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Speaker 1: This is mesters in Business with very Renaults on Blueberg Radio.

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Speaker 1: This week on the podcast, I have a special guest.

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Speaker 1: His name is Gary Kropovka. He's the president of World Quants,

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Speaker 1: a highly regarded quantitative investment firm. UH. Gary has a

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Speaker 1: fascinating background, really insightful UH twenty years at ge Sam

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Speaker 1: and Goldman Sachs Asset Management, where he was co head

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Speaker 1: of the quantitative investment Strategies team. Ge Sam runs a

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Speaker 1: ton of capital UH, and last year he moved over

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Speaker 1: to world Quan, which in and of itself was spun

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Speaker 1: out from Millennium Management in two thousand and seven. Millennium

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Speaker 1: Management is another giant quantitative hedge funds, and World Quan

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Speaker 1: runs a nice lug of capital for them. As innovative

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Speaker 1: as so many different quantitative approaches are, World quant Is

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Speaker 1: really stands out. They're an unusual shop. They do a

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Speaker 1: lot of really interesting things that read led by UM,

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Speaker 1: a very a kind of clastic and brilliant founder and

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Speaker 1: CEO UM And really this is just a very intriguing

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Speaker 1: and fascinating conversation if you are at all interested in

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Speaker 1: quantitative investing, understanding one of the key drivers of markets

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Speaker 1: today or just to get a sense of what people

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Speaker 1: with advanced computer and mathematical degrees think about UM, the

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Speaker 1: financial engineering that's taking place in the markets these days.

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Speaker 1: You're going to find this to be a fascinating conversation. So,

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Speaker 1: with no further ado, my interview of Gary Krapovka of

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Speaker 1: World quant This is Mester's in Business with very Renaults

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Speaker 1: on Bloomberg Radio. Our special guest this week is Gary Propovka.

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Speaker 1: He is the president of World Quants, a highly regarded

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Speaker 1: quantitative shop spun out of Millennium Management back in two

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Speaker 1: thousand and seven. Gary has a BA in mathematics and

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Speaker 1: a master's degree in financial engineering from Colombia. He's also

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Speaker 1: on the board of trustees of Rutgers University. Gary Kropovka,

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Speaker 1: Welcome to Bloomberg. Thank you so much, Barry. Great to

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Speaker 1: be here. So I'm I'm kind of fascinated by your background.

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Speaker 1: You you spend time UM at the quantitative investment strategies

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Speaker 1: co heading that at Goldman Sachs, and you have your

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Speaker 1: financial engineering degree from Colombia. Any overlap with Emmanuel Derman,

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Speaker 1: you seem to have followed his footsteps. Yeah, I actually

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Speaker 1: I think I predated Emmanuel Derman because I I was

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Speaker 1: in the program when it first start bit back in

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Speaker 1: the early two thousands. UM I did follow em Manuel,

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Speaker 1: I guess to to Goldman facts UM. You know, after

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Speaker 1: he had he was there, but separate paths, but there's

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Speaker 1: definitely a correlation among the two UM A share. I

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Speaker 1: went to Columbia, you know, after I joined the quant

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Speaker 1: group at Goldman UM, there was you know, looking looking

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Speaker 1: around the space, there were a lot of folks with

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Speaker 1: some pretty advanced degrees, and decided to try to marry

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Speaker 1: the computer science as well as the engineering with some

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Speaker 1: of the business side to uh, you know, to to

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Speaker 1: be better trained in the in the quant field. So

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Speaker 1: so you eventually become co head of quantitative Investment Strategies

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Speaker 1: at ge SAM. What what was that experience like? So,

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Speaker 1: I would say I spent at over twenty years in

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Speaker 1: the same group, and you know, I it really drove

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Speaker 1: what I love about, you know, my job, which is

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Speaker 1: fun dative investing. It's something that I have a huge

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Speaker 1: passion for. I love, you know, dealing with data and

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Speaker 1: figuring out problems. And you know, there were certainly a

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Speaker 1: lot of investment problems that we dealt with in that

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Speaker 1: in that group, and you know, really compelled me to

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Speaker 1: go and join World Quant for for you know, even

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Speaker 1: other opportunities. But you know, while I was at Goldman,

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Speaker 1: did a number of different things on the research side,

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Speaker 1: on the portfolio management side, on the product development side,

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Speaker 1: the client side, and so had a had a host

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Speaker 1: of experiences that I cherished, had a great experient, great

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Speaker 1: time there, learned a ton and uh and now I'm

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Speaker 1: here at the World Pants for the last roughly six months.

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Speaker 1: So we're going to talk more about World Quant in

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Speaker 1: a in a few minutes. Let's let's stick with the

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Speaker 1: big data you referenced at Goldman and elsewhere. You know,

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Speaker 1: big data is almost a cliche these days. How is

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Speaker 1: it used in quantitative investment? Yeah, I would say, you know,

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Speaker 1: when I think about big data, and you know, it's

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Speaker 1: a it's a large term. But I would say, you know,

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Speaker 1: we're all consumers, not just in the investment or in

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Speaker 1: the quant group, but this whole concept around big data

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Speaker 1: is affecting each and every one of our lives. We're

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Speaker 1: all trying to have a have an information edge, We're

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Speaker 1: trying to make better decisions, we're trying to you know,

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Speaker 1: utilize as much data to make informed decisions of where

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Speaker 1: we're spending our time, whether it's things like going on

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Speaker 1: vacation or you know, figuring out where you what restaurant

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Speaker 1: you want to go to. And so, you know, the

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Speaker 1: world has moved beyond things like zag. It's um and

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Speaker 1: really trying to understand the idea. There's a lot of

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Speaker 1: things that will provoke what you want to do or

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Speaker 1: where you want to spend your time and where do

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Speaker 1: you want to invest in. And so this whole concept

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Speaker 1: of big data is really to take you know, anything

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Speaker 1: and everything that may be applicable to a company and

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Speaker 1: try to learn from it. And so, you know, there's

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Speaker 1: just this massive amount every time we click on something,

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Speaker 1: time we move, there's all this data that's being captured.

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Speaker 1: And really, you know, one of the great things about

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Speaker 1: being a quantitative investor is that we have tools and

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Speaker 1: techniques to take all this awesome amount of data which

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Speaker 1: comes in many forms and I could touch on that,

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Speaker 1: but it comes in many forms and convert that into

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Speaker 1: some insight or some informational edge that helps us predict

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Speaker 1: companies or particular asset class. So this whole concept of

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Speaker 1: big data absolutely here to stay I'd say it's much

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Speaker 1: broader than the investing business. It's happening, you know, all

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Speaker 1: of our lives. We're all sitting with you know, the

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Speaker 1: phones in our pockets that have massive amounts of information

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Speaker 1: and so really the goal of all this big data

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Speaker 1: is to create an informational edge to know something that

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Speaker 1: maybe somebody else doesn't or um in order to be

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Speaker 1: able to leverage that in in pursuit of learning something else.

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Speaker 1: So give us, give us an example, how can you

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Speaker 1: use a data set, uh, specifically to identify opportunities that

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Speaker 1: other people that aren't looking at that data might miss. Sure,

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Speaker 1: So I think there's there's tons of data out there

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Speaker 1: that you know, one can glam that. We could take

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Speaker 1: an example of, you know, looking through analyst reports and

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Speaker 1: you know a lot of people read analysts reports, and

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Speaker 1: so you know, things you can do is try to

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Speaker 1: pick up on their sentiment and so how we're analysts

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Speaker 1: starting to change their mind about a particular company. You know,

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Speaker 1: it is a pretty common example of you know, figuring

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Speaker 1: out how you know, you can train a computer to

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Speaker 1: read all of these words that some of these analysts

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Speaker 1: are putting together. UM. That might be one example, UM

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Speaker 1: looking at you know, what's in the newspaper and trying

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Speaker 1: to gauge sentiment around you know, what's popular and maybe

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Speaker 1: what topics are interesting and what companies may be related

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Speaker 1: to those topics, and or those topics trending positively or negatively.

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Speaker 1: Those are some examples of of ideas where you know

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Speaker 1: there's something out there that you know not as company,

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Speaker 1: may not be coming out of a company's financials, but

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Speaker 1: it's something that's happening around the company that might be impactful.

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Speaker 1: So you know, those are two examples of of items

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Speaker 1: that you know you'd constitute as big data because you're

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Speaker 1: looking at massive amount of whether it's research reports or

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Speaker 1: news articles, to kind of get a gauge of can

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Speaker 1: I have a better picture of that company's fortunes? And

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Speaker 1: I would say, you know, one of the things that

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Speaker 1: we do at work Pond is you know, there's not

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Speaker 1: just three ideas or five ideas. There's millions of ideas

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Speaker 1: of ways to to navigate and have a view on

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Speaker 1: a company, and big data forwards us the opportunity big

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Speaker 1: data along with some you know, some great analytical tools

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Speaker 1: to be able to kind of have views on particular companies. Huh,

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Speaker 1: so so how does that play into things like smart

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Speaker 1: data or factor based approaches. Is that something that you

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Speaker 1: can apply um large data sets towards identifying new variations

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Speaker 1: on absolutely? And I think you're touching on an important

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Speaker 1: component if we think about the quant industry, you know,

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Speaker 1: really started with a lot of these kind of let's

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Speaker 1: call them smart betas or traditional measures of factors. So

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Speaker 1: thinking about things like value or momentum, value being a

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Speaker 1: cheap company relative to its book value as an example, momentum,

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Speaker 1: So if the stock is starting to trend in a

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Speaker 1: favorable direction, will it continue that particular trend? And so

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Speaker 1: you know, the whole idea around analyzing all the data

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Speaker 1: you know as quants. For the original quants, you really

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Speaker 1: wanted to play off the law of large numbers, and

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Speaker 1: so you had a lot of a lot of data

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Speaker 1: yet information on each and every company, thousands of companies,

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Speaker 1: and you try to rank companies by these particular metrics

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Speaker 1: price to book or some measure of momentum. And you'd

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Speaker 1: create a portfolio around those kind of quote unquote smart datas.

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Speaker 1: And you know that tried and true works over time,

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Speaker 1: and I think you know, as the industry is evolved,

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Speaker 1: the smart data strategies, Um, there's now more interesting other

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Speaker 1: ways of evolving and utilizing things like big data to

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Speaker 1: be able to similarly look at those look at factors.

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Speaker 1: So very similar to rank companies. So quants always want

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Speaker 1: to play the breath game, meaning spread out their bets,

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Speaker 1: have a lot of different views on particular companies. But

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Speaker 1: what the alternative data and big data allows us to

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Speaker 1: do is really play the depth game, so know a

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Speaker 1: lot more about a particular company as opposed to just

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Speaker 1: their price to book. So you know, back to your

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Speaker 1: original question. The smart data strategies you know, which are

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Speaker 1: largely common um implementable, absolutely use large amounts of data, um,

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Speaker 1: you know in a pretty uh academically proven, you know,

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Speaker 1: well thought out, but have been around for many decades.

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Speaker 1: So one of the phrases I've been reading about is

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Speaker 1: variation and of that customized data. What what is comdomized data? Yeah,

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Speaker 1: so it is an interesting topic customization, I would say

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Speaker 1: when we think about when you think about customized data

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Speaker 1: in the industry, Um, you know, there's there's really two

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Speaker 1: things that are happening. One is, um, what types of

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Speaker 1: i'll call it bets would you like to make? So

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Speaker 1: you know, do you want to bet on value stocks,

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Speaker 1: do you want to bet on companies that are um

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Speaker 1: have higher dividend payers, And you're able to customize what

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Speaker 1: you want to place a wager on UM. The other

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Speaker 1: part of the customization, which continues to be a pretty

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Speaker 1: interesting trend in the industry, is there are certain E

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Speaker 1: s G. Factors that one may want to hold near

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Speaker 1: and dear and want to have companies in their portfolios

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Speaker 1: that express their the beliefs that they, you know, have

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Speaker 1: and want to express. So for example, you know, I

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Speaker 1: don't want to invest in tobacco stocks, or I don't

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Speaker 1: want to invest in, you know, something that is going

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Speaker 1: to negatively impact the environment, and so you can, you know,

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Speaker 1: with quant tools, you can figure out, okay, what are

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Speaker 1: those companies or how do they fall into those categories,

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Speaker 1: whether it's an industry or the percentage of revenues that

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Speaker 1: company is going to get from you know, let's say

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Speaker 1: emissions UM and then be able to create a portfolio

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Speaker 1: to identify, you know, whether it's a factor bet around

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Speaker 1: value or momentum and or um you know, different types

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Speaker 1: of exposures that they want. So for example, things like

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Speaker 1: tobacco or or emission, so you can customize the what

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Speaker 1: your equity portfolio looks like relative to a benchmark or

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Speaker 1: just an absolute. So let's talk a little bit about

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Speaker 1: what you do at World Quants. What does the president

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Speaker 1: of the firm's jobs responsibility look like? Great barrier. Yes,

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Speaker 1: So so as as president, which I'm extremely fortunate to

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Speaker 1: have joined such an incredible team, you know, I'd say,

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Speaker 1: really three things that I focus on. You know, one

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Speaker 1: is overall business strategy, help with the operating of the

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Speaker 1: operating of the firm, and then add some leadership on

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Speaker 1: the investing on the investing side, and it really that

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Speaker 1: breaks down into kind of four key elements that you know,

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Speaker 1: in terms of my role, and I work very closely

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Speaker 1: with our CEO, Igor Tolchinsky, UM and really thinking about

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Speaker 1: the following four things. One is vision, so you know,

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Speaker 1: where where should we be spending our time? I would say, interestingly,

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Speaker 1: we've got you know, roughly all over six hundred quantitative people,

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Speaker 1: and so you know, we feel like we could solve

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Speaker 1: a lot of interesting problems. UM. And really one of

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Speaker 1: one of our jobs is to ensure that we're focusing

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Speaker 1: on the right ones to solve and so you know,

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Speaker 1: the you know, setting out that vision, UM, keeping people focused, UM,

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Speaker 1: making sure that incentives are aligned, were allocating resources to

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Speaker 1: tackling the right problems and and remaining focused on those

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Speaker 1: types UM. Speed and one of the things that you know,

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Speaker 1: in an organization that has over six people, you want

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Speaker 1: to make decisions quickly. UM Igory does a terrific job

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Speaker 1: of you know, of of of leading in attempt to

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Speaker 1: help him with that in terms of making decisions, making

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Speaker 1: sure things escalate very quickly, UM, so that we can

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Speaker 1: continue our focus and our vision. And then the last

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Speaker 1: thing I spend a decent amount of time on is talent.

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Speaker 1: And you know, how do we acquire talent? How do

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Speaker 1: we promote a culture of collaboration? UM? Intellectual stimulation. You

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Speaker 1: know a lot of quants in general, we like to

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Speaker 1: be intellectually stimulated. So how do we continue to do

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Speaker 1: that and create a culture where ideas can be shared

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Speaker 1: and collaborated across the firm? So those are those are

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Speaker 1: where I've been spending my time over the last six months.

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Speaker 1: What sort of programs do you have to incentivize your staff? Sure? So,

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Speaker 1: so we have many different ways that we try to

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Speaker 1: incentivize our people. UM. In terms of the you know,

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Speaker 1: what we do for our for our researchers, and so

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Speaker 1: we have real different challenges that we have around around

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Speaker 1: the world to you know, to to incentivize people's work.

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Speaker 1: And so, you know, that's just yet another piece of

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Speaker 1: the puzzle where um, you know, we're trying to promote

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Speaker 1: a particular activity or particular research and be able to, um,

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Speaker 1: you know, incentivize them, call them out, reward them for uh,

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Speaker 1: you know, for for doing some some really good work.

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Speaker 1: And so you know, we have many of these, and

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Speaker 1: I think one of the unique things about this firm

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Speaker 1: is that we have many different competitions where uh, you know,

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Speaker 1: where people can our our teams can be incented to

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Speaker 1: uh to do different things and to use their mind

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Speaker 1: a little differently and have the right uh incentive structure

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Speaker 1: to be able to to to be rewarded for those

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Speaker 1: So so you're creating these um for lack of a

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Speaker 1: better word, competitions internally to solve an investing problem or

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Speaker 1: equation or issue, and everyone who works in the firm

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Speaker 1: can basically throw the hat in the ring and say

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Speaker 1: this is the way I think we can solve this problem,

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Speaker 1: and then you run the tests and figure out who's

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Speaker 1: the winner on that or is it real time and hey,

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Speaker 1: this is the best results based on your suggestion. Yes,

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Speaker 1: so we we did. You have we have several competitions

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Speaker 1: around around the firm UM with you know, set incentives

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Speaker 1: for each of them, and we kind of have a

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Speaker 1: group of people that try to tackle this and instead

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Speaker 1: of it being relative to others in the firm, they're

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Speaker 1: we're saying, okay, here's a particular UM strategy that we

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Speaker 1: want to spend some time on. Let's see what you

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Speaker 1: can develop UM. And so that's you know, that's an

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Speaker 1: area where you know, we have projects that you might

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Speaker 1: not fit into the core research that we do on

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Speaker 1: a daily basis, but you know, maybe a little more um,

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Speaker 1: you know, a little more out there. Maybe we're trying

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Speaker 1: to look at a different asset class and we want

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Speaker 1: to uncover. So we realize, you know, the the upfront

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Speaker 1: R and D or the research is going to take

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Speaker 1: a little bit longer, and so we want to incentivize

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Speaker 1: them to go out and um, you know, and really

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Speaker 1: think creatively about about capturing and we incentivize them accordingly

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Speaker 1: because they're taking time out of their kind of core

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Speaker 1: to really push the envelope a little bit more in

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Speaker 1: terms of um, you know, in terms of figuring out

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Speaker 1: something unique. One of the questions I was going to

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Speaker 1: ask you is, hey, how is World quant differentiated from

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Speaker 1: other firms? But but things like the accelerated platform, these

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Speaker 1: sound somewhat different than what we typically hear about at

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Speaker 1: a at a lot of shops. Are these common in

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Speaker 1: the worlds of quant or is this a little more

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Speaker 1: unique to what you guys do? Yeah, I think I think,

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Speaker 1: Um sometimes people may do this, um, you know, for

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Speaker 1: you know, to try to recruit people, and I've heard

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Speaker 1: of people doing that, but putting it as a systematic way,

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Speaker 1: you know, internally, I think is something quite unique. I

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Speaker 1: would say when we think about our you know, our group,

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Speaker 1: and really one of the compelling opportunities that is I

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Speaker 1: had when I when I thought of of joining and

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Speaker 1: fortunate enough to join World Font is you know, we've

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Speaker 1: got over six people around the globe. We operate in

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Speaker 1: twenty three offices thirteen countries. UM. So we've got unbelievable

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Speaker 1: global diversity. And so I think that's you know, one

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Speaker 1: thing that makes us UM quite unique. UM. So we

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Speaker 1: operate in many different places we have many different opinions. UM.

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Speaker 1: We've we've always promoted diversity, diversity of thought, UM, diversity

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Speaker 1: of alpha's or drivers of return when we invest. And

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Speaker 1: so you know, having programs that can continue to incentivize

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Speaker 1: people UM and really create a collaborative and you know,

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Speaker 1: I would say competitive in a in a good way, UM,

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Speaker 1: where where people continue to be intellectually stimulated. I think

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Speaker 1: that's really what you know, really drives the firm, the collaboration. UM.

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Speaker 1: We just recently did a a research tour, a virtual

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Speaker 1: research tour, and myself and Igo and a few other

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Speaker 1: the senior folks kind of did a did a tour

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Speaker 1: around and and you know, it's unbelievable when you know

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Speaker 1: people can promote the collaboration they're sharing with us some

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Speaker 1: of the research and the first thing they say is,

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Speaker 1: I'd like to acknowledge the four or five people that

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Speaker 1: helped with this with this research project. And so you know,

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Speaker 1: just the idea around true collaboration, true appreciation for where

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Speaker 1: you're getting assistance from. You know, I think it was

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Speaker 1: really makes makes this place a pretty unique, unique uh

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Speaker 1: neque place to be. World Tom was spun out of

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Speaker 1: Millennium by Igor Tolchinsky, who is the founder and CEO.

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Speaker 1: Tell us a little bit about your boss. Yeah. So,

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Speaker 1: so when I first met Igor Um, he's just so

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Speaker 1: intellectually stimulating. I mean, a brilliant, brilliant investor, brilliant man. Um.

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Speaker 1: You know, extremely charitable. Um. Some of the things that

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Speaker 1: he's done, UM. You know, so we are just a

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Speaker 1: really spectacular and you can see a lot of those

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Speaker 1: on the web. He's written some really interesting books and

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Speaker 1: just his vision, his ability to articulate, um, you know

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Speaker 1: where we're going, uh, and and and collaborate very well. Uh.

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Speaker 1: To the other thing that is just very impressive is

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Speaker 1: his decision making. And I think I've observed a lot

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Speaker 1: of quants over the years. You know, you kind of

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Speaker 1: get into the analysis paralysis. UM. You know, the there's

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Speaker 1: always another test you can run on something. You know,

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Speaker 1: Igor to his credit, is a decision maker. UM. And

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Speaker 1: it is. You know, it's just great to be able

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Speaker 1: to partner with him for six months of the last

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Speaker 1: six months and you know, look forward for for many

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Speaker 1: many years and decades to come. But he is, you know,

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Speaker 1: someone who really does make decisions, takes in all the

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Speaker 1: information um, and you know, has really built an unbelievable business.

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Speaker 1: Uh here at will on. So when I normally speak

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Speaker 1: to a firm and I say, hey, what's your firm's

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Speaker 1: investment philosophy? Usually I get a sentence that sums everything

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Speaker 1: up in in one nice little SoundBite. I get the

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Speaker 1: sense that you're operating a whole lot of different approaches.

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Speaker 1: It might be a little harder to pin you down

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Speaker 1: to one philosophy of of the firm. What is World

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Speaker 1: Quants investment philosophy? So, so World World Plants investment philosophy

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Speaker 1: is really, you know, pretty pretty simple global leverage our

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Speaker 1: people and provide them the tools and technology two to

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Speaker 1: make returns for our investors. I mean that's really you know,

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Speaker 1: in a nutshell, you know what we're trying to do. Um.

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Speaker 1: We we have a very systematic way in which we

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Speaker 1: do it. We try to leverage the law of large

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Speaker 1: numbers and have millions of different alphas that we can leverage.

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Speaker 1: We put them together in a portfolio and then we

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Speaker 1: execute and make them a reality through trading. So, you know,

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Speaker 1: the investment processes is quite simple and straightforward. But the

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Speaker 1: uniqueness of our philosophy is that we are extremely global

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Speaker 1: in terms of our people. Um, we do believe in

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Speaker 1: in playing the breath game. We have we have a

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Speaker 1: lot of alpha's, a lot of ways to look at

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Speaker 1: companies and we try to leverage that throughout our process

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Speaker 1: and create portfolios that driver turned for our clients. So

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Speaker 1: let's talk a little bit about the past year, which

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Speaker 1: was some people have called it unprecedented. When you are

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Speaker 1: crunching numbers to try and find a pattern, how can

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Speaker 1: you deal with the possibility of events which have simply

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Speaker 1: never occurred before. Yeah, Berry, that's a terrific question. Um.

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Speaker 1: You know, that really separates the you know, the quantz

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Speaker 1: quote unquote and the quant investors. And so, you know,

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Speaker 1: one of the things that makes our jobs so interesting,

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Speaker 1: I find is the ability to adapt and really to

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Speaker 1: to be market practitioners as well as as quants, and

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Speaker 1: I think that really makes great quant investors. So, you know,

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Speaker 1: if we think about two thousand twenty and also in

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Speaker 1: two thousand twenty one thus far, you know we've seen

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Speaker 1: you know, obviously unprecedented events. Um. You know, whether it's

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Speaker 1: around COVID or you know other types of you know

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Speaker 1: of of events that that have happened over the last year,

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Speaker 1: which which ramifications have caused very large moves in you know,

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Speaker 1: kind of common let's call them factors or expressions or

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Speaker 1: buckets of particular stocks or characteristics of stocks. You know,

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Speaker 1: for example, you know, things like momentum we talked about

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Speaker 1: before value. UM, these have had some pretty unprecedented moves. UM.

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Speaker 1: You know, there's been you know, for value, about fifteen

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Speaker 1: UH standard standard aviation moves that are that we're above

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Speaker 1: two in two thousand and UM. You know, one just

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Speaker 1: massive moves. When you think about a simple five center

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Speaker 1: deviation move means that that happens once one day every

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Speaker 1: approximate fourteen thousand years. So to your point, there's been

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Speaker 1: no shortage of massive moves UM, you know, largely because

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Speaker 1: there's been such a big shift. And so I think

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Speaker 1: as quant investors, the way we try to approach it

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Speaker 1: is to I is to adapt as quickly as we

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Speaker 1: possibly can for some unforeseen event. Obviously we try to

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Speaker 1: predict whatever we can in advance. UM. But to the

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Speaker 1: extent UH you know you have something like uh COVID,

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Speaker 1: you know, you want to think about companies that are

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Speaker 1: going to be largely affected because of that, And there's

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Speaker 1: two approaches. One is you can try to risk manage,

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Speaker 1: which is usually what we would do, which is, you know, listen,

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Speaker 1: this is a once in a life time events. Let's

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Speaker 1: try to immunize our portfolios from those. So, whether you

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Speaker 1: know it's a it helps or hurt stocks, let's try

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Speaker 1: to immunize ourselves. And the other is to say, okay,

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Speaker 1: let's try to get a sense whether there's going to

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Speaker 1: be some type of trend here or there's some you know,

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Speaker 1: ability to to create alpha or some excess returns UM

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Speaker 1: when these events happen. So you know, you can think

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Speaker 1: about binary events, so things like elections that have happened

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Speaker 1: UM and what the ramifications are. You can think about

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Speaker 1: things like trade UM. You could think about companies exposure

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Speaker 1: to you know, things like bitcoin when they announce and

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Speaker 1: what do you do about it? And so, you know,

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Speaker 1: we think about the world in characteristics. So we call

430
00:25:43,760 --> 00:25:47,120
Speaker 1: them factors, and so you can create these quote unquote

431
00:25:47,160 --> 00:25:50,320
Speaker 1: factors and say I want to have a portfolio that

432
00:25:50,800 --> 00:25:55,119
Speaker 1: whether those factors do well or poorly, I'm my portfolio

433
00:25:55,160 --> 00:25:57,560
Speaker 1: will not be affected. So that's really the way we've

434
00:25:57,640 --> 00:26:00,040
Speaker 1: We've thought about a lot of two thousand, twenty and

435
00:26:00,320 --> 00:26:02,639
Speaker 1: twenty one and our investment team has just done a

436
00:26:02,720 --> 00:26:06,159
Speaker 1: terrific job of being able to navigate that and identify

437
00:26:06,280 --> 00:26:09,040
Speaker 1: some of these risks that they haven't seen before. We

438
00:26:09,119 --> 00:26:12,160
Speaker 1: try to codify it in a systematic way and then

439
00:26:12,320 --> 00:26:15,199
Speaker 1: focus our attention on, you know, on really where we

440
00:26:15,240 --> 00:26:17,560
Speaker 1: believe we can make money UM, and that's a lot

441
00:26:17,560 --> 00:26:20,800
Speaker 1: of these millions of alphas that we believe have been

442
00:26:20,880 --> 00:26:23,679
Speaker 1: contested for for years. So that's how we think about,

443
00:26:23,960 --> 00:26:26,479
Speaker 1: you know, dealing with some of these unprecedented moves that

444
00:26:26,520 --> 00:26:29,320
Speaker 1: we've seen in you know, things like short interest and

445
00:26:29,440 --> 00:26:33,119
Speaker 1: momentum and value that have happened over the last twelve

446
00:26:33,119 --> 00:26:36,359
Speaker 1: months or so. Huh So. So I'm intrigued by the

447
00:26:36,400 --> 00:26:40,560
Speaker 1: concept of of something UM that's so many standard deviations

448
00:26:40,600 --> 00:26:42,520
Speaker 1: away from the norm that it's really a one in

449
00:26:42,560 --> 00:26:47,280
Speaker 1: a fourteen thousand year events, those sort of tail risks.

450
00:26:47,920 --> 00:26:52,800
Speaker 1: How can we anticipate them on a quantitative basis? And

451
00:26:52,800 --> 00:26:57,359
Speaker 1: and more specifically, UM, think back to January six and

452
00:26:57,600 --> 00:27:01,879
Speaker 1: the attempted insurrection in the US capital. How can you

453
00:27:01,960 --> 00:27:06,680
Speaker 1: quantify that? And we've learned since that that actually came

454
00:27:07,400 --> 00:27:09,960
Speaker 1: pretty close to I don't know if I would call

455
00:27:10,000 --> 00:27:15,440
Speaker 1: it successful, but but pretty close to having the rioters

456
00:27:15,680 --> 00:27:20,720
Speaker 1: access UM, various people in Congress, maybe even the Vice president.

457
00:27:21,760 --> 00:27:27,160
Speaker 1: How do you factor that into to your UM analyzes. Yes,

458
00:27:27,320 --> 00:27:29,119
Speaker 1: as I would say, you know, I'll take it up

459
00:27:29,160 --> 00:27:31,520
Speaker 1: a step in terms of just in general how we

460
00:27:31,600 --> 00:27:34,160
Speaker 1: think about it. But it's really about, you know, trying

461
00:27:34,160 --> 00:27:38,679
Speaker 1: to identify things that will impact UM companies and you know,

462
00:27:38,720 --> 00:27:42,160
Speaker 1: what are the ramifications and and I think that's really

463
00:27:42,200 --> 00:27:44,919
Speaker 1: the way we try to think about that. So you know,

464
00:27:44,920 --> 00:27:48,399
Speaker 1: in that specific case, in terms of what would happen

465
00:27:48,400 --> 00:27:51,640
Speaker 1: to particular companies UM, you know, those those events are

466
00:27:52,040 --> 00:27:56,000
Speaker 1: relatively UM you know, quick moves. We try to be

467
00:27:56,119 --> 00:27:59,480
Speaker 1: very diversified in many different ways. And you know, that's

468
00:27:59,480 --> 00:28:01,160
Speaker 1: probably one of the first times I've used that term,

469
00:28:01,160 --> 00:28:04,560
Speaker 1: but I would say the diversification point is so critical

470
00:28:04,680 --> 00:28:07,560
Speaker 1: in investing UM, whether you're a quant investor or you're

471
00:28:07,560 --> 00:28:10,520
Speaker 1: any type of investor. It's it's definitely an extremely helpful

472
00:28:11,160 --> 00:28:14,760
Speaker 1: UM attribute when you have events like this occur. And

473
00:28:14,800 --> 00:28:17,880
Speaker 1: so you know, creating you know, different ways to look

474
00:28:17,920 --> 00:28:22,040
Speaker 1: at risks UM as quickly as you possibly can, and

475
00:28:22,119 --> 00:28:25,720
Speaker 1: adapting a portfolio, you know, we think leads to very

476
00:28:25,760 --> 00:28:28,960
Speaker 1: successful outcomes in the long run. How did you guys

477
00:28:29,000 --> 00:28:32,919
Speaker 1: look at what was taking place with things like Robin

478
00:28:32,960 --> 00:28:36,320
Speaker 1: Hood and read it to me that was reminiscent of

479
00:28:36,920 --> 00:28:40,160
Speaker 1: you know, late nineties action, although it certainly was faster

480
00:28:40,360 --> 00:28:43,960
Speaker 1: and maybe more powerful than we've seen in the past.

481
00:28:44,240 --> 00:28:48,520
Speaker 1: How do you look at these sort of group behavior

482
00:28:48,600 --> 00:28:54,480
Speaker 1: that that social networks can foster. Yeah, again we um so,

483
00:28:54,840 --> 00:28:57,480
Speaker 1: I think we look at in terms of, you know,

484
00:28:57,560 --> 00:29:00,760
Speaker 1: from a from a liquidity standpoint, what what are the

485
00:29:01,240 --> 00:29:04,400
Speaker 1: know how is this affecting the amount of the amount

486
00:29:04,440 --> 00:29:08,400
Speaker 1: of ability to trade our securities? Um? You know, we

487
00:29:08,640 --> 00:29:11,200
Speaker 1: really do try to minimize that I mentioned earlier. We

488
00:29:11,320 --> 00:29:14,160
Speaker 1: try to minimize the amount of risk we take from

489
00:29:14,160 --> 00:29:17,680
Speaker 1: any particular factor and things like you know, short interest

490
00:29:17,840 --> 00:29:20,000
Speaker 1: is something that you know is a is a pretty

491
00:29:20,040 --> 00:29:23,360
Speaker 1: common factor that you know, folks UM like us would

492
00:29:23,440 --> 00:29:27,480
Speaker 1: would try to identify and minimize. Um. Are you know

493
00:29:27,760 --> 00:29:31,200
Speaker 1: how much our our stocks will move because of that? UM,

494
00:29:31,200 --> 00:29:35,080
Speaker 1: I'd say, you know, big picture thinking about liquidity, obviously,

495
00:29:35,400 --> 00:29:38,120
Speaker 1: there there is a big retail you know, retail input

496
00:29:38,720 --> 00:29:40,880
Speaker 1: um to liquidity. They tend to you know, trade it

497
00:29:41,920 --> 00:29:45,200
Speaker 1: trade you know, stocks that are that are relatively cheap

498
00:29:45,200 --> 00:29:47,920
Speaker 1: in priced um. You know, and I think there's some

499
00:29:48,080 --> 00:29:50,920
Speaker 1: you know, some pretty interesting data around that I would say,

500
00:29:50,960 --> 00:29:53,640
Speaker 1: for you know, for our purposes. You know, we look

501
00:29:53,680 --> 00:29:56,520
Speaker 1: at things like liquidity and depths of market and how

502
00:29:56,560 --> 00:29:59,280
Speaker 1: that's being impacted. And I would say, over the last

503
00:29:59,360 --> 00:30:02,560
Speaker 1: twelve months, you know, interestingly that the world of market

504
00:30:02,600 --> 00:30:05,560
Speaker 1: micro structure has gotten pretty complicated, you know, to the

505
00:30:05,600 --> 00:30:09,000
Speaker 1: extent you could trade you could trade you know, ABC

506
00:30:09,200 --> 00:30:14,360
Speaker 1: stock in forty different venues in the US is interesting enough,

507
00:30:14,760 --> 00:30:18,360
Speaker 1: you know, across sixteen different exchanges, or roughly about sixteen

508
00:30:18,360 --> 00:30:20,959
Speaker 1: different exchanges. And so you know, we spend a lot

509
00:30:21,000 --> 00:30:24,360
Speaker 1: of our time looking at things like volumes and spreads

510
00:30:24,520 --> 00:30:28,200
Speaker 1: and and overall liquidity UM. And so that's really where

511
00:30:28,240 --> 00:30:31,400
Speaker 1: we see um, you know, those effects. And I would say,

512
00:30:31,680 --> 00:30:33,480
Speaker 1: you know, it looks like over the last twelve months

513
00:30:33,480 --> 00:30:36,680
Speaker 1: it's been a pretty rocky um, you know, rocky area.

514
00:30:36,720 --> 00:30:38,880
Speaker 1: But you know we're pretty much back to you know,

515
00:30:38,920 --> 00:30:42,520
Speaker 1: kind of pre pandemic levels when I think about quote sizes,

516
00:30:43,040 --> 00:30:46,680
Speaker 1: bid ask spreads, UM, you know for for SMP type names.

517
00:30:46,680 --> 00:30:48,720
Speaker 1: So it looks like things are kind of getting a

518
00:30:48,760 --> 00:30:51,960
Speaker 1: little bit back to normal in terms of of market liquidity,

519
00:30:52,080 --> 00:30:56,960
Speaker 1: depth and spreads. So you mentioned value earlier. I think

520
00:30:57,040 --> 00:31:01,760
Speaker 1: this is up until this quarter. I think the underperformance

521
00:31:01,800 --> 00:31:06,600
Speaker 1: of value versus growth, it could be the longest run

522
00:31:06,640 --> 00:31:11,320
Speaker 1: we've seen of growth dominating value since since at least

523
00:31:11,320 --> 00:31:15,320
Speaker 1: since the CRISP database goes back to nineteen seventeen or

524
00:31:15,400 --> 00:31:19,680
Speaker 1: something like that. How do you think about something that's

525
00:31:20,360 --> 00:31:23,880
Speaker 1: rather unusual in those terms. How does the FED factor

526
00:31:23,920 --> 00:31:27,160
Speaker 1: into this or is that even an input to to

527
00:31:27,320 --> 00:31:31,840
Speaker 1: what you're building in your models. Yeah, no, it's it's

528
00:31:31,920 --> 00:31:35,720
Speaker 1: it's exactly. It's very consistent with again thinking about it

529
00:31:35,760 --> 00:31:39,320
Speaker 1: as a as a very diversified portfolio, and you know,

530
00:31:39,480 --> 00:31:43,479
Speaker 1: value investing over the long term has done reasonably well. UM.

531
00:31:43,520 --> 00:31:46,320
Speaker 1: I'm very impressed that you went back to the CRISP database,

532
00:31:46,480 --> 00:31:50,400
Speaker 1: So kudos to you. UM. When I think about you

533
00:31:50,600 --> 00:31:54,080
Speaker 1: value again, value on itself, we tend to take an

534
00:31:54,080 --> 00:31:56,880
Speaker 1: approach where we want to be more diversified. We don't

535
00:31:56,880 --> 00:31:58,680
Speaker 1: want to just bet on value. We want to have

536
00:31:59,000 --> 00:32:01,200
Speaker 1: things that have growth at tributes and really have some

537
00:32:01,720 --> 00:32:04,560
Speaker 1: you know, we call it idiosyncratic or some specific type

538
00:32:04,600 --> 00:32:07,800
Speaker 1: of return where we think that's our edge. And and

539
00:32:07,960 --> 00:32:10,360
Speaker 1: in terms of other types of factors like value or

540
00:32:10,400 --> 00:32:14,200
Speaker 1: growth or low volatility. UM, those are something that we

541
00:32:14,240 --> 00:32:17,480
Speaker 1: want to have a very modest amount of exposure or

542
00:32:17,960 --> 00:32:19,920
Speaker 1: you know, we really don't want to We don't necessarily

543
00:32:19,960 --> 00:32:22,400
Speaker 1: make a lot of money on that particular aspect because

544
00:32:22,400 --> 00:32:26,240
Speaker 1: it's very common and it's also subject to very sharp moves,

545
00:32:26,680 --> 00:32:28,120
Speaker 1: and so you know, we aim to have a little

546
00:32:28,160 --> 00:32:31,360
Speaker 1: bit more consistent, persistent results. But to your point, you're right,

547
00:32:31,400 --> 00:32:35,200
Speaker 1: this is it's been an unbelievable um challenge for Value.

548
00:32:35,240 --> 00:32:37,960
Speaker 1: We've we have seen a little bit of a turnaround, um,

549
00:32:38,000 --> 00:32:41,080
Speaker 1: you know, since since the election, UM, and so you know,

550
00:32:41,160 --> 00:32:43,280
Speaker 1: value that start us to do a little bit better.

551
00:32:43,880 --> 00:32:45,920
Speaker 1: But your point is well taken, But I think it

552
00:32:46,000 --> 00:32:50,240
Speaker 1: just speaks to our philosophy of you want to have,

553
00:32:50,680 --> 00:32:53,000
Speaker 1: you know, many different ways of looking at the fortunes

554
00:32:53,000 --> 00:32:58,760
Speaker 1: of a company and diversification. Diversification, diversification is key and

555
00:32:58,960 --> 00:33:02,600
Speaker 1: at World Plant we that with millions of alpha's, we

556
00:33:02,680 --> 00:33:06,600
Speaker 1: have many different portfolio managers, many different ways of combining

557
00:33:06,600 --> 00:33:09,240
Speaker 1: our alpha's, and so you know, we kind of live

558
00:33:09,280 --> 00:33:13,320
Speaker 1: and breathe from diversifying of our people to our alpha's,

559
00:33:13,360 --> 00:33:17,240
Speaker 1: to our portfolio managers, and then to our execution. So again,

560
00:33:17,320 --> 00:33:19,920
Speaker 1: I think you know, your observation is spot on, and

561
00:33:19,920 --> 00:33:21,920
Speaker 1: I would say we as a as a group try

562
00:33:21,960 --> 00:33:26,600
Speaker 1: not to take too many bets in one place. Huh. Interesting.

563
00:33:27,000 --> 00:33:31,120
Speaker 1: You know you mentioned certain strategies are popular, and I

564
00:33:31,160 --> 00:33:35,040
Speaker 1: can't help but think back to the um quant quake

565
00:33:35,200 --> 00:33:38,760
Speaker 1: that took place about eight years ago, where a lot

566
00:33:38,800 --> 00:33:43,240
Speaker 1: of quantitative strategies were very similar at different shops, and

567
00:33:43,600 --> 00:33:48,120
Speaker 1: we saw what had become a fairly crowded trade. Maybe

568
00:33:48,160 --> 00:33:50,880
Speaker 1: maybe it's a decade ago, it's even longer ago. What

569
00:33:50,880 --> 00:33:53,920
Speaker 1: what do you make of that crowded trades? Yeah, so

570
00:33:53,920 --> 00:33:56,160
Speaker 1: so it was more than a decade ago, is you

571
00:33:56,160 --> 00:33:58,200
Speaker 1: know if I think it was almost eight thirteen and

572
00:33:58,240 --> 00:34:00,840
Speaker 1: a half years ago? Okay, I think I think there

573
00:34:00,880 --> 00:34:04,960
Speaker 1: was a huge lesson learned for for Kuant investors. Um.

574
00:34:05,000 --> 00:34:07,920
Speaker 1: I think it was a period where, uh, you know,

575
00:34:08,040 --> 00:34:11,000
Speaker 1: there was you know, some some shops had a fair

576
00:34:11,000 --> 00:34:14,560
Speaker 1: amount of complacency where they didn't continue to use their research.

577
00:34:14,640 --> 00:34:17,840
Speaker 1: There was more into there was you know, there there

578
00:34:17,840 --> 00:34:19,920
Speaker 1: should have been a lot more pushing in terms of

579
00:34:19,960 --> 00:34:22,560
Speaker 1: research and and I think you look back and you

580
00:34:22,600 --> 00:34:25,760
Speaker 1: saw events that you know, for a number of reasons.

581
00:34:25,800 --> 00:34:27,640
Speaker 1: One is there was a fair amount of leverage in

582
00:34:27,640 --> 00:34:30,840
Speaker 1: the system, and so you're able to amplify your returns

583
00:34:30,840 --> 00:34:33,759
Speaker 1: with leverage UM and leverage is great if you're always

584
00:34:33,760 --> 00:34:37,840
Speaker 1: going to have high, high positive returns, but when you don't,

585
00:34:38,280 --> 00:34:40,839
Speaker 1: you know, leverages is a you know as a very

586
00:34:40,840 --> 00:34:42,960
Speaker 1: big challenge because people call up and ask you for

587
00:34:43,000 --> 00:34:44,919
Speaker 1: money and you need to pay them. So I think

588
00:34:44,960 --> 00:34:47,200
Speaker 1: you know that that really was one of the biggest

589
00:34:47,200 --> 00:34:50,040
Speaker 1: issues of of oh seven UM. But I'd also say

590
00:34:50,040 --> 00:34:53,520
Speaker 1: there was crowded traits, as you correctly point out. And

591
00:34:53,600 --> 00:34:55,319
Speaker 1: so I think one of the goals that we have

592
00:34:55,360 --> 00:34:58,680
Speaker 1: at World pant is continue to differentiate, continue to create

593
00:34:58,840 --> 00:35:04,120
Speaker 1: unique ways of making money for our clients investing in

594
00:35:04,160 --> 00:35:08,400
Speaker 1: our almost three hundred researchers, to try to continue to

595
00:35:08,440 --> 00:35:12,280
Speaker 1: innovate and be much less crowded than other people. Again,

596
00:35:12,320 --> 00:35:14,560
Speaker 1: we want to be unique. We don't want to be

597
00:35:14,760 --> 00:35:18,160
Speaker 1: susceptible to those large movements in terms of those quote

598
00:35:18,200 --> 00:35:22,279
Speaker 1: unquote crowded trades. And that's really a huge goal and

599
00:35:22,320 --> 00:35:25,640
Speaker 1: frankly was a big lesson learned for I believe the

600
00:35:25,680 --> 00:35:29,480
Speaker 1: quand industry. UM that happens, you know, almost fourteen years ago.

601
00:35:29,960 --> 00:35:32,719
Speaker 1: So let's talk a little bit about the future of

602
00:35:32,840 --> 00:35:37,160
Speaker 1: quant investing. You mentioned previously that the industry has learned

603
00:35:37,200 --> 00:35:41,080
Speaker 1: from past mistakes. It's involved UM. Tell us a little

604
00:35:41,080 --> 00:35:45,120
Speaker 1: bit about the direction the industry is in evolving towards,

605
00:35:46,719 --> 00:35:49,680
Speaker 1: Sir Barry, I think the you know, the quand industry

606
00:35:49,800 --> 00:35:53,360
Speaker 1: will continue to evolve in in places like data, in

607
00:35:53,400 --> 00:35:57,799
Speaker 1: places like storage, in places like analytics, UM and the

608
00:35:57,840 --> 00:36:00,759
Speaker 1: tools that are that one can use to try to,

609
00:36:01,680 --> 00:36:04,120
Speaker 1: you know, figure out the fortunes of a company have

610
00:36:04,239 --> 00:36:08,040
Speaker 1: increased exponentially, and so you know, the amount of data

611
00:36:08,120 --> 00:36:10,360
Speaker 1: that's out there, amount of data that can be stored,

612
00:36:10,760 --> 00:36:13,920
Speaker 1: amount of data that can be analyzed, the simulations that

613
00:36:13,960 --> 00:36:18,600
Speaker 1: one can run has grown, like I said, absolutely exponentially,

614
00:36:19,000 --> 00:36:22,480
Speaker 1: and really for a quant investor, it's terrific because you know,

615
00:36:22,520 --> 00:36:24,440
Speaker 1: the world is kind of coming in our direction. The

616
00:36:24,440 --> 00:36:26,480
Speaker 1: amount of data. We think, you know, one of our

617
00:36:26,560 --> 00:36:29,719
Speaker 1: edges to be able to take data, synthesize it and

618
00:36:29,760 --> 00:36:32,919
Speaker 1: create information and drive returns. And you know, we think,

619
00:36:32,920 --> 00:36:36,239
Speaker 1: here a world point, we're extremely well positioned to be

620
00:36:36,320 --> 00:36:39,239
Speaker 1: able to do that. And so, you know, honestly, I

621
00:36:39,239 --> 00:36:42,040
Speaker 1: think it's a you know, it's an absolute golden age

622
00:36:42,080 --> 00:36:44,799
Speaker 1: for us as Kuan investors UM in terms of kind

623
00:36:44,840 --> 00:36:49,080
Speaker 1: of where the industry is evolving really interesting. Any of

624
00:36:49,080 --> 00:36:54,040
Speaker 1: this evolution surprised you what what has taken place that um,

625
00:36:54,080 --> 00:36:56,600
Speaker 1: either you didn't see coming, or you saw coming and

626
00:36:56,640 --> 00:37:01,319
Speaker 1: didn't think would happen, and it happened anyway, right, I

627
00:37:01,320 --> 00:37:04,120
Speaker 1: would tell you know, one of the surprises is is

628
00:37:04,440 --> 00:37:09,239
Speaker 1: the adoption of you know, more and more quantitative investing

629
00:37:09,480 --> 00:37:16,080
Speaker 1: strategies in general. Um. Just given uh, everyday people's thoughts

630
00:37:16,120 --> 00:37:19,239
Speaker 1: on you know, the use of computers and use of

631
00:37:19,280 --> 00:37:24,799
Speaker 1: your phone to drive information, It's happening across most every industry.

632
00:37:25,280 --> 00:37:28,800
Speaker 1: I guess I'm surprised, happily surprised that more and more

633
00:37:29,120 --> 00:37:33,560
Speaker 1: kind of investment folks aren't employing more and more quantitative strategy.

634
00:37:33,760 --> 00:37:37,040
Speaker 1: Good for us from where we sit, but I'm just surprised.

635
00:37:37,280 --> 00:37:39,880
Speaker 1: You know, I think everybody wants to you know, if

636
00:37:39,920 --> 00:37:41,879
Speaker 1: you're at a dinner party, you're you're asked, you're getting

637
00:37:41,880 --> 00:37:44,239
Speaker 1: asked a question, it's got to be empirically back that

638
00:37:44,320 --> 00:37:46,560
Speaker 1: You're gonna look it up as quickly as you possibly can,

639
00:37:47,360 --> 00:37:50,440
Speaker 1: and you want to test that there whatever someone said,

640
00:37:50,520 --> 00:37:53,920
Speaker 1: whatever hypothesis, um, you know, and there's there's a lot

641
00:37:53,960 --> 00:37:56,279
Speaker 1: of skeptics and they can be proven yea or nay

642
00:37:56,440 --> 00:37:58,719
Speaker 1: very quickly. And I'm just you know, I guess I'm

643
00:37:58,880 --> 00:38:01,480
Speaker 1: I'm surprised that that's not happening more and more in

644
00:38:01,520 --> 00:38:03,880
Speaker 1: the in the investment industry. So that would be one

645
00:38:03,880 --> 00:38:06,480
Speaker 1: of the I would say, my my biggest surprises. But

646
00:38:06,520 --> 00:38:09,920
Speaker 1: I'm but I'm okay with that. Huh. You mentioned earlier

647
00:38:09,960 --> 00:38:14,399
Speaker 1: trying to read sentiment data from analysts reports. I've read

648
00:38:14,440 --> 00:38:19,240
Speaker 1: about firms trying to actually scrape market wide sentiment data

649
00:38:19,400 --> 00:38:23,640
Speaker 1: off of social networks like Twitter. What what does that

650
00:38:23,719 --> 00:38:28,160
Speaker 1: look like? And can you really find an investable edge

651
00:38:28,719 --> 00:38:34,280
Speaker 1: from the characters of millions of people who know um

652
00:38:34,360 --> 00:38:37,520
Speaker 1: relatively little, although they may not know that they know

653
00:38:37,640 --> 00:38:43,640
Speaker 1: relatively little? What what signal is in all that noise? Sure, Marry,

654
00:38:43,680 --> 00:38:46,800
Speaker 1: I think you're you're touching on a really important components.

655
00:38:46,840 --> 00:38:49,560
Speaker 1: You think about all this alternative data, you know, it's

656
00:38:49,600 --> 00:38:51,279
Speaker 1: it's what do you do with it? And and how

657
00:38:51,280 --> 00:38:53,759
Speaker 1: do you utilize it? And I think you know, a

658
00:38:53,840 --> 00:38:59,120
Speaker 1: diversified approach of you using things like satellite images, using

659
00:38:59,160 --> 00:39:03,279
Speaker 1: things like social media, um, you know, can be quite impactful,

660
00:39:03,480 --> 00:39:05,799
Speaker 1: you know, some of which might be very very short run.

661
00:39:06,160 --> 00:39:10,200
Speaker 1: Some of it might have more longer term ramifications, things

662
00:39:10,239 --> 00:39:13,839
Speaker 1: like credit card transactions, web clicks. I mean, there's so

663
00:39:13,920 --> 00:39:17,400
Speaker 1: much alternative data out there that you know, if you

664
00:39:17,440 --> 00:39:20,239
Speaker 1: can think about how best to utilize it. Again, it's

665
00:39:20,320 --> 00:39:23,759
Speaker 1: that whole concept of marrying kind of technical acumen and

666
00:39:23,800 --> 00:39:29,120
Speaker 1: so you understand data, you understand uh something about you know,

667
00:39:29,160 --> 00:39:32,800
Speaker 1: putting data together to create some type of expected return,

668
00:39:32,880 --> 00:39:36,680
Speaker 1: but also marrying that with some business acumen. You know,

669
00:39:36,719 --> 00:39:40,279
Speaker 1: I think is is really exploding. And so you know,

670
00:39:40,360 --> 00:39:42,879
Speaker 1: whether it's social media, where it's at allite imaging, whether

671
00:39:42,960 --> 00:39:46,520
Speaker 1: it's you know, clicking on you know, getting vendors that

672
00:39:46,520 --> 00:39:50,279
Speaker 1: that provide some of this data all anonymized to be

673
00:39:50,320 --> 00:39:53,400
Speaker 1: able to have a view of where company's fortunes maybe

674
00:39:54,120 --> 00:39:56,879
Speaker 1: is certainly something that the industry is seeing. Um, there's

675
00:39:56,880 --> 00:39:59,520
Speaker 1: a massive amount of data vendors out there. There is

676
00:39:59,560 --> 00:40:01,680
Speaker 1: some content validation and some of those data vendors, but

677
00:40:01,719 --> 00:40:04,200
Speaker 1: there's a lot of data out there to be able

678
00:40:04,200 --> 00:40:07,000
Speaker 1: to employ not just social media, but other types of

679
00:40:07,000 --> 00:40:10,000
Speaker 1: of data that you know, can be informative of a

680
00:40:10,000 --> 00:40:13,719
Speaker 1: company's fortunes. Yeah, I've been kind of fascinated by the

681
00:40:13,800 --> 00:40:18,520
Speaker 1: satellite data and how granular it can get not just

682
00:40:19,080 --> 00:40:24,279
Speaker 1: tracking ships carrying goods or oil around the world, but

683
00:40:24,520 --> 00:40:28,040
Speaker 1: how deep the ships are sitting in the water. That

684
00:40:28,360 --> 00:40:32,680
Speaker 1: gives some insight as to how are they traveling? Full

685
00:40:32,800 --> 00:40:37,440
Speaker 1: half full? Three corps. That's just astonishing stuff. Yeah, I

686
00:40:37,440 --> 00:40:40,120
Speaker 1: mean it's it really is. And I think you know, listen,

687
00:40:40,120 --> 00:40:43,040
Speaker 1: I think we all we all have our phones and

688
00:40:43,040 --> 00:40:45,319
Speaker 1: and you know I could I could kindly tracked my

689
00:40:45,440 --> 00:40:48,120
Speaker 1: kids on on Life three sixty and figuring out where

690
00:40:48,160 --> 00:40:50,919
Speaker 1: they are. Um, you know this is happening. It's part

691
00:40:50,920 --> 00:40:54,880
Speaker 1: of our everyday lives, um and uh. And you know

692
00:40:55,160 --> 00:40:57,920
Speaker 1: it's it could be insightful information. You know certainly helped me,

693
00:40:58,400 --> 00:41:00,840
Speaker 1: you know, with my kids and and you know other parts,

694
00:41:00,840 --> 00:41:04,240
Speaker 1: whether it's uh, you know, tankers or whether it's uh,

695
00:41:04,280 --> 00:41:07,600
Speaker 1: you know, clicks. Uh. You know, these are insights that

696
00:41:08,120 --> 00:41:11,800
Speaker 1: you know, can be you know, potentially telling. Again, I

697
00:41:11,800 --> 00:41:15,040
Speaker 1: would go back to my other common about diversification. So

698
00:41:15,239 --> 00:41:19,080
Speaker 1: in isolation, these you know, will you know almost for

699
00:41:19,120 --> 00:41:21,480
Speaker 1: certain will not work all the time. Um, But if

700
00:41:21,520 --> 00:41:23,960
Speaker 1: there's some level of insight that you can gain from

701
00:41:24,000 --> 00:41:25,600
Speaker 1: a piece of this data or a way to look

702
00:41:25,640 --> 00:41:29,400
Speaker 1: at this data, and then you marry that with millions

703
00:41:29,400 --> 00:41:31,640
Speaker 1: and millions of other things. You know, you can have

704
00:41:31,680 --> 00:41:35,200
Speaker 1: a pretty good sense of that company's fortune. So you know,

705
00:41:35,239 --> 00:41:38,520
Speaker 1: again it's it's really about diversification and not thinking about

706
00:41:39,040 --> 00:41:42,200
Speaker 1: you know, these pieces of data in isolation. UM. You know,

707
00:41:42,320 --> 00:41:44,879
Speaker 1: we had talked a little bit about value and other

708
00:41:44,920 --> 00:41:47,520
Speaker 1: types of factors. Again, I think you know, the approach

709
00:41:47,600 --> 00:41:50,839
Speaker 1: that that one that most quants take UM is really

710
00:41:50,880 --> 00:41:54,960
Speaker 1: to think about diversification um as as a really helpful

711
00:41:54,960 --> 00:41:58,640
Speaker 1: way to produce UM you know, consistent results for clients.

712
00:41:58,719 --> 00:42:01,160
Speaker 1: And I think that's really, you know, the key to

713
00:42:01,840 --> 00:42:03,799
Speaker 1: how most wants and at World want you know, we

714
00:42:03,840 --> 00:42:07,000
Speaker 1: think about diversification at pretty much every step of the way.

715
00:42:07,200 --> 00:42:11,000
Speaker 1: But it's our people, whether it's the expected returns that

716
00:42:11,040 --> 00:42:14,400
Speaker 1: we try to generate or portfolio managers and how we

717
00:42:14,480 --> 00:42:17,440
Speaker 1: go about executing and making that a reality. So you

718
00:42:17,560 --> 00:42:23,440
Speaker 1: talked several times about how gigantic these data sets are

719
00:42:23,480 --> 00:42:27,839
Speaker 1: and how fast they're growing, How how big can these get?

720
00:42:28,040 --> 00:42:31,640
Speaker 1: And at what point do they become unmanageable? I mean,

721
00:42:31,640 --> 00:42:36,759
Speaker 1: when is too much data too much? Yeah? UM, we

722
00:42:37,000 --> 00:42:40,560
Speaker 1: certainly have not found that out yet. UM. You know,

723
00:42:40,600 --> 00:42:43,600
Speaker 1: the nice thing about it is there's the amount of data,

724
00:42:44,000 --> 00:42:48,640
Speaker 1: you know, is increasing exponentially. There's some unbelievable stats on

725
00:42:48,760 --> 00:42:52,040
Speaker 1: just that massive amount of growth UM. And I think,

726
00:42:52,080 --> 00:42:55,240
Speaker 1: you know, frankly, we've spent an enormous amount of time

727
00:42:55,600 --> 00:42:58,120
Speaker 1: figuring out how to take in that data, how to

728
00:42:58,160 --> 00:43:00,799
Speaker 1: collate it, how to check that data. Um. You know,

729
00:43:00,800 --> 00:43:04,240
Speaker 1: again it's the gory details of data, but it's um

730
00:43:04,280 --> 00:43:06,840
Speaker 1: but it's fascinating. I know, I d C I d

731
00:43:07,000 --> 00:43:09,799
Speaker 1: c UM. You know reported a quote them more than

732
00:43:09,880 --> 00:43:14,360
Speaker 1: five billion consumers interact with data every day. Five billion

733
00:43:14,440 --> 00:43:18,680
Speaker 1: consumers interact with data every day. By they say that

734
00:43:18,760 --> 00:43:22,040
Speaker 1: number will be six billion, or three quarters of the

735
00:43:22,080 --> 00:43:26,000
Speaker 1: world's population. So data is getting created again exponentially. I

736
00:43:26,000 --> 00:43:27,960
Speaker 1: think this is the thing that we spend a lot

737
00:43:27,960 --> 00:43:31,560
Speaker 1: of time on is how do we ingest that, How

738
00:43:31,600 --> 00:43:33,920
Speaker 1: do we come up with processes to be able to

739
00:43:34,400 --> 00:43:37,279
Speaker 1: you know ingest it, how do we store it, how

740
00:43:37,280 --> 00:43:40,439
Speaker 1: do we analyze it? Again? And that's that's really, uh,

741
00:43:40,520 --> 00:43:42,600
Speaker 1: you know, one of our integral parts of what we

742
00:43:42,680 --> 00:43:45,200
Speaker 1: do and how we do it. And you're seeing this

743
00:43:45,280 --> 00:43:48,239
Speaker 1: in the investment industry. You're seeing this in many different industries.

744
00:43:48,719 --> 00:43:51,520
Speaker 1: But I think that's one of the exciting parts um,

745
00:43:52,080 --> 00:43:54,000
Speaker 1: you know, and it's a lot of data. But again

746
00:43:54,040 --> 00:43:57,000
Speaker 1: I think that's really you know, we've been waiting for

747
00:43:57,040 --> 00:43:59,360
Speaker 1: these times for for a long time. To continue to

748
00:43:59,400 --> 00:44:01,880
Speaker 1: have more and more data, it allows us a huge

749
00:44:01,880 --> 00:44:05,040
Speaker 1: opportunity to drive an edge because we think we know

750
00:44:05,239 --> 00:44:07,520
Speaker 1: what to do with that type of data. Um, to

751
00:44:07,640 --> 00:44:10,640
Speaker 1: pair that with some of our you know, smart researchers

752
00:44:10,640 --> 00:44:13,319
Speaker 1: and figuring out what are the insights. So, you know,

753
00:44:14,320 --> 00:44:16,959
Speaker 1: I think the other challenge that we face in terms

754
00:44:16,960 --> 00:44:20,120
Speaker 1: of your comment about too much is again signal to noise?

755
00:44:20,200 --> 00:44:23,200
Speaker 1: All right, What's what's a signal? Meaning what what gives

756
00:44:23,239 --> 00:44:26,279
Speaker 1: you insight? And what's just noise? And so part of

757
00:44:26,320 --> 00:44:30,480
Speaker 1: our jobs as researchers and portfolio managers, as good quants

758
00:44:30,480 --> 00:44:33,319
Speaker 1: at worklan is is to kind of distinguish between the

759
00:44:33,400 --> 00:44:36,840
Speaker 1: signal meaning does this have some value, does it provide

760
00:44:36,840 --> 00:44:40,960
Speaker 1: me insight? Or is it really just noise and you know,

761
00:44:41,040 --> 00:44:46,319
Speaker 1: not really worthy of of of allocating any investment to it. Huh.

762
00:44:46,480 --> 00:44:49,120
Speaker 1: Quite interesting. Let let me change gears on you a

763
00:44:49,120 --> 00:44:53,840
Speaker 1: little bit. We recently heard rumblings about possible changes in

764
00:44:54,080 --> 00:44:57,759
Speaker 1: tax policy coming out of the new administration. I know

765
00:44:57,920 --> 00:45:00,520
Speaker 1: at Goldman, I know a gam You did a lot

766
00:45:00,520 --> 00:45:05,759
Speaker 1: of work on UM tax efficiency from from your new perch.

767
00:45:05,920 --> 00:45:12,239
Speaker 1: How do you think about things like tax efficiency in investing?

768
00:45:12,320 --> 00:45:16,560
Speaker 1: Is that something that's still within your bailiwick or or

769
00:45:16,680 --> 00:45:19,560
Speaker 1: is it more institutional and you're you're less focused on

770
00:45:19,640 --> 00:45:22,960
Speaker 1: tax Yeah, so, I mean the way we think about it,

771
00:45:23,000 --> 00:45:26,040
Speaker 1: and I'm happy to spend some time on just generally

772
00:45:26,040 --> 00:45:28,680
Speaker 1: tax efficient investing. I think it is. It's you know,

773
00:45:28,719 --> 00:45:30,920
Speaker 1: it's a very useful piece and I've had some prior

774
00:45:30,920 --> 00:45:33,680
Speaker 1: experience in it, but more substantively on you know, kind

775
00:45:33,680 --> 00:45:35,440
Speaker 1: of what we do now at work on you know,

776
00:45:35,520 --> 00:45:37,600
Speaker 1: if there is a change to tax policy, we're gonna

777
00:45:37,680 --> 00:45:39,239
Speaker 1: you know, figure out how it's going to impact a

778
00:45:39,239 --> 00:45:41,560
Speaker 1: particular company. You know, our corporate tax is going to

779
00:45:41,640 --> 00:45:44,280
Speaker 1: go up or down UM, and how will that impact

780
00:45:44,560 --> 00:45:46,880
Speaker 1: you know, cash flow or you know something on on

781
00:45:46,920 --> 00:45:49,560
Speaker 1: a company's statements. UM. So that that's really how we

782
00:45:49,600 --> 00:45:52,400
Speaker 1: would tackle it, and you know, we'll we'll understand how

783
00:45:52,440 --> 00:45:55,359
Speaker 1: we should you know, update our accounting UM for for

784
00:45:55,400 --> 00:45:58,279
Speaker 1: those types of events and adapt accordingly, like like you

785
00:45:58,320 --> 00:46:01,799
Speaker 1: would expect most in sters to do. UM. You know,

786
00:46:01,840 --> 00:46:03,640
Speaker 1: in the ray of tax efficient investing, you know, I'm

787
00:46:03,640 --> 00:46:05,440
Speaker 1: happy to spend a few minutes there, but you know,

788
00:46:05,800 --> 00:46:07,960
Speaker 1: we don't, uh, that's really not one of our core

789
00:46:08,040 --> 00:46:11,840
Speaker 1: focuses at world point. You're you're looking more as to

790
00:46:12,239 --> 00:46:16,120
Speaker 1: how the changes in taxes impact either the bottom line

791
00:46:16,120 --> 00:46:20,480
Speaker 1: for the companies or their position relative to their competitors

792
00:46:21,160 --> 00:46:26,360
Speaker 1: UM and what the tax code means, uh to their valuation.

793
00:46:26,440 --> 00:46:28,880
Speaker 1: Is that is that a fair description? Like, I know,

794
00:46:28,960 --> 00:46:33,879
Speaker 1: you guys aren't tax loss harvesting the way a traditional

795
00:46:34,760 --> 00:46:39,120
Speaker 1: UM advisor would. You're you're running a very different portfolio

796
00:46:39,640 --> 00:46:43,680
Speaker 1: for for a different audience. So your perspective is what

797
00:46:43,760 --> 00:46:45,920
Speaker 1: does this mean to the companies that we may or

798
00:46:45,960 --> 00:46:48,200
Speaker 1: may not own, and and how does it affect them

799
00:46:48,719 --> 00:46:52,759
Speaker 1: relative to their competitors? Is that a fair statement? Yeah?

800
00:46:52,760 --> 00:46:54,560
Speaker 1: I think that's a that's a fair statement. Again, we

801
00:46:54,840 --> 00:46:57,640
Speaker 1: want to see as as Biden you know, institutes new policies,

802
00:46:57,680 --> 00:47:00,759
Speaker 1: how that will affect corporations and frank we that goes

803
00:47:00,880 --> 00:47:04,239
Speaker 1: you know beyond you know, tax policy, other types of policies,

804
00:47:04,280 --> 00:47:06,640
Speaker 1: and so you know, if there's international policies that will

805
00:47:06,640 --> 00:47:10,200
Speaker 1: affect trade or or any type of you know of

806
00:47:10,200 --> 00:47:13,319
Speaker 1: of things that come out of Washington or Frankly, any

807
00:47:13,400 --> 00:47:15,960
Speaker 1: any other government around the world. Given we are a

808
00:47:16,000 --> 00:47:18,680
Speaker 1: global organization, you know, we're gonna attempt to take that

809
00:47:18,719 --> 00:47:22,200
Speaker 1: into account, um, to try to understand it, understand what

810
00:47:22,239 --> 00:47:25,320
Speaker 1: the ramifications are two companies and being able to position

811
00:47:25,360 --> 00:47:28,319
Speaker 1: our portfolios accordingly. And that's that's you know, we do

812
00:47:28,360 --> 00:47:31,239
Speaker 1: that whether it's a regulatory issue or an event that

813
00:47:31,280 --> 00:47:35,520
Speaker 1: we talked about again. Our ability to adapt and understand

814
00:47:35,560 --> 00:47:38,200
Speaker 1: what's going on in markets, what's going to affect companies

815
00:47:38,239 --> 00:47:41,160
Speaker 1: or particular asset classes is really you know, one of

816
00:47:41,200 --> 00:47:45,000
Speaker 1: the fun parts of the job as being a quantitative investor. Huh,

817
00:47:45,200 --> 00:47:47,480
Speaker 1: what what are other fun parts of the job? What

818
00:47:47,480 --> 00:47:50,799
Speaker 1: what do you enjoy doing most? Um, as presidents of

819
00:47:50,800 --> 00:47:56,200
Speaker 1: world want so, I will tell you I've had such

820
00:47:56,200 --> 00:48:01,800
Speaker 1: a great time of of walking out of meetings action steps. Uh,

821
00:48:01,840 --> 00:48:05,880
Speaker 1: it's been you know, seeing seeing people intellectually stimulated around

822
00:48:06,120 --> 00:48:07,640
Speaker 1: you know again where a lot of it is on

823
00:48:07,719 --> 00:48:10,080
Speaker 1: zoom and so you know, we sit there and and

824
00:48:10,120 --> 00:48:13,919
Speaker 1: just you know, watching how people dialogue has just been

825
00:48:14,320 --> 00:48:17,319
Speaker 1: you know, so incredibly exhilarating. Um. You know a lot

826
00:48:17,320 --> 00:48:20,200
Speaker 1: of the great ideas and you know, watching how respectful

827
00:48:20,239 --> 00:48:22,840
Speaker 1: people heard of each other and challenging them in in

828
00:48:23,080 --> 00:48:27,319
Speaker 1: thoughtful ways and almost hearing them think, uh, you know

829
00:48:27,600 --> 00:48:30,239
Speaker 1: right in real time. Is it's just been been incredible.

830
00:48:30,719 --> 00:48:33,680
Speaker 1: Uh in terms of uh, you know, the the organization,

831
00:48:33,760 --> 00:48:37,920
Speaker 1: it's it's just highly productive, highly collaborative. Um, there's just

832
00:48:38,320 --> 00:48:40,759
Speaker 1: a lot of great decision making that goes on. We

833
00:48:40,840 --> 00:48:44,400
Speaker 1: just recently did a research off site where we just

834
00:48:44,480 --> 00:48:48,160
Speaker 1: walked through and have many, many decisions. We pride ourselves that,

835
00:48:48,480 --> 00:48:51,480
Speaker 1: you know, we're very action oriented, and so you know,

836
00:48:51,520 --> 00:48:53,399
Speaker 1: that's been you know, some of the fun things that

837
00:48:54,040 --> 00:48:56,120
Speaker 1: that I've been fortunate enough to uh, you know, to

838
00:48:56,200 --> 00:48:58,720
Speaker 1: observe in my in my six months. Let me jump

839
00:48:59,160 --> 00:49:02,840
Speaker 1: to my favorite questions that I asked all of my guests,

840
00:49:02,920 --> 00:49:06,080
Speaker 1: starting with what are you streaming these days? Give us

841
00:49:06,120 --> 00:49:09,560
Speaker 1: your your favorite Netflix or Amazon Prime show or any

842
00:49:09,600 --> 00:49:16,080
Speaker 1: podcast you might be listening to. What's keeping you entertained? Sure? Um,

843
00:49:16,120 --> 00:49:19,280
Speaker 1: I would say been a fan of House of Cards. Um,

844
00:49:19,719 --> 00:49:23,279
Speaker 1: My daughter and I watched Million Little Things. Um. Joe

845
00:49:23,360 --> 00:49:27,160
Speaker 1: Rogan's interviews with Elon musk Are are pretty pretty impressive.

846
00:49:27,200 --> 00:49:29,360
Speaker 1: And I would have to say, you know, one of

847
00:49:29,360 --> 00:49:32,600
Speaker 1: my favorite videos is a four minute and thirteen second

848
00:49:33,080 --> 00:49:36,880
Speaker 1: Jason Garrett speeches. He talks about one World Trade Center.

849
00:49:36,880 --> 00:49:39,440
Speaker 1: It's just it's an amazing video that you know, all

850
00:49:39,440 --> 00:49:42,120
Speaker 1: my friends, uh get a text from me on a

851
00:49:42,120 --> 00:49:45,640
Speaker 1: pretty regular basis, just just level sets. It's a great video.

852
00:49:46,280 --> 00:49:49,879
Speaker 1: H really interesting. Uh tell us about your mentors who

853
00:49:49,920 --> 00:49:55,040
Speaker 1: helped to shape your career. Sure, I would say my

854
00:49:55,120 --> 00:49:58,920
Speaker 1: dad had unbelievable work ethic. Um it was a six

855
00:49:59,000 --> 00:50:01,400
Speaker 1: day a week guy. Um. One of my my first

856
00:50:01,440 --> 00:50:05,640
Speaker 1: bosses was was a guy named Gustic Conomos, who unfortunately

857
00:50:05,680 --> 00:50:08,920
Speaker 1: passed away at nine eleven. Um. But you know, was

858
00:50:08,920 --> 00:50:13,359
Speaker 1: was was able to balance enormous credibility or industry credibility

859
00:50:13,840 --> 00:50:16,719
Speaker 1: with a sense of humor. And uh, you know, us

860
00:50:16,800 --> 00:50:18,360
Speaker 1: always used to tell me I may have taught you

861
00:50:18,440 --> 00:50:21,120
Speaker 1: everything you know, but I didn't teach you everything I know.

862
00:50:21,600 --> 00:50:23,960
Speaker 1: And I always always think that's a pretty funny, uh

863
00:50:24,239 --> 00:50:26,600
Speaker 1: funny quote. But uh, you know, And and the last

864
00:50:26,600 --> 00:50:29,400
Speaker 1: one I would say on the quant spaces two gentlemen

865
00:50:29,400 --> 00:50:32,400
Speaker 1: and Bob Jones and Don Mulvehill. Bob was the founder

866
00:50:32,440 --> 00:50:35,640
Speaker 1: of the g Sam uh you know chront equity business

867
00:50:35,680 --> 00:50:37,600
Speaker 1: back in the day, and you know, taught me a

868
00:50:37,600 --> 00:50:39,960
Speaker 1: lot and really helped shape my career and my interest

869
00:50:40,000 --> 00:50:43,160
Speaker 1: in quantitative investing. And then Don was you know, an

870
00:50:43,160 --> 00:50:46,240
Speaker 1: age old colleague and boss of mine that really taught

871
00:50:46,239 --> 00:50:50,080
Speaker 1: me a tremendous amount about UM investing, in dealing with

872
00:50:50,160 --> 00:50:53,440
Speaker 1: clients and you know to too great early role models

873
00:50:53,440 --> 00:50:57,719
Speaker 1: that I had had in the industry quite quite interesting.

874
00:50:58,120 --> 00:51:00,600
Speaker 1: Tell us about some books? What do you what are

875
00:51:00,600 --> 00:51:02,400
Speaker 1: you some of your favorites and what are you reading

876
00:51:02,520 --> 00:51:07,840
Speaker 1: right now? Sure? So, uh, some of my favorite especially

877
00:51:07,840 --> 00:51:11,360
Speaker 1: since I had a decent amount of time between between

878
00:51:11,400 --> 00:51:14,200
Speaker 1: taking on the role at at World quand UM. You know,

879
00:51:14,239 --> 00:51:16,560
Speaker 1: I was able to read David Rubinstein's How to Lead,

880
00:51:16,600 --> 00:51:19,960
Speaker 1: which I thought was just terrific. Um Sartin to Tella

881
00:51:20,040 --> 00:51:22,440
Speaker 1: had the hit Refresh, which I thought was quite good.

882
00:51:22,880 --> 00:51:26,360
Speaker 1: I was also able to read our books from our CEO,

883
00:51:26,560 --> 00:51:29,280
Speaker 1: who has two good ones, Finding Alpha's and The Unrules,

884
00:51:29,280 --> 00:51:32,000
Speaker 1: So I gotta plug those two. Those were quite quite

885
00:51:32,000 --> 00:51:34,920
Speaker 1: good and just interesting ways of thinking. UM. And then

886
00:51:34,920 --> 00:51:36,799
Speaker 1: the one I'm reading now, which I think is a

887
00:51:36,840 --> 00:51:40,839
Speaker 1: pretty cool book. It's called Outrageous Good Fortune. It's about

888
00:51:40,840 --> 00:51:44,240
Speaker 1: a guy named Michael Burke, UM football hero you penn

889
00:51:44,440 --> 00:51:49,520
Speaker 1: c I agent overthrew Communist government um Ran Intelligence for

890
00:51:49,600 --> 00:51:55,799
Speaker 1: Eastern Europe Ran Ringling Brothers Circus. He was the executive

891
00:51:55,840 --> 00:51:59,160
Speaker 1: at TBS Sports, president of the Yankees, and president of MSG.

892
00:51:59,440 --> 00:52:01,880
Speaker 1: So talk about a pretty packed life, but that books

893
00:52:02,080 --> 00:52:04,160
Speaker 1: called outrageous good Fortune. I'm in the middle of that

894
00:52:04,280 --> 00:52:08,600
Speaker 1: and it's, uh, it's pretty amazing, really really quite interesting.

895
00:52:09,400 --> 00:52:12,000
Speaker 1: What sort of advice would you give to a recent

896
00:52:12,239 --> 00:52:16,680
Speaker 1: college grad who was interested in pursuing a career in

897
00:52:16,880 --> 00:52:24,600
Speaker 1: quantitative finance? UM, I would say to those that are

898
00:52:25,719 --> 00:52:29,480
Speaker 1: uh their First of all, they're welcome. We'd love to

899
00:52:29,520 --> 00:52:34,000
Speaker 1: see them, UM to enjoy the journey. UH, substantively network.

900
00:52:34,600 --> 00:52:36,719
Speaker 1: I think you learned so much from asking a lot

901
00:52:36,719 --> 00:52:41,080
Speaker 1: of questions about what people do and how they do it. UM.

902
00:52:41,239 --> 00:52:44,280
Speaker 1: Be a sponge. UM. Surround yourself with some really smart

903
00:52:44,360 --> 00:52:48,160
Speaker 1: people UM that are equally driven UM. And then you know,

904
00:52:48,160 --> 00:52:50,960
Speaker 1: the last thing I would say, for particularly for Kuan investors,

905
00:52:51,719 --> 00:52:54,920
Speaker 1: is you know, marry the how and the why. And

906
00:52:54,960 --> 00:52:56,359
Speaker 1: what I mean by that is, you know a lot

907
00:52:56,400 --> 00:53:00,520
Speaker 1: of people either have the correlation understanding or the causation

908
00:53:00,600 --> 00:53:05,360
Speaker 1: understanding correlation and they understand the math behind it causation,

909
00:53:05,440 --> 00:53:09,120
Speaker 1: they understand the practical effects. So it could work again. UM.

910
00:53:09,120 --> 00:53:12,840
Speaker 1: Marrying those two I think really makes for UM, you know,

911
00:53:12,880 --> 00:53:17,719
Speaker 1: a phenomenal quantitative investor. Uh, quite quite interesting. And our

912
00:53:17,800 --> 00:53:21,040
Speaker 1: final question, what do you know about the world of

913
00:53:21,160 --> 00:53:26,040
Speaker 1: quantitative investing in trading today that you wish you knew

914
00:53:26,560 --> 00:53:32,279
Speaker 1: years ago when you were first starting out UM. But

915
00:53:32,320 --> 00:53:37,120
Speaker 1: I would say, besides buying UM it was a monster beverage,

916
00:53:37,120 --> 00:53:39,880
Speaker 1: which I think is up about six hundred thousand percent.

917
00:53:40,440 --> 00:53:44,040
Speaker 1: The SMP is only a less than a thousand percent UM,

918
00:53:44,120 --> 00:53:46,880
Speaker 1: and I would say, I would say, there's there's really

919
00:53:46,880 --> 00:53:49,640
Speaker 1: nothing I would want to know in advance. And it

920
00:53:49,760 --> 00:53:52,800
Speaker 1: might sound a little weird, but I think it spoils

921
00:53:52,800 --> 00:53:55,040
Speaker 1: the excitement. I'm one of the great things about being

922
00:53:55,040 --> 00:53:58,440
Speaker 1: in this quantitative business that really finance in general, is

923
00:53:58,520 --> 00:54:02,080
Speaker 1: just the expert exploration and the quest for learning. That's

924
00:54:02,080 --> 00:54:04,560
Speaker 1: something that has driven me, you know in my career

925
00:54:04,640 --> 00:54:08,480
Speaker 1: that I've I've truly enjoyed and and knowing you know stuff,

926
00:54:08,640 --> 00:54:11,000
Speaker 1: would you know what would kind of spoil that journey?

927
00:54:11,000 --> 00:54:13,440
Speaker 1: And so, you know, I the hiccups that I've had

928
00:54:13,640 --> 00:54:16,719
Speaker 1: across the around the years and and the successes I

929
00:54:16,760 --> 00:54:20,840
Speaker 1: think have made the journey awesome. And I'd say respectfully,

930
00:54:20,840 --> 00:54:23,959
Speaker 1: No thanks on on you know the other pieces, because

931
00:54:24,000 --> 00:54:26,640
Speaker 1: it wouldn't have made the journey is fun. We have

932
00:54:26,800 --> 00:54:30,440
Speaker 1: been speaking with Gary Kropovka. He is the president of

933
00:54:30,480 --> 00:54:34,839
Speaker 1: World Quantz. If you enjoy this conversation, well please check

934
00:54:34,880 --> 00:54:39,200
Speaker 1: out any of our previous almost four hundred prior conversations.

935
00:54:39,719 --> 00:54:43,560
Speaker 1: You can find those at iTunes, Spotify, wherever you feed

936
00:54:43,600 --> 00:54:47,760
Speaker 1: your podcast fix. We love your comments, feedback and suggestions

937
00:54:47,960 --> 00:54:51,800
Speaker 1: right to us at m IB podcast at Bloomberg dot net.

938
00:54:52,239 --> 00:54:55,960
Speaker 1: Sign up for my daily reads at ridoltz dot com.

939
00:54:56,080 --> 00:54:59,800
Speaker 1: Check out my weekly column on Bloomberg dot com slash Opinion.

940
00:55:00,400 --> 00:55:04,240
Speaker 1: Follow me on Twitter at Ritholtz. I would be remiss

941
00:55:04,280 --> 00:55:06,480
Speaker 1: if I did not thank the crack team that helps

942
00:55:06,520 --> 00:55:10,560
Speaker 1: put these conversations together each week. Nick Falco is my

943
00:55:10,680 --> 00:55:15,880
Speaker 1: audio engineer. Michael Boyle is my producer. Attika Valbrunn is

944
00:55:15,920 --> 00:55:19,920
Speaker 1: our project manager. Michael Batnick is my head of research.

945
00:55:20,680 --> 00:55:25,200
Speaker 1: I'm Barry Ritholtz. You've been listening to Master's Business on

946
00:55:25,280 --> 00:55:26,280
Speaker 1: Bloomberg Radio.