Download Article
Stewart: Welcome to another edition of the InsuranceAUM.com podcast. My name's Stewart Foley, I'll be your host. Hey, welcome back. It's so great to have you. And this is a unique podcast because at the end of just about every podcast I say, "If you have ideas for a podcast, please shoot me a note. It's Stewart@InsuranceAUM.com." And no one ever does, until now. And Travis Gibson, at Loomis Sayles, was kind enough to send in this suggestion. And it came across saying, "Following your call for suggestions, I'd love to hear more from people working as quantitative analysts, quantitative developers, statisticians, et cetera in the industry. I'm a quant, so I'm a hundred percent biased. Anyway, would love to hear what other people in the industry are working on in this regard."
So that was music to our ears over here. And we went to our clients with this request, and we got back three firms who volunteered to talk about quantitative methods in insurance asset management. And those folks include Kyle Johnson with Principal, Lisa Larsson and Laura Zhang with StepStone, and Sashi Naik from Fidelity. Thanks to all of you for being on. We're going to have an interesting conversation. We're going to go about this one by one, but I wanted to give you all a chance to say hi, and welcome you first. So we're going to start with the folks from StepStone, and that's Lisa Larsson and Laura Zhang. Ladies, thanks for being on. Thanks for taking the time. We're thrilled to have you.
Lisa: Thrilled to be here, Stewart.
Stewart: Okay, so let's start the way we always do, which is Lisa, where did you grow up and how in the world did you get into the quant side of the business?
Lisa: That is a fantastic question, and it's always dangerous to tell people too much when they don't know you yet. But I always say that I was raised by wolves because I am from the upper Peninsula of Michigan, which is essentially unpopulated. And back when they started Obamacare, they actually just left it off the map because they didn't realize there was anyone up there. So always was a math nerd, did undergrad in math, Masters in Math Finance, right during the GFC, went to a PhD, also in math, and then realized I needed to do something practical with my life, so I ended up in the financial industry. And currently, I am a Partner and leading our Portfolio Management Team at StepStone. And we kind of developed models for top-down portfolio management, so risk, return, liquidity fees, valuation across private markets, asset classes, and use all those models to build smarter portfolios. So delighted to be here with you today, Stewart.
Stewart: We're thrilled.
Lisa: And the topic is near to my heart.
Stewart: Yeah, for sure. I rode a motorcycle around the Great Lakes and was in the UP. And you are without the local accent that I've heard prior.
Lisa: It sounds a bit like in the movie Fargo, if that's kind of what people have a reference for. That's how I used to sound, and for some reason I don't anymore. It's weird.
Stewart: There you go. All right, Laura, how about you? Can you tell us a little bit about where you grew up and how did you become a quad?
Laura: Glad to be here. It's been quite a journey. So I grew up in China, and I was an English major back in college, so it was all about Jane, Austen, Hemingway, and don't ask me why. And later decided, "Yeah, I need to do something practical with my life." So I went to work for a hedge fund operation, and I was there soaking data for four years. So I kept wondering, "What can I do with all this data, not just trade settlements, reconciliations?" So I came to US for my master degree in business analytics, hoping to get some formal training in this analytical world. And there I met Lisa at StepStone. I got this chance to work in this amazing team. And then, I got more exposure in real estate and here I am developing data-driven insights for real estate house view and work to quantify this due diligence process for real estate funds. So it's been a fascinating journey.
Stewart: That is a great story. So everybody's kind of got their own way we're going to go about this. And in the case of StepStone, you wanted to work on some examples. And the first thing we're going to talk about is insurance dedicated funds. Where do you want to start, Lisa?
Lisa: Yeah. I think we'll start with Laura. So Laura and I work together on this product, and we really think that it highlights how we take some of our quantitative tools and make a real product that people can invest in and meets their needs. So Laura, I'll turn it over to you on this one.
Laura: Yeah, of course. So I will start to talk about what is so unique about this product. So first of all, this fund, it's 100% invested in private real estate. And the way it works is it takes in a lump sum investment from the clients upfront, and StepStone deploys the capitals over the next couple of years. And the interesting part is around year five, the fund turns self-funding, meaning that the distributions coming from the real estate investments are used to fund the capital costs from the new real estate commitments. And then, the funds keep growing until it unwinds in dozens of years. So the structure of this product, it really underscores the crucial role of portfolio construction and liquidity management, which are two really fascinating topics in private markets.
So you may ask how do we manage the portfolio construction for such a long-lived vehicle? So first, we need to answer the questions on the client target returns and risk tolerance. So since StepStone has a massive global proprietary database of historical returns, we can actually look at the performance, the volatility, as well as the correlations of very granular investment strategies. And then, we can quantify the diversification benefits investing in private markets.
So in this particular example, over the last 20 years, private real estate has shown minimal correlation with S&P 500, which is only like 2%. This is real powerful for strategic asset allocation because the clients can earn higher returns without increasing the risk exposure.
And then next question, how do we allocate the different strategies, like why and how much? So it's a function of the strategies' target returns, the risk levels, as well as the cash flow profiles.
First, the non-cost strategies, these are high return strategies, and it can be executed through the primary funds or secondaries and co-investments. So primary funds is very important for deal sourcing and offers high return potentials, but it typically takes three, two years to fully deploy. That can create potential cash drag on returns because the money is simply not deployed fast enough. And also like primary funds, it has this J-curve effect where the first year return, it's like dragged down by management fees, operating expenses. But on the contrary, like secondaries and co-investments, it's a great way to ramp up the portfolio quickly and mitigates the J-curve return. And also, StepStone has great control over the deployment speed that can help with liquidity management as well.
And the third strategy I want to mention here, it's open-ended funds in real estate. That's the most liquid product in private real estate. And if the portfolio has extra cash, it's a great way to get in and out from these funds to maximize the returns.
And that brings another very interesting feature of this product, which is liquidity management. And as mentioned earlier, the way it works is it takes in a lump sum cash from the clients, and StepStone strategically manage the cash to maximize returns. So it's essentially solving two questions. One, we need to invest cash in high return strategies really quickly, and that means we cannot only invest. But on the other hand, we need to meet the obligations of the capital costs. That means that we cannot over-invest. So these self-conflicting constraints, it's very interesting to navigate around, and it's why it's extremely valuable to have data to help us understand the performance and cash flow profiles of all these different strategies. And with our strategic asset allocation and pacing models, it's very helpful to address these challenges in a scientific and structured way. So, next I'll hand over to Lisa to take us through those two crucially important models in private markets portfolio construction.
Stewart: That's great. Thank you, Laura. Lisa?
Lisa: Yeah. No, so backing up a bit, Laura mentioned strategic asset allocation and pacing were kind of the key quantitative tools that helped us to actually create a real product that works for the insurance market. But what is the potential benefit to private markets, and what are the risks? And how can you start to think about incorporating private markets into a portfolio?
And on that topic, I think the first step in any portfolio construction process is understanding risk, understanding return, and in our area of the world, understanding liquidity. So there are a lot of assets across private equity, private debt, infrastructure, and also real estate. Some have a longer hold, some have a shorter hold, some have more yield, some have yet less yield. And the returns are just sensitive to risk factors.
So when we think about blending these assets in a rational way, that is definitely the first step in any private markets program. And this is a very data-driven process. So each year we create kind of capital markets assumptions across private markets, asset classes, and strategies. And these are supposed to tell you the go-forward long-term potential of each investment type, but also just the risk and the interdependence.
And then, this is just very quantitative, so understanding historical data, developing models to understand the sensitivity of macro projections on private assets, and also collaborating with StepStone's global research themes to get an idea of how assets today are just different than assets that were labeled the same in the past. So for example, in non-core real estate, there are lower leverage levels than we saw pre-GFC. So what does the next shock look like? Will it look the same as what we have in the historical data? Those are kind of model-based and conversation-based things that we can understand with our research teams who are on the ground investing in these assets. And so, all of this analysis along with client-specific goals and constraints, of which there are always many, this allows us to come up with just practical options to achieve the desired return while minimizing risk and hitting those liquidity targets.
So one question that we frequently get asked on strategic asset allocation is, "How much private market should I actually have in my portfolio?" And I think this is fascinating, because whenever you say, "what should my allocation to X be?" In the back of any quantitative person's mind, there's an efficient frontier that shows up. And in this case it's not at all driven by an efficient frontier, it's driven entirely by liquidity. So when you ask what your allocation should be, I ask you, "What are your obligations? What is your portfolio supposed to fund? How much cash is coming into your portfolio? And in a downturn situation, what are your cash needs?" And all of those questions together will help determine how much private markets you could have in your portfolio in a kind of structured, disciplined way.
And then, there's another part of our process that's practical, and I think we just don't take efficient frontiers as gospel. So I think that there can be portfolios that are very close to that efficient frontier and just have better properties. So they're better diversified across asset classes, they have more yield or other things, and they're just off the efficient frontier. And so, we just look in a neighborhood. We don't say, "Oh, this is the optimal portfolio. You're welcome." Those other portfolios can actually make a ton of sense for investors. And another example is that near-efficient portfolio, that could be just more favorable in terms of J-curve, which is another topic that we have to deal with in private markets.
Stewart: Yeah. So if we were to ask, of the tools that you use, and I want to move on. I want to get Kyle into the conversation here. If we were to say, "What percentage of the quantitative tools that you're using at StepStone are homegrown and what percentage of them are commercially available?"
Lisa: So Stewart, that is a fantastic question. And essentially, all of our data and all of our models are homegrown. Private markets is this area of the world where there is no Bloomberg. There hasn't been massive amounts of academic study because everybody has access to words or other databases. So the amount of models that we have just off the shelves that have been studied for the last 70 years is relatively low. Our data also has interesting features that we have to be quants about, right? We have to be simple, disciplined, structured. We have to understand the world in a simplified way that makes sense and is useful and practical.
So I think that there's a lot of heavy lifting on the private market side because there isn't a lot of stuff that you can just read a paper and implement. And that's what makes it super fun, Stewart. This is kind of the key reason why doing quantitative work in private markets is just, it's creative, it's fun. You get to do new things. I love it.
Stewart: That's awesome. I think it's fair to mention that the database is called SPI, and it is available, depending upon which side of the ball you're on, you can buy a subscription to SPI and actually get access to the quantitative research and the qualitative research that StepStone does. Is that fair to say, so people know that that's available?
Lisa: That's absolutely right. Sashi and Kyle, they're like, "Why are you waxing poetic about data?" But in private markets, it's just the data is the crucial element. It's hard to get. You have to amass it over years. And we've been very disciplined and very careful over a number of years, and we're so proud of SPI. So yeah, if anyone wants to reach out and talk to us about that data set, we're more than happy to talk more.
Stewart: That's great. Kyle, thanks for your patience. Kyle Johnson, CFA, Associate Director of Quantitative Research at Principal Asset Allocation. Welcome, sir. And where did you grow up? And what was your journey into being what non-quants call quant?
Kyle: First of all, thanks sir, for having us on. Thanks for facilitating this conversation. I grew up in Minneapolis, Minnesota. So, so far, we are two for three in terms of Midwest representation.
Stewart: Yeah, absolutely. Well, I'm from Missouri, so that counts too. And what about the journey for you? Did you know as a kid that you had high aptitude for math or for quantitative methods? When did it click for you?
Kyle: Yeah. So actually, growing up, so I started, there was a math program at the University of Minnesota that I started at in sixth grade, seventh grade, it's so far back now. But I started there and I was like, "Oh wow, I'm actually, I have some aptitude for this. I ended up going to school at the University of Iowa for business and then a Master's in Actuarial Science, which seemed like a relatively appropriate application. I wanted to go into some business type thing and I wanted to use my mathematical and quantitative skills.
So the first few years of my career after that, I worked in a number of actuarial and data analytics roles, and over that time period got my FSA. I'm now a recovering actuary. But as I was going through that process, I had determined that I really like the structured application, a quantitative process. You actually get to do the thing, and so a structured application of quantitative processes and quantitative thought processes. And the quantitative investment side of things seemed to be a relatively logical extension of my prior work.
Stewart: Yeah. And there's nobody on the planet that knows much about the actuarial profession or the FSA that doesn't have a great deal of respect for that designation. So congratulations on that. You're also a CFA. What in your mind is a quant? How do you think about that when you hear the term? What is a quant to you?
Kyle: Yeah. I like to think that Principal thinks about it in the same way. So a quant is you can be relatively disciplined in a thought process, right? And sort of, relatively disciplined, what does that mean? You can get up every morning at 4:30 in the morning and go and lift weights, whether it's hot or cold, dry or wet. You can be disciplined, you can get up and you can do that. Right? Other than maybe adding the numbers on the weights up on either side of the barbell, there's probably not a lot of quantitative discipline there.
Separately, you can be quantitative, but you don't really have necessarily an application for it. One example that I like to use, and one thing that I've seen over and over again, is sports announcers can be relatively quantitative without an application. How many times have you heard a sports announcer come up and say, "Patrick Mahomes is five for seven on third downs in the third quarter of stadiums that begin with consonants?"
Stewart: Yeah, exactly. And you go, "Okay. Well now what?" Right?
Kyle: That's quantitative. You're coming up with something, right? Hill of beans on what you do from there, right? It's a neat little factoid, but you can't really do anything with that, right? And so, when I think of what a quant is, it is both a disciplined application of quantitative processes in a means to an end, and in this case, building and implementing equity and global macro investment products.
Stewart: And that's interesting because it kind of leads me to my next question, which is what makes quantitative products unique in your mind?
Kyle: Yeah. So quantitative products, they are built off of measurable, predictable, and controllable building blocks. And in a quantum, you take these, you research these building blocks, and then you can combine them in different ways. I kind of think about it like a baking recipe, but you can add 10% of this, 20% of this, and you can kind of vary the proportions and the elements of these different building blocks. And that allows you to produce investment products with relatively predictable factor exposures, relatively predictable volatility, and importantly, relatively predictable correlations with other asset classes and strategies.
And the ability to do that, the ability to customize solutions, the ability to customize different exposures and put them together makes it a really compelling combination for acid allocators. We can come in and we can serve as, we can help balance out a client's portfolio to lower their drawdown risk or increase their overall targeted expected return. And sitting in an asset allocation team, the approach that we take is we really think that we have a unique insight on the views and the needs of asset allocators, and we can help design unique solutions that fit nicely within their tool set and solve problems for them.
Stewart: And it's also, I think, interesting that a lot of times investment strategies are based on anomalies, or mispriced assets, where the price of the asset for whatever reason doesn't reflect the intrinsic value. And generally speaking, the theory as I understand it anyway, is mean reversion, right? So if this thing is eventually all assets are priced at where they should be, and if I can buy, in the immortal words of Warren Buffett, "If I can buy dimes for nickels, good things will happen to me." How do you use quantitative methods to exploit asset class anomalies?
Kyle: That's a good question. So again, you hit on something really important there, Stewart, which is an anomaly has to have a reason for existing. And you can go on and on with, 'Well, Greek cement production is correlated with Euro area growth, the Euro area growth factor." And you can run a bunch of regressions and you can come up with stuff. I would argue very strongly that you found a spurious correlation or there is a much cleaner way to express that concept. And so, you have to start with, to your point, a reason that there's an anomaly, a reason that there's an anomaly that exists.
Two classic examples are valuation and momentum, right? So valuation would be when people tend to hoard and they tend to take bad news and they tend to get a little bit more pessimistic on the bad news, on bad news. And you get 3, 4, 5, 6, 7 quarters of bad news and all of a sudden you started selling that nickel for it was 9 cents, it was 8 cents, it was 7 cents, and now that's 5 cents and it's never getting back. And so, if you can find a structured way of identifying statistically cheaper companies generally in combination with something else with durable cash flows and a dependable business idea, generally companies that will tend to outperform in the long run.
Conversely, there's a concept of momentum, which is generally things that have been going up keep going up. In equity land, that happens somewhat differently in other asset classes, but that's an exploitation of another psychological anomaly with which when you go to buy a stock or when you go and buy a fund, you want to see that it's been going up. And it's psychologically easier to do, right? So if you're buying things that keep going up, over time you will probably do reasonably well. Momentum tends to have a characteristic where it works a decently large amount of the time, and then it really doesn't work. So you get base hits and then you give up a triple play or you give up a double play.
Value tends to be a very kind of plods along, it doesn't do very well, it doesn't do very well, it doesn't do very well. And then, you have two or three years of very, very large over-performance.
And so, the reason that I'm highlighting these two strategies is now you can get the view of, "Okay. Well, you have something that works most of the time and then really doesn't work. And you have something else that doesn't work a lot of the time and then really does work. And it turns out when you put these two things together, you can get a relatively well-structured product that is relatively well-balanced."
Stewart: That's awesome. Thank you so much. And we are moving along to Sashi Naik. And Sashi, you are with Fidelity. You are a Senior Portfolio Manager there. And you have been at this for a minute and I'm thrilled to have you on. The first question that comes up is, where'd you grow up and how did you become a quant at Fidelity?
Shashi: Thank you, Stewart, thank you so much for having me. Really excited to talk about quantitative investments at Fidelity. So just to give a brief background on me, so I grew up in India. I cannot say as Lisa and Kyle said that they were mathematic. I wasn't a math nerd at all, although I was a techie. I always knew that I liked computers and I liked programming. So although my background in my undergraduate studies is accounting and then an MBA, I kind of self-taught myself a whole bunch of programming languages and ended up in IRD consulting.
Having spent a brief amount of time there, I realized that I needed to broaden my horizon and always being good at math. I wasn't a math nerd, but I was good at it, so I thought maybe why not look at options trading or things like that, and decided to enroll in a quantitative finance program at Boston University. And once I graduated from my Master's in Mathematical Finance program, I was hired as a quant analyst at one of the quant firms in Boston named PanAgora Asset Management. And from there, I was hired at GeoCapital, and from GeoCapital to Fidelity again. So those technically are two different firms, but as far as my job is concerned, it's the same job. And I can elaborate that when I start digging into what QRI is and SES is.
Stewart: Absolutely. That's very cool. We have a friend, an advisor friend of InsuranceAUM's named Bill Pachaca who was involved to PanAgora at one point. So shout out to Bill there. So Sashi, talk to us a little bit about QRI and SES at Fidelity.
Shashi: So when investors think about Fidelity, they think about this big behemoth that it's mostly fundamental investments, star portfolio managers and things like that. QRI, which stands for Quantitative Research and Investing, is an endeavor/group within Fidelity that was started around 2020, I think right around pandemic, to kind of bring all the quants at Fidelity, which were not in kind of all big star roles and kind of have an umbrella around them. So, to create a group to include all the quants in one place. So that was kind of the genesis of the QRI group. Fidelity has always had quants within its ranks. It's not as if this is a new enterprise at Fidelity, but they were just dispersed and kind of reporting within different managers and different groups. So this was an effort to bring all those quants within one group, and that's how QRI was formed.
Systematic equity strategies is like a subgroup within QRI, and our story is slightly nuanced in the fact that we came over from Geo. GeoCapital is a company that manages all of Fidelity's index funds. If investment professionals have ever invested in Fidelity index funds like Spartan, and index funds of the past, they were all managed at Geo. And we had this tiny little world of managing active quant investments using quantitative techniques, which was a really tiny world of about $10 billion in a firm that managed a trillion dollars.
So Fidelity kind of came in and decided to move this whole group from GeoCapital into this newly established QRI to kind of give these strategies a new home and a new direction. So that's our genesis of SES within QRI. Although we have been at Fidelity only for the last two and a half years, we have managed the same exact strategies for close to 17 years. So these strategies that we manage have now a 17-year track record. So that's kind of the background for QRI and SES within QRI.
Stewart: That's super cool. So can you talk at a high level about what kind of strategies those are, just so that people get an idea of applications of quantitative methods?
Shashi: Right. So what our group specializes in is providing benchmark level strategies. So we try to give exposure. Most of them, of course, in our name itself, they're all equity strategies by design. What we are trying to do is give investors a very similar return and risk profile as respect to benchmarks, for example, S&P 500, Russell 1000 Value, Russell 1000 Growth. But using quantitative techniques, we kind of try to tilt our portfolios away from those benchmark exposures and what you see in benchmark intake that are slightly different in terms of sectors, industries, or stock levels, using our quantitative design investment philosophy based on our philosophy. And then, using those what we call factors, we kind of tilt our portfolios away from what the benchmark looks like and kind of try and deliver outperformance over the underlying benchmark.
So our idea is that yes, we will try to give you a very similar risk return profile of the underlying equity benchmark, but at the same time we'll try and harness some "alpha," if people are familiar with that word. It's a word that's commonly used in the quantitative landscape. But then, when you talk about alpha and beta it kind of falls flat, people don't really understand the difference between those two.
Stewart: And what would be awesome is can you explain a little bit about the word and the difference? It's just a learning opportunity for our audience. They'd love to hear it, I'm sure.
Shashi: Absolutely. So beta is essentially your benchmark return exposure. So if you are invested in an index fund like an S&P 500 index fund, that essentially is your beta. Now here comes a fund that my team manages that try to deliver returns that are over and above those benchmark returns. That is what in the most simplistic terms can be termed as an alpha. Now obviously, there's a statistical regression-based definition of what alpha and beta means and all those things, but not trying to get into the minutia of that, but just trying to understand that any performance that you get over and on top of the benchmark by not taking outrageously ridiculous bets to achieve those outperform returns is essentially how we look at alpha.
Stewart: Very interesting. And I realize that these are proprietary strategies and that you can't go into too many of the details, but can you provide us with an overview of the investment and portfolio construction process there?
Shashi: It's very similar to what a lot of quantitative portfolio, if you're in the landscape, you will get these words. Even Kyle mentioned these two things, valuation, momentum. So a lot of quants will keep talking about these factors, how we think about these factors, all the factors that the fundamental guys look at like price to equity ratio, free cash flow to price, return to enterprise value. So as quants, we also look at these same exact measures. We just look at them through a quantitative framework. We don't go and analyze balance sheets or return statements, but we just kind of stuck them in a database and analyze all these metrics that any fundamental investor/analyst must use.
But what these quantitative tools allow us to do is to look at a whole host of other things that may not be humanly possible. So other than the valuation signals, we look at momentum signals, we look at quality of a company's signals like return on assets, return on equity. We look at growth type signals like cash to assets, how much cash the company has, just giving examples there.
What we have also done at SES is also invested a lot of our efforts into looking at what we call non-traditional kinds of data sets. For example, what are the options markets saying about a particular stock, trying to look at earnings transcripts. We have a natural language processing of we actually go in and try and understand what the sentiment is being described in a particular earnings transcript without actually having us to read it. And then, also we look at are there any insider who's buying or selling stocks?
But there are a lot of things that we are able to look at when we analyze a group of stocks. And not only that, just because this is a quantitative process, we are not limited by the number of stocks we can analyze. If there's a Russell 3000 benchmark that has 3,000 names in it, our process is perfectly capable of going and analyzing each and every individual security within that benchmark and coming up with our proprietary buy and sell signals.
Stewart: That's really interesting. I have gotten such great education on quantitative methods today from this group, and I've got a couple of fun ones for you out the door if you'll allow me. And this is for everyone. I've got a very soft spot in my heart for students who are new to the industry, coming out of college, whatever else. What advice would you give someone who was either in school or recently out of school who was interested in getting into this area of finance? Kyle, I'll start with you. I feel like you've got some good insights there. I want to go to Lisa, Laura, and Shashi respectively. Be happy to do it. But if you wanted to get into this business, Kyle, how would you do it?
Kyle: So I would start with observe yourself and think about what gives you energy. And whatever gives you energy, do more of that. It's a competitive field and you're going to be up against people who are both very talented and they like what they're doing. And you're going to go much farther, and actually in general be much happier in life, if you go and you do what gives you energy. So if you like the mathematical side of things, if you like the programmatic side of things, try to make inroads there and lean into your strengths and lean into what gives you energy. And then, as a student, try to find the student groups and get involved in those and try to show some effort. And people will generally gravitate to talent.
Stewart: That's awesome. Anybody else got anything to add there?
Lisa: I would just jump in that, I don't know, Kyle, what gives me energy is learning and curiosity and finding something new. And also taking the time to... Like you say, it's easy to ask good questions and it's hard work to find good answers that are logical and simple and executable. And so, I think that ask a million questions. There are so many quantitative jobs out there, you should not hesitate to apply for a lot of them. Ask each one a lot of questions in those interviews, and just look at what you're getting back from those answers. Have you learned something? Are these curious people? Are they going to help you grow into the niche area of finance that you end up in? I think good questions, good answers, that's just critical.
Stewart: Good stuff.
Shashi: Yeah, and I would second in that. Based on what Kyle and Lisa said, if after having done all those things, you feel like this is for you, then I highly recommend getting that coursework in software programming languages, programming techniques, and statistical techniques. Those will go a long way in getting you that first internship. And that first internship can be your door into a whole world that will open up to you. So yeah, I would...
Stewart: That is great advice. All right. So here's the last one, the fun one. So we normally do this, a lunch table of four, but given that we've got folks here, a lunch table of five, it's everybody gets to invite one person to lunch. And it can be anyone alive or dead. And I guess it would be with the four of you and four guests, that would be on a lunch table of eight. So our expense account's going to get a little bit of a ding, but hey, what the heck? Who wants to start with who they'd most like to have lunch? Lisa?
Lisa: I'll start because I'm going to pick something very boring. I want to have lunch with Warren Buffett because again, just droning on about this point of having these simple, logical ideas. Quantitative work, it's not just about calculating. It's really about coming up with logic that is airtight, that makes sense, that you can tell other people about and they're like, "Yes, that's solid." And so, I think that Warren Buffett, he has all of these quotes that we've just mentioned because he had this very simple nuts and bolts approach to things. It makes sense or it doesn't. And having kind of that extra layer there at lunch would be delightful.
Stewart: Awesome. Sashi, how about you?
Shashi: I think I would go with Jim Simmons, the founder of Renaissance Technologies. It is just amazing to see what kind of returns that firm has been able to deliver and would just be mind-boggling to actually ask questions and figure out what strategies they used and how they deployed them and how they were able to scale this up to the assets that they were able to manage and deliver these returns. That would be very interesting talk.
Stewart: Very cool. How about you, Laura?
Laura: I actually haven't given thought to this question. And if I have just one, I would love to have launch with Lisa Larsson here.
Lisa: I love that. I love that.
Stewart: Very nice, very nice. That's awesome. How about you Kyle?
Kyle: So I'm going to take this a little a bit different direction. And the other people, they touched on finding the logic behind things and finding what's true and different ways of arguing and getting to the truth. And in that vein, Plato comes to mind, so the Greek philosopher.
Stewart: Wow, there you go.
Kyle: You have to be doing something somewhat special if people are still talking about you, what, 2,000-3,000 years later?
Stewart: Absolutely.
Kyle: And just one of the great, all-time, historical minds. Maybe I'd get something out of it, maybe not, but that'd be kind of fun.
Stewart: That's awesome. I've gotten a great education today. I really appreciate all of you being on, so thank you so much.
Lisa: Thanks, Stewart.
Shashi: Thanks, Stewart.
Laura: Thank you.
Kyle: Thanks, Stewart.
Stewart: My pleasure. We've been joined today by Kyle Johnson, CFA, Associate Director of Quantitative Research at Principal Asset Allocation, Lisa Larsson, Partner at StepStone, and Laura Zhang, also with StepStone, and Sashi Naik, CFA, Senior Portfolio Manager at Fidelity. Thanks everybody so much for the education, and thanks for taking the time today.
Information provided in the program is for informational, educational purposes only. To the extent any investment information is deemed to be a recommendation, it is not meant to be impartial investment advice, or advice in a fiduciary capacity, and is not intended to be used as a primary basis for you or your clients’ investment decisions.
Fidelity and its representatives may have a conflict of interest in the products or services mentioned in this material, because they have a financial interest in them and receive compensation directly or indirectly in connection with the management, distribution, and/or servicing of these products or services, including Fidelity funds, certain third-party funds and products, and certain investment services.
Views expressed are as of the day of the recording, based on the information available at that time, and may be changed based on market or other conditions. Unless otherwise noted, the opinions provided are those of the speakers, and not necessarily those of Fidelity Investments or its affiliates.
Alternative investments are investment products other than the traditional investments of stocks, bond, mutual funds, or ETFs. Examples of alternative investments are limited partnerships, limited liability companies, hedge funds, private equity, private debt, commodities, real estate, and promissory notes. Some of the risks associated with alternative investments are: Alternative investments maybe relatively illiquid. It may be difficult to determine the current market value of the asset. There may be limited historical risk and return data. A high degree of investment analysis maybe required before buying. Costs of purchase and sale may be relatively high.
Fidelity does not assume any duty to update any of the information. Advisor Group is a client of Fidelity Institutional, and is an independent company, unaffiliated with Fidelity Investments. Its business models, needs, and results may not reflect the experience of other Fidelity clients.
Third-party trademarks and service marks are the property of their respective owners. All other trademarks and service marks are the property of FMR LLC or its affiliates. All other trademarks and service marks are the property of FMR LLC, or its affiliated companies.
Fidelity Institutional provides investment products through Fidelity Distributors Company LLC; clearing, custody, or other brokerage services through National Financial Services LLC or Fidelity Brokerage Services LLC, members NYSE, SIPC; and institutional advisory services through Fidelity Institutional Wealth Advisor LLC, copyright 2024 FMR LLC, all rights reserved.
1175069.1.0
The InsuranceAUM.com podcast is available on
