Speaker 1: Hello, and welcome to another episode of Odd Lots. I'm
Speaker 1: Joe Wisenthal, Managing editor of Bloomberg Markets, and I'm Tracy Alloway,
Speaker 1: Executive editor of Bloomberg Markets. Hey, Tracy, do you know
Speaker 1: what the fastest sport in the world is? Uh? Sailing?
Speaker 1: Race car driving? Uh, those are pretty good guesses, but no,
Speaker 1: Actually the answer is high lie high lie highlight. Highlight
Speaker 1: high Okay, highlight. It's actually originally a game that originated
Speaker 1: in the Spanish Basque country. It's kind of like racquetball,
Speaker 1: except the players play on this gigantic court. The ball
Speaker 1: goes nearly two miles the rackets of these gigantic crald
Speaker 1: things that the players wear over their hands. The ball
Speaker 1: is hard as a rock, and oh yeah, if it
Speaker 1: were to hit you in the head, the ball could
Speaker 1: kill you. This sounds like a made up sport. Um,
Speaker 1: why are we talking about this? It's a good question because,
Speaker 1: in addition to how crazy and intense the game is,
Speaker 1: you know, as I said, two hundred mile an hour
Speaker 1: ball is hard as a golf ball, potentially deadly. People
Speaker 1: actually gamble on highlight, kind of like horse racing. Uh.
Speaker 1: The people a bunch of players play in a tournament
Speaker 1: of sorts and then people bet on whether the different
Speaker 1: players will win place or shows. So there's this big
Speaker 1: gambling element to it that's really interesting. Our guest today
Speaker 1: that we'll be talking to is Steven Skina. He's a
Speaker 1: professor at stony Brook University and he wrote a book
Speaker 1: all about gambling on Highlight Online and how he cracked
Speaker 1: the system and he made a bunch of money. He
Speaker 1: cracked the system, so he beat the house. Yeah, it's
Speaker 1: basically impossible theoretically, you know, gambling, the house is always
Speaker 1: supposed to lose. But in these games we're sort of
Speaker 1: betting against other people and the crowd sets the odds.
Speaker 1: It's actually possible. And not only did Steven beat the
Speaker 1: system and make a bunch of money, there's some interesting
Speaker 1: lessons in terms of eating the stock market and odds
Speaker 1: games in general. Okay, so I'm excited because not only
Speaker 1: am I about to learn about a sport which I've
Speaker 1: never heard of before, but I'm also interested in making money.
Speaker 1: So this sounds too all right, I think you've seen
Speaker 1: it all well, think fast, experience the wall to wall
Speaker 1: action and NonStop excitement that is Miamie Highlive see world
Speaker 1: class athletes climbed the walls to catch a rock hard
Speaker 1: ball flying. It speeds over a hundreds of miles an hour.
Speaker 1: Think excitement, Think Miami, Higlan, I think fast Steven, thank
Speaker 1: you very much for joining us. Thank you, it's nice
Speaker 1: to be here. Tracy has never heard of highlight before.
Speaker 1: What is highlight and how did you get interested in? So?
Speaker 1: Highlight is as as you said, a besque game. Originally,
Speaker 1: it's sort of like a variation on handball. Um. The
Speaker 1: reason people are exposed to it in the United States
Speaker 1: is typically because in Florida it's a betting venue. Um.
Speaker 1: There are these stadiums called front Times in Miami and
Speaker 1: Dania near Fort Lauderdale where you can watch mostly basque
Speaker 1: players play the sport. And that's actually exactly how I
Speaker 1: discovered it. My family used to go down to Florida,
Speaker 1: my North Miami beach every winter for a couple of weeks,
Speaker 1: and we went occasionally to UH the Dania Highlight front Time.
Speaker 1: That was exactly the way that we got involved. You know,
Speaker 1: our family would every year take its vacation visiting the
Speaker 1: relatives in Florida. We would drive down and we would
Speaker 1: UH one night go to the Highlight front time. Is
Speaker 1: it fun to watch these games? It sounds intense from
Speaker 1: Joe's description, it's I think it's incredibly exciting. I mean,
Speaker 1: first of all, it's fun to watch them make these
Speaker 1: plays because the ball is moving very fast, they have
Speaker 1: to make, you know, great catches and very difficult throws.
Speaker 1: But also the scoring system involved in Highlight has some
Speaker 1: infra sting mathematics instructor that makes it kind of fun
Speaker 1: to watch. So depending up on how you bet and um,
Speaker 1: the chances of winning change extremely rapidly with every point,
Speaker 1: and so it's it's it's very exciting because the situation
Speaker 1: is always changing. Yeah, so I said it was kind
Speaker 1: of like horse race betting and that you could bet
Speaker 1: on wind, place their show. But in a way it's
Speaker 1: a little more complicated. Why don't you just sort of
Speaker 1: describe how the similarities real quickly and the differences. So
Speaker 1: it is like horse race betting and that you can
Speaker 1: bet on win, place and show, and that's certainly what um,
Speaker 1: what we're gonna be doing. The difference is the scoring system. UM. Basically,
Speaker 1: in Highlight the winner is the first one to get
Speaker 1: the seven points, and they have eight teams that are
Speaker 1: playing in any given match, but because of the size
Speaker 1: of the court, only two teams can play at once.
Speaker 1: The teams wait in a line. Their their uniform numbers
Speaker 1: are one through weight, corresponding to where they start in line.
Speaker 1: And originally the first two players play each other. Then
Speaker 1: the winner keeps playing, gets a point and keeps playing.
Speaker 1: The loser goes to the end of the line, and
Speaker 1: they keep playing until you get to seven points. And
Speaker 1: if you think about that kind of a scoring system,
Speaker 1: it gives an advantage to the people who start early
Speaker 1: because um, obviously they get first cracks at getting points,
Speaker 1: and even if they lose, they are more likely to
Speaker 1: be they're gonna be the first player to come up
Speaker 1: for a second time. So they make the scorches them
Speaker 1: even more complicated. Whereafter every trip through the queue once
Speaker 1: meaning every player has played its first point, now every
Speaker 1: subsequent point counts for two. And this makes for a
Speaker 1: very complicated scoring system. That means that even if you're
Speaker 1: very very close to winning, if you suddenly lose that point,
Speaker 1: you go to the end of the line and you
Speaker 1: might not get another chance to play again. And the
Speaker 1: betting system, it's a paramutual odds what exactly to that man.
Speaker 1: So paramutual means that you're betting against the other players,
Speaker 1: and it's not me betting against the house. If I
Speaker 1: was betting against the house, the odds of me winning
Speaker 1: are very small. That's why the house is usually big.
Speaker 1: But in a para mutual system, what happens is the money,
Speaker 1: all the money that is bet in the accompetition is
Speaker 1: thrown into a pool. The house skims off a fee
Speaker 1: something and the rest is divided among the winners. So
Speaker 1: in order to you know, to have a successful betting system,
Speaker 1: you have to be better than the other players. So
Speaker 1: the other betters like basically there are a bunch of
Speaker 1: dumb people like me and my family who used to
Speaker 1: go there from time to time and bet and we
Speaker 1: didn't know anything. And so theoretically, if you're really smart
Speaker 1: and studied, we're the fish that you could take advantag.
Speaker 1: That was the attraction. I mean again, it's it's a
Speaker 1: very exciting sport. It's probably a hard sport to know
Speaker 1: the players very well. You know, I don't think most fans.
Speaker 1: Most fans are not that intense, but they go once
Speaker 1: a year, twice a year. And before we get to
Speaker 1: your sort of rigorous approach. A story in your book.
Speaker 1: You won the first ever bet you placed on Highlights, right, So,
Speaker 1: so the reason we got really hooked on this was
Speaker 1: that when and our parents drove down the floor and
Speaker 1: let us go to highlight one night, they also let
Speaker 1: us make one bet. They gave us two dollars and
Speaker 1: they said, you make one bet, and we followed the
Speaker 1: bet that was listed in the local tow sheet, knowing nothing,
Speaker 1: and astonishingly, it was a trifecta combination of first place
Speaker 1: and show that astonishingly one. And so we won, you know,
Speaker 1: a hundred four dollars. And this was an amazing amount
Speaker 1: of money to a bunch of kids back in the seventies.
Speaker 1: And uh, that's what I was, probably about twelve or
Speaker 1: so at the time. Turning two hundred dollars into a
Speaker 1: hundreds exciting any time, but but when you're a kid
Speaker 1: in the seventies, it must be absolutely Uh. I could
Speaker 1: see how you would then get hooked for life on
Speaker 1: the game. Um, all right, let's fast forward a little bit,
Speaker 1: and so you're a professor at a Stony Brook. Talk
Speaker 1: us about how you started on your path to systematizing
Speaker 1: a gambling system. For highlight and what you did. So
Speaker 1: when you look at the scoring system again, a highlight
Speaker 1: game is played in discreete points player one place player two.
Speaker 1: One of them wins, the other goes to the end
Speaker 1: of the line. You could imagine simulating the result of
Speaker 1: a highlight match by flipping a coin for every particular
Speaker 1: point player one. If player one is maybe better than
Speaker 1: player two, maybe you'd say it as a six chance
Speaker 1: of winning the first point. And if you could figure
Speaker 1: out the odds that one player is going that every
Speaker 1: player has against every other player in that they might
Speaker 1: encounter in a match, you can build a simulation to
Speaker 1: use random numbers to play through and simulate each match.
Speaker 1: So is it like it sounds like a series of
Speaker 1: tree charts almost right? You assigned probabilities for each outcome
Speaker 1: and then you have them sort of branching across all
Speaker 1: the possible outcomes. Right, so you you could view this
Speaker 1: as a tree process. It's a it is a branching process.
Speaker 1: It's a tree process where at every point in the
Speaker 1: tree is every note in the tree is basically two
Speaker 1: players playing each other with a certain score and a
Speaker 1: certain status of players in the queue to come. Then
Speaker 1: depending upon who wins it, you go to a different
Speaker 1: state in the process, and this process ends when you
Speaker 1: have identified who comes in for a second and third.
Speaker 1: So this part sounds is where it seems to really
Speaker 1: diverge from say horse racing, where you just have one event,
Speaker 1: there's one, two, and three, not really all these different
Speaker 1: permutations and sequences. So horse racing is not at the
Speaker 1: scret event kind of a game. This is maybe a
Speaker 1: little bit more akin to I would say, um baseball
Speaker 1: than football. Baseball is a bunch of the scret events.
Speaker 1: There's pitches and things happen. In basketball, things are very continuous.
Speaker 1: In horse racing, things seem continue with So when you
Speaker 1: started developing the system, when are we talking about how
Speaker 1: long ago is this? This is something we started in
Speaker 1: the I would say early nineties. If you have to
Speaker 1: get back there, it's probably the story about in the
Speaker 1: early nineties. Okay, so you broke highlight down into this
Speaker 1: series of discrete events, then what's next in terms of
Speaker 1: the creation of your gambling system. So again, once you
Speaker 1: have the ability to view this as this tree process
Speaker 1: or as this um you can simulate one game, you
Speaker 1: can now simulate a million games or or or more
Speaker 1: and see what this probability distribution is of outcomes, and
Speaker 1: you can start to look at for every combination of first, second,
Speaker 1: and third, how often did it come in? And from
Speaker 1: this that gives you some insight into what things you
Speaker 1: should bet on. But things get a little bit more complicated. First,
Speaker 1: you have to accurately model how good the players are,
Speaker 1: so you have a good guess as to how often
Speaker 1: player one is gonna be player two. And more that
Speaker 1: you have to get a build a model of how
Speaker 1: the public is going to bet um. Again, it's a
Speaker 1: paramutual system. I'm betting against the public. If everyone else
Speaker 1: in the public was someone who programmed the computer and
Speaker 1: did the analysis the way I did, I would have
Speaker 1: no advantage. Are you looking for almost pricing descrepants? Ease
Speaker 1: between where you think the outcome of the game is
Speaker 1: going to come and where people are actually betting exactly
Speaker 1: so that that that again there is a a based
Speaker 1: on our simulation, basically an underlying real probability distribution which
Speaker 1: would in some sense and for a price as to
Speaker 1: what would be a fair return for a two dollar
Speaker 1: bet on that outcome. And then you know we look
Speaker 1: for pricing disreferences, so let's talk about them. Are there
Speaker 1: some persistent biases that you learned about in how the
Speaker 1: public bets? Like you know, I'm the public is silly
Speaker 1: enough to mostly bet on by powerball tickets. People are irrational,
Speaker 1: So what kind of irrationality is did you see that
Speaker 1: you can take advantage of? What one interesting property is
Speaker 1: that since it's very very hard for all the players
Speaker 1: with high uniform numbers to do very well. If you
Speaker 1: could imagine players six, seven, and eight, in order for six, seven,
Speaker 1: and eight to do well well, six has to beat
Speaker 1: seven in order to do well, but that puts seven
Speaker 1: at the end of the line, and seven has to
Speaker 1: eat eight, but that puts eight at the end of
Speaker 1: the line. It's very very hard for there to be
Speaker 1: combinations where all the big numbers come in and essentially
Speaker 1: almost impossible. And yet you would always see people betting
Speaker 1: on this because they didn't know that that way, they
Speaker 1: were betting on essentially an outcome that essentially couldn't happen,
Speaker 1: and the odds must look pretty juicy for those characters.
Speaker 1: For those players, I mean, and it's up to the
Speaker 1: payoff would be good if they want. Because there's only
Speaker 1: one person betting on that during the course of any match,
Speaker 1: but it's never gonna happen. So so so here's our
Speaker 1: system would look for these disrepencies. The other thing that
Speaker 1: we would look for that's sort of related to the
Speaker 1: kind of models people build for trading, is we'd have
Speaker 1: to look at what our impact on the betting pool is. So, um,
Speaker 1: if we bet on something and we win, it doesn't
Speaker 1: pay for us to bet a lot of money on
Speaker 1: that outcome because we're just dividing the pool among all
Speaker 1: the winning tickets, and so every subsequent ticket we would
Speaker 1: buy would would have a lower and lower expected value.
Speaker 1: All right, So you have the mathematics, all the trees,
Speaker 1: you have the nature of how people bet, so the payoffs.
Speaker 1: You also have this calculation about how your own bets
Speaker 1: might affect the results. Now let's talk about putting it
Speaker 1: into practice. What did you do then? So the question
Speaker 1: now is how do we actually go bet on this thing?
Speaker 1: We we couldn't have somebody stationed at the HIGHLFE front
Speaker 1: on every day making our bets the front and we
Speaker 1: were interested in betting in was in Connecticut, I was
Speaker 1: in New York. This was not you know, I'm not
Speaker 1: that crazy, but it turned out that Connecticut did have
Speaker 1: an O TB and off track betting operation where they
Speaker 1: had a phone system where you could dial it in
Speaker 1: and dial in your bets. And so we programmed the
Speaker 1: computer computers back then had these things called modems for
Speaker 1: the kids listening, which I remember that I remember the
Speaker 1: A O L dial up modem, these dial up modems,
Speaker 1: and so you could in some sense, therefore, you had
Speaker 1: a device that you could program that could make phone
Speaker 1: calls and could likewise push buttons in some sense push
Speaker 1: buttons on phones. And so we built a system that
Speaker 1: would um take our bets and convert that into the
Speaker 1: dial tone instructions that would be necessary to place this
Speaker 1: bet at the Connecticut oft TACH betting operation. And so
Speaker 1: we built essentially a complete, you know, programmed trading system
Speaker 1: in highlight. Every day, it would identify go over the web,
Speaker 1: identify what we're the game matches, and who was playing.
Speaker 1: It would simulate each one a million times. It would
Speaker 1: determine the most profitable betting outcomes, and then it would
Speaker 1: phone it into O T B attempt to implement our trade.
Speaker 1: This is amazing. This sounds like algorithmically driven high frequency
Speaker 1: highlight trading essentially. What I love about that is that
Speaker 1: you're exactly right, except there's this very old school part
Speaker 1: because there's high frequency algorithmic highlight trading except for the
Speaker 1: very last part, and it involves dial tones going through
Speaker 1: a phone tree, and so it's this incredibly modern seeming
Speaker 1: idea and then this very old school actually process of
Speaker 1: placing the trades at the very end. I can't say
Speaker 1: that the you know, the the We wrote a book
Speaker 1: about this book called Calculated Bets and market for it
Speaker 1: ended up being not high live fans because it's not
Speaker 1: actually a big universe, but but if they're out to be,
Speaker 1: yet a lot of play in people who were doing
Speaker 1: trading and building these program trading operations, because it is
Speaker 1: essentially the same idea that people are using in the
Speaker 1: same technologies, and it kind of explains basically how these
Speaker 1: things work. I want to just step back and ask
Speaker 1: the dumb question, how would you do? What's an algorithm?
Speaker 1: We hear it all the time, algorithmic trading, and people
Speaker 1: have some idea that it means computers in math, But
Speaker 1: what does this actually mean for someone in plain English?
Speaker 1: As it as it is said in in the world
Speaker 1: of algorithmic trading. It is typically a it's really I guess,
Speaker 1: a programmed procedure from making decisions so that uh, you know,
Speaker 1: there is a decision in a program trading system. There
Speaker 1: has to be somebody making a decision to buy or
Speaker 1: sell this particular stock at this particular time. So a
Speaker 1: set of rules basically, so it can be sort of
Speaker 1: a set of rules as usually some level of input.
Speaker 1: I would say, it could be a simulation, it could
Speaker 1: be sets of rules. It's some kind of a procedure
Speaker 1: that that the sides that this bet or this series
Speaker 1: of bets are profitable, and goes and executes them without
Speaker 1: human involvement. And now you set up the trading system,
Speaker 1: they're dialed in. How much money did you make? Well,
Speaker 1: we may we made okay by percentage wise. Again, recognize
Speaker 1: that the betting pool and highlight is very small. So
Speaker 1: that I told you that a you know, making too
Speaker 1: many bets on any particular match would would rapidly saturate
Speaker 1: the pool and none of the bets would be profitable.
Speaker 1: But but over the course of our trade, we made
Speaker 1: over return on our investment. It was sizeable enough that
Speaker 1: it got to be a little scary to UM run
Speaker 1: it on university research machines, and so we eventually ended.
Speaker 1: It wasn't so much that I have deep regrets about
Speaker 1: turning the system off. What time frame we we had
Speaker 1: it running for about a six month period. Six months, Yes,
Speaker 1: would you would you have gotten to the point if
Speaker 1: you had continued with it where you would just be
Speaker 1: the entire your market UM if we kept um again,
Speaker 1: if if we made our bets bigger and bigger than
Speaker 1: we could have easily become the entire market you know.
Speaker 1: In fact, one of the one of the things that
Speaker 1: was key to our systems again we our system bet
Speaker 1: on Trifecta's combinations of wind, Place and show, which are
Speaker 1: rare events and but but really didn't make it payoff
Speaker 1: was they had a special type of bet called to
Speaker 1: Trifecta box where we could buy a particular set of
Speaker 1: all combinations of three numbers UM cheaper than we could
Speaker 1: the corresponding tickets. And our goal was really to have
Speaker 1: as little impact on the pool as possible, and that
Speaker 1: was really what was necessary. It really wasn't a big
Speaker 1: margin here. If someone wants to do this, I mean,
Speaker 1: I know that it's harder, but what are the key
Speaker 1: areas of mathematics to study. So again I am a
Speaker 1: computer scientist, and so in this case there was um,
Speaker 1: you know, to understand things about Monte Carlo simulations. Again
Speaker 1: we were talking about this tree process. Um, you could
Speaker 1: view this as building a tree that you exhaustively analyze
Speaker 1: where you could simul late at using something called Monte
Speaker 1: Carlo simulation, where you really did use random numbers to
Speaker 1: describe the path down the trade. So knowing computer science
Speaker 1: is a good thing. Um, you know, knowing something about statistics.
Speaker 1: You know, data science is a new field called data science,
Speaker 1: which is the kind of area where my my lab works.
Speaker 1: And uh then this kind of field that this is
Speaker 1: the kind of stuff that I think is good learning
Speaker 1: how to build models, this kind of thing. So you
Speaker 1: mentioned you came out of this book Calculated Beds, and
Speaker 1: it wasn't a huge hit among high life fans because
Speaker 1: there aren't that many high life fans, but it got
Speaker 1: a lot of followers among people who play the market,
Speaker 1: people in banks. What are some of the key lessons
Speaker 1: in terms of what you did and how else they
Speaker 1: apply to someone wanting to play the markets and setting
Speaker 1: up a trading system. So the first thing that I
Speaker 1: would say is that that markets are in general relatively efficient,
Speaker 1: even in high Lie where we had you know, these
Speaker 1: crazy you know, the people who were watching embedding workers only,
Speaker 1: these people who went once a year and didn't know anything,
Speaker 1: the pools of dumb money. I was surprised how hard
Speaker 1: it was for us to build a system that actually
Speaker 1: did um, did have a positive return. I thought it
Speaker 1: was gonna be a lot easier than that than it
Speaker 1: turned out to be. And that's that's probably a lesson
Speaker 1: that most markets are are more efficient than you would think.
Speaker 1: You know, even in horse ray. They've been done studies
Speaker 1: in horse racing, and the markets there are relatively efficient.
Speaker 1: You know, the fact is that there's a large um
Speaker 1: transaction cost essentially of the house keeps, and that's a
Speaker 1: large transaction cost, and that's white people lose. So certain
Speaker 1: things are models are harder to build than than you
Speaker 1: would think. That's I guess one lesson here. The other
Speaker 1: is that if you're careful and you're you're thinking hard enough,
Speaker 1: and you beat on it and maybe there's something there.
Speaker 1: Does the lesson of not saturating the market apply to
Speaker 1: broader financial markets because obviously we hear a lot today
Speaker 1: about um high frequency trading, algorithmically driven trading, and it's
Speaker 1: impact on markets. What do you think, Yeah, so it's
Speaker 1: certainly the case that in any market, you if you
Speaker 1: bet enough, you're eventually betting against yourself. And uh, the
Speaker 1: the advantage of the financial markets that they're generally large
Speaker 1: enough that you can put in a tremendous amount of
Speaker 1: capital and play before it, you know, you really start
Speaker 1: betting against yourself. But again, you know many in many
Speaker 1: hedge funds, in some sense there's they will occasionally occasionally
Speaker 1: a hedge funds will return capital if they can't think
Speaker 1: they can invest it efficiently enough. And that's basically because
Speaker 1: of these saturation effects. But what about if you get
Speaker 1: a market that becomes dominated by algorithmic trading and they
Speaker 1: all kind of feed on each other. Does that end
Speaker 1: up having the same effect. It's an interesting question. Um,
Speaker 1: if everybody was doing the exact same thing in a market,
Speaker 1: then there wouldn't be an interesting market going on. And
Speaker 1: so the question of whether whether algorithmic trading is going
Speaker 1: to eventually get into a world where you know, sometimes
Speaker 1: people are betting against them where nothing is happening, It
Speaker 1: depends upon the traders doing different things. I guess markets
Speaker 1: would only get into trouble if all the different traders
Speaker 1: were doing the exact same things. That's I guess when
Speaker 1: you get into bubbles and when you get into into crashes,
Speaker 1: there any sort of final key lessons for markets from
Speaker 1: what you did. Has anyone has anyone written to you
Speaker 1: and said they've used your book and made a fortune?
Speaker 1: And I feel I have heard from every person who
Speaker 1: has read the book the people the book is and
Speaker 1: I acted books that did reasonably well, but it's still
Speaker 1: the case that uh, a lot of people felt very
Speaker 1: very close to this book because it does tell a
Speaker 1: story that's that's a kiss akin to what a lot
Speaker 1: of traders basically do. And UM, you know, I hear
Speaker 1: from people. I occasionally hear from people who want to
Speaker 1: want me to get involved in their betting scheme. I
Speaker 1: heard from a Russian syndicate recently that wanted UH to
Speaker 1: do trading in um soccer pools and uh. And I've
Speaker 1: I've hung around a gambling syndicate in Macau where they've
Speaker 1: been in horse racing and uh. And so you know,
Speaker 1: so there are these these and I've also spoken through
Speaker 1: a lot of traders, and uh again we met at
Speaker 1: a financial conference. So it's been an interesting leading into
Speaker 1: a world that's quite different from me as a computer scientist.
Speaker 1: Your system was ultimately based on pure mathematics. Does that
Speaker 1: take the emotion out of winning and making the bet?
Speaker 1: It was true that that that there was sort of
Speaker 1: the fact that there was a real event happening, that
Speaker 1: they were really these besques tossing a ball around was
Speaker 1: really an abstraction. You know, every day I would get
Speaker 1: email from my machine about how we did, and every
Speaker 1: night the computer would play a million simulations of this
Speaker 1: game that was going to happen tomorrow. But and and
Speaker 1: somehow we were divorced from the real aspect of it.
Speaker 1: And that may be true in certain certain aspects of
Speaker 1: the markets. I mean, people are busy trading stocks around
Speaker 1: in some ways, quite independent of whether or not these
Speaker 1: are You know that there are companies there, and that
Speaker 1: people are working and people are building things, and there's
Speaker 1: things happening. So there's a certain sense in which this
Speaker 1: was an abstraction of the world that that may may
Speaker 1: have felt a little bit funny when you think about it.
Speaker 1: And last question, are you doing any betting on anything?
Speaker 1: Or these days or back to hip pure academic stuff.
Speaker 1: I am, I am up again. I am a professor.
Speaker 1: I I you know, I live a clean life. But
Speaker 1: but again, my my research area these days is related
Speaker 1: to data science data analysis. We do a lot of
Speaker 1: projects related to data modeling and uh things like this,
Speaker 1: and so every once in a while my work touches
Speaker 1: on some kind of a model related to that does
Speaker 1: have relations with financial markets and other things. Thank you
Speaker 1: very much for joining. Its fascinating and just talking with
Speaker 1: Thank you, there's a lot of fun. So Tracy, are
Speaker 1: you Are you a high life fan? Though? I kind
Speaker 1: of want to go watch a game. I want to
Speaker 1: I want to bet on a game. I want to
Speaker 1: be the dumb money on the sidelines of the game. Um.
Speaker 1: I love that conversation. I thought that was like, I
Speaker 1: don't know, I just I thought it was fascinating. So
Speaker 1: I learned a lot about the sport. And it also
Speaker 1: is really helpful in bringing to life some the concepts
Speaker 1: that we talk about all the time and markets like algorithms.
Speaker 1: Absolutely like all these things in terms of simulations, decision trees, algorithms,
Speaker 1: and particularly that part about how you know the end
Speaker 1: we were talking about about how if you get too
Speaker 1: big at a market, or if everybody's chasing the same
Speaker 1: algorithmic strategy, how can all break down? Yeah, exactly, And
Speaker 1: my absolute favorite part was when he described the betting
Speaker 1: system and you have like all the math and then
Speaker 1: you have the algorithm. But how in the nineties and
Speaker 1: finally ended up where you had to like have your
Speaker 1: computer make dial tones to enter in the debates. There's
Speaker 1: a marriage of old style and modern trading techniques. I
Speaker 1: thought was hilarious to imagine. Yeah, that was great. All right,
Speaker 1: that is all for Odd Lodge. Thank you for listening.
Speaker 1: I'm Joe Wisenthal, Managing Editor Bloomberg Markets, and you can
Speaker 1: follow me on Twitter at the stall War. And I'm
Speaker 1: Tracy Alloway, Executive editor of Bloomberg Markets and I'm on
Speaker 1: Twitter at Tracy Alloway. Thanks again. Joe and I are
Speaker 1: very proud of our new podcast, Odd Lots, but we
Speaker 1: are also very proud of Bloomberg's other growing suite of
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Speaker 1: addition to our own podcast, please don't miss Benchmark with
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