  You will be surprised.  Mark and Cris were.Full episode transcript hereFor more on Cam Harvey, click hereFollow Mark Zandi @MarkZandi, Cris deRitis @MiddleWayEcon, and Marisa DiNatale on LinkedIn for additional insight Questions or Comments, please email us at [e-mail]. We would love to hear from you.  To stay informed and follow the insights of Moody's Analytics economists, visit Economic View. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome to Inside Economics. I'm Mark Sandy, the chief economist of Moody's Analytics. And this is a special bonus cut podcast. Chris, Chris DeReedies is joining me. This is a, it's not a bonus because you're on, Chris. Sorry to say. Wow. Well, I mean, really?

You took that. The ego there. No, no, no. It's always special when you're on, Chris. You're always on. Yeah. That is true. You know, this, this, we had an anniversary.

the podcast. We missed it. We totally. Completely. No one said a thing to me. We had, I think it was a couple of podcasts ago, or maybe it was last week. I can't remember. We had our 100th podcast.

Can you believe that? It's like two years. We've been doing this for almost two years. It's like hard to imagine. So it is special talking to you, but I talk to you all the time. So, no, this is a special bonus podcast because we have Campbell Harvey. Hey, Cam. Thank you for inviting me.

Yeah, Cam is a professor of finance at Duke University. And the way I've been reading your work for many years. And I think of you as the father of using the yield curve, the treasury yield curve as a predictor of recession. And so all roads lead back to you. And you told me that indeed they do because your PhD thesis was. exactly on this topic. The EO curve is a predictor of future recession. When did you do, when were you in grad school? When did you get your PhD? So my PhD is dated 1986, but the idea goes back even before I got to the University of Chicago. Oh, oh, you got your PhD at Chicago? I didn't know that. And so where was

the idea, where did the idea come from? How did that, what's the genesis of this as a, oh, and let me, let me, let me, let me stop for a second and to provide a little context. I just take it for granted that people know what we're talking about. But, you know, everyone, the strong consensus view of economists and many others is that the economy is headed towards recession. And broadly, there's two reasons for that. One is the circumstance. We have high inflation and the Federal Reserve is raising interest rates aggressively. And if you go back historically and look when you're in a high inflation, high rate world, you end up in recession a lot more often than not. In fact, you have to look pretty hard to find a time when you don't end in recession. And the second reason is that the leading indicators that economists tend to use to assess the probability that the economy is going to go into in your term recession, or not all of them, but many of them are signaling red, flashing red.

And the most prominent is the yield curve, the shape between or the difference between long-term interest rates, say, 10-year treasury yields and short-term interest rates. And we're going to come back and discuss exactly how you measured the yield curve can. But when that curve is typically positively sloped higher long-term rates than short rates, but sometimes it becomes inverted, short rates rise above long rates. And when that happens, historically, we end up in recession, you know, some period after that, 12, 18 months after that. So with that as a preface, and it's very prescient, I mean, very prescient. If you go back and look at, you know, the recession since World War II, there's been 12 of them, and you look at the yield curve, it inverts before the recessions hit.

And invariably, it depends on how you measure the old curve, you don't get false. positives. You don't get the curve inverting and recession not happening. So it's a really pressure indicator. So economists look at that and they say, oh my gosh, you know, if history is any guide whatsoever, we're going in. Okay. So with that as a law, first of all, let me ask you this, Cam, did I say anything there that you would take umbrage with or would like to elaborate on? But then then let's go back to, let's go back to ground zero. Where did this idea come from? Where did this regularity that you observed, you know, how did it pop in your mind and became ultimately your thesis? So the story is looking back on it pretty bizarre. And let me kind of go through what happened.

So I was a first year master student and I had a couple of internship offers. And one was an all Toronto. That's my hometown. And one was at a major media company, and the other was at the largest copper miner in the world at the time. And I chose to go to work for the corporate development division of this large copper miner. And the first day on the job, they gave me a task. And the task was to build a model to forecast real GDP growth. And at that in real time, I didn't think it was a big deal. So what do I know? No big deal.

23 years old. I can do this. And they give me a job. And looking back on it, it just doesn't make any sense. Like copper and GDP are so highly correlated that, the decision to open a new mine or close a mine is critically dependent upon your view of the economy. Yet you've got this student intern in charge of developing a GDP forecasting model.

But again, in real time, I thought it was no big deal. So I start and I realize that I'm at a significant disadvantage at the time that we were were these econometric services you could subscribe to for tens of thousands of dollars. They've got these simultaneous equation models. That would be us, Cam, by the way. That would be us. Go ahead. And there's just no way I could build like a hundred equation model,

assemble all the data and deliver a credible forecast. It was just impossible to think about it for one person versus, these companies that have hundreds of economists working for them. So I had to take a different approach. And the first thing I thought about was looking at something simple. And what I wanted to look at were asset prices. So given my finance training, those asset prices should reflect expectations of what's going to happen in the future. And I thought about stocks, I thought about bonds. I did a little research on stocks without looking at the data. And there was a prominent research stream at the time spearheaded by a Chicago professor, Eugene Fama, that looked at

the relation between the stock market and real activity. And it looked at those papers and realized that this was not going to be the measure that I really wanted to look at. And there were multiple reasons for this. And one reason, kind of a joke at the time, was that the stock market had forecast nine of the last five recessions. And the reason that was a Samuelson quote, wasn't it? Wasn't that a Samuelson quip? I believe. Yeah.

Yeah. You're correct. Yeah. It was actually a quote in 1950s. In the 1950s. Nothing really has changed. Right. So there's many reasons why it could be like an unreliable indicator.

Like the essential intuition is that valuation of a stock is based upon the discounted present value of the cash flows. And those cash lows are driven by earnings and earnings will be impacted by real activity. But there's a lot of other stuff going on that could make it really noisy, including you need to discount those cash lows. by like a rate that reflects both what's happening in the economy and just shifts in risk. The cash flow is not so obvious because it changes through time. And the duration of stocks is very long. So you put all this stuff together and it could be very unreliable as it was at the time. So I decided instead of looking at the stock market to look at the bond market.

And the bond market had a number of advantages. So number one, there was a fixed maturity. Number two, the cash flows were known. So the coupon is stated. And then number three, if we're looking at U.S. treasury bonds, the risk is minimal in terms of default risk and things like that. So you put that all together, along with the basic economic intuition, that a nominal interest rate is made up of the real interest rate, expected inflation, and a risk premium. Assume that risk premium is fairly low. And you've got this expected real rate.

You've got expected inflation. The expected real rate, according to almost every standard economic theory, is linked. to expected real economic growth. So I've got something, and the reason that I looked at a yield curve rather than just rates is very simple that I didn't want to deal with the expected inflation component. So to take a difference, I could isolate the expected growth. So that's kind of where I went. So I put this together and it looked very promising.

And I was about to present it to the higher ups in the company. I show up and I'm told that the entire corporate development group has been laid off, including me. Oh, geez. Oh, geez. What was the company's name? Balkan Bridge Copper. Oh, okay. Largest in the world at the time.

They interestingly failed, of course. They're putting a 23-year-old kid in charge of like a critical input. You knew they were going down. That was the indicator right there. That's a good indicator. This is unbelievable. And this is really harsh, right? You're in the middle of your internship as a student and you're laid off.

Like, that usually doesn't happen at a, firm of this prominence. And in addition, this is in Canada. The Canadian is supposed to be like much nicer. Yeah. And to send the kid to the street, wow. So I was not too pleased, obviously. But things kind of work out.

So I had an extra four weeks. And I started to do additional research on the idea because I was kind of excited about the idea. And then I went back for my second year and showed a few professors what I'd come up with. And they said, oh, wow, this is a really good idea. You need to apply for a PhD. Oh, wow. And I had no idea. Like I was the first person in my family with a bachelor's degree. And maybe they got the idea that I could get a quick master's degree, but PhD, I had no idea. So Chicago, no less. Well, that's the thing.

I didn't even know where to apply. Novel laureates, right. Yeah. And so they helped me. I put my application together to various schools. And in hindsight, it was a strong application because what I did was I included my paper and I'd written up. So my master's program, they actually gave me, they combined a few courses and allowed me to just work on the paper. So I included the paper. I applied to these

programs and they say, well, this person's doing research already. And let's take a risk on him. So that's how I ended up in Chicago. That's a great, that's such a cool story. Yeah. But look, this was not easy, even though I got to Chicago with my idea, Chicago's got a very high standard. Indeed, three people on my committee went on to win Nobel Prizes. So Fama, Fama was, he was, yeah, no, my chair, Merton Miller. Oh, Merton Miller. Oh, okay, wow. That is just, that's incredible.

You may be the only economist in the world that P.A. with the three advisors as Nobel laureates? That could be a record. Yeah, well, that's... Really? At the time, it was interesting because the students kind of knew that they would win,

but it took many years for them to win. Yeah, oh, sure. Yeah, right. But nevertheless, it was kind of... Yeah. They were very rigorous. And I, my paper, the quality data, really, if you're looking at treasuries is after the Fed Treasury Accord.

So you really can't go back that far in history because the rates were so manipulated. Which is in the 50s. I can't remember. When was that accord? It was 1953. 53. Right. It could be wrong.

In around there. I think that's right. Yeah. So I've got like an economic theory. So that's essential. So you just can't have an empirical. finding and think you're going to get a dissertation from Chicago. So there is a theory, and then there is empirical result, which appeared quite striking, but nevertheless,

think of it as being four out of four for recessions, and my committee saying, well, this could just be lucky. Yeah. And they were impressed that I got the double dip recession and nobody else got that. So none of the big forecast. So 1980, there was a recession, then there was one in 82, and the curve signaled that, inverted before the 80s. Yeah, inverted, it went positive, then it went positive. And if you look at the yield curve and look at a real GDP growth, it's a mirror. Yeah. It was super impressive that it actually got that. But again, you get four out of four. It could be lucky.

And I think a couple of things worked in my favor. The most important being that the idea had sound economic intuition and theoretical foundation. So when that occurs, then even if there's not that much data, people will kind of go along with it. And the other thing that they really liked was the fact that it was so simple and that it was competitive or beat these econometric services that cost tens of thousands of dollars to subscribe to. And the cost of my forecast was at the time 25 cents, which was the cost of the Wall Street Journal back then. Right. So that was good. And so they signed off.

And then we kind of go to the out of sample period. So after you publish your dissertation, what happens? And usually there's two things that happen. In the good scenario, the effect that you document gets weaker. And in the bad scenario, the effect completely goes away. Right. But that's the way it works in science. Yeah.

But in my situation, the effect didn't go away. And the first real challenge I had was October 1987. So I'm a junior professor and the stock market had crashed. An economist believed that there would be a recession in 1988. So it's widespread agreement that. that real GDP growth was going to be negative and we're going to go down. And I remember being at a conference and all this doom and gloom, and I was the most junior person.

And I said, well, I've got this model, this yield curve model that tends to do a good job historically in predicting real GDP growth. And I think real GDP growth is going to be 4.2% in 1988. And the reaction was almost laughter. What a joke. Like, who is this kid? And the model is obviously a false model. And that was the first test in that growth was over 4%.

In 1988, there was no recession. And then the next four inversions of the old curve, each, were followed by a recession. So for the data that I looked at, so in sample four recessions, out of sample four recessions, eight out of eight. And you might put an asteris on the COVID recession.

Yeah. Because obviously, the yield curve didn't forecast COVID. Right. But in real time, in 2019, when the yield curve inverted at the end of June, there was widespread expectation that we were going to go into a recession.

Right. So our CFO survey at Duke University, 70% thought we're going into a recession. So we'll never know the counterfactual, but nevertheless, the foundation was there. And I will count that as one of the eight. out of eight. Got it. Hey, so there's a lot to unpack there.

Let me first, though, begin with, there's lots of ways of measuring the yield curve. You know, 10-year treasury yield versus the two-year treasury yield, 10-year treasury yield versus three-month treasury bill, 10-year treasury yield. Usually it's the 10-year yield as your long-term interest rate. And then there's a lot of short-term rates. The other would be the federal funds rate, the rate the federal reserve controls. Which of those measures are your favorite? favorite? Or do you have a favor? You can't pick. Which one do you look at most regularly?

So I had to pick back in 1986. So in 1986, I looked at the 10-year minus the three-month. And the logic was I want some yields that are some treasury bonds that are liquid. I chose the three-month because I'm forecasting quarterly GDP. So I kind of make a lot. I kind of make sense to use a three-month rate. And I chose the 10-year because it was highly liquid, the most liquid, and still is. So I looked at the 10-year minus the three-month, and that one is the one I referred to as delivering no-fault signals. Now, others have looked afterwards at, let's say, the 10-year minus the two-year. Yeah. And all of these yields are correlated.

So if you look at that yield curve versus the 10-year minus three-month, it's got high correlation. But the way I look at it is, okay, well, my original idea was 10-year minus three-month. It's eight out of eight. And there's not really a good reason to switch it out. So if it was four out of eight, so it failed multiple times in forecasting, then that's a good reason to switch it out to something else. No false, I can't recall, no false positives with the 10 year three months? That's correct. Okay.

So eight out of eight with no false signals. So that's important because you could be eight out of eight and have like 20 false signals. Right. This has got zero. And I didn't see like a good. reason to swap it out. And there are like an infinite number of choices. So people say, well, the 10 year minus two year, but it could be the eight and a half year minus the one half year. You could data mine this to find something. AI. Yeah. Yeah. And in this case,

as I said, there's no false signal. If you look at the 10 year minus two year, there is. Yeah. And in 1998. And I think you go with the original model until it fails. And then you reexamine it. One quick technical question on the three months. Is that on an equivalent bond basis or not? Yeah, you need to be careful here with the historical data.

The yields are quoted on a discount basis. A lot of people make this mistake. that if the Treasury bill, the 12-month Treasury bill is trading at $90, the discount yield is 10%, but we know the true yield is greater than 10. So you need to make conversions. There's all sorts of conventions that you have to. So you convert, you do it on an equivalent bond basis. You look at 10-year treasury versus three-month on an equivalent bond basis?

Yes. Yeah, okay. There's so much, and Chris, I'm going to let you in just. Just a second. I know. Just a quick technical clarification. Go ahead. Do you consider any type of threshold in terms of the extent of the inversion or the number of days?

Is it if it's one basis point, it's inverted? Yes. Yes. So what I did in my dissertation, I looked at the average over the quarter. Oh, it's quarterly. If you invert for one day or one week, that just doesn't count. Again, the measurement of GDP is a quarter. It's not a day.

Right. So take this into account. And so after a three-month period where you've got an inversion on average, then I declare that a code read event in terms of my model. So 10-year yield, three-month on an equivalent bond basis for a three-month period, a quarter, that's the signal, recession. That is the signal, and that is what is correlated with economic growth. So my model shows that that spread is a strong predictor of real economic growth.

How far ahead? What's the typical lead? So the lead varies, and it varies between, let's say, six months and 18 months. Okay. So let me tell you what the, model does really well and what it doesn't do as well at. So obviously, given what we've already talked about, it's really good at predicting recessions, given its 8 out of 8. It's also, and this is interesting, it's also very good at predicting the duration of recessions. So

the length of the inversion is highly correlated with the length. of the recession. And there's a third aspect that it doesn't do as well at, and that is the extent or the depth of the recession. So I've been criticized on social media. Well, the Harvey model, it doesn't do very well getting the depth of recessions. And I'm thinking, well, if I get a forecast of the recession event accurately. And then the duration was pretty good for a single variable. There's just one variable. So, yeah, it doesn't do everything, but at least historically, it's done really well.

Got it. Let me ask you, I want to get back to why the O'Curve is a good predictor. And then, of course, obviously, I want to go to, is it a good predictor? predictor today of recession dead ahead. But before I do that, a couple other kind of nuts and bolts questions. One is, why isn't the yield curve useful or seemingly useful overseas? I mean, if I go, if we go look at yield curves and other developed economies, you don't see that kind of relationship. But what is your thinking around that? Why is the U.S. yield curve such a good predictor, but others are not. Yeah, so my early research looked at other countries, and you're correct that the other countries

are, don't have this strong relation like the U.S. has, and an obvious reason for this is manipulation in the bond market. One country that was particularly interesting for me was my home country, Canada. So you think of Canada as, well, it is highly tied to the U.S. So the business cycle in Canada just mirrors what happens in the U.S. So the Canadian yield curve should have very little information. So I wrote this paper where I tried to forecast the difference between Canadian economic growth and U.S. economic growth.

So the part that wasn't explained by what happens in the U.S. And it turned out that the difference between the Canadian and U.S. yield curves was very powerful in predicting the difference. So for a country close to home, that indicator is quite important. But if you go to other countries, for example, Japan, there's no relation. And is it a surprise to you? No. Given what happens in the Japanese government and bond market, there's no surprise. Right. Which gets to another quick question. Do you think the Fed's quantitative easing, quantitative tightening is messing with the curve in terms of its signaling? Because it's no longer totally a market-driven measure. It's now affected by policy.

So yes, and I just want to emphasize something that the model, the economic model I use, is so simple that there is no Fed. It's really simple. And historically, the Fed has been very active and manipulating yields. Operation Twist is a good example, the original one. Indeed, I think that we talk about, quantitative easing and quantitative tightening. In my opinion, given the massive size of the bond market today, that it was probably easier 30, 40 years ago for the Fed to manipulate the yield curve. The market is just so large now, it's so difficult for the Fed to deal with it. So yes, there is a series of interventions that adds noise to this indicator. And there's very little I can do about that. The model is what it is. It's a simple model. And it gives us some information, which appears to be valuable. If I was in the business, again, if I was asked to develop a model for forecasting real GDP, just like I had the task as a student intern, I would look well beyond the O curve.

So the YO curve is important, but there's obvious other information that needs to be taken into account. Yeah. Okay. So you're saying, and just for the listener, QEQT, that's the Federal Reserve, buying Treasury securities, mortgage securities, and then, of course, in QT, allowing the securities run off the balance sheet. So they are big, they've become, and that's since the financials, crisis. So they become really large players, the Fed, in the bond market. And that is, as you're saying, has to have some impact. The question is how big an impact. I mean, they've got nine,

you have nine trillion on the balance sheet now. You know, before the financial crisis, it might have been a half a billion. I don't know. I mean, I don't know. I can't remember. No, it was four or five hundred billion dollars. Now it's nine trillion dollars. So it must have some effect. Here's the other thing. The bond market, U.S. bond market has, it feels like, correct me if I'm wrong, become more internationalized, globalized over time as well. It used to be pretty much a domestic, you know, investors would buy treasury bonds and hold them. Now, if you look at the ownership, it's, you know, obviously all around the world. So what's going on overseas is also having an impact on here. Do you think that's also messing with the recession signal in the,

occur? I think that's probably second order. Second order. So I think Fed Treasury is first, first order. And I say it's second order because of the influence of the U.S. economy and the world economy. So the U.S. economy is the most important driver of world economic growth. Yes, it's the size of the economy is smaller compared to the rest of the world. compared to the past, but it's still a very important driver. So what other countries in terms of their buying of U.S. treasuries is also correlated with expectations of what's going to happen in the U.S. Okay. Okay. Very good. Chris, anything else you want to ask with regard to kind of the nuts and bolts of the before we move on to, you know, what's the intuition behind why it's such a good predictor?

Anything else you wanted to bring up? No, I think, no. Okay. Yeah, I think we go. Just want to make sure. I didn't miss anything. Okay, let's turn to that question. And there's a couple three, and I'm sure there are more explanations for, you know, why, you know, what's going on here.

I guess the most obvious is the curve represents the collective wisdom of, you know, bond investors who are putting their money where their mouth is. So if a bond investor thinks, oh, this economy is going to go to hell and inflation is going to fall, I'm going to buy long-term bonds. And of course, short rates are kind of pinned to where they are because of monetary policy. The Fed's got its foot on the brakes. It can't come down or at least can't come down as much. And you get that inversion. Is that kind of your way of explaining why the curve is a good predictor or is it something else? Yeah, did you've hit it. Exactly. It's a, it's a basic hedging argument. So you see there's a problem, a simple way to think about it, there's a flight to quality, and that's the tenure. So prices bid up, yields come down, and that flattens or potentially inverts the yield curve. So it's really straightforward.

Straightforward. Yeah. Okay. Here's another, this is, now this is a Zandi, I think, explanation. And I want to try it out on you. And I've tried. it on Chris, I think you're somewhat sympathetic to this, but let me play it and play it for you for a second. So when you have a positively shaped yield curve, and generally, you know, those are the good times, you know, when it's really positively shaped the boom times, a financial intermediaries, banks can make a boatload of money, right? Their net interest margin, the difference between their funding costs and their lending rates is very wide. They have a lot of incentive to go out and extend send out a lot of credits. You get a lot of credit flowing into the economy to businesses and to households. Of course, in the boom times, the economy gets to full employment, inflationary

pressures developed, the Fed steps on the brakes. At some point, it really steps hard. Curve goes flat, starts to invert. These intermediaries, the banks can't make money. Their interest margin goes negative or flat. Their funding costs are greater than the lending rates, and they stop lending, which is really first problematic because credit is necessary to keep the economy moving, but it's really problematic after a period of very strong credit growth because you've got a lot of businesses and households coming back saying, hey, I need to refinance this debt. I'm like, I can't pay you back. You know, no one ever thought I would pay you back at this point in time.

We've got to refinance. And the banks say, oh, yeah, you can refinance, but now you've got to pay me much higher rate or the lending terms are much higher, much. much more significant, the underwriting standards. And so businesses can't afford that. The lending rates too high. The terms are too on onerous. And they say, oh, I got to pull back on hiring. I got to pull back on investment. I can't expand. Thus, I go into recession. What do you think of that as an explanation for the intuition by the curve? Does that make sense to you? It does make sense. Indeed, the first paper I presented that,

the University of Chicago had a mechanism similar to that. Damn, I thought this was a Zandi idea. This is no, gosh, I knew I could have it. I never published it, so you can have it. But it's a really interesting idea that as we become really flat, that actually puts pressure on the banks. So when we're really positively sloped, they're making a boat load of money, more likely to make that low.

to a corporation because they are making all of this money. And when it flattens, it's less likely. And this leads to companies making less investment, less employment, and that feeds into slower economic growth. So I think that that explains some of the mechanism, but it doesn't really explain like the cause. So what is the reason that we're flattening? So given that we're flatting, it makes sense that banks become more cautious in terms of their lending. But how do you get to that flattening? So the mechanism that you describe is a credible mechanism that leads to perhaps extra predictive power for the yield curve. It's not clear, though, that's the cost. Yeah, I think of the credit cycle kind of driving the business cycle.

Or, you know, there obviously causality is running in both directions here. But, you know, key aspect of the business cycle in terms of boom bust is credit, boom bust. And so the credit flows are driven by the shape of the curve and then an interest margin. And that helps amplify the ups and downs in the business cycle. That's kind of the causal relationship that I have in mind. Yeah. So I think you're right that we talk about causality, but everything really is connected. Everything is dodging this here.

And I think you point out something that's really important, that I'm looking at the Treasury yield curve, that if we were doing this job of forecasting real GDP, we would want to. to look at other information and probably the number one place I would start is credit. Because if you look at credit spreads, they're also highly correlated with future economic growth. So there's another variable that you could look at to bolster the accuracy of your forecast. Okay. Okay. So collective wisdom of bond investors, maybe some aspect of the the credit cycle. Is there any other intuition behind it? Not that there needs to be, but any other kind of causal link or relationship that could help explain why the curve is such a good predictor

recession? There are many different ways to go at this. And we actually tackled two of them, the basic hedging argument. I think is the most powerful one. And just this idea that interest rates, they contain information about real economic activity that's expected. So again, the foundation is very intuitive and it's not really a surprise that this works. Let me also mention that while I documented the yield curve predicting real GDP, there was an earlier paper by somebody at the Fed in 1965, Ruben Kessel, and he had a long time series of the yield curve, and he noticed that there was a cyclical behavior. He didn't link it to forecasting economic growth or anything like that, but he did notice that there were cycles. And that was influential paper for me, which obviously.

I cite in my dissertation. Got it. Got it. Okay. So here we are today. And I just look, the, and correct me if I'm wrong, Chris, the 10-year, three-month, I think on an EBI basis, equivalent bond basis, inverted in October of last year. So here we go.

November, December, January, February. We're four months in. That's three-month moving averages. We are, you know, inverted. So the signal that you use is saying recession anytime between mid this year and kind of early in in 2024. Is that right? Is that what you're taking away? That's correct.

So the end of December, so-called code red in terms of this indicator. So the indicator is forecasting a recession. Got it. Okay. let me ask you this. Is the yield curve, do you agree with that forecast? Do you think we're going into recession? No. Second half, you do not. I do not. Okay. Okay. Okay. This is blowing my mind. This is blowing my mind. Everything leading up to that said yes, you were going to answer yes. Okay. All right. Cam, why? Why is this time different? By the way, those deadly words, this time is different.

Why is this time different? And by the way, Kim, I am like so on the same page with you. But go ahead. Go ahead. Yeah. Let me just first establish something really important. Yeah. Every time is different. Okay.

Okay. That's a good point. Good point. Yes. The yield curve model that I've got is a very simple model. And it is true that it's eight out of eight, but no false signals. it is naive to think that this model will never produce a false signal. And I believe there's a number of reasons why it's producing a false signal this time.

Okay. So, and I can go through. Yeah, we definitely got to go through them. I'm like dying. This is like better than, you know, this is like, we got to sell tickets to this podcast. This is really cool. Oh, yeah, go ahead. Go ahead. So one thing that's unusual is the employment situation.

Okay. And we know that employment is a lagging or maybe coincident indicator. And that's not what I'm talking about. I'm not talking about the fact that the unemployment rate is low. It's always low before a recession. It always increases. But what's unusual is, is the excess demand, where we've got the ratio of job openings to unemployed, is very high.

And what that means is when we slow down, and I believe that the yield curve is accurately forecasting slower growth, just to be clear, when we slow down, there's a buffer. And that means we're not going to see a spike. We will see an increase in unemployment, but not a spike in unemployment. And that's one of the first reasons to kind of second guess the forecast. Before you go, let's just let me just let me restate just so everyone can get their mind around it. You're saying this time is different because the labor market's different. you know, the labor market is extraordinarily oversubscribed. There's just 11 million unfilled positions out there.

That's a record number. And it also reflects kind of the idea that businesses know that their number one problem is retaining and holding on to workers. And that kind of supercharged labor market. market, hard to see the kind of layoffs you would need to see, the increase in unemployment you would need to see to go into recession. Is that roughly right? That is roughly right. And it's even beyond this. Even beyond this. Look at the, at the, if you dissect the type of unemployment that we've seen, it's very interesting because what makes the headline are all the tech

layoffs. And those are so different than the types of layoffs we had, for example, in the global financial crisis. So you lose your job at Lehman Brothers. Where are you going to go? You're going to go to Bear Stearns? You're going to go to one of the banks that are basically looking for a handout? You're facing a very long period of unemployment, as many did during the global. level financial crisis. These tech workers, you work for an A-level firm, whether it be Facebook, Twitter, alphabet, and you're laid off, those workers have a very low duration of unemployment because they are highly sought at for just non-tech corporations. Many companies would love to have one of those X workers at these A-level places in the technology sector.

So if you empirically look at the data, and this is interesting, that it appears as if the duration is a little longer, but I think it's purely by choice. That, oh, well, take some time off. I know with the snap of the fingers, I can get a job. And indeed, we're trying to get some of these workers at our MBA program at Fuqua, at Duke. They're highly desired. Moody's too. Yeah, for sure.

So I think that that's another aspect. So the duration is lowered, the structural makeup of what we've seen in terms of layoffs. So all of this is, think of this as the first factor. What's the second? The second factor is fascinating to me. This is all fascinating to me, Cam. This is something totally unexpected. And it has to do with the yield curve.

And before the global financial crisis, the yield curve was also strongly inverted for a long period of time. So the duration of the inversion was very, very long, just like the recession was long. And I was screaming code red and nobody listened. I was with you. I was with you. Maybe I was in screaming as you were.

Nobody's too strong. So definitely the case the Fed didn't listen. Yeah. And they were so late to the game. And so, or think about like a CEO or a CFO, you know, during the global financial crisis, they had to make significant layoffs. Their firm could be in distress. And they could credibly say, we were blindsided by this.

We had no idea this was going to happen. And my peers within the industry, they were also blindsided. So it was a surprise. So today, it's a different story. So after the global financial crisis recession, people started to see, to predict a power the ill curve. It got a lot of media attention. This is not really my area of research anymore, but I get asked about the ill curve all the time. And I think given the publicity that the yield curve has got, that it's harder for a CEO to say if a recession occurred, well, we were

completely surprised. It's hard for the CEO to make a major cash. investment and borrow to finance that in the face of an inverted yield curve. Think about it, that making an investment or betting the firm in a situation with an inverted yield curve where you've got a record of eight out of eight and no false signals, you need to think twice about that. Or major hiring, no, you're not going to do that. You're going to wait. And this is related to this idea of self-fulfilling a prophecy.

So you get the inversion. People see it and say, oh, well, that's bad news. I'm going to change my behavior. I'm not going to pull the trigger on this investment project. I'm not going to hire like 100 new employees. I'm going to wait and see. And that feeds in to the slower economic growth. Okay, so that's the self-fulfilling prophecy, and it actually makes the yield curve causal.

So a negatively sloped yield curve could actually cause slower economic growth, given that it's now a popular indicator. So what is this number two factor? Well, the yield curve has caused companies to be cautious and to exercise risk management. and to take actions now that hopefully will protect them in the future for the extreme downside. So it's better to take actions now, to slow economic growth, and to reduce the probability that you need to take drastic actions in the depth of a serious recession. So that is. That is, so this is, again, fascinating to me that there's a causal link here.

The predictive power is still there. But this risk management reduces the probability of a hard landing. Interest, that is fascinating. So you're saying, look, the fact that everyone is focused on this as an indicator and is predicting recession means that they become more cautious. that will allow the economy to kind of cool off and not experience the boom and bust that it typically does because it cooled off in anticipation of all of this. That is fascinating, yeah. The self-fulfilling aspect of it is actually going to reduce the odds that the economy actually goes into recession

because people are responding earlier than they typically would. Or at least a hard recession. It could be like a soft one, like. Like 2001, which is not really a big deal. You might not even happen like a year over year that's negative. That wouldn't have been a recession. I don't think of without 9-11, right? Probably.

It might not have been. Yeah. So anyway, interesting. Okay. That's, and now I'm cognizant in a little bit of time. So I want to make sure I get through all the reasons. Is there a reason number three? So there are multiple reasons.

Oh, goodness. Yeah. Let's just do like one more reason. Okay. Okay. So one of, if you look at the global financial crisis recession, housing was a big part of it. Yep. And if you look at equity to debt in the housing market, it looks sharply different than before the global financial crisis where there's much more equity. So even if housing is down, but even if it goes down further, that's not going to trigger

a big problem, nor, and reason number four, our financial system is sound, in my opinion, right now. So in the global financial crisis, that was actually the cause of the problem. And given that it's much more prudent today, I think it's less likely that that accelerates any issues. So the economy is on sounder, sounder. fundamental ground than it is typically before recession, therefore no recession. Yeah. Hey, Chris, does this all sound familiar to you? I'm just asking Chris, this is,

so Chris is a true believer in your yoke curve and saying we're, you know, he's relying very heavily on that as a predictor of future recession. Anything to say, I'm turning back to you, Chris. Anything you'd like to say or push back on? Oh, gosh. So I'm fully, on board with the idea that the next recession would be mild, right, for the reasons you outlined here. And actually, my question view was around the self-fulfilling prophecy aspect. And I can see that certainly firms are acting much more conservatively today in anticipation. I think it's a very careful calibration, though, right? Because they're pulling back on that investment. If they pull back too far, if households pull back too far, then you will have the

recession, right? So it's got a little bit of a dance there. But I do agree that, You know, there's no, there's no, or there's very little evidence of access or that would lead to a very significant recession. So I still use the yield curve, certainly as a signal. I find it difficult to ignore completely. But combined with some other factors, I still see that there is significant risk. And we're right on that edge, as you put it, that, yeah, households are in pretty good shape. They have a lot of equity. but there are some cracks in consumer credit, housing, home building is weak.

So there are some areas where a little bit of a shock could certainly tip us into recession, in my opinion. Yeah, I don't think we're that far apart here. So I totally agree there's a risk. And I believe that growth will slow. So I think the yield curve is accurately forecast. that. I just think the probability of a hard landing is is pretty low, given the economic scenario right now. That said, the big wild card, in my opinion, is what the Fed is going to do. And the Fed could make the model nine out of nine.

It's interesting. It's interesting. So do you think the Fed is responding to the yield curve as well? No. So I think the Fed is using this very blunt instrument, the Fed funds rate. And this blunt instrument, given what they're doing, thinking that just raising the rate is going to erase inflation, could drive us into a hard landing. And we all know that the Fed was very late to the game where we had essentially zero. interest rates for an extended period of time that didn't make any sense, where you've got strong economic growth, you've got low unemployment, you've got record stock market, and the rates are very low or zero, even though inflation was increasing. They're late to the game, and I believe that they will be late again. They will overshoot, and there's evidence that they're already doing this, in my opinion.

So, and I think that one of the major problems here, you mentioned housing, that that is the problem. That is fairly intuitive that housing inflation takes a while to make it into the CPI. Yes. If you think about, okay, rents, let's say, go up by 10%. Well, if you've got a lease for the next 11 months, you don't feel that until you have to renew in 11 months. So that's exactly what we had. If you look early on, you see the rents going up, the housing prices going up, and it takes a while to work its way through the CPI and then the Fed response. It's the same thing now that the rental component, the shelter component is 40% of the PCE deflator, 33%

of the CPI. And you can look at the data. You can see the rents coming down. You see the housing prices coming down. You see new construction going down. Permits going down. All of this is consistent with this really important component coming down over the next six months. Yet the Fed is perhaps going to do 50 basis points next time.

And that could be enough to push us. into the recession. You know, it's very interesting when I asked you for the reasons why the O Curve is falsely predicting recession, I think that of broadly speaking, there's two sets of reasons. One are fundamental reasons, you know, the labor market, the housing market, the things that you mentioned. The other are what I would consider to be more technical measurement issues.

I mentioned the QE QT. We talked about, you know, global investors, you know, increasingly in the marketplace. The third I want to throw out there is that the Fed over time through business cycles have become increasingly clearer with regard to the path of future monetary policy, that their so-called forward guidance is becoming clearer and clearer and clearer. and now they're like crystal clear, as crystal clear as you can be. And that is also influencing the shape of the curve in the future, more so than it has in the past. And it's interesting, you went to the fundamental reasons. You didn't go to kind of the technical reasons.

Or is it that you don't think those technical reasons, as you said, they're just second order kind of reasons. They're not by themselves sufficient to make the curve less predictive of future recessions? Does that make sense? Yeah, I think I did use the word second order and also used the word noise. Noise. A lot of stuff happens that, again, this is a very simple indicator and other stuff will happen. And some of it, maybe first order, some of it's second order.

And it has the ability to mess up the predictive power. Yeah, interesting. Well, Cam, if we go into recession, how embarrassing would that be for? for you. It adds you when tells you a little. Yeah. So I thought about this a little bit. And again, this is not.

Of course, that was a tongue-in-cheek question. I got it. Yeah, it's just tongue-in-cheek. Yeah. So I'm talking to you about the yield curves predictive power, but this research is something that I did many, many years ago, and I've moved on to a different area. Yeah.

But again, I think we need to look at this scientifically. Yeah. So this is a model that I proposed, and the model is done very well in terms of predictions and lack of false signals. And my job is not to just support the model. Yes, it's my model. But I'm a scientist. and any model is going to be eventually wrong. It has to be.

It is a simplification of reality. And that simplification is going to fail at some point. So I'm not going to be embarrassed. So if it works or it doesn't work, there's no embarrassment. If you're right, this is science. If you're right, you will become the Oracle. You will become the Oracle. You are now the Oracle of the Yilk curve.

When do I listen to it and when don't I listen to it? Yeah, you'll the Yolkir whisper. You'll be the Yolkir whisper. That'll be a very good spot to be in. Yeah. Indeed, I did a podcast that they called exactly that. The Yolkir whisperer. Oh, actually, that would be a good one for the, I mean, we can't steal that, but that's a good one.

Well, Cam, you know, we took the hour and I really appreciate it. It was a fantastic conversation and really put things into clear real. relief. So thank you for that. And Chris, any parting words that you want to say? Like I'm, you know, well, stay tuned. How about stay tuned? Stay tuned. Stay tuned. Those are good parting words. Yeah. Well, Cam, thank you so much. And please, we'll definitely have you back, you know, down the road here to see how this thing after this thing all plays out. So thanks again. Appreciate it. Thank you for inviting me. And indeed, I recommend this podcast to my students.

So keep up the good work. Thanks so much. And you heard that dear listener. We've got a fan, hopefully your fans as well. And we'll talk to you soon. Take care now.