1
00:00:02,560 --> 00:00:11,920
Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. This is Masters in

2
00:00:12,000 --> 00:00:16,200
Speaker 1: Business with Barry Ritholt on Bloomberg Radio.

3
00:00:16,600 --> 00:00:20,840
Speaker 2: This week on the podcast, yet another extra special guest.

4
00:00:21,320 --> 00:00:25,680
Speaker 2: John Philip Buschow is chief Scientist, head of research, chairman

5
00:00:25,760 --> 00:00:31,080
Speaker 2: and co founder at CFM. They're a quantitative trend following

6
00:00:31,280 --> 00:00:35,120
Speaker 2: Hedge Fund. They run over twenty billion dollars in client money.

7
00:00:35,159 --> 00:00:38,280
Speaker 2: They've been around for almost thirty five years, put together

8
00:00:38,320 --> 00:00:42,280
Speaker 2: a very impressive track record. They also run a number

9
00:00:42,360 --> 00:00:48,480
Speaker 2: of interesting academic research labs and things like that. Jean

10
00:00:48,560 --> 00:00:53,080
Speaker 2: Philippe has published something like three hundred plus academic papers.

11
00:00:53,479 --> 00:00:56,800
Speaker 2: They are deep into all the things that drive markets

12
00:00:56,800 --> 00:01:00,880
Speaker 2: from a quantitative perspective. I thought this conversation was fascinating

13
00:01:00,880 --> 00:01:03,520
Speaker 2: and I think you will also with no further ado,

14
00:01:04,240 --> 00:01:09,960
Speaker 2: My interview of CFMS Jean Philippe the show. So what

15
00:01:09,959 --> 00:01:13,000
Speaker 2: do people call you? JP? John Phillip Philippe, What what

16
00:01:13,000 --> 00:01:13,200
Speaker 2: do you?

17
00:01:13,600 --> 00:01:16,919
Speaker 3: Philippe? In France JP and Anglo Saxon Countries JP.

18
00:01:17,120 --> 00:01:19,959
Speaker 2: All right, it seems a little informal, but I'll go

19
00:01:20,000 --> 00:01:24,240
Speaker 2: with JP. So, so JP, let's start with your background

20
00:01:25,000 --> 00:01:29,360
Speaker 2: PhD and theoretical physics from NS. You spent some years

21
00:01:29,400 --> 00:01:34,680
Speaker 2: at very prestigious research in the institutions I mentioned Cavendish Labs.

22
00:01:35,400 --> 00:01:37,000
Speaker 2: What was the original career plan.

23
00:01:38,080 --> 00:01:40,440
Speaker 3: Yeah, I was planning to be a physicist, but then,

24
00:01:40,640 --> 00:01:43,399
Speaker 3: you know, studying statistical physics, and we can go into

25
00:01:43,400 --> 00:01:47,000
Speaker 3: that later if you wish. I realized that physics can

26
00:01:47,040 --> 00:01:51,120
Speaker 3: offer much more than studying physics, and and then I,

27
00:01:51,520 --> 00:01:56,600
Speaker 3: you know, was always fascinating, fascinated by numbers. I've always

28
00:01:57,000 --> 00:02:03,600
Speaker 3: liked statistics and financial markets spit statistics every day, and

29
00:02:03,680 --> 00:02:07,240
Speaker 3: I thought, you know, this is a very interesting complex system.

30
00:02:07,480 --> 00:02:12,640
Speaker 3: There are crisis, crashes, jumps, this system seems to be

31
00:02:12,760 --> 00:02:16,480
Speaker 3: driven by its own dynamics. Physicists have to do something

32
00:02:16,520 --> 00:02:18,640
Speaker 3: about this, and so.

33
00:02:17,440 --> 00:02:20,040
Speaker 2: The sounds very similar to chaos theory.

34
00:02:20,120 --> 00:02:22,880
Speaker 3: Yeah, exactly, mean that was the high days of chaos theory.

35
00:02:23,720 --> 00:02:27,520
Speaker 2: So I pulled some phrases from some of your papers.

36
00:02:29,040 --> 00:02:32,480
Speaker 2: One was titled, and I'm going to mangle this disordered

37
00:02:32,520 --> 00:02:37,160
Speaker 2: systems and complex phenomena which can be either physics or finance.

38
00:02:37,240 --> 00:02:40,600
Speaker 2: That sounds like, but what are the dynamics of glassy

39
00:02:40,680 --> 00:02:42,400
Speaker 2: systems and granular media?

40
00:02:42,840 --> 00:02:48,560
Speaker 4: That sounds fascinating, Yeah, but you know it's all the

41
00:02:49,320 --> 00:02:55,160
Speaker 4: problem is how to interacting elements give rise to something surprising.

42
00:02:55,960 --> 00:03:00,000
Speaker 3: You know. Granuar matter is grains that you know, interract

43
00:03:00,040 --> 00:03:02,800
Speaker 3: with one another, and then you have these strange phenomena

44
00:03:02,880 --> 00:03:06,400
Speaker 3: called avalanches where you drop a grain on the slope

45
00:03:06,800 --> 00:03:09,800
Speaker 3: and most of the time nothing happens, but sometimes there's

46
00:03:09,840 --> 00:03:13,680
Speaker 3: a big landslide that takes all the grains down. And

47
00:03:13,720 --> 00:03:18,160
Speaker 3: so this again is very reminiscent of financial markets, right,

48
00:03:18,200 --> 00:03:22,040
Speaker 3: I mean, many things happen, nothing much follows, and then

49
00:03:22,080 --> 00:03:25,200
Speaker 3: sometimes there's a crash, right, And so this was really

50
00:03:25,240 --> 00:03:27,000
Speaker 3: intriguing for physicists like me.

51
00:03:27,360 --> 00:03:30,519
Speaker 2: So I'm as you're talking, I'm just thinking of a

52
00:03:30,760 --> 00:03:35,600
Speaker 2: concept in physics that really applies to markets, the three

53
00:03:35,640 --> 00:03:42,080
Speaker 2: body problem. When you have those three gravitational masses interacting

54
00:03:42,120 --> 00:03:47,360
Speaker 2: with each other, it's fairly unpredictable, which kind of seems

55
00:03:47,440 --> 00:03:48,920
Speaker 2: like markets themselves.

56
00:03:49,200 --> 00:03:52,119
Speaker 3: Yeah, I mean, there's there are two ways to be unpredictable.

57
00:03:52,200 --> 00:03:55,680
Speaker 3: One is that the system is by itself unpredictable, that

58
00:03:55,760 --> 00:03:59,600
Speaker 3: even with deterministics laws like the three body problem, you

59
00:03:59,640 --> 00:04:03,400
Speaker 3: can't see much after a few seconds, days or weeks.

60
00:04:04,200 --> 00:04:07,880
Speaker 3: But there are other kinds of unpredictability. When there's a

61
00:04:07,920 --> 00:04:12,080
Speaker 3: true source of exognous noise that hits the system and

62
00:04:12,120 --> 00:04:15,560
Speaker 3: you can't say anything. So that's the traditional way economists

63
00:04:15,600 --> 00:04:18,440
Speaker 3: think about markets. They are kind of buffeted by things

64
00:04:18,480 --> 00:04:21,760
Speaker 3: you can't predict because they come from outside. And then

65
00:04:21,960 --> 00:04:24,880
Speaker 3: I think the physics hunch is that, hey, but there

66
00:04:24,920 --> 00:04:30,120
Speaker 3: can be self generated shocks, self generated randomness that come

67
00:04:30,240 --> 00:04:36,000
Speaker 3: from large assemblies of individuals, i e. Traders, you know,

68
00:04:36,080 --> 00:04:38,880
Speaker 3: agents that trade and buy and sell to each other,

69
00:04:39,440 --> 00:04:45,080
Speaker 3: and this can generate you know, intrinsic randomness that is

70
00:04:45,120 --> 00:04:47,919
Speaker 3: not of the same kind as the three body problem,

71
00:04:48,320 --> 00:04:51,320
Speaker 3: but really comes from the interaction of a huge number

72
00:04:51,480 --> 00:04:53,039
Speaker 3: of elements.

73
00:04:53,520 --> 00:04:58,880
Speaker 2: So I see the parallels between theoretical physics and finance.

74
00:04:59,520 --> 00:05:02,719
Speaker 2: What I led you to begin shifting in the early

75
00:05:02,839 --> 00:05:10,120
Speaker 2: nineties from studying theoretical physics to becoming fascinated by market

76
00:05:10,160 --> 00:05:12,680
Speaker 2: microstructure and econophysics.

77
00:05:13,240 --> 00:05:17,000
Speaker 3: Yeah, so, as I said initially, I've always been excited

78
00:05:17,080 --> 00:05:20,320
Speaker 3: by data and trying to make sense of data. So

79
00:05:20,839 --> 00:05:25,000
Speaker 3: there was something there anyway, But what really drove the

80
00:05:25,160 --> 00:05:29,839
Speaker 3: transition was, in a sense, the nineteen eighty seven crash

81
00:05:30,360 --> 00:05:33,800
Speaker 3: and the Black Shoal theory. So I didn't know anything

82
00:05:33,800 --> 00:05:37,560
Speaker 3: about that, and then I wrote a paper on what

83
00:05:37,640 --> 00:05:42,520
Speaker 3: I was working on, physics systems with large jumps, if

84
00:05:42,520 --> 00:05:45,520
Speaker 3: you want large crashes that happened from time to time.

85
00:05:47,040 --> 00:05:51,359
Speaker 3: And someone who was working in the banking industry called

86
00:05:51,360 --> 00:05:54,320
Speaker 3: me and said, hey, it's really interesting because it resembles

87
00:05:54,400 --> 00:05:57,800
Speaker 3: what happens in finance, and in particular what just happened

88
00:05:57,800 --> 00:06:00,440
Speaker 3: the nineteen eighty seven crash. And there's this theory, the

89
00:06:00,440 --> 00:06:04,440
Speaker 3: black steals theory, that is a theory that only works

90
00:06:04,680 --> 00:06:08,000
Speaker 3: in a world where there are no crashes, where you know,

91
00:06:08,080 --> 00:06:12,479
Speaker 3: all the motions are small and predict they're random, but

92
00:06:12,520 --> 00:06:15,280
Speaker 3: they're kind of predictable even if they're random in some

93
00:06:15,360 --> 00:06:18,240
Speaker 3: strange way. And I thought this is really weird. And

94
00:06:18,520 --> 00:06:21,440
Speaker 3: this guy said, why don't you try to generalize black

95
00:06:21,440 --> 00:06:24,359
Speaker 3: seals to a world where where there are crashes? And

96
00:06:24,440 --> 00:06:27,080
Speaker 3: I thought, well, that's that's really interesting. So I read

97
00:06:27,120 --> 00:06:29,440
Speaker 3: black Shoals and I thought it can't be right. They

98
00:06:29,480 --> 00:06:32,279
Speaker 3: must be wrong, these guys. So I kind of redid

99
00:06:32,320 --> 00:06:36,640
Speaker 3: everything myself and found something that looked more interesting than

100
00:06:36,640 --> 00:06:40,600
Speaker 3: baccheors because it could be extended to non Gaussian statistics,

101
00:06:40,640 --> 00:06:44,279
Speaker 3: as they are called non normal distribution Bell curves and

102
00:06:44,320 --> 00:06:47,599
Speaker 3: so on, and so it looked to me interesting and

103
00:06:47,640 --> 00:06:51,200
Speaker 3: I thought, okay, maybe we can do software out of

104
00:06:51,240 --> 00:06:55,960
Speaker 3: that and commercialize it. And so I went and knocked

105
00:06:55,960 --> 00:06:59,800
Speaker 3: on several doors and suddenly the door of Jean Pillard opened,

106
00:06:59,800 --> 00:07:05,680
Speaker 3: and was someone who had founded actually CFM in ninety one,

107
00:07:05,839 --> 00:07:09,920
Speaker 3: that was ninety four. And I started, you know, explaining

108
00:07:09,960 --> 00:07:12,440
Speaker 3: what I had been doing and that I was interested

109
00:07:12,480 --> 00:07:15,800
Speaker 3: in transferring ideas from physics to finance, and he said,

110
00:07:15,880 --> 00:07:18,720
Speaker 3: why don't we create something together, and so at the

111
00:07:18,720 --> 00:07:22,080
Speaker 3: time we created a company called Science and Finance and

112
00:07:22,160 --> 00:07:25,600
Speaker 3: this was done, you know, in in two weeks. It

113
00:07:25,720 --> 00:07:30,880
Speaker 3: was like amazing the way we met, and there was

114
00:07:30,920 --> 00:07:35,080
Speaker 3: a fluid that was flowing between us immediately. And so

115
00:07:36,360 --> 00:07:40,440
Speaker 3: CFM then merged with Science and Finance in nineteen ninety

116
00:07:40,520 --> 00:07:43,560
Speaker 3: so it's now the same firm. But the idea he

117
00:07:43,680 --> 00:07:47,280
Speaker 3: had at the time, he had this this small CTA

118
00:07:48,320 --> 00:07:50,920
Speaker 3: trading firm, and he thought, I need to beef up

119
00:07:50,960 --> 00:07:53,640
Speaker 3: my research team, and this guy seems to be interesting.

120
00:07:53,760 --> 00:07:57,280
Speaker 3: So we just partnered and that's how it all started.

121
00:07:57,600 --> 00:08:01,880
Speaker 2: And that CTA firm specialized in manage future Yeah, exactly much.

122
00:08:02,280 --> 00:08:05,080
Speaker 2: Now I know most of the future straders. I know,

123
00:08:05,640 --> 00:08:08,760
Speaker 2: they all seem to be trend followers. How do you

124
00:08:08,800 --> 00:08:14,320
Speaker 2: think about applying quantitative research and theoretical physics to dealing

125
00:08:14,360 --> 00:08:15,200
Speaker 2: with futures?

126
00:08:15,720 --> 00:08:18,559
Speaker 3: Yeah, well that that was exactly Jean Pierre Haguilar's idea.

127
00:08:19,560 --> 00:08:22,600
Speaker 3: He said, Okay, I'm doing trend following is good, but

128
00:08:22,680 --> 00:08:25,200
Speaker 3: it's not you know, rocket science. Maybe we can do

129
00:08:25,320 --> 00:08:28,440
Speaker 3: much better. And so he said, why don't we work

130
00:08:28,480 --> 00:08:32,720
Speaker 3: on something more beefy than just trend following, And so

131
00:08:33,440 --> 00:08:35,440
Speaker 3: you know that that started the whole thing. And the

132
00:08:35,480 --> 00:08:39,199
Speaker 3: idea of the main idea is data. You know, physicists

133
00:08:39,240 --> 00:08:42,800
Speaker 3: are good at looking at data and extracting structures, you know,

134
00:08:42,880 --> 00:08:46,640
Speaker 3: looking at data and imagining that from that data you

135
00:08:46,679 --> 00:08:50,480
Speaker 3: can build theories. You can identify what's important and what's not.

136
00:08:51,320 --> 00:08:54,439
Speaker 3: And that's I think the way you know it all

137
00:08:54,520 --> 00:08:58,160
Speaker 3: works in physics that you you you scrutinize data and

138
00:08:58,200 --> 00:09:00,920
Speaker 3: then there's a flash and you think I can model that.

139
00:09:01,600 --> 00:09:04,120
Speaker 3: And it's really the same process in finance, at least

140
00:09:04,559 --> 00:09:07,800
Speaker 3: as far as we wear concern, and we are concerned now,

141
00:09:08,000 --> 00:09:10,920
Speaker 3: it hasn't changed. It's the same process. Huh.

142
00:09:11,040 --> 00:09:15,960
Speaker 2: Really really quite fascinating you keep your professorships at NS

143
00:09:16,280 --> 00:09:20,079
Speaker 2: and you've maintained a foot in academia even as you're

144
00:09:20,080 --> 00:09:23,760
Speaker 2: building and running an asset management firm. Tell us about that,

145
00:09:23,800 --> 00:09:27,080
Speaker 2: you're still publishing papers. What keeps you interested in the

146
00:09:27,120 --> 00:09:28,920
Speaker 2: academic side of finance.

147
00:09:29,000 --> 00:09:31,679
Speaker 3: Well, first of all, it is me. You know, I

148
00:09:32,000 --> 00:09:36,640
Speaker 3: feel I'm a researcher's researcher at heart, and I need

149
00:09:36,679 --> 00:09:40,120
Speaker 3: to continue. It's like you know, people running the marathon.

150
00:09:40,280 --> 00:09:43,080
Speaker 3: They are doing something else in life and then there's

151
00:09:43,120 --> 00:09:45,760
Speaker 3: an urge to run the marathon. For me, there's an

152
00:09:45,880 --> 00:09:50,839
Speaker 3: urge to understand what I'm doing, and understand also things

153
00:09:50,880 --> 00:09:54,480
Speaker 3: that I'm not doing even now, you know, even physics problems,

154
00:09:54,520 --> 00:09:57,800
Speaker 3: I can get excited about them, or trying new things

155
00:09:57,920 --> 00:10:02,880
Speaker 3: like the mL revolution and how does mL work? Why

156
00:10:03,400 --> 00:10:08,319
Speaker 3: do large language models work so well? Learn so well?

157
00:10:08,679 --> 00:10:11,800
Speaker 3: I think it's fascinating. I want to understand. But there's

158
00:10:11,840 --> 00:10:15,840
Speaker 3: another reason for doing this is that to attract talents,

159
00:10:16,440 --> 00:10:19,920
Speaker 3: you need to identify them, you need to attract them,

160
00:10:20,360 --> 00:10:23,760
Speaker 3: you need to be their professor at one point. And

161
00:10:23,800 --> 00:10:26,480
Speaker 3: I think a lot of the success of CFM has

162
00:10:26,559 --> 00:10:31,240
Speaker 3: been attracting talents, and I think part of that only

163
00:10:31,320 --> 00:10:33,880
Speaker 3: part of that. Of course, it's a teamwork is due

164
00:10:33,920 --> 00:10:36,880
Speaker 3: to the fact that I'm still very connected in academic

165
00:10:36,920 --> 00:10:42,319
Speaker 3: circles and young students. They've listened to me giving talks, lecturing,

166
00:10:42,920 --> 00:10:46,920
Speaker 3: They've read my papers, and so they feel let's go

167
00:10:47,000 --> 00:10:49,000
Speaker 3: and work for that firm because it seems that they're

168
00:10:49,000 --> 00:10:50,280
Speaker 3: really doing cool stuff.

169
00:10:50,600 --> 00:10:54,040
Speaker 2: So is that the thinking behind establishing the research division

170
00:10:54,080 --> 00:10:57,080
Speaker 2: at CFM. I mean, you run that as a full

171
00:10:57,880 --> 00:11:01,480
Speaker 2: academic research department as oppose to you know, a lot

172
00:11:01,480 --> 00:11:06,120
Speaker 2: of asset management shops. They have a couple of cfps

173
00:11:06,120 --> 00:11:10,160
Speaker 2: and MBAs and their CFAs and they're working on their

174
00:11:10,240 --> 00:11:13,360
Speaker 2: quantitative models. You guys seem like you've taken it to

175
00:11:13,400 --> 00:11:14,720
Speaker 2: a whole different level.

176
00:11:14,920 --> 00:11:15,160
Speaker 5: Yeah.

177
00:11:15,280 --> 00:11:19,720
Speaker 3: I mean most, maybe even all, our researchers have a PhD.

178
00:11:21,080 --> 00:11:24,040
Speaker 3: It doesn't mean that we're an academic lab. We're really

179
00:11:24,120 --> 00:11:27,120
Speaker 3: working on concrete stuff. We're really there to, you know,

180
00:11:27,200 --> 00:11:31,520
Speaker 3: make models that work, build portfolios that are robust, model risk,

181
00:11:32,200 --> 00:11:36,480
Speaker 3: model execution, control costs. All these things are bread and

182
00:11:36,520 --> 00:11:39,920
Speaker 3: butter for everyday work. But at the same time, we

183
00:11:40,120 --> 00:11:43,120
Speaker 3: feel that when we find something that is beyond the

184
00:11:43,240 --> 00:11:46,960
Speaker 3: kind of daily work and that can be published because

185
00:11:46,960 --> 00:11:49,240
Speaker 3: it brings something to the academic debate or to do

186
00:11:49,320 --> 00:11:52,640
Speaker 3: public debate. You know, why how do markets work? Why

187
00:11:53,080 --> 00:11:58,319
Speaker 3: are there crashes? Are market sufficients? What about the economy?

188
00:11:58,440 --> 00:12:02,240
Speaker 3: Do people understand invation? We need new theories to understand inflation,

189
00:12:03,400 --> 00:12:08,000
Speaker 3: monetary policy and all these things. We believe it's our role.

190
00:12:08,480 --> 00:12:11,280
Speaker 3: Also because we have access to so much data, and

191
00:12:11,320 --> 00:12:14,040
Speaker 3: we're privileged, you know, academics they don't have access to

192
00:12:14,120 --> 00:12:16,959
Speaker 3: so much data, and so we have to give back

193
00:12:17,120 --> 00:12:20,760
Speaker 3: in a way. And the reason we're doing this, as

194
00:12:20,800 --> 00:12:23,000
Speaker 3: I said, it's not only because we're driven to do that,

195
00:12:23,559 --> 00:12:28,800
Speaker 3: but also because it creates an atmosphere where people are

196
00:12:28,880 --> 00:12:31,679
Speaker 3: happy to work at CFM. I hope you know, I

197
00:12:31,720 --> 00:12:33,880
Speaker 3: don't want to put words in their mouth.

198
00:12:34,120 --> 00:12:37,240
Speaker 2: Well, you guys, open up a big, expanded, a big

199
00:12:37,280 --> 00:12:39,840
Speaker 2: New York officer. You don't seem to be having much

200
00:12:39,880 --> 00:12:43,120
Speaker 2: difficulty recruiting people there. What's the head count there? Now?

201
00:12:43,559 --> 00:12:47,640
Speaker 3: We have one hundred and fifteen researchers and fifteen percent

202
00:12:47,880 --> 00:12:49,080
Speaker 3: of them are in New York.

203
00:12:50,559 --> 00:12:54,199
Speaker 2: What motivated expanding the New York office as much as

204
00:12:54,200 --> 00:12:54,480
Speaker 2: you have?

205
00:12:54,800 --> 00:12:57,319
Speaker 3: Well, first of all, a lot of our investors are

206
00:12:57,320 --> 00:13:00,600
Speaker 3: in the US, so we need to be there interact

207
00:13:00,640 --> 00:13:05,400
Speaker 3: with them, and we need to have a presence, if

208
00:13:05,480 --> 00:13:09,240
Speaker 3: only for investor relation, but also because there's a lot

209
00:13:09,280 --> 00:13:11,480
Speaker 3: of talents in the US that we want to grab

210
00:13:12,520 --> 00:13:15,800
Speaker 3: and attract. There's a lot of data, a lot of brokers,

211
00:13:15,840 --> 00:13:17,720
Speaker 3: so it makes a lot of sense. So we've been

212
00:13:17,720 --> 00:13:22,720
Speaker 3: in New York for twenty years, and you know, it's

213
00:13:22,800 --> 00:13:25,120
Speaker 3: obvious that you know it is a hub and we

214
00:13:25,160 --> 00:13:26,160
Speaker 3: should expand there.

215
00:13:26,640 --> 00:13:30,439
Speaker 2: So you mentioned earlier your co founder Jean Pierre Aguilar.

216
00:13:31,360 --> 00:13:34,920
Speaker 2: He passed away in two thousand and nine. What was

217
00:13:34,960 --> 00:13:38,320
Speaker 2: the impact on the firm. How did you guys manage

218
00:13:38,640 --> 00:13:41,040
Speaker 2: around That's a big loss when you lose a founder.

219
00:13:41,200 --> 00:13:43,280
Speaker 3: Yeah, it was a tragedy because he died in a

220
00:13:43,840 --> 00:13:46,680
Speaker 3: glider accident. You know, we knew that he was gliding.

221
00:13:46,880 --> 00:13:51,360
Speaker 3: We knew that gliding was dangerous, but in a sense,

222
00:13:51,440 --> 00:13:55,200
Speaker 3: and it really means bad risk management, right, we never

223
00:13:55,320 --> 00:13:59,199
Speaker 3: thought that he could crash. It never occurred to us,

224
00:13:59,240 --> 00:14:03,160
Speaker 3: which was straight because these things happen, and so it

225
00:14:03,200 --> 00:14:06,360
Speaker 3: was tragic because we were not prepared. And it was

226
00:14:06,400 --> 00:14:09,600
Speaker 3: tragic because he was not only a friend, but he

227
00:14:09,800 --> 00:14:12,960
Speaker 3: was you know, the public figure of CFM. He was

228
00:14:13,000 --> 00:14:16,760
Speaker 3: not involved in constructing models. I mean quants in a way.

229
00:14:16,800 --> 00:14:19,760
Speaker 3: What's great about quant investing is that you don't need

230
00:14:19,840 --> 00:14:23,760
Speaker 3: star traders, you don't need you know, pms that know everything.

231
00:14:24,120 --> 00:14:28,600
Speaker 3: It's a collective effort and so when someone disappears or

232
00:14:28,640 --> 00:14:31,640
Speaker 3: resigns or dies, it's not a tragedy. But in the

233
00:14:31,680 --> 00:14:33,760
Speaker 3: case of Jean Pierre, it was even that he was

234
00:14:33,840 --> 00:14:37,560
Speaker 3: not really involved in the construction of models. He was

235
00:14:37,640 --> 00:14:43,200
Speaker 3: just very inspiring, generous, and he was really great. Was

236
00:14:43,320 --> 00:14:45,440
Speaker 3: he had a vision, you know when we met and

237
00:14:45,480 --> 00:14:48,080
Speaker 3: he thought, Okay, with that guy, we can build something,

238
00:14:48,600 --> 00:14:53,040
Speaker 3: build something great. It's amazing, you know, to think that

239
00:14:53,120 --> 00:14:57,760
Speaker 3: he was so enthusiastic about creating what we created together,

240
00:14:57,920 --> 00:15:01,000
Speaker 3: and so we owe him a lot. When he passed away,

241
00:15:01,040 --> 00:15:05,400
Speaker 3: it was really difficult to well, you know, there were

242
00:15:05,440 --> 00:15:09,320
Speaker 3: several issues. One is that he had fifty seven percent

243
00:15:09,360 --> 00:15:12,800
Speaker 3: of the company, so we had to negotiate with the

244
00:15:13,080 --> 00:15:17,720
Speaker 3: estate to get back control. That was pretty difficult, but

245
00:15:18,080 --> 00:15:21,600
Speaker 3: we went through that. And also we needed to reassure

246
00:15:21,640 --> 00:15:24,520
Speaker 3: our investors. You know, Jean Pierre was he seemed to

247
00:15:24,560 --> 00:15:27,120
Speaker 3: be the public figure. He was a public figure, and

248
00:15:27,160 --> 00:15:30,040
Speaker 3: it seemed to be the inspired inspiration behind everything, and

249
00:15:30,080 --> 00:15:33,240
Speaker 3: so we had to we you know, communicate that we

250
00:15:33,240 --> 00:15:36,920
Speaker 3: were at the helm and that we would navigate that

251
00:15:37,120 --> 00:15:41,760
Speaker 3: and it worked and so it was it was very stressful,

252
00:15:42,600 --> 00:15:45,640
Speaker 3: but it was very rewarding as well to go through

253
00:15:45,640 --> 00:15:46,280
Speaker 3: that and.

254
00:15:46,560 --> 00:15:49,040
Speaker 2: The firm carries on as his legacy.

255
00:15:49,600 --> 00:15:50,000
Speaker 3: Yeah.

256
00:15:50,160 --> 00:15:53,360
Speaker 2: Coming up, we continue our conversation with John Philip Schau,

257
00:15:54,200 --> 00:15:58,560
Speaker 2: head of Research and Chief Scientists at CFM, talking about

258
00:15:58,640 --> 00:16:00,800
Speaker 2: the growth of Capital Fund Management.

259
00:16:01,160 --> 00:16:02,320
Speaker 6: I'm Barry Redults.

260
00:16:02,400 --> 00:16:16,760
Speaker 2: You're listening to Masters in Business on Bloomberg Radio. I'm

261
00:16:16,760 --> 00:16:20,600
Speaker 2: Barry Ridults. You're listening to Masters in Business on Bloomberg Radio.

262
00:16:21,000 --> 00:16:24,040
Speaker 2: My extra special guest this week is Jean Philip the show.

263
00:16:24,480 --> 00:16:27,880
Speaker 2: He is the head of Research, chairman and chief scientist

264
00:16:28,280 --> 00:16:33,760
Speaker 2: at Capital Fund Management Hedge fund managing over twenty billion dollars,

265
00:16:33,840 --> 00:16:39,040
Speaker 2: a quantitative shop specializing in managed futures and other quant

266
00:16:39,480 --> 00:16:45,440
Speaker 2: type funds. So let's talk a little bit about the

267
00:16:45,480 --> 00:16:49,680
Speaker 2: building of the fund. You meet you cofind found Science

268
00:16:49,720 --> 00:16:55,080
Speaker 2: and Finance in nineteen ninety four with Jean Pierre Aguilar.

269
00:16:55,360 --> 00:16:58,280
Speaker 2: What was the thought process did you think you were

270
00:16:58,320 --> 00:17:03,200
Speaker 2: building a quant fund research shop? What was the original plan.

271
00:17:03,080 --> 00:17:04,920
Speaker 6: A quand fund From day one.

272
00:17:05,040 --> 00:17:08,240
Speaker 3: From day one a quand fund. But from day one

273
00:17:08,320 --> 00:17:13,240
Speaker 3: we knew that we wanted to be strongly associated with academia.

274
00:17:13,440 --> 00:17:16,280
Speaker 3: We knew that the only way to innovate, you know,

275
00:17:16,480 --> 00:17:19,199
Speaker 3: again coming back to the fact that financial markets are

276
00:17:19,240 --> 00:17:23,520
Speaker 3: complex systems, it's really difficult to beat the market. We

277
00:17:23,600 --> 00:17:26,000
Speaker 3: know that everybody's trying to beat the market. If we

278
00:17:26,040 --> 00:17:29,240
Speaker 3: want to have something else to say and not follow

279
00:17:29,280 --> 00:17:32,520
Speaker 3: the crowd, we have to innovate, and innovating is hard.

280
00:17:32,720 --> 00:17:35,240
Speaker 3: You have to spend time, you have to have new

281
00:17:35,280 --> 00:17:39,400
Speaker 3: ideas that nobody else has, and so this means investing

282
00:17:39,480 --> 00:17:42,880
Speaker 3: heavily in research. So the two are not contradictory. We

283
00:17:42,960 --> 00:17:46,240
Speaker 3: really wanted to be a con fund. We had already.

284
00:17:46,359 --> 00:17:49,960
Speaker 3: We knew already about Renaissance. We knew that these guys

285
00:17:49,960 --> 00:17:52,560
Speaker 3: at Renaissance they were very close in spirit and in

286
00:17:52,640 --> 00:17:55,359
Speaker 3: culture to what we were, and so we thought we

287
00:17:55,400 --> 00:17:59,200
Speaker 3: were going to try to emulate them. Of course, they're

288
00:17:59,240 --> 00:18:02,040
Speaker 3: so great that there's no way to emulate them, but anyway,

289
00:18:02,359 --> 00:18:03,160
Speaker 3: this was the aim.

290
00:18:03,600 --> 00:18:06,000
Speaker 2: They had a forty year head start on you guys.

291
00:18:06,040 --> 00:18:10,000
Speaker 2: So yeah, But so it's funny you mentioned renaissance technologies.

292
00:18:10,560 --> 00:18:13,760
Speaker 2: I think of when I'm doing my research for for

293
00:18:14,040 --> 00:18:19,040
Speaker 2: CFM kind of reminded of D. E. Shaw and AQR

294
00:18:19,240 --> 00:18:22,920
Speaker 2: and a few other little bit of Millennium, although they

295
00:18:22,960 --> 00:18:28,960
Speaker 2: do so much of everything. The thought process behind being

296
00:18:29,000 --> 00:18:31,800
Speaker 2: a QUND shop when there's so many other QUND shops.

297
00:18:32,359 --> 00:18:34,800
Speaker 2: If we don't create our own models, if we don't

298
00:18:34,840 --> 00:18:38,840
Speaker 2: create our own findings and innovations, we're just an also run.

299
00:18:38,960 --> 00:18:41,920
Speaker 2: Is that ran? That is that the thought process behind it,

300
00:18:42,840 --> 00:18:45,600
Speaker 2: like we have we have to do this otherwise because

301
00:18:45,680 --> 00:18:49,960
Speaker 2: it's all of these other cound shop that I've mentioned,

302
00:18:50,080 --> 00:18:53,359
Speaker 2: none of them are quite the academic lab that.

303
00:18:53,320 --> 00:18:58,280
Speaker 3: They AQR is. I think that closest to us in

304
00:18:58,320 --> 00:19:00,679
Speaker 3: that front. I think, you know, in the songs they

305
00:19:00,680 --> 00:19:04,439
Speaker 3: took the initially the completely different term. They thought we

306
00:19:04,600 --> 00:19:08,440
Speaker 3: have to be completely secretive about everything and be a

307
00:19:08,520 --> 00:19:11,240
Speaker 3: kind of black hole where everything goes in but nothing

308
00:19:11,280 --> 00:19:14,920
Speaker 3: goes out. And that was not our philosophy. We thought that,

309
00:19:15,080 --> 00:19:17,159
Speaker 3: you know, life is too short as well. This is

310
00:19:17,200 --> 00:19:20,159
Speaker 3: not only we want to make money for ourselves, for

311
00:19:20,200 --> 00:19:24,760
Speaker 3: our investors. We want to excel, but not at any costs.

312
00:19:24,760 --> 00:19:27,040
Speaker 3: We think that there's something else in life, that there's

313
00:19:27,080 --> 00:19:30,160
Speaker 3: a legacy that we want to leave. And this legacy

314
00:19:30,280 --> 00:19:31,800
Speaker 3: is intellectual as.

315
00:19:31,680 --> 00:19:35,399
Speaker 2: Well, pursuing the truth as to what drives markets and

316
00:19:35,440 --> 00:19:36,760
Speaker 2: what leads to alpha.

317
00:19:36,560 --> 00:19:37,760
Speaker 6: And returns exactly.

318
00:19:38,280 --> 00:19:43,040
Speaker 2: You know, every new discovery of alpha eventually gets arbitraged away.

319
00:19:43,119 --> 00:19:44,200
Speaker 2: Is that is that the thinking?

320
00:19:44,880 --> 00:19:47,439
Speaker 3: Yeah, not exactly, I mean transfollowing. You know, it's not

321
00:19:47,560 --> 00:19:50,120
Speaker 3: arbitrage the way at all. And actually, if you think

322
00:19:50,160 --> 00:19:53,919
Speaker 3: about it, it's very hard to arbitrage trend following. You know,

323
00:19:54,240 --> 00:19:58,359
Speaker 3: if if people trend follow is going to lead to

324
00:19:58,480 --> 00:19:59,320
Speaker 3: more trendfor.

325
00:19:59,119 --> 00:20:02,600
Speaker 2: Right, it's not only a farm of French factor, but

326
00:20:02,840 --> 00:20:04,160
Speaker 2: it takes on its own life.

327
00:20:04,280 --> 00:20:07,240
Speaker 3: Right, Well, Fama doesn't like momentum but anyway.

328
00:20:06,960 --> 00:20:09,560
Speaker 2: But isn't it part it's not part of the original

329
00:20:09,720 --> 00:20:14,280
Speaker 2: three factor model, but wasn't it one of the later models?

330
00:20:14,400 --> 00:20:17,240
Speaker 3: Reluctantly, I think he had to add it. But it's

331
00:20:17,280 --> 00:20:20,359
Speaker 3: a disgrace for efficient market theory. So he doesn't like

332
00:20:20,840 --> 00:20:21,680
Speaker 3: momentum at all.

333
00:20:22,720 --> 00:20:25,920
Speaker 2: Listen, you know, if the math is there, it doesn't

334
00:20:25,920 --> 00:20:28,400
Speaker 2: matter if you like it, if it works, if it's

335
00:20:28,440 --> 00:20:30,240
Speaker 2: a valid factor, it's a valid factor.

336
00:20:30,320 --> 00:20:34,720
Speaker 3: Agree, I agree, But you know that's again the physicists

337
00:20:34,720 --> 00:20:40,160
Speaker 3: point of view. Experiments is about everything else. But sometimes

338
00:20:40,200 --> 00:20:43,240
Speaker 3: when you talk to economists, they have a strange view

339
00:20:43,280 --> 00:20:48,280
Speaker 3: that theorems and axioms are supersede any empirical observation. I

340
00:20:48,320 --> 00:20:52,280
Speaker 3: was told that by an economist, and so there's a

341
00:20:52,400 --> 00:20:54,120
Speaker 3: very strong difference in perception.

342
00:20:54,880 --> 00:21:00,000
Speaker 2: I recently had Richard Thaylor and Alex Amus in the studio,

343
00:21:00,200 --> 00:21:04,120
Speaker 2: and I was shocked to learn from them they still

344
00:21:04,160 --> 00:21:10,040
Speaker 2: aren't teaching behavioral finance in economics course at a college level,

345
00:21:10,040 --> 00:21:13,560
Speaker 2: which is kind of shocking. I think everything we've learned.

346
00:21:13,840 --> 00:21:17,360
Speaker 2: So let's talk about another technology. There have been over

347
00:21:17,440 --> 00:21:21,639
Speaker 2: the past decade, but especially the past few years, huge

348
00:21:21,640 --> 00:21:25,800
Speaker 2: advances and artificial intelligence and machine learning, to say nothing

349
00:21:25,800 --> 00:21:29,720
Speaker 2: about large language models. How are you guys thinking about

350
00:21:29,840 --> 00:21:33,600
Speaker 2: real world investing driven by AI and what sort of

351
00:21:33,680 --> 00:21:35,239
Speaker 2: opportunities does this open up?

352
00:21:35,720 --> 00:21:39,360
Speaker 3: Well, you know, AI is really an advanced form of

353
00:21:39,920 --> 00:21:43,680
Speaker 3: data analysis, and in a way, we've been doing machine

354
00:21:43,760 --> 00:21:48,040
Speaker 3: learning forever. The thing is that techniques have evolved there.

355
00:21:48,800 --> 00:21:51,959
Speaker 3: It's now much more efficient. There many more things that

356
00:21:52,000 --> 00:21:55,200
Speaker 3: one can do, in particular reading text. For many years

357
00:21:55,200 --> 00:21:58,040
Speaker 3: we were just using numbers, and actually for many years

358
00:21:58,080 --> 00:22:01,960
Speaker 3: we were just using prices and volume and not anything else,

359
00:22:02,480 --> 00:22:07,440
Speaker 3: and well and fundamental information about companies. But now there's

360
00:22:07,560 --> 00:22:10,040
Speaker 3: so much data that you can use. There's new data

361
00:22:10,080 --> 00:22:12,920
Speaker 3: set every day that we're presented by the data vendors,

362
00:22:13,520 --> 00:22:16,560
Speaker 3: and so there's a need to handle that data to

363
00:22:16,680 --> 00:22:19,840
Speaker 3: read sometimes huge data files. For example, if you think

364
00:22:19,880 --> 00:22:26,399
Speaker 3: about microstructure high frequency data, you know there's events happening

365
00:22:26,440 --> 00:22:30,080
Speaker 3: in the other book of major exchanges at the millisecond

366
00:22:30,160 --> 00:22:33,679
Speaker 3: level or even faster. This generates a huge amount of

367
00:22:33,720 --> 00:22:37,600
Speaker 3: information that has to be dealt with, analyzed, and machine

368
00:22:37,680 --> 00:22:42,159
Speaker 3: learning helps you very much doing that, reading texts that

369
00:22:42,400 --> 00:22:45,720
Speaker 3: no human would be able to read and extracting information

370
00:22:46,080 --> 00:22:49,600
Speaker 3: statistical information from that text. So for us, it is

371
00:22:50,560 --> 00:22:54,040
Speaker 3: I wouldn't say a revolution, but it's an acceleration of

372
00:22:54,119 --> 00:22:56,920
Speaker 3: things that we were trying to do before, and obviously

373
00:22:57,040 --> 00:23:00,119
Speaker 3: we're much in tune with that. We've actually created to

374
00:23:00,160 --> 00:23:05,200
Speaker 3: the nomil ab at CFM to help transferring technology from

375
00:23:05,640 --> 00:23:09,440
Speaker 3: what mL people are constructing and what researchers at CFM

376
00:23:09,520 --> 00:23:13,400
Speaker 3: may be using, but also to try to understand how

377
00:23:13,440 --> 00:23:16,840
Speaker 3: these things work right, because we're very uncomfortable with the

378
00:23:16,880 --> 00:23:21,160
Speaker 3: idea of black boxes. Black box is something that can

379
00:23:21,600 --> 00:23:26,400
Speaker 3: improve the research process, but when you think about implementing

380
00:23:26,440 --> 00:23:30,480
Speaker 3: that in production and having models trading with these models,

381
00:23:30,560 --> 00:23:32,479
Speaker 3: you really want to be sure that the machine has

382
00:23:32,520 --> 00:23:37,560
Speaker 3: done something that makes sense, and so understanding what machine

383
00:23:37,640 --> 00:23:41,359
Speaker 3: learning is actually doing? Why are these things working? To

384
00:23:41,440 --> 00:23:43,919
Speaker 3: start with? You know, what is strange is that it

385
00:23:44,040 --> 00:23:48,159
Speaker 3: works so well, but nobody understands why. When you're driving

386
00:23:48,160 --> 00:23:50,800
Speaker 3: a car, the car works really well, but we know

387
00:23:50,960 --> 00:23:55,120
Speaker 3: exactly why it works how it works machine learning, nobody

388
00:23:55,200 --> 00:23:59,040
Speaker 3: really understands what's the magic, and I think it's a

389
00:23:59,160 --> 00:24:02,080
Speaker 3: huge intellect challenge and we want to be part of that.

390
00:24:03,119 --> 00:24:06,639
Speaker 2: How much of that is pattern recognition? Because when I

391
00:24:06,720 --> 00:24:10,800
Speaker 2: think about the work, whenever I read about LMS, it's

392
00:24:10,880 --> 00:24:14,480
Speaker 2: really just statistically what makes the most sense for the

393
00:24:14,520 --> 00:24:17,200
Speaker 2: next letter, the next word, the next sentence. It's kind

394
00:24:17,200 --> 00:24:21,639
Speaker 2: of hard to just think of crafting a document based

395
00:24:21,680 --> 00:24:26,560
Speaker 2: on probabilities of the most likely word if you have

396
00:24:26,720 --> 00:24:30,080
Speaker 2: these few words beginning. But apparently that's a big part

397
00:24:30,200 --> 00:24:32,840
Speaker 2: of how they work. Or am I grossly overse.

398
00:24:33,200 --> 00:24:36,280
Speaker 3: So why does it work? And can it work in

399
00:24:36,320 --> 00:24:40,440
Speaker 3: finance too? You know? So is it because the language

400
00:24:40,640 --> 00:24:44,960
Speaker 3: or images have such a strong structure that you know

401
00:24:45,080 --> 00:24:50,000
Speaker 3: there's an internal logic to language, or to pictures, or

402
00:24:50,040 --> 00:24:53,560
Speaker 3: to other things that the model is able to capture.

403
00:24:53,760 --> 00:24:59,320
Speaker 3: And using these breathively simple ideas of statistical prediction of

404
00:24:59,520 --> 00:25:04,000
Speaker 3: what's going to happen next is enough to generate meaningful sentences.

405
00:25:04,440 --> 00:25:08,280
Speaker 3: But maybe maybe this part of that the structure of

406
00:25:08,359 --> 00:25:12,000
Speaker 3: the of the data. Is it the case in finance too, well,

407
00:25:12,160 --> 00:25:12,480
Speaker 3: he may.

408
00:25:12,400 --> 00:25:15,280
Speaker 2: Be literally exactly where to go. If you're training an

409
00:25:15,520 --> 00:25:18,960
Speaker 2: LLM on billions and billions of documents, pages, books, whatever,

410
00:25:19,520 --> 00:25:22,920
Speaker 2: and it now has a giant data source of when

411
00:25:22,920 --> 00:25:25,919
Speaker 2: you get these first few words or these first few sentences,

412
00:25:26,440 --> 00:25:30,399
Speaker 2: here's what's most common, here's what's second most common. And

413
00:25:30,520 --> 00:25:33,920
Speaker 2: here's a reference check for you to say, how does

414
00:25:33,960 --> 00:25:40,120
Speaker 2: this compare grammatically structurally to the giant corpus we have.

415
00:25:40,680 --> 00:25:44,680
Speaker 2: I can see the math behind that, because there's only

416
00:25:44,760 --> 00:25:48,200
Speaker 2: so many trillions of combinations of letters where it sentences.

417
00:25:48,680 --> 00:25:52,159
Speaker 2: But when you now apply it to markets, which seem

418
00:25:52,280 --> 00:25:56,160
Speaker 2: to be so random tick to tick day to day,

419
00:25:57,600 --> 00:26:00,280
Speaker 2: can you apply the same sort of logic to to

420
00:26:00,440 --> 00:26:00,919
Speaker 2: invest there?

421
00:26:01,440 --> 00:26:06,320
Speaker 3: Well, that are two problems. One is fundamental, are there

422
00:26:07,080 --> 00:26:11,680
Speaker 3: structures that you can extract? And we believe that there are,

423
00:26:12,240 --> 00:26:14,359
Speaker 3: because otherwise we wouldn't be there. I mean, you know,

424
00:26:14,359 --> 00:26:17,400
Speaker 3: trend following is a structure. It's a pretty trivial one,

425
00:26:17,440 --> 00:26:20,200
Speaker 3: but it is a structure. Now, many other types of

426
00:26:20,240 --> 00:26:24,480
Speaker 3: structures in the data that we've extracted without using mL

427
00:26:25,119 --> 00:26:29,000
Speaker 3: or using mL now or recovering with mL, or even

428
00:26:29,080 --> 00:26:33,800
Speaker 3: more complicated one with emls. But the major difference between

429
00:26:34,400 --> 00:26:39,080
Speaker 3: finance and languages or pictures is one the amount of data.

430
00:26:39,800 --> 00:26:42,320
Speaker 3: Because in the end, you know, stock markets have only

431
00:26:42,359 --> 00:26:45,399
Speaker 3: existed since I don't know, nineteen hundred or eighteen.

432
00:26:45,200 --> 00:26:46,960
Speaker 6: Hundred year one small data set.

433
00:26:47,000 --> 00:26:50,520
Speaker 3: Small said, except if you go to high frequency, as

434
00:26:50,600 --> 00:26:52,320
Speaker 3: I said, you know, if you go to tick by

435
00:26:52,359 --> 00:26:55,960
Speaker 3: tick or the book data, there's huge amounts of data

436
00:26:56,000 --> 00:27:00,280
Speaker 3: and there you can think that there's more to but

437
00:27:03,119 --> 00:27:06,160
Speaker 3: what it was I saying, yeah, so so, so there's

438
00:27:06,200 --> 00:27:09,960
Speaker 3: the problem of the availability of data and the frequency

439
00:27:10,040 --> 00:27:12,680
Speaker 3: at which you want to predict. So for high frequency,

440
00:27:12,720 --> 00:27:15,359
Speaker 3: I think there's a lot of structure. For lower frequency,

441
00:27:15,520 --> 00:27:19,040
Speaker 3: it's not clear yet that it is going to be useful,

442
00:27:19,680 --> 00:27:25,720
Speaker 3: you know, used as a kind of technical model which

443
00:27:25,760 --> 00:27:29,440
Speaker 3: only looks at prices without reading texts. For reading text

444
00:27:29,480 --> 00:27:31,680
Speaker 3: we know that there's there's a lot of structure, which

445
00:27:31,840 --> 00:27:35,680
Speaker 3: corresponds to to the structure of language. But having said

446
00:27:35,800 --> 00:27:39,360
Speaker 3: everything you said, there's still something strange about lll m's

447
00:27:39,560 --> 00:27:42,800
Speaker 3: or you know, generative AI is that with this process

448
00:27:42,800 --> 00:27:47,600
Speaker 3: of constructing sentences that are statistic statistically valid, you can

449
00:27:47,760 --> 00:27:51,359
Speaker 3: invent new things. And that's the thing that really is

450
00:27:51,400 --> 00:27:55,120
Speaker 3: really strange, right, I mean, you can learn pictures, for example,

451
00:27:55,160 --> 00:27:58,919
Speaker 3: you know this celebrity database where you have made the

452
00:27:59,000 --> 00:28:02,040
Speaker 3: machine learn these these pictures and then you ask the

453
00:28:02,080 --> 00:28:06,080
Speaker 3: machine to generate new ones and it does, and these

454
00:28:06,119 --> 00:28:09,520
Speaker 3: are pictures that look exactly I mean that you look

455
00:28:09,560 --> 00:28:12,520
Speaker 3: and you think it's a celebrity, but the celebrity doesn't exist.

456
00:28:13,000 --> 00:28:16,960
Speaker 3: So there's something still weird about this that, as I said,

457
00:28:17,080 --> 00:28:18,400
Speaker 3: nobody really understand.

458
00:28:19,760 --> 00:28:20,800
Speaker 2: Really kind of interesting.

459
00:28:21,040 --> 00:28:24,560
Speaker 3: And so continuing on that, what we are trying to

460
00:28:24,560 --> 00:28:28,000
Speaker 3: do is to do the same thing with financial markets. So,

461
00:28:28,040 --> 00:28:30,280
Speaker 3: as I said, one hundred years of data is not

462
00:28:30,400 --> 00:28:33,359
Speaker 3: a lot, but maybe you can use these gen AI

463
00:28:33,520 --> 00:28:39,560
Speaker 3: models to generate a millionaires of fictitious financial markets. That's interesting,

464
00:28:40,440 --> 00:28:41,120
Speaker 3: very interesting.

465
00:28:41,360 --> 00:28:43,479
Speaker 2: So let's talk a little bit about trend following and

466
00:28:43,560 --> 00:28:48,680
Speaker 2: managed futures. It's had a few real standout years in particular,

467
00:28:48,880 --> 00:28:55,800
Speaker 2: twenty twenty two real challenging year and manage futures at

468
00:28:56,800 --> 00:29:01,040
Speaker 2: we're top of the asset quilt? What does that episode

469
00:29:01,040 --> 00:29:05,360
Speaker 2: tell us about what strategies work, why they work? And

470
00:29:05,400 --> 00:29:08,920
Speaker 2: the question I always find with trend following why did

471
00:29:09,000 --> 00:29:13,040
Speaker 2: so few investors tend to stay with them? They all

472
00:29:13,040 --> 00:29:17,920
Speaker 2: seem to get nervous and tap out right before things.

473
00:29:17,960 --> 00:29:21,720
Speaker 2: It's almost when you see people giving up. It's just

474
00:29:21,800 --> 00:29:23,240
Speaker 2: about when the turn occurs.

475
00:29:23,400 --> 00:29:25,440
Speaker 6: I agree, So what is it?

476
00:29:25,520 --> 00:29:25,600
Speaker 4: What?

477
00:29:25,720 --> 00:29:28,040
Speaker 2: First of all, why was twenty twenty two such a

478
00:29:28,080 --> 00:29:31,800
Speaker 2: standout year? I don't know just I mean, aside from

479
00:29:31,800 --> 00:29:34,960
Speaker 2: the fact that we had big FED rate hikes and

480
00:29:35,160 --> 00:29:40,280
Speaker 2: fixed income and equities both got sill act double digits

481
00:29:40,480 --> 00:29:44,280
Speaker 2: kind of rare occurs the same year. I think you

482
00:29:44,320 --> 00:29:46,400
Speaker 2: have to go back about forty to forty one years

483
00:29:46,800 --> 00:29:51,640
Speaker 2: to see both of them down significantly. How do you

484
00:29:51,760 --> 00:29:57,080
Speaker 2: think about how do you think about what environment leads

485
00:29:57,120 --> 00:29:59,800
Speaker 2: to best results for trend following.

486
00:30:00,000 --> 00:30:02,560
Speaker 3: It's very difficult to say, because otherwise we would have

487
00:30:02,600 --> 00:30:06,760
Speaker 3: a meta model that albutrages and you know, increases the

488
00:30:06,800 --> 00:30:09,480
Speaker 3: weight of trend following when it's going to work. Well,

489
00:30:09,920 --> 00:30:12,840
Speaker 3: I think it's there probably is more research to do,

490
00:30:13,000 --> 00:30:16,400
Speaker 3: and where we've been trying, we haven't found anything that's

491
00:30:16,520 --> 00:30:19,280
Speaker 3: very convincing. But you know, beginning of twenty twenty six

492
00:30:19,400 --> 00:30:22,240
Speaker 3: is also a very good period for trendsforward. Actually, since

493
00:30:22,600 --> 00:30:25,720
Speaker 3: we wrote a paper in twenty fourteen called two hundred

494
00:30:25,800 --> 00:30:28,920
Speaker 3: Years of trend Following, and we were reporting on the

495
00:30:28,920 --> 00:30:32,400
Speaker 3: fact that since eighteen hundred, if you pay per trade,

496
00:30:32,800 --> 00:30:36,760
Speaker 3: a very simple trend following strategy, you make money every

497
00:30:36,800 --> 00:30:39,840
Speaker 3: decade with ups and downs. You know, there are years

498
00:30:39,880 --> 00:30:42,440
Speaker 3: that are not so good years. But as you say,

499
00:30:42,520 --> 00:30:46,760
Speaker 3: I mean, what is striking and about the very point

500
00:30:46,800 --> 00:30:50,840
Speaker 3: you made about people getting out of trend following just

501
00:30:50,880 --> 00:30:54,760
Speaker 3: before it gets back on is I think it's ingrained

502
00:30:54,880 --> 00:30:59,200
Speaker 3: in people's behavior to chase performance. So if performance has

503
00:30:59,240 --> 00:31:02,120
Speaker 3: been bad for a few years, everybody declares. And that

504
00:31:02,240 --> 00:31:05,520
Speaker 3: was the case in twenty fourteen when we wrote our paper,

505
00:31:06,000 --> 00:31:08,960
Speaker 3: transfollowing had been flat for the last five years, and

506
00:31:09,040 --> 00:31:11,880
Speaker 3: people said, okay, well, trensfalling is dead now, and we

507
00:31:11,880 --> 00:31:13,880
Speaker 3: were actually convinced that it was not the case. That

508
00:31:14,080 --> 00:31:19,040
Speaker 3: you know, transfalling is such a strong behavioral bias that

509
00:31:19,800 --> 00:31:23,160
Speaker 3: performance chasing is so ingrained in every one of us

510
00:31:23,240 --> 00:31:28,560
Speaker 3: even rational, we can't help, and so we bet and

511
00:31:28,800 --> 00:31:31,280
Speaker 3: it was confirmed that trenfollowing would come back. And since

512
00:31:31,280 --> 00:31:34,000
Speaker 3: twenty twenty four is twenty fourteen, it's been very good.

513
00:31:34,000 --> 00:31:37,880
Speaker 2: Actually overall, well, you've had a market that very much

514
00:31:38,000 --> 00:31:41,040
Speaker 2: was trending, mostly in one direction. For I mean, you

515
00:31:41,080 --> 00:31:43,320
Speaker 2: have Q four of twenty eighteen, and I think twenty

516
00:31:43,320 --> 00:31:47,040
Speaker 2: sixteen was so so. But for the past fifteen years,

517
00:31:47,560 --> 00:31:50,120
Speaker 2: the bias has been pretty much in one direction. If

518
00:31:50,120 --> 00:31:52,080
Speaker 2: you're on the right side of that, you should do

519
00:31:52,160 --> 00:31:52,640
Speaker 2: pretty well.

520
00:31:52,800 --> 00:31:54,920
Speaker 3: Yeah, but I'm not speaking about being long. I mean

521
00:31:55,160 --> 00:31:56,400
Speaker 3: I'm really speaking about you know.

522
00:31:56,440 --> 00:31:59,960
Speaker 2: Different asset classes, different trends of your long and short.

523
00:32:00,280 --> 00:32:00,560
Speaker 3: Yeah.

524
00:32:00,600 --> 00:32:03,000
Speaker 2: Sure, so it doesn't matter as long as the trend

525
00:32:03,080 --> 00:32:04,040
Speaker 2: is in place you want.

526
00:32:03,880 --> 00:32:04,640
Speaker 6: To part exactly.

527
00:32:06,160 --> 00:32:09,360
Speaker 2: And for people who are not familiar with managed futures,

528
00:32:10,400 --> 00:32:13,720
Speaker 2: there's a decent amount of leverage used in that product,

529
00:32:13,800 --> 00:32:16,959
Speaker 2: so you have to really manage the risk of the downside.

530
00:32:17,040 --> 00:32:19,520
Speaker 2: But how do you think about, you know, the potential

531
00:32:19,600 --> 00:32:23,280
Speaker 2: upside relative to the risk you're taking in a futures product.

532
00:32:23,600 --> 00:32:26,320
Speaker 3: What do you mean exactly? I mean, I mean, we

533
00:32:27,200 --> 00:32:30,200
Speaker 3: just think about risk we want to risk. Is a

534
00:32:30,320 --> 00:32:33,000
Speaker 3: very complex object. Actually, you know, there's a volatility, but

535
00:32:33,040 --> 00:32:35,880
Speaker 3: that's also correlation. If you deal with the portfolio of

536
00:32:35,920 --> 00:32:39,520
Speaker 3: futures that has like one hundred and fifty futures, there's

537
00:32:39,600 --> 00:32:44,840
Speaker 3: a very subtle correlation structure between all the assets that

538
00:32:44,880 --> 00:32:47,320
Speaker 3: you have in your portfolio. So if you think about risk,

539
00:32:47,400 --> 00:32:51,640
Speaker 3: you really have to think about how all these products

540
00:32:51,760 --> 00:32:54,680
Speaker 3: interact with one another, talk to one another. And so

541
00:32:54,720 --> 00:32:57,200
Speaker 3: it's not only a question of volatility that goes up

542
00:32:57,240 --> 00:32:59,440
Speaker 3: and down that you have to control, but also a

543
00:32:59,480 --> 00:33:03,160
Speaker 3: question of how these assets co move together or anti

544
00:33:03,200 --> 00:33:05,920
Speaker 3: co move together. But the way we think about upside

545
00:33:06,000 --> 00:33:07,600
Speaker 3: risk is the same as the way we think about

546
00:33:07,680 --> 00:33:09,600
Speaker 3: downside risk is it's just a question of risk.

547
00:33:10,240 --> 00:33:14,520
Speaker 2: So you mentioned one hundred and fifty different of assets.

548
00:33:14,560 --> 00:33:19,640
Speaker 2: I'm assuming some of these are commodities with modities, bones, stockspawns,

549
00:33:19,720 --> 00:33:22,760
Speaker 2: interest rates, right, But what else is in the full

550
00:33:22,800 --> 00:33:23,960
Speaker 2: list of one hundred and fifty.

551
00:33:24,120 --> 00:33:28,440
Speaker 3: Well, you know there's different the maturities that different countries,

552
00:33:28,560 --> 00:33:33,200
Speaker 3: futures in China. I mean, if you count everything, it

553
00:33:33,240 --> 00:33:35,240
Speaker 3: goes up to you know, one hundred. I don't have

554
00:33:35,280 --> 00:33:38,680
Speaker 3: the exact number, but in the one hundred and fifties altogether.

555
00:33:39,160 --> 00:33:43,400
Speaker 2: Has CFN been looking at prediction markets things like CALCI

556
00:33:43,480 --> 00:33:44,880
Speaker 2: and polymarket.

557
00:33:44,920 --> 00:33:47,400
Speaker 3: No, we haven't. They're not liquid enough for us.

558
00:33:47,600 --> 00:33:50,520
Speaker 2: Really, you need size and they can't provide it exactly.

559
00:33:51,040 --> 00:33:55,040
Speaker 2: Really really quite fascinating. Coming up, we continue our conversation

560
00:33:55,160 --> 00:33:59,920
Speaker 2: with Jean Felipe Buschot, co founder and chief scientists at

561
00:34:00,080 --> 00:34:06,000
Speaker 2: Capital Fund Management, talking about how market structures are changing today.

562
00:34:06,400 --> 00:34:07,440
Speaker 6: I'm Barry Results.

563
00:34:07,520 --> 00:34:22,960
Speaker 2: You're listening to Master's Business on Bloomberg Radio.

564
00:34:24,680 --> 00:34:25,680
Speaker 6: I'm Bury Redults.

565
00:34:25,719 --> 00:34:28,960
Speaker 2: You're listening to Masters in Business on Bloomberg Radio. My

566
00:34:29,120 --> 00:34:33,200
Speaker 2: extra special guest this week, John Philip Buschow, chief scientist

567
00:34:33,360 --> 00:34:37,520
Speaker 2: and chairman at CFM, a quantitative hedge fund managing over

568
00:34:37,960 --> 00:34:41,840
Speaker 2: twenty billion dollars in assets. So let's just talk a

569
00:34:41,880 --> 00:34:45,879
Speaker 2: little bit about risk management. I know that when you're

570
00:34:45,880 --> 00:34:51,440
Speaker 2: dealing with leveraged or long short or futures, there's a

571
00:34:51,719 --> 00:34:57,040
Speaker 2: very robust thought process around risk management. Tell us a

572
00:34:57,080 --> 00:35:02,000
Speaker 2: little bit about how you think about out correlation and risk.

573
00:35:03,040 --> 00:35:06,000
Speaker 3: Yeah, we have a discipline and systematic approach not only

574
00:35:06,080 --> 00:35:10,520
Speaker 3: to alpha signals, to building prediction, but also to risk management.

575
00:35:10,880 --> 00:35:15,680
Speaker 3: We have a pretty sufisticated tool to predict the velocity

576
00:35:15,760 --> 00:35:19,160
Speaker 3: of tomorrow, the velocility of our portfolio tomorrow, and we're

577
00:35:19,160 --> 00:35:22,160
Speaker 3: pretty good at that. So of course, you know, we

578
00:35:22,200 --> 00:35:25,920
Speaker 3: know in financial markets are difficult beasts, and even if

579
00:35:25,960 --> 00:35:28,160
Speaker 3: you have the best model of the world, you can

580
00:35:28,239 --> 00:35:31,920
Speaker 3: still have an expected events that blow up your portfolio.

581
00:35:31,920 --> 00:35:35,440
Speaker 3: That's that's something that we can't say will never happen.

582
00:35:36,120 --> 00:35:38,359
Speaker 3: But in a way with you know, if you don't

583
00:35:38,400 --> 00:35:40,960
Speaker 3: want to make take any risk, you shouldn't be in

584
00:35:41,000 --> 00:35:43,480
Speaker 3: financial markets. You shouldn't be in that business. So we

585
00:35:43,640 --> 00:35:47,640
Speaker 3: accept that there might be I don't know, a completely

586
00:35:47,680 --> 00:35:52,359
Speaker 3: unexpected event that you know, breaks the whole financial markets

587
00:35:52,440 --> 00:35:55,680
Speaker 3: everywhere in the world and you know everything is going

588
00:35:55,719 --> 00:35:58,000
Speaker 3: to fail and there's nothing to do about that, So

589
00:35:58,080 --> 00:36:03,200
Speaker 3: that can happen. But barring these extreme events, we think

590
00:36:03,200 --> 00:36:06,279
Speaker 3: we're pretty good at predicting what's going to happen. And

591
00:36:06,600 --> 00:36:10,320
Speaker 3: over the last thirty five years of the existence of CFM,

592
00:36:10,360 --> 00:36:16,240
Speaker 3: we're actually anniversary is this year, we're celebrating our thirty

593
00:36:16,280 --> 00:36:21,880
Speaker 3: fifth anniversary in June in Paris. Very proud of that. So,

594
00:36:22,200 --> 00:36:25,840
Speaker 3: you know, it kind of resisted these thirty five years,

595
00:36:25,920 --> 00:36:31,000
Speaker 3: although we've become much better with time. But having said that,

596
00:36:31,040 --> 00:36:35,720
Speaker 3: there's always an element that you have to be ready

597
00:36:35,760 --> 00:36:39,600
Speaker 3: to intervene, even if you're a contrap and so on

598
00:36:39,600 --> 00:36:44,080
Speaker 3: several occasions in the past thirty five years, we decided

599
00:36:44,120 --> 00:36:47,120
Speaker 3: that our risk model couldn't know about things that we

600
00:36:47,800 --> 00:36:51,080
Speaker 3: humans knew, Like you know, I don't know the Brexit

601
00:36:51,200 --> 00:36:54,000
Speaker 3: votes or in the let's.

602
00:36:53,800 --> 00:36:57,640
Speaker 2: Talk about that because this year, just the past twelve months,

603
00:36:58,040 --> 00:37:02,000
Speaker 2: between the tariffs and venezuel Ella and now the ongoing

604
00:37:02,040 --> 00:37:06,799
Speaker 2: war and Iran. How does global market volatility around all

605
00:37:06,800 --> 00:37:11,680
Speaker 2: these geopolitical events, How does a quad shop deal with that?

606
00:37:12,320 --> 00:37:15,840
Speaker 2: What I'm hearing is the humans have to do what

607
00:37:15,920 --> 00:37:18,160
Speaker 2: humans do and sometimes override the machines.

608
00:37:18,239 --> 00:37:20,960
Speaker 6: Sometimes, yes, I mean when when.

609
00:37:20,920 --> 00:37:28,840
Speaker 2: Unexpected geopol political events disrupt the world, our models just

610
00:37:28,920 --> 00:37:31,680
Speaker 2: not built to really work their way through that.

611
00:37:32,080 --> 00:37:37,279
Speaker 3: You're right, some events are okay, and like the war

612
00:37:37,320 --> 00:37:40,600
Speaker 3: in Iran for the moment, is not, you know, something

613
00:37:40,640 --> 00:37:44,200
Speaker 3: that our risk models are completely blind to. Right, It

614
00:37:44,200 --> 00:37:47,040
Speaker 3: doesn't mean that they've predicted at all. It just means that,

615
00:37:47,440 --> 00:37:50,160
Speaker 3: you know, we're comfortable with the risk that our model

616
00:37:50,560 --> 00:37:54,320
Speaker 3: have predicted, and they've adapted sufficially fast toward to the events,

617
00:37:54,360 --> 00:37:57,880
Speaker 3: so that we're comfortable with the risk level no human intervention.

618
00:37:58,400 --> 00:38:01,840
Speaker 3: On the other hand, in some cases it is completely unexpected,

619
00:38:01,960 --> 00:38:07,359
Speaker 3: like tariffs and Liberation Day, this created havoc, although strangely enough,

620
00:38:07,880 --> 00:38:11,520
Speaker 3: Libration Day was announced. You know, everybody knew what it

621
00:38:11,760 --> 00:38:14,680
Speaker 3: was going to be said, and still everybody.

622
00:38:14,360 --> 00:38:15,319
Speaker 6: Was apprised of it.

623
00:38:15,880 --> 00:38:19,520
Speaker 2: I don't believe despite I'm tariff man, it's the most

624
00:38:19,520 --> 00:38:23,120
Speaker 2: beautiful in the dictionary. I think the one hundred, one

625
00:38:23,200 --> 00:38:27,279
Speaker 2: hundred and fifty percent tariffs on specific countries later found

626
00:38:27,360 --> 00:38:31,160
Speaker 2: to be completely unconstitutional, But at the time I think

627
00:38:31,320 --> 00:38:37,480
Speaker 2: people were genuinely shocked by this. And then a week later,

628
00:38:37,680 --> 00:38:39,960
Speaker 2: you know, the little bit of a taco trade where

629
00:38:40,600 --> 00:38:43,880
Speaker 2: let's just put a pin in this for ninety days. Again,

630
00:38:44,000 --> 00:38:46,720
Speaker 2: back to the volatility, how do you deal? I agree

631
00:38:47,120 --> 00:38:50,200
Speaker 2: oil is trending upwards, and then you have a tweet

632
00:38:50,280 --> 00:38:53,759
Speaker 2: the world the war's over, and then it resumes, and

633
00:38:53,760 --> 00:38:56,000
Speaker 2: then you have a tweet I think we've got a deal,

634
00:38:56,440 --> 00:38:58,560
Speaker 2: and then the other side says we're not even negotiating.

635
00:39:00,120 --> 00:39:03,200
Speaker 2: I don't recall a period in history where the president

636
00:39:03,239 --> 00:39:08,760
Speaker 2: of the United States just constantly disrupted the normal flow

637
00:39:08,840 --> 00:39:14,000
Speaker 2: of market activity. How disruptive is this to a quant model.

638
00:39:14,200 --> 00:39:16,600
Speaker 3: Well, as I said, the two cases seem to be

639
00:39:16,640 --> 00:39:20,239
Speaker 3: pretty different. Liberation Day was really a surprise and we

640
00:39:20,280 --> 00:39:23,280
Speaker 3: had to manually intervene. There was something in our models

641
00:39:23,280 --> 00:39:26,040
Speaker 3: that was completely blind to these things, and we had

642
00:39:26,080 --> 00:39:28,520
Speaker 3: to make a judge on the call. I think the

643
00:39:28,560 --> 00:39:32,160
Speaker 3: idea really is that humans should use their best judgment

644
00:39:32,239 --> 00:39:36,279
Speaker 3: in these cases and decide whether it's reasonable that the

645
00:39:36,360 --> 00:39:39,359
Speaker 3: risk model knows something about what's going on or not.

646
00:39:40,000 --> 00:39:42,480
Speaker 3: In some cases it does, in some cases it doesn't.

647
00:39:42,960 --> 00:39:46,680
Speaker 3: I think the the tricky part is not to overreact,

648
00:39:47,080 --> 00:39:50,279
Speaker 3: because you said you don't remember periods of the world

649
00:39:50,360 --> 00:39:53,560
Speaker 3: where things like this happened. But you know, looking back,

650
00:39:54,280 --> 00:39:56,680
Speaker 3: I've been in the markets for thirty five years and

651
00:39:56,760 --> 00:40:00,720
Speaker 3: everything every year there seems to be something unexpect that happens.

652
00:40:00,760 --> 00:40:03,040
Speaker 6: You know, just not every day, not every day, but

653
00:40:03,160 --> 00:40:03,640
Speaker 6: every year.

654
00:40:03,640 --> 00:40:04,680
Speaker 2: It seems like a lot.

655
00:40:04,719 --> 00:40:07,880
Speaker 3: Right, But in a way, every day means that it

656
00:40:07,920 --> 00:40:11,200
Speaker 3: becomes a new normal, I guess, and so it's not

657
00:40:11,239 --> 00:40:11,640
Speaker 3: that bad.

658
00:40:11,760 --> 00:40:13,200
Speaker 6: It's if it's every day.

659
00:40:13,520 --> 00:40:16,040
Speaker 3: But really, you know, this idea that this time is

660
00:40:16,040 --> 00:40:19,920
Speaker 3: different is something that's strange. If you look at the

661
00:40:19,960 --> 00:40:25,600
Speaker 3: world and the history of financial markets. It's really being normal.

662
00:40:25,640 --> 00:40:29,040
Speaker 3: That's not normal, and we've become used to that.

663
00:40:30,000 --> 00:40:34,319
Speaker 2: So let's stay with the idea of modeling. You've been

664
00:40:34,400 --> 00:40:39,000
Speaker 2: kind of skeptical of certain applications of deep learning in finance.

665
00:40:39,440 --> 00:40:43,879
Speaker 2: There's overfitting. You know, no one's ever seen a bad

666
00:40:44,680 --> 00:40:48,839
Speaker 2: back test because they all seem to work perfectly in

667
00:40:48,880 --> 00:40:52,320
Speaker 2: the past. You have a lot of signal and noise issues.

668
00:40:52,920 --> 00:40:55,919
Speaker 2: What are some of the problems with models that you're

669
00:40:56,000 --> 00:40:57,239
Speaker 2: focusing on improving.

670
00:40:57,640 --> 00:41:00,920
Speaker 3: Yeah, well, for example, this exactly what you just said.

671
00:41:01,280 --> 00:41:05,680
Speaker 3: Can you have indicators that tell you whether your back

672
00:41:05,719 --> 00:41:10,000
Speaker 3: test is overfitted or not? And for many years we

673
00:41:10,680 --> 00:41:14,000
Speaker 3: struggled with that, and we use judgment again to say

674
00:41:14,400 --> 00:41:17,560
Speaker 3: this is plausible, this is not plausible. We can believe

675
00:41:17,600 --> 00:41:21,680
Speaker 3: that we kind of replace the trader that trades every

676
00:41:21,800 --> 00:41:28,600
Speaker 3: day his signals or his beliefs to a higher level

677
00:41:28,719 --> 00:41:32,520
Speaker 3: where we are traders of models. We know we kind

678
00:41:32,520 --> 00:41:35,319
Speaker 3: of judge models. We say this model is good enough

679
00:41:35,320 --> 00:41:37,839
Speaker 3: to go in production, this model is not convincing enough,

680
00:41:38,280 --> 00:41:40,920
Speaker 3: but it would be great to have something more systematic.

681
00:41:41,320 --> 00:41:44,439
Speaker 3: And over the years we've been struggling, and I think

682
00:41:44,480 --> 00:41:49,040
Speaker 3: with some success, to have meta models that predict whether

683
00:41:49,640 --> 00:41:54,320
Speaker 3: your back test is really fudged or if it's decent

684
00:41:54,440 --> 00:41:57,880
Speaker 3: enough to go in production. So we're kind of industrial

685
00:41:58,120 --> 00:42:03,279
Speaker 3: and industrializing this process of selecting models that will go

686
00:42:03,320 --> 00:42:04,799
Speaker 3: into production. Does that make sense?

687
00:42:04,880 --> 00:42:08,880
Speaker 2: Yeah, No, that makes perfect sense. You're quant, and we've

688
00:42:08,880 --> 00:42:11,959
Speaker 2: seen some issues with a lot of quant shops in

689
00:42:12,000 --> 00:42:16,960
Speaker 2: the US where crowding became a structural risk. You have

690
00:42:17,000 --> 00:42:20,840
Speaker 2: all these system systematic strategies, and math is math, so

691
00:42:21,080 --> 00:42:25,520
Speaker 2: essentially you end up with a crowded trade. We had

692
00:42:25,560 --> 00:42:29,799
Speaker 2: what people called the quant quake way back when. How

693
00:42:29,840 --> 00:42:32,000
Speaker 2: do you think about that? How do you manage that

694
00:42:32,120 --> 00:42:34,400
Speaker 2: problem when you're constructing portfolio?

695
00:42:34,520 --> 00:42:36,759
Speaker 3: Sure it's something that we you know, every day we

696
00:42:36,800 --> 00:42:39,959
Speaker 3: think about this. We were in the quant quake in

697
00:42:39,960 --> 00:42:43,960
Speaker 3: two thousand and oh seven, and actually we were fortunate

698
00:42:44,080 --> 00:42:47,040
Speaker 3: enough to be out of the markets or to have

699
00:42:47,280 --> 00:42:53,800
Speaker 3: the leveraged already two weeks before the worst day of

700
00:42:53,840 --> 00:42:54,479
Speaker 3: the quont quake.

701
00:42:54,920 --> 00:42:57,320
Speaker 2: Was that an external signal or one of your model

702
00:42:57,360 --> 00:42:58,200
Speaker 2: signals or what like?

703
00:42:58,480 --> 00:43:01,640
Speaker 3: Was it was something in the perfour months already in July.

704
00:43:01,800 --> 00:43:05,719
Speaker 3: The one quick the really bad day happened maybe ninth

705
00:43:05,760 --> 00:43:07,480
Speaker 3: of August, I don't remember exactly but.

706
00:43:07,560 --> 00:43:09,839
Speaker 6: It was early August days of summer.

707
00:43:09,560 --> 00:43:14,160
Speaker 3: Right, but starting like tenth of July already there was

708
00:43:14,200 --> 00:43:17,760
Speaker 3: something really very strange in our portfolio. And we started

709
00:43:18,320 --> 00:43:21,799
Speaker 3: reflecting on what was going on and decided someone was

710
00:43:21,960 --> 00:43:27,320
Speaker 3: leveraging and you know, hitting us by shorting our lungs

711
00:43:27,320 --> 00:43:30,759
Speaker 3: and buying our shorts, and you know, this process, this

712
00:43:30,760 --> 00:43:35,040
Speaker 3: thought process of imagining that even if a fund that

713
00:43:35,200 --> 00:43:38,080
Speaker 3: was like ten percent correlated with ours, not a lot,

714
00:43:38,120 --> 00:43:41,520
Speaker 3: but ten percent and having every day a kind of

715
00:43:42,160 --> 00:43:46,640
Speaker 3: systematic deleveraging policy, it would create exactly the kind of

716
00:43:46,680 --> 00:43:49,760
Speaker 3: signals that we were seeing in our portfolio. So we thought, okay,

717
00:43:49,800 --> 00:43:52,839
Speaker 3: this is maybe going to lead to a crash because

718
00:43:52,840 --> 00:43:56,200
Speaker 3: people are going to you know, suffer suffering at one point,

719
00:43:56,200 --> 00:43:59,160
Speaker 3: they're going to cascades and skate and so on, and

720
00:43:59,520 --> 00:44:02,839
Speaker 3: so that was rationale for getting out. So in some

721
00:44:02,920 --> 00:44:06,120
Speaker 3: cases you have you're lucky enough to have strong enough

722
00:44:06,160 --> 00:44:09,680
Speaker 3: signals that tell you that your standard risk model is

723
00:44:09,719 --> 00:44:11,280
Speaker 3: wrong and you should do something else.

724
00:44:13,040 --> 00:44:17,480
Speaker 2: That's really that's really fascinating. So you mentioned almost thirty

725
00:44:17,480 --> 00:44:20,640
Speaker 2: five years of doing this, What do you think that

726
00:44:21,760 --> 00:44:25,400
Speaker 2: I'm gonna say that again? So you are now thirty

727
00:44:25,440 --> 00:44:30,160
Speaker 2: five almost thirty five years into being a market quant

728
00:44:30,920 --> 00:44:36,239
Speaker 2: what's the most important thing about markets that the mainstream

729
00:44:36,480 --> 00:44:38,080
Speaker 2: funds still get wrong?

730
00:44:39,320 --> 00:44:43,520
Speaker 3: Well, I think it's this question of what is price doing?

731
00:44:44,200 --> 00:44:48,200
Speaker 3: What are moving prices? And a lot of people still

732
00:44:48,239 --> 00:44:51,799
Speaker 3: believe that there's something like a fundamental value and that

733
00:44:51,880 --> 00:44:57,160
Speaker 3: the price is really moving because fundamentals are moving whereas

734
00:44:57,440 --> 00:45:00,640
Speaker 3: we believe, and this is going you know, this touches

735
00:45:00,960 --> 00:45:05,160
Speaker 3: very recent academic papers that Gabex and coaches and to

736
00:45:05,320 --> 00:45:09,080
Speaker 3: economists have put forward. They've called it the inefficient inelastic

737
00:45:09,120 --> 00:45:13,520
Speaker 3: market hypothesis, and we've contributed to that debate as well.

738
00:45:14,000 --> 00:45:16,720
Speaker 3: And the idea is really that markets are not driven

739
00:45:16,760 --> 00:45:21,839
Speaker 3: by fundamentals, or at least they are. There are to

740
00:45:21,880 --> 00:45:24,520
Speaker 3: some extent driven by fundamentals, but this is a small

741
00:45:24,640 --> 00:45:28,080
Speaker 3: long term effect on short run. Short run meaning from

742
00:45:28,080 --> 00:45:31,680
Speaker 3: one day to one year's pretty long already. It's really

743
00:45:31,719 --> 00:45:35,840
Speaker 3: flows that matter. That is people buying or selling stuff,

744
00:45:36,239 --> 00:45:39,200
Speaker 3: whatever the reason they buy or sell, is going to

745
00:45:39,200 --> 00:45:41,839
Speaker 3: move prices. It's not going to move prices on a

746
00:45:41,880 --> 00:45:45,799
Speaker 3: short timescale and then disappear. It's really going to leave

747
00:45:45,800 --> 00:45:49,400
Speaker 3: a trace in markets. And this is really a fundamental

748
00:45:49,880 --> 00:45:53,400
Speaker 3: change of point of view that I think that you

749
00:45:53,440 --> 00:45:58,719
Speaker 3: know is going to percolate and convince more and more

750
00:45:58,760 --> 00:46:02,960
Speaker 3: people looking for But having this change of tech is

751
00:46:03,000 --> 00:46:05,680
Speaker 3: really important because in one case, what you need to

752
00:46:05,719 --> 00:46:09,080
Speaker 3: do to make money is to predict fundamentals. In the

753
00:46:09,120 --> 00:46:11,200
Speaker 3: other case, you need to predict what people are going

754
00:46:11,239 --> 00:46:14,239
Speaker 3: to do. And so in a sense, crowding can be

755
00:46:14,280 --> 00:46:16,960
Speaker 3: a good thing because if there's crowding, it's easier to

756
00:46:16,960 --> 00:46:19,400
Speaker 3: predict what the crowd is going to do. And so

757
00:46:20,160 --> 00:46:23,239
Speaker 3: if whatever the reason, people do things, they move prices

758
00:46:23,480 --> 00:46:25,600
Speaker 3: and you're able to predict what people are going to

759
00:46:25,640 --> 00:46:29,920
Speaker 3: do because you have, you know, behavioral models and structural

760
00:46:29,960 --> 00:46:33,400
Speaker 3: models that tell you, you know, everything else being equal, people

761
00:46:33,440 --> 00:46:35,839
Speaker 3: are more likely to do this and that then you

762
00:46:35,880 --> 00:46:38,480
Speaker 3: can build models. And I think that's the reason why

763
00:46:38,520 --> 00:46:42,160
Speaker 3: we've been successful is this change of philosophy. We were

764
00:46:42,160 --> 00:46:45,840
Speaker 3: not kind of anchored to fundamentals. We're anchored to flows.

765
00:46:46,239 --> 00:46:49,120
Speaker 2: So this sounds a little bit like the ben Gram line.

766
00:46:49,480 --> 00:46:52,840
Speaker 2: I think it's Gram in the short run markets or

767
00:46:52,960 --> 00:46:55,440
Speaker 2: voting machines and the long run their weighing machines. Is

768
00:46:55,480 --> 00:46:59,799
Speaker 2: that is that the balance between flows and fundamentals.

769
00:47:00,239 --> 00:47:02,680
Speaker 3: Yeah, I think it's an old idea. I mean, you know,

770
00:47:03,360 --> 00:47:06,440
Speaker 3: Canes also said things like that. You know, but in

771
00:47:06,480 --> 00:47:09,400
Speaker 3: the long run, we're all dead, right, Panes dead was

772
00:47:09,440 --> 00:47:12,640
Speaker 3: saying that, So it's really a question of whether you're

773
00:47:12,640 --> 00:47:24,880
Speaker 3: going to be solvable. I'm getting tired. I'll take that again.

774
00:47:25,600 --> 00:47:27,600
Speaker 6: What's the word was that, Kip?

775
00:47:27,640 --> 00:47:30,080
Speaker 2: By the way, Knes or Graham? I initially thought it

776
00:47:30,120 --> 00:47:31,760
Speaker 2: was Canes, and then I checked myself.

777
00:47:32,040 --> 00:47:36,320
Speaker 3: I'm not sure, but what Kines said is that you

778
00:47:36,960 --> 00:47:43,879
Speaker 3: what was his quote? Markets can remain irrational longer than

779
00:47:43,920 --> 00:47:44,880
Speaker 3: you can remain solid.

780
00:47:44,920 --> 00:47:48,400
Speaker 2: So he never said that, but it's always attributed to him. Really,

781
00:47:48,520 --> 00:47:52,319
Speaker 2: there's a great website called Quote Investigator. I see, and

782
00:47:52,400 --> 00:47:55,800
Speaker 2: you give them a quote. Because people as a sort

783
00:47:55,800 --> 00:48:01,200
Speaker 2: of authority, you know, appeal to authority, they'll put somebody

784
00:48:01,280 --> 00:48:06,719
Speaker 2: sophisticated as the source. Einstein never said compounding is the most.

785
00:48:06,480 --> 00:48:09,480
Speaker 5: Powerful force in the universe, but they always attributed to

786
00:48:09,560 --> 00:48:12,239
Speaker 5: I didn't know that you would be I spend way

787
00:48:12,280 --> 00:48:15,560
Speaker 5: too much time perusing quotes on the site, but you

788
00:48:15,560 --> 00:48:20,320
Speaker 5: would be shocked who said markets are a voting machine

789
00:48:20,880 --> 00:48:22,920
Speaker 5: in the short run, but a weighing machine in the

790
00:48:22,960 --> 00:48:23,520
Speaker 5: long run.

791
00:48:23,760 --> 00:48:25,480
Speaker 3: I don't think it's Canes by the way, No, I

792
00:48:25,719 --> 00:48:26,399
Speaker 3: don't remember if.

793
00:48:26,320 --> 00:48:28,040
Speaker 2: It was Canes or Graham, but I thought it was

794
00:48:28,080 --> 00:48:31,520
Speaker 2: one or the other, and it'll tell me. Yeah, Benjamin Graham,

795
00:48:31,560 --> 00:48:34,640
Speaker 2: But the first thing that came to mind was Canes

796
00:48:34,680 --> 00:48:38,040
Speaker 2: because or Galbraith. They have so many favorite quotes from.

797
00:48:37,960 --> 00:48:42,919
Speaker 3: Yeah, well he said many relevant things today. Yes, absolutely,

798
00:48:43,080 --> 00:48:46,319
Speaker 3: but actually we have models that predict exactly that that

799
00:48:46,360 --> 00:48:48,320
Speaker 3: you know, on the short run you can have trends

800
00:48:48,360 --> 00:48:51,600
Speaker 3: and the irrational behavior, and on the long run it reverts

801
00:48:51,640 --> 00:48:54,640
Speaker 3: back to fundamentals. But the long run from our estimate

802
00:48:54,760 --> 00:48:57,960
Speaker 3: is like five ten years. I'm trying a long time scale.

803
00:48:58,200 --> 00:49:01,480
Speaker 2: I'm trying to remember I'd have been French of Farm

804
00:49:01,480 --> 00:49:04,640
Speaker 2: of French who said, you can't really tell if a

805
00:49:04,719 --> 00:49:07,320
Speaker 2: manager is skilled innt you until you have twenty years

806
00:49:07,320 --> 00:49:09,839
Speaker 2: of data, because it could just be good luck over

807
00:49:10,080 --> 00:49:13,360
Speaker 2: five or ten years, which is kind of fascinating. I

808
00:49:13,440 --> 00:49:18,040
Speaker 2: want to stay with the efficient market or the inelastic

809
00:49:18,120 --> 00:49:23,800
Speaker 2: market hypothesis. I'm curious as to your thoughts on EMH.

810
00:49:24,440 --> 00:49:28,320
Speaker 2: I've always thought markets were kind of sort of eventually efficient,

811
00:49:29,000 --> 00:49:33,399
Speaker 2: but not very efficient in the short run. What's your

812
00:49:33,440 --> 00:49:35,520
Speaker 2: criticism of EMH.

813
00:49:35,920 --> 00:49:39,239
Speaker 3: Well, It really depends what you mean my efficient. If

814
00:49:39,280 --> 00:49:43,160
Speaker 3: you mean that they're very close to unpredictable, then you're write.

815
00:49:43,200 --> 00:49:46,200
Speaker 3: But I think it's a very dumb down version of EMH.

816
00:49:46,680 --> 00:49:49,360
Speaker 3: The question is whether our prices reflect something from the

817
00:49:49,480 --> 00:49:56,600
Speaker 3: mental that is, in principle noble, that reflects reality or

818
00:49:56,640 --> 00:50:01,600
Speaker 3: not at all. And one I think smoking gun of

819
00:50:01,600 --> 00:50:06,600
Speaker 3: that is do you have long term mean reversion? That is,

820
00:50:07,000 --> 00:50:10,680
Speaker 3: can prices do random things in particular trending, which is

821
00:50:10,719 --> 00:50:14,279
Speaker 3: really completely against the EMH. On the short run, that

822
00:50:14,440 --> 00:50:18,520
Speaker 3: is from a week to six months, markets are trending

823
00:50:18,560 --> 00:50:21,880
Speaker 3: over six months, one year, and then on the longer

824
00:50:21,920 --> 00:50:27,280
Speaker 3: time scale they kind of hover around some long term

825
00:50:27,360 --> 00:50:30,279
Speaker 3: trend and I think this is true, but this is

826
00:50:30,360 --> 00:50:33,080
Speaker 3: really at odds with efficient market, which tells you that

827
00:50:33,160 --> 00:50:38,200
Speaker 3: every day markets are around the correct price and there's

828
00:50:38,239 --> 00:50:40,480
Speaker 3: no trend, no mean reversion.

829
00:50:40,640 --> 00:50:45,319
Speaker 2: Ever, which is which you know that that's not exactly

830
00:50:45,360 --> 00:50:49,080
Speaker 2: what your day to day experiences if you're in the markets.

831
00:50:48,880 --> 00:50:53,799
Speaker 2: It seems it's not magic that the collective votes of

832
00:50:53,960 --> 00:50:58,760
Speaker 2: all market participants don't magically bring you to the correct

833
00:50:58,800 --> 00:50:59,520
Speaker 2: in quotes.

834
00:50:59,400 --> 00:51:02,760
Speaker 3: But that's that's the assumption of efficient market, or actually

835
00:51:04,040 --> 00:51:09,000
Speaker 3: not even an assumption. It's the argument that collectively, you

836
00:51:09,000 --> 00:51:13,040
Speaker 3: know if you have. That's the difference between having rational

837
00:51:13,080 --> 00:51:18,640
Speaker 3: investors that all take decisions based on noisy observation, but

838
00:51:18,760 --> 00:51:23,640
Speaker 3: independent from one another. Then because they're independent, they realize

839
00:51:23,680 --> 00:51:27,640
Speaker 3: the mean I mean, some overpriced, some underprice, and then

840
00:51:27,719 --> 00:51:30,239
Speaker 3: it's a voting machine and the vote comes out right

841
00:51:30,280 --> 00:51:33,920
Speaker 3: because there's enough investors and they're uncorrelated to one another.

842
00:51:34,120 --> 00:51:36,400
Speaker 3: But the problem with markets is that it's not the

843
00:51:36,400 --> 00:51:39,960
Speaker 3: way it works. The people are influenced by what other

844
00:51:40,040 --> 00:51:43,120
Speaker 3: people are doing and what other people are saying. So

845
00:51:43,160 --> 00:51:47,399
Speaker 3: instead of having independent guys doing random stuff, they're kind

846
00:51:47,400 --> 00:51:52,520
Speaker 3: of one guy who's doing only one thing, which is

847
00:51:54,040 --> 00:51:58,359
Speaker 3: a fictitious body that aggregates everybody in the same way.

848
00:51:59,120 --> 00:52:01,719
Speaker 2: Let's jump to our favorite questions that we ask all

849
00:52:01,719 --> 00:52:05,200
Speaker 2: of our guests, starting with tell us about your mentors

850
00:52:05,280 --> 00:52:07,920
Speaker 2: who helped shape the direction of that career.

851
00:52:08,120 --> 00:52:11,520
Speaker 3: That's an easy one. I have, you know, several mentors,

852
00:52:11,560 --> 00:52:14,160
Speaker 3: but two of them are really close to my heart.

853
00:52:14,239 --> 00:52:16,680
Speaker 3: One is but man that brought of course, you know,

854
00:52:17,000 --> 00:52:21,080
Speaker 3: the fractal guy. I knew him personally, Oh really, yes,

855
00:52:21,280 --> 00:52:24,600
Speaker 3: and he did a lot of things in physics as well, sure,

856
00:52:25,760 --> 00:52:28,920
Speaker 3: and so he influenced a lot my wife. My wife

857
00:52:29,120 --> 00:52:35,120
Speaker 3: was a physicist before turning a playwright now, and and

858
00:52:35,160 --> 00:52:37,719
Speaker 3: she worked on fracture surfaces. The way you know, when

859
00:52:37,760 --> 00:52:42,000
Speaker 3: you break a material, what emerges from from from the

860
00:52:42,040 --> 00:52:46,080
Speaker 3: fracture is a kind of very rough landscape that is fractal.

861
00:52:46,200 --> 00:52:48,800
Speaker 3: And Manda Brought had worked on that, and there was

862
00:52:48,840 --> 00:52:50,080
Speaker 3: a lot of interaction and.

863
00:52:50,239 --> 00:52:52,400
Speaker 2: The fractal at a molecular level or at.

864
00:52:52,280 --> 00:52:56,960
Speaker 3: A yeah, well fractal from the kind of very fine

865
00:52:57,080 --> 00:53:03,120
Speaker 3: structure to microscopic length skill. And so Manelrad also did

866
00:53:03,320 --> 00:53:06,680
Speaker 3: his work on financial markets, and for me it was

867
00:53:06,760 --> 00:53:12,399
Speaker 3: really a revelation. It was something very influencing and out

868
00:53:12,440 --> 00:53:16,560
Speaker 3: of the dogma of Brownian statistics and Gaustian phenomena and

869
00:53:16,600 --> 00:53:18,800
Speaker 3: so on, and so it was also very close to

870
00:53:18,800 --> 00:53:21,680
Speaker 3: what I was doing myself in physics. So you know,

871
00:53:22,040 --> 00:53:25,440
Speaker 3: it was clear that he influenced me enormously on that.

872
00:53:26,760 --> 00:53:30,520
Speaker 2: Didn't he write a book on market crashes and how

873
00:53:30,520 --> 00:53:31,880
Speaker 2: there's a fractal.

874
00:53:31,520 --> 00:53:35,879
Speaker 3: Yeah, with the misbehavior of market that's right there, you go,

875
00:53:35,960 --> 00:53:37,359
Speaker 3: I'm actually quoted in that book.

876
00:53:38,080 --> 00:53:39,160
Speaker 2: Oh that's fascinating.

877
00:53:39,800 --> 00:53:43,600
Speaker 3: And then Pierre Gilldzene who was a Nobel Price in

878
00:53:43,640 --> 00:53:48,000
Speaker 3: physics of French physicists, who was so fantastic, and you

879
00:53:48,040 --> 00:53:51,600
Speaker 3: know both these two, and and also Phil Anderson who

880
00:53:51,640 --> 00:53:54,000
Speaker 3: was a Nobel Price in physics as well in the US.

881
00:53:54,719 --> 00:54:00,880
Speaker 3: These three people, they convinced me that you shouldn't be

882
00:54:00,960 --> 00:54:03,880
Speaker 3: stuck to your own field. You should broaden your scope,

883
00:54:04,280 --> 00:54:06,319
Speaker 3: and what you learn from one field can be very

884
00:54:06,400 --> 00:54:10,120
Speaker 3: useful understanding another field. The three of them, they're really

885
00:54:10,320 --> 00:54:15,080
Speaker 3: kind of hovered around and not got tied to their

886
00:54:15,120 --> 00:54:20,000
Speaker 3: specific initial field. And I think this creates well, at

887
00:54:20,120 --> 00:54:26,359
Speaker 3: least for me, this created this The inhibited me in

888
00:54:26,360 --> 00:54:29,160
Speaker 3: the sense that I thought, Okay, maybe I'm not legitimate

889
00:54:29,239 --> 00:54:32,279
Speaker 3: to speak about finance because I'm a physicist, but you know,

890
00:54:33,400 --> 00:54:37,080
Speaker 3: doesn't matter. If I have things that I strongly believe in,

891
00:54:37,440 --> 00:54:40,120
Speaker 3: I should better say them and go to the end

892
00:54:40,120 --> 00:54:42,480
Speaker 3: of them. So I think they were really influential in

893
00:54:42,520 --> 00:54:42,919
Speaker 3: that way.

894
00:54:43,000 --> 00:54:47,000
Speaker 2: So since we mentioned the misbehavior of markets, let's talk

895
00:54:47,040 --> 00:54:49,520
Speaker 2: about some books. What are some of your favorites? What

896
00:54:49,560 --> 00:54:50,560
Speaker 2: are you reading right now?

897
00:54:50,840 --> 00:54:51,640
Speaker 6: Wow? So many?

898
00:54:51,880 --> 00:54:55,600
Speaker 3: That's really a very broad question. So my last book

899
00:54:55,719 --> 00:55:03,560
Speaker 3: is a book on John and Paul By in Leslie Johnny.

900
00:55:03,880 --> 00:55:05,799
Speaker 3: It's a beautiful book. I really loved it.

901
00:55:05,880 --> 00:55:07,640
Speaker 6: What's the name of it, John and Paul?

902
00:55:08,440 --> 00:55:10,600
Speaker 2: Is it John and PAULA love story Memory?

903
00:55:10,680 --> 00:55:11,000
Speaker 6: Correct?

904
00:55:11,360 --> 00:55:15,719
Speaker 2: I'm a big Beatles fan, and.

905
00:55:14,200 --> 00:55:15,200
Speaker 3: Oh you should read it.

906
00:55:15,560 --> 00:55:17,480
Speaker 6: I'm very very I'm going to.

907
00:55:17,480 --> 00:55:21,920
Speaker 2: Make a recommendation to you for a YouTube channel called

908
00:55:22,719 --> 00:55:26,640
Speaker 2: you Can't Unhear This. They take apart Beatles songs in

909
00:55:26,719 --> 00:55:29,960
Speaker 2: ways that just little things that were done in the

910
00:55:30,040 --> 00:55:33,120
Speaker 2: recording process that in a million years you never would

911
00:55:33,160 --> 00:55:35,759
Speaker 2: have noticed, and then once you hear it, you just

912
00:55:35,840 --> 00:55:38,960
Speaker 2: can't unhear it. And if you're a Beatles fan, it's

913
00:55:39,000 --> 00:55:41,920
Speaker 2: a rabbit hole. You'll you'll love this. What else besides

914
00:55:42,000 --> 00:55:44,200
Speaker 2: John and Paul, Yeah.

915
00:55:44,239 --> 00:55:46,640
Speaker 3: I'm reading someone that's something that I actually have read

916
00:55:46,680 --> 00:55:51,359
Speaker 3: for years that Missus Dollaway Virginia or Wolf. Yeah, I'm

917
00:55:51,400 --> 00:55:54,120
Speaker 3: read a great admirer of Virginia.

918
00:55:53,800 --> 00:56:01,280
Speaker 2: Wolf really really interesting. Do you do much streaming TV

919
00:56:01,719 --> 00:56:03,759
Speaker 2: podcast anything like that or we can skip.

920
00:56:03,520 --> 00:56:07,200
Speaker 3: Both podcasts a bit, but they're kind of French podcast.

921
00:56:07,000 --> 00:56:09,200
Speaker 2: So let's let's you know, people are always looking for stuff.

922
00:56:09,360 --> 00:56:12,920
Speaker 2: I'm going to ask that, So what are you streaming today?

923
00:56:12,960 --> 00:56:14,600
Speaker 2: What sort of podcasts are you listening to.

924
00:56:14,719 --> 00:56:16,960
Speaker 3: Yeah, you know, in France, we were very fortunate we

925
00:56:17,000 --> 00:56:21,080
Speaker 3: have something called Hoskulture. It's a radio where there's an

926
00:56:21,280 --> 00:56:24,239
Speaker 3: enormal I mean, you could stay tuned all day if

927
00:56:24,280 --> 00:56:27,319
Speaker 3: you want it. There's so many interesting things going on

928
00:56:27,680 --> 00:56:33,040
Speaker 3: about everything cultural literature, but also you know, movies, politics,

929
00:56:33,320 --> 00:56:33,880
Speaker 3: and so.

930
00:56:34,440 --> 00:56:35,520
Speaker 2: We have NPR here.

931
00:56:35,560 --> 00:56:36,440
Speaker 6: It's very similar.

932
00:56:36,520 --> 00:56:38,640
Speaker 2: You can it's a rabbit hole you could fall down.

933
00:56:39,120 --> 00:56:43,600
Speaker 3: Yeah, okay, so life of big peeple of major celebrities

934
00:56:44,360 --> 00:56:49,400
Speaker 3: in culture, in in cinema and theater, all these things.

935
00:56:49,440 --> 00:56:53,000
Speaker 3: So I'm really a big fan of Hostalture.

936
00:56:52,920 --> 00:56:57,000
Speaker 2: And our final two questions, First, what sort of advice

937
00:56:57,040 --> 00:57:00,359
Speaker 2: would you give to a recent college grad interest did

938
00:57:00,560 --> 00:57:05,760
Speaker 2: in a career in either quantitative investing or theoretical physics.

939
00:57:06,480 --> 00:57:12,480
Speaker 3: Well, study theoretical physics and study everything that's related to data,

940
00:57:12,640 --> 00:57:17,720
Speaker 3: and pay attention to data. And think about something that

941
00:57:17,800 --> 00:57:20,840
Speaker 3: you strongly believe in and that you feel has not

942
00:57:21,000 --> 00:57:24,920
Speaker 3: been investigated, and it doesn't matter if it's big or small.

943
00:57:25,440 --> 00:57:28,560
Speaker 3: Make the effort of building something you strongly believe in.

944
00:57:29,600 --> 00:57:34,040
Speaker 2: Really good answer. And our final question, what do you

945
00:57:34,040 --> 00:57:36,720
Speaker 2: know about the world of investing today? Might have been

946
00:57:36,800 --> 00:57:39,600
Speaker 2: useful thirty five years or so ago when you were

947
00:57:39,600 --> 00:57:40,600
Speaker 2: first getting started.

948
00:57:41,000 --> 00:57:43,800
Speaker 3: That is extremely competitive, much more than we thought.

949
00:57:44,280 --> 00:57:48,680
Speaker 2: Really wow, that's really fascinating. Jean Philip, thank you so

950
00:57:48,800 --> 00:57:52,040
Speaker 2: much for being so generous with your time. We have

951
00:57:52,160 --> 00:57:58,120
Speaker 2: been speaking with Jean Philippe Boucheau, cfm's co founder, chairman,

952
00:57:58,320 --> 00:58:02,560
Speaker 2: and chief scientist. If you enjoy this conversation, well check

953
00:58:02,600 --> 00:58:04,840
Speaker 2: out any of the six hundred plus we've done over

954
00:58:04,880 --> 00:58:11,280
Speaker 2: the past twelve years. You could find those at Apple Podcasts, Spotify, YouTube, Bloomberg,

955
00:58:11,400 --> 00:58:15,720
Speaker 2: wherever you find your favorite podcasts. I would be remiss

956
00:58:15,760 --> 00:58:17,680
Speaker 2: if I didn't thank the crack team that helps me

957
00:58:17,720 --> 00:58:21,800
Speaker 2: put these conversations together each week. Meredith Frank is my

958
00:58:21,960 --> 00:58:26,480
Speaker 2: video producer. Anna Luke is my podcast producer. Sean Russo

959
00:58:26,760 --> 00:58:30,400
Speaker 2: is that my head of research. I'm Barry Ritoults. You've

960
00:58:30,400 --> 00:58:38,600
Speaker 2: been listening to Masters in Business on Bloomberg Radio.