When machines take over markets | R L Shankar | TEDxGLIMChennai

let’s start with a quick show of hands

let’s start with a quick show of hands how many of you here like taking

how many of you here like taking

how many of you here like taking afternoon naps good god I’m in so much

afternoon naps good god I’m in so much

afternoon naps good god I’m in so much trouble today I mean not about tell

trouble today I mean not about tell

trouble today I mean not about tell o’clock all right the benefits of these

o’clock all right the benefits of these

o’clock all right the benefits of these naps are well documented let me add a

naps are well documented let me add a

naps are well documented let me add a little known benefit to this list assume

little known benefit to this list assume

little known benefit to this list assume you are a u.s. investor who regularly

you are a u.s. investor who regularly

you are a u.s. investor who regularly takes afternoon naps the chances of you

takes afternoon naps the chances of you

takes afternoon naps the chances of you getting a cardiac arrest on the sixth

getting a cardiac arrest on the sixth

getting a cardiac arrest on the sixth May of 2010 would have been

May of 2010 would have been

May of 2010 would have been significantly lower than somebody who

significantly lower than somebody who

significantly lower than somebody who doesn’t takes these naps sounds

doesn’t takes these naps sounds

doesn’t takes these naps sounds interesting right here is why 6 May 2010

interesting right here is why 6 May 2010

interesting right here is why 6 May 2010 began like any other normal trading day

began like any other normal trading day

began like any other normal trading day in the US markets market us up a bit

in the US markets market us up a bit

in the US markets market us up a bit down a bit nothing much that we hadn’t

down a bit nothing much that we hadn’t

down a bit nothing much that we hadn’t seen before so let’s say you took your

seen before so let’s say you took your

seen before so let’s say you took your beauty nap at 2:30 p.m. and then woke up

beauty nap at 2:30 p.m. and then woke up

beauty nap at 2:30 p.m. and then woke up at 3:30 p.m. right what happened to the

at 3:30 p.m. right what happened to the

at 3:30 p.m. right what happened to the market the market didn’t do much right

market the market didn’t do much right

market the market didn’t do much right so there’s nothing much to worry about

so there’s nothing much to worry about

so there’s nothing much to worry about however if we had stayed awake during

however if we had stayed awake during

however if we had stayed awake during this period this is what you would have

this period this is what you would have

this period this is what you would have witnessed around 2:30 p.m. the markets

witnessed around 2:30 p.m. the markets

witnessed around 2:30 p.m. the markets crashed dramatically the Dow Jones index

crashed dramatically the Dow Jones index

crashed dramatically the Dow Jones index which is a proxy for the financial

which is a proxy for the financial

which is a proxy for the financial health of the stock markets fell by a

health of the stock markets fell by a

health of the stock markets fell by a massive 9.6 percentage as a result over

massive 9.6 percentage as a result over

massive 9.6 percentage as a result over 1 trillion dollars of investors wealth

1 trillion dollars of investors wealth

1 trillion dollars of investors wealth was wiped out let’s put that number in

was wiped out let’s put that number in

was wiped out let’s put that number in perspective right one trillion dollars

perspective right one trillion dollars

perspective right one trillion dollars is twice the combined size of Singapore

is twice the combined size of Singapore

is twice the combined size of Singapore and Hong Kong twice the combined size of

and Hong Kong twice the combined size of

and Hong Kong twice the combined size of Singapore and Hong Kong economy was

Singapore and Hong Kong economy was

Singapore and Hong Kong economy was wiped out within 15 minutes you should

wiped out within 15 minutes you should

wiped out within 15 minutes you should have definitely taken that nap right

have definitely taken that nap right

have definitely taken that nap right however if that sounded strange what

however if that sounded strange what

however if that sounded strange what came next was even stranger but the next

came next was even stranger but the next

came next was even stranger but the next 15 minutes the market recovered and went

15 minutes the market recovered and went

15 minutes the market recovered and went back to where it was earlier as if

back to where it was earlier as if

back to where it was earlier as if nothing had happened right this crash is

nothing had happened right this crash is

nothing had happened right this crash is referred to as a flash crash there’s

referred to as a flash crash there’s

referred to as a flash crash there’s been six years since a crash happened

been six years since a crash happened

been six years since a crash happened during the past six years we have

during the past six years we have

during the past six years we have learned a lot not a lot about this crash

learned a lot not a lot about this crash

learned a lot not a lot about this crash these learnings have in turn

these learnings have in turn

these learnings have in turn fundamentally enhanced our understanding

fundamentally enhanced our understanding

fundamentally enhanced our understanding of the financial markets

of the financial markets

of the financial markets these learnings are by no means specific

these learnings are by no means specific

these learnings are by no means specific to the US market

to the US market

to the US market some of them apply even to the Indian

some of them apply even to the Indian

some of them apply even to the Indian market where we are assembled today so

market where we are assembled today so

market where we are assembled today so it is some of these insights that I

it is some of these insights that I

it is some of these insights that I would like to share now let’s start with

would like to share now let’s start with

would like to share now let’s start with a simple thought experiment right I’ll

a simple thought experiment right I’ll

a simple thought experiment right I’ll give that you are sitting in a bar

give that you are sitting in a bar

give that you are sitting in a bar that’s a good place to visualize

that’s a good place to visualize

that’s a good place to visualize yourself in right now right so you

yourself in right now right so you

yourself in right now right so you you’ve got a nice window conifer gonna

you’ve got a nice window conifer gonna

you’ve got a nice window conifer gonna see for yourself after some time you see

see for yourself after some time you see

see for yourself after some time you see your friend stepping out hey woody be

your friend stepping out hey woody be

your friend stepping out hey woody be ten steps later oh let’s say fifteen

ten steps later oh let’s say fifteen

ten steps later oh let’s say fifteen steps later of course it depends on the

steps later of course it depends on the

steps later of course it depends on the amount of alcohol is consumed right

amount of alcohol is consumed right

amount of alcohol is consumed right let’s assume that is consumed a lot of

let’s assume that is consumed a lot of

let’s assume that is consumed a lot of alcohol

alcohol

alcohol it’s going to be absolutely difficult to

it’s going to be absolutely difficult to

it’s going to be absolutely difficult to predict where he would be ten steps

predict where he would be ten steps

predict where he would be ten steps later right this part is often referred

later right this part is often referred

later right this part is often referred to as a drunkard walk or in statistics

to as a drunkard walk or in statistics

to as a drunkard walk or in statistics terms a random walk

terms a random walk

terms a random walk many believe that stock prices are like

many believe that stock prices are like

many believe that stock prices are like drunkard it’s very difficult to predict

drunkard it’s very difficult to predict

drunkard it’s very difficult to predict their future prices so what is it that

their future prices so what is it that

their future prices so what is it that we can predict let’s revisit the bar

we can predict let’s revisit the bar

we can predict let’s revisit the bar right so now after stepping out the

right so now after stepping out the

right so now after stepping out the drunkard our friend unchanged his dog

drunkard our friend unchanged his dog

drunkard our friend unchanged his dog and they both start walking like before

and they both start walking like before

and they both start walking like before it is very difficult to predict where

it is very difficult to predict where

it is very difficult to predict where our friend would be but there is

our friend would be but there is

our friend would be but there is something else that we can predict with

something else that we can predict with

something else that we can predict with a lot of confidence right the distance

a lot of confidence right the distance

a lot of confidence right the distance between the dog and the Drunken assuming

between the dog and the Drunken assuming

between the dog and the Drunken assuming the drunkard did not treat the dog badly

the drunkard did not treat the dog badly

the drunkard did not treat the dog badly the dog will always stay close to that

the dog will always stay close to that

the dog will always stay close to that drunkard no matter where he won this off

drunkard no matter where he won this off

drunkard no matter where he won this off stock market is full of dogs and

stock market is full of dogs and

stock market is full of dogs and drunkards not literally of course right

drunkards not literally of course right

drunkards not literally of course right what do I mean by this

what do I mean by this

what do I mean by this now let’s say we have two stocks Google

now let’s say we have two stocks Google

now let’s say we have two stocks Google and poodle poodle stands for a poor

and poodle poodle stands for a poor

and poodle poodle stands for a poor man’s Google something that I made up

man’s Google something that I made up

man’s Google something that I made up right so don’t look for it and Bloomberg

right so don’t look for it and Bloomberg

right so don’t look for it and Bloomberg or something like that

or something like that

or something like that so the key assumption here is that both

so the key assumption here is that both

so the key assumption here is that both of them belong to the same sector have

of them belong to the same sector have

of them belong to the same sector have similar business models and hence their

similar business models and hence their

similar business models and hence their prices are driven by similar factors not

prices are driven by similar factors not

prices are driven by similar factors not surprisingly they prices track each

surprisingly they prices track each

surprisingly they prices track each other now let’s assume that over the

other now let’s assume that over the

other now let’s assume that over the next two periods the price of poodle

next two periods the price of poodle

next two periods the price of poodle falls while that of Google remains

falls while that of Google remains

falls while that of Google remains stagnant the prisoners

stagnant the prisoners

stagnant the prisoners what is likely to happen in the next

what is likely to happen in the next

what is likely to happen in the next period before we make the call because

period before we make the call because

period before we make the call because obviously scan use outlets for any

obviously scan use outlets for any

obviously scan use outlets for any information relevant to poodle let’s say

information relevant to poodle let’s say

information relevant to poodle let’s say we do not find any news or information

we do not find any news or information

we do not find any news or information that would justify this dramatic crash

that would justify this dramatic crash

that would justify this dramatic crash in price of poodle perhaps is just a

in price of poodle perhaps is just a

in price of poodle perhaps is just a temporary aberration and eventually the

temporary aberration and eventually the

temporary aberration and eventually the prices would converge what you’re

prices would converge what you’re

prices would converge what you’re essentially betting here is that the

essentially betting here is that the

essentially betting here is that the Google and poodle or the dog and

Google and poodle or the dog and

Google and poodle or the dog and drunkard which eventually converge this

drunkard which eventually converge this

drunkard which eventually converge this is what is referred to as space trading

is what is referred to as space trading

is what is referred to as space trading as we are not looking at stocks in

as we are not looking at stocks in

as we are not looking at stocks in isolation but in groups off took let’s

isolation but in groups off took let’s

isolation but in groups off took let’s make it a bit more realistic right if

make it a bit more realistic right if

make it a bit more realistic right if all of that I am doing is tracking these

all of that I am doing is tracking these

all of that I am doing is tracking these two stocks I’m gonna spend a lot of time

two stocks I’m gonna spend a lot of time

two stocks I’m gonna spend a lot of time praying that they would eventually

praying that they would eventually

praying that they would eventually diverge because unless they diverge I

diverge because unless they diverge I

diverge because unless they diverge I don’t have a trading opportunity right

don’t have a trading opportunity right

don’t have a trading opportunity right however if I were to track three stocks

however if I were to track three stocks

however if I were to track three stocks I can now work with three pairs if I

I can now work with three pairs if I

I can now work with three pairs if I were to track hundred stocks symbol

were to track hundred stocks symbol

were to track hundred stocks symbol algebra tells us that it can work with

algebra tells us that it can work with

algebra tells us that it can work with close to 5,000 pairs my opportunity said

close to 5,000 pairs my opportunity said

close to 5,000 pairs my opportunity said immediately explodes

immediately explodes

immediately explodes however with great opportunities come

however with great opportunities come

however with great opportunities come great pain it’s almost impossible for

great pain it’s almost impossible for

great pain it’s almost impossible for the human brain to simultaneously

the human brain to simultaneously

the human brain to simultaneously process information about 5,000 pairs

process information about 5,000 pairs

process information about 5,000 pairs this is where algorithms step in and

this is where algorithms step in and

this is where algorithms step in and algorithm is nothing but a series of

algorithm is nothing but a series of

algorithm is nothing but a series of instructions to a computer right it’s

instructions to a computer right it’s

instructions to a computer right it’s relatively straightforward to ask the

relatively straightforward to ask the

relatively straightforward to ask the computer do the to the following read

computer do the to the following read

computer do the to the following read prices of various stocks identify the

prices of various stocks identify the

prices of various stocks identify the pairs that are moving away from each

pairs that are moving away from each

pairs that are moving away from each other

other

other and for the ones that have really moved

and for the ones that have really moved

and for the ones that have really moved far away from each other place bets that

far away from each other place bets that

far away from each other place bets that they will eventually converge this is

they will eventually converge this is

they will eventually converge this is referred to as algorithmic trading a

referred to as algorithmic trading a

referred to as algorithmic trading a practice where algorithms scan the

practice where algorithms scan the

practice where algorithms scan the market identify opportunities and

market identify opportunities and

market identify opportunities and execute trades all with no human

execute trades all with no human

execute trades all with no human intervention right so it’s very

intervention right so it’s very

intervention right so it’s very important to clarify that pace trading

important to clarify that pace trading

important to clarify that pace trading is not the only algorithmic trading

is not the only algorithmic trading

is not the only algorithmic trading strategy right now let’s make this

strategy right now let’s make this

strategy right now let’s make this a bit more interesting now right let’s

a bit more interesting now right let’s

a bit more interesting now right let’s add a bit more color what will happen if

add a bit more color what will happen if

add a bit more color what will happen if I were to put these algorithms on drugs

I were to put these algorithms on drugs

I were to put these algorithms on drugs or hallucinogens right what I get is

or hallucinogens right what I get is

or hallucinogens right what I get is what is referred to as high-frequency

what is referred to as high-frequency

what is referred to as high-frequency trading something that I’m going to talk

trading something that I’m going to talk

trading something that I’m going to talk about now let me start with a very

about now let me start with a very

about now let me start with a very simple illustration in US stocks are

simple illustration in US stocks are

simple illustration in US stocks are primarily traded in New York and New

primarily traded in New York and New

primarily traded in New York and New Jersey

Jersey

Jersey however contracts that are tightly

however contracts that are tightly

however contracts that are tightly linked to these stocks are traded in

linked to these stocks are traded in

linked to these stocks are traded in Chicago these contracts could be simple

Chicago these contracts could be simple

Chicago these contracts could be simple bets on whether the price is going to go

bets on whether the price is going to go

bets on whether the price is going to go up or down given their tight linkages

up or down given their tight linkages

up or down given their tight linkages it’s natural that the prices will move

it’s natural that the prices will move

it’s natural that the prices will move together and hence they form the ideal

together and hence they form the ideal

together and hence they form the ideal dog and drunkard pick an algorithm that

dog and drunkard pick an algorithm that

dog and drunkard pick an algorithm that can continuously monitor the prices at

can continuously monitor the prices at

can continuously monitor the prices at these two locations will be able to

these two locations will be able to

these two locations will be able to exploit any temporary divergence in

exploit any temporary divergence in

exploit any temporary divergence in prices by some estimates if a trading

prices by some estimates if a trading

prices by some estimates if a trading firm can capture the monopoly of

firm can capture the monopoly of

firm can capture the monopoly of exploiting these prices it stands to

exploiting these prices it stands to

exploiting these prices it stands to make about twenty billion dollars in

make about twenty billion dollars in

make about twenty billion dollars in annual profit that’s twenty billion

annual profit that’s twenty billion

annual profit that’s twenty billion dollars in annual profit but how can a

dollars in annual profit but how can a

dollars in annual profit but how can a firm capture this monopoly right

firm capture this monopoly right

firm capture this monopoly right everything else remaining constant the

everything else remaining constant the

everything else remaining constant the firm that is able to communicate the

firm that is able to communicate the

firm that is able to communicate the fastest with these two exchanges will

fastest with these two exchanges will

fastest with these two exchanges will have the upper hand it’s been surprising

have the upper hand it’s been surprising

have the upper hand it’s been surprising that this has triggered a new kind of

that this has triggered a new kind of

that this has triggered a new kind of arms race a race for higher and higher

arms race a race for higher and higher

arms race a race for higher and higher speed training that happens at

speed training that happens at

speed training that happens at exceptionally high speeds is referred to

exceptionally high speeds is referred to

exceptionally high speeds is referred to as high frequency trading in 2012

as high frequency trading in 2012

as high frequency trading in 2012 Financial Times published a report by by

Financial Times published a report by by

Financial Times published a report by by they said that hey cities account for 84

they said that hey cities account for 84

they said that hey cities account for 84 percentage of trading in u.s. stock

percentage of trading in u.s. stock

percentage of trading in u.s. stock markets 84 percentage really investors

markets 84 percentage really investors

markets 84 percentage really investors suggest pension funds mutual funds hedge

suggest pension funds mutual funds hedge

suggest pension funds mutual funds hedge funds brokerage and other institutional

funds brokerage and other institutional

funds brokerage and other institutional investors accounted for a mere 16

investors accounted for a mere 16

investors accounted for a mere 16 percentage it’s very tempting to

percentage it’s very tempting to

percentage it’s very tempting to identify such prolific growth only with

identify such prolific growth only with

identify such prolific growth only with Western markets like us the Indian

Western markets like us the Indian

Western markets like us the Indian market where algorithmic trading was

market where algorithmic trading was

market where algorithmic trading was introduced in the year 2008 presents a

introduced in the year 2008 presents a

introduced in the year 2008 presents a perfect platform to validate this

perfect platform to validate this

perfect platform to validate this hypothesis my co-authors and I have done

hypothesis my co-authors and I have done

hypothesis my co-authors and I have done extensive research on this subject

extensive research on this subject

extensive research on this subject in a project that was sponsored by New

in a project that was sponsored by New

in a project that was sponsored by New York University stone school a National

York University stone school a National

York University stone school a National Stock Exchange of India we found that as

Stock Exchange of India we found that as

Stock Exchange of India we found that as early as 2010 algorithms accounted for

early as 2010 algorithms accounted for

early as 2010 algorithms accounted for 15% age of trading in big stocks

15% age of trading in big stocks

15% age of trading in big stocks subsequent research shows that the share

subsequent research shows that the share

subsequent research shows that the share had increased to 70 percentage by 2013

had increased to 70 percentage by 2013

had increased to 70 percentage by 2013 within five years this new breed of

within five years this new breed of

within five years this new breed of traders had captured 70 percentage of

traders had captured 70 percentage of

traders had captured 70 percentage of the market right the message is loud and

the market right the message is loud and

the market right the message is loud and clear machines dominate trading in

clear machines dominate trading in

clear machines dominate trading in markets this is the new normal

markets this is the new normal

markets this is the new normal whether it is India or us machines

whether it is India or us machines

whether it is India or us machines dominate trading in US markets let’s

dominate trading in US markets let’s

dominate trading in US markets let’s next look at a dramatic illustration of

next look at a dramatic illustration of

next look at a dramatic illustration of how this race for higher and higher

how this race for higher and higher

how this race for higher and higher speeds has played out in the markets in

speeds has played out in the markets in

speeds has played out in the markets in his bestseller flash boys the author

his bestseller flash boys the author

his bestseller flash boys the author Michael Lewis introduces us to Dan

Michael Lewis introduces us to Dan

Michael Lewis introduces us to Dan Spivey a trader based out of Chicago

Spivey a trader based out of Chicago

Spivey a trader based out of Chicago like other traders he was very

like other traders he was very

like other traders he was very frustrated with the poor transmission

frustrated with the poor transmission

frustrated with the poor transmission speeds provided by the traditional

speeds provided by the traditional

speeds provided by the traditional carriers how bad was it you may wonder

carriers how bad was it you may wonder

carriers how bad was it you may wonder in 2007 it took 16 milliseconds for a

in 2007 it took 16 milliseconds for a

in 2007 it took 16 milliseconds for a signal to do a two-way trip between

signal to do a two-way trip between

signal to do a two-way trip between Chicago and New Jersey let’s put that

Chicago and New Jersey let’s put that

Chicago and New Jersey let’s put that number in perspective right a blink of

number in perspective right a blink of

number in perspective right a blink of an human eye takes 300 to 400

an human eye takes 300 to 400

an human eye takes 300 to 400 milliseconds thousand milliseconds is

milliseconds thousand milliseconds is

milliseconds thousand milliseconds is one second right so by the time we blink

one second right so by the time we blink

one second right so by the time we blink our eye a signal would have made twenty

our eye a signal would have made twenty

our eye a signal would have made twenty round trips between Chicago and New

round trips between Chicago and New

round trips between Chicago and New Jersey we might find this positively

Jersey we might find this positively

Jersey we might find this positively baffling but the traders founded

baffling but the traders founded

baffling but the traders founded woefully inadequate right so smiley

woefully inadequate right so smiley

woefully inadequate right so smiley sensed a business opportunity if he

sensed a business opportunity if he

sensed a business opportunity if he could find a faster way of communicating

could find a faster way of communicating

could find a faster way of communicating data between these locations the speed

data between these locations the speed

data between these locations the speed bandits would pay him a handsome reward

bandits would pay him a handsome reward

bandits would pay him a handsome reward so he started a telecommunication firm

so he started a telecommunication firm

so he started a telecommunication firm the name of the firm was spread networks

the name of the firm was spread networks

the name of the firm was spread networks and his objective was to build a

and his objective was to build a

and his objective was to build a straight a line as possible between

straight a line as possible between

straight a line as possible between these two locations why because when spy

these two locations why because when spy

these two locations why because when spy we started examining the cable roots of

we started examining the cable roots of

we started examining the cable roots of the carriers we found that there are

the carriers we found that there are

the carriers we found that there are lord of places where the roots were not

lord of places where the roots were not

lord of places where the roots were not straight that makes sense right if you

straight that makes sense right if you

straight that makes sense right if you have mountains or rivers the tables have

have mountains or rivers the tables have

have mountains or rivers the tables have to go around them and not through them

to go around them and not through them

to go around them and not through them however these twists and turns greatly

however these twists and turns greatly

however these twists and turns greatly resolve in drop of transmission speeds

resolve in drop of transmission speeds

resolve in drop of transmission speeds so spy we decided to build a straight a

so spy we decided to build a straight a

so spy we decided to build a straight a line as possible

line as possible

line as possible it meant literally blasting holes to the

it meant literally blasting holes to the

it meant literally blasting holes to the mountains and digging tunnels under the

mountains and digging tunnels under the

mountains and digging tunnels under the riverbed which he did finally Spivey had

riverbed which he did finally Spivey had

riverbed which he did finally Spivey had a line through which he could transmit a

a line through which he could transmit a

a line through which he could transmit a two-way signal within 13 milliseconds

two-way signal within 13 milliseconds

two-way signal within 13 milliseconds right so if you are anchoring yourself

right so if you are anchoring yourself

right so if you are anchoring yourself in the eye blink world we went from one

in the eye blink world we went from one

in the eye blink world we went from one by twentieth of an eye blink to one by

by twentieth of an eye blink to one by

by twentieth of an eye blink to one by twenty-fifth

twenty-fifth

twenty-fifth of an eye blink mind boggling right so

of an eye blink mind boggling right so

of an eye blink mind boggling right so how much did it cost by B to build this

how much did it cost by B to build this

how much did it cost by B to build this build in life 300 million dollars just

build in life 300 million dollars just

build in life 300 million dollars just so that hft traders could shave off

so that hft traders could shave off

so that hft traders could shave off three milliseconds or one by hundreds of

three milliseconds or one by hundreds of

three milliseconds or one by hundreds of an eye blink the arms race was truly on

an eye blink the arms race was truly on

an eye blink the arms race was truly on despite this high stake race the beauty

despite this high stake race the beauty

despite this high stake race the beauty about hatchapee was that they managed to

about hatchapee was that they managed to

about hatchapee was that they managed to stay away from the public spotlight for

stay away from the public spotlight for

stay away from the public spotlight for a very long time that is until the day

a very long time that is until the day

a very long time that is until the day of the flash crash suddenly the acronym

of the flash crash suddenly the acronym

of the flash crash suddenly the acronym hft started popping up everywhere and

hft started popping up everywhere and

hft started popping up everywhere and the Google search for the keyword

the Google search for the keyword

the Google search for the keyword hatchapee went through the roof that’s

hatchapee went through the roof that’s

hatchapee went through the roof that’s what we do then you don’t know something

what we do then you don’t know something

what we do then you don’t know something right we just go ahead and good so

right we just go ahead and good so

right we just go ahead and good so everybody started looking what hey chip

everybody started looking what hey chip

everybody started looking what hey chip D was but what actually did happen on

D was but what actually did happen on

D was but what actually did happen on that day at 2:32 p.m. a mutual fund

that day at 2:32 p.m. a mutual fund

that day at 2:32 p.m. a mutual fund group not a chip t remember send a huge

group not a chip t remember send a huge

group not a chip t remember send a huge cell order to the market due to the

cell order to the market due to the

cell order to the market due to the intense selling pressure the prices

intense selling pressure the prices

intense selling pressure the prices started falling initially hfts viewed

started falling initially hfts viewed

started falling initially hfts viewed this as the temporary aberration and

this as the temporary aberration and

this as the temporary aberration and started buying but when the prices

started buying but when the prices

started buying but when the prices continued to fall they sold whatever

continued to fall they sold whatever

continued to fall they sold whatever they had bought earlier some of them

they had bought earlier some of them

they had bought earlier some of them continue to aggressively south and some

continue to aggressively south and some

continue to aggressively south and some decided to stop trading as a result

decided to stop trading as a result

decided to stop trading as a result there is hardly any buyer in the market

there is hardly any buyer in the market

there is hardly any buyer in the market and the market tanked so here is a

and the market tanked so here is a

and the market tanked so here is a remarkable statistic between 245 13p

remarkable statistic between 245 13p

remarkable statistic between 245 13p to 4527 p.m. a total of 14 seconds hft

to 4527 p.m. a total of 14 seconds hft

to 4527 p.m. a total of 14 seconds hft is traded about 27,000 contracts or

is traded about 27,000 contracts or

is traded about 27,000 contracts or roughly 2000 contracts per second or

roughly 2000 contracts per second or

roughly 2000 contracts per second or thousand contracts per eye blink right

thousand contracts per eye blink right

thousand contracts per eye blink right what was the role of the hfts in this

what was the role of the hfts in this

what was the role of the hfts in this crash this is where a lot of ambiguity

crash this is where a lot of ambiguity

crash this is where a lot of ambiguity is and that’s why we need proper

is and that’s why we need proper

is and that’s why we need proper research to educate us a recent paper by

research to educate us a recent paper by

research to educate us a recent paper by researchers from MIT at University of

researchers from MIT at University of

researchers from MIT at University of Maryland

Maryland

Maryland among other schools summarizes it nicely

among other schools summarizes it nicely

among other schools summarizes it nicely they say while ketchup teas did not

they say while ketchup teas did not

they say while ketchup teas did not cause the flash crash they contributed

cause the flash crash they contributed

cause the flash crash they contributed to it due to their excessive training

to it due to their excessive training

to it due to their excessive training this is not semantics right this is not

this is not semantics right this is not

this is not semantics right this is not a trivial case of potatoes potatoes

a trivial case of potatoes potatoes

a trivial case of potatoes potatoes tomatoes tomatoes it’s a sharp

tomatoes tomatoes it’s a sharp

tomatoes tomatoes it’s a sharp difference between what they did and

difference between what they did and

difference between what they did and what they were believed to have done now

what they were believed to have done now

what they were believed to have done now so what is the learning what have you

so what is the learning what have you

so what is the learning what have you learned in the past six years here is

learned in the past six years here is

learned in the past six years here is what we have learned algorithms

what we have learned algorithms

what we have learned algorithms specifically Hatcher T’s dominate

specifically Hatcher T’s dominate

specifically Hatcher T’s dominate trading in most markets that’s the new

trading in most markets that’s the new

trading in most markets that’s the new normal as soon as a flash crash

normal as soon as a flash crash

normal as soon as a flash crash demonstrated this new normal

demonstrated this new normal

demonstrated this new normal unfortunately is inherently unstable the

unfortunately is inherently unstable the

unfortunately is inherently unstable the billion dollar dollar question or should

billion dollar dollar question or should

billion dollar dollar question or should we say the trillion dollar question is

we say the trillion dollar question is

we say the trillion dollar question is how do we improve the stability of these

how do we improve the stability of these

how do we improve the stability of these markets researchers regulators

markets researchers regulators

markets researchers regulators policymakers exchanges have thrown their

policymakers exchanges have thrown their

policymakers exchanges have thrown their hats in the ring unfortunately we still

hats in the ring unfortunately we still

hats in the ring unfortunately we still do not have a definite answer until we

do not have a definite answer until we

do not have a definite answer until we find an answer we can only hope that

find an answer we can only hope that

find an answer we can only hope that where every time HF please go on a

where every time HF please go on a

where every time HF please go on a rampage like this we somehow managed to

rampage like this we somehow managed to

rampage like this we somehow managed to doze off

doze off

doze off this was 23rd April 2013 and around 1 7

this was 23rd April 2013 and around 1 7

this was 23rd April 2013 and around 1 7 at 107 on that day the markets witnessed

at 107 on that day the markets witnessed

at 107 on that day the markets witnessed a micro crash losing about 200 billion

a micro crash losing about 200 billion

a micro crash losing about 200 billion dollars in investors but what happened

dollars in investors but what happened

dollars in investors but what happened on that day on that day The Associated

on that day on that day The Associated

on that day on that day The Associated Press Twitter account was hacked so the

Press Twitter account was hacked so the

Press Twitter account was hacked so the trading algorithms read the fake tweet

trading algorithms read the fake tweet

trading algorithms read the fake tweet then realized that the tweet was fake

then realized that the tweet was fake

then realized that the tweet was fake started selling and the market crashed

started selling and the market crashed

started selling and the market crashed all of this happened within one minute

all of this happened within one minute

all of this happened within one minute all of this happened before

all of this happened before

all of this happened before Associated Press realized that his

Associated Press realized that his

Associated Press realized that his account was hacked right

account was hacked right

account was hacked right so in summary right in the olden days

so in summary right in the olden days

so in summary right in the olden days what would that trade are done the

what would that trade are done the

what would that trade are done the trader would have just picked up the

trader would have just picked up the

trader would have just picked up the telephone and asked hey did they really

telephone and asked hey did they really

telephone and asked hey did they really bomb the white house but now by the time

bomb the white house but now by the time

bomb the white house but now by the time the trader picks up the phone unlocks

the trader picks up the phone unlocks

the trader picks up the phone unlocks the screen dials the number and says hey

the screen dials the number and says hey

the screen dials the number and says hey a gazillion orders would have gone to

a gazillion orders would have gone to

a gazillion orders would have gone to the exchange The Shard is a crazy a fast

the exchange The Shard is a crazy a fast

the exchange The Shard is a crazy a fast word out there thank you

word out there thank you

word out there thank you [Applause]