Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking. Hey there, everyone, and welcome to Forward Thinking,
Speaker 1: the podcast that looks at the future and says they say,
Speaker 1: the best things in life are free. I'm Jonathan Strickland.
Speaker 1: I'm Lauren foc Obad. I now, Joe. I look at you,
Speaker 1: and I just think you're not You're not in the
Speaker 1: right place, Joe, you're not. You don't belong here. You
Speaker 1: belong in a swamp. That's that's choice number two, actually
Speaker 1: choice number one. Oddly, you're wearing the same thing in
Speaker 1: both scenarios, is that you're wearing a rumpled Oxford shirt
Speaker 1: that's been rolled up to the elbows. You've gotta tie
Speaker 1: that's a little, you know, dislodged, and your hair's all
Speaker 1: must up, and you're standing in the middle of a
Speaker 1: crowd of people all with sheets of paper in their hands,
Speaker 1: and you're making these crazy hand signals to some guy
Speaker 1: who stands in front of a bunch of numbers that
Speaker 1: keep on changing, and you just keep screaming out buy
Speaker 1: and sell in random increments. Yeah, you're imagining me as
Speaker 1: an extra from the movie Wall Street, which was about
Speaker 1: the place Wall Street which is about money. Yes, yeah,
Speaker 1: you've nailed it. Actually, that's my personality. I write about science,
Speaker 1: technology in the future, but my real passion is international finance.
Speaker 1: Yeah I can, especially the part that involves standing someplace
Speaker 1: and shouting. Oddly enough, you would be doing the same
Speaker 1: thing in the swamp, just to whatever creatures happened to
Speaker 1: be stopping by. Okay, So yeah, this isn't um the
Speaker 1: easiest topic for everyone to find something interesting about international finance, stocks,
Speaker 1: commodities markets. But it turns out that there are crazy
Speaker 1: machines involved in stocks markets. Yes, and as you read
Speaker 1: about these crazy machines, you start to envision a future,
Speaker 1: a future that's similar to say, the Terminator film series.
Speaker 1: So we wanted to talk today about that future and
Speaker 1: about some of these machines and what they do. So
Speaker 1: before we get into the future, let's talk about the past.
Speaker 1: You guys know what happened on May six? Can you
Speaker 1: tell us? John? I can. I was waiting for response,
Speaker 1: which your your stunned silence tells me that I need
Speaker 1: to explain. That was the day. It was when the
Speaker 1: superhero the Flash smashed into the side of a mountain. Oh,
Speaker 1: the Flash crash. Okay, well, you got the name right,
Speaker 1: but but the particulars are a little different from what
Speaker 1: you are envisioning. It didn't involve Barry Allen smashing into
Speaker 1: the side of a cliff. No, the flash crash was
Speaker 1: really about a bad day on the for the TAL
Speaker 1: the dal Jones Industrial average. It dropped about nine percent,
Speaker 1: which was around a thousand points total that day, and
Speaker 1: recovered most of those losses within the span of less
Speaker 1: than an hour. Actually, but six hundred points of that
Speaker 1: one thousand point loss came in the space of five minutes,
Speaker 1: and it required about twenty minutes for the market to recover.
Speaker 1: And there are a lot of different reasons why the
Speaker 1: stock market dropped that day. One of the reasons was
Speaker 1: that Greece was undergoing that terrible debt crisis that was
Speaker 1: gripping a lot of Europe. Actually, Greece was just sort
Speaker 1: of the hotbed for it. And then there are other
Speaker 1: issues as well. But some of it has to do
Speaker 1: with these crazy machines all acting on what they what
Speaker 1: they perceived to be market trends in mass and it
Speaker 1: began to kind of create an escalating effect. I guess
Speaker 1: it's not so much the machines that are crazy, but
Speaker 1: it's what the machines do. These your computers executing what
Speaker 1: we call high frequency trading. Yeah, which you might wonder, well,
Speaker 1: what does that mean? High frequency trading is pretty much
Speaker 1: what sounds like. You're trading lots and lots and lots
Speaker 1: of shares throughout a day, like your shares are are.
Speaker 1: You're buying and selling uh, sometimes multiple times within a second. Yeah,
Speaker 1: and actually usually not that many shares that you usually
Speaker 1: keep the number of shares to a couple of hundred,
Speaker 1: but you're but you're flipping them tens of thousands of
Speaker 1: times a day in order to rack up these fractions
Speaker 1: of its scent each time. Right, because stock prices are
Speaker 1: actually averaged out quite a few decimal places these days,
Speaker 1: so you could be talking about point zero zero zero
Speaker 1: five cents in a trade. But if you have enough
Speaker 1: of those over the course of a day that that
Speaker 1: starts to amount to some real money. Okay, so high
Speaker 1: frequency trading was partially responsible for the flash crash, but
Speaker 1: surely that's the only time something like that has happened. Oh,
Speaker 1: actually there is another blip. In by blip, I mean
Speaker 1: like a hundred thirty six billion dollar market value blip. Um.
Speaker 1: By the way, I accept blips. If anyone has a
Speaker 1: hundred thirty six billion dollar blip they'd like to give me,
Speaker 1: also blimps. I am a Dirgible fan, so just throwing
Speaker 1: that out there, I actually will accept cheap Dirgibles here.
Speaker 1: That's true. That's true. I like to think of them
Speaker 1: as reasonably priced. But go on, okay, So in this instant,
Speaker 1: the APIs the Associated Press's Twitter account was hacked and
Speaker 1: it sent out news that bombs had gone off in
Speaker 1: the White House and that Obama had been injured. Um.
Speaker 1: Of course that never happened, but programs that were monitoring
Speaker 1: the news sold off holdings within seconds and the market plummeted.
Speaker 1: These are these are two two separate but connected uh
Speaker 1: trends here, right, We've got computers that are following algorithms
Speaker 1: that are looking for market trends, like if they see
Speaker 1: something happening in the market, they pounce on it, whether
Speaker 1: that's buying or selling whatever stocks they happen to have.
Speaker 1: And then the other trend is machines that are taking
Speaker 1: in information from the real world and acting upon it.
Speaker 1: So both of these things are happening in increasingly large
Speaker 1: numbers and frequencies in the United States in particular, in
Speaker 1: other countries as well. We're not the only ones who
Speaker 1: do it, but investors of this kind sometimes call themselves quants,
Speaker 1: alluding to some kind of metaphorical quantum unit of money,
Speaker 1: which of course does not exist, but it's kind of
Speaker 1: cute um and and and the strategy here is too,
Speaker 1: is to yeah, flip all of these shares back and
Speaker 1: forth based on minute changes in the stock market thousands
Speaker 1: of times a day, and then remove all your funds
Speaker 1: from the market by the close of business. Right, so yeah,
Speaker 1: you you get in and out every single day. It's
Speaker 1: kind of it's kind of mind boggling, and it's and
Speaker 1: it's operating at a speed that human people literally cannot
Speaker 1: compete at. Right. Yeah. No, that by the time you
Speaker 1: would see a offering on a stock, it would already
Speaker 1: be too late in this world, you know, too late
Speaker 1: a thousand times over. We're talking about machines that can
Speaker 1: that can make a decision in a fraction fraction of
Speaker 1: a second, millionth of a second. And it might be
Speaker 1: responding to like we talked about something in the news,
Speaker 1: or some kind of external predictor or me responding to
Speaker 1: the just the market prices themselves, right right? Yeah, Now,
Speaker 1: according to one story I read in two thousand twelve,
Speaker 1: high speed computer algorithms, or al goes as some call them,
Speaker 1: I don't. I can live for about of all trading
Speaker 1: in US stocks, which sounds like a lot until you
Speaker 1: hear other numbers. I've heard that estimated at as high
Speaker 1: as eight percent, and as much as for UK stocks
Speaker 1: for other European stock markets. Right, and so here we're
Speaker 1: talking about not just uh high frequency here, it's obviously
Speaker 1: all about speed as well. So it's not just how
Speaker 1: many times are your trading stocks throughout the day, but
Speaker 1: how quickly can you pounce upon a trend opportunity in
Speaker 1: order to make that fraction of assent. So let's before
Speaker 1: we get into actually how that happens and what and
Speaker 1: what lengths people and companies have gone to in order
Speaker 1: to get that that advantage of speed. Maybe it's time
Speaker 1: to take a little step back, right, Let's let's turn
Speaker 1: the clock back and take a look at how stock
Speaker 1: markets even came into being and how they work in general,
Speaker 1: so that we can understand what's going on at these
Speaker 1: incredible speeds. So if you were to go back to
Speaker 1: uh say, the Middle Ages, and um, you know, you know,
Speaker 1: dust yourself off a little bit and look around. The
Speaker 1: earliest examples of what would eventually become stock exchanges really
Speaker 1: grew out of the the money lending and later the
Speaker 1: banking movements that we're just getting started. Really, money lending
Speaker 1: had been around for a while, but banking was pretty
Speaker 1: darn new, um, and so at that point one of
Speaker 1: the things that started to become commonplace was trading of debts. So, Lauren,
Speaker 1: let's say that you had borrowed a small sum of
Speaker 1: money and you fully intend to pay it back, and
Speaker 1: you are probably going to be able to, and I
Speaker 1: happen to hold the the debt the promise you're going
Speaker 1: to be paying me. Joe, let's say that you have
Speaker 1: borrowed a ludicrous sum of money and you have very
Speaker 1: little intent to pay it back, but no one knows that. Well,
Speaker 1: I needed to invest in my alchemy lab. Okay, so
Speaker 1: you have gone into I turn all this lead into gold.
Speaker 1: You'll get your money back and then some So Joe
Speaker 1: has invested heavily in Al's chemicals, and uh so, Joe's
Speaker 1: money is potentially never going to be seen again. Now
Speaker 1: I have a chance, a pretty good chance of getting
Speaker 1: the loan paid off by Lauren. But someone else holding
Speaker 1: Joe's loan may say, hey, I'll trade you this this
Speaker 1: loan for that loan. Whereas you know, yours has almost
Speaker 1: a guarantee of being paid off. Mine doesn't have a
Speaker 1: very strong guarantee, but the payoff would be much larger.
Speaker 1: And so you would have people start to trade those
Speaker 1: kinds of those debts, and this has got to be
Speaker 1: a pretty big thing. However, there was no there was
Speaker 1: no formal place to meet to do this. You wonder,
Speaker 1: you wonder if the higher risk, bigger debts are more
Speaker 1: attractive to people who are better at extracting payment, yeah right,
Speaker 1: or less good at math yeah well, uh yeah, Like
Speaker 1: the Borgias were probably pretty darn good at it at
Speaker 1: the by the at their height. But then you had,
Speaker 1: around the the early fift hundreds the Belgian Stock Exchange,
Speaker 1: which traded in bonds and promissory notes. No promisory notes,
Speaker 1: that's essentially saying I promised to pay you X amount
Speaker 1: of money. Yeah, it's it's a legal document, but it
Speaker 1: is essentially an iou uh. And then in shipping you
Speaker 1: started to see the people who owned ships would sell
Speaker 1: shares in a voyage, the idea being that by raising
Speaker 1: this money they could outfit the voyage and then if
Speaker 1: the voyage were successful, they would pay out profits from
Speaker 1: that voyage to their backers. Just very similar to the
Speaker 1: kind of stuff we would see and say a movie
Speaker 1: like The Producers, where you get this idea of selling
Speaker 1: shares in a in a show. In that case, they
Speaker 1: were trying to uh sell shares and show that was
Speaker 1: guaranteed to fail, so they wouldn't have to pay anyone back.
Speaker 1: But this was this is the basis of what would
Speaker 1: eventually be come kind of again a stock market. And
Speaker 1: in fact, in London you get your first stock market
Speaker 1: in seventeen seventy three and nineteen years later that's when
Speaker 1: the New York Stock Exchange was formed. And so that
Speaker 1: takes us to how these these systems work. So you've
Speaker 1: got buyers and sellers right, and you have a market there.
Speaker 1: Often the market is actually the entity that's holding stocks
Speaker 1: and is selling to you. Uh, you have a price
Speaker 1: which is guided by the market itself and basically guided
Speaker 1: by the amount of money someone was last willing to
Speaker 1: buy or sell a stock at. So if you want
Speaker 1: to buy stock. Joe. Let's say that you're raid by
Speaker 1: into Al's chemicals today, you know, because that that business
Speaker 1: really turned around after the Middle Ages. I want to
Speaker 1: invest in lead. Okay, So Joe wants to invest in lead.
Speaker 1: He he thinks lead is going places. So Joe, you
Speaker 1: you say that you're willing to pay how much are
Speaker 1: you willing to pay for a share of lead? A dollar?
Speaker 1: So Joe willing to pay one dollar for a share
Speaker 1: of lead? And how many shares are you wanting to
Speaker 1: purchase in total? So Joe is willing to pay one
Speaker 1: dollar per share of lead for up to one hundred
Speaker 1: shares of lead. Now let's say that you have a
Speaker 1: that that would, by the way, be called a bid.
Speaker 1: That that's your bid for a share for a hundred shares.
Speaker 1: Now the seller might say, you know, I'm willing to
Speaker 1: sell uh fifty shares of lead, but my asking price,
Speaker 1: my ask is a hundred a hundred dollars. So there's
Speaker 1: a big gap there. Right, you're asking you we're willing
Speaker 1: for a dollar, they're asking for a hundred. This would
Speaker 1: be a bad what they call spread. But let's say
Speaker 1: as much you were you were being very realistic, or
Speaker 1: maybe maybe the price is much lower. We'll say that
Speaker 1: asking for a dollar oh five per share, Then you
Speaker 1: have a spread of five cents per share. It's not
Speaker 1: that big a deal. If it's the market that's selling this.
Speaker 1: The market ends up in kind of pocketing that five
Speaker 1: cent difference every time these trades are happening, and that's
Speaker 1: how the market makes money. And again, the stock price
Speaker 1: is reflected by that last transaction. So if your transaction
Speaker 1: was the last one, and you actually bought say a
Speaker 1: hundred hundred years of lead for a dollar or five apiece,
Speaker 1: dollar or five would be what the stock price would be.
Speaker 1: So that's the basis of this. So if you were
Speaker 1: able to act very very quickly, and and you saw
Speaker 1: that a stock price had changed to a specific amount,
Speaker 1: and you were able to leap on it right at
Speaker 1: that moment before anything any other changes in the market happened,
Speaker 1: before anyone else made a purchase, and then sold it
Speaker 1: for a different amount, which would change the stock price.
Speaker 1: You could stand to benefit from that, either by selling
Speaker 1: something slightly higher than what it was or buying it
Speaker 1: when it was slightly lower. You know, there's a lot
Speaker 1: of different maneuvers you could do within the stock market,
Speaker 1: but you know, you're limited by how fast you can
Speaker 1: do them, which is where things like computers come in.
Speaker 1: Oh well, I mean because in in ye olden days,
Speaker 1: and by ye olden days, I mean for a great
Speaker 1: part of the twentieth century. In fact, this involved a
Speaker 1: lot of literal running around. I mean high high technology
Speaker 1: at the time was hiring the fastest and loudest kid
Speaker 1: that you could to go work on the exchange floor. Um.
Speaker 1: And or sometimes pigeons. Legends say that pigeons were used
Speaker 1: to trade in real time on the outcome at Waterloo.
Speaker 1: The Rothchild family supposedly used pigeons to uh, to learn
Speaker 1: the outcome of the Battle of Waterloo before anyone else
Speaker 1: that they could then act upon the market accordingly. So
Speaker 1: I guess they had a strategy like, okay, if the
Speaker 1: French win, go with this. So it was kind of
Speaker 1: an interesting Uh. That's that's an interesting legend. Whether or
Speaker 1: not it's true, you know, it could be apocryphal, we
Speaker 1: don't know sure, but um, but all of this did
Speaker 1: definitely change when computers started to become a thing. Um.
Speaker 1: In October, a computer system replaced that original trading floor
Speaker 1: of the of the New York Stock Exchange, and uh
Speaker 1: it tripled the number of exchanges that could be made
Speaker 1: within months or that were literally made within months. Yeah,
Speaker 1: so you suddenly see a lot more volume as a result,
Speaker 1: you see uh more you know, a higher frequency but
Speaker 1: not high frequency trading. It that would come later, but
Speaker 1: it's certainly that that's set the stage and now we've
Speaker 1: gotten to a point where we really want to eliminate
Speaker 1: latency as much as possible. Latency is that period between
Speaker 1: recognizing a potential trend and acting upon it, and latency
Speaker 1: depends on a couple of different things in this world.
Speaker 1: It depends on how good your computer algorithm is a
Speaker 1: recognizing trends and then and then executing a command. And
Speaker 1: it depends upon the actual communication connection, physical hardware that
Speaker 1: you're using, right, even the infrastructure, right the cables that
Speaker 1: connect you to the New York Stock Exchange. In fact,
Speaker 1: at this point, you have companies that are willing to
Speaker 1: pay top dollar to have to have machines all stored
Speaker 1: in Essentially, they're all putting them in the same building
Speaker 1: that's near the New York Stock Exchange in order to
Speaker 1: have as as close to connect to the stock exchange
Speaker 1: is possible, and to keep things fair, the engineers in
Speaker 1: that building equalize the cable that runs to each of
Speaker 1: these servers, like like adding a few feet of cable
Speaker 1: when necessary, so that these messages that are moving essentially
Speaker 1: y'all at the speed of light are going to equal out. Yeah,
Speaker 1: that's kind of incredible that a few feet can make
Speaker 1: a difference. So the actual physical speed of data transfer
Speaker 1: depends upon the medium you're using to transmit that information, right,
Speaker 1: because the speed of light is a constant, and it's
Speaker 1: a hypothetical ideal that we're never really going to reach. Right,
Speaker 1: that's the speed of light and a vacuum. And so
Speaker 1: what we're dealing with is like various extremely fast transmissions,
Speaker 1: say like the stream of a microwave transmission through the air,
Speaker 1: a fiber optic cable across the street, you know. Right,
Speaker 1: As it turns out, light does travel at different speeds
Speaker 1: through different media, right, I mean, it all depends upon
Speaker 1: the medium or electromagnetic waves, because we're gonna be talking
Speaker 1: not into really about light. That's true. That's true. So
Speaker 1: if you are talking about optic cable, so fiber optic
Speaker 1: cables you can transmit lots of information through fiber optic
Speaker 1: cables by doing lots of tricky things like having multiple
Speaker 1: cores and even a different geometric layout of how the
Speaker 1: light itself travels through that cable. So you may have
Speaker 1: heard about the fastest supercomputers in the world using fiber
Speaker 1: optic to move massive amounts of data in very little time.
Speaker 1: But this is this is that difference between speed and
Speaker 1: throughput that we've talked about before in this podcast. It's
Speaker 1: not that the data is moving faster, it's it's able
Speaker 1: to move more data at the same time, so it's
Speaker 1: it's not necessarily traveling at the speed of light. So
Speaker 1: speed of light through optical cable is around two hundred
Speaker 1: thousand kilometers per second. But you know if that sounds
Speaker 1: pretty fast two kilometers per second. I mean, I'm not
Speaker 1: saying that's slow. But if you were to say, set
Speaker 1: up a microwave tower and a receiver and send a
Speaker 1: message that way, then travels at around three hundred thousand
Speaker 1: kilometers per second, so markedly faster. Right, You're going much
Speaker 1: faster than you would be if you were going through
Speaker 1: optic cable. So there are companies that are setting up
Speaker 1: these microwave relay towers to transmit information between say the
Speaker 1: New York Stock Exchange and a trading houses in Chicago
Speaker 1: that deal with futures, right, because the activity in one
Speaker 1: can affect the other. And if you know about it
Speaker 1: ahead of time, ahead of your competition, you stand to
Speaker 1: make a lot of money. Yeah, I mean even by
Speaker 1: by fractions of a second faster than your competition. And
Speaker 1: there are problems with microwaves that you don't run into
Speaker 1: with optical cable, Like it's a direct line of sight
Speaker 1: kind of issue, right, and if there's a storm, it
Speaker 1: can interfere with transmission. Right. So I mean the same
Speaker 1: reasons that we don't use satellites for this kind of
Speaker 1: thing because latency. I mean, you can move a lot
Speaker 1: more data. But yeah, but for that information to travel
Speaker 1: all the way up into space and then relay with
Speaker 1: the satellite and then be be back down to some
Speaker 1: receiving station on Earth, that that information has to travel
Speaker 1: a huge distance obviously all the way up into lower
Speaker 1: th orbit and then back down and then or not
Speaker 1: even lower thorbit, that could be much higher than lower orbit.
Speaker 1: Uh so then you have a real latency problem. Sure,
Speaker 1: and people are working with microwaves on that entire line
Speaker 1: of sight issue, right, No, sure, A couple of different
Speaker 1: companies have been working on building microwave towers, like I said,
Speaker 1: between New York and Chicago. They're also looking at the
Speaker 1: possibility of using microwaves to transmit information across the Atlantic,
Speaker 1: which would be a lot trickier because you know, building
Speaker 1: towers out in the middle of the ocean not easy. However,
Speaker 1: there's at least one guy who suggests we use drones.
Speaker 1: Microwave drones, Yeah, drones that can actually the stock so
Speaker 1: you think, you know, you'd have to have a drone
Speaker 1: that would be very energy efficient to be able to
Speaker 1: maintain flight over the Atlantic Ocean without needing to recharge
Speaker 1: every few minutes. It would have to have some kind
Speaker 1: of crazy power source, perhaps one of the ones we've
Speaker 1: talked about in one of our previous episodes. But if
Speaker 1: you were able to do it, then in theory, you
Speaker 1: could have a drone microwave relay station to transmit information
Speaker 1: across the Atlantic Ocean, which would travel faster than it
Speaker 1: would with a fiber optic cable. So, assuming that there
Speaker 1: weren't these atmospheric problems in the way obviously that we
Speaker 1: mentioned earlier, right, sure, And I mean, as as crazy
Speaker 1: as this all sounds, it might in fact be more
Speaker 1: reasonable than the three hundred million dollar cable laying project
Speaker 1: that's underway to shave uh sixty five milliseconds to just
Speaker 1: under sixty from London too. So here's the thing. If
Speaker 1: there's money to be made, someone's doing it. That's really
Speaker 1: the message we're getting at here is that you know,
Speaker 1: you're you're hearing like these incredible like you're just taking
Speaker 1: maybe a little bit more than five milliseconds off of
Speaker 1: a transmission line that seems so tiny to us, but
Speaker 1: the fact is that can trans translate into me, got dollars? Yeah, yeah,
Speaker 1: for for these for these crazy high frequency trader people. Okay,
Speaker 1: So I have a question. So obviously lots of people
Speaker 1: are going to be trying to get there. They're buying
Speaker 1: signal out there faster to get out ahead of the competition.
Speaker 1: But I can foresee that one way to get out
Speaker 1: ahead of the competition is not to get there faster,
Speaker 1: but to make smarter decisions, or to trick your opponent
Speaker 1: into making dumber decisions. Right, So I have to guess
Speaker 1: if people are spending millions of dollars shaving half of
Speaker 1: a second off of a transmission time. They're probably also
Speaker 1: spending money on software that's designed to do this, am
Speaker 1: I right? Yeah, yeah, So you've got software, like we said,
Speaker 1: the algorithms that are designed to specifically look for trends
Speaker 1: in the market. That's one type, right, They're looking for
Speaker 1: any kind of indication that a certain type of market
Speaker 1: condition is about to happen and so therefore act in
Speaker 1: advance of that, right, which I mean you've got these
Speaker 1: algorithms watching Twitter blogs and stuff like that. Right, then
Speaker 1: you've got those exactly the ones that are looking at
Speaker 1: the news itself and not just market conditions, but also
Speaker 1: external conditions that could then affect the market. Remember that
Speaker 1: the market is going to lag behind any news, but
Speaker 1: it's not gonna lag a lot as news, especially if
Speaker 1: machines making these decisions, because as news propagation gets faster,
Speaker 1: and if they are if there are ways of getting
Speaker 1: that news into a machine readable format faster and faster,
Speaker 1: whether or not that means that you've translated the news
Speaker 1: into machine readable format or you've created a program that
Speaker 1: has really good natural language recognition software, then you're going
Speaker 1: to have machines reacting to real world news essentially immediately
Speaker 1: after it happens, or at least immediately after it's been reported.
Speaker 1: So then you have this market being even more reactive
Speaker 1: to real world conditions, sometimes perhaps overreactive, like in the
Speaker 1: example of the false story about the bomb um. Further
Speaker 1: marking the software waters here, we've got some some algorithms
Speaker 1: that are undertaking something like like denial of service attacks.
Speaker 1: They're basically clogged clogging exchanges with quotes to to test
Speaker 1: the market or confuse other software, or to just plane
Speaker 1: slow down the entire system. Yeah, this is kind of
Speaker 1: like when you hear it is very much like a
Speaker 1: denial of service. I'm glad you put it that way,
Speaker 1: because it's like pinging a server for a response. That's
Speaker 1: you know, a basic command, right you paying a server
Speaker 1: to see how long it takes for your message to
Speaker 1: go out and for a response to come back. And
Speaker 1: if you send lots of pings to a server from
Speaker 1: lots of different sources at once, you can overwhelm the
Speaker 1: server and that's a denial. That's a kind of denial
Speaker 1: of service. Attach quotes are kind of similar. It's sort
Speaker 1: of like, uh, almost like a false bid for a
Speaker 1: stock and if you're able to send a lot of
Speaker 1: them out, it may end up clogging up the whole
Speaker 1: system or just fooling other algorithms into thinking that a
Speaker 1: particular stock is something it should move on. And then
Speaker 1: as those algorithms move on your decoy, you can then
Speaker 1: you know, twirl your mustache and then go after the
Speaker 1: actual target you were interested in all along. Right, I
Speaker 1: should mention that some of these, some of these pings
Speaker 1: could be completely legit trying trying to test and see
Speaker 1: what price of stock is at it an given point,
Speaker 1: and you do have to do that pretty frequently and
Speaker 1: pretty quickly in order to engage in this high frequency
Speaker 1: trading stuff. And furthermore, unlike a denial of service attack,
Speaker 1: this is in no way illegal. It's just considered a
Speaker 1: little bit rude. Yeah, maybe some people don't even look
Speaker 1: at it as rude. Now, now, granted, this is one
Speaker 1: of those conditions that could change over time. Right if
Speaker 1: if enough, if enough people or let's just say computers
Speaker 1: abuse this system and it becomes an actual problem as
Speaker 1: opposed to something that is, you know, generally looked upon
Speaker 1: as dirty pool, but it's all allowed, then you might
Speaker 1: you might actually see companies get a little bit of
Speaker 1: a wrist slap for this kind of behavior. Um, so
Speaker 1: it may turn out that perhaps five years down the road,
Speaker 1: algorithms cannot have this sort of behavior built into them.
Speaker 1: You know. I I don't want to be unfair to
Speaker 1: the stock trading community, but I kind of have to
Speaker 1: see that within this world, there's not a lot of
Speaker 1: like middle ground between not allowed and we're going to
Speaker 1: do it. Yeah, there's not like this huge gray area
Speaker 1: where it's like, well, we could do that, but that
Speaker 1: wouldn't be very nice. I mean, it seems kind of like,
Speaker 1: if it's not illegal, you should do it till it's illegal.
Speaker 1: It's essentially the message. Well again, it's all about if
Speaker 1: it's if it's profitable, then it makes sense, right, I
Speaker 1: mean literally makes sense in the case of fractions of
Speaker 1: the center. Okay, so now let's talk about how thing
Speaker 1: this is actually affecting the world today. We've already talked
Speaker 1: about the flash crash and the other the the blip,
Speaker 1: the hundred thirty six billion dollar blip. But what is
Speaker 1: the future going to be like? In this world of
Speaker 1: high frequency trading, computer algorithms are reacting to news and
Speaker 1: other changing conditions. What's that future, Like some industry insiders
Speaker 1: are really gloomy about it. Actually they're they're worried about
Speaker 1: about jobs. And I mean there's definitely been a trend
Speaker 1: towards these algorithms replacing some of the people who originally
Speaker 1: worked in in fields trying to figure out what stocks
Speaker 1: in particular markets were doing. Um. So, so they're worried
Speaker 1: about that. They're worrying, worrying about the dependence on technology
Speaker 1: changing the market into something unidentifiable and coldly automated, kind
Speaker 1: of the sort of buzz that you hear about any
Speaker 1: segment of the tech industry. Um. Not that I'm saying
Speaker 1: that they're you know, wrong to to have this concern.
Speaker 1: And then they're also concerned about stuff like glitchens and
Speaker 1: hacks undermining market stability and are already really delicate economy. Yeah,
Speaker 1: that that delicate economy that was brought about by a
Speaker 1: lot of people just saying maybe maybe their messages, Look,
Speaker 1: we don't need help messing up our economy. We've been
Speaker 1: doing it for years done and so yeah, and like
Speaker 1: we said, it's it's not even a fair playing ground
Speaker 1: now if you are a person trying to play in
Speaker 1: this world. I mean you. You just can't move at
Speaker 1: the speed of these algorithms. I mean, you literally cannot
Speaker 1: read something in the space of time that it would
Speaker 1: take a machine to read it and make a decision
Speaker 1: and enact that decision. Right. So, you know, it's one
Speaker 1: of those things where if you look at a stock price,
Speaker 1: you can pretty much guarantee that whatever that stock is
Speaker 1: trading at is no longer the price that you're looking at,
Speaker 1: at least on Uh. You know, it might be a
Speaker 1: fraction of ascent different, but it will be different, and
Speaker 1: depending upon the type of trading you're engaged in, that
Speaker 1: could be a big deal. Uh. Now, we might see
Speaker 1: if these systems do cause big issues, some regulations step in. Uh,
Speaker 1: it will probably be I mean, it's going to be
Speaker 1: a reactive kind of thing, I think, and I doubt
Speaker 1: we're going to see any proactive regulation, but we'll probably
Speaker 1: In fact, so far, the regulations seem to be opening
Speaker 1: up the market to more of this kind of trading, right,
Speaker 1: So it'll only be I think in the wake of
Speaker 1: something uh spectacular or catastrophic, however you wanna describe it.
Speaker 1: I think that's going to be the required event before
Speaker 1: we see any regulations. Step in. But I imagine that
Speaker 1: that would happen eventually. Should this prove to be more
Speaker 1: harm than good, I mean, because of ultimately this is
Speaker 1: about making money, and if it means that people are
Speaker 1: not able to make money because they're all these bullips
Speaker 1: that create a false market where nothing the value of
Speaker 1: everything is not accurately reflected in the prices, which, by
Speaker 1: the way, you could already argue for a lot of companies.
Speaker 1: But if if this made that problem bad enough, I
Speaker 1: would imagine that would cause some regulations to arise as
Speaker 1: a result. Also, you know, there is a difference between
Speaker 1: high frequency trading and the kind of investment that your
Speaker 1: average investor is doing. Right, It's not right. This isn't
Speaker 1: care right. Well, you know, this is like we said,
Speaker 1: you know, like you were saying, Lauren, It's all about
Speaker 1: getting in and getting out in the same day and
Speaker 1: making lots of small trades throughout the day, you know,
Speaker 1: thousands of times a set. And sometimes there are hobbyists
Speaker 1: are professionals who are doing this on on purpose a
Speaker 1: lot really hard. This isn't This isn't about investing in
Speaker 1: a company or even people who want to invest in
Speaker 1: a company they believe in as a way of supporting
Speaker 1: that company and then earning rewards off of that support
Speaker 1: in the long run, assuming that company is actually successful.
Speaker 1: You know, that's a very different approach. That's a very
Speaker 1: different strategy. Although I did want to put in that
Speaker 1: if all this high speed stuff sounds weird and like
Speaker 1: counterproductive to that ostensible goal of of an investor and
Speaker 1: a company, you know, mutually benefiting each other with that
Speaker 1: money exchange. Traditional stock theory does state that all trades
Speaker 1: are of benefit to the overall economy because they keep
Speaker 1: the market flexible and more welcoming to investors. Yeah, it's
Speaker 1: all about liquidity, right exactly. So yeah, this is a
Speaker 1: so it's you know, is it scary? Sure, because you're
Speaker 1: talking about a lot of different automated processes that are,
Speaker 1: you know, we hope working to their best advantage, but
Speaker 1: could conceivably if they all all react in a similar way,
Speaker 1: either drive the market up and and it would end
Speaker 1: up being a false market that way, or make it
Speaker 1: crashed down. Hopefully, should that ever happen, the market would
Speaker 1: be able to correct itself relatively quickly the way it
Speaker 1: did back in two But you know, it's certainly one
Speaker 1: of those things where you know, you feel like the
Speaker 1: control is slipping further and further away from human hands.
Speaker 1: And that's always something that gives us a little bit
Speaker 1: of a pause. Oh sure, and it's not even with
Speaker 1: high frequency trading, like like I was saying a second ago, Um,
Speaker 1: you know, algorithms are starting to replace lots of traditional
Speaker 1: stock analysts, so so changes are definitely happening across the board. Yeah, yeah,
Speaker 1: it's just that you might not for those other methods.
Speaker 1: You might not have something where it's trading as frequently.
Speaker 1: It maybe all right, right, you're not going to cause
Speaker 1: these kind of data sensitive crashes, right, Yeah, you might
Speaker 1: just have an algorithm that's looking at the market in
Speaker 1: general and saying which of these prospects seemed the most
Speaker 1: promising for X payoff? Because you know, again, like if
Speaker 1: you've ever in uh been part of a four oh
Speaker 1: one K, you've you've probably seen multiple choices that you
Speaker 1: can put your money into. Some of them are higher
Speaker 1: risk but higher payoff at the risk pays off, some
Speaker 1: of them are lower risk but lower payoff, same sort
Speaker 1: of thing. These algorithms are just trying to identify those
Speaker 1: those trends. Uh, mathematically, you know, Uh. You know, I
Speaker 1: remember reading from one analyst who said that according to
Speaker 1: what they could tell that you could be just as
Speaker 1: effective in the stock market if you threw a dart
Speaker 1: at a list of stocks as you could if you
Speaker 1: were a strategist really studying the market, which is a
Speaker 1: little cynical. I'm sure all of us have felt that way,
Speaker 1: if we've looked at our four oh one case at
Speaker 1: some point, especially over the past three years. Yeah, let's
Speaker 1: not talk about it. I've Yeah, I like to think
Speaker 1: I've funded several kickstarters magically through the money lost in
Speaker 1: my four oh one case. So that's that's the way
Speaker 1: I like to think of it. Y'all obviously have not
Speaker 1: been funding Alchemy like I have. Yeah, it's it's do
Speaker 1: I tells you. Returns on Alchemy chemical is just really
Speaker 1: but you know, I don't know. I think that overall,
Speaker 1: having more data and more software devoted to crunching that
Speaker 1: data in any system is a good thing. I mean,
Speaker 1: I cannot I cannot look at that and say, no,
Speaker 1: knowing stuff is bad. Um And and you know the
Speaker 1: fact that we've got these um online investing tools is
Speaker 1: also a pretty fabulous thing. You know, It's it's letting
Speaker 1: people that might have never gotten into this kind of
Speaker 1: gig experiments. Yeah, yeah, it's definitely. It's definitely opened up
Speaker 1: those the opportunities quite a bit. I mean, you know,
Speaker 1: it doesn't take you don't have to think back that
Speaker 1: far to imagine a world where the stock trading the
Speaker 1: only thing anyone knew about it was from films like
Speaker 1: Wall Street because it was something that only, yeah, a
Speaker 1: very elite few were engaged in, and most of them
Speaker 1: were big companies or very wealthy individuals who were engaged in,
Speaker 1: and the rest of us were, you know, feeling like
Speaker 1: it was a world that that was mythical, like the
Speaker 1: land of dragons and unicorns. But these days we can
Speaker 1: all have access to it. Well, that kind of wraps
Speaker 1: up our discussion about the stock market, the computer algorithms,
Speaker 1: the robots that are going to own everything that we
Speaker 1: have built over the last a few centuries. Never hook
Speaker 1: sky net up to it. Okay, well I'll take that
Speaker 1: under consideration. I don't know Skynet's really convincing. I was
Speaker 1: talking to it the other day and it had some
Speaker 1: great ideas. So guys, here's another great idea. Go visit
Speaker 1: f W thinking dot com. That's a website where we've
Speaker 1: got all of the blog post, podcast videos, other content
Speaker 1: that is really cool. It's all about the future and
Speaker 1: science and technology and you should definitely check that off
Speaker 1: you haven't been there, and also remember you can you
Speaker 1: can join in on the conversation. We are on various
Speaker 1: social media platforms including Facebook, Twitter, and Google Plus. You
Speaker 1: can find us with the handle fw thinking and we
Speaker 1: will talk to you again really soon. For more on
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