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Hi, welcome to Investing the Templeton Way podcast.

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I'm your host Lauren Templeton.

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And I'm your co-host, Scott Phillips.

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We're really excited to introduce today's guest, Ryan Myers.

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Ryan is portfolio manager at Causeway Capital.

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He joined Causeway in 2013 and has been a portfolio manager since January 2021.

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His responsibilities include alpha, research, stock, selection, and portfolio construction.

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And I'm here to causeway Capital, Mr. Myers served as chief investment officer of iron,

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castle, asset management.

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An investment partnership focused on midcap US equities.

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From 2007 to 2008, Mr. Myers worked as an analyst at Canyon Partners where he covered the

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cable, media, telecom, and satellite sectors.

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Prior to that, Mr. Myers was an associate for Oak Tree Capital Management and the Distress

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Opportunities Group and he began his professional career as an investment banking analyst at Goldman Sachs

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and Technology, Media, and Telecom Group.

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Mr. Myers earned a BA, Magna Cum Laude, and Economics from Harvard University, where he was elected

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to Phi Beta Kappa.

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He earned an MBA from the Stanford Graduate School of Business.

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Mr. Myers also serves on the Board of Trustees of the Yosemite Conservancy, an organization dedicated

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to supporting projects and programs that preserve Yosemite National Park and enrich the visitor

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experience.

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And it's with great pleasure.

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We welcome Ryan Myers.

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Thank you for having me, Lauren, and Scott.

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Yeah, so tell me specifically about your role at Causeway and what Causeway focuses on.

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Right, so I am a portfolio manager at Causeway for our emerging markets equity strategy and

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our international small cap equity strategy.

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And Causeway in general, we're a global investment manager, approximately 40 billion under

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management.

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And we really bring a unique approach to investing where we try to blend both fundamental

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and quantitative inputs in all of our strategies.

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Great.

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Well, tell me what got you started in investing?

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What led you to this career path?

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Yeah, I mean, fortunately my parents got me saving very early, although it was sort of just

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in your standard savings accounts.

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In college, I became much more interested in high school in economics.

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By studying, I gained an appreciation for this historical importance of economics in sort of

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shaping history, I feel that people are really a product of the economic times, the macroeconomic

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backdrop that they're brought up in.

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Also, you know, people respond to incentives.

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And so understanding that is very interesting to me.

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I took my first corporate finance class in college and just became much more interested in taking

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the macro down to the micro understanding companies.

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And I think I'm naturally curious.

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And so I just love learning about new industries, learning about business models.

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And so, you know, from there, it was sort of a natural extension to sort of go into the

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buy side and finding companies to invest in.

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Yeah, it's interesting that you were interested in economics in high school.

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I mean, it's the dismal science.

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And you must have had a very good economics teacher, too.

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I did.

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That kept you interested in it.

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I did.

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I'd say I became much more interested in college.

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I got to study with people like Larry Summers and Martin Feldstein.

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And they really bring a lot of passion to the subject, which makes it easier as a student always.

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Yeah, that's amazing.

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I'm jealous of that.

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I was economics major at the University of the South.

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We call it the Harvard of the South.

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It is not Harvard.

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I had some good economics teachers, though.

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I do appreciate them.

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Well, tell me, in your role at Causeway.

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And you're focused on emerging markets and international small cab equities.

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Tell me what are your views on the current markets, specifically towards small cab international

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equities or emerging market equities?

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Yeah, I think it's an interesting time.

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Certainly, we're, you know, today in March 2023, I think there's a divergence going on where

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we see a lot of headwinds to growth in the US and to a lesser extent in developed international.

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But when you turn to emerging markets, you see China getting a lot of tailwinds from its reopening.

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You see India as well. Between the two of them,

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they're about 45% of the emerging markets index.

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And they're both expected to put up mid to high single digit GDP growth this year.

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And so you have a very big, you know, counter-influence to the slowing growth in the US

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internationally.

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And so I think it's a very interesting time to be investing internationally.

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When you get to small cap specifically, it's also interesting that, you know, small caps typically

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trade at a premium to their large cap peers because of the perceived growth.

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And yet now, especially internationally, we see that that space trading roughly in line from

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the multiples perspective with larger caps, which hasn't really happened very often historically,

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really have to go back to the TMT bubble, the global financial crisis to see that,

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them trading at parity basically from a multiples perspective.

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Yeah, sure.

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We've seen some of that, too.

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Yeah, Ryan, following up on that, the international space appears from our perspective very

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discounted versus large-cap US names.

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And it was interesting to get your insight into the kind of the deeper trenches of the international

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market, the small caps and how they're trading in line with the international broader multiples.

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I'm wondering, to what extent are you, or you see differentials between, let's say, Europe and

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Asia, define all regions attractive, does one stand out versus the other?

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What do your thoughts on that?

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We, you know, we're finding more opportunities in emerging right now.

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I think whenever you enter sort of a risk-off stance, like as happened in the last few

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weeks and months, I think,

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the perceived riskier parts of the investment landscape tend to sell off a little more.

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So that includes international, that includes small cap, that includes emerging markets.

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And so I think the intersection of all three is very interesting now.

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We're overweight countries like Korea, Thailand, our international small cap strategy

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currently and part of that is the function of just the fact that, from a valuation perspective,

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some of those countries appear very attractive, especially when you compare it to the US,

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which the US has outperformed international, both emerging and developed international for the better

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part of a decade now.

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But these are inherently cyclical, right?

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I mean, throughout the 2000s, international was outperforming the US, late 1980s, late 1970s, same thing.

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And so, you know, inevitably this is cyclical.

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I think Europe is relatively attractive as well.

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You're seeing, I think there's been a preference for dividends in Europe for some time.

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And so the dividend yield in Europe has been higher than that in the US for some time.

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But more recently, the buyback yield is now higher in Europe than in the US.

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And it hasn't been that case for 20 years.

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And so I think there's a lot, a lot going for international right now.

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Also the dollar, the US dollar has been a major headwind to US-based international investors,

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again for the better part of a decade.

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And if you believe that the Fed is near the end of its current hiking cycle, I think you can

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make a strong argument that having non-US dollar exposure may finally prove to be a tailwind

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for US-based investors.

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But is that consistent with what you two are seeing as well?

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It is, you know, just as kind of pivoting off this conversation towards something that's probably

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evolving as we speak and gaining steam in these international global perspectives.

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The banking headlines out of the US have been, I think, shaken up a lot of investors who

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may have ignored balance sheets at their own risk.

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But balance sheets are a huge factor in investing.

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And you kind of ignore them at your own peril.

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So I'm wondering, when you look at things going on in the US, whether it's Silicon Valley Bank

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or Credit Suisse, which will just for two is a global franchise for these purposes, you've

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seen bad behaviors, chasing you, misallocation of resources, how do you compare what we're

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seeing kind of being exposed through higher interest rates in the US versus the international

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markets?

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On down these same rabbit holes, what is your view on credit and capital allocation in these markets?

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Is it a better picture than what we're seeing unfold here?

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Yeah, it's a good question.

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I think in general, generally speaking, international banks are a little better off than what

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we're seeing in the US.

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Obviously in the US, what got Silicon Valley Bank into trouble was both a mismatch in

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duration on their balance sheet, as well as a lot of paper losses that ultimately became

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realized when they saw depositor outflows.

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And I'd say, especially within emerging markets, it's interesting because this cycle,

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emerging market central banks really led the Fed in terms of increasing interest rates.

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So you saw the likes of Brazil, other large EM countries proactively increasing interest rates

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in advance of the Fed in 2021, beginning in early 2021.

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And so it had a few effects.

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First of all, they were used to the higher rates before the US was.

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And it also insulated their currencies as well to some degree.

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And so looking forward, I think if you look at the performance of European banks and

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emerging market banks, they've largely held in pretty well because they've avoided some

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of those issues that we're seeing in US regional banks.

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Now undoubtedly, we're going to see more regulation coming down for regional banks in the US, which

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will ultimately decrease lending and be yet another drag to economic growth in the near term.

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But I think what we're hearing too and as we're learning more about Silicon Valley Bank,

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Signature Bank, that they had fairly unique issues as well and probably not the proverbial

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canary in the coal mine that we thought a few weeks ago.

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But it's a reminder that we are in a tougher credit environment.

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And that's certainly not going away.

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As you said, it really pays to understand capital structure and balance sheets in this environment.

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And we've seen just in the last few weeks, a sell-off in some of the more highly levered companies,

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those that need to tap the capital markets in the next few years have certainly sold off more

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than clean balance sheet companies.

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No question.

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And then I also think this would be interesting to get your view.

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When you look at the emerging markets in particular, 8% inflation, that really

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shook up things here in the US, but they've seen inflation.

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You go to Brazil.

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They've had 1,000% inflation.

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So there's this institutional memory of these corporate managers in place.

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They know what can go wrong.

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They've seen it all.

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And while people in the US are a little bit flat-footed from all this, I'm wondering if you

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see just a more conservative backdrop from a balance sheet standpoint in some of these markets,

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because these guys know what can happen.

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They get starved of capital every so often, and they know that they have to be prepared for that.

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Yeah, that's fair.

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And certainly from a macroeconomic perspective, emerging markets have had many crises.

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If you go back to the 90s, certainly you had Mexico in 94.

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You had the Asian financial crisis in '97 - '98.

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And over time, a lot of adjustments because of that, you've seen a lot more floating exchange rates.

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You see a lot fewer external vulnerabilities in the form of current account deficits than we used to see.

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And so, certainly that would extend to the lessons learned into banks as well.

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You know, I'd say in general, banks are far more well capitalized than they have been historically.

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Mostly as a result of lessons learned from the global financial crisis.

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And so, if you look at some of the European bank balance sheets, they're very strong.

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Relative to the US and relative to certainly how they were post GFC.

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And so, yeah, you could argue that they're pretty well insulated against any near-term financial crisis.

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So, another question that came to mind.

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I know we've talked a lot about these macroeconomic factors,

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grabbing the headlines, they have everyone's attention. But I'm wondering just from a process standpoint,

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tell us a little bit about how you differentiate your weights between macrofactors as microfactors.

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Is your portfolio constructed more on a bottom-up basis?

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Or do you have macro e-factors that filter in or maybe exclude some markets?

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Just give us an idea of how you approach these matters.

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Yeah, well, how we approach international small caps specifically as we're, you know,

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from a primarily a quantitative approach, we're able to blend together multiple sources of alpha.

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You know, our foundation as a firm is really in value investing.

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And so, value receives the largest weight in the final assessment of any stock.

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But again, from a diversification of alpha approach, we're able to blend in measures of growth,

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momentum. What we call competitive strength, which is really quality assessing a company's

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competitive mode. But then also blend in certain top-down metrics. And so, in international small

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caps that are a little more driven by company specific issues, management decisions,

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the weight is basically 90% company specific 10% top-down. When we're looking at the larger cap

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emerging market space, it's 75% company specific 25% top-down.

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Well, we find is that small caps tend to be a lot more idiosyncratically driven.

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I mean, their success or failure really comes down to the decisions that

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management is making. And so, we put a lot of emphasis on company specific metrics. But at the

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same time, you can't ignore what's happening at the macro level. You know, we see their 24 emerging

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market countries in our EM universe, their 46 international small cap countries in our universe.

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And so, you need to make trade-offs, ultimately, between the opportunities set in all of those countries.

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And so, the macro piece certainly helps us, you know, looking at things like

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changes in GDP growth, sovereign CDS, interest rate curves, inflation, budget forecasts, current account

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deficits, foreign exchange reserves, that all matters as a US-based investor, ultimately, when you're

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going to take your profits out in US dollars, it definitely pays to understand those aspects as well.

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Absolutely. And then how do you rule of law on property rights factor in, do you just take some

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countries off the table because you're not comfortable? I'd say that the really

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dicey countries generally aren't in our universe to begin with. Most of the countries, we invest in

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have, you know, some history of strong property rights. We do have certain political risk

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inputs into our top-down models, so we do try to make an assessment. At Causeway too, we have a full

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fundamental team. And so, we're not making decisions in isolation. We try to get their input on our

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entire portfolio periodically to get anything that you can't quantify, basically. We're

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realists in the fact that, you can't quantify everything in the world. There are certain

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aspects that are just outside the scope of quantitative analysis. Usually it comes down to corporate

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events, but sometimes it's regulatory changes, litigation, M&A, things like that that are

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very difficult. So our fundamental team has been very valuable, especially recently given recent events,

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obviously Russia, Ukraine last year, but even more specific

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changes, such as the Japanese government change their reimbursement policy for certain

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healthcare companies a couple of years ago, and that impacted some of our healthcare holdings.

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Our real estate team alerted us to certain irresponsible lending practices on the

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part of various Chinese developers and so we exited some of those positions. And so having

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some of that real world feedback, it has been very helpful for us as well. 

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How do you think about, where obviously you invest in certain emerging market

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countries, or the geopolitical risks are elevated. So, how do you approach that?

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We need to feel protected as shareholders, just that there's certain property rights in place.

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And I think the tricky thing with emerging markets is just - if you're just reading about

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the country generally and their laws and the books, they all kind of look okay, but then there's a matter of

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enforcement. And you've got to really figure out to what extent you are protected.

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And that there's never a placement for getting in and getting local knowledge of the markets and understanding

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how things really work. Like India is a fantastic example of that. And then China has just changed so much

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over our investment careers. You know, we held stocks in China. I would say coming out of the

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taper tantrum. So that was mid-2015 when the emerging markets basically collapsed in value.

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There were a number of attractive businesses at very low valuations. We bought

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Alibaba then. But then, by mid-2020, we were completely out of China because we were just

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unsettled by what was going on geopolitically. Well, and from a regulatory perspective, I mean,

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you saw a crackdown on online education companies, real estate companies. It's just like one domino

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after the next and ultimately, you would have to know someone within the Chinese government to

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know, okay, what's next on the chopping block to be really comfortable? It's exactly right. So it

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changed so much in that five years. We were very uncomfortable with that. 

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I pushed a little bit on your process. So you're running a quantitative process. Tell me about your

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00:23:39,760 --> 00:23:47,360
universe. Is that, is your universe that MSAC, MSCI, Acrex, US, small cap, international,

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universe? That's right. Yeah. How many stocks are in that universe? Yeah. So it's a fascinating

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universe of stocks. Um, there are over 4,300 stocks in that Acrex, US, small cap index 

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00:24:02,000 --> 00:24:09,680
and, if you take a step back and look at the overall international opportunity set, Acrex, US, IMI,

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small caps are 14% weight, you know, the way that MSCI constructs their indices. 

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But actually, they're close to two thirds of that universe by number. And then even if you include the US,

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even if you take a very broad global universe and take the Acrex, IMI, those international small

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cap companies are still 47%. So, close to half of the global investment universe is those international

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small cap companies. And so we just see, you know, a wealth of of opportunity set,

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given that, that broad index. And the index is unique too and that you don't have the concentration

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issues that you have in larger cap indices. And so the largest single constituent in that index

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is only 20 basis points. The top 10 or 1.7%. I mean, you compare that to the MSCI, USA, which is very

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similar to S&P 500. You know, you're looking at 24% weight for the top 10 companies. And

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so because of that lack of concentration, it's also very difficult for companies to replicate

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that universe passively. There's one ETF, the SS from, from Vanguard, that generates

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5% tracking error every year to the underlying index that it's seeking to replicate. And that's

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no ding on Vanguard. It's a byproduct of just the fact that this very broad flat index is very

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difficult to replicate on a daily basis, which creates an amazing opportunity for an active manager.

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Yeah, what about country exposure within the index? Is there a consolidation in certain countries or

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I mean the largest weights are to Japan, UK. But they're still relatively small.

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I think Japan's the largest that about 20% of the index. But what's also unique is that 

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China has a much lower weight in small caps than it does in large caps. It obviously has these

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very big mega cap companies like Alibaba and Tencent. But when you go into small caps,

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China is only about 2 to 3% weight in the index. And so if you're concerned about some of

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those issues that we spoke about, it's just much less of a presence overall. Yeah, that's an interesting

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point. So you start with this universe and then you're going to run your model. You'll have your

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00:26:57,760 --> 00:27:04,960
alpha model and you are using certain factors to weight stocks. And can you tell me a little bit

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00:27:04,960 --> 00:27:11,840
about the factors that you're using? I saw in a past interview you talked about value being the largest

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00:27:11,840 --> 00:27:18,560
component and then growth and then some type of quality factor as well. That's right. Yeah,

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00:27:18,560 --> 00:27:24,560
value receives the largest weight in our weighting.  Every stock in our

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00:27:24,560 --> 00:27:32,080
universe gets a unique weighting. We try to appeal to the marginal buyer of every stock and sort of

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00:27:32,080 --> 00:27:39,520
upweight the most attractive aspect of every stock. But that said, value still receives the largest

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00:27:39,520 --> 00:27:46,720
weight across the board. We have sectors, specific models that our fundamental analysts help us

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00:27:46,720 --> 00:27:53,360
derive, focus on what are the most important value metrics for every sector. Growth matters,

293
00:27:53,360 --> 00:27:59,280
but long-term growth as well, shorter term growth in the form of estimate upgrades. We find that

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once the sentiment is changing, that's generally a good time to get on board. Momentum does work,

295
00:28:08,560 --> 00:28:14,800
especially in less efficient asset classes such as international small cap. We look at the

296
00:28:14,800 --> 00:28:23,920
focal companies momentum. But also the momentum of the broader network. And so we look at the

297
00:28:23,920 --> 00:28:30,720
momentum of companies competitors and suppliers and customers. Generally what you see, it's very interesting.

298
00:28:30,720 --> 00:28:40,160
There's a trickle down of information from large caps to small caps. For example, if Apple reports

299
00:28:40,160 --> 00:28:48,160
really great iPhone sales, it may take a day or two for that positive news to filter down to some

300
00:28:48,160 --> 00:28:57,360
of Apple smaller cap suppliers. We find a lot of value added from just looking at the broader momentum

301
00:28:57,360 --> 00:29:06,400
of companies network. And then finally, as you said, yeah, competitive strength. We developed

302
00:29:06,400 --> 00:29:15,440
that factor group a few years ago, really in reaction to value underperforming for so long.

303
00:29:16,240 --> 00:29:23,120
And it really came down to avoiding value traps. The companies that look cheap, but they look cheap

304
00:29:23,120 --> 00:29:30,720
into perpetuity is their earnings decline to zero. And we really took a lot of inspiration from

305
00:29:30,720 --> 00:29:38,320
Michael Porter and his five forces framework. Obviously, he was more focused at the industry level.

306
00:29:38,320 --> 00:29:45,200
We're trying to apply that to the company level. But we're looking for companies with the strong

307
00:29:45,200 --> 00:29:54,160
competitive mode. And typically those are companies that exhibit long-term uptrends in margins and market

308
00:29:54,160 --> 00:30:02,000
share in returns. We also examine the industry structure for further and more concentrated industries.

309
00:30:02,000 --> 00:30:08,560
But then we also look at balance sheet as well. We want to make sure that these companies have

310
00:30:08,560 --> 00:30:15,920
a balance sheet that will not get them into trouble during times like this. And so ultimately,

311
00:30:15,920 --> 00:30:22,960
through those factors, we're trying to identify the companies with the business models, with the pricing

312
00:30:22,960 --> 00:30:33,360
power, with the balance sheets to do well in not great economic times like we're seeing.

313
00:30:34,960 --> 00:30:41,200
Well, this is a really unsophisticated question, but you take the 4300 companies. You run your

314
00:30:41,200 --> 00:30:47,600
screen on the 4300 companies - that's going to differ a bit per industry, especially when you're

315
00:30:47,600 --> 00:30:54,240
looking at valuation metrics. You're going to come up with some type of score. Are you ranking these

316
00:30:55,360 --> 00:31:04,800
on the total 4300 or within industry? And then how do you decide the industry and country allocation?

317
00:31:04,800 --> 00:31:11,120
So you're trying to match the index or you're looking at the total overall rank of the 4300

318
00:31:11,120 --> 00:31:19,120
companies like a composite ranking. Yeah. Ultimately, you're right. We're trying to rank the

319
00:31:19,120 --> 00:31:25,760
entire universe, the advantage of a quantum approach is we can do that very easily on a daily basis.

320
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And look at the entire opportunity set, you know, from most attractive to lease attractive.

321
00:31:31,120 --> 00:31:36,560
You're right. There are some issues with comparing to companies. And so that's why we

322
00:31:36,560 --> 00:31:43,520
we typically sector neutralize and country neutralize our value scores. And so we're not 

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directly comparing emerging markets to developed. Obviously there's a valuation difference there. We're not

324
00:31:49,920 --> 00:31:55,760
directly comparing financials to, let's say, IT. Obviously there's sort of a structural

325
00:31:55,760 --> 00:32:02,160
multiple difference there as well. So we're looking for, for chief companies within their sector

326
00:32:02,160 --> 00:32:09,680
and within their country when we're doing those comparisons. We do, and that tends to create a fairly

327
00:32:09,680 --> 00:32:15,840
balanced portfolio just by itself. We do have certain portfolio level constraints so we'll constrain

328
00:32:15,840 --> 00:32:24,560
our country exposure to plus or minus 5% of the index. We have a fairly tight, even at the

329
00:32:24,560 --> 00:32:30,800
stock level plus or minus 2% versus the index. As I said, in the case of international small gaps,

330
00:32:30,800 --> 00:32:38,240
the largest constituent is only 20 basis points by itself. And so really it's just an upward cap

331
00:32:38,240 --> 00:32:44,240
on exposure. So how many stocks are typically in your portfolio? In our international small cap

332
00:32:44,240 --> 00:32:53,680
strategy, typically we're about 135 today, ranges between 120 and 200. We're a little more

333
00:32:53,680 --> 00:33:01,360
concentrated versus your typical pure quantitative manager, which some of them have hundreds of stocks.

334
00:33:02,320 --> 00:33:09,040
And partly because of our fundamental team gives us more confidence, more conviction in the stocks

335
00:33:09,040 --> 00:33:15,600
that we do hold. Yeah, I'm surprised that you actually said 120 to 200 because I also read

336
00:33:15,600 --> 00:33:21,440
that you're using your optimizing the portfolio for 100 different risk factors. And I would have

337
00:33:21,440 --> 00:33:27,120
just guessed your answer to be like 300, 400 companies because when you get those super optimized

338
00:33:27,120 --> 00:33:34,000
portfolios, you need all the holdings. Yeah, that's true. It's very easily to go sort of overboard

339
00:33:34,000 --> 00:33:42,480
on the risk and really over-neutralize a portfolio. We do have a sophisticated approach to risk where

340
00:33:42,480 --> 00:33:49,760
really whenever you're buying a stock, you're buying, I like to think of it as you're buying a basket

341
00:33:49,760 --> 00:33:56,720
of risks with uncertain payoffs. And a lot of those risks are company-specific, but far more

342
00:33:56,720 --> 00:34:02,640
are shared risks within the country you're investing in or the sector or the currency or even the

343
00:34:02,640 --> 00:34:10,640
style of the company that you're buying. And so our risk model tries to, basically

344
00:34:10,640 --> 00:34:19,280
assess how well a stock will fit into the portfolio, how well it will diversify existing risk exposures.

345
00:34:19,280 --> 00:34:26,640
And so ultimately when we optimize the portfolio, we're trying to find the highest expected return

346
00:34:27,120 --> 00:34:33,520
per unit of risk. But it's just so fascinating again going back to the universe,

347
00:34:33,520 --> 00:34:40,160
the international stock in the universe. If you take the average correlation between every single pair

348
00:34:40,160 --> 00:34:48,160
of stocks in the universe, it's only about 0.25. So the universe itself is so well diversified that

349
00:34:48,160 --> 00:34:55,280
if you start putting these into a portfolio, you get much greater diversification benefits than you do.

350
00:34:55,840 --> 00:35:02,720
Let's say, you know, US large cap portfolio, where the correlations tend to be much, much higher, much

351
00:35:02,720 --> 00:35:09,600
heavier. Yeah. Well that makes sense. So you're, you rank the companies, you're optimizing for

352
00:35:09,600 --> 00:35:14,960
certain risk factors. And then at what point do that does the fundamental team come in and take over?

353
00:35:14,960 --> 00:35:22,400
Is it right? I mean, that that seems like a lot of stocks for the group to cover, but at what point

354
00:35:22,400 --> 00:35:29,200
do they jump in and do stock-specific analysis or are they looking at industries? How does it work?

355
00:35:29,200 --> 00:35:36,480
Yes, so they give us input on trade dates. So whenever we're doing a rebound, so the portfolio

356
00:35:36,480 --> 00:35:42,240
will send the new stocks that are entering the portfolio to our fundamental team. We also have a

357
00:35:42,240 --> 00:35:50,560
standing monthly meeting where we will basically review the entire portfolio. And especially the

358
00:35:50,560 --> 00:35:56,720
largest active weights. And to the extent that they see something that's really not being picked up in

359
00:35:56,720 --> 00:36:03,360
our quantitative models, they'll provide that feedback. And ultimately, we can we can kick a stock out

360
00:36:03,360 --> 00:36:09,680
of the trade list even before it enters the portfolio. Or, you know, if something happens after the

361
00:36:09,680 --> 00:36:17,520
fact, obviously we had we had a few Russian holdings in early 2022 before the invasion,

362
00:36:17,520 --> 00:36:27,040
but some feedback from them helped us exit those names fairly quickly. And so ultimately, it's

363
00:36:27,040 --> 00:36:34,400
an innocent until proven guilty approach, I like to say. So we're not in the

364
00:36:34,400 --> 00:36:42,800
business of just overwriting our models every day. It really has to be something truly outside the scope

365
00:36:42,800 --> 00:36:48,400
of our of our models that's not being captured. But, you know, call it two to three times a

366
00:36:48,400 --> 00:36:56,480
quarter, we will trim a position back where we will kick it off the the buylist even before it's in

367
00:36:56,480 --> 00:37:02,480
our portfolio. Sure. It's very interesting to me. You know, I think most people don't realize how quantitative

368
00:37:02,480 --> 00:37:11,360
John Templeton was. He was so quantitative. He just did it in a time where you couldn't download

369
00:37:11,360 --> 00:37:18,720
information from Bloomberg to sell. And you just didn't have the computing ability that we have now.

370
00:37:18,720 --> 00:37:24,800
And it was much harder to do, which gave him an edge. But all the strategies I started and ran

371
00:37:24,800 --> 00:37:32,240
with him over the years all had a major quantitative component. And the way his three foundations

372
00:37:32,240 --> 00:37:39,440
manage money - they have a very quantitative process involved with manager selection and monitoring

373
00:37:39,440 --> 00:37:46,800
managers. And I'm always quick to remind the committee that if you're going to run a quantitative process,

374
00:37:46,800 --> 00:37:52,160
you have to consistently adhere to it. If you're going to override it all the time, you're really

375
00:37:52,160 --> 00:37:58,960
defeating all the benefits you get from what running a quantitative process. So it's really a very

376
00:37:58,960 --> 00:38:06,160
interesting approach to international small caps and emerging markets. I think it's really

377
00:38:06,160 --> 00:38:13,200
smart what you guys are doing. I have two more questions. I mean, maybe more, but two that immediately

378
00:38:13,200 --> 00:38:21,120
come to mind. One is, I would suspect there's a good bit of turnover in this strategy. Am I right or wrong?

379
00:38:21,120 --> 00:38:27,840
There's a fair amount. It really depends on the alpha decay of every factor you're looking at. So

380
00:38:27,840 --> 00:38:35,280
value in particular tends to be a factor that pays off over a long period of time as you're well aware of.

381
00:38:35,280 --> 00:38:42,560
And so once you blend in certain measures like momentum and earnings growth and sentiment,

382
00:38:42,560 --> 00:38:49,440
those tend to turn over a little faster in terms of what ranks high or low, depending on your time frame.

383
00:38:49,440 --> 00:38:57,520
But overall, we target a turnover below 100% annually in all of our strategies. And some of our

384
00:38:57,520 --> 00:39:04,160
more fundamentally oriented strategies are even far lower, more like 30%. And it really ultimately comes

385
00:39:04,160 --> 00:39:10,800
down to your weight on value because we all know that value pays off over a long period of time.

386
00:39:10,800 --> 00:39:18,720
And going back to John Templeton, obviously he was one of the first sort of adherence to behavioral

387
00:39:18,720 --> 00:39:25,120
finance too, which is really a core tenant in quantitative investing because, one thing that

388
00:39:25,120 --> 00:39:32,720
we like to do before we introduce a new factor is really understand why it works, right? And usually it

389
00:39:32,720 --> 00:39:39,680
comes down to some risk-based reason or behavioral-based reason or a limits to arbitrage-based

390
00:39:39,680 --> 00:39:46,080
reason. But, you take value and you say, okay, why does value work? It's been around for

391
00:39:46,080 --> 00:39:54,080
decades, even longer. It should be arbitraged out by now. And yet, you come back to some

392
00:39:54,080 --> 00:40:00,160
risk-based reasons, which are, okay, people think value companies are riskier, they may be prone to default.

393
00:40:00,160 --> 00:40:07,600
You get into a whole host of behavioral reasons why value works, right? 

394
00:40:07,600 --> 00:40:13,760
People like to buy the story stock that's very popular that you can easily, you know, talk about

395
00:40:13,760 --> 00:40:20,480
this, you know, long-term growth projection, the future. There's also sort of a lottery aspect

396
00:40:20,480 --> 00:40:26,480
of people investing where they want to go for the company that's going to get them, you know, 10x their

397
00:40:26,480 --> 00:40:32,960
money, right? But, but at the end of the day, those companies usually fail to perform, fail to really

398
00:40:32,960 --> 00:40:40,800
match the high level of expectations that are set. And so, so these things work and they continue to work.

399
00:40:40,800 --> 00:40:48,880
And the quantitative approach or at least some quantitative input

400
00:40:48,880 --> 00:40:56,960
to your process allows at least some level of removing those emotional biases that are really hardwired

401
00:40:56,960 --> 00:41:02,800
into into everyone, right? It's a discipline. And that's what I like about value investing. But,

402
00:41:02,800 --> 00:41:12,320
I think John Templeton was so wise when I started investing with him to make the strategies very quantitative.

403
00:41:12,320 --> 00:41:16,480
Because I think he was like, she's 24, she's never going to be able to manage her emotions.

404
00:41:17,120 --> 00:41:23,360
So, I'm just going to remove that component for her. And we're going to be managing the simple quantitative

405
00:41:23,360 --> 00:41:28,160
strategy and I'm not going to let her ever ride the strategy. I think that was genius. And then,

406
00:41:28,160 --> 00:41:33,680
you do that for a number of years and you become accustomed to occasionally losing money,

407
00:41:33,680 --> 00:41:39,920
not being upset about it and seeing it as a part of the process. And you're not always right 100%

408
00:41:39,920 --> 00:41:45,840
of the time. He said he was right 60% of the time. So, all of that makes so much sense to me.

409
00:41:45,840 --> 00:41:54,000
How do you make the decision to rebalance? Is it, we just rebalance every quarter

410
00:41:54,000 --> 00:42:01,440
or is there something you're looking for that causes the rebalance? We do. We actually run

411
00:42:01,440 --> 00:42:08,720
sort of a theoretical optimization every day so at any point in time you have the

412
00:42:08,720 --> 00:42:14,640
theoretically optimal portfolio that you would pick and you have your current portfolio, right?

413
00:42:14,640 --> 00:42:21,760
And over time, if you don't trade, those two will diverge. Now, to get from your current portfolio

414
00:42:21,760 --> 00:42:29,200
to that optimal portfolio, you have to trade. You have to undergo turnover. That's market impact.

415
00:42:29,200 --> 00:42:36,240
That's direct trading costs in the form of commissions and stamp duties and exactly. 

416
00:42:36,240 --> 00:42:41,680
And that's guaranteed negative alpha, right? When you spend that money,

417
00:42:42,240 --> 00:42:48,800
you're immediately losing that portion to alpha. And so, it's really a trade-off. And that's something

418
00:42:48,800 --> 00:42:56,000
that we try to analyze really on a daily basis. We see, okay, how much theoretical alpha could we pick

419
00:42:56,000 --> 00:43:03,120
up by trading the portfolio? And we look at that question under different turnover scenarios. And so,

420
00:43:03,120 --> 00:43:10,080
that enables us to both make the decision of when to trade, but then also how much turnover to use

421
00:43:10,080 --> 00:43:17,680
in a particular trade. So, in more volatile periods like we're seeing now, we may end up trading a little

422
00:43:17,680 --> 00:43:25,120
more often, expanding a little higher turnover overall because of the changing opportunities.

423
00:43:25,120 --> 00:43:32,720
But ultimately, to come back to your question, it usually works out to about once or twice per month

424
00:43:32,720 --> 00:43:39,840
that we're trading. And typically, it's, you know, relatively lower turnover. Again, we're

425
00:43:39,840 --> 00:43:46,000
targeting something below 100% annual turnover. But it's a great question in something that we

426
00:43:46,000 --> 00:43:52,480
analyze quite a lot because ultimately it's a question of

427
00:43:52,480 --> 00:44:00,560
how much can you improve your portfolio by undergoing that turnover? And then how much are you

428
00:44:00,560 --> 00:44:07,360
tinkering with your model or strategy? You know, John Templeton always said there are a hundred measuring

429
00:44:07,360 --> 00:44:11,920
sticks about you. If you've focused on what worked best five years ago, you're going to miss what's

430
00:44:11,920 --> 00:44:18,400
going to work best in the next five years. So, I imagine that you have to be adjusting some of those

431
00:44:18,400 --> 00:44:24,400
factors over time. What does that process look like? I mean, is it you all meet in a conference room

432
00:44:24,400 --> 00:44:31,520
and talk about it or, how does it work? Yeah, you're right. I think quantitative investing

433
00:44:32,480 --> 00:44:40,480
needs to be evolutionary because, you know, certain aspects do tend to be arbitrage to weigh over time

434
00:44:40,480 --> 00:44:45,520
to some extent. And so you need to adjust, but at the same time, as you said, you don't want to

435
00:44:45,520 --> 00:44:50,880
kick something out of your model just because it hasn't worked in the last year. And value is a great

436
00:44:50,880 --> 00:44:56,400
example. Value went through a very large extended period of underperformance

437
00:44:56,400 --> 00:45:05,520
in the 2010s. I've been telling you recently. And so, so where does that leave us? You know,

438
00:45:05,520 --> 00:45:10,720
two years ago, we could have looked at a back test over the last ten years and said, well, why do

439
00:45:10,720 --> 00:45:17,840
we even have value in our model? It hasn't been working. And that's where it really comes down to

440
00:45:17,840 --> 00:45:24,880
having a long-term perspective, looking at these performances over very long periods of time,

441
00:45:24,880 --> 00:45:33,280
understanding the reasons why a particular factor works, again, coming back to identifying a risk-based

442
00:45:33,280 --> 00:45:39,360
or behavioral-based reason why it works. And therefore, why it should continue to work going forward.

443
00:45:39,360 --> 00:45:47,440
But ultimately, you know, we're constantly looking at new data sets. Obviously, just with the proliferation

444
00:45:47,440 --> 00:45:57,520
of data, there are vendors sort of knocking on our door every day with some new data set. And some of them

445
00:45:57,520 --> 00:46:03,760
add value, some of them don't. I'd say, you know, 90% of the new factors we look at ultimately don't

446
00:46:03,760 --> 00:46:12,640
go into our models. We want to make sure that we understand why a factor works. We want to

447
00:46:12,640 --> 00:46:18,960
make sure it works over a long period of time. And we also want to make sure we're not replicating anything

448
00:46:18,960 --> 00:46:26,240
in our model that's in there already. If you introduce a new factor that's very highly correlated

449
00:46:26,240 --> 00:46:35,040
to something else, that sort of just confuses the model, creates some other issues too.

450
00:46:35,680 --> 00:46:43,440
And, you know, you ultimately, you don't want to, when we bring a new factor, ideally, we want to see it

451
00:46:43,440 --> 00:46:49,680
very lowly correlated with the other factors that are in our model. Yeah. So it's a very high bar.

452
00:46:49,680 --> 00:46:57,440
Yeah. So I have heard you comment on machine learning and how you guys are using machine learning

453
00:46:57,440 --> 00:47:06,880
to scrub earnings call transcripts. Can you speak about that? And then also about any uses of artificial

454
00:47:06,880 --> 00:47:13,280
intelligence or how that might be influencing your portfolio or you might be thinking about using these

455
00:47:13,280 --> 00:47:20,800
tools? Yeah. It's obviously a hot topic these days, especially with everyone's talk about 

456
00:47:20,800 --> 00:47:28,320
ChatGPT and how high school students are now not writing their own essays. They're just asking,

457
00:47:28,320 --> 00:47:37,760
please write me an essay on 20th century mercantilism or something. I'd say, you know, one of the pitfalls

458
00:47:37,760 --> 00:47:47,360
of quantitative investing is the danger to overfit a model. You must look for patterns where patterns

459
00:47:47,360 --> 00:47:53,120
don't exist or where they shouldn't continue to exist going forward. And so I could.

460
00:47:53,120 --> 00:47:58,240
I'm just coming up with a hypothetical example. Let's say, you know, all of the companies that start with

461
00:47:58,240 --> 00:48:04,720
the letter R, last year outperformed the market. Well, do you really want to create a strategy around

462
00:48:04,720 --> 00:48:10,240
buying the companies that start with the letter R? Probably not because there's no reason that that

463
00:48:10,240 --> 00:48:17,520
should continue going forward. And one of the big dangers with machine learning is it's sort of the

464
00:48:17,520 --> 00:48:25,920
ultimate overfitting machine. It will find patterns whether there's a pattern to be seen or not,

465
00:48:25,920 --> 00:48:32,720
whether there's some causal relationship or not. And so we've spent a lot more time

466
00:48:32,720 --> 00:48:42,320
than we typically do in proving some of our more machine learning-based factors. And as you mentioned,

467
00:48:42,320 --> 00:48:49,840
we're spending a lot of time using natural language processing to analyze conference call transcripts.

468
00:48:49,840 --> 00:48:57,760
And ultimately, you know, we've been at it for over two years now. We still haven't gotten

469
00:48:57,760 --> 00:49:04,640
comfortable enough to add it to our models because we've basically been in a road testing phase for

470
00:49:04,640 --> 00:49:12,320
some time where we now have it automated so that every single transcript that comes out in a day

471
00:49:12,320 --> 00:49:20,800
is summarized the next day and sent to our fundamental analysts. And it picks out the specific phrases and

472
00:49:20,800 --> 00:49:28,160
keywords that it's tacking onto to derive sort of a sentiment score. And ultimately, we want to make sure

473
00:49:28,160 --> 00:49:34,000
there are fundamental analysts who are actually listening to these calls who actually know the companies

474
00:49:34,000 --> 00:49:42,400
agree with the assessment of the algorithms of the read of the transcript. Because if that doesn't

475
00:49:42,400 --> 00:49:50,240
match up, then again, we're just sort of overfitting a model. And does that usually? 

476
00:49:50,240 --> 00:49:57,440
Well, we've made a lot of refinements. We're getting to a point where we're starting to feel comfortable.

477
00:49:57,440 --> 00:50:04,640
We will likely add that component to our models sometime this year, but after a lot of work. And

478
00:50:04,640 --> 00:50:13,520
it really just underscores the importance of understanding how the model is working and understanding

479
00:50:14,880 --> 00:50:22,720
why that factor or that sentiment score should continue to work. And because I think, again it's a major

480
00:50:22,720 --> 00:50:31,440
pitfall to that technology. It's amazing technology for what it does, but it's dangerous just to

481
00:50:31,440 --> 00:50:40,240
just to close your eyes and have a machine pick stocks. Sure. And if I were the fundamental

482
00:50:40,240 --> 00:50:49,440
analysts on your team, I might be absolutely not. I'm not sure because I'm worried about my job.

483
00:50:49,440 --> 00:50:56,960
Yeah, it's happened. And what's interesting though is we've seen companies start to adapt to

484
00:50:56,960 --> 00:51:05,520
a machine learning world. And so it's very interesting. Some companies before they deliver their

485
00:51:05,520 --> 00:51:12,400
prepared remarks, they will run it through machine learning algorithms to make sure that it's interpreted

486
00:51:12,400 --> 00:51:18,720
positively, which is just crazy. And I'm sure there are avoiding certain words and have got

487
00:51:18,720 --> 00:51:26,480
the advice to repeat certain words. That's a big set of advice that you will hear.

488
00:51:26,480 --> 00:51:33,040
Whole new world of earnings, game and ship. Oh, totally. And in the early

489
00:51:33,040 --> 00:51:38,400
days of machine learning, it was like, it was all about, how many analysts are congratulating

490
00:51:38,400 --> 00:51:45,280
the management team? How many times is the word "congratulations" appear in the Q&A? And that was

491
00:51:45,280 --> 00:51:53,120
an easy signal. But we think that there's a lot of gamesmanship now going on with that regard.

492
00:51:53,120 --> 00:52:00,400
I'm sure. Well, let me ask you this question. And you can answer it for your strategy. You can answer

493
00:52:00,400 --> 00:52:09,440
it for yourself or you can answer it for Causeway as just a business. What is your competitive

494
00:52:09,440 --> 00:52:15,520
advantage? If you had to name one thing, what is the competitive advantage of Ryan Meyers,

495
00:52:15,520 --> 00:52:23,520
Causeway, or the quantitative approach to international small caps, stock solution? Choose either one.

496
00:52:25,440 --> 00:52:32,080
With regard to international small caps specifically, it's an area that would be difficult to cover purely

497
00:52:32,080 --> 00:52:38,400
fundamentally. I would say with 4,300 stocks, let's say you have an analyst. Let's say you have an

498
00:52:38,400 --> 00:52:47,040
analyst covering 100 of those stocks. You still have to have 43 analysts because of the market caps,

499
00:52:47,040 --> 00:52:53,200
you know, you're only going to be able to run, we measure capacity or we estimate capacity

500
00:52:53,200 --> 00:53:03,200
to be 2 to 3 billion overall. And so it's an interesting space because it's very difficult to cover

501
00:53:03,200 --> 00:53:12,160
purely fundamentally. One of the big disadvantages to pure fundamental

502
00:53:12,160 --> 00:53:19,680
analysis is it takes time, right? I mean, you're going to spend days, weeks analyzing a company,

503
00:53:19,680 --> 00:53:26,800
talking with management teams, and at the end of the day, you may ultimately say, okay, well,

504
00:53:26,800 --> 00:53:34,960
that's fairly valued. Let's move on to the next one. And that's a big advantage to a quantitative

505
00:53:34,960 --> 00:53:43,280
approach. In that we can assess the universe, we can identify mispriced things really on a daily basis.

506
00:53:44,080 --> 00:53:50,800
But with that said, quantitative analysis has plenty of pitfalls to it as it tries to over-simplify the

507
00:53:50,800 --> 00:53:57,840
world. It misses certain aspects that are outside the scope of quantitative analysis. And so,

508
00:53:57,840 --> 00:54:03,440
you know, coming back to your question, I think a big competitive advantage of our approach and

509
00:54:03,440 --> 00:54:10,080
of Causeway is really the blending of quantitative and fundamental inputs. Because at the end of the day,

510
00:54:10,080 --> 00:54:18,560
I think both have their strengths, both have their limitations. And so, combining them in some respect,

511
00:54:18,560 --> 00:54:27,840
hopefully gives you the benefits of both approaches and minimizes the limitations. But it's hard to do.

512
00:54:27,840 --> 00:54:36,080
I mean, they're ultimately different approaches to the same question. And sometimes you arrive at

513
00:54:36,080 --> 00:54:43,440
a different conclusion. Yeah. And I loved earlier how you highlighted the behavioral aspect.

514
00:54:43,440 --> 00:54:50,080
Because that, to me, from a quant standpoint, that may be the biggest gift. Not only do you avoid

515
00:54:50,080 --> 00:54:58,400
the story stocks, most likely, fear of the value-based manager. But it's probably how your experience with John

516
00:54:58,400 --> 00:55:04,480
Templeton was. It's going to force you into some names as a fundamental guy. You probably, and I don't know

517
00:55:04,480 --> 00:55:10,720
about this one. But in so there are so many advantages to that starting point. Do you ever find,

518
00:55:10,720 --> 00:55:15,280
when you're putting together your portfolio, the stock shows up, you're

519
00:55:15,280 --> 00:55:20,720
fundamentally like, I don't know about this. But you just have to go with a model, or how do you

520
00:55:20,720 --> 00:55:27,520
all sort those out those situations? Yeah. I mean, I look at certain examples of because we're diverse.

521
00:55:27,520 --> 00:55:33,600
I mean, I'm a value investor at heart, too. And so I want to get a bargain. But occasionally our model

522
00:55:33,600 --> 00:55:40,080
will come up with these stocks that score pretty average on a value metric. But the

523
00:55:40,080 --> 00:55:47,120
growth in momentum is just all there and all the stars are aligning. And yeah, it sort of makes me

524
00:55:47,120 --> 00:55:55,120
question when, we have a stock right now, you know, an Indian small cap company that does

525
00:55:55,120 --> 00:56:01,840
sort of automated driving software. It trades at 40x next year's earnings. And as a value investor,

526
00:56:01,840 --> 00:56:07,440
I'm like, oh my goodness, what are we doing? And yet that stock has been one of our best performing

527
00:56:07,440 --> 00:56:15,120
stocks over the last few months. And so I think at some level you need to be comfortable

528
00:56:15,120 --> 00:56:22,720
with the diversification of alpha approach, the fact that sometimes

529
00:56:22,720 --> 00:56:29,360
you do have to pay for a stock up for a stock, right? I mean, if you just take a very simple approach and

530
00:56:29,360 --> 00:56:35,840
say the prices sort of you're taking an annuity approach to earnings.

531
00:56:35,840 --> 00:56:42,160
And ultimately, you should be willing to pay more for a company where those earnings are growing higher.

532
00:56:42,160 --> 00:56:49,680
And/or where the volatility of those earnings is lower. But, you know, you have to really,

533
00:56:50,560 --> 00:56:57,360
 have a systematic approach to that and be comfortable at the end of the day that your process

534
00:56:57,360 --> 00:57:04,480
is finding good bargains whether they're trading at a 3 P/E or a 30 P/E. 

535
00:57:04,480 --> 00:57:11,360
Yeah. And really, what I was getting at was kind of the other side of that coin, like the

536
00:57:11,360 --> 00:57:17,040
names that look scary or dicey. They're in the headlines for the wrong reasons, but you're

537
00:57:17,040 --> 00:57:23,040
quantitative, a model is probably saying no guys. This is one of them. You've got to buy it.

538
00:57:23,040 --> 00:57:29,600
You're right. Yeah. Now, I think, and I think my background in distressed debt gives me 

539
00:57:29,600 --> 00:57:38,800
some comfort in thinking that ultimately most assets have some value, right? It's just a matter of

540
00:57:38,800 --> 00:57:44,240
coming up with the fair price for that asset. And I think as a credit investor,

541
00:57:45,440 --> 00:57:52,640
you approach the world thinking, okay, what can go wrong with my investment thesis?=

542
00:57:52,640 --> 00:57:58,160
I don't have to have the equity worth anything for me to make money. I just need to

543
00:57:58,160 --> 00:58:03,440
make sure if I bought these bonds at 60 cents on the dollar, I just better 

544
00:58:03,440 --> 00:58:09,360
sure that the company's worth that much. And as long as it is, then I'll make my money back.

545
00:58:10,160 --> 00:58:17,920
As an equity investor, I think inherently we're trying to say, okay, what can go right? But, you know,

546
00:58:17,920 --> 00:58:25,120
the value part of the investor mindset is also asking, you know, what can go wrong? And I better be

547
00:58:25,120 --> 00:58:32,880
ultra conservative on my assumptions. And the stock still has upside from here. And so,

548
00:58:33,920 --> 00:58:40,720
I think you're right.Sometimes you really, it helps to sort of follow some

549
00:58:40,720 --> 00:58:48,800
frameworks, some rules-based approach to investing. Because, yeah, Lauren, you said that

550
00:58:48,800 --> 00:58:57,360
John Templeton was right 60% of the time. 60% looks pretty good to me. I'll take it. 

551
00:58:57,360 --> 00:59:02,560
Exactly. If you can get a successful investment, 60% of the time, you're definitely going to succeed.

552
00:59:03,520 --> 00:59:10,160
Yeah, that was really well said. I appreciate those comments. 

553
00:59:10,160 --> 00:59:17,360
I have one, one last question for you. What is keeping you up at night right now?

554
00:59:17,360 --> 00:59:23,360
What are you sitting there worried about when it comes to the market or this particular strategy?

555
00:59:23,360 --> 00:59:31,200
It seems like there's always a lot to potentially worry about.

556
00:59:32,960 --> 00:59:39,520
I think the geopolitical situation is something that we try to think about and try to review periodically.

557
00:59:39,520 --> 00:59:45,440
You know, we get a lot of questions from our investors right now. Since we invest in Taiwan,

558
00:59:45,440 --> 00:59:53,040
is China going to invade Taiwan?  Well, we don't think so, imminently.

559
00:59:53,040 --> 00:59:59,120
 But, we also didn't think that Russia was going to invade Ukraine early last year. And so I think assessing

560
01:00:00,000 --> 01:00:07,280
the geopolitical risks is something that's difficult. And it's difficult to do from a systematic perspective.

561
01:00:07,280 --> 01:00:15,200
You know, how do you really assess some of these larger geopolitical risk? It's very difficult to do.

562
01:00:15,200 --> 01:00:22,000
You know, the macro backdrop, I think I'm less worried about because we're all used to

563
01:00:22,000 --> 01:00:31,120
going through investment cycles. And I think a lot of people are saying,

564
01:00:31,120 --> 01:00:36,160
well, the Fed was too late to tighten and now they're potentially overthinking.

565
01:00:36,160 --> 01:00:42,480
It's that I think I'm less worried about. I think it will ultimately

566
01:00:42,480 --> 01:00:50,640
work out. I think inevitably whatever happens, we're in a higher cost of capital

567
01:00:50,640 --> 01:00:56,560
environment than we've been for the last 10 years. And I don't think that's going to change. 

568
01:00:56,560 --> 01:01:01,360
And as long as that doesn't change, I think value investing is going to have

569
01:01:01,360 --> 01:01:08,400
a decent runway from here. And so we're certainly optimistic looking forward from that front.

570
01:01:08,400 --> 01:01:15,120
fingers crossed. Yeah. Exactly. I really appreciate the time you've spent with us

571
01:01:15,120 --> 01:01:21,440
today. And this has been a great hour. I've learned a lot. And I appreciate hearing about your background

572
01:01:21,440 --> 01:01:27,040
and your strategies. So thank you so much. Well, thank you both as well. It's been a pleasure.

573
01:01:27,040 --> 01:01:34,000
Thank you for listening to Investing the Templeton Way. Please be sure to subscribe on your favorite

574
01:01:34,000 --> 01:01:40,000
podcasts player. To view the show notes and resources mentioned in today's show,

575
01:01:40,720 --> 01:01:48,240
head to investingthetempletonway.com.

576
01:01:48,240 --> 01:01:58,240
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