- 1:03:01: The Financial Crisis was caused by the rating agencies changing their business model to get paid by the issuer rather than the buyer
Summary
An interview and Q&A with billionaire and founder of the quantitative hedge fund Renaissance Technologies, James Simons. In this interview, James discusses his quantitative approach to investing and how this has evolved over his career. James also talks about fundamental trading and how his management style has helped make Renaissance Technologies so successful.
Video Segments:
0:00 Introduction
5:20 Were you precocious about business as a child?
7:06 When did you start thinking about business?
12:15 Your first investment was leveraged contracts on futures?
13:05 What got you interested in business?
15:13 Did any code breaking have applicability to finance?
17:40 Investing in foreign currency after Stoneybrook?
29:19 Interesting history?
31:34 Joining Stoney Brook mathematics department?
37:03 Leaving Stoney Brook to trade?
37:57 Fundamental trading technique?
39:54 Track record of Medallion fund?
44:28 How many employees do you have?
47:25 Employees are top of their field?
49:53 How do you manage lots of talented people?
52:42 A theory as to why Renaissance is so successful?
56:26 How did you know about the Bernie Madoff ponzi scheme?
1:03:01 The 2008 financial crisis?
1:08:47 Start of Q&A
1:09:14 Has the rise of computes in markets changed your perspective on fundamental investing?
1:11:11 Are quants destined to slowly drive themselves out of business?
1:12:47 What is your favourite algorithm?
1:13:59 How did you protect your intellectual capital?
1:16:52 The balance between improving your model and keeping it simple enough to understand?
1:18:12 Is Medalion the same as it was 10 years ago?
1:19:38 At any point in time did you doubt yourself?
1:21:48 Is your internal compass better than others?
1:23:53 Inductive or deductive driven investment strategy?0
1:25:15 Have you encountered any unsolved finance problems?
1:26:20 Advice to future quants?
Transcript
00:00
welcome everyone I think you in for a
00:03
real treat
00:05
first of all I’d like to welcome you all
00:08
to MIT Sloan we’re here today because
00:14
this is part of this is the there’s been
00:17
a world made for the the Sussman
00:19
fellowship which is given every couple
00:22
of years and it was funded to honor the
00:27
achievements and the opportunity stone
00:32
Sussman is given to a bunch of people in
00:35
the fund management business
00:36
he’s also somebody who was very much a
00:40
Pena and the whole hedge fund arena we
00:45
have it Donald for those who weren’t
00:49
here last week runs paloma partners and
00:52
a Chinese private equity investment firm
00:56
and it’s very it’s very much involved
01:02
you know with all things investing to
01:06
this day this year we have awarded the
01:10
fellowship to Jim Simons when in fact
01:13
the award really is almost ours for Jim
01:17
having agreed to show up Jim is an
01:22
extraordinary man genuinely
01:25
extraordinary it’s very hard to describe
01:28
how extraordinary because if you think
01:32
about this you know every day he goes to
01:35
work or when he did go to work it’s a
01:38
discovery in a battle it’s a competition
01:40
it’s like an athlete winning the
01:42
Olympics pretty much every day that’s
01:44
probably the best analogy it’s a
01:46
fiercely competitive environment and
01:48
anybody who knows finance knows how
01:50
unbelievably difficult it is and how
01:51
many people would like to eat your meal
01:55
Jim
01:56
not only is a great mathematician and
01:58
again those who are last week will know
02:01
about this but he’s also what I
02:03
described as a real Mensch an extremely
02:07
nice person who when I first met him
02:09
which would have been around 1990 or 91
02:12
and his fund was maybe 200 Jim will
02:14
correct me maybe 250 million which by
02:17
then was fairly large but I did it
02:19
today’s standard it’s very small had
02:22
this certain irreverence and confidence
02:24
and directness and one of the things
02:27
features that that makes Jim so very
02:29
special and you’ll notice it today he
02:31
gives extremely concise direct and
02:34
unambiguous answers to any question you
02:36
ask him the other thing to note about
02:40
Jim is for those who know some of the
02:42
people who work for Jim I know some of
02:43
the history it’s unbelievably difficult
02:46
to manage intelligent people and I don’t
02:51
know many people who do it as well as
02:52
Jim does and it’s worse when there’s a
02:55
lot of money involved Jim
03:00
one other thing to say the few things
03:02
angusamy things to say but one of the
03:03
things to savor Jim is that his track
03:05
record is so extraordinary that to most
03:10
academics it’s inconceivable and it’s
03:13
somewhat ironic that we’re here at MIT
03:15
in a and this is probably the best
03:17
finance faculty in the world at least so
03:19
my friends tell me and there’s a there’s
03:23
a paradox here because Jim never hires
03:25
finance guys or MBA is so such a we you
03:28
never used to and it’s quite wonderful
03:30
to have him speak to this audience and
03:33
I’m sure the other departments here as
03:34
well but it is interesting that you know
03:37
you have two powerpoints studying all
03:39
kinds of features of the financial
03:40
markets and Jimmy does away with Jim
03:44
does a way of publishing papers instead
03:46
just cracks them so Jim Andrew over to
03:52
you thank you
03:55
[Applause]
04:04
well I want to I want to start by
04:06
joining Andre and my MIT colleagues in
04:09
thanking Jim for joining us today and
04:11
being here and I have to say that this
04:16
is a real pleasure and an honor for me
04:18
because I think it’s fair to say that
04:21
Jim Simons and Renaissance Technologies
04:22
is certainly the most successful
04:25
quantitative investor in the history of
04:28
investing but perhaps actually you can
04:30
drop the qualifier quantitative and so
04:33
there’s a really really interesting set
04:35
of issues that we want to get to today
04:37
before I do that though I need to lower
04:41
your expectations of the interviewer
04:43
because you know this three lectures
04:46
that are three fireside chats that Jim
04:48
is agreed to
04:50
mathematics money and making a
04:52
difference only one of those M’s is
04:54
proprietary and confidential and that
04:57
happens to be today’s topic of money so
05:00
so Jim and I agreed on some ground rules
05:02
I get to ask all sorts of nosy questions
05:04
and he gets to say pass because it’s
05:07
confidential and I’m sure that the
05:09
audience will have a chance to ask those
05:11
questions too and I’ll have a chance to
05:14
say no okay so um Jim oh I’m gonna ask
05:20
you if you don’t mind to recount a bit
05:22
of your biography the way we did last
05:24
week but instead of Tom’s focus on your
05:27
mathematics career I’d like to turn it
05:30
around and focus on your business and
05:31
finance career so I’m gonna start the
05:34
very beginning you were a very
05:37
precocious mathematics student you
05:39
mentioned last week that when you were 2
05:42
or 3 years old you are already doing the
05:43
powers of two um were you also
05:46
precocious from a business perspective
05:49
did you think about any of these issues
05:50
as a child as a child I thought I had no
05:56
interest in business which is not to say
05:59
I had no interest in money but I had no
06:02
interest in business and but
06:07
you know was a little kid I had a friend
06:10
who was very rich okay it’s nice to be
06:13
very rich I deserved that but just I
06:18
just focused on math and for quite a
06:20
while
06:21
so unlike Warren Buffett who had a
06:24
newspaper route business or Bob Merton
06:26
who I think was trading stocks when he
06:28
was 10 you had nothing to do with with
06:30
finance nothing to do with finance
06:33
okay so uh when did you first get
06:37
interested in business when you were at
06:39
MIT you mentioned something about that
06:40
last week I’ll have to call you back
06:48
what can I say I hope that wasn’t a
06:52
margin call if it had been I would have
07:01
said the same thing so when did you
07:07
start thinking about business you
07:08
mentioned as an undergraduate you had
07:10
some friends who are you know doing some
07:13
business in Colombia well I met some I
07:17
made friends at MIT with two Colombian
07:20
boys and they at a certain point started
07:26
a business and in fact it was my
07:28
encouragement that they started that
07:30
business and my father and I invested a
07:33
small amount in that business which
07:36
turned out eventually to be a big
07:41
success so what what possessed you to
07:44
think about that I mean that well it’s a
07:46
certain amount of initiative to actually
07:47
well there’s asked me to think about
07:49
that particular investment was that I
07:51
had when we graduated MIT three of us
07:58
one of whom was the Colombian boy and
08:01
his friend was in Bogota
08:03
cerebus road motor scooters from
08:09
Cambridge to Bogota now we’d expected to
08:14
go all the way to Buenos address but by
08:17
the time we got to Bogota we were
08:19
exhausted so we said we stopped in
08:22
Bogota and I stayed there a week or so
08:25
and I saw this country Columbia and it
08:27
was really a place that you could do
08:29
anything I real I was told if you start
08:33
a business a manufacturing business and
08:36
you’re making something that was
08:37
imported previously imported to Colombia
08:41
the government would shut off those
08:43
imports and give you clear roads to run
08:47
so I thought my friend should start some
08:50
kind of business like that but which
08:53
they did but that was my first
08:57
interaction with money
09:01
was when I was the first year well it
09:07
was a second year I went out to Berkeley
09:09
to finish my PhD I spent two years there
09:11
in the first year early on I got married
09:15
and we I had five I got $5,000 worth of
09:21
wedding gifts so I my wife and I decided
09:26
well I decided to shoot what she was
09:28
willing that we should invest this and I
09:31
I had a couple of stocks which for no
09:35
good reason I thought might do well and
09:38
so I want open an account in San
09:42
Francisco with with Merrill Lynch I
09:44
bought these two stocks I went home and
09:48
four months they did absolutely nothing
09:50
so they didn’t go down they didn’t go up
09:53
so I went back and I said you have
09:56
anything that’s a little more exciting
09:59
and he said yes he said you should buy
10:03
soybeans Merrill Lynch thinks that two
10:07
dollars and fifty cents now they’re
10:08
going to go up to three dollars and 50
10:10
cents what are you talking about I did
10:12
soybeans
10:13
I knew about stocks I didn’t know about
10:16
soybean users yes you could buy a
10:18
contract this 5,000 bushes you could buy
10:20
two contracts he did a lot of leverage
10:23
and so on all right so I bought two
10:26
contracts of soybeans and within a week
10:30
it had gone up quite a lot and I’d made
10:33
several thousand dollars maybe two or
10:36
three
10:36
now that was exciting and I came back to
10:41
the math department and I said to one of
10:44
the older guys I told him what happened
10:46
he said I said have any idea what I
10:49
should do is it absolutely sell it
10:51
immediately which was extremely good
10:53
advice because within a day or two it
10:56
hadn’t gone back down and it was
10:58
bouncing around and actually had a
11:01
little loss I closed out two position
11:04
and then I thought well I should have
11:09
taken a smaller position and then I
11:10
could have held it more
11:13
and I did I bought one contract of
11:16
soybeans and was going back and forth
11:20
early in the morning to watch the
11:23
opening in Chicago because it was it was
11:28
early in the morning in San Francisco
11:31
and Chicago open to trade these things
11:33
and I was going back and forth across
11:36
the Bay Bridge watching bored and and
11:40
then and I and I had a little profit at
11:43
a certain point and I realized I am
11:47
either gonna trade soybeans or write a
11:50
thesis I was in the middle of starting
11:54
to write a thesis and I could see I
11:56
can’t do both at the same time so I sold
12:00
that one contract for I think a very
12:04
small profit and that was the last time
12:06
I traded anything for a number of years
12:09
but I did write a thesis and got a job
12:12
here at MIT as a result so so just for
12:16
clarification your first investment was
12:19
a couple of stocks and then the second
12:22
investment was soybean futures contracts
12:25
yeah and these contracts as I recall are
12:28
leveraged like 25 or 50 to 1 is that
12:32
right it’s very highly leveraged I don’t
12:35
know I octane yeah it was yeah I felt
12:38
that you were you were a suitable
12:40
investor for that as a graduate student
12:44
well he didn’t ask any questions
12:49
he was just doing his job I came in I
12:52
had enough money to buy this stuff so he
12:54
he figured it was all ok yeah and so you
12:58
had never invested before that you
13:00
didn’t have to take a course in finance
13:01
or business nothing okay great so now
13:05
you’re an assistant professor at MIT and
13:07
it’s pretty clear based on your thesis
13:09
and the early work that you did that you
13:12
were gonna have a very good career in
13:14
math and you did so what got you
13:18
interested in business
13:20
at that same time because you continue
13:22
to have an interest in it didn’t you
13:24
continue having an interest in business
13:26
well when I came back to teach at MIT
13:32
the first intercession I went down to
13:35
Bogota to visit with my friends and told
13:38
him I was coming and I won’t leave until
13:41
we have found a business and they found
13:45
one while I was there and decided to
13:47
partner up and I knew there would be
13:49
there were very smart guys and they had
13:51
a very good sense of business which I
13:54
don’t think I ever had and so they
13:58
started this this business my father and
14:01
I invested a small amount and I had a
14:04
borrow from everybody but I did and so
14:08
that was the the first the first thing
14:12
and then there was there was not much I
14:15
could do about it so I kept doing doing
14:18
math and but I actually since I borrowed
14:25
some money I needed to pay it back and
14:28
it was there was a place in Princeton
14:33
called the Institute for Defense
14:34
analyses which was a very highly
14:38
classified joint and it specialized in
14:42
cracking Russian codes and protecting
14:45
her own so it was under the auspices of
14:48
the NSF and they paid a lot for
14:50
mathematicians so I applied to them and
14:54
got a job and enjoyed the job and was
14:58
able to start paying down some of my
15:00
debts because they it paid maybe double
15:03
what I was getting at at MIT Wow so so
15:07
well and I liked that place it was it
15:11
was interesting now you talked last week
15:14
about some of the work that you did
15:16
there
15:16
but one of the things that I wanted to
15:18
ask you and I didn’t think it was
15:19
appropriate to ask last week because the
15:21
focus is on mathematics
15:22
did any of the work that you were doing
15:26
there any of the mathematical tools that
15:28
you were developing
15:29
have any applicability to some of the
15:33
work that you did later on in finance in
15:35
a general sense yes now I didn’t get
15:38
into finance for 10 years after that I
15:41
left there in 68 and really didn’t get
15:44
into finance until the late 70s but I
15:49
learned about computers I learned about
15:54
you know the fun of coming up with some
15:57
algorithm which might crack a code most
16:00
of the time it didn’t but once in a
16:01
while you were lucky and and I didn’t
16:05
know how to program at all and never did
16:07
learn how to program but they had
16:08
programmers but I liked the idea of
16:10
developing algorithms seeing them put on
16:13
the computer and seeing you know if it’s
16:16
if it’s going to work so that experience
16:20
was very influential when I went into
16:25
the hedge fund business and then
16:28
gradually started to make it systematic
16:31
as well as opposed to the fundamental
16:35
training which we did at the beginning
16:38
and so anyway I was a mathematician I
16:42
was getting frustrated with some of the
16:45
research I was doing worked on a problem
16:47
for two years didn’t get anywhere and
16:51
it’s never been solved so well he could
16:53
see it was a hard problem that’s a good
16:56
one too and the South American business
17:03
had was beginning to throw off some
17:05
money so I had some money and I thought
17:11
I would and start investing and and I
17:17
had an interest in foreign currencies I
17:19
don’t know why but I did and I read a
17:24
lot about that so we started I started
17:29
and I got a partner investing in in
17:31
foreign currencies and that did very
17:36
well with this before you left for stony
17:41
brook or
17:42
Oh No was after I left I’d been in
17:44
Stoneybrook for six years by that okay
17:47
you know I was I went to Stony Brook in
17:50
68 and and it was 76 or 77 that we
17:54
started doing this and but I thought we
17:57
could I looked at the charts and they
18:00
looked like there was some structure to
18:05
these historical charts that one could
18:07
perhaps exploit so I hired the best
18:11
Crypt analyst in the world guy named
18:14
Lenny Baum who you may have heard of the
18:20
bomb Welsh algorithm the EEM algorithm
18:23
expectancy Maxim maximization he
18:27
discovered that so he came to work with
18:30
me and and we built a little system even
18:35
though I was trading fundamentally at
18:38
the time you know seat-of-the-pants sort
18:42
of thing which way this is wind blowing
18:45
we developed this sort of primitive
18:49
currency trading system but we didn’t
18:55
actually put it into practice because
18:57
one day when he didn’t show up for work
19:01
until the middle of the afternoon and I
19:07
should say that Lenny loved to read the
19:11
broad tape that was this tape we call it
19:13
was the Duke we’d called it the doomsday
19:15
machine because it just clicked that
19:18
this broad tape would roll all day long
19:22
giving the financial news of the world
19:25
and he liked to study that he was
19:28
supposed to be studying making systems
19:30
but he liked to read that tape so he
19:32
came in late and I said where you’ve
19:34
been and he said Margaret Thatcher has
19:39
been sitting on the pound and it has to
19:43
go up I said oh well I wish you’d come
19:47
here
19:49
this morning he said why because
19:52
Margaret Thatcher just stood up and
19:56
Margaret Thatcher just stood up and the
19:59
pound was way up he said how much is it
20:00
up I said well it’s up nickels five
20:03
cents so far he says it’s gonna go up 50
20:06
cents a dollar by pounds we should buy
20:09
pounds okay five pounds sure enough it
20:13
went way way up and that was the last
20:17
time Lennie wanted to look at any
20:19
systems he just felt his good intuition
20:24
would be suitable and we’d make a lot of
20:29
money and and we did we did doing
20:34
fundamental trading we started a fund
20:37
called limb Rory and the fees were 25%
20:44
of profits no fixed fee which was you
20:47
know sort of a reasonable thing and what
20:51
Lenny is my partner the first year the
20:55
fund doubled after fees and the next
20:58
year it multiplied by six after fees so
21:02
it had two times six is twelve so
21:07
everyone had 12 times as much money as
21:09
they started with and it was it was
21:13
fantastic and it was all fundamental
21:15
training still I felt that okay we can’t
21:24
we were lucky in certain ways I’ll tell
21:29
you one good story about luck gold which
21:37
was illegal to trade had become legal to
21:42
trade and the gold market gold prices
21:45
were going up and we bought gold and in
21:50
the firm
21:51
we bought gold we had a pretty big
21:52
position in fact Lenny and I split the
21:55
position half of it belonged to him in
21:58
some sense and half B belong to me
22:01
and it was a two hundred dollars and
22:04
fifty cents I’ve been a children for
22:06
$250 to 300 400 500 550 I think I said
22:13
Lenny you know I think we should sell
22:15
this already he said no no you don’t
22:17
know how far we’ll go you don’t know how
22:19
far it will go so I sold my half and it
22:24
kept going up and one day it reached
22:27
$800 and that day I happened to be
22:33
speaking to a friend of mine who was a
22:36
stockbroker but we would just I was just
22:38
chatting with him over the phone and I
22:41
said what’s new he said well what’s new
22:43
was this my wife went into my closet
22:46
this morning and cleaned it out of all
22:48
my old gold cufflinks and tie clasps and
22:52
she’s now down selling it
22:56
I said well dick I mean are you having
22:59
financial difficulties he said no no but
23:03
she’s a jeweler which he was and she
23:06
only had to stand in the short line I
23:08
said the short line he says don’t you
23:11
know there’s lines and lines of people
23:13
selling gold I said no but I’m very glad
23:19
you told me I hung up with him I picked
23:23
up the phone which went right to the
23:25
floor of the exchange and I’ve got
23:26
plenty to come over and I said Lenny
23:29
sell the gold he said no you don’t know
23:32
how far it’s been I was the boss and I
23:36
said sell the effing gold he said okay
23:43
okay and he sold the gold it was it was
23:45
eight hundred and ten dollars or
23:47
something like that the next morning we
23:51
came in and it was eight hundred and
23:52
twenty dollars and he was so mad by the
23:55
end of that day it was six hundred and
23:57
fifty dollars the market collapsed and
24:00
went nowhere but down after that until
24:03
it got back to two hundred and fifty
24:05
dollars a three hundred not not in a
24:08
week but it just collapsed now that was
24:12
totally good luck I mean it wasn’t
24:14
good that I realized that if everyone is
24:16
selling something it may be a time to
24:18
sell it yourself but but it was it was
24:25
luck it was just it was just luck so we
24:31
went we did well but I felt that this
24:35
should be systematized there should be a
24:37
way to systematize it and I brought in
24:40
another mathematician a very strong
24:42
mathematician named Gen X and to do
24:47
fundamental trading but he knew about
24:49
that we had made this currency system
24:51
and he looked at it and he got a good
24:54
programmer into the firm and he realized
24:59
this system could work for all all
25:01
commodities really it was it was a
25:04
pretty good system sort of so we started
25:08
trading that system and it did pretty
25:11
well and he did research have improved
25:15
it and improved it we were still
25:18
fundamental trading but that wasn’t even
25:20
going so well I had gotten interested in
25:25
venture capitals to some extent so this
25:27
memoride company was also starting to
25:30
invest in start-up companies and and ax
25:37
was running this training and at a
25:42
certain point the investors in Limerick
25:47
they didn’t like this liquid the venture
25:53
capital they liked the training and so I
25:57
decided to break up the company Lim Roy
26:00
and make a fund called medallion gym ax
26:06
would run that fund and we put the the
26:10
venture stuff into a liquidation only
26:16
fund and actually it ended up doing
26:19
pretty well so now we had the medallion
26:22
fund and everyone invested in the
26:27
medallions on
26:29
and it did very well for about six
26:32
months and then it started losing money
26:34
and it was losing money steadily now he
26:39
and his team had developed a very
26:42
complex system a very complex system and
26:47
it had many dimensions in it one thing
26:52
or another and I said you know I have to
26:56
understand what this system is actually
26:58
doing
26:59
he said it’s oh it’s too tough week I
27:01
can’t explain it to you it had this Bell
27:03
and that whistle so I said come on I’m
27:06
gonna project this into the two
27:09
principal dimensions and see what it
27:12
looks like and it was nothing but a
27:14
trending system plain and simple
27:17
trending it had this these other little
27:20
geek things whatever they were but it
27:23
was basically a trending system and
27:25
trending which in in commodities and
27:28
currencies to which historically was a
27:32
very strong thing had in the last
27:36
several years
27:37
just just sort of gone away with no
27:40
reason to think it would ever come back
27:42
so I said we’re closing the fund and he
27:48
was very annoyed but I was the boss so
27:53
we closed the fund and I told the
27:55
investors we’re gonna spend I’m gonna do
27:58
a study period and we’re not going to
28:01
trade at all of course we’re not gonna
28:04
charge any fees Oh at that point it was
28:06
five five and twenty who was a five
28:09
percent fixed fee and twenty percent of
28:11
profits and and everyone stuck with us a
28:16
few people redeemed but everyone stuck
28:18
with us and for six months and we
28:22
brought back someone who had left the
28:25
firm it’s a long story
28:27
we brought back this other very good guy
28:29
axe left and he and I especially he he
28:34
had some ideas of much shorter term
28:36
trading not high frequency in an out in
28:41
five minutes but
28:42
trading on a much shorter term and he
28:45
developed a pretty good system and
28:48
together I helped him and it got better
28:51
and after six months we went back in
28:55
business only only systematic training
28:59
and from then on we never looked back it
29:05
was it was just went from strength to
29:07
strength and I hired a lot of scientists
29:11
a lot a lot of computers and over the
29:14
years the system got better and better
29:17
and better so Jemma we’re gonna focus on
29:20
the Renaissance medallion fund in a few
29:21
minutes but I want to bring you back a
29:23
little bit because there are some
29:25
interesting precursors that I think
29:27
speak to the success that you enjoyed
29:30
one is that when you’re an idea is that
29:34
where you first met Lenny BAM was here
29:35
and down the street and as I recall his
29:40
early work the bomb Welch algorithm was
29:43
really designed to estimate hidden
29:44
Markov models that’s right which for
29:48
many of you I think you know that’s the
29:49
precursor for a lot of the techniques
29:51
that are used today including deep
29:52
learning so it’s an interesting history
29:55
to that yeah in terms of what you what
29:58
you encountered there yeah he developed
29:59
that algorithm with this guy named Lloyd
30:01
Welsh which was supposed to access
30:05
estimate hidden Markov models whatever
30:09
that is but there’s a lot of parameters
30:11
in and it was an algorithm which just
30:16
kept climbing
30:17
it kept re-estimated and re estimating
30:21
and with each restoration the expectancy
30:23
of these particular parameters whatever
30:26
they were got better and better and it
30:28
changed the parameters and to cut better
30:30
however no one could prove that it
30:34
worked no one could prove that it worked
30:36
it clearly did you could start at any
30:39
place as well it definitely worked but
30:43
how did you prove Oaxaca with proven so
30:46
actually I worked on that a little while
30:48
I was at I da and trying to prove
30:53
that actually works but it climbs at
30:56
every step but I couldn’t and anyway I
31:01
left I da and he when he and his friend
31:06
Petrie finally figured it out and they
31:10
wrote it was a a long paper it may have
31:12
been two or three papers now today it
31:16
turns out I’m told you can prove that in
31:19
just a couple of pages because there was
31:21
some theorem of which they were unaware
31:23
which would have made it short but but
31:26
anyway there was the algorithm speech
31:29
recognition it was very good for speech
31:31
recognition a whole lot of things yeah
31:33
yeah so um I’ll get to the medallion in
31:37
a minute but I want to just ask you two
31:39
more things that lead up to the
31:40
medallion fund one was you left I da to
31:44
join Stony Brook’s math department here
31:46
and at the time Stony Brook’s math
31:48
department wasn’t nearly as strong as it
31:51
is now can you tell us about that and
31:53
what motivated you and and what your
31:56
experiences were there well I got fired
32:00
from I da I got fired did I tell this
32:05
the last last time but I think it’ll be
32:07
worth repeating because not everybody
32:08
was here so okay so I always say getting
32:13
fired once is it could be a good
32:15
experience you just don’t want to make a
32:17
habit of it
32:18
I did I got fired okay the head of this
32:27
place I DEA was in Washington DC which
32:34
was a was a big organization and one of
32:36
its units it was this small unit in
32:38
Princeton he was named was Maxwell
32:41
Taylor some of the older folks in the
32:44
audience might remember that name and he
32:48
wrote an article lead article in The New
32:49
York Times Magazine section about how
32:52
we’re winning in Vietnam were doing
32:54
great we have to stay the course so on
32:57
this was 1968 and I did not have the
33:02
same opinion as he and I wrote a letter
33:05
to The Times
33:06
the first sentence of which was not
33:08
everyone who works for general Taylor
33:10
subscribes to his views or something
33:13
like that and I gave my views which was
33:15
he got out of there as fast as we can
33:17
and nobody said anything you know
33:22
nobody said anything they could have
33:25
tried to lift my security clearance but
33:29
no reason for it a few months later a
33:34
guy claiming to be a stringer for News
33:38
Newsweek magazine said he’s doing an
33:42
article on people who work for the
33:43
Defense Department and a repose to the
33:46
war and he’s having trouble finding
33:47
anyone in that category could he
33:51
interview me I was 29 years old no one
33:54
had ever asked to interview me before so
33:57
I was very excited and he said well okay
34:01
so how are you responding to this I said
34:04
well and I DEA you’re supposed to do and
34:10
at least half your time on their work
34:13
but you could also spend up to half your
34:17
time on your homework and I had been
34:19
doing a lot of math in that period as I
34:24
said so my attitude is my policy is
34:27
until the war is over I’ll do only my
34:30
own work and then when it’s over I’ll do
34:33
an equal amount of time doing only their
34:36
work and so that allow balance of so
34:40
that’s what I said then I went back to
34:43
the office and I decided I better tell
34:45
my boss that I gave this interview it
34:49
would have been more intelligent if I
34:50
had told him before I gave the interview
34:53
because he would have said don’t give
34:55
any interviews but he said well what did
34:58
you say I said well I said about the
35:00
half and half and so he said okay he
35:03
went into his inner office and called
35:06
Maxwell Taylor he came out in five
35:08
minutes and I said well you fired
35:12
I said I’m fired I see you can’t fire me
35:16
my title is permanent
35:18
number permanent member and he said well
35:22
you know but difference between a
35:24
permanent number and a temporary member
35:26
I said no he says a temporary member has
35:29
a contract but I was a permanent member
35:32
and I didn’t have a contract so I left
35:36
that’s of course I had to leave and I
35:40
had to look for a job I had three kids
35:42
but I was certain I would get a pretty
35:45
good job because I had just done some
35:47
actually quite important mathematics and
35:49
it was I’ve been giving talks and so on
35:52
so I knew I’d get a good job but as a
35:59
professor somewhere but Stony Brook came
36:02
along and offered me the position of
36:05
being chair of their math department and
36:08
it was a weak department with one of two
36:12
exceptions and they wanted to build it
36:15
up and they’ve been trying for a long
36:16
time to find a older distinguished
36:19
person to come as chair and they
36:21
couldn’t find anybody but they found me
36:23
and I thought this would be really fun
36:29
I’d like to build something and so I
36:33
took the job and the university had a
36:37
lot of money at that time which it
36:39
doesn’t have so much today they had a
36:42
lot of money Rockefeller was the
36:45
governor and he loved the state
36:47
university so I hired a lot of great
36:51
people it was a wonderful experience I
36:54
did a lot of mathematics during those
36:57
first few years myself it was a very
37:00
very productive time so that’s so then
37:03
what led you to start doing your
37:05
currency trading because you at some
37:07
point you left Stony Brook to do
37:08
currency trading yeah the time I left
37:10
Stony Brook first I went half time and
37:12
then I left altogether and what was in
37:15
the training business yes and so what
37:18
what led you to do that that why did
37:20
well because I as I said I was stuck on
37:24
a problem I had come into some money
37:28
I was trying that out I liked it
37:32
and I thought well I’ll just have a new
37:34
career my father was very opposed to it
37:38
he said look you have tenure you have
37:40
this wonderful job they can’t take it
37:41
away from you I did have a contract in
37:45
that sense and what why do you want to
37:48
take this risk but I I thought it would
37:52
work I thought it would work out yeah
37:54
and I was pretty confident and so in
37:56
your fundamental trading for currencies
37:58
can you share with us how you did it
38:01
well I mean it was totally non
38:03
quantitative would you say and so do you
38:07
how we use for example technical
38:09
analysis people are no Charlie didn’t do
38:12
our technical analysis I read all the
38:15
newspapers The Economist there was a lot
38:19
of writing I just pay a lot of attention
38:22
to two currencies and in these
38:26
currencies has just been tradable in the
38:30
open market because some some countries
38:32
still had fixed currencies fixed to the
38:35
dollar and you couldn’t well you could
38:39
trade it but it would it was fixed but
38:44
so it was malicious fundamental
38:46
fundamental stuff and it worked it
38:50
worked reasonably well I would say work
38:52
reasonably well and so but that was it
38:58
but but the problem with a business like
39:03
that is I walk in one day everything was
39:08
going my way I’m a genius the next day
39:12
I’d walk in everything was against me oh
39:14
I’m a dope it was a very stomach
39:17
wrenching business whereas with a system
39:22
that you can develop okay you have a
39:25
system you do what the computer says to
39:28
do you haven’t made a historical study
39:32
of the system that you’re using and it
39:35
worked with a very high probability the
39:38
system was going to work and so I
39:42
I was much more satisfied with that
39:45
approach and and we hired scientists and
39:49
so on and to build these systems and
39:52
improve them okay so now let me talk
39:54
about the medallion fund so you know
39:56
when I teach introductory finance I
39:58
usually start with a single equation on
40:00
the board and the equation is
40:02
mathematics plus money equals finance
40:05
and I would argue that the medallion
40:07
fund pretty much epitomizes that because
40:09
the system that as you described has
40:12
yielded just extraordinary returns and
40:16
at this point the track record is
40:18
confidential but you did give an
40:20
interview one of you very few interviews
40:23
that you gave in 2002 how luxe and so I
40:26
want to just read to you what was
40:28
written at that time about the medallion
40:30
track record Symonds by contrast just
40:35
keeps getting better consider his
40:37
performance over the past decade and
40:39
this is between 1988 when it was
40:42
launched and 2000 since its inception in
40:45
March 1988 Symons flagship three point
40:48
three billion dollar medallion fund has
40:50
amassed annual returns of thirty five
40:53
point six percent compared with eighteen
40:57
percent for the SP during that that was
41:00
after fees and at that time the fees for
41:03
the medallion fund at its peak was five
41:06
and forty four so five percent fixed fee
41:09
and forty four percent of the profits so
41:13
that that track record yielded two
41:16
thousand four hundred and seventy eight
41:18
point six percent return over the eleven
41:20
years from 98 to 88 to 99 and the
41:26
next-best fund in the hedge fund
41:28
databases at the time was the Soros Fund
41:31
the Quantum Fund which was only one
41:34
thousand seven hundred and ten percent
41:36
so and but that was of two thousand so
41:40
first question how’s the track record
41:43
been since then because nobody knows for
41:46
sure I know
41:53
and if you and a few other people know
41:56
the tracrac that has continued good we I
41:59
don’t know if at that time we had
42:01
already raised the fees to five and
42:03
forty four first we raised them to five
42:05
and thirty six and then the investors
42:09
are all complained but they just wanted
42:12
to have more so how can I get more and
42:14
then five and forty four and there was
42:18
still a very good return at five and
42:20
forty four so no one wanted to redeem
42:22
but we realized that there was a limit
42:26
to how much we could manage we
42:29
understood the system and you know it
42:34
could manage a certain amount but it
42:36
couldn’t have managed huge you know huge
42:40
amounts and trillions
42:43
hundreds of billions that certainly
42:44
couldn’t manage that kind of money so we
42:47
decided to and because we were making so
42:51
much money the fund was growing
42:55
internally first we prevented any
42:59
outsiders from no new investing
43:03
investments from outsiders except for
43:06
the employees and then we decided to buy
43:12
in the outsiders that was in oh three I
43:17
think oh three or four no five by the
43:20
end of oh five we had bought out all the
43:23
outside investors and it was just owned
43:25
by owned by the employees and it did
43:30
grow to some extent but because it did
43:36
and it could manage that much but at a
43:38
certain point it’s been it’s been capped
43:43
off and we started and in that same year
43:47
oh five we started some funds for the
43:51
public which have done very nicely and
43:55
they have no
43:57
clash with medallion there there much
44:00
longer term expectations but those funds
44:04
have done very nicely and so at the
44:08
moment as it that there’s 45 billion in
44:11
those funds being managed and but the
44:16
medallion fund has always stayed
44:17
medallion fund has stayed at a certain
44:19
size which I won’t share yeah but it’s
44:23
not as big as 45 million yes can you
44:27
share with us how many employees you
44:28
have yeah we have three hundred and ten
44:32
to twenty or something like that yeah
44:34
counting everyone we have a lot of
44:36
scientists we really you know you have
44:43
to in a business like this just keep
44:46
making things better keep improving the
44:50
system because other parts of it are
44:54
gonna wear out after a while people will
44:57
catch on to this so they’ll catch on to
44:59
that so you you just have to like in any
45:01
business in any business you just have
45:03
to make things better and better and
45:05
better because that’s what everyone else
45:07
is trying to do and so so we hire the
45:12
best scientists we can people have said
45:16
to me although yo you know you’re you’re
45:18
not doing the world a favor these people
45:20
could be doing great scientists you know
45:23
for they’ll make all this money and then
45:26
they’ll give it to charity I’m not
45:28
worried that it’s gonna ruin the world
45:29
by having good scientists working at
45:32
Renaissance but we do have good
45:34
scientists working there and and that’s
45:37
been that’s been the model the model has
45:42
been first hire the smartest people you
45:47
possibly can sensible principal work
45:58
collaboratively let everyone know what
46:02
everyone else is doing now some
46:07
firms that do have these systems they
46:11
have little groups of people this is
46:12
ours and this is theirs and and they’ll
46:15
get paid accordingly and so on to how
46:17
their system goes up we have one system
46:20
and once a week there’s a research
46:27
meeting if someone has something new to
46:29
present it gets presented it gets shoot
46:33
or shoot up and and and looked at from
46:35
everyone has a chance to the code is
46:39
there they can run the code and see what
46:42
they think is this really work and so on
46:45
so it’s a very collaborative enterprise
46:48
and and I think that’s the best way to
46:52
accelerate science is people working
46:54
together and so that’s that’s that and
47:00
we have great infrastructure wonderful
47:04
infrastructure so people can get right
47:05
to work we’ve had people come in start
47:10
to work as I got oh I’m doing this after
47:13
after three days I’ve never been in any
47:15
place where you could get up and running
47:16
so quick so it’s well organized and we
47:21
have great people so you know obviously
47:26
much of what Renaissance does is
47:28
confidential and in particular the even
47:32
the people that you have are
47:34
confidential but I think it’s fair to
47:36
say that if you looked at the quality of
47:40
the colleagues you have they are
47:43
probably among the top scientists in
47:46
their field in many different fields is
47:49
that fair to say well I don’t think
47:53
there’s anyone who would well okay I’ll
47:55
tell you a funny story we had a we have
48:02
a Renaissance a colloquium every week
48:05
someone comes and gives a talk the
48:07
scientists and and it’s open to the
48:10
public and one day an astronomer and
48:13
young astronomer came in a friend of his
48:16
already worked at Renaissance
48:18
and this guy came and he gave a very
48:21
good talk he gave a very good talk
48:23
and I took them aside afterwards and
48:26
says you know your friend is here and
48:30
you would like working here you would
48:33
like working here we would like to have
48:34
you work here
48:36
and he said well it sounds very
48:38
appealing but I’m right now I’m in a
48:41
project that I science project that I
48:44
really want to complete before I think
48:46
about so he won the Nobel Prize he won
48:52
the Nobel Prize he was one of the two
48:55
teams that learned that the universe
48:58
instead of decelerating was actually
49:02
accelerating and it was it was big news
49:05
and so I think he made the right
49:08
decision you know most people would
49:11
rather have been Nobel Prize so so he’s
49:16
the only scientists of Nobel Prize
49:19
quality that we almost got and and I
49:23
don’t think anyone else in the firm is
49:25
probably that good although some of them
49:27
have been terrific I some of them they
49:33
don’t give Nobel prizes in mathematics
49:35
but what they do in physics of course
49:39
and we have a lot of people who were
49:40
physicists experimental physicists do
49:43
well astronomers do well they look at a
49:46
lot of data and analyze it and that’s
49:49
and that’s what we do analyze data so
49:52
that leads me to my next question how do
49:54
you manage with all of these incredibly
49:58
talented people often with really huge
50:01
egos you talked about collaboration but
50:04
having been a chair of a department and
50:07
you’re having a manager an apartment
50:09
it’s not always easy to get big egos to
50:11
collaborate well a department chair does
50:18
not have that much power right
50:22
and I’m sure and any professors in the
50:26
audience know that you don’t have to do
50:29
what your department UOB says you have
50:31
to teach this class okay it teaches a
50:33
class but as far as your research goes
50:35
you can do what you want so but we did
50:43
at Renaissance say you know we’d like
50:45
you to work over in this area or work
50:47
over in that area but but nonetheless
50:51
other groups there are groups that work
50:54
on different things and in the research
50:57
area but because they see what’s going
51:00
on every week and everyone else’s group
51:02
they can sometimes and often do make a
51:05
suggestion hey you know what we’re doing
51:09
over here I think could affect what you
51:11
want to do over there the the the way
51:15
people are paid everyone gets a piece of
51:19
the profits and but they’re judged it’s
51:24
not why don’t you accomplish this year
51:27
you know I’d have every year people come
51:29
in would convince me and say you know I
51:32
made so much money for the company my
51:34
work made so much money for the company
51:37
last year I deserve a big raise I said
51:39
oh yeah well that was that was good work
51:41
didn’t it derive from Shawn selves work
51:44
he says yeah yeah but we really made it
51:46
better and I said well and didn’t you
51:49
work with Joe and Susan on this yes yes
51:53
I agree I did I did that so I said you
51:57
know if I added up all the money that
52:00
everyone who comes in here tells me they
52:03
made for the company this year it would
52:05
be five times as much assess the company
52:08
made so you know but we look back on
52:13
three years four years five years how
52:16
they’ve done and they’ll get raises
52:19
accordingly and and that’s the way it
52:24
works and people no one’s perfectly
52:29
happy with everything and I can’t say
52:31
there’s no one who thinks he should be
52:33
paid more
52:34
which is human nature but everyone’s
52:37
pretty happy it’s a it’s a very happy
52:39
place yeah so very happy so this this
52:42
leads me to the the final point that I
52:45
wanted to make about the medallion fund
52:46
and what what you built over the years
52:48
so you must know that that you and your
52:52
colleagues at Renaissance have been an
52:53
inspiration to many many quantitative
52:56
investors many students here many
52:58
faculty myself included and the favorite
53:01
topic among quants getting together for
53:05
beer or or stronger is how do you do it
53:09
and why is it the case that even to this
53:11
day there’s nobody close to Renaissance
53:15
and so I have my own conjecture that I’d
53:18
like to run by you and and get you to
53:20
react early and my conjecture is a
53:23
little different it’s not about the
53:24
systems it’s not about any particular
53:27
magic formula or or algorithm but rather
53:32
being at a management school I guess I’m
53:35
biased I actually think it’s about the
53:37
management specifically I think it’s the
53:41
combination of the fact that you
53:44
actually ended up being a very good prop
53:47
trader first before you even thought
53:51
about the mathematics you actually
53:52
became a good trader and then with that
53:55
intuition of what it means to make money
53:58
and lose money you ended up being a good
54:01
people picker and you ended up building
54:05
around you an extraordinary team and
54:08
that team has grown based upon the
54:10
culture that you created if you just
54:12
mentioned that at the end of every year
54:14
you have these awkward conversations
54:16
with people who can adjudicate among
54:18
these very big egos except somebody the
54:21
command of the respect of anybody so do
54:24
you agree or disagree with that
54:25
characterization more or less I mean it
54:29
was it was certainly good to have done
54:32
fundamental trading to you know just
54:34
understand the mechanics of markets and
54:38
so on
54:40
of course we don’t do that people don’t
54:44
do that
54:44
and I have to say I left Renaissance
54:48
when I was 72 so that was almost nine
54:53
years ago and the management there just
54:59
carried on we had some great leaders and
55:04
we haven’t missed a beat they’ve done
55:08
just as well maybe better than they
55:10
wouldn’t have if I had stuck around but
55:13
I felt it was time for the younger
55:16
people to take over I was had started
55:21
spending more of my time with our
55:23
foundation which is a topic of next
55:27
week’s encounter and so I thought okay
55:34
what it was two people who were called
55:40
executive I don’t know I don’t remember
55:43
what that title was but they had a very
55:45
high title and gradually I’d given them
55:47
more and more responsibilities so when I
55:50
left it was it was just fine and and I
55:58
always keep pushing them to hire very
56:00
smart young people and that’s I think my
56:04
biggest contribution I’m the chair and
56:07
we meet every every month and so on but
56:10
just hiring great young people into the
56:15
incident into the business is the best
56:19
thing you can do in your tenure as chair
56:21
of Stony Brook’s math department
56:22
prepared you for that in some ways yeah
56:24
sure so I want to turn to a few
56:28
miscellaneous topics now and again feel
56:31
free to tell me that not interested in
56:34
them as early as 2003 renaissance
56:38
technologies raised concerns about the
56:40
Bernie Madoff Ponzi scheme how did you
56:45
get wind of that and what motivated you
56:49
to even say anything to the SEC we had
56:55
had money invested with Madoff for a
56:58
long time not not too firm but
57:02
relatives of mine our foundation had an
57:07
investment with with Madoff and I knew
57:13
him a little bit and he was really
57:16
amazing he kept coming up with with
57:19
these very very steady returns very
57:22
steady returns come rain or shine
57:27
so at a certain point I said this guy
57:30
has to know something that we don’t know
57:35
he certainly knew something that we
57:37
didn’t know I had all the the tickets
57:45
the what he calls the confirmations for
57:49
going back two years so I asked one of
57:52
the guys in the at Renaissance and well
57:55
in the company I was apprentice on set
57:57
to look analyze these trades that he was
58:00
going and tell me what you learn what’s
58:04
his secret so this guy went to work and
58:09
it was his conclusion well when they put
58:15
on a position they they buying something
58:18
they generally get a very good price
58:22
maybe the low of the day if they’re
58:25
buying maybe the high of the day if
58:26
they’re selling but most of the time
58:28
they’re not putting on positions they
58:31
stick with the position that accounts he
58:34
said for maybe ten percent of their
58:35
profits they claim they have t-bills
58:39
sometimes and so was an interest but 80
58:42
percent of the profits was a complete
58:44
mystery it was a complete mystery now
58:48
what they did was let’s see they would
58:55
put on a big position according to the
58:59
tickets with stocks which would the
59:06
collection of which would be
59:08
approximately the SP and then
59:13
they would buy a put or a call to
59:17
protect themselves against outside moves
59:24
well from what we understood they had a
59:28
huge amount of money under management so
59:32
you would think when they put on these
59:34
puts or calls or whatever it was it
59:38
would it would move the market actually
59:39
in those things but we could see no no
59:44
evidence of that they said they were
59:47
putting on these puts and calls but you
59:50
look at the pudding call market there
59:53
was no evidence of any such activity so
59:57
I thought well let’s let’s get out of
60:04
this thing even medallion had a little
60:07
bit invested in it it with medallion had
60:10
extra cash at that time and we had put
60:13
it with me so we sold it and and then
60:18
nothing happened
60:20
and several years went by one of my
60:25
relatives called me and said you know
60:27
you still like Madoff I said well I
60:34
can’t tell you to take your bunny out of
60:37
it because he’s been going for a long
60:39
time and he keeps on going and he’s he
60:42
must know something I don’t I said I
60:45
took my money out but I couldn’t advise
60:48
someone to take their money out it never
60:51
dawned on me that it was a Ponzi scheme
60:53
I didn’t know what the heck he was doing
60:56
but I just didn’t like the looks of it
60:59
so we couldn’t understand what he was
61:02
doing said that’s why we got out five
61:03
years later the crap hit the fan and he
61:10
was he was outed and and it was
61:16
everyone knows what happened next so and
61:20
actually they look back six years for
61:24
any profits that you’ve made have made
61:25
so we our foundation had to give back
61:29
some money to the two people who had
61:33
lost it but it was it was just the
61:40
craziest thing the craziest thing in the
61:42
world
61:43
Madoff the irony is that the fake track
61:47
record that Madoff posted was actually
61:50
not as good as the real track record the
61:51
medallion fund that’s true that’s true
61:54
well it was pretty steady I have to say
61:57
that it was a pretty pretty it was
61:59
pretty steady but it was and then the
62:03
the I don’t know the sec started
62:07
investigating us because some people had
62:11
said oh look these Renaissance people we
62:12
don’t know what they do we because of
62:14
course no one knew exactly what what
62:16
made of did and of course we didn’t tell
62:18
people what we would doing they couldn’t
62:20
see our portfolio they couldn’t see
62:22
anything by that time I think we had
62:27
already given all the money back to the
62:30
investors so I could say well look we we
62:35
can’t be doing anything wrong because
62:36
it’s all our own money we’ve already
62:40
given back all the money to the
62:42
investors but they did study us and work
62:47
us over for a while and of course they
62:51
couldn’t find anything bad and then they
62:54
went home but it was as a result of made
62:57
up that we will show examined by the SEC
63:00
right so right around that time of
course was the financial crisis and that
probably precipitated Madoff unraveling
what do you make of that natural crisis
in the aftermath you talk about 2008
yeah well
it should never have happened
it should never have happened
the there were these mortgage-backed
securities had been created they’d
always existed mortgage-backed
securities but very fancy ones were
getting created and they had all kinds
f this and that and so on and so forth
and in the old days the rating agencies
their customers were the buyers of bonds
the bond rating agencies so they wanted
to do right by their customers but at a
certain point and then you’d get a
report every week or a newsletter or
something like that but with the
internet coming along people were
sharing this who didn’t subscribe so the
rating agencies decided ok we’re not
going to charge the buyers of the bonds
we’re going to charge the sellers of the
bonds now if you think about it that’s a
conflict of interest because they really
want to get the bundt have the bonds
sold so maybe they won’t be so tough in
rating them and that’s what happened the
stuff was sold which you’d have to be a
moron and stamp triple-a and you know
people were getting mortgages no docks
you’d walk in you’d get a mortgage how
64:54
much money do you have oh I have
64:55
$100,000 and how much money do you make
64:57
oh I make $200,000 ok fine we’ll give me
65:02
this much of a mortgage well they didn’t
65:04
even ask the doc for doc for documents
65:06
in many cases or your income tax forms
65:09
or they do and why were the bank’s being
65:12
so lenient because they could sell them
65:17
to people who would package up these
65:20
these mortgages and put them there would
65:23
they ultimately end up as a
65:25
mortgage-backed security stamped
65:28
double-a triple-a so everything had just
65:34
become very wax and and Bear Sterns for
65:41
example which was a firm that we had
65:43
always had great confidence in they were
65:46
very conservative outfit they almost
65:52
went down the drain because of this
65:56
fortunately they didn’t we had money
65:58
with them and as soon as it looked like
66:01
they were going to be in trouble we
66:04
bailed out and got out three days before
66:07
they folded then we were working with
66:13
Lehman Brothers and we had a lot of
66:17
money with Lehman Brothers but this is
66:19
this is medallion and so on we had a lot
66:21
of money with Lehman Brothers and some
66:24
of our outside funds also did but it was
66:28
beginning to look not so good for them
66:30
and I called up the head of Lehman
66:35
Brothers had said you know dick we’re
66:39
gonna have to take some of our money
66:40
I’ll I’m gonna have to take half of it
66:43
out I’m uncomfortable with that much
66:47
being with you and he said ok fine so we
66:50
did that and then things were looking
66:55
worse and worse and we had a some
66:59
insight into his into their balance
67:01
sheet and we knew it was stuffed with
67:02
these a lot of the assets were these
67:05
mortgage-backed securities and I called
67:07
him and I remember I was driving and I
67:11
said dick what we’re gonna have to take
67:13
out the rest of the money and he said
67:16
all he said I thought you called me to
67:19
buy these new bonds that were issuing
67:21
their oversubscribed but for you I’ll
67:24
you know I’ll give you a piece and I
67:28
said well I I don’t want to buy your
67:32
bonds but I’ll wait a few days and see
67:34
how they sell before I take the rest of
67:38
the money huh
67:39
and a few days went by then the list of
67:43
buyers of these bonds came up and it was
67:46
the most unsophisticated group of you
67:49
know an obscure Teachers Retirement Fund
67:52
no no reputable big outfit was buying
67:57
these bonds and said I call him okay
68:01
we’re taking the rest of the money out
68:03
and that was three months I think before
68:07
Lehman Lehman collapsed so but if the
rating agencies had done their job this
would not have happened and but no one
wants to blame the rating agencies
because who’s ever heard of rating aids
I mean the newspapers want to blame the
banks right they want to blame the big
players but it was it was you know maybe
not quite as simple as I’m saying but it
was a mortgage-backed collapse and these
bonds were rated improperly that’s what
happened so let me now since we’re
getting a short on time I want to make
sure there’s plenty of time for audience
68:52
questions so maybe we can open it up and
68:55
and while we’re looking for our
68:57
questions raise your hand and then Kelly
69:00
and Italy will pass a mic to you while
69:03
we’re getting our first question maybe I
69:06
was the first one with the white shirt
69:08
of the hold of his hand so yeah
69:14
hi I’m Myles I’m a junior at Harvard I
69:17
was wanting to ask so over the years
69:19
obviously the the general markets have
69:21
changed with the advent of more computer
69:23
technology has that shifted your view on
69:25
fundamental versus quantitative and
69:27
investing I mean early you seemed to
69:29
kind of point to the fact that at the
69:30
end of the day fundamental investing is
69:32
very wishy-washy and based on intuition
69:33
do you think that that is always true or
69:36
do you think there are people that truly
69:37
have an advantage in in fundamental
69:39
investing that people have in doing fun
69:43
of is it possible to do yes I mean
69:45
obviously Renaissance is is quantitative
69:47
but are you always Pro quant over
69:50
fundamental or do you think
69:52
scream for fun knowing that he’s had a
69:55
great career I know I don’t think he has
69:58
a computer on the premises except maybe
70:01
to count his money but no very it’s a
70:08
perfectly legitimate way to invest then
70:13
I guess what are the skill sets that
70:14
differentiates a good fundamental
70:15
investor from a good quantitative
70:16
investor say it again what what are the
70:18
different skill sets that separate a
70:20
good fundamental from a good
70:21
quantitative investor oh I think it’s a
70:24
it’s a world of difference I think a
70:27
good fundamental investor let’s say in a
70:30
company he wants to evaluate the
70:33
management have a sense of the human
70:35
beings that are running this thing he
70:38
wants to have a sense of where the
70:40
market might be going and it’s you know
70:47
it’s a set of skills and some people of
70:50
a very good habit
70:54
quantitative stuff is a it’s a different
70:56
set of skills and which suited me and so
71:04
does that answer your question
71:11
mr. Simon some ask wants with
71:13
increasingly powerful tools seek out
71:16
inefficiencies and the markets to
71:18
exploit and we keep exploiting them
71:21
until there’s nothing left to exploit
71:23
that’ll overcome transaction costs are
71:25
we destined to slowly drive ourselves
71:28
out of business and also how long as we
71:31
weak wants or weak watch as we keep
71:34
seeking the inefficiencies to exploit
71:36
and thereby diminishing them that’s a
71:39
good question
71:40
yes inefficiency do eventually get
71:44
traded out if they’re discovered but the
71:48
market is not static it’s dynamic things
71:54
change and therefore this room I think
71:57
for new inefficiencies to materialize
72:01
and so
72:04
I think it’s never gonna be you know all
72:08
inefficiencies are out of it there’s
72:10
nothing to just discover on the other
72:14
hand you know so far we’ve managed to
72:18
you know our returns have been more or
72:22
less stable for a long time so but we
72:26
keep finding new things and throwing out
72:29
things that are no longer working to new
72:33
things emerge as quants are looking for
72:36
new things so the quants are exploiting
72:38
other quants I have no idea okay well
72:44
she’s giving no hi what’s your favorite
72:49
algorithm what’s my favorite algorithm
72:54
I’ll tell you my favorite algorithm my
73:02
favorite algorithm is something that I
73:05
worked out when I was at the Institute
73:07
for Defense analyses and it has to do
73:11
with it has to do with solving a certain
73:18
classical problem in the field and I
73:22
solved that but it’s classified it is I
73:29
solved this problem and they made a
73:30
special purpose machine at NSA and I
73:34
heard that thirty years later it was
73:37
still they were still using this special
73:38
purpose machine to implement this this
73:41
algorithm so that’s that’s my favorite
73:43
algorithm and it’s classified so there’s
73:48
a guy right there well hi you’ll get
73:54
your turn hi right here I was wondering
74:00
what I was wondering what you did over
74:03
time to kind of protect your
74:05
intellectual capital you had a lot of
74:08
people working for you how did you keep
74:10
everybody rowing in the same direction
74:13
and how did you protect kind of this
74:15
special yeah the question it’s a good
74:18
question
74:18
well everyone signs forever non-compete
74:24
no not a pro man on computer for now
74:27
forever non-disclosure and after you’ve
74:32
been there a couple years there’s a
74:34
non-compete agreement that you’re
74:36
invited to sign and pretty much everyone
74:39
does because there’s a lot of money
74:41
that’s out of your bonus a certain
74:44
amount is held back for a while and then
74:49
invested in medallion actually and then
74:52
you get it all the time but so there’s
74:55
always you always have a lot of money on
74:58
the table which you’ve not yet gotten
75:01
received which keeps people from running
75:04
off we’ve only had one incident a couple
75:08
of Russian guys left and stole some of
75:14
our secrets and well we had a lawsuit
75:19
against them and so on and so forth
75:22
and and well that they’re not in
75:27
business anymore and the system but they
75:30
had made off with this now pretty
75:32
antiquated so we’re not worried about
75:35
that but it’s a very good question but
75:39
the main reason people don’t want to
75:42
leave it’s it’s a very nice atmosphere
75:43
it’s it’s fun to work there people get
75:46
paid a lot of money there’s no doubt
75:49
about that and it’s fun so we we’ve had
75:56
people retire and but they’ve put the
75:59
exception of those two Russians they’ve
76:00
never gone into any investment business
76:03
they’ve just retired and I don’t know
76:06
done this and that one guy went up to
76:09
the Broad Institute and became a
76:14
terrific scientist genetic scientist
76:17
working for bird so I think if
76:23
turnover is very important in any
76:26
company and if a company has a great
76:28
deal of turnover there’s something
76:30
there’s something wrong and and you know
76:34
with something right if turnover is very
76:39
well of course one thing that could be
76:40
right is you’re paying too much
76:44
but-but-but it’s good to have low
76:46
turnover and that’s what Renaissance has
76:50
[Music]
76:51
at the beginning of your talk you
76:54
mentioned that you wanted to understand
76:56
how the system that has presented you
76:58
you want to understand how it works and
77:01
at another point you also mentioned that
77:03
once you have the system it’s all about
77:05
like making it better and better so my
77:08
question is the balance between those
77:10
two because as you try to make your
77:12
system better and better there is the
77:14
risk of making it more complex to a
77:18
point that you don’t understand it
77:20
anymore how do you balance improving
77:22
your model and keeping it simple enough
77:24
to understand the tech job well that’s a
77:30
good question
77:32
it’s completely understandable because
77:37
you can understand that if you wanted to
77:43
spend a week doing nothing but
77:46
understanding the system it’s all
77:48
written down and so it’s perfectly
77:51
understandable
77:52
there are a lot of you know predictive
77:56
signals there’s a lot of stuff going on
77:58
it is very complicated but it’s not not
78:03
understandable so we understand it
78:11
I’m a Jedi I work with grace dan I’m
78:15
sorry yes
78:16
so you mentioned continuous improvements
78:19
of the systems I wonder is medallion of
78:24
today the core of it at least similar to
78:27
what it was ten or 20 years ago or has
78:30
medallions reinvented itself over that
78:33
time to be completely different things
78:35
you know I didn’t my ears not so good
78:37
can you understand what he asked yeah so
78:39
is the medallion of today pretty much
78:42
the same as it was ten or twenty years
78:44
ago or has it reinvented itself
78:46
oh it’s continuously reinventing itself
78:48
I think there are some parts of it that
78:51
would probably have been there for ten
78:53
years or maybe even 20 years but that’s
78:57
less and less as as new as new things
79:03
come along so like I said you just have
79:09
to keep you just have to keep running
79:11
people will discover some of the things
79:13
that you’ve discovered then they’ll get
79:15
traded out so you have to keep coming up
79:19
with with more and more things and we
79:23
have a great computer system and and you
79:29
know and great scientists very good ones
79:33
so that’s the answer
79:37
hi Jim so you mentioned during the
79:42
beginning phases of the medallion there
79:44
was a short period of time when you guys
79:46
weren’t doing so well I’d like to ask to
79:49
you any point in time did you doubt
79:51
yourself and if so how did you will
79:54
yourself to continue and eventually
79:56
succeed well in that period
80:02
well we shut it down I wasn’t certain
80:08
but I did feel that we could improve it
80:13
to the point where we were happy to
80:16
continue trading so and
80:22
I’ve never doubted that things would
80:27
keep working reasonably well I think you
80:34
know we’ve been lucky to a certain
80:36
extent you know luck has placed quite a
80:39
role in life and a lot of people don’t
80:43
you know the guy’s business fails he
80:50
says was bad luck
80:52
if a guy’s business succeeds he says oh
80:54
you know I’m a hard worker and naturally
80:56
it succeeded but there’s luck everyone
81:01
so so far we’ve been pretty lucky but I
81:06
haven’t been I haven’t been very worried
81:11
you know there are times when when a
81:13
month goes by we don’t make money one
81:15
month there it’s very rare we don’t make
81:19
money in a month but once in a while
81:20
that happens but it’s always come back
81:28
okay there’s a woman right there I’m
81:35
looking at you who didn’t you didn’t you
81:38
raise your hand all right well we’ll see
81:44
if we’d get you you’re big on behavioral
81:50
finance and dr. Simmons it seems like
81:54
when you talk about your past you talk
81:56
about your gut instincts and you kind of
81:59
just pass it off as you know I felt this
82:02
way but I was wondering if you had more
82:04
I don’t know if your internal compass is
82:07
a little bit better than most in guiding
82:10
you through tough decisions like the
82:11
last person asked again I couldn’t
82:13
understand true the acoustics in this
82:15
room are not great yeah I think that the
82:18
she was asking about the role of human
82:21
behavior in quantitative investing that
82:24
the fact is that you do have some kind
82:26
of a gut instinct about when a system is
82:28
working or underperforming what role
82:31
does that play this intuition and
82:33
judgment
82:34
thinking about these strategies well I
82:37
mean if you see something that’s that’s
82:38
steadily losing you don’t that does not
82:41
take into ition to determine that
82:45
something is wrong and you ought to stop
82:47
doing that but well intuition you know
82:55
scientists some scientists have pretty
82:59
good intuition scientific intuition how
83:03
does that happen in math you might say
83:06
hey this this operation worked over
83:10
there maybe it’ll work over here I’ll
83:14
give it a try so as people come into the
83:18
firm they learn what has worked and
83:22
sometimes they say oh oh if we perturb
83:25
that a little bit it could work even
83:27
better or stuff like that some people
83:31
you know have better scientific
83:33
intuition but I think it’s scientific
83:35
intuition it’s not market intuition that
83:38
the the guys who work there are usually
83:45
so we’re just about out of time I saw I
83:49
want to have it one last question and
83:50
then we’re gonna wrap up with it would
83:53
you say that fundamental approach in
83:56
your investment modeling is primarily
83:58
inductive reasoning based or deductive
84:02
by nature in other words data-driven to
84:05
come up with your models or more logic
84:07
driven to come up with your models well
84:10
we certainly are logical it’s hard to
84:14
work without logic
84:21
there’s a lot of data sometimes one
84:27
might come up with a number of things
84:32
and just try them all out and see in
84:35
front of one of them works now the
84:38
danger of that is if you try and up
84:41
things something’s gonna work but you
84:43
have to be sure but the statistics are
84:47
still in your favor if there was so much
84:51
data that even though we tried a million
84:53
things and one of them worked the
84:57
probability of that was very very slim
85:00
and therefore you were probably okay so
85:03
you know we do try try a bunch of stuff
85:07
and I don’t know if that answers your
85:10
question but okay so so Jim and wrapping
85:14
up I’m gonna ask you two quick questions
85:16
it related one is that you moved from
85:20
Stony Brook to doing trading because you
85:23
were working on a problem that you were
85:25
struggling with and to this day it’s
85:27
still unsolved you went back to working
85:28
on it it’s a tough problem I imagine did
85:31
you encounter any unsolved finance
85:34
problems that that you think about and
85:37
struggle with unsolved finance problems
85:41
well it doesn’t seem like they’re given
85:44
the track record of medallion well I
85:47
think there’s a lot of people who will
85:48
worry about how they’re gonna pay their
85:50
rent which is perhaps an unsolved
85:53
problem as far as they’re concerned yeah
85:55
I don’t know what an unsolved financial
86:00
problem really means but okay have you
86:07
had any financial problems uh uh a whole
86:10
bunch I would love to get access to the
86:14
Renaissance research staff to have them
86:16
working with us on it well but the the
86:18
last question is for all of the future
86:21
quants in the audience any advice about
86:25
how they ought approach this field and
86:28
career I think any potential coin should
86:30
just not get into the business we don’t
86:32
need to have a
86:33
a whole lot of people in this business
86:38
well on behalf of what advice could I
86:47
give you it’s just you know work hard
86:50
hire good people and it’s not easy to
86:54
get into the business business you need
86:57
big databases and a lot of computers and
87:00
stuff like that to to even startup but
87:04
you know if you have an idea you can
87:08
test it out and think it’s good you know
87:13
more power to you I could say well Jim
87:17
on behalf of all of us here at MIT we
87:20
want to thank you so much for sharing
87:21
your wisdom with us and I I think that
87:24
your career in finance is just
87:27
extraordinary and it’s been an
87:29
incredible inspiration to many many
87:30
people and will continue to be an
87:32
inspiration but what I want to tell
87:34
everybody is what might be even more
87:37
inspiring is what you can talk about
87:38
next week this not only have you made
87:40
tens of billions of dollars for
87:42
investors and billions for yourself but
87:44
you’ve also given away a tremendous
87:46
amount of money for philanthropic
87:49
purposes and we’re going to hear about
87:51
that next Wednesday so I urge all of you
87:52
to come back and hear the the third and
87:55
the three MS of money
87:59
mathematics money and making a
88:01
difference so thank you very much
88:02
[Applause]