Quantamental Investing & Emerging Markets with Jason Hsu

Emerging markets represent a dynamic and increasingly influential segment of the global economy. These regions, often characterized by faster economic growth, industrialization, and a burgeoning middle class, offer significant investment opportunities. However, investing in emerging markets comes with its unique set of challenges, including political instability, currency volatility, and less-developed financial infrastructure. To navigate these complexities, investors are increasingly turning to quantamental investing, a hybrid approach that combines quantitative analysis with fundamental insights. By leveraging advanced data analytics and machine learning algorithms, quantamental investing seeks to exploit hidden patterns and opportunities within emerging markets to make better informed decisions. In this episode, Jason Hsu shares his investment philosophy and quantamental investing strategy, focusing on Asia's emerging markets and their neglected long-term opportunities.

Jason has authored more than 40 peer-reviewed articles. He is an associate editor for Journal of Investment Management, and also serves on the editorial board for several publications including Journal of Index Investing, Journal of Investment Consulting, and Journal of Investment Management.

Jason graduated with a BS (summa cum laude) in physics from the California Institute of Technology, was awarded an MS in finance from Stanford University, and earned his Ph.D. in finance from UCLA, where he conducted research on the equity premium, business cycles, and portfolio allocations.

Awards & Recognition

o    2019 CFA Institute Graham and Dodd Top Award for “What is Quality?”

o    2018 Bernstein Fabozzi/Jacobs Levy Outstanding Article Award for “Does Past Performance Matter in Investment Manager Selection?” Journal of Portfolio Management

o    2016 CFA Institute Graham and Dodd Scroll Award for “Will your Factor Deliver? An Examination of Factor Robustness and Implementation Costs”

o    2015 Bernstein Fabozzi/Jacobs Levy Outstanding Article Award for “A Study of Low-Volatility Portfolio Construction Methods” Journal of Portfolio Management

o    2015 William F. Sharpe Award – ETF/Indexing Paper of the Year for “A Framework for Assessing Factors and Implementing Smart Beta Strategies”

o    2013 Bernstein Fabozzi/Jacobs Levy Outstanding Article Award for “The Surprising Alpha from Malkiel’s Monkey and Upside-Down Strategies” Journal of Portfolio Management

o    2013 William F. Sharpe Award – ETF/Indexing Paper of the Year for “A Framework for Examining Asset Allocation Alpha”

o    2011 CFA Institute Graham and Dodd Scroll Award for “A Survey of Alternative Equity Index Strategies”

o    2011 Financial Analyst Journal Readers’ Choice Award for “A Survey of Alternative Equity Index Strategies”

o    2009 Outstanding Service to UCLA Anderson School of Management

TRANSCRIPT BEGINS:

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Hello and welcome to the Investing the Templeton Way podcast. I'm your host Lauren Templeton.

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And I'm your co-host Scott Phillips. And today's guest is Jason Hsu. Jason is the founder and

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president of Rayliant Advisors. Prior to launching Rayliant, Jason was the co-founder and vice-chairman

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of Research Affiliates, where he was on the forefront of the smart beta revolution with Rob

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Arnott. Jason then spun out the Asian business from Research Affiliates and began Rayliant.

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Jason is very well published and has authored more than 40 peer-reviewed articles. He has won

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many awards for these contributions. Jason is an associate editor for the Journal of Investment

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Management and also serves on the editorial board for several publications including the Journal of

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Index Investing, Journal of Investing Consulting and the Journal of Investment Management. Jason is

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a member of the board of directors at the Anderson School of Management at UCLA as well as a

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professor of finance. He graduated with a BS summa cum laude in physics from the California Institute

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of Technology and was awarded an MS in Finance from Stanford University and earned a PhD in finance

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from UCLA where he conducted research on the equity premium, business cycles and portfolio

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allocations. Welcome Jason. Thank you so much for joining us today and I'm just curious how you

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went from a background in physics to finance. Well, a lot of people in our industry went from

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physics or mathematics into finance because at the sort of the core of quantitative

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investment management is really this belief that through data and through statistics applied

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to a large amount of data, you could identify patterns. Be it, patterns in how the stock market behaves

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or patterns in how investors behave and with those patterns you can have an edge over other people

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who are not as sort of data focused and as quantitative. It is one of the three sources of alpha, right?

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Better information, a better model or the third source of alpha would be exploiting human

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behavior. So that's very interesting going from physics to finance and I know that you're well known

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for the term "quantimental investing." Can you describe what "quantimental investing" is? I'm sure

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most of our listeners are familiar but if you could briefly review that that would be helpful.

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Yeah, a lot of portfolio managers are either traditional stock pickers or they're sort of pure

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quant who just believe in statistics and have sort of the data tell the story. What I'm trying to do

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and I think it is starting to be the trend, right? It's, a holy grail is to combine both, right? As a

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quantitative investor, someone who believes in the statistics and the big data, I'd have developed

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a tremendous respect for people who sort of have gut intuition for how to pick stocks, how to

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how to pick market cycles and it's about me trying to model what they do, try to quantify what they do

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and also recognizing that there are a lot of things you can't quantify. That is purely art and

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not science and so you want to bring that art into the process, right? Into a, otherwise more quantitative

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process and hopefully by combining the two, you can, you know, remove some of the biases of a purely

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statistical based investing process but also remove some of the biases of a portfolio manager who

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made himself suffer from behavioral biases and where, you know, he clearly lacks a bandwidth

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of looking at a thousand stocks, right? He can probably handle a handful. So really combining both, 

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is ultimately how you kind of fill the gaps. What was your introduction towards investing in the

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markets, Jason? A lot of people kind of iterate through different things or maybe they read something

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seminal or saw a speech from Warren Buffett. What kind of latched you into thinking about the markets

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and pursuing this path? So, it started at Caltech. Most people think of Caltech as just physics and

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math. Caltech actually has one of the very first behavioral finance laboratory where they ran

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market experiments, bringing, you know, undergrads and then oftentimes also bringing businesspeople

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in the Pasadena area to play these simulated market games. And I worked on the team that conducted

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those experiments and that was how I went from someone who really was much more of a scientist,

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data scientist to someone who go, wow, you know, the data tells us there are a lot of irrational human

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behaviors even by, by very well-trained engineers like Caltech, undergrads and graduate students.

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And then I became fascinated with how markets work, right? Both the very rational part of it, but also

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the very irrational participants that come into it. Yeah, those irrational participants are where

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we think we have an edge, but when you're thinking through that and this application of

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quantimental investing, I know that you're really an expert in applying this to the Chinese market.

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Can you walk us through some advantages to applying that particular style of investing to the Chinese

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market and some differences between the Chinese market and developed markets when it comes to

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quantimental investing? Yeah, first and foremost, the Chinese stock market is very retail-dominated.

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It's 85% retail that's measured by trading volume. You compare that to the US, right? The US

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doing normal times is about 5% retail trading, you know, Robinhood.com and the meme stock periods

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notwithstanding. And so, you can immediately see like, you know, markets that are like the US

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predominantly institutional versus markets that's predominantly retail driven like China.

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One will be a lot more inefficient. The Chinese stock market is extremely inefficient. And so when

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you apply the quantitative techniques that includes a lot of behavioral-based factors,

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that's trying to understand what do people get wrong, right? And what you'll see is there are a lot of

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great companies in China who are very undervalued. And then occasionally, there are a lot of basically

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meme stocks, right? I call China like GameStop, you know, every day. I just going to be some speculative

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meme stocks that rally for no good reason. And the bubble can get quite large for an extended period

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of time. And that's the kind of market you're dealing with. And that's the kind of market where you

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will find more patterns, whether sentiment-driven, the value-oriented investing works really well in that

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market. And you don't expect that to change anytime soon until it transforms from a retail-oriented

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market to an institutional market. And one of the aspects of investing in China that you've done

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a really good job of delineating is, just, you know, from a Western perspective, we think of

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Alibaba, the ADR, Tencent, and the big large Mega cap, you know, tech names that are listed, you know,

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over here with ADRs, but you've done a good job of really breaking out the different segments of

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the market and looking at them individually and exploiting the ones that make the most sense from

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an excess return standpoint. Now, I think you've had some counterintuitive findings there. Could you

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just share that with our audience. Yeah, so there are some really interesting features of the

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Chinese stock market that you wouldn't expect, right? Because generally most people would say, "Okay,

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that's a more inefficient market." And it's a market where there's probably a lot of manipulation

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because governance is poor. Now, investors are right to say there's a lot of manipulation when

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it comes to earnings reporting. But what is very surprising and very counterintuitive is that

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manipulation is generally to under-report earnings and earnings growth. And that is very hard to believe

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and very hard to understand. If you're going to manipulate numbers, why would you manipulate them downward?

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And this is because the stock exchanges in China are not for profit businesses, so they don't just

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care about listing stocks. They're almost a part of the government bureaucracy, so they worry about

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if I list a company who doesn't make money or whose stock price falls a lot, there's sort of personal

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liability from the exchange officials for listing a bad company, right? There'll be investigations

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too, did you take bribes? And so, there's an explicit change rule that if you lose money for a year,

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there'll be a major investigation to see, well, why are you bad at running your business? If you do

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it again for a second year, there's sanction and you do it again in a third year, you're being prepared

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for de-listing. So, companies are terrified, right? And they understand, there's business cycle, but

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of course, the exchange officials aren't so patient with those explanations. So, whenever they're at the

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height of kind of the industry cycle, they'll claw back and underreport earnings just so that there's

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a reserve that, during a bad year, they could then use that reserve and use it as earnings for that year,

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to basically smooth earning over their industry business cycle. So very surprising finding, but it is

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systematic and widespread in China that most firms underreport earnings growth.

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Yeah, that's very surprising. And then another aspect that I've heard you speak about several times,

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which I actually have some experience with, but just that listing in China,

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listing a company in China on one of the exchanges, the Shenzhen or the Shanghai exchange,

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is very difficult compared to listing in the US. Could you comment on that aspect as well?

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Yeah, and I just mentioned that the exchanges are an extension of the government bureaucracy.

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And so, they're not in a rush to list more companies, so there's more trading volume and so they

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can earn a listing fee. The exchange officials are often quite burdened by the fact that people

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want to list because their assumption is there are a lot of unscrupulous business operators

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who want to juice up their earnings, list the company and defraud retail investors. And when that

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happens, people will protest outside the exchange and then they'll be a giant embarrassment to a bureaucracy.

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And so, what the exchange officials often do when they see a new filing is to basically sit

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on it for a while because if you're in a rush to list or maybe there's something wrong with you,

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maybe you're not financially stable. And additionally, there are rules that basically mandate a company

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to be profitable and have a business plan to continue to grow profits before they'll even consider

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your case. And so, this is very different from the US, because I remember seeing the Lift IPO and

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we're like first page, first paragraph of their prospectus is like we do not have a business model

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to profit, right? So, you know, well, that can work in the US. That definitely doesn't fly in China.

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So, they want you to be already profitable and have a credible plan of sustained profitability growth.

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And of course, that is problematic in the sense that, well, if you're already profitable

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and you kind of got everything figured out, right? You're not really looking for risk capital to help

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you grow, right? If you just imagine like a Tesla or say even an Amazon would have a very hard time

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IPOing China and investors would have missed out on the opportunities to really participate before

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the company sort of got it all figured out. So, you know, China is starting to rethink that rule,

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but I would say it's listing standards today is probably the highest versus, you know, anywhere else

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in the world. Yeah, I think that's often misunderstood by investors for sure. And when you're talking

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about the Chinese market, I think it's important for us to sort of delineate for investors that

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may not understand the different markets, the A-share versus the H-share versus ADR-listed securities.

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So can you explain that just very briefly to anyone who's listening who might not understand?

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Yeah, so most of us, you know, when we think of investing in China, we think of AliBaba, right? We

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think of Jack Ma. You know, it's almost like, you know, Jack Ma is Jackie Chan, right? And Jackie Chan

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used to be kind of the only Asian face we started to recognize in the West. But, you know, China is

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much, much, much more than these sort of big tech names like the Tencent's of the BABAs. What surprises

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a lot of people is, you know, the wealthiest guy today in China doesn't do any tech, right? He sells water.

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All right, there's no IP, there's no technology, right? And it's not sold to foreigners, so it's not a big

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export product. It's for domestic consumption growth. And the last wealthiest guy in China sold

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spicy hot pot, right? Again, domestic consumption, not technology. So, onshore A-shares are generally

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companies that most of us have not heard of. They get your access to a lot of the more domestic

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economies, probably a lot more what you would think of, you know, small mid-company that still have a

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lot of growth. I haven't got international. The ADR is by comparison right? That's AliBaba,

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that's your Tencent's. And they're generally, you know, IPO overseas, rather an onshore because

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they're sort of broader audience, right? They got the name recognition, and they want to fetch kind

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of the NASDAQ like high valuation. So probably the biggest difference between ADRs and the A-shares,

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as far as all, huge valuation gap, right? You're paying for a very premium brand when you buy a BABA

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or Tencent when it first hits the market, whereas if you're buying A-shares are generally quite cheap,

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even if they have phenomenal dividends, phenomenal earnings growth. Something else that's probably

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useful for investors to pay attention to is ADRs, American Depository Receipts, at least when it comes

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to Chinese companies. Oftentimes, are ADRs of a Cayman shell company? So, think of AliBaba, the Cayman

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entity, right? What the heck is that thing? Right? Versus the AliBaba onshore. What is AliBaba Cayman

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entity claims to have a lot of marketing licensing contracts with the actual AliBaba who like sells

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all the stuff in China and makes, you know, all the tens of billions of dollars. And all that profit

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is really kind of then repatriated to the Cayman entity through these licensing and marketing agreements.

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So, it's sort of a wonky arrangement that, you know, tax authorities tend to want to challenge,

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right? Both the SEC looks at that and say, well, are you really AliBaba? And the Chinese

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authority says, hey, you know, are you trying to, you know, sort of just take profits out of China

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without paying taxes? Are you trying to, you know, break sort of the FOREX control and the

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foreign ownership control requirements? So, the ADR structure, or they call the VIE structure that's

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used for the ADRs has come under a lot of attack. And again, this is sort of a very subtle issue

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that most investors who bought a share in AliBaba have never been warned about. Right? And I think

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it's got glossed over in the prospectus. I think it's important to point out. And we actually

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used to own shares in AliBaba. And it was one of our concerns, that legal structure, the risk of

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that legal structure is something I think that investors should familiarize themselves with.

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Now, I know the Chinese market over the past three years, including this year hasn't been the best

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place to be. My great uncle, John Templeton was known for saying, bull markets are born on pessimism,

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grow on skepticism, mature on optimism, and die on euphoria. The time of maximum pessimism is the

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best time to buy. Is it the point of maximum pessimism in China right now? Absolutely. I mean,

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I'm a big fan of, you know, Sir Templeton. And I try to imitate how you invest, right? So

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it's maximal fear, maximal pessimism when it comes to China. And it's not just

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sort of Western portfolio managers, you know, Western asset owners being pessimistic about China.

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I was just in China for about two months during the summer. And I traveled to different cities,

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spoken with people, business owners. And I would say, domestic sentiment is extremely pessimistic as

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well. And it's not like people are not spending, people are not investing, not because they have

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a balance sheet problem. A lot of people go, you know, the real estate price decline has caused a

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balance sheet crisis and people have no money to spend and then, you know, no money invests. That's

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not at all true, right? There's massive amount of savings. Its about probably 30 trillion

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US dollars  worth of deposits in kind of the banking system or the shadow banking system. And there's

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another 30 trillion in sort of, corporate deposits. And that money is just not going into investing,

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CAPEX, household spending, because people are pessimistic. They're really, really down. But if you

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think about that, its, you know, if there's a catalyst, that's just a lot of money waiting to be deployed.

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And so, I'm a big believer, you know, maximal fears where you can earn or not just the equity

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risk premium, but really a lot of fear premium, right? People are afraid not because there's sort of,

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you know, truly pessimistic future, but just in a short run. You know, I think the emotion's gotten

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the better of them. Yeah, you know, when we, Lauren mentioned, we owned AliBaba for a while at

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our firm. I think we bought it in late 2014, 15, when it IPOed, it went down 50 some odd

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percent and it became a value stock by our measure then. But you know when I kind of trace back

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that period to when we sold in 2020 sometime, a lot has changed in China, just the environment.

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And I'm wondering like you've spent time there and you've got the length of perspective to make

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these judgments. Do you think it's still glorious to get rich in China? Do you think something's kind

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of changed in the social fabric, the entrepreneurial spirit. Are there things that, you know,

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would kind of prey upon a pessimist fear, so to speak? Or are they over exaggerated in the share

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prices? What's your perspective? So, I think there's absolutely a shift in policy and then

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China's so policy driven. And I think it's got a real impact too. I would say the private sector,

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the entrepreneurs and then kind of the skilled laborers. But I would say, and this is again where I think,

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you know, Sir John Templeton's wisdom is absolutely critical in that. Sure, there's sort of shifts

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in the fundamentals and that may be hard to predict, and you don't know whether China will

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zap back or if it'll continue to trend downward. But what I think is clear is the current sentiment.

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Prices in such negativity, it prices in the possibility and a probably a meaningful possibility that China

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returns to the mild cultural revolution type, you know, closed economy that doesn't have any competitive

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free enterprises. Prices in a complete decoupling of China from the US that the two greatest economic

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powers like, stop trading with each other, right? These things are being priced, their prices in a

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invasion of Taiwan that leads to potentially, you know, outright conflict between China and the

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rest of the world. And then again, those are just such unlikely extreme left-tailed Black Swan events

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and then all three Black swans are being priced in. So, you know, prices simply reflect sort of irrational

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fear. So, I agree with you that China has changed and it's useful for people to pay attention to

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that change and to track it. But I would say what's been hyped up in media headlines is probably

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driving more fear and it is sort of sensible analysis. Yeah, I think that's an important nuance

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that comes through in valuations that, you know, skilled investors can see. It's like, yeah, we've

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seen all the headlines. We know what's happened and, you know, the Chinese education stocks going to

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zero overnight and we know about, you know, the tech dust up and, you know, we know about the property

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bubble and we know about all these other things that are keeping Western investors away. But you know

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that when you get more granular and you get down into the economy, the wealth is still being created.

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And that means that there's an opportunity for a shrewd investor. So I'm wondering when you are

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focused on the A-shares and you're looking for those opportunities. What do you look for in

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a particular stock? What stands out is a sign of potential excess return or alpha generation to you.

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So, you know, basically we look at a number of factors, you know, quant signals that we sort of,

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we want to make sure all the boxes are ticked before we take a position. So first of all, you know,

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we look for sort of a blended set of quality scores, right? So, we like firms that have, you know,

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strong growth based on healthy margin. And there are a lot of unhealthy growth in China that's

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driven by leverage and those stocks we shy away from. They're just too exposed to, you know, the

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whims of Beijing in terms of injecting or taking away liquidity. So, we really focus on firms that

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are really high quality and have exhibited sustained quality growth. Clearly, you know, we only like them

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when they are cheap. All right. So when there's a lot of fear, that's not really associated with

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the industry or the business, but there's just an overall broad fear. So, they're ignored and unloved.

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So, you know, valuation is really important for us. And then recognizing that, look, you can buy

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a value stock and the value stock could stay depressed or get even more value for a long period of

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time. You want to blend that in with, you know, some sentiment scoring so that a catalyst is either

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happening, brewing, or has already happened. And that kind of the mean reversion that the values

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finally going to be discovered by market. So, these are kind of the really key statistics we look at.

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One of the follow up questions I have in mind is you, I think I've seen in either a previous

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podcast or maybe somebody you're writing, and you talk about the importance of localized knowledge

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in China. Now, I fully agree with that. And I think that applies to a lot of emerging markets.

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Can you give us a sense of how that adds to your process or maybe another way to frame this

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question is at what point, within the quantimental framework does quant shut off and something more

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Judgment-fundamental come in from the process standpoint? Absolutely. Especially, you know, we're now

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adding a meaningful ESG component to how we invest in China. And it's not ESG for the sake of,

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you know, the ESG fad. It's really recognizing that ESG is a tremendous sort of value driver in the

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in selecting Chinese types of governance. If you get the governance wrong in China, it could mean

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you own state-owned enterprise that doesn't really care about the shareholders. And there's some really

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high quality state-owned enterprises that act as a very responsible large shareholder. And there are

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regional ones that do a bad job and you really got to understand that. So, governance is super important.

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You know, obviously, governance when it comes to you, do you, are dealing with a firm that has

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a strong management team and proper checks and balance where you're dealing with a family held

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business where the family may have a lot of connected businesses that siphon off resources from

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the main company that's gone listing. So, governance is huge for us. And oftentimes, as a governance

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research, it's much more sort of a fundamental active manager who does like a really deep dive into

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the company and to the management. Right. So, we can use the quant process to highlight what to look at.

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We really need the humans to look deeper to governance element. And around social or around

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environmental, you know, China has, you know, Beijing has been fairly active when it comes to

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identifying social policies, environmental policies. They, you know, announced that in their sort of,

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you know, five-year plans. And again, because the change with every regime, every administration,

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you can't use historical data, right. Historically, like this initiative is important. All of it,

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changes after the government has made some progress or attention shifts. And so again, we need

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an analyst who really looks at what are top of mind for Beijing when it comes to social policies.

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Right. What is education, right. What are its housing prices. What is top of mind for them.

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You know, they've now committed to hitting carbon neutrality. So, we know just going to be a huge

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amount of subsidies for anything that's green related. And that again, you can't use quant.

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You can't use data. You've got to have an analyst on a ground.

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So, at your company, you employ both quantitative and then you put the fundamental overlay on it,

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where you're really looking into these issues like governance, etc. That makes so much sense.

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And your comments regarding state owned enterprises, because I think most people hear SOE and they're running

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the other way, it is really important to understand the difference in SOEs. Do you want to expand on that a

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bit? Yeah. So usually, I think investors are right when they say, "Yeah, SOEs is you don't want to touch

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them because they're not really even companies." Think of SOE is a bit of an equivalent to our DMVs and

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our post office. And that's generally true. If you look at what's broadly true in emerging markets,

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the big SOE's, they're very inefficient. And every few years there's this political graft and

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then corruption. And then those are not companies you want to be an owner of in a long run.

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But in China, again, you have some of the SOE's that you definitely don't want to be invested with.

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But there are some that are called central SOEs. Central SOEs are basically, it's kind of easy to identify them.

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They're basically companies whose names start with China. So, in China, you can't just go file for

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a trade market or a registered company that starts with China. That is only reserved for the most

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centrally connected state-owned enterprises. And because there's such a face of the party,

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if those companies do poorly, it means the party has been a bad steward of a national treasure.

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And so, in some ways, it's the A-teams that are assigned to be Chairman and CEO of the centrally

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connected state-owned enterprise. And the people who are running those, they understand

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every party member is watching them like a hawk. They make a mistake, they're gone, right? They're

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probably in prison. If they do a really good job, they get promoted into much more senior

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sort of party positions. And so, people are super careful, super responsible. So, in a way,

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this is like what we teach in MBA textbooks, right? You want a large shareholder who

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really monitors management and replace them whenever they misbehave. And this is sort of the classic

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textbook case of a responsible large shareholder, right? The party being a large shareholder of the

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state-owned enterprises. So generally, tend to be things we really love as value investors, right? They

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pay big dividends, right? They're great moat, right? Because they're usually national monopoly. So

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no one else can be in their business, right? And so, whenever they're cheap, right? They're great

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buys, right? Because you know, they're not going away. There's almost a government put, right? Anything

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happens and they make a mistake. Management is immediately fired. The government comes in and sort of

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bailed them out with just sort of unlimited loan supports and business supports. So, the state

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owned enterprises, essentially connect the state enterprise, actually outperform the average company

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in China. About two and a half percent per annum, consistent. Interesting. That is very interesting,

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data. Speaking of loans, do you have any comments on the banking industry in China?

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Yeah, so the banking industry in China, there are two things that's quite interesting about them.

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You know, so first of all, you know, they are generally all state-owned, right? So, all the major

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banks in China are state-owned. And so, everything I said about, centrally connected,

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state-owned enterprises apply to them. You know, they have enormous margin, pay massive dividends,

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and there's a government put, like, you know, the Chinese central bank will basically always

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supply them with unlimited liquidity and backstop any and everything else. So, there's a government

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put on there, making them less risky. And so, when they're cheap, they're great buys.

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But there's something that's quite interesting about these banks is, do they have bad debt? Absolutely.

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They have bad debt because, you know, they do tend to lend to other

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state-owned enterprises and some of the state-owned enterprises are inefficient, right? So, like the

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the DMV version of the Chinese state-owned enterprise, but their bad loan reserve completely overstate

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the problem. So, when you take a look at Chinese bank and you say, "Oh, they're all these bad loan

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reserve," right? Maybe they have even more bad loans than what they're reserving for.

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This is actually generally not true. So, what happens is, again, as a state-owned bank, right? And

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someone who's running it, you don't want to keep having, you know, record profits because the

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government would say, "Look, you know, bank is supposed to serve as an economy, right? If you're making

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record profits, you know, are you taking, you know, keeping too much of the returns?" So, a lot of these

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banks, you know, whatever they have, record years, they, they excessively, you know, stuff that into

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bad loan reserve. And this is just a way for them to perfectly smooth earnings and don't appear to

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be way too profitable. That is interesting. Well, with, I hate to, you know, just focus the conversation

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on China because you have a variety of products and people can go to your website, you have an

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emerging markets product, you have a developed markets product, and these are actively managed ETFs.

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And those are the products that US investors can access; I'm assuming. But tell me, you know,

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although China, the Chinese market is very interesting, I think there's some opportunities there.

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It is definitely towards the more pessimistic side of the market. Are there any other markets that

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capture some of the same characteristics as China? Like I'm thinking specifically of India,

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as far as market depth, inefficiencies, etc. What are your thoughts there?

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So, we definitely love India. So, a lot of people are talking, you know, might India be the next China.

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We're definitely a big believer that India might be the next China, but it doesn't become the next

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China by replacing China. You know, I think it becomes the next China in the sense that it's per

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capita GDP today is less than half of China, which means it's got a phenomenal headroom to grow.

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Right? Just like, you know, China said 13,000 and it's looking at, hey, you know, how does it get caught

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up to Taiwan and Korea about, you know, 35,000? India is looking at, oh, how can it continue to grow

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And emerge and then get caught up to China. And then ultimately with other of the, you know,

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Asian tigers. So I would say India right now, there's probably too much hype around it. So

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incorect hype, right? I think the long-term prospect and long term thesis is wonderful, but the short term

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hype below, you know, we're going to move all the iPhones, manufacturing, and semiconductor manufacturing

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and tech manufacturing from China and move it to India. Right? That's just unrealistic, right? That's

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sort of its people failing to understand just how long it takes to develop all the ecosystem for

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high precision, high value add tech manufacturing, right? It takes decades and then and then tens and

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hundreds of billions, right? We're not just talking about factories; we're talking about all the

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underlying infrastructure roads, ports, you know, cheap low-cost steady electricity and all that. And then

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you know, a labor force that is specialized for manufacturing, you know, clean-room manufacturing.

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You know, India has been successful on its own path, right? It's done a lot of good work in

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call center outsourcing, in software development outsourcing, things that China doesn't do. And if

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China ever wants to do that, it would struggle for decades and trying to figure it out. And the same

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thing, right? So, India is on a path and it's sort of emerging along its own path of excellence and

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comparative advantage. And so I think that is, you know, ultimately where India will be

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successful, right? It's going to deepen its specialization, not, you know, all of a sudden India is

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going to be a manufacturing hub for the for the world. Can you speak specifically about your emerging

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markets product and other markets that may have the depth to access like what markets are you focused

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on in that emerging markets product? And what else are you seeing? What other markets are interesting?

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Yeah, so we love emerging markets again right now. Emerging market is cheap because it's had 10 years

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of underperformance versus the US, right? But of course, people forget, right? From the 90s to for

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sort of two that from 2000 to 2010, emerging markets outperform the US handily, right? And so, they

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all go through cycles and then the starting point like the valuation you pay at the starting point

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matters a lot, right? EM is a lot cheaper than the US today and that sort of sets it up for at least

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wealth of outperformance. But when we look at EM, there are really two parts to EM. One part is really

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the Asia growth start, right? It's about, you know, a very skilled workforce and I can work grueling

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long hours that attract or foreign capital, the outsource, you know, manufacturing,

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outsource, you know, development, duties, growth economies and, you know, they'll export, earn FOREX,

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build domestic consumption, right? That's kind of Asian growth story and that we love that about EM

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and we're very careful in fighting, well, who is the next China, right? So there was initially,

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you know, Taiwan and Korea and China now we're thinking, you know, it's sort of happening in

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Indonesia, in Vietnam, there's India. So that we like and we're constantly looking for kind of the

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next one that's going to really emerge and transfer all of value to their listed companies and so

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we as investors can participate. There's the other part, parts that we tend not to like historically

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we're starting to like them now, which are the resource-based economies in EM, as you talk about,

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you know, parts of Middle East, you talk about Latin America, Brazil in particular.

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So those economies historically, I don't like them because they're said, you know, they're

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almost like the curse of an endowment, right? They have so much endowment that they don't have to develop,

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right? They just say, hey, you know, US big companies come in here and extract our resources, pay us,

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you know, our fair share and you can take everything else. So, they under-invest in infrastructure and

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education and growth and so on and so forth. And as a result, they simply are subject to these

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commodities, boom, boom-bust cycles and then really no sustained trend growth. And so, you know,

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you look at Latin America and then, you know, that story checks out, right? It's just no sustained growth.

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Their per-capita GDP just don't ever increase at anything like what we see for kind of EM Asia.

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But I'm starting to like them today because in a funny sort of way, right? Because of China,

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because China is now competing with the West for resource, right? And because of the second

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comer, the way it builds relationship like any second mover is it has to go in and offer a much better

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value proposition, pay a much better price. So, if I am a resource rich economy and I used to only

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depend on the US and take whatever terms I can get, now I got another bidder, right? A high bidder

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00:37:16,640 --> 00:37:21,040
who's saying, I'll come in and build you ports and roads and all that, you know, could I get some

364
00:37:21,040 --> 00:37:28,080
mineral extraction, right? Could I get in on some of this? And so, I think that's likely to accelerate

365
00:37:28,080 --> 00:37:33,120
both the development for these economies which never really invests in much of infrastructure.

366
00:37:33,120 --> 00:37:38,640
And also, they're going to get getting better terms of trades. And that of course is never a bad thing

367
00:37:38,640 --> 00:37:45,760
if you're a seller of scarce commodities. Yeah, it's a really provocative idea, especially because

368
00:37:45,760 --> 00:37:52,880
I think most Western investors kind of point out emerging markets and low PEs and

369
00:37:52,880 --> 00:37:58,640
everyone gets all excited about that. And then they think, well, let's look through the stocks and

370
00:37:58,640 --> 00:38:06,240
its basic materials and banks and a lot of capital-intensive industries that go boom and bust.

371
00:38:06,240 --> 00:38:11,920
And they kind of get turned off by that is they know that the business cycle there is similar

372
00:38:11,920 --> 00:38:18,880
tied to commodities. But one of the things that I know just looking at your EM portfolio is you

373
00:38:18,880 --> 00:38:23,520
done a really good job of differentiating. I see some of the resource-based names that you're

374
00:38:23,520 --> 00:38:30,560
referring to, but you've also unearthed some more atypical names. What are you zeroing in on,

375
00:38:30,560 --> 00:38:36,320
you know, here in terms of alpha signals? And what do you see to get that kind of differentiated

376
00:38:36,320 --> 00:38:44,640
approach? Because I think it's atypical and very fascinating. Yeah, so when we look at kind of our

377
00:38:44,640 --> 00:38:50,640
picks in the end, we tend to shy away from things that are kind of household names.

378
00:38:50,640 --> 00:38:56,720
You know, the mega-cap where all the, you know, investment banks and then sales analysts are covering

379
00:38:56,720 --> 00:39:02,960
because you're not going to have as much in terms of edge if you're simply buying names that everyone

380
00:39:02,960 --> 00:39:08,560
else is buying that, you know, every analyst is sort of covering. All right, so again, it's not

381
00:39:08,560 --> 00:39:14,880
that we don't love AliBaba and Tencent, those are wonderful companies. They're big weights in an EM portfolio

382
00:39:14,880 --> 00:39:21,280
because they're familiar comfortable names. And I just don't think you need an active manager like

383
00:39:21,280 --> 00:39:25,440
Myself to help you buy a share of BABA. If you got a view, you can go buy BABA by yourself, right?

384
00:39:25,440 --> 00:39:31,920
We're thinking about, well, how do we get you access to a company in Vietnam, right? Who's, you know,

385
00:39:31,920 --> 00:39:38,640
kind of the, you know, top partner for Foxconn, who's really, you know, moving up the skill curve in

386
00:39:38,640 --> 00:39:44,400
terms of making smartphones and high-end electronics. We're going to get you access to, you know, someone

387
00:39:44,400 --> 00:39:50,320
who's, you know, doing e-commerce in Brazil. So instead of thinking Brazil, it's just Petrobras,

388
00:39:50,320 --> 00:39:55,600
we're going to get you access to someone who's trying to create the next Amazon in Brazil that you've

389
00:39:55,600 --> 00:40:01,040
not heard of. We're trying to get you access to guy who's selling water in China. So, if someone who's

390
00:40:01,040 --> 00:40:06,880
selling semiconductor in China. So that's, that's how we differentiate ourselves. And I think

391
00:40:06,880 --> 00:40:11,600
that's also where the opportunities are. Yeah. And so, from a quantimental standpoint,

392
00:40:11,600 --> 00:40:17,040
there's your process for you into looking at those names, I guess. Does it create the discipline that

393
00:40:17,040 --> 00:40:21,280
creates the virtue of making these, you know, investments that are a little bit off the beaten path?

394
00:40:21,280 --> 00:40:26,480
And then as a follow up question, what are the challenges from getting that localized research that

395
00:40:26,480 --> 00:40:31,680
we discussed earlier when you traverse in a different country like Brazil or Latin America or

396
00:40:31,680 --> 00:40:41,520
Vietnam or wherever? Yeah. So, the benefit of being a data driven quant shop is that it's a lot easier

397
00:40:42,160 --> 00:40:50,960
to go find data vendors and buy data operating from, you know, US and operating from kind of our hubs in

398
00:40:50,960 --> 00:40:58,480
Asia. Then having to sort of travel to Brazil and standing outside a factory and try to get a meeting

399
00:40:58,480 --> 00:41:04,880
with the CFO or count trucks coming out of the factory. And so, as a quant manager, we can get

400
00:41:04,880 --> 00:41:12,160
really deep and get to a lot of data without, you know, a lot of challenges that a traditional manager

401
00:41:12,160 --> 00:41:17,600
might have. And so, we can cover so many more companies much more efficiently and effectively.

402
00:41:17,600 --> 00:41:21,840
Now again, we lack the same depth as someone who could actually go stand outside a factory and try to

403
00:41:21,840 --> 00:41:28,400
get a meeting with the CFO and talk to factory workers. But the breadth, I think, you know, makes up for it

404
00:41:28,400 --> 00:41:34,240
and also, our ability to immediately get access to information is just much higher. And so, you know,

405
00:41:34,240 --> 00:41:41,360
we tend to have a more diversified portfolio and the ability to get sort of into the less million

406
00:41:41,360 --> 00:41:46,800
names in harder to visit places. We have data on them and then we can use sort of, data to triangulate

407
00:41:46,800 --> 00:41:53,120
you know, what's happening. And then this is where once we just pre-screened that, we can bring in

408
00:41:53,120 --> 00:41:57,600
sort of more fundamental analysis and say, "Oh, what are we missing?" Right? You know when you're

409
00:41:57,600 --> 00:42:02,240
looking at this company and no one's really heard of, and this is what data tells us, you know, is there

410
00:42:02,240 --> 00:42:08,880
something that we're missing and then the fundamental manager can then work off a much more select list

411
00:42:08,880 --> 00:42:15,280
to get concentrated? Yeah. And I would imagine there are fewer institutional managers competing with you

412
00:42:15,280 --> 00:42:20,880
for those. That's right. Simply because emerging markets have done horribly for 10 years, which makes

413
00:42:20,880 --> 00:42:25,600
them a great place to look for value, but also, they're going to be a little bit smaller and more thinly

414
00:42:25,600 --> 00:42:34,560
traded. So, it makes a ton of sense. Well, that's, these are, it's exciting to learn all of this about

415
00:42:34,560 --> 00:42:40,560
the different products you have to offer. I know for developed markets; do you still feel like the

416
00:42:40,560 --> 00:42:46,720
one-dimensional overlay or the quantimental approach to investing? Is there enough?

417
00:42:46,720 --> 00:42:53,520
Is there enough inefficiency in developed markets to exploit anything there?

418
00:42:54,800 --> 00:43:02,160
Now let's say inefficiencies in developed markets tend to be, you know, episodical. So, US has become

419
00:43:02,160 --> 00:43:12,640
more inefficient by comparison to itself. As I was mentioning, historically the US retail participation

420
00:43:12,640 --> 00:43:20,640
is sub-5 percent. Since COVID, it's jumped to 30 percent. And then that's very exciting for us

421
00:43:20,640 --> 00:43:30,400
because retail flow tends to be a supplier of alpha to discipline managers. Not immediately,

422
00:43:30,400 --> 00:43:36,240
because sometimes retail flow can create bubbles, right? We all saw what GameStop did to a few

423
00:43:36,240 --> 00:43:41,600
professional hedge fund managers. So, we know you can be on the other side of retail and then get

424
00:43:41,600 --> 00:43:49,360
hurt. You're not careful. But ultimately, retail never win in the long run. You know, there's,

425
00:43:50,080 --> 00:43:54,080
they're just trading way too much, way too aggressively, on too little information, get too

426
00:43:54,080 --> 00:44:02,000
leveraged to concentrate it. So, I would say episodically, US can become inefficient. And those are

427
00:44:02,000 --> 00:44:08,160
opportunities where I think a disciplined, quantum manager, a fundamental manager could have an edge.

428
00:44:08,160 --> 00:44:12,960
And so, we still like applying that process, as long as the process, we use machine learning.

429
00:44:12,960 --> 00:44:18,800
So, the machine is sort of looking at, is this an environment for us to express our view aggressively.

430
00:44:18,800 --> 00:44:24,880
And when they're very little retail, and it's just all, you know, experienced high frequency,

431
00:44:24,880 --> 00:44:30,320
experienced quantum manager, experienced stockpickers, we're going to be very benchmark-like and just

432
00:44:30,320 --> 00:44:35,600
participate in what the market will naturally bring for us. But you know, when they're sort of,

433
00:44:35,600 --> 00:44:40,960
you know, largest agreement between retail and the professionals and their, you know,

434
00:44:40,960 --> 00:44:46,560
unreasonable valuation levels will tend to take on larger bets. In many cases, we'll get a lot

435
00:44:46,560 --> 00:44:52,960
more value tilted in our portfolio. We'll need to take some paying as a value investor, hoping to avoid

436
00:44:52,960 --> 00:45:02,160
kind of a, you know, bubble bursting in the market. Sure. And I'm assuming your products are long

437
00:45:02,160 --> 00:45:10,800
only. Would your fundamental process work on the short side? I'm sure, you know, if someone's

438
00:45:11,440 --> 00:45:19,360
happy to come in and do the shorting for our portfolio or license our signal, do a shorting,

439
00:45:19,360 --> 00:45:24,400
that it'll do fairly well. Now, I'm a long-term investor, and so, you know, I tend to, you know,

440
00:45:24,400 --> 00:45:30,400
think more along the line of a Warren Buffet, right? You want to sort of compound and let the market help

441
00:45:30,400 --> 00:45:36,320
you out and you can add alpha, it's great. You compound on top of that. But, you know, shorting against

442
00:45:37,360 --> 00:45:44,320
a generally rising market, even if it's a less attractive stock, you know, that's, you know,

443
00:45:44,320 --> 00:45:47,600
that is not a recipe for a long-term compounding.

444
00:45:47,600 --> 00:45:54,800
Right. Well, this is a really silly question, but it will highlight how non-tech I am as a person,

445
00:45:54,800 --> 00:46:02,560
but with AI and machine learning, I mean, I'm sure people are just applying, quant 

446
00:46:03,200 --> 00:46:09,840
types of investing more often. I mean, Scott and I have quant screens that we run before we do

447
00:46:09,840 --> 00:46:18,480
analysis on a company, probably not nearly as robust as what you do, but do you worry that

448
00:46:18,480 --> 00:46:24,240
with the proliferation of machine learning and more AI and people having access to that,

449
00:46:24,240 --> 00:46:30,400
that it'll take away the edge of what you're doing or, and also, how are you applying that at your,

450
00:46:30,400 --> 00:46:39,280
at your business? Yeah, so we definitely have adopted machine learning very early on. We recognize that,

451
00:46:39,280 --> 00:46:46,400
you know, the idea outcome is, of course, you know, always use the most advanced technology

452
00:46:46,400 --> 00:46:52,000
to help the human make good decisions. Right. So, we're always very cognizant that this is not about,

453
00:46:52,000 --> 00:46:56,160
oh, the human's not good enough, so we let the machine make all the decisions. This is about,

454
00:46:56,160 --> 00:47:01,520
there are a lot of things that humans sort of don't want to do anymore, or we just want to,

455
00:47:01,520 --> 00:47:06,560
don't want to do it 8,000 times a day. And so, we get a very smart machine to do it fast and do it

456
00:47:06,560 --> 00:47:13,360
well. And so that's really how we think about machine learning, AI. Those are tools to make our

457
00:47:13,360 --> 00:47:18,400
analysts and our portfolio managers just more effective, more successful. So, they focus on

458
00:47:18,400 --> 00:47:23,520
whether it's a real edge for the human. It's not about, oh, you know, the human is no good, so,

459
00:47:23,520 --> 00:47:28,320
you know, let the machine take over, right. There's not a man versus machine competition. It's

460
00:47:28,320 --> 00:47:36,640
really a partnership. That's how we think about it. Now, I would say, what we realize is, you know,

461
00:47:36,640 --> 00:47:41,520
if you're really going to get value out of using AI, using machine learning, you've got to understand

462
00:47:41,520 --> 00:47:49,040
what does it not do? A lot of people sort of glorify what machine learning can do. And then,

463
00:47:49,040 --> 00:47:54,000
and I think, you know, there's, there's a lot of myth out there, right. We, at least today don't have

464
00:47:54,000 --> 00:48:00,640
machines that can think for themselves, right. So, we don't have machines that can outthink an experienced

465
00:48:00,640 --> 00:48:06,080
portfolio manager, right. We have a machine where if you teach them enough formulas from a, you know,

466
00:48:06,080 --> 00:48:12,000
it's fun to see if they're curriculum from my MBA textbook, you sort of feed them enough, sort of

467
00:48:12,000 --> 00:48:16,800
patterns that everyone knows. It'll repeat those, right. It's very good at sort of repeating what

468
00:48:16,800 --> 00:48:24,560
is known, but it isn't today, smart enough to go out, you know, there are many things that don't actually,

469
00:48:24,560 --> 00:48:30,560
you know, that's, you know, that's not actually sort of taught in the books and they can figure it out,

470
00:48:30,560 --> 00:48:34,560
or there were many things we taught in the book that don't work anymore, right. And some machines -

471
00:48:34,560 --> 00:48:39,120
it s not quite there yet. So, you don't want to just trust it and then trust the black box,

472
00:48:39,120 --> 00:48:43,440
because you know, look, machines look at patterns and we know a lot of patterns are in the past,

473
00:48:43,440 --> 00:48:47,760
right. They're not going to repeat, right. Times have changed. People have changed. Regulations have changed.

474
00:48:47,760 --> 00:48:52,640
Markets become more efficient. So, the machine sort of, just look at data without understanding

475
00:48:52,640 --> 00:48:57,040
general equilibrium and market efficiency, they'll make a lot of mistakes.

476
00:48:57,040 --> 00:49:04,640
Accounting changes too. So, it's not that they're new accounting standards every year. So that's

477
00:49:04,640 --> 00:49:09,920
something to keep in mind. My last question for you today, Jason, is just how investors should

478
00:49:09,920 --> 00:49:17,360
think about currency exposure and do you hedge any currency risk in your portfolios or how do you

479
00:49:17,360 --> 00:49:26,240
think about that? So, I would say generally for developed markets, we tend to advocate hedging,

480
00:49:26,240 --> 00:49:32,160
because in developed markets, look, you know, the currency sort of just mostly fluctuates. There's no

481
00:49:32,160 --> 00:49:37,600
reason to believe any particular currency should strengthen versus any other currency over time.

482
00:49:38,480 --> 00:49:43,120
And if you don't hedge it, it just drives more volatility and especially for people who,

483
00:49:43,120 --> 00:49:49,680
your American investor, you're investing overseas, but you consume in the US dollars. So, you really

484
00:49:49,680 --> 00:49:53,840
don't need to take US dollar risk. But when it comes to emerging markets, we're a big proponent

485
00:49:53,840 --> 00:49:56,960
for not hedging. And the reason is, I think there's a famous saying that says,

486
00:49:56,960 --> 00:50:03,440
Diversification means you want to buy things that are short term, negative correlated,

487
00:50:04,160 --> 00:50:10,400
but long term, you know, both of these assets should have a positive expected return. So, if you think

488
00:50:10,400 --> 00:50:15,360
about emerging market currencies and emerging market stock market, they're negative correlated,

489
00:50:15,360 --> 00:50:20,800
because a lot of the emerging markets are export-oriented. What is resource or its high value add technology.

490
00:50:20,800 --> 00:50:27,680
If the currency is weak, export goes up and it's generally very good for the export sector,

491
00:50:27,680 --> 00:50:33,200
which dominates their stock market. So short term, negative correlation, but oh, long horizon,

492
00:50:33,920 --> 00:50:37,280
emerging market currency tend to strengthen. You look at Taiwan and look at South Korea.

493
00:50:37,280 --> 00:50:41,840
You look of course, Japan when it was a emerging economy, now it's come to a developed,

494
00:50:41,840 --> 00:50:48,400
you look at renminbi, as you export, right? And earn a lot of FOREX, you have currency strengthens.

495
00:50:48,400 --> 00:50:53,760
And that's just how economics work. That's our currency work. So, you definitely want to hold on to

496
00:50:53,760 --> 00:51:00,800
EM currencies and EM stocks because short term, negative correlation, but there's a thesis for

497
00:51:00,800 --> 00:51:08,880
positive returns for both over time. I see. Well, thank you so much. Scott, do you have any final questions?

498
00:51:08,880 --> 00:51:15,360
I do. It's actually the trickiest question because it's no right answer, but let's just talk about

499
00:51:15,360 --> 00:51:23,440
your views on sell discipline in terms of portfolio turnover, like what drives you to leave a stock?

500
00:51:23,440 --> 00:51:28,400
Is it finding a better one? Is it, the thesis has changed? How does the quantimental

501
00:51:29,040 --> 00:51:35,600
factor get involved. Just any thoughts there? Yeah, particularly in EM investing,

502
00:51:35,600 --> 00:51:40,320
the sell discipline can oftentimes be far more important than the buy this one because

503
00:51:40,320 --> 00:51:48,080
in EM, right? It's not like you've got a great company and you hold on to it forever because

504
00:51:48,080 --> 00:51:54,480
oftentimes a great company, once you go discover it, the bubble forms and the price goes completely

505
00:51:54,480 --> 00:51:59,600
unreasonable. And so, you've earned your alpha from picking the right stock and then you can earn

506
00:51:59,600 --> 00:52:04,880
additional alpha from other people going crazy about it. And that means you definitely want to take

507
00:52:04,880 --> 00:52:11,600
profit and sell and maybe even get to a underweight or outright remove the stock completely from

508
00:52:11,600 --> 00:52:18,560
the portfolio. And that's just because emerging markets, the volatility, the excess volatility is

509
00:52:18,560 --> 00:52:24,720
far larger than the true underlying fundamental movement and you've got to, you know, you've got

510
00:52:24,720 --> 00:52:30,160
at some point, you know, take your gain even if there are taxes involved and sell all the stock,

511
00:52:30,160 --> 00:52:34,560
a company you love simply because the price has become unreasonable. So, it's absolutely critical

512
00:52:34,560 --> 00:52:41,120
in the EM. Awesome. Well, thank you so much for addressing these questions

513
00:52:41,120 --> 00:52:47,440
to us today. Jason, is there anything else that you want investors to know about you or understand

514
00:52:47,440 --> 00:52:57,520
about your strategy? Well, I would say, you know, we, are quants that over time have a lot of humble

515
00:52:57,520 --> 00:53:03,120
pies and evolve to become quantimental investors by recognizing that sure, we have a lot of

516
00:53:03,120 --> 00:53:08,560
advantage over traditional stock pickers because we can take the emotions out of the process by

517
00:53:08,560 --> 00:53:14,320
introducing more discipline through data and machine. We can cover more stocks. Just again,

518
00:53:14,320 --> 00:53:20,880
machines are good at breadth, but we also realize that there's a lot of the art in investing,

519
00:53:20,880 --> 00:53:24,960
right? And there's a lot of this has not happened before in this time. It is truly different.

520
00:53:24,960 --> 00:53:31,760
That only a stock picker, right? Who's on the ground reading tea leaves and doing the art part

521
00:53:31,760 --> 00:53:36,320
of investing. So, with deep respect for that and always looking to incorporate that either learning

522
00:53:36,320 --> 00:53:41,920
from them and trying to, you know, model it, or bring them on board to help with the process.

523
00:53:42,640 --> 00:53:47,360
And that same humility is where we also recognize that we could do a lot of right things investing in

524
00:53:47,360 --> 00:53:53,840
our right stocks. And in a short run, the market will disagree with us, right? It's not that we are

525
00:53:53,840 --> 00:53:59,920
wrong, but it is that you can be right and still lose a lot of money over a short period of time.

526
00:53:59,920 --> 00:54:06,000
So, you know, never take that too personally and then definitely never, you know,

527
00:54:06,000 --> 00:54:10,480
leverage out of hubris because you think you're right and the market's wrong, right? The market

528
00:54:10,480 --> 00:54:14,800
doesn't really care about that. Yes, it can take you out of the game permanently.

529
00:54:14,800 --> 00:54:22,960
Leverage can. Well, we certainly appreciate your time today. Please tell us where our listeners can go

530
00:54:22,960 --> 00:54:30,480
to read more of your research. You're very well published and to learn more about your investment

531
00:54:30,480 --> 00:54:40,000
opportunities. Well, Lauren Scott, thank you for giving me a chance to expand my social media

532
00:54:40,000 --> 00:54:46,240
reach. Everyone, please do follow me on LinkedIn. That's the primary social media platform I use.

533
00:54:46,240 --> 00:54:52,080
I put out a newsletter called the bridge, generally trying to bridge, you know,

534
00:54:52,080 --> 00:54:56,880
the development market with the emerging markets helping you understand what's happening in,

535
00:54:56,880 --> 00:55:04,560
you know, emerging Asia, especially. You can always sort of Google me and find many of the papers.

536
00:55:04,560 --> 00:55:11,280
They're more math in nature, that's out there in the public domain. And if you find something

537
00:55:11,280 --> 00:55:16,320
interesting that you want to talk about, email me or LinkedIn mail me and I'm generally

538
00:55:16,320 --> 00:55:21,440
pretty good at responding. Awesome. Well, thank you so much, Jason. This has been a pleasure.

539
00:55:21,440 --> 00:55:24,560
Thank you, Jason. Thanks, Scott. Thanks, Lauren.