openpolitics.com

yeah nobody I mean these are huge

18:03

numbers to make millions 5 million 10

18:06

million oh that’s a lifetime’s worth of

18:07

money you don’t ever need to work again

18:10

and everybody wanted that you know I

18:12

could quit working this year I made

18:14

enough money in one year I’ll never have

18:15

to work for the rest of my life and that

18:17

was the goal of everyone it appeared to

18:19

me huh this is money okay and Aspen’s

18:27

talking about making money making money

18:29

making money every year you’re making

18:31

money and then one year you blow up now

18:33

the difference between this being your

18:35

money and being a hedge fund is if this

18:38

is your money fantastic you’re making

18:40

money you’re down here you’re bankrupt

18:42

if it is somebody else’s money if it’s a

18:44

hedge fund that does this every year

18:47

they’re taking a percentage they’re

18:50

taking some of that as profit as their

18:52

bonus effectively so they make some of

18:54

that they make some more they make some

18:56

more all of this money they’re putting

18:58

into their own bank account and then

19:01

when they lose money that’s their

19:03

clients money that’s a lot it’s not

19:04

their money so you’ve got you can so you

19:06

can see why it’s very easy for people to

19:08

abuse this kind of thing I think it’s

19:12

fantastic the people who take risk

19:15

should be compensated for taking risk

19:19

but only if they are actually taking

19:22

risk themselves taking risk with other

19:24

people’s money you should not get

19:26

compensated for I’m sorry I did that the

19:29

Donald where that fits into economic

19:32

theory but taking risk with other

19:33

people’s money does not get rewarded

19:34

sadly though it does in this business

19:38

no but now

19:46

there was a moment when I thought when I

19:50

questioned why I was ever involved in

19:53

Wall Street goodbye I need it right now

19:56

on the double

19:57

hi that’s something I thought that

20:01

people would be more judicious and more

20:04

conservative in their lending and I was

20:06

involved in it and I thought well wait a

20:08

second these guys are out of control

20:09

totally the piece of software per se you

20:13

know that’s a sort of inanimate object

20:15

yes people used it but you know if

20:18

people had used it and put good

20:20

mortgages into it who never would have

20:21

caused a problem at all but when you put

20:24

you know mortgages that you have a

20:26

fairly high certainty that people cannot

20:29

repay and then half of all the mortgages

20:32

issued in a given year that type of

20:33

mortgages yes the industry has gotten

20:36

out of control

20:39

trillions of dollars a year basically

20:41

went through that model these bonds

20:46

within two and three years of being

20:47

issued went from triple-a to

20:50

unwrite I mean just catastrophic

20:51

collapse a lot of trading firms that

20:55

kept these the riskier pieces in their

20:58

portfolio saw them drop to next to

21:00

nothing and given the leverages the

21:02

amount of leverage under the amount that

21:03

the banks had borrowed they were

21:05

suddenly in a financial panic

21:14

Saturday after midnight still studying

21:17

I know long hours will not stop when I

21:20

enter a future job as a client

21:27

because I was primarily a technologist I

21:30

did not fully understand what was going

21:32

on I think part of my motivation

21:34

post-crash for becoming a quant is to

21:37

gain that understanding having been

21:39

through the personal experience of

21:41

seeing the destruction of my firm looked

21:45

again at my resume that I put out there

21:48

the same headhunter called again today

21:51

to see if I would like to take a job in

21:53

my former field as a financial

21:55

technologist I declined again of course

22:01

no invitation for a quanzhou Piett

22:13

people that are in the business right

22:15

now probably refuse to talk to the

22:17

public if they were to talk to the press

22:18

they would be fired

22:19

so only limited few people in the

22:22

business have the option of talking to

22:24

the press once you’re in the world right

22:28

I mean your phones are ringing you know

22:31

from the moment I woke up in the morning

22:32

and I remembered you know a lot of these

22:34

guys I do quite well they try to wake me

22:37

up 6 o’clock oh I thought you were

22:39

asleep you know can you be up till 11

22:41

o’clock

22:44

you have to be wired you have to be

22:47

alert every second you have to be

22:49

engaged and and you have to be perfect

22:52

and you have to be right all the time

22:54

the software fails people lose millions

22:57

or billions can’t happen you can’t you

23:00

can’t be wrong you have to be perfect it

23:01

says it’s a lot of stress my wife was in

23:07

the business with me

23:08

we both would wake up in the morning and

23:10

describe similar nightmares phones were

23:13

ringing we couldn’t answer them and then

23:15

we sort of grew out of that and we both

23:18

realized that we didn’t know what day of

23:21

the week was that’s right boy there’s

23:24

always videos person departures Barclays

23:27

dangerously Pleasant read the planet

23:30

record is brought to you by the Deaf

23:31

1.6% it wonder the up 1.2 percent

23:35

so is the CAC in Paris hey Joel boy

23:43

stirs

23:50

banking is completely lost touch with

23:53

its purpose its original purpose and is

23:56

now becoming dangerous

23:57

it used to be that when some of these

23:59

derivatives were first invented they

24:01

were to help your farmer for example

24:03

hedge the value of his crop so he was he

24:06

wasn’t speculating on the price of wheat

24:08

he was busy growing it now there are

24:12

more people trading these these

24:14

commodity derivatives and then are

24:15

actually involved in the production of

24:17

the commodity so which is completely

24:20

bizarre I know a lot and quite a lot of

24:24

people in this business who are feeling

24:27

a bit jaded now people are starting to

24:31

ask questions my nice friends I started

24:35

to ask questions about the role in

24:36

society you may be making lots of money

24:38

but are you is it something to tell your

24:40

grandchildren oh yeah I was a banker I

24:42

was there when I caused the the

24:46

2008-2009 crisis etc what are they

24:50

actually doing with their lives or their

24:52

or just moving this money around this

24:54

isn’t necessary such a business to be

24:56

proud of I think that’s probably

25:03

planning 30 35 pounds responsibility is

25:07

just not a one-way street when it’s

25:09

successful you’re responsible well you

25:10

can’t be unresponsible when the same

25:12

same item is is a failure you have to

25:15

have some type of responsibility and I

25:17

could say I wasn’t but I was involved I

25:21

made a comfortable very comfortable

25:23

living and and I was proud of what I had

25:28

done I never I myself never saw this

25:32

kind of debacle

25:38

pretty big muscle to see a little Wilder

25:43

this is a you know they’re yellow on the

25:46

inside different color a chef and the

25:50

city loves this wild taste I only do it

25:54

for one chef because if I did too many

25:56

there wouldn’t be enough you know the

25:59

model is Hippocratic oath I will

26:02

remember that I didn’t make the world

26:04

and it doesn’t satisfy my equations

26:06

that’s obviously that’s it that’s about

26:09

having a a mature appreciation that

26:13

whatever you do that the models are

26:15

never going to be perfect

26:16

I will never sacrifice reality for

26:18

elegance without explaining why I have

26:21

done so so it’s again it’s a competition

26:24

between the real world and the elegant

26:27

world of mathematics and sometimes the

26:28

real world is just dirty

26:31

nor will I give the people who use my

26:34

model false comfort about its accuracy

26:36

instead I will make explicit its

26:38

assumptions and oversights quanta are

26:41

asked the following by some trader they

26:43

say well look you’ve just measured the

26:45

risk in this portfolio it’s too big okay

26:48

to quant back to the drawing board

26:50

I want you redoing numbers and come up

26:52

with a smaller risk it doesn’t mean

26:55

change the portfolio it means change the

26:57

maths to make it look less risky people

27:00

can use the models to hide risk though I

27:06

will use models boldly to estimate value

27:08

I will not be overly impressed by

27:10

mathematics people make finance too

27:14

mathematical so mathematical that many

27:17

people who have to implement the models

27:19

don’t understand what’s going on and

27:21

once you have too much mathematics it’s

27:23

difficult to see where the mistakes are

27:25

I understand that my work may have

27:27

enormous effects on society and the

27:29

economy many of them beyond my

27:31

comprehension so this is a serious

27:34

business it’s what it’s saying the

27:37

quantitative finance banking has become

27:39

so enormous it’s it’s outstripped all

27:42

other all other businesses and really it

27:44

should just be a service for these other

27:46

businesses rather than we are everybody

27:49

is now working to

27:51

she service the banks move is what it

27:53

feels like it’s it’s completely changed

27:56

the nature of the world always banking

27:59

again so there’s a nice little picture

28:01

of the book of me and Emanuel Derman

28:06

with our with our Karl Marx beards on

28:08

because obviously it’s it’s basically

28:11

that the inspiration was a kind of

28:12

communist manifesto you take the

28:15

combined the communist manifesto with

28:17

the Hippocratic oath and this is what

28:19

you’ve got when I first came to the

28:21

field I was sort of optimistic about

28:23

using quantitative methods on the

28:24

financial markets and I don’t think

28:26

they’re useless but them but I’m trying

28:31

to think how to say it I don’t think you

28:32

can use quantitative methods to explain

28:34

markets either people like borer

28:37

Einstein or Schrodinger or Fineman

28:39

discovered things that um that seem to

28:42

be God’s true for most you know even if

28:44

they’re they’re not 100 percent accurate

28:45

and I’m I don’t think that’s possible in

28:48

finance I sort of think it’s an illusion

28:50

it’s the world the financial world and

28:53

their world of people is changing the

28:54

whole time history doesn’t repeat itself

28:57

whereas in physics history repeats

28:58

itself all the time you can do the same

29:00

experiment over and over again so I

29:02

don’t know somewhere somewhere somewhere

29:03

after five or six years in the field I

29:05

began to realize that it wasn’t the same

29:09

thing as doing physics in physics if you

29:11

wake up in the morning and think of an

29:13

equation or think of some theory you

29:16

actually have a small hope in hell that

29:18

you might actually be right but in

29:20

finance if you write down some set of

29:21

assumptions and you look at yourself

29:22

honestly it may be useful but you know

29:25

it’s not going to be right in some

29:27

absolute sense

29:30

because you’re dealing with people and

29:31

and people don’t work that way

29:43

another weekend trying to remember all

29:46

the parts of the city I haven’t seen

29:48

since I started the course longing to

29:51

visit art galleries eat out every night

29:53

to live the day at the library seem to

30:01

have more hours than the normal 12

30:06

studying alone with other people doing

30:08

the same thing I feel like a monk in a

30:11

monastery it’s peaceful the library is

30:14

quite old sometimes we have to cover the

30:17

air-conditioner with old Soviet

30:19

mathematical journals from the 60s

30:37

once I dreamt of doing pure science

30:40

working on rocket ships working at a

30:42

small start-up company

30:47

there has to be a way to be creative as

30:49

a quant – like designing new financial

30:51

products and the math to price them

30:59

do you think it’s always possible for

31:02

people to express a worry they have

31:05

about the things they’re building or

31:07

writing it’s possible people may not

31:09

listen to them in the end most of these

31:11

people are employees people don’t always

31:14

listen to you but yeah it’s possible to

31:16

do it and I think people should do it

31:17

and I think people who use the model

31:19

should should understand that but I

31:22

don’t honestly believe that the models

31:25

are responsible for what happened in the

31:28

world I think what’s just one for what

31:29

happened in the world is that they’ve

31:30

been an increasing number of they’ve

31:35

been an increasing number of crises

31:36

since 1990 financial crises in the world

31:38

since 1994 and every time people are

31:41

used to people are used to constant

31:44

growth and acceleration and every time

31:46

it slowed down the government stepped in

31:49

and tried to stimulate it again by

31:51

lowering interest rates just like

31:52

they’re doing now and so you get these

31:55

sort of a rise and a collapse and then

31:56

people don’t like the collapse so they

31:58

lend money cheaply enforcer’ rise again

32:00

and each time the oscillations get

32:02

bigger and bigger and they doing exactly

32:04

the same I have no idea what’s the right

32:06

thing to do but they’re doing exactly

32:07

the same thing now which is trying to

32:09

stimulate the economy every time it

32:11

looks like it stops growing fast

32:17

it shocks me that as a person who runs

32:20

many businesses that we can talk about

32:23

an economy shrinking by 1% is also

32:27

growing by 1% is fantastic this

32:31

difference of 2% how can that difference

32:34

in 2% have such a big impact on the

32:36

world around us 1% plus or minus in my

32:40

businesses I won’t notice the economist

32:42

sir they think that they’re scientists

32:45

so they come up with these what they

32:47

call laws they’re not laws laws of

32:51

gravity that’s a law anything that Isaac

32:54

Newton comes up with it is a law but

32:56

when the Economist comes up it’s just a

32:58

framework an idea it may work it may not

33:01

sometimes it’s that’s not a law but they

33:03

think their laws and so they build up

33:05

this whole edifice of theory based upon

33:09

this very shaky foundations and they get

33:11

all sorts of nonsense coming out of it

33:20

let’s ease off

33:25

I think that the natural world is

33:29

something you learn to appreciate

33:32

through a struggle in the financial

33:36

world

33:36

you know money is a man-made phenomenon

33:39

right it’s like a game right where you

33:41

make the rules well money is a game that

33:43

people make the rules for but out there

33:46

the day-to-day activity is not about

33:48

making money the day-to-day activity

33:50

trying to grow an animal a healthy

33:53

animal or a group of healthy atoms

33:55

that’s a big difference

34:04

that is beautiful

34:06

believe it or not that is beautiful the

34:08

beautiful thing about this is it says

34:09

that in the risk-neutral

34:11

I’ve got to keep emphasizing this is the

34:14

risk-neutral version when mu equals R if

34:18

it was the real world if this was the

34:20

real version it would have some dim UD

34:23

T’s in it

34:23

now let’s do some manipulations now some

34:25

of these manipulations are

34:26

straightforward six over zet in which

34:29

case there are no Zed’s in there at all

34:30

if you say to me the d by d big t

34:33

version because we want them we are

34:35

trying to find the stochastic

34:36

differential equation not for log said

34:39

so you’re going to end up with new minus

34:40

1/2 Sigma squared let me backtrack it a

34:43

wee bit here and we have a stochastic

34:47

differential equation for Z then we can

34:51

also write down stochastic differential

34:52

equation for F there was a very very

34:57

short period of time when conser in the

35:00

doghouse so to speak the people were

35:01

saying but all banking is changed

35:03

forever a Kwan serveth I’ve finished

35:06

there’ll be no more these credit

35:08

instruments and I said you know second

35:10

guys you really don’t know your history

35:11

you don’t know human nature this will

35:14

all blow over you know in a matter of

35:16

months because we’re back to the big

35:17

bonus is everything goes back to as it

35:21

was if people don’t complain now then it

35:25

serves them right when the next

35:26

financial crisis happens

35:31

twelve hours to go before the evening

35:33

classes start

35:35

I feel United with my classmates but the

35:38

enormous workload it’s actually the fees

35:42

that we’re trying to maximize right of

35:44

course we have to maximize returns we

35:47

have to do a good job in managing their

35:49

money otherwise where we’re going to do

35:51

pretty poorly at collecting those fees I

35:54

wanted to feel challenged again and

35:56

enrolled in a quant program

35:58

it cost me $60,000 tuition which means

36:03

more debt that I now have to take on the

36:07

incentive fee structure basically means

36:10

that maximizing the twr is like

36:13

maximizing fees think about that that’s

36:16

kind of tricky

36:18

this course is a year and a half

36:20

full-time one and a half years no salary

36:24

expenses living in downtown Manhattan

36:26

plus paying full-time tuition so no

36:30

alcohol for me not a drop at least till

36:33

the end of the first semester I can’t

36:35

afford to lose a day to a hangover

36:37

hardly any social life for the time

36:39

being

36:43

most of the other students are Asian or

36:46

East Europeans math is their first

36:48

language and our common language we

36:51

Americans are the minority maximizing

36:55

the number of times that we’re going to

36:56

penetrate the previous high-water mark

36:59

we’re actually maximizing incentive so

37:02

you can see that this this type of

37:04

optimization is very hedge fund like

37:07

does everybody get it so far

37:12

used to be the physicists were splitting

37:14

the atom whose splitting the atom these

37:15

days building bridges who is people

37:18

building bridges everybody wants to move

37:19

into this field scientific creativity is

37:23

becoming financial creativity which is

37:25

all of the bogus

37:39

Kwan’s are essential to modern banking

37:41

because so much of it is based upon new

37:44

techniques like the latest thing is the

37:46

algorithmic trading that high-frequency

37:47

trading for what you need math skills it

37:52

used to be you know historically you

37:53

just have like floor traders and brokers

37:55

you know screaming and shouting down on

37:58

the floor of exchanges and trading

37:59

stocks you know and the order came down

38:01

and they would run up and they sort of a

38:03

muscle there he added we’re a different

38:05

color jackets you know the classic

38:06

pictures we’ve we’ve all seen Matthew

38:09

Goldstein almost obscene list for PES

38:12

below Reuters do same from the ears to

38:15

the Kefauver format high-frequency

38:16

trading on cotton the reality is so much

38:20

of this doesn’t even take place there I

38:22

mean that’s becoming such a lesser part

38:24

of trading in what goes on it goes on in

38:26

the back rooms and it goes on in these

38:27

these modeling’s where these programs

38:29

are put together by computer geeks

38:31

basically so high-frequency trading is

38:33

just about taking all this data

38:35

analyzing very very rapidly and then

38:37

putting on trays that may last

38:38

milliseconds what worries me the most is

38:42

I was disturbed to hear that some firms

38:45

get faster access to the markets than

38:50

other people I forget what they call it

38:52

now but people get like a tenth of a

38:55

second advantage big firms which i think

38:57

is unfair hedge funds try and get the

39:00

black boxes as close to an exchange as

39:02

possible because it takes time for the

39:06

signal to get from the black box to the

39:09

exchange to buy or to sell now of course

39:11

that is dictated by the speed of

39:13

lightning

39:14

now we’re talking about trading at the

39:16

speed of light

39:19

the classic crash was the 87 the 19th of

39:25

October 1987 crash that happened within

39:27

a day all that the big move the 20% fall

39:30

and S&P 500 was within a day the next

39:33

crash could be within minutes so what is

39:36

the black box a black box is just

39:39

something that has it has inside some

39:41

kind of formula

39:43

maybe secret or maybe not that takes in

39:45

lots of data and the data might be stock

39:50

prices and might be other information

39:51

and it tells you what to trade what to

39:54

buy and sell and my favorite is is

40:00

Google search terms trading based on

40:04

what people are searching for it’s not a

40:13

black box in the sense that um you know

40:16

if you if you saw the algorithms you

40:18

could fit what you want you and me might

40:20

not be able to figure it out but but

40:21

wiser minds maybe could and computers

40:23

can certainly read it so it’s a black

40:25

box in a sense that it’s almost hard for

40:26

the human mind to get their arms or you

40:29

know wrap themselves around to really

40:30

understand what’s going on

40:36

and you know people have said for years

40:38

that Goldman itself is a black box we

40:41

don’t really know how it makes all this

40:42

money in the billions of dollars and you

40:44

know the big bonuses we hear about the

40:46

New York Stock Exchange building is big

40:47

facility out in New Jersey which is you

40:49

know right near in New York and and

40:51

basically it’s being built for

40:52

high-frequency traders so they can have

40:53

their equipment very close in a very can

40:56

you know tightly knit factory

40:58

essentially to do high-frequency trading

41:00

well who gets to have their server

41:02

where’s there going to be a lottery you

41:04

know you know does someone pay more to

41:06

get closer I mean it’s sort of a it’s

41:08

sort of absurd when you think about this

41:10

is what it’s come down to the

41:12

battleground is ultimately going to be

41:14

who has the most resources who can pay

41:17

the best salaries to hire the best

41:19

brains in reality we’re talking maybe

41:21

about a dozen or so really top players

41:22

you know and not everyone can be a

41:24

customer of goldman sachs not everyone

41:26

can be a customer of morgan stanley or

41:28

no berkeley also it does the the high

41:32

frequency trading means people more

41:33

concerned with the price of something

41:36

and not its value value means what it’s

41:39

really it’s really worth price is just

41:43

what people buy and sell for and if you

41:45

buy something now sell is second or two

41:49

later all you care about is the price

41:51

you sold it for is greater than the

41:52

price you bought it for it’s actual

41:54

value who cares it sort of flies in the

41:57

face of what we sort of think about what

41:59

what the what the markets are really

42:01

about the companies themselves almost

42:03

don’t matter what they do doesn’t matter

42:05

it’s just the fact which way their

42:07

stocks move is all that matters and

42:10

what’s sort of great thing about it that

42:13

I’ve I’ve seen from the standpoint is

42:15

the systemic risk that might be involved

42:16

it’s so much of this trading just takes

42:19

automatically and just takes place so

42:21

quickly that the the human element gets

42:24

more and more divorced from it I mean

42:25

the human beings are obviously

42:26

responsible for for writing the programs

42:29

but there’s no human being intercepting

42:31

between these trades and we saw this a

42:33

year ago with United Airlines there was

42:35

a false of bankruptcy rumor some wire

42:37

service inadvertently and transmitted an

42:40

old story about a UI

42:41

bankruptcy filing the problem is all

42:45

these news reading algorithms saw that

42:47

and immediately started sell sell sell

42:49

in a matter of minutes United Airlines

42:51

stock is cut in half that is clearly a

42:53

case where the computers have gone wild

42:59

banking is taking over the entire planet

43:01

and is having such a major impact on the

43:05

man in the street and it really should

43:07

not banking is supposed to be to take

43:09

money from people with too much to give

43:12

to people with too little who maybe want

43:14

to start a business if you’re a business

43:16

idea but that’s not what banking is

43:18

about anymore

43:19

banking is just about gambling on these

43:20

these numbers not realizing that behind

43:24

these numbers there are human beings

43:26

with jobs

43:34

there’s always been a joke about the New

43:37

York Stock Exchange becoming a museum at

43:38

some point and they’ll just have it for

43:40

like a show there and people running

43:42

around this is the way we used to trade

43:43

stocks you know isn’t it so quaint in

43:45

everything at the same time one can

43:58

argue though that if there’s this big

44:01

backlash in high-frequency trading

44:02

we may see revival to some form of human

44:05

element inside that people may say you

44:07

know as flawed as human beings are we

44:10

don’t want to give everything over to

44:11

the machines either

44:15

just walk past a crowded Wall Street

44:18

full of Chinese tourists asking me if

44:20

this was the actual stock exchange Wall

44:25

Street as a location is not any longer

44:28

what it was many banks moved their front

44:31

offices uptown and their back offices to

44:34

newer and cheaper spaces in New Jersey

44:37

now deutsche bank is the only major firm

44:40

left on Wall Street proper nearby is

44:43

Goldman Sachs

44:45

with no name on the door also about to

44:48

move there are almost no large firms

44:51

headquartered in the neighborhood that

44:53

was the cradle of American Finance only

44:56

the New York Stock Exchange remains its

44:59

facade one of the most iconic symbols of

45:01

global capitalism

45:19

I’m always trying to encourage young

45:21

people to do what I’m doing I mean it’s

45:23

a young person’s you know it’s pretty

45:25

some pretty physically intensive they

45:33

really haven’t grown that much I may not

45:39

make it to Christmas no I like a bigger

45:44

we sell a much we typically sell a much

45:46

bigger oyster right what do you find

45:48

more satisfying

45:50

well software is much more mental you

45:53

know the pleasure in the mental exertion

45:56

is pretty intense I get million lines of

45:58

software’s a lot – man you have it all

45:59

memorized right and there’s a pleasure

46:01

of like ask ruble Scrabble doing that

46:04

kind of word puzzle kind of thing uh

46:07

although it’s not that healthy you sit

46:09

in front of a machine you have the

46:10

terminal face effect you know it’s not

46:13

the same as this oh yeah this is pretty

46:15

uh not good I mean day like today pretty

46:19

idyllic right you’re just out in the

46:20

water a nice feeling bringing food and I

46:25

think we we still about 150,000 oysters

46:29

which is uh that’s $100,000 you know of

46:35

course I live off interest you know so I

46:39

don’t this is nice to have I make some

46:42

pocket money etc etc and the overhead

46:45

here is pretty small

46:49

you have to come to grips with nature

46:51

like I these these oysters should be

46:53

bigger every year the ones that I pick

46:55

in September and October are ready by

46:58

November why they aren’t I don’t know

47:00

and there’s nothing you can do about it

47:01

right we’re in software you can do

47:03

something about everything

47:04

you can modify you can get you can creat

47:07

make you know this virtual world you can

47:09

make what you want here you know you

47:12

have to live constrained by the real

47:16

world