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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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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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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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alpha model and you are using certain factors to weight stocks. And can you tell me a little bit
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about the factors that you're using? I saw in a past interview you talked about value being the largest
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component and then growth and then some type of quality factor as well. That's right. Yeah,
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value receives the largest weight in our weighting. Every stock in our
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universe gets a unique weighting. We try to appeal to the marginal buyer of every stock and sort of
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upweight the most attractive aspect of every stock. But that said, value still receives the largest
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weight across the board. We have sectors, specific models that our fundamental analysts help us
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derive, focus on what are the most important value metrics for every sector. Growth matters,
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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,
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especially in less efficient asset classes such as international small cap. We look at the
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focal companies momentum. But also the momentum of the broader network. And so we look at the
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momentum of companies competitors and suppliers and customers. Generally what you see, it's very interesting.
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There's a trickle down of information from large caps to small caps. For example, if Apple reports
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really great iPhone sales, it may take a day or two for that positive news to filter down to some
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of Apple smaller cap suppliers. We find a lot of value added from just looking at the broader momentum
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of companies network. And then finally, as you said, yeah, competitive strength. We developed
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that factor group a few years ago, really in reaction to value underperforming for so long.
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And it really came down to avoiding value traps. The companies that look cheap, but they look cheap
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into perpetuity is their earnings decline to zero. And we really took a lot of inspiration from
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Michael Porter and his five forces framework. Obviously, he was more focused at the industry level.
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We're trying to apply that to the company level. But we're looking for companies with the strong
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competitive mode. And typically those are companies that exhibit long-term uptrends in margins and market
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share in returns. We also examine the industry structure for further and more concentrated industries.
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But then we also look at balance sheet as well. We want to make sure that these companies have
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a balance sheet that will not get them into trouble during times like this. And so ultimately,
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through those factors, we're trying to identify the companies with the business models, with the pricing
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power, with the balance sheets to do well in not great economic times like we're seeing.
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Well, this is a really unsophisticated question, but you take the 4300 companies. You run your
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screen on the 4300 companies - that's going to differ a bit per industry, especially when you're
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looking at valuation metrics. You're going to come up with some type of score. Are you ranking these
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on the total 4300 or within industry? And then how do you decide the industry and country allocation?
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So you're trying to match the index or you're looking at the total overall rank of the 4300
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companies like a composite ranking. Yeah. Ultimately, you're right. We're trying to rank the
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entire universe, the advantage of a quantum approach is we can do that very easily on a daily basis.
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And look at the entire opportunity set, you know, from most attractive to lease attractive.
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You're right. There are some issues with comparing to companies. And so that's why we
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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
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directly comparing financials to, let's say, IT. Obviously there's sort of a structural
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multiple difference there as well. So we're looking for, for chief companies within their sector
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and within their country when we're doing those comparisons. We do, and that tends to create a fairly
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balanced portfolio just by itself. We do have certain portfolio level constraints so we'll constrain
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our country exposure to plus or minus 5% of the index. We have a fairly tight, even at the
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stock level plus or minus 2% versus the index. As I said, in the case of international small gaps,
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the largest constituent is only 20 basis points by itself. And so really it's just an upward cap
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on exposure. So how many stocks are typically in your portfolio? In our international small cap
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strategy, typically we're about 135 today, ranges between 120 and 200. We're a little more
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concentrated versus your typical pure quantitative manager, which some of them have hundreds of stocks.
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And partly because of our fundamental team gives us more confidence, more conviction in the stocks
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that we do hold. Yeah, I'm surprised that you actually said 120 to 200 because I also read
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that you're using your optimizing the portfolio for 100 different risk factors. And I would have
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just guessed your answer to be like 300, 400 companies because when you get those super optimized
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portfolios, you need all the holdings. Yeah, that's true. It's very easily to go sort of overboard
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on the risk and really over-neutralize a portfolio. We do have a sophisticated approach to risk where
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really whenever you're buying a stock, you're buying, I like to think of it as you're buying a basket
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of risks with uncertain payoffs. And a lot of those risks are company-specific, but far more
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are shared risks within the country you're investing in or the sector or the currency or even the
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style of the company that you're buying. And so our risk model tries to, basically
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assess how well a stock will fit into the portfolio, how well it will diversify existing risk exposures.
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And so ultimately when we optimize the portfolio, we're trying to find the highest expected return
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per unit of risk. But it's just so fascinating again going back to the universe,
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the international stock in the universe. If you take the average correlation between every single pair
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of stocks in the universe, it's only about 0.25. So the universe itself is so well diversified that
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if you start putting these into a portfolio, you get much greater diversification benefits than you do.
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Let's say, you know, US large cap portfolio, where the correlations tend to be much, much higher, much
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heavier. Yeah. Well that makes sense. So you're, you rank the companies, you're optimizing for
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certain risk factors. And then at what point do that does the fundamental team come in and take over?
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Is it right? I mean, that that seems like a lot of stocks for the group to cover, but at what point
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do they jump in and do stock-specific analysis or are they looking at industries? How does it work?
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Yes, so they give us input on trade dates. So whenever we're doing a rebound, so the portfolio
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will send the new stocks that are entering the portfolio to our fundamental team. We also have a
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standing monthly meeting where we will basically review the entire portfolio. And especially the
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largest active weights. And to the extent that they see something that's really not being picked up in
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our quantitative models, they'll provide that feedback. And ultimately, we can we can kick a stock out
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of the trade list even before it enters the portfolio. Or, you know, if something happens after the
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fact, obviously we had we had a few Russian holdings in early 2022 before the invasion,
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but some feedback from them helped us exit those names fairly quickly. And so ultimately, it's
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an innocent until proven guilty approach, I like to say. So we're not in the
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business of just overwriting our models every day. It really has to be something truly outside the scope
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of our of our models that's not being captured. But, you know, call it two to three times a
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quarter, we will trim a position back where we will kick it off the the buylist even before it's in
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our portfolio. Sure. It's very interesting to me. You know, I think most people don't realize how quantitative
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John Templeton was. He was so quantitative. He just did it in a time where you couldn't download
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information from Bloomberg to sell. And you just didn't have the computing ability that we have now.
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And it was much harder to do, which gave him an edge. But all the strategies I started and ran
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with him over the years all had a major quantitative component. And the way his three foundations
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manage money - they have a very quantitative process involved with manager selection and monitoring
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managers. And I'm always quick to remind the committee that if you're going to run a quantitative process,
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you have to consistently adhere to it. If you're going to override it all the time, you're really
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defeating all the benefits you get from what running a quantitative process. So it's really a very
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interesting approach to international small caps and emerging markets. I think it's really
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smart what you guys are doing. I have two more questions. I mean, maybe more, but two that immediately
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come to mind. One is, I would suspect there's a good bit of turnover in this strategy. Am I right or wrong?
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There's a fair amount. It really depends on the alpha decay of every factor you're looking at. So
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value in particular tends to be a factor that pays off over a long period of time as you're well aware of.
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And so once you blend in certain measures like momentum and earnings growth and sentiment,
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those tend to turn over a little faster in terms of what ranks high or low, depending on your time frame.
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But overall, we target a turnover below 100% annually in all of our strategies. And some of our
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more fundamentally oriented strategies are even far lower, more like 30%. And it really ultimately comes
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down to your weight on value because we all know that value pays off over a long period of time.
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And going back to John Templeton, obviously he was one of the first sort of adherence to behavioral
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finance too, which is really a core tenant in quantitative investing because, one thing that
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we like to do before we introduce a new factor is really understand why it works, right? And usually it
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comes down to some risk-based reason or behavioral-based reason or a limits to arbitrage-based
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reason. But, you take value and you say, okay, why does value work? It's been around for
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decades, even longer. It should be arbitraged out by now. And yet, you come back to some
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risk-based reasons, which are, okay, people think value companies are riskier, they may be prone to default.
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You get into a whole host of behavioral reasons why value works, right?
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People like to buy the story stock that's very popular that you can easily, you know, talk about
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this, you know, long-term growth projection, the future. There's also sort of a lottery aspect
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of people investing where they want to go for the company that's going to get them, you know, 10x their
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money, right? But, but at the end of the day, those companies usually fail to perform, fail to really
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match the high level of expectations that are set. And so, so these things work and they continue to work.
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And the quantitative approach or at least some quantitative input
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to your process allows at least some level of removing those emotional biases that are really hardwired
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into into everyone, right? It's a discipline. And that's what I like about value investing. But,
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I think John Templeton was so wise when I started investing with him to make the strategies very quantitative.
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Because I think he was like, she's 24, she's never going to be able to manage her emotions.
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So, I'm just going to remove that component for her. And we're going to be managing the simple quantitative
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strategy and I'm not going to let her ever ride the strategy. I think that was genius. And then,
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you do that for a number of years and you become accustomed to occasionally losing money,
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not being upset about it and seeing it as a part of the process. And you're not always right 100%
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of the time. He said he was right 60% of the time. So, all of that makes so much sense to me.
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How do you make the decision to rebalance? Is it, we just rebalance every quarter
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or is there something you're looking for that causes the rebalance? We do. We actually run
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sort of a theoretical optimization every day so at any point in time you have the
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theoretically optimal portfolio that you would pick and you have your current portfolio, right?
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And over time, if you don't trade, those two will diverge. Now, to get from your current portfolio
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to that optimal portfolio, you have to trade. You have to undergo turnover. That's market impact.
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That's direct trading costs in the form of commissions and stamp duties and exactly.
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And that's guaranteed negative alpha, right? When you spend that money,
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you're immediately losing that portion to alpha. And so, it's really a trade-off. And that's something
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that we try to analyze really on a daily basis. We see, okay, how much theoretical alpha could we pick
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up by trading the portfolio? And we look at that question under different turnover scenarios. And so,
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that enables us to both make the decision of when to trade, but then also how much turnover to use
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in a particular trade. So, in more volatile periods like we're seeing now, we may end up trading a little
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more often, expanding a little higher turnover overall because of the changing opportunities.
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But ultimately, to come back to your question, it usually works out to about once or twice per month
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that we're trading. And typically, it's, you know, relatively lower turnover. Again, we're
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targeting something below 100% annual turnover. But it's a great question in something that we
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analyze quite a lot because ultimately it's a question of
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how much can you improve your portfolio by undergoing that turnover? And then how much are you
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tinkering with your model or strategy? You know, John Templeton always said there are a hundred measuring
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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
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01:01:34,000 --> 01:01:40,000
podcasts player. To view the show notes and resources mentioned in today's show,
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01:01:40,720 --> 01:01:48,240
head to investingthetempletonway.com.
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01:01:48,240 --> 01:01:58,240
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