Speaker 1: M This is Masters in Business with Very Renaults on
Speaker 1: Bluebird Radio. My special guest this week is Hornda Silva.
Speaker 1: He is a fascinating quant a pioneer in low vall
Speaker 1: and factor based investing. He runs the Analytic Investors Group
Speaker 1: at Wells Fargo Asset Management. His team runs over twenty
Speaker 1: billion dollars in an assortment of quantitative strategies and really
Speaker 1: just a fascinating guy. UM who has spent a lot
Speaker 1: of time studying factors, studying quantitative investing, thinking about what
Speaker 1: does and doesn't work and when and why. I find
Speaker 1: a lot of quants tend to be UM a little
Speaker 1: more singularly focused, and he's a pretty broad based, holistic
Speaker 1: sort of guy. He He's quite a fascinating background and
Speaker 1: really interesting set of hobbies. UM. So, with no further
Speaker 1: ado my conversation with Wells Fargoes horind Da Silva. This
Speaker 1: is Mesters in Business with Very Reholts on Bloomberg Radio.
Speaker 1: My special guest this week is Horirenda Silva. He is
Speaker 1: a leader and portfolio manager at the Analytic Investors Group
Speaker 1: of Wells Fargo Asset Management. His team runs a variety
Speaker 1: of quantitative strategies with assets of over twenty billion dollars.
Speaker 1: Harin is known as a pioneer in both low volatility
Speaker 1: and factor based investing. He has won numerous awards, including
Speaker 1: multiple CFA Institute, Graham and Dot Awards, as well as
Speaker 1: multiple Bernstein Fabosi Awards from institutional investor Harendra da Silva.
Speaker 1: Welcome to Bloomberg. Thanks to having me on, Barry. I
Speaker 1: had to chat with you. Yep. I've been looking forward
Speaker 1: to talking to you because you have such an interesting
Speaker 1: background and your career has taken so many interesting paths.
Speaker 1: Tell us about your life a bit. How did you
Speaker 1: get into the financial services business? How do you go
Speaker 1: from Sri Lanka to UC Irvine. Well, apart from watching
Speaker 1: that episode of The Graduate as a kid, we had
Speaker 1: the advice to the guy who was go West young
Speaker 1: man um that was kind of the original inspiration. But
Speaker 1: I really you know, growing up in Sri Lanka, it
Speaker 1: was traditional for people to go to school or the
Speaker 1: university in the UK. And so I studied as an
Speaker 1: engineer undergrad and had the misfortune to graduate which was
Speaker 1: the height of a recession. And uh, you know, at
Speaker 1: the time, I had two choices. I could go onto
Speaker 1: graduate school. Why could go back to Sri Lanka, and
Speaker 1: you know, Sri Lanka was in the middle of a
Speaker 1: civil war at the time, so the choice was not difficult,
Speaker 1: and I made the decision to go to graduate school
Speaker 1: at the University of Rochester to study finance. And I
Speaker 1: was interested in finance because I saw a lot of
Speaker 1: similarities between finance and engineering, which was, you know, the
Speaker 1: ability to kind of design products, design strategies, and the
Speaker 1: idea that you could build something and actually put it
Speaker 1: to the test really appealed to me. So that's why
Speaker 1: I initially went to the University of Rochester to study
Speaker 1: finance and study finance for several years and then started
Speaker 1: working for a consulting firm called Analysis Group, and I
Speaker 1: was had by another stroke of good fortune and got
Speaker 1: assigned to a project for Merrill Lynch in the mid
Speaker 1: eighties where we were taking managers to go into the
Speaker 1: Marylands Consults program and if you remember remember that program,
Speaker 1: but it was one of the first rap programs. And
Speaker 1: so I was twenty five years old and I got
Speaker 1: to visit something like money managers in person. So you
Speaker 1: go to the money manager, you'd listen to their story
Speaker 1: and you try to capture in a quantitative way what
Speaker 1: they were doing. And that's when I first got really
Speaker 1: kind of interested in factor investing because I realized that,
Speaker 1: you know, you go and talk to, for example, a
Speaker 1: bunch of growth managers, and talking to them, you realized
Speaker 1: that all the ones that were doing well at that
Speaker 1: particular time well focused on a particular factors, and not
Speaker 1: only were the growth focused then maybe for example, focused
Speaker 1: on earning the acceleration, and those were the guys who
Speaker 1: are doing well at that time. And then you'd go
Speaker 1: talk to the value managers, for example, and you realize, well,
Speaker 1: the only ones that are doing really well are the
Speaker 1: ones who are f for example, divident deals. And that's
Speaker 1: when I realized, wow, you know, the factors actually explained
Speaker 1: a lot, and I kind of asked myself the question,
Speaker 1: which obviously wasn't related to the project I was working on.
Speaker 1: Is it the manager or is it the factor. I'm
Speaker 1: hearing a parallel between what you described in your background
Speaker 1: with mechanical engineering and the ability to test that in
Speaker 1: the real world with factor investing and being able to
Speaker 1: quantify what's driving returns. Am I reading too much into that?
Speaker 1: Or is there some parallel there? No? I think there
Speaker 1: is a parallel. I think it's the same idea of
Speaker 1: kind of what makes this work. And you know it
Speaker 1: maybe even a biased view because in an engineer, you
Speaker 1: think there's always there's a way to there's a formula,
Speaker 1: or there's a way to quantify something. So obviously if
Speaker 1: I had a history background, that would not think are
Speaker 1: you thinking was a manager or the process as opposed
Speaker 1: to something they were doing that was focusing on the factor.
Speaker 1: So I think the engineering aspect really kind of affected
Speaker 1: the way I looked at the problem. Very very interesting.
Speaker 1: And some of the factors that you cover include not
Speaker 1: just the traditional factors um, but you spend a lot
Speaker 1: of time focusing on low volatility. Tell us a little
Speaker 1: bit why that factor is significant, what what are the
Speaker 1: common elements in the factors that that you find intriguing. Well,
Speaker 1: that factor, to me was probably the one that was
Speaker 1: I call it the most neglected factor because I was
Speaker 1: working on my PhD thesis in the early nineties when
Speaker 1: Farmer and French came out with this paper that basically said,
Speaker 1: if you want to describe what they what academics kind
Speaker 1: of called the cross section of returns, In other words,
Speaker 1: which stocks go up and which stocks to go. That
Speaker 1: value book to market and far Camp were very good
Speaker 1: at explaining it, and that data did a really poor job,
Speaker 1: and that high data stocks had the same return as
Speaker 1: low beata stocks. To me, everybody when they read the
Speaker 1: paper in nine two focused on, oh yeah, the value
Speaker 1: premium and a small cap premium. And what I found
Speaker 1: really curious was, Wow, you can build a portfolio of
Speaker 1: Lottle better stocks that's going to have a way better
Speaker 1: returned risk profile than a portfolio of high data stocks
Speaker 1: or beta one portfolio. And I was really fascinated by
Speaker 1: why a no one focused on it, and most people
Speaker 1: didn't think it was really that interesting. So what I call,
Speaker 1: you know, what's now referred to in our team as
Speaker 1: a low all anomaly is this idea that if you
Speaker 1: build a portfolio a little beata stocks, you get a
Speaker 1: much better shop ratio, a much better return risk ratio
Speaker 1: than the market portfolio. Translate that for the lady listener,
Speaker 1: what are loval what are low beta stocks? And how
Speaker 1: do what is the significance of the sharp ratio? Typically
Speaker 1: a little bit of stock is what we mean by
Speaker 1: a little bit of stock is a stock that doesn't
Speaker 1: move a lot with the market moves in the mark,
Speaker 1: it will go up less in the market. It will
Speaker 1: go down less in the morning, go down less in
Speaker 1: the market exactly. And what if you put them in
Speaker 1: a portfolio. The portfolio obviously, if you buy a portfolio
Speaker 1: of a little bit of stocks and be it as
Speaker 1: a point eight, they'll actually move less than the market.
Speaker 1: So portfolio will go up less in the market and
Speaker 1: go down lest in the market. But what it also
Speaker 1: means is that the because you're invested in equities, you
Speaker 1: get the equity of risk premium. So the long run
Speaker 1: return of equities with a lot less volatility so on
Speaker 1: a risk of the dropacious, it's better than traditional portfolios,
Speaker 1: right right. And what the Farmer, all the work the
Speaker 1: Farmer and French did showed was that low beta stocks
Speaker 1: have actually the same return over the very long run
Speaker 1: as HIGHBERDA stocks or better one stocks. And what's really
Speaker 1: fascinating is if you look at a little bit of stocks.
Speaker 1: You know, when you first tell people, they tell you, oh, yeah,
Speaker 1: a little bit of stock. Oh yeah, that's just that's Amazon. Sorry,
Speaker 1: that's that's electric utility, And you go no, no, no, no,
Speaker 1: that's not electric utilities. A little bit of stock is
Speaker 1: the stock that doesn't move with the market. So early
Speaker 1: on in its cycle, Amazon was indeed a little bit
Speaker 1: of stock because what was driving it was this idiot
Speaker 1: think credit returned related to doing business in a very
Speaker 1: unusual way. As the company evolved, it became increasingly a
Speaker 1: Beta one stock, and now it's a bet at one
Speaker 1: point to one point three stock because it's a very,
Speaker 1: very large cap company. It's the very large component of
Speaker 1: the economy, and it's no longer very geosyncratic Richard. So
Speaker 1: if you look at our portfolios very you find that
Speaker 1: companies like that are now very high data. For example,
Speaker 1: even Tesler was actually in a little bit of portfolio
Speaker 1: four years ago. It's no longer in a little bit
Speaker 1: of portfolio because it's megacap and it moves so much
Speaker 1: with the market. But when people think of a little
Speaker 1: bit of portfolios, you know, the other thing you see
Speaker 1: often in a little bit of portfolios that you've got
Speaker 1: to be very careful with is biotech companies, right, because
Speaker 1: the biotech company will often have two or three patterns,
Speaker 1: and it's going through a process for regulatory approval after
Speaker 1: the drug, so it's not really moving. The moving in
Speaker 1: the market at all, So stuff like that very often
Speaker 1: you see those in a little bit of portfolios. So
Speaker 1: it really is kind of it's kind of fascinating to
Speaker 1: to watch this and if you look at sectors, for example,
Speaker 1: spent more of my life studying this anomaly and anything else.
Speaker 1: But if you look in the sixties, energy was low BEATA.
Speaker 1: Nobody really cared about it. It was almost utility. Went
Speaker 1: through the oil crisis in the seventies, it became very
Speaker 1: high data because it was the fact it sort of
Speaker 1: started moving the market. Through the seventies and eighties, oil
Speaker 1: companies were very HIGHBATA, you know, when the market was
Speaker 1: dominated by the Seven Sisters. Then it became LOWBATA, and
Speaker 1: as we went through this last crisis with energy consumption falling,
Speaker 1: they became high BEATA once again. So it really is
Speaker 1: sounding a little bit geek like, but it's fascinating to
Speaker 1: watch the evolution of companies and industries because it's not
Speaker 1: as simple as oh yeah, a little bit of stocks
Speaker 1: are different paying utilities. So let's talk a little bit
Speaker 1: about factor investing, and in general, we've seen many factors
Speaker 1: significantly underperformed over the past decade. Why is that? What
Speaker 1: are your thoughts here, Well, when you say factors under perform,
Speaker 1: you know you're saying the return to the factor was
Speaker 1: not the expected. Sign is that the well, you know,
Speaker 1: the granddaddy of underperformance. The past decade has been value
Speaker 1: versus growth, but there has been some other factor surprises
Speaker 1: to the downside. I guess um when when we had
Speaker 1: the big blow up in in volatility a couple of
Speaker 1: years ago, that seemed to affect some factor performance and
Speaker 1: small cap has dramatically lagged for for quite a while.
Speaker 1: It's starting to catch up over the past year, but
Speaker 1: the past decade was not kind two small caps. When
Speaker 1: you look at the world of factors, and I know
Speaker 1: there are many, many more factors than just you know,
Speaker 1: the three or six that most people are familiar with,
Speaker 1: how do you look at what's doing well, what's doing poorly?
Speaker 1: And how do you contextualize that? Yes, so I really
Speaker 1: look at it from two dimensions. One is the does
Speaker 1: the factor matter? Right? So does the factor describe what's
Speaker 1: going on in the market? And when I mean by describe,
Speaker 1: if you look at the factor, can I tell you
Speaker 1: whether a group of stocks either out performing on the performing?
Speaker 1: So that to me the sign doesn't really matter when
Speaker 1: in the first dimension is is this factor meaningful? So
Speaker 1: just to pick up, you know, a random factor. You
Speaker 1: could pick a factor as where the name of the
Speaker 1: company is in the alphabet, right, that's a factor. You'll
Speaker 1: find that that study is actually completely useless in explaining
Speaker 1: whether a group of stocks went up or down, right,
Speaker 1: because it's just random. On the other hand, small camp
Speaker 1: as you mentioned, is actually really useful in terms of
Speaker 1: people are running away from risk. Usually small cap companies
Speaker 1: do really poorly if they're running towards risk, as they've
Speaker 1: done a little bit in the last three to six months.
Speaker 1: Small cap companies really generally do well. The correlation isn't one,
Speaker 1: but generally, regardless of the environment, there is a difference
Speaker 1: in performance in large cap companies with the small cap companies,
Speaker 1: and the same versus value the same in sort of
Speaker 1: quality related factors. Now, the expectation that people form on
Speaker 1: these factors is they look at the long run return,
Speaker 1: you know, whether it's from a Farmer French study or elsewhere,
Speaker 1: and then they say, wow, you know, this factor on
Speaker 1: average has been positive, so I expected to be positive
Speaker 1: going forward, And that I think is one of the
Speaker 1: misunderstandings with factor investing because you know when you when
Speaker 1: you pace the quantitative back, the first thing that goes
Speaker 1: through my head is what's the hit rate? Right? Because
Speaker 1: when I when I tell you something is going to outperform,
Speaker 1: the first thing you need to be doing, is an investor,
Speaker 1: is to say, Okay, how often is that going to happen.
Speaker 1: My conjecture is that even the best factors will outperform
Speaker 1: six or seven out of out of ten times. So
Speaker 1: you can think of that in meaning it's going to
Speaker 1: miss three or four out of ten times. Yeah, so
Speaker 1: the underperformed three or four at ten times. So you
Speaker 1: think of a coin, if you have a sixty percent
Speaker 1: chance of winning, you can get a bunch of classes
Speaker 1: in a row even with a sixty percent chance of winning.
Speaker 1: So so the degree under performance and the length of
Speaker 1: periods and the performance can be really, really long. And
Speaker 1: that I think has not been adequately communicated to people
Speaker 1: who are investing in these strategies because the other things
Speaker 1: is factors of momentum, and this is only recently coming
Speaker 1: to the four where people are talking about it, and
Speaker 1: this is something so you're not let me interrupt you
Speaker 1: a second or and you're you're not talking about momentum
Speaker 1: itself as a factor. You're talking about the momentum of
Speaker 1: other factors impacting that factor exactly. And I think that
Speaker 1: is probably probably one of the most least appreciated things
Speaker 1: in investing, and it's something to be really really aware of.
Speaker 1: And I noticed this when I first got into investing,
Speaker 1: because I look at these investment firms and you know,
Speaker 1: they'd look growing like gangbasters because their performance was great.
Speaker 1: You know, the head of the firm was viewed as
Speaker 1: a genius. And I look at it really closely as
Speaker 1: a you know, ex engineer, and go, it's not what's
Speaker 1: blading up on this one and this factor with its
Speaker 1: earnings acceleration or undeliveraged companies, whatever. The fact is that's
Speaker 1: been in favor for the last five years, and as
Speaker 1: long as it's in favor, they're going to do really,
Speaker 1: really well. And then suddenly the factor starts under performing
Speaker 1: and the managers say, as well, you know, my style
Speaker 1: is out of favor, but it's going to come back.
Speaker 1: So cynically, you know, if you're an investor, you know
Speaker 1: this right, because if you're doing well, you're a genius.
Speaker 1: But if you're doing coolly your styles out of favor. Um,
Speaker 1: it's an asymmetrical bet. Yeah, it's an asymmetrical bet. So
Speaker 1: beyond momentum, factors have persistence that tends to continue to exist.
Speaker 1: Are some factors more persistent than others that we can
Speaker 1: to really get into wonky geek territory here, But how
Speaker 1: consistent does persistence apply to different factors? Is it similar
Speaker 1: or or some factors do they tend to enjoy that
Speaker 1: momentum for longer periods than other factors. It's remarkably consistent.
Speaker 1: I mean I in all the work I've done, the work,
Speaker 1: the time and effort I put into modeling each factor
Speaker 1: individually has not been fruitful. What I see very consistently
Speaker 1: is that factors tend to the factors that I worked
Speaker 1: in the last year tend to continue to work. The
Speaker 1: ones that have worked in the last two to three years,
Speaker 1: they tend to work well, but a little less. So.
Speaker 1: The persistence is very strong over one year, less strong
Speaker 1: over two to three years. And then after three years
Speaker 1: you actually start to see some mini version. When you
Speaker 1: look at manager cycles, you will really really stop saying that,
Speaker 1: but I mean they managed cycles. Is the tendency for
Speaker 1: certain types of managers to perform. And so I think
Speaker 1: when you're when you're thinking about factor investing, it's really
Speaker 1: important to actually weigh the factors that have been working well,
Speaker 1: not only recently, but also think about the mean reversion factor. Right,
Speaker 1: So if you think about, you know, value factors right now,
Speaker 1: right now, it's kind of a nice time for value
Speaker 1: right because the last six months have been very strong.
Speaker 1: So the fact that has momentum not great momentum because
Speaker 1: it's only been strong for three to four months, maybe
Speaker 1: six months, but the mean reversion aspect of it is
Speaker 1: actually quite strong. So what's really fascinating about this? When
Speaker 1: I first started running money using this tract of momentum approach,
Speaker 1: I was doing it for a uh the US client
Speaker 1: using US stocks, and it was a client was based
Speaker 1: out of Japan, and they came to me and said, hey,
Speaker 1: what this work in Japan? And you know, being the
Speaker 1: typical quant, I said, yeah, well, let me check, right,
Speaker 1: So I collected all this data in Japan and I
Speaker 1: found that the same fact exists for Japanese stocks that
Speaker 1: you see this same factor momentum and then you know,
Speaker 1: subsequently repested it in in emerging markets and in Europe
Speaker 1: and you see the same. So I think what you're
Speaker 1: seeing is really and you can depending on which camp
Speaker 1: you fall into. I dated the economic cycle. Oh, it's
Speaker 1: just human behavior, right. If you think about the way
Speaker 1: our decision making process sense to work, is we tend
Speaker 1: to like things, so we tend to focus on things
Speaker 1: that are work recently. So that recentcly bias, I think,
Speaker 1: is there in the way we buy stocks and the
Speaker 1: way we think of fads coming in and out of favor,
Speaker 1: and that's what's driving That's what something you need to
Speaker 1: take into account when you're tractor investing, because I think
Speaker 1: the idea that yeah, value work, small cap works, quality works.
Speaker 1: So you take these you know, five words, six factor tilts,
Speaker 1: and you're going to beat the market regardless of what happens. Well,
Speaker 1: that's going to work if you have a very long
Speaker 1: or a reason. But if those factors out of favor
Speaker 1: for the last three years, you're in for a little
Speaker 1: bit of pain for the next two or three years.
Speaker 1: So Horne, let's talk a little bit about the idea
Speaker 1: of managers expressing their philosophy in their portfolio. I I
Speaker 1: can't help but think that you're a part lo val,
Speaker 1: part value sort of quant. Is that a fair just ryption? Yeah,
Speaker 1: I would say it's it's part global active quant active
Speaker 1: quant because I really think, I mean, love all is
Speaker 1: an anomaly. But as a manager, you can express a
Speaker 1: lot more views than just having a low Boll view
Speaker 1: in the portfolio. I want to talk to you about
Speaker 1: some of the funds you guys specifically manage, but before
Speaker 1: I do, I have to ask you a question. What
Speaker 1: is the fundamental law of active management? Well, that is
Speaker 1: actually a formula, and the idea behind the formula is
Speaker 1: that you can relate your investing success to really three things.
Speaker 1: So if you think about investing in US lodge cap stocks,
Speaker 1: the first thing that's going to matter for you in
Speaker 1: terms of your relative success of investing is how big
Speaker 1: your universe is, so what we call brand. The second
Speaker 1: is your bill lead forecast, which is as a quant,
Speaker 1: measured by the correlation between your predictions and what actually happens.
Speaker 1: The quants called that your information coefficient, how much information
Speaker 1: you have. And then the third thing is how effectively
Speaker 1: you transfer your information into your portfolio, So you overweight
Speaker 1: the stocks you like, and are you underweight the stocks
Speaker 1: don't like. And that's what quants referred to as the
Speaker 1: transfer coefficient. So the three decisions that are the three
Speaker 1: inputs to determining your success is one is how big
Speaker 1: the universe? You know, what the breadth of your investment decisions.
Speaker 1: The second is how well are you forecasting? And the
Speaker 1: third is how effectively are you transferring all your information
Speaker 1: into the portfolio? The transfer coefficient, and that's a formula
Speaker 1: that was very useful when you think about how much
Speaker 1: our performance you can get from a portfolio. And it's
Speaker 1: a formula that my colleagues Steve Sali and Roger Clark
Speaker 1: and I developed um in the nineties and then published
Speaker 1: a paper in the two thousand timeframe. That's where that
Speaker 1: question came from. So now let's talk about some of
Speaker 1: the specific funds that that you manage over at Wells Fargo,
Speaker 1: starting with the Global Dividends Opportunity Funds. From the name,
Speaker 1: I'm going to guess that it is both global and
Speaker 1: dividend focused. Um, what do we make of the trend
Speaker 1: of falling dividends at least here in the US over
Speaker 1: the past call it a few decades, Yeah, I mean
Speaker 1: that's been kind of a corporate trend right because of
Speaker 1: the tendency to have buy back as a methodology forgive
Speaker 1: beturning money to to shareholders. So I mean it's a
Speaker 1: challenge from a form divid and focused strategy in that
Speaker 1: particular portfolio. The other thing we do is use covered calls.
Speaker 1: So Analytic got it start as a firm in doing
Speaker 1: covered call strategies. So we actually use a vault of
Speaker 1: the forecasting model to identify overvalue call options and then
Speaker 1: use that to generate additional income for the portfolio. Interesting,
Speaker 1: it's a little bit unusual, but this is typical quant
Speaker 1: approach in that you're not just using one way to
Speaker 1: generate income. You're using dividends as well as covered calls
Speaker 1: as a way to generate income for the portfolio. So historically,
Speaker 1: covered call writing was always a challenge because you're balancing, um,
Speaker 1: the risk of a stock that's working out getting cold
Speaker 1: away versus the versus the income you get from selling
Speaker 1: the calls. I'm going to assume giving your background and
Speaker 1: low vall that um you managed to offset that because
Speaker 1: you're going to have a lower beta group of names.
Speaker 1: They're they're less likely to get called away when the
Speaker 1: stock starts to run up or I should say less
Speaker 1: likely to run up and therefore have the stock called away. Yeah,
Speaker 1: so that I mean that portfolio is team managed, so
Speaker 1: it's actually uses some of the other skills within Wells
Speaker 1: frougu as well, So we're not the only manager. So
Speaker 1: we just manage the option portflio. And the reason the
Speaker 1: way we avoid the stop getting called away is by
Speaker 1: using index calls. Because if you use the index calls,
Speaker 1: all you're susceptible to the market run up, not the
Speaker 1: individual stocks themselves going up, right, right, you still run
Speaker 1: the risk of having to buy in to replace the
Speaker 1: the the underlying right well, the underlying wouldn't get called
Speaker 1: away because you're selling the calls on the SMPI index
Speaker 1: very example, so you would have to be you'd be
Speaker 1: on the hook for the payment. But the key with
Speaker 1: call writing that most people don't think about is you
Speaker 1: have to have a way to value the call. Right,
Speaker 1: So if volatility is over priced, you know, as it
Speaker 1: was at the start of this year, for example, that's
Speaker 1: a great time to be selling calls. Then you're getting
Speaker 1: paid for exactly getting paid for the risk. But having
Speaker 1: a call writing strategy where you're always selling the same
Speaker 1: call that is usually not going to be as successful
Speaker 1: as something that is actually constantly looking at you know,
Speaker 1: one month, two month, three month horizon and different strikes
Speaker 1: and trying to figure out where's the most amount of
Speaker 1: miss pricing coming in the marketplace. Now, for example, as
Speaker 1: we are in sitting here in you know, in the
Speaker 1: and the may longer dated calls actually more miss priced
Speaker 1: than shot it calls. So you really need to be
Speaker 1: thinking about increasing the tenor of your call if you're
Speaker 1: in a call writing strategy. Very very interesting. What about
Speaker 1: the Low Volatility US Equity Fund? What what's the philosophy
Speaker 1: behind that? So that's a fairly vanilla fund um from
Speaker 1: if you think about everything that we do, because it's
Speaker 1: long US stock. The idea is to have a bottlatility
Speaker 1: of about seventy percent of the equity market and and
Speaker 1: a similar return. The fund itself is new, but we've
Speaker 1: run that strategy now since the early two thousands. So
Speaker 1: it's you know, I'm going to show my age because
Speaker 1: it's I've been involved with that type of for sixteen
Speaker 1: years and we're building a portfolio that has that has
Speaker 1: a low beta, so the average beata for all the
Speaker 1: stocks in the portfolio is around point six point seven.
Speaker 1: But at the same time, we're tilting towards the characteristics
Speaker 1: that are in favor. So if you look at the
Speaker 1: portfolio right now, you'll see that it has a very
Speaker 1: big loading on price to sales as a characteristic. That's
Speaker 1: a factor that has really really been rewarded in the marketplace.
Speaker 1: Investors are really focusing on that right now. Trading earnings
Speaker 1: is not useful as because any trailings earnings number contains
Speaker 1: the pandemic. So using forward looking numbers like forward pe
Speaker 1: price to sales are really really important. Asset turnover is
Speaker 1: a factor that is really important right now as well,
Speaker 1: so you'll see a lot of stocks with high asset
Speaker 1: turnover um in that portfolio. So we tend to look
Speaker 1: at a very broad range of factors, not just sort
Speaker 1: of you know, value, growth, quality, small cap, but really
Speaker 1: kind of try to capture the pross section of factors
Speaker 1: that fundamental investors look at. Let's talk a little bit
Speaker 1: about where we are in this market cycle today. Do
Speaker 1: you look at us as sort of late cycle or
Speaker 1: was last year a reset and this is a relatively
Speaker 1: young market, or do you not care about any of
Speaker 1: those things I do care um from the perspective of
Speaker 1: looking at things in a historical context, because that's often
Speaker 1: a kind of a useful guide as you're trying to
Speaker 1: figure out which factors too blobal weight and underweight, and
Speaker 1: you know, does it make sense? And I would say,
Speaker 1: given the way factors are behaving right now, it's much
Speaker 1: more of a really early cycle. There's a huge focus
Speaker 1: on in the US and globally on estimate revisions. So
Speaker 1: stocks with high being revised upwards are doing incredibly well,
Speaker 1: so people are really focused on that as a factor.
Speaker 1: Small cap is doing well, Low price to sales stocks
Speaker 1: are doing well. Those are all that associated early in
Speaker 1: the fact in the cycle. If you look at interest
Speaker 1: rate sensitivity as a factor, you know that is something
Speaker 1: really people are really focused on because there's concerned that
Speaker 1: there's going to be a rise in rates. And you
Speaker 1: should think about equities having duration. Most people don't, but
Speaker 1: equities do have duration, and different equities have different types
Speaker 1: of different levels of duration. So that's a measure managing
Speaker 1: newport fill. So when you say duration, harand let me
Speaker 1: interrupt yourself. When you say duration, most people think in
Speaker 1: terms of fixed income and bonds is having a duration. Hey,
Speaker 1: this is a ten year bond, of twenty year bond
Speaker 1: and nine bond, what have you. What does duration mean
Speaker 1: when it comes to equity. I'm assuming there's some sensitivity
Speaker 1: to changes in interest rate policy. What do you mean
Speaker 1: by duration, he said to me. The duration of a
Speaker 1: stock is how sensitive a stock is in the tenure yield.
Speaker 1: So if it's insitive, then you'll see that it's it's insensitive.
Speaker 1: You'll see that it's not affected by changes in rates.
Speaker 1: And you know, when you look at individual companies, it's
Speaker 1: really fascinating. You stop. When you look at them carefully
Speaker 1: from that perspective, you'll see that some companies, for example,
Speaker 1: have a lot of floating rate debt, so as rates
Speaker 1: rise there interest payments are going to rise. So those
Speaker 1: companies tend to be more interest rate sensitive than others. Huh,
Speaker 1: that's really that's really interesting. So much has happened since
Speaker 1: you started in the industry today. What do you think
Speaker 1: are the biggest differences in asset management relative to years
Speaker 1: ago or so? Well, there's a lot been a lot
Speaker 1: of good developments, I think, you know, on the positive side,
Speaker 1: um fees have come down, the trading costs have come down,
Speaker 1: so your ability to trade a portfolio mo uh, you know,
Speaker 1: to have higher turnover to capture factor rotation. That's become
Speaker 1: a lot easier. The costs of data um have come down,
Speaker 1: but at the same time, the amount of data available
Speaker 1: for purchase has gone up. So if you look at
Speaker 1: us as a group as a team, you know our
Speaker 1: data costs is millions, but it seems to always go up,
Speaker 1: not come down. What are your thoughts on some of
Speaker 1: these alternative data sources. I know people are buying satellite
Speaker 1: data where you can see movement of tankers and ships
Speaker 1: that are I've even seen some people say we could
Speaker 1: tell how loaded the ship is by how low it
Speaker 1: sits in the water relative to its waterline. Do you
Speaker 1: do you have anything it's on these alternative data sources? Yeah,
Speaker 1: I think those that type of data is really useful
Speaker 1: in terms of updating your earnings estimate forecasts, because that's
Speaker 1: ultimately what it comes down to. The challenge with that
Speaker 1: data is that it's really really time sensitive. So when
Speaker 1: I think of data quality, I'm always trying to think
Speaker 1: about what horizon do I need to invest with to
Speaker 1: actually use that. Now that's really useful for me in
Speaker 1: a short run trading model. But that's not the sweet
Speaker 1: spot for from our size because we can't turn those
Speaker 1: portfolios over enough to capture that. So I I prefer data,
Speaker 1: for example that looks at you know, um. Just to
Speaker 1: pick up example of something I'm working on now, is
Speaker 1: what's the carbon footprint of a company and how can
Speaker 1: it be measured? Right? Because we know the cost of
Speaker 1: emitting carbon is going to go up in the future.
Speaker 1: That's going to be a big factor in the profitability
Speaker 1: of companies and their behavior, and they need to invest
Speaker 1: in terms of new plants, encuipment. So what data can
Speaker 1: be used to capture that. That's kind of a moment intermediate,
Speaker 1: long horizon factor. And the more data I can collect
Speaker 1: on that dimension, the better if I am. But it
Speaker 1: doesn't rely on me getting to some information quicker than
Speaker 1: somebody else and the information kind of dying at the
Speaker 1: next learnings announcement that you know that makes a lot
Speaker 1: of sense. In fact, since you mentioned low carbon I'm
Speaker 1: going to jump ahead to another question. What are your
Speaker 1: thoughts on E s G. On environmental, social and corporate
Speaker 1: governance as potential factors. I've I've heard people describe them
Speaker 1: as risk screens. What do you think of E s G.
Speaker 1: I think they're really important as risk screens. I think
Speaker 1: if you what we've found is that if you use
Speaker 1: E s G related factors actually incredibly important in describing
Speaker 1: the future return volatil the of a start. The thing
Speaker 1: that's most important is governance, and you'll find that companies
Speaker 1: with poor governance the returns are very very fat tailed
Speaker 1: and as a portfolio manager, you need to account for
Speaker 1: that because most risk models missed that. Right, a poorly
Speaker 1: government company has a significant chance of a really negative return,
Speaker 1: but it doesn't happen very often. It will happen once
Speaker 1: every fifteen years, once every thirty years. So in a
Speaker 1: typical risk model it actually doesn't show up, but it
Speaker 1: does show up if you look at you know, long
Speaker 1: time series of data and beef. I find these E
Speaker 1: s G factors are really really important in risk forecasting.
Speaker 1: They're not useful in return forecasting because I think you
Speaker 1: can make the case that people like these stocks or
Speaker 1: the like these stocks for other reasons, you know, just
Speaker 1: like you have people like since some people like sin stocks, right,
Speaker 1: that's where there's a sin stock etf um So I
Speaker 1: think the return aspect for me, is less important than
Speaker 1: the volatility aspect. Huh. That's really interesting. It's it's sort
Speaker 1: of the Charlie Manger approach, which is, don't be more smart,
Speaker 1: be less stupid. In other words, and I love phrases it,
Speaker 1: but you're you're looking to screen out potential disasters with
Speaker 1: E s G rather than screen in additional alpha. Right.
Speaker 1: That's to me, that's exactly the way to think about
Speaker 1: because these companies have something in their behavior where there's
Speaker 1: going to be a chance that they have a bad
Speaker 1: outcome in the next twenty years. Right. But if you
Speaker 1: have a portfolio of poorly government companies and suppose you
Speaker 1: have a hundred stocks in them, there's a significant chance
Speaker 1: that one of them is going to have a bad
Speaker 1: outcome next year. So that they is a really key
Speaker 1: dimension in using E s G in a portfolio because
Speaker 1: most people think don't think about it that way. But
Speaker 1: I found that with D s G factors, and increasingly
Speaker 1: with environmental factors like carbon or water pollution, or you know,
Speaker 1: even something is like plastics, the company's plastic emissions, is
Speaker 1: there a way to measure that and quantify and incorporate
Speaker 1: in the portfolio Because that's increasingly going to be something
Speaker 1: that investors care about and something that the company will
Speaker 1: have to care about in the way people assess their
Speaker 1: future profitability. That's really that's really kind of kind of intriguing.
Speaker 1: So so here we are, the economy is just starting
Speaker 1: to reopen. People are more concerned with inflation than they
Speaker 1: are with unemployment. It's means, what factors do you see
Speaker 1: really taking advantage of the post COVID reopening, anything stand
Speaker 1: out in particular, and what do you think is um
Speaker 1: the wrong factor for this phase of the recovery. Well,
Speaker 1: I would stay away from anything that uses recent accounting data.
Speaker 1: So just to just to give you something tangible, you know,
Speaker 1: people focus on things like r o E and r
Speaker 1: o A right return on equity return and affect those
Speaker 1: numbers are really have been affected by the company's performance
Speaker 1: in COVID. So that's those are factors I would focus on.
Speaker 1: I mentioned trailing learning zeal as a fact that you
Speaker 1: should not look at, so that anything that uses trailing
Speaker 1: accounting data you've got to stay away from. But if
Speaker 1: you want to look at the valuation, which I think
Speaker 1: there's a lot of evaluation factors that are you should
Speaker 1: be looking at looking at the ratio of price to
Speaker 1: sales is a really excellent factor right now given where
Speaker 1: we are in the cycle. Staying away from companies with
Speaker 1: a lot of debt, especially debt that is floating rate
Speaker 1: as opposed to fix rate, is something that's that you
Speaker 1: should be looking at doing. Going towards companies that have
Speaker 1: high operating margins in terms of their business model. That's
Speaker 1: a little bit difficult to do because of the accounting
Speaker 1: data problem for the last year. Uh, that's something that
Speaker 1: you should be incorporating into your portfolio. And the other
Speaker 1: thing that I would really emphasize in the current cycle
Speaker 1: because of the change we're seeing in the way companies
Speaker 1: do business, is staying away from companies where there's a
Speaker 1: lot of disagreements about their future earnings. So one thing
Speaker 1: people don't have attention to is looking at analyst dispersion.
Speaker 1: So if you look at an earnings forecast, everybody looks
Speaker 1: at the mean, but you should also look at is
Speaker 1: look at the difference between the high and the lower
Speaker 1: or the spread between analysts, because whenever they has a
Speaker 1: big spread, that means there's a lot of disagreement as
Speaker 1: to the future profitability of the company, and that factor
Speaker 1: is something that's going to be really important in this
Speaker 1: stage of the cycle. Really interesting. I have one curveball
Speaker 1: question to h to throw at you. Um, what sort
Speaker 1: of motorcycle do you like to ride? Oh? My goodness,
Speaker 1: how much time do you have? Well I did read
Speaker 1: that you have bite pretty much all over the world.
Speaker 1: How much of an exaggeration is that? Uh? No, that
Speaker 1: that that is true. I mean motorcycle is reatively speaking,
Speaker 1: are cheap. Um. And one of my hobbies is exploring
Speaker 1: the world on a motorcycle. So I like to keep
Speaker 1: bikes at different out of the world. I can you know,
Speaker 1: I can show up, I leave my clothes on the
Speaker 1: bike and can hop on and ride. But my my
Speaker 1: daily rider, because I ride to work, is an electric
Speaker 1: Hollie Davidson, which is actually a wonderful bike. It's a
Speaker 1: called Hallie Davidson Live Wire. Um. It's quiet, it's fast, um.
Speaker 1: And when I leave early morning for work, I don't
Speaker 1: disturb my neighbors. Um. And we have so solar panels
Speaker 1: at home so it doesn't cost me anything to run. Um.
Speaker 1: My favorite bike to ride on the weekends is to
Speaker 1: have an obscure Italian bike called the bi motor, the Due,
Speaker 1: which is a two stroke, very light sport bike. And
Speaker 1: then my favorite bike for touring is you know BMWs
Speaker 1: because you can get them service anywhere in the world,
Speaker 1: and there it's almost like a train. They almost never takedown, right,
Speaker 1: They're big, they're solid, they're comfortable, and they could go
Speaker 1: on and on and on. Do you find I asked
Speaker 1: this question as a kid who used to write dirt
Speaker 1: bikes and and some of the smaller one fifties do
Speaker 1: you find like traffic is so heavy these days and
Speaker 1: people are just not paying attention. It's a little more
Speaker 1: challenging to to be on a motorcycle. There is considerably
Speaker 1: more distracted driving um in southern California. Actually, in California,
Speaker 1: it's legal to filter or split lanes, So when you're
Speaker 1: driving down the middle of lanes, what you'll often see
Speaker 1: is the you know, the person next to you and
Speaker 1: driving a car has their phone in their lap. And
Speaker 1: I don't know why, there's a tendency if you're look
Speaker 1: if you're texting, you always put the phone in your
Speaker 1: lap and then you look down away from where you're
Speaker 1: driving and you text, and I see that probably you
Speaker 1: know the time in the in the morning, so that
Speaker 1: it is a hazard that one has to deal with,
Speaker 1: and it does make it um so that usually when
Speaker 1: I get to work in the morning, I'm really awake
Speaker 1: because my adrenaline is flowing to see, to say the
Speaker 1: very least, And you have a trip planned later for
Speaker 1: parts of Asia. Where are you heading to this year? Well,
Speaker 1: this is actually a continuation of a trip that was
Speaker 1: got canceled last year because of COVID. So last year
Speaker 1: I left the motorcycle. I was writing in Riga in Latviere,
Speaker 1: and I'm writing this year. The plan is to write
Speaker 1: from Riga to Saint Petersburg to Moscow and then next
Speaker 1: year to ride from Moscow all the way to the
Speaker 1: east coast of Russia to Ladivostok, which is right next
Speaker 1: to Tokyo. Right, I've never been to Moscow, but St.
Speaker 1: Petersburg is an amazing city and you can spend weeks there.
Speaker 1: It's just an unbelievable amount of things to see and do. Yeah,
Speaker 1: I'm really looking forward to that. I've put lots of
Speaker 1: wonderful things about that city. All right, So I know
Speaker 1: I only have you for a couple more minutes. Let's
Speaker 1: jump to our favorite questions that we ask all of
Speaker 1: our guests, starting with what are you streaming these days?
Speaker 1: Give us some of your favorite Netflix, Amazon Prime podcast entertainment.
Speaker 1: So I'm I'm going to be a disappointment on that
Speaker 1: dimension because I don't think I watched Netflix in over
Speaker 1: a year, so I'm not. I didn't grow up with
Speaker 1: televisions in Sri Lanka or Scream, so I'm I'm almost
Speaker 1: never watched them. Well, all right, listen, I would be
Speaker 1: more productive if I wasn't if I wasn't watching. I'm
Speaker 1: a big reader. I mean I read more, I more
Speaker 1: than anybody else I know, so I'm probably got the
Speaker 1: world's biggest Amazon books built. So so let me let
Speaker 1: me jump to that question. Then tell us about some
Speaker 1: of your favorite books. What if some are your all
Speaker 1: time favorites, and what are you reading now? Well, what
Speaker 1: I'm reading now? There's two books that I'm really enjoying.
Speaker 1: One is called The Well Gardened Mind, m Um. It's
Speaker 1: a book on how gardening and nature affects the way
Speaker 1: we think. And this book, I think, to an investor
Speaker 1: is really fascinating because it talks about how you exposure
Speaker 1: to nature affects your decision making. So if we take
Speaker 1: two people, for example, or two groups of students and
Speaker 1: they're about to take the exam, one of them books
Speaker 1: through an urban environment, are the group box to an arboretum,
Speaker 1: so they exposed to nature. The one that boxer an
Speaker 1: arboretum will have higher test case. And that's because of
Speaker 1: something that's called attention restoration. Because we are also focused
Speaker 1: in such short time periods now that after a while
Speaker 1: we get attention fatigue, so you have to figure out
Speaker 1: where to restore that. And if you're involved in trading
Speaker 1: or building portfolios, this is something that that's really really critical.
Speaker 1: So this book is a really fascinating book from st
Speaker 1: from that standpoint, because I think, especially in the COVID environment,
Speaker 1: we're all working longer hours and you have to figure
Speaker 1: out a way to restore your attention during the day.
Speaker 1: And that's kind of one of the big things that
Speaker 1: got out of this book. Um. But the other book
Speaker 1: I'm reading is I'm a I'm a total Formula one fanatic. Um.
Speaker 1: I'm reading this book by one of my favorite designers,
Speaker 1: Adrian Newey, and he wrote a book last year called
Speaker 1: How How to Build a Car and Adrian Nui, I
Speaker 1: think is one of the b he's the design of
Speaker 1: a red bull. If we didn't know, but he's one
Speaker 1: of the best card designers ever to go through Formula one.
Speaker 1: And the book goes through his designed philosophy and how
Speaker 1: his philosophy evolved over time and all the different cars
Speaker 1: that he designed, but it also decided describes his career.
Speaker 1: And you know, if most people don't realize it, but
Speaker 1: Adrian was, you know, one of the key partners that Williams,
Speaker 1: which is now one of the worst Formula one teams,
Speaker 1: and it was a disagreement with the owners of the
Speaker 1: company that left him. They do him leaving, So if
Speaker 1: not for that disagreement, Williams would probably be continue to
Speaker 1: be the number one team in UH in Formula one.
Speaker 1: So it kind of highlights to me one of the
Speaker 1: importance of realizing that teams are really important to a
Speaker 1: business and if you let key team members go UH,
Speaker 1: that's going to have a big impact on your business.
Speaker 1: So there is a really strong tide of how to
Speaker 1: build an effective team in a very high performance environment
Speaker 1: in this book Give us Another the last one m
Speaker 1: The most other book I read more recently, which is
Speaker 1: a little more academic, is annoyed. Sure, yeah, and I
Speaker 1: think you've talked about that. I think I've seen that
Speaker 1: in your podcast. Um. The other one that I really
Speaker 1: liked that I read recently was actually by one of
Speaker 1: the people who was one of the founders of Analytic,
Speaker 1: were involved in its founding, which is a book called
Speaker 1: A Man for All Markets. Sure, and I think it's
Speaker 1: a book that anybody going into quantity financial really by
Speaker 1: at Top was definitely one of the smart st guys
Speaker 1: I've ever met, UM, and so that I really really
Speaker 1: enjoyed reading. UM. So, I you know, pretty pretty wide array,
Speaker 1: but I'm really fascinated by this interaction between the how,
Speaker 1: what what we have faced with effects our decision making.
Speaker 1: And another book I read recently that I really liked
Speaker 1: is called The Nature of Fear about Survival Lessons in
Speaker 1: the while that's how we what happens to us when
Speaker 1: we are fearful, because if we think about investors, you know,
Speaker 1: one of the issues that's happened is with for one case,
Speaker 1: and people having to manage their own portfolios. I don't
Speaker 1: think people realize how they're environment affects their decision making,
Speaker 1: and that's why it's really important to show kind of
Speaker 1: average investor investing, you know, having exposure in for one case,
Speaker 1: not to make decisions very often. You know, generally they
Speaker 1: say look at it once a year. But also realize
Speaker 1: what type of mood you're in when you're making that decisions.
Speaker 1: And I think that's really underappreciated. To make sure that
Speaker 1: you're not in a fearful state or what can cause
Speaker 1: you to be in a fearful state when you look
Speaker 1: at that. So you know, do some all things like
Speaker 1: download your statement, look at it, you know, wait a month,
Speaker 1: then make the decision, but don't be in a hurry
Speaker 1: and allow yourself time to think about the decision you're
Speaker 1: going to make getting put on the decision. But you know,
Speaker 1: if I can think about this team, it's this broader
Speaker 1: team of thinking about how your state of mind affects
Speaker 1: your decision and how can you manage your state of mind?
Speaker 1: Really interesting. Tell us about any mentors you might have had,
Speaker 1: who who helps guide your career. I was pretty lucky,
Speaker 1: I think when I went to the University of Rochester.
Speaker 1: So there was a gentleman by the name of Paul
Speaker 1: McAvoy who was the dean of the Business school, and
Speaker 1: I was making a living at the time, you know,
Speaker 1: in addition to going to school programming, and he hired
Speaker 1: me as a programmer for some research projects. And Paul
Speaker 1: was a very well known economist. He was on the
Speaker 1: President's Council of Economic Advisors. So working with them, I
Speaker 1: really learned that kind of think about how to sell problems,
Speaker 1: but also think about applying economics mhm, you know, much
Speaker 1: broader context than major policy or interest rate policy, and
Speaker 1: thinking about you know, the whole Kansian world of animal
Speaker 1: spirits and how they affect markets. So that was he
Speaker 1: was probably I think one of the most instrumental people
Speaker 1: to me. Uh. And then also Shin Kasu, who was
Speaker 1: the founder of Analytic. He was the head of the
Speaker 1: con department at the University of California at Irvine. It
Speaker 1: was very fortunate to work with him. And then also
Speaker 1: I would say the gentleman who was my PhD advisor
Speaker 1: and also kind of a well known figure in in finance,
Speaker 1: professor Robert Howgen, who wrote a book called The Inefficient
Speaker 1: Market Hypothesis and the Incredible January Effects. A very very
Speaker 1: color a full character, but somebody was always willing to
Speaker 1: question markets and do unusual things to build portfolios in
Speaker 1: terms of using at the time large scale optimizers and
Speaker 1: building large scale factor models in the world where everybody
Speaker 1: in the eighties we were convinced that markets were perfectly etician,
Speaker 1: and now we know that's not the case. To say
Speaker 1: the very least, what sort of advice would you give
Speaker 1: to a recent college grad who was interested in a
Speaker 1: career in either factor investing or quantitative finance. I think
Speaker 1: the hardest thing in starting in the business right now
Speaker 1: is figuring out whether you're invest actually invests interested in
Speaker 1: investing all your interested in what you think is investing,
Speaker 1: because investing is really about doing research to figure out
Speaker 1: what's going on in the market and then figuring out
Speaker 1: way to exploit that for the benefit of your clients.
Speaker 1: Right it's not about frequent trading or moving faster than
Speaker 1: somebody else. And what I find when I talk to
Speaker 1: younger people is they're really focused on trying to get
Speaker 1: an edge by getting this short run informational advantage, and
Speaker 1: that's not that's not sustainable. And you also can't use
Speaker 1: that to invest institutional money because people tend to have
Speaker 1: longer horizons. So my piece of advice to them is
Speaker 1: understand what type of investing you're interested in, and whether
Speaker 1: you like doing that type of research, because the hardest
Speaker 1: thing about doing research is eight percent at the time,
Speaker 1: it's a dead end. So if you don't enjoy the journey,
Speaker 1: it can be really frustrating, right, I mean, think about it.
Speaker 1: Think about it this way. You're souted like being a chef,
Speaker 1: but of the dishes you make taste absolutely horrible, for
Speaker 1: four out of five get sent back to the kitchen.
Speaker 1: Really really interesting, and in our final question, what do
Speaker 1: you know about the world of research and portfolio management
Speaker 1: today that you wish you knew back in the nineties
Speaker 1: when you were really first getting started. Without a doubt Bory.
Speaker 1: For me, it would be the impact of human eimations
Speaker 1: and sentiments on markets. I think by the time I
Speaker 1: did not appreciate how much that mattered, and now I
Speaker 1: see that it matters a great degree. And I think
Speaker 1: something I worked very hard at is trying away to
Speaker 1: build a way to quantify that. But I didn't have
Speaker 1: an appreciation for how much emotion and people's attitude and
Speaker 1: sentiment matters in the way assets are price and I
Speaker 1: think that is not taught enough in schools. Huh. Really
Speaker 1: quite quite interesting. Thank you her In for being so
Speaker 1: generous with your time. We have been speaking with horendraw
Speaker 1: to Silver. He is the leader of Wells Fargoes quantitative
Speaker 1: strategy group known as Analytic Investors. They manage over twenty
Speaker 1: billion dollars and assets. If you enjoy this conversation, well,
Speaker 1: be sure and check out all of our previous UH interviews.
Speaker 1: There are nearly four hundred of them, and you can
Speaker 1: find them at iTunes or Spotify or any of your
Speaker 1: favorite podcast sites. We love your comments, feedback and suggestions
Speaker 1: right to us at m IB podcast at bloom Berg
Speaker 1: dot net. You can sign up from my daily reads
Speaker 1: at Reholts dot com. Check out my weekly column on
Speaker 1: Bloomberg dot com slash Opinion. Follow me on Twitter at
Speaker 1: rit Holts. I would be remiss if I did not
Speaker 1: thank the crack staff that helps put these conversations together
Speaker 1: each week. UH Tico val Bron is our project manager.
Speaker 1: Tim Harrow is my audio engineer. Michael Boyle is my producer.
Speaker 1: Michael Batnick is my head of research. I'm Barry Ridholts.
Speaker 1: You're listening to Master's Business on Bloomberg Radio.