1
00:00:04,519 --> 00:00:07,480
Speaker 1: Hello, and welcome to the first episode of Odd Lots.

2
00:00:07,600 --> 00:00:09,959
Speaker 1: I'm Joe Wisnthal, co host of What You Miss and

3
00:00:10,440 --> 00:00:13,960
Speaker 1: editor at Bloomberg markets Now. I'm Tracy Alloway, executive editor

4
00:00:14,040 --> 00:00:16,720
Speaker 1: of Bloomberg Markets. Here at Odd Lots, we want to

5
00:00:16,760 --> 00:00:23,680
Speaker 1: have a discussion every week about economics, finance, markets, market structure,

6
00:00:23,680 --> 00:00:27,920
Speaker 1: which Tracy loves, maybe some politics and culture thrown in

7
00:00:28,480 --> 00:00:31,400
Speaker 1: stuff that doesn't necessarily fit into the normal day to

8
00:00:31,480 --> 00:00:34,640
Speaker 1: day conversation. And we couldn't think of any guests better

9
00:00:34,720 --> 00:00:39,960
Speaker 1: to have than Tom Kin, my guinea pig. You're the

10
00:00:39,960 --> 00:00:44,160
Speaker 1: guinea pig. Tom King, as everyone should know, is the

11
00:00:44,200 --> 00:00:48,559
Speaker 1: host of Bloomberg Surveillance on TV and radio here Bloomberg.

12
00:00:48,640 --> 00:00:52,760
Speaker 1: He knows more about markets and economics and world events

13
00:00:52,760 --> 00:00:55,080
Speaker 1: than just about anyone else in the room. He's a

14
00:00:55,240 --> 00:00:59,680
Speaker 1: very eclectic background in music and mathematics. And I wanted

15
00:00:59,720 --> 00:01:01,720
Speaker 1: to have wanted to have Tom Keene on the show

16
00:01:01,720 --> 00:01:04,920
Speaker 1: because Tom is always interviewing people, but he'd never answering

17
00:01:05,040 --> 00:01:08,440
Speaker 1: questions and never behind try to avoid it like the plagues.

18
00:01:09,560 --> 00:01:11,560
Speaker 1: So we're gonna doing this for you, Joe. I'm doing

19
00:01:11,560 --> 00:01:15,479
Speaker 1: this for Tracey. Everybody knows that, but um, who are

20
00:01:15,520 --> 00:01:17,880
Speaker 1: you Tom? Why are you here? How did you get

21
00:01:17,880 --> 00:01:22,479
Speaker 1: to be Tom Keane? Who? Everybody? I get it a lot,

22
00:01:22,680 --> 00:01:26,720
Speaker 1: And what I would suggest is it's one part short

23
00:01:26,840 --> 00:01:30,440
Speaker 1: term stuff, one part long term stuff, and one part

24
00:01:30,520 --> 00:01:34,240
Speaker 1: blind luck. The long term thing is is being acutely

25
00:01:34,280 --> 00:01:36,680
Speaker 1: aware when I was a kid, and evermore every day

26
00:01:36,840 --> 00:01:40,800
Speaker 1: knowing how twisted the early years where I found something

27
00:01:40,840 --> 00:01:43,959
Speaker 1: like bet stan Freeburg record and you don't know Stan

28
00:01:44,000 --> 00:01:46,080
Speaker 1: Freeburg is he was a great comedian. He just died

29
00:01:46,120 --> 00:01:49,720
Speaker 1: this year. And then recently it was about the privilege

30
00:01:49,760 --> 00:01:53,520
Speaker 1: of running into Matt Winkler and basically Matt and I,

31
00:01:53,680 --> 00:01:56,720
Speaker 1: with the support of l Mayers who runs Bloomberg Media,

32
00:01:56,720 --> 00:01:59,760
Speaker 1: and Ted Fine who runs TV, they're the ones that

33
00:02:00,000 --> 00:02:02,880
Speaker 1: made all this happen. Matt win Clair, for our listeners

34
00:02:02,920 --> 00:02:06,640
Speaker 1: who don't know, is the guy who founded Bloomberg News. Essentially,

35
00:02:06,680 --> 00:02:09,160
Speaker 1: so you got lucky. You met Matt win Clair, you

36
00:02:09,280 --> 00:02:12,480
Speaker 1: just hired me because the bow tie. But to make

37
00:02:12,520 --> 00:02:15,880
Speaker 1: a long story short, you get lucky and I met

38
00:02:15,919 --> 00:02:20,079
Speaker 1: Matt and we basically invented what you see. That's that's

39
00:02:20,080 --> 00:02:22,280
Speaker 1: a safe statement. What were you doing before then? Because

40
00:02:22,280 --> 00:02:25,120
Speaker 1: when I think of you, I think you projected, or

41
00:02:25,160 --> 00:02:28,720
Speaker 1: of someone who's been doing radio for decades. But what

42
00:02:28,760 --> 00:02:31,560
Speaker 1: were you doing before you did radio and TV? Well,

43
00:02:31,720 --> 00:02:33,880
Speaker 1: before the media thing. I was in the investment business.

44
00:02:33,919 --> 00:02:36,640
Speaker 1: But there's a whole side car thing in music and

45
00:02:36,680 --> 00:02:41,519
Speaker 1: an entertainment um for example. And I gauge it off

46
00:02:41,600 --> 00:02:45,360
Speaker 1: my oldest child's age. I used to hold him in

47
00:02:45,400 --> 00:02:48,600
Speaker 1: my arms and do the stock report in Boston. And

48
00:02:48,680 --> 00:02:51,240
Speaker 1: this is the vanilla days, not cross asset, the dal

49
00:02:51,360 --> 00:02:54,919
Speaker 1: Jones industrial average up forty two points today, eight forty two,

50
00:02:54,919 --> 00:02:58,400
Speaker 1: blah blah blah. You know that. And so I did

51
00:02:58,400 --> 00:03:00,720
Speaker 1: a little bit of investment stuff back then, but a

52
00:03:00,720 --> 00:03:02,760
Speaker 1: lot of it was just the music business as well,

53
00:03:03,000 --> 00:03:06,080
Speaker 1: which is the show business aspect, which a lot of

54
00:03:06,120 --> 00:03:09,040
Speaker 1: people in business media try to ignore every day, and

55
00:03:09,080 --> 00:03:12,079
Speaker 1: they're wrong. I mean, the f T s pink. It's

56
00:03:12,120 --> 00:03:15,040
Speaker 1: pink for a reason. It's pink. It's like what you did.

57
00:03:15,080 --> 00:03:20,880
Speaker 1: I mean, you invented the modern headline in modern business journalism.

58
00:03:21,080 --> 00:03:24,160
Speaker 1: So it's just you know that Wisenthal wrote that headliner.

59
00:03:24,280 --> 00:03:27,880
Speaker 1: You just know that how has her investment experience informed

60
00:03:27,880 --> 00:03:32,160
Speaker 1: your career? A huge, huge um, it's a massive type

61
00:03:32,160 --> 00:03:36,440
Speaker 1: two and that you learn so much enjoying losing money. Um,

62
00:03:36,800 --> 00:03:38,880
Speaker 1: it's it's for those of you gaus see and it's

63
00:03:38,920 --> 00:03:42,200
Speaker 1: log normal. Uh, you learn way more on the downside

64
00:03:42,200 --> 00:03:45,240
Speaker 1: and the upside. Lots of fat tail risks. There's fat

65
00:03:45,240 --> 00:03:48,320
Speaker 1: tail risk, but that's not that that's over emphasized. It's

66
00:03:48,360 --> 00:03:52,480
Speaker 1: the joy of losing money within the fat tails, which

67
00:03:52,520 --> 00:03:58,680
Speaker 1: is I think that's you learn factors more losing money

68
00:03:59,160 --> 00:04:02,360
Speaker 1: than making money factors. But I can tell you that

69
00:04:02,440 --> 00:04:05,160
Speaker 1: the way I learned to lose money was enjoying losing

70
00:04:05,160 --> 00:04:08,160
Speaker 1: money in the options market. And then so when you're

71
00:04:08,320 --> 00:04:10,600
Speaker 1: doing the show, whether on radio or TV, how do

72
00:04:10,640 --> 00:04:13,960
Speaker 1: you apply that the fact that you learned so much

73
00:04:14,000 --> 00:04:16,040
Speaker 1: more when you lost money. When you think about it,

74
00:04:16,080 --> 00:04:18,920
Speaker 1: it's a humility show. It's a it's a humility thing

75
00:04:19,040 --> 00:04:24,599
Speaker 1: of knowing every day how dumb you are and trying

76
00:04:24,600 --> 00:04:28,680
Speaker 1: to always work at getting smarter, laughing at your mistakes.

77
00:04:29,720 --> 00:04:32,159
Speaker 1: There's a there's a lot there's less now after the

78
00:04:32,160 --> 00:04:36,479
Speaker 1: financial crisis, but there's lots of people strutting around with

79
00:04:36,560 --> 00:04:42,640
Speaker 1: a certain intellectual arrogance about economics, finance, investment. Right now

80
00:04:42,680 --> 00:04:45,840
Speaker 1: nobody has arrogance and international relations. Did you have to

81
00:04:45,920 --> 00:04:48,640
Speaker 1: learn how much you don't know, Like, where's the point

82
00:04:48,680 --> 00:04:50,520
Speaker 1: earlier in your career where you thought you knew it all?

83
00:04:50,560 --> 00:04:55,800
Speaker 1: And then you know certitude of yeah, you you you

84
00:04:55,920 --> 00:05:01,000
Speaker 1: learn from a wide set of mistakes and cycles, which

85
00:05:01,440 --> 00:05:05,599
Speaker 1: gives you a humility which forces you to get smarter.

86
00:05:05,640 --> 00:05:08,200
Speaker 1: For example, I went to a wedding this weekend and

87
00:05:08,360 --> 00:05:12,320
Speaker 1: half the wedding was from Uruguay. I know nothing about

88
00:05:12,440 --> 00:05:16,080
Speaker 1: Montevideo except one of my kids. Friends all went down

89
00:05:16,120 --> 00:05:19,320
Speaker 1: there because it couldn't get a job in America. And

90
00:05:19,440 --> 00:05:23,960
Speaker 1: I read seven articles this weekend in Uruguay just to

91
00:05:23,960 --> 00:05:27,760
Speaker 1: to begin to I have no clue about Uruguay. There's

92
00:05:27,800 --> 00:05:32,480
Speaker 1: that kind of madness, but compounded over I know nothing

93
00:05:32,480 --> 00:05:34,839
Speaker 1: about Uruguay. I know nothing about Montevideo. Is a great

94
00:05:34,920 --> 00:05:37,320
Speaker 1: humble bread because you slide in how easily you knew

95
00:05:37,360 --> 00:05:40,280
Speaker 1: the capital. Now I have to ask you talk about

96
00:05:40,279 --> 00:05:43,880
Speaker 1: certitude on Wall Street and in addition to having a

97
00:05:43,960 --> 00:05:47,839
Speaker 1: musical background, you also have a mathematical background. And it

98
00:05:47,880 --> 00:05:50,559
Speaker 1: seems like one of the areas in markets where people

99
00:05:50,640 --> 00:05:54,200
Speaker 1: start to get really certain and have that certain mathematical

100
00:05:54,279 --> 00:05:56,839
Speaker 1: swagger is when it comes to models, and you love

101
00:05:56,920 --> 00:06:00,200
Speaker 1: talking about models, how does your experience in mathematic nex

102
00:06:00,279 --> 00:06:03,760
Speaker 1: feed in. Yeah, that the background there was growing up

103
00:06:03,800 --> 00:06:08,200
Speaker 1: with it. I have the clearest memories of gooding up

104
00:06:08,200 --> 00:06:11,160
Speaker 1: on my tiptoes and looking over my father's desk as

105
00:06:11,200 --> 00:06:16,359
Speaker 1: he did very sophisticated triple integration of space satellites and

106
00:06:16,400 --> 00:06:18,320
Speaker 1: I would literally play on the floor with the French

107
00:06:18,400 --> 00:06:21,640
Speaker 1: curves is a million years ago and like like think

108
00:06:21,680 --> 00:06:24,400
Speaker 1: spot Nick and I g y and all that, and

109
00:06:24,800 --> 00:06:29,679
Speaker 1: all of that became a mathewiness which culminated in Max

110
00:06:29,760 --> 00:06:34,000
Speaker 1: peters fabulous program at the University of Colorado. Max Peters

111
00:06:34,080 --> 00:06:38,200
Speaker 1: was a highly decorated Italian infantryman up the spine of

112
00:06:38,240 --> 00:06:40,400
Speaker 1: Italy and World War Two, and he went out to

113
00:06:40,480 --> 00:06:44,440
Speaker 1: Colorado and put together, uh, the mother of all grinds

114
00:06:44,600 --> 00:06:49,640
Speaker 1: in engineering academics. And I was extremely fortunate to parachute

115
00:06:49,640 --> 00:06:52,960
Speaker 1: into that for a couple of years. So you take,

116
00:06:53,480 --> 00:06:55,000
Speaker 1: you know, what I get in math and what I

117
00:06:55,040 --> 00:06:57,000
Speaker 1: don't get, and trust me, is a lot I don't get.

118
00:06:57,720 --> 00:07:02,440
Speaker 1: And then you overlay that into some of the certitude

119
00:07:02,440 --> 00:07:05,960
Speaker 1: of quantitative finance and you get a massive humility. I

120
00:07:06,480 --> 00:07:10,920
Speaker 1: think the math overlay is a it's a massive type one.

121
00:07:11,000 --> 00:07:14,120
Speaker 1: You've got it, but what it really is, and I

122
00:07:14,200 --> 00:07:16,480
Speaker 1: see it every day, and it's getting worse. It's a

123
00:07:16,520 --> 00:07:18,560
Speaker 1: little better in a couple of last couple of years.

124
00:07:18,960 --> 00:07:23,880
Speaker 1: Is the math phobia within economics, finance, investments just stunning,

125
00:07:24,040 --> 00:07:29,800
Speaker 1: It's just breathtaking. You see rampant math phobia. Because other

126
00:07:29,840 --> 00:07:33,200
Speaker 1: people have argued that it's just the opposite, that economics

127
00:07:33,240 --> 00:07:36,360
Speaker 1: and finances become too mathy, to the point where people

128
00:07:36,440 --> 00:07:40,080
Speaker 1: can't explain in clear English what they're talking about. Yeah, well,

129
00:07:40,120 --> 00:07:42,480
Speaker 1: let me part that debate. You're absolutely right, Joe. The

130
00:07:42,480 --> 00:07:45,880
Speaker 1: basic idea is there was an era, particularly coming out

131
00:07:45,880 --> 00:07:49,000
Speaker 1: of World War Two, of of math, too much math,

132
00:07:49,080 --> 00:07:53,640
Speaker 1: math anxiety, etcetera, etcetera. And then at the undergraduate level,

133
00:07:53,680 --> 00:07:55,960
Speaker 1: not at the PhD, not at the doctor trek level

134
00:07:55,960 --> 00:07:59,360
Speaker 1: of the graduate level, but at the undergraduate level, a

135
00:07:59,480 --> 00:08:03,400
Speaker 1: vast majority of people don't have the dynamics in their

136
00:08:03,440 --> 00:08:08,440
Speaker 1: head to do even basic martiality and microeconomics or you know,

137
00:08:08,720 --> 00:08:11,160
Speaker 1: name the flavor of macro you want to do. The

138
00:08:11,280 --> 00:08:14,920
Speaker 1: British are very different. In the French they have much better,

139
00:08:15,400 --> 00:08:18,960
Speaker 1: as a rule of thumb, undergraduate mathematics than we do.

140
00:08:19,080 --> 00:08:22,360
Speaker 1: If I, if I talked to British students. Their knowledge

141
00:08:22,360 --> 00:08:26,840
Speaker 1: of first order difference equations off the chart honors undergraduate

142
00:08:26,880 --> 00:08:28,440
Speaker 1: programs in the US. Some of them they have no

143
00:08:28,520 --> 00:08:31,560
Speaker 1: clue what what that is. I'm pleased to say I've

144
00:08:31,600 --> 00:08:37,440
Speaker 1: I've forgotten almost all the mathematics I learned in college. However, however,

145
00:08:37,480 --> 00:08:39,800
Speaker 1: I want to know. So when you see something like

146
00:08:39,840 --> 00:08:42,960
Speaker 1: the events of August when the market sold off and

147
00:08:43,000 --> 00:08:46,880
Speaker 1: a lot of people were talking about mathematical formulas and

148
00:08:47,000 --> 00:08:50,400
Speaker 1: model based equations and risk parody at the center of

149
00:08:50,440 --> 00:08:53,560
Speaker 1: that sell off, what do you think I think some

150
00:08:53,640 --> 00:08:58,720
Speaker 1: of it was extremely valid this time around. Uh I,

151
00:08:58,720 --> 00:09:02,360
Speaker 1: I think that the modtle fatigue is much more in

152
00:09:02,400 --> 00:09:05,440
Speaker 1: the macro area. The work Olivia Blanchard did at the

153
00:09:05,440 --> 00:09:08,800
Speaker 1: i m F with Joe Stiglson others really important to

154
00:09:08,840 --> 00:09:15,640
Speaker 1: ask the non sophisticated and the very sophisticated differential equation

155
00:09:15,679 --> 00:09:18,960
Speaker 1: models that pro PhDs use, and I don't pretend to

156
00:09:18,960 --> 00:09:22,360
Speaker 1: be fluent in them. They're very suspect after what we

157
00:09:22,400 --> 00:09:26,040
Speaker 1: went through in August of oh seven. Stepping back, so

158
00:09:26,080 --> 00:09:29,920
Speaker 1: you have this interest in lifelong interest in mathematics in music,

159
00:09:29,960 --> 00:09:32,120
Speaker 1: which I was also want to get to. But then

160
00:09:32,200 --> 00:09:34,680
Speaker 1: how did that when did it click that markets and

161
00:09:34,800 --> 00:09:38,000
Speaker 1: investments where they were in the earliest memories, earliest it

162
00:09:38,080 --> 00:09:41,680
Speaker 1: was permeating in my house. My my grandfather knew a

163
00:09:41,880 --> 00:09:44,440
Speaker 1: w CO and the point and figure guy. He did

164
00:09:44,480 --> 00:09:48,479
Speaker 1: point and figure charts. My mother did point and figure charts,

165
00:09:49,840 --> 00:09:56,920
Speaker 1: totally totally permeating investment theory and investment analysis, you know,

166
00:09:57,040 --> 00:10:00,400
Speaker 1: just original Graham, Don and Coddle up on. So in

167
00:10:00,400 --> 00:10:02,640
Speaker 1: addition to everything else you were always interested in, you

168
00:10:02,679 --> 00:10:05,560
Speaker 1: always knew that this was something you wanted to know.

169
00:10:05,760 --> 00:10:08,920
Speaker 1: You know, I didn't know, it just was there. It

170
00:10:09,040 --> 00:10:11,960
Speaker 1: was just there kind of thing. I'm also, I don't

171
00:10:12,000 --> 00:10:14,720
Speaker 1: think a lot of people know about your musical background.

172
00:10:14,800 --> 00:10:18,440
Speaker 1: But why do you give us the sixteed and here's

173
00:10:18,840 --> 00:10:21,559
Speaker 1: Tom King's music? The ninety second version is real simple.

174
00:10:21,640 --> 00:10:25,160
Speaker 1: At eight years old, I walked into a place called Stutsman's,

175
00:10:25,200 --> 00:10:28,920
Speaker 1: which is legendary and the acoustic music business. With my father.

176
00:10:29,000 --> 00:10:32,760
Speaker 1: There were six Grutch White Falcons lined up and where Rochester,

177
00:10:32,840 --> 00:10:36,040
Speaker 1: New York Kodak and my father bought me a forty

178
00:10:36,080 --> 00:10:39,320
Speaker 1: two dollar you know, acoustic guitar, and then I just

179
00:10:39,360 --> 00:10:44,400
Speaker 1: began grinding away and there were three or four iterations

180
00:10:44,440 --> 00:10:47,280
Speaker 1: of it. But to make a long story short, I

181
00:10:47,440 --> 00:10:50,960
Speaker 1: ended up doing the New England singer songwriter thing, juggling

182
00:10:50,960 --> 00:10:53,800
Speaker 1: a bunch of other stuff. There's a place in Nashville

183
00:10:53,840 --> 00:11:00,240
Speaker 1: called the Blueberg Cafe, which is magical Bloomberg. You know.

184
00:11:00,360 --> 00:11:01,920
Speaker 1: It was just the New England folks and it was

185
00:11:01,960 --> 00:11:06,439
Speaker 1: sort of you know in terms of artists around it. Uh,

186
00:11:06,480 --> 00:11:11,079
Speaker 1: it was post Tracy Chapman, um Susan Vega was really

187
00:11:11,120 --> 00:11:15,040
Speaker 1: happening with Luca and Solitude standing and then a whole

188
00:11:15,040 --> 00:11:18,000
Speaker 1: host of people came on, really jumped, started by a

189
00:11:18,000 --> 00:11:21,000
Speaker 1: guy named David Wilcox who did an album called Either

190
00:11:21,120 --> 00:11:24,240
Speaker 1: Hurricane for A and M Records, which just there was

191
00:11:24,320 --> 00:11:27,160
Speaker 1: like this mini folk boom and what was so cool?

192
00:11:27,760 --> 00:11:30,120
Speaker 1: We knew when we this is before the internet. That's

193
00:11:30,160 --> 00:11:33,560
Speaker 1: a key statement. Even we had no idea what was

194
00:11:33,600 --> 00:11:36,880
Speaker 1: coming in digital, but we knew how lucky we were

195
00:11:37,040 --> 00:11:40,840
Speaker 1: to do it. When we were doing it, we knew

196
00:11:40,880 --> 00:11:44,480
Speaker 1: it couldn't last. What was the greatest guitar you've ever owned?

197
00:11:45,520 --> 00:11:47,720
Speaker 1: The one I got now with the greatest guitar my

198
00:11:47,880 --> 00:11:51,640
Speaker 1: my concert Gibson J one hundred, which was picked out

199
00:11:51,679 --> 00:11:55,440
Speaker 1: by Eric Schoenberg up in Boston was stolen and I

200
00:11:55,440 --> 00:11:57,480
Speaker 1: got it back four years ago. I told Dave Drummond

201
00:11:57,480 --> 00:12:00,360
Speaker 1: and Google. I got it off Google Images. There was

202
00:12:00,400 --> 00:12:03,319
Speaker 1: my guitar and Google images. But that and I've got

203
00:12:03,320 --> 00:12:06,840
Speaker 1: some others now. But I think that's you know, I

204
00:12:06,840 --> 00:12:09,400
Speaker 1: guess the best ones that when my father had, but

205
00:12:09,559 --> 00:12:14,520
Speaker 1: that's been lost. So with your very very idiosyncratic background

206
00:12:15,040 --> 00:12:20,480
Speaker 1: in mathematics, it's almost like Joe Wisenthal an investment. When

207
00:12:20,520 --> 00:12:22,920
Speaker 1: you do your show today at Bloomberg and you look

208
00:12:22,960 --> 00:12:25,000
Speaker 1: around the world, what do you see as the most

209
00:12:25,040 --> 00:12:29,480
Speaker 1: important story going on right now? Um, I think the

210
00:12:29,559 --> 00:12:32,000
Speaker 1: number one stories. One of my kids said to me, Daddy,

211
00:12:32,000 --> 00:12:35,160
Speaker 1: when does this get got to be fun? And I

212
00:12:35,200 --> 00:12:40,800
Speaker 1: think there's a massive understanding by people of a certain

213
00:12:40,920 --> 00:12:45,960
Speaker 1: vintage that the kids don't have. They have lots of

214
00:12:46,240 --> 00:12:51,240
Speaker 1: wonderful digital stuff and medical stuff, etcetera, etcetera, but the

215
00:12:51,360 --> 00:12:57,440
Speaker 1: optimism has been shattered. And the answer Jeff emmel I

216
00:12:57,480 --> 00:13:00,000
Speaker 1: was with two years ago, I'm guessing and he said, look,

217
00:13:00,000 --> 00:13:02,800
Speaker 1: all we need is three point two percent GDP and

218
00:13:02,840 --> 00:13:05,440
Speaker 1: that solves a lot of problems. That's a very smart

219
00:13:05,480 --> 00:13:10,160
Speaker 1: comment by the applied mathematician from Dartmouth. We don't have that.

220
00:13:10,480 --> 00:13:14,800
Speaker 1: The the younger people, people under about thirty two, have

221
00:13:15,080 --> 00:13:19,160
Speaker 1: never known normal. So when you look at the world,

222
00:13:19,160 --> 00:13:24,920
Speaker 1: you don't necessarily see problems of inequality, you see generational problems. No,

223
00:13:25,040 --> 00:13:28,400
Speaker 1: I think they're both there, but I think in two

224
00:13:28,400 --> 00:13:33,840
Speaker 1: thousand and fifteen that the generational issues are less spoken,

225
00:13:34,280 --> 00:13:37,440
Speaker 1: which to an extent speaks of the anger into politics today.

226
00:13:37,720 --> 00:13:39,480
Speaker 1: When do you think it wasn't normal? Or when was

227
00:13:39,600 --> 00:13:41,680
Speaker 1: it was? Well, you know, you stand on the floor

228
00:13:41,679 --> 00:13:44,280
Speaker 1: of the Republican convention. Ex conventions are going you go,

229
00:13:44,520 --> 00:13:48,080
Speaker 1: well this is surreal or the Democratic doesn't matter which

230
00:13:48,160 --> 00:13:52,960
Speaker 1: part party. But the answer is we are programmed for

231
00:13:53,120 --> 00:13:57,679
Speaker 1: certain nominally real GDP that ain't happened. There's a quarter here,

232
00:13:57,679 --> 00:14:00,840
Speaker 1: at quarter there, Macroeconomic Advisor is right now, hiss, third

233
00:14:00,920 --> 00:14:04,480
Speaker 1: quarter at one. The next quarter is a little bit better.

234
00:14:04,520 --> 00:14:08,920
Speaker 1: But we we have not had the run rate of

235
00:14:09,040 --> 00:14:14,640
Speaker 1: GDP that provides base psychological comfort to a lot of people,

236
00:14:15,600 --> 00:14:19,960
Speaker 1: whether it's over educated torps like my kids, or you know,

237
00:14:20,120 --> 00:14:23,280
Speaker 1: people really struggling millions of Americans. Do you think I mean?

238
00:14:23,320 --> 00:14:27,600
Speaker 1: I remember thinking in and when we had the Raging

239
00:14:27,640 --> 00:14:29,720
Speaker 1: Dead ceiling debate, and I think that was the first

240
00:14:29,760 --> 00:14:32,760
Speaker 1: time that we saw this huge I would say, the

241
00:14:32,880 --> 00:14:36,680
Speaker 1: crisis and the economy seeming to really spill over into politics,

242
00:14:36,720 --> 00:14:39,640
Speaker 1: and we had this start division the Tea Party and leadership.

243
00:14:39,960 --> 00:14:42,680
Speaker 1: But it hasn't faded as much as I would have guessed,

244
00:14:42,680 --> 00:14:47,920
Speaker 1: given how far unemployment has dropped. I mean, the economy

245
00:14:48,000 --> 00:14:49,840
Speaker 1: is much better than it was in eleven, but we

246
00:14:49,880 --> 00:14:53,600
Speaker 1: still have look at Donald Trump and leading in the polls,

247
00:14:53,640 --> 00:14:56,800
Speaker 1: and we have rise of more radical politicians everywhere. Do

248
00:14:56,840 --> 00:14:58,960
Speaker 1: you think it's something beyond economics. It has to do

249
00:14:58,960 --> 00:15:02,320
Speaker 1: with the media environment, the Internet. It has to do

250
00:15:02,400 --> 00:15:04,280
Speaker 1: the media, and it has to do with speed of

251
00:15:04,320 --> 00:15:07,080
Speaker 1: trans Twitter and all. I mean the Cypress crisis alone

252
00:15:07,400 --> 00:15:10,840
Speaker 1: with Twitter was surreal that Saturday morning when Cyperus drew up.

253
00:15:11,120 --> 00:15:15,520
Speaker 1: How the Twitter dialogue changed the discussion. But what I

254
00:15:15,520 --> 00:15:19,360
Speaker 1: would what is under emphasized from a Newtonian mechanics standpoint

255
00:15:19,480 --> 00:15:23,360
Speaker 1: is inertial force and the word chronic. And the answer

256
00:15:23,560 --> 00:15:26,960
Speaker 1: is you totally right about ten and eleven. And what's

257
00:15:27,000 --> 00:15:29,720
Speaker 1: different now is it may not be forced masure like

258
00:15:29,760 --> 00:15:33,880
Speaker 1: it was then, but there's just this chronic weight of

259
00:15:34,000 --> 00:15:38,200
Speaker 1: gridlock in Washington. This chronic sense of g d P

260
00:15:38,440 --> 00:15:42,600
Speaker 1: under performing even while unemployment supposedly gets better. And that

261
00:15:42,640 --> 00:15:46,680
Speaker 1: goes back to productivity. You know, we could board everybody

262
00:15:46,760 --> 00:15:53,080
Speaker 1: with three ratio productivity analysis just act productivity in the

263
00:15:53,120 --> 00:15:59,040
Speaker 1: media is uh appallingly reported. There's capital, there's labor, and

264
00:15:59,080 --> 00:16:01,920
Speaker 1: there's a separate ray show wrapped around something called total

265
00:16:02,040 --> 00:16:07,440
Speaker 1: factor productivity or TFP. And the pros know all that

266
00:16:08,040 --> 00:16:09,800
Speaker 1: and they sort of just, you know, when we talk

267
00:16:09,880 --> 00:16:13,960
Speaker 1: about productivity, gloss over it. But the answers laboring happening.

268
00:16:14,000 --> 00:16:17,680
Speaker 1: And certainly for a part of America, this Angus Deaton

269
00:16:17,760 --> 00:16:20,680
Speaker 1: winning the Nobel Prize a big deal, big big deal.

270
00:16:20,880 --> 00:16:24,720
Speaker 1: This is the death of aggregate aggregation, of summoning everything

271
00:16:24,760 --> 00:16:28,160
Speaker 1: together and that that's a really big deal that I

272
00:16:28,480 --> 00:16:31,600
Speaker 1: talked to Shower today and I chance to talk about it. Well,

273
00:16:31,640 --> 00:16:34,280
Speaker 1: that brings us to again to your show. When you

274
00:16:34,360 --> 00:16:37,400
Speaker 1: go out and talk to people, what makes some good

275
00:16:37,440 --> 00:16:40,680
Speaker 1: interviews and who are the best interviewees that you think

276
00:16:40,680 --> 00:16:43,200
Speaker 1: you've had. It's a it's a chemistry. It's a mixture,

277
00:16:43,760 --> 00:16:47,880
Speaker 1: uh And there's always exceptions. There's hyper academic people that fail,

278
00:16:48,000 --> 00:16:51,240
Speaker 1: and and I do think it's a chemistry We keep

279
00:16:51,360 --> 00:16:53,480
Speaker 1: very careful track of who we like and who we

280
00:16:53,520 --> 00:16:57,360
Speaker 1: don't like. And I would say the third rail is

281
00:16:57,400 --> 00:17:00,840
Speaker 1: we don't want people that are scripted or consulted. That

282
00:17:00,920 --> 00:17:03,880
Speaker 1: was the rage to three five years ago. There's less

283
00:17:03,920 --> 00:17:06,760
Speaker 1: of that now. We have less and less people on

284
00:17:06,960 --> 00:17:09,399
Speaker 1: talking points, which is where a consultant comes in and

285
00:17:09,400 --> 00:17:12,199
Speaker 1: tells them four things to say. That's going away. But

286
00:17:12,400 --> 00:17:14,920
Speaker 1: mostly it's you know, it's a media phrase. Pop. They've

287
00:17:14,920 --> 00:17:17,920
Speaker 1: gotta have Pop, particularly in radio is critical. So we

288
00:17:18,040 --> 00:17:21,720
Speaker 1: talked about these sort of big general generational issues that

289
00:17:21,760 --> 00:17:24,720
Speaker 1: you see is the main thing? What about this moment

290
00:17:24,800 --> 00:17:27,959
Speaker 1: specifically when you look at financial markets? What are the

291
00:17:27,960 --> 00:17:30,879
Speaker 1: big things that you're watching. We're going to get into

292
00:17:30,960 --> 00:17:34,760
Speaker 1: prediction season soon for what do you? What do you?

293
00:17:34,760 --> 00:17:36,679
Speaker 1: What do you have your on? What would you What

294
00:17:36,760 --> 00:17:38,520
Speaker 1: are the charts that you look at first thing in

295
00:17:38,560 --> 00:17:41,040
Speaker 1: the morning. My chart of the year's inflation adjusted come

296
00:17:41,040 --> 00:17:43,399
Speaker 1: out of these back sixty seventy years. I've shown it

297
00:17:43,440 --> 00:17:45,920
Speaker 1: in TV probably ten times. You can steal. It's a

298
00:17:45,960 --> 00:17:50,760
Speaker 1: great chart, great great chart, and it looks like a

299
00:17:50,960 --> 00:17:56,719
Speaker 1: persistent decline in commodity prices over many years. And then

300
00:17:56,760 --> 00:18:00,280
Speaker 1: there's a China aberration, and we are, off the top

301
00:18:00,320 --> 00:18:03,320
Speaker 1: of my head, two thirds to three quarters of our

302
00:18:03,359 --> 00:18:08,399
Speaker 1: way back to normal, which is commodity long term commodity deflation.

303
00:18:08,680 --> 00:18:11,120
Speaker 1: So you don't think the long term, we're not. It's

304
00:18:11,119 --> 00:18:13,439
Speaker 1: not over yet. If we're going to return to normal,

305
00:18:13,640 --> 00:18:15,720
Speaker 1: I would suggest not that it's not over. I'm not

306
00:18:15,720 --> 00:18:17,960
Speaker 1: going to make a prediction. I would say the people

307
00:18:18,040 --> 00:18:21,320
Speaker 1: predicting it is over ore on tenuous ground. That's Thomas

308
00:18:21,400 --> 00:18:24,600
Speaker 1: media experience coming through that, refusing to make a prediction.

309
00:18:24,800 --> 00:18:27,440
Speaker 1: Do you think one thing I feel is like everyone's

310
00:18:27,480 --> 00:18:31,360
Speaker 1: talking about deflation and central banks around the world failing

311
00:18:31,400 --> 00:18:34,440
Speaker 1: to hit their policies and how are they going to reflate?

312
00:18:34,480 --> 00:18:37,640
Speaker 1: They can't do it, And then you see these conversations

313
00:18:37,800 --> 00:18:40,679
Speaker 1: the Phillips curve, this idea that the employment and the

314
00:18:40,720 --> 00:18:44,480
Speaker 1: inflation ratear inversely related, and how that's dead and broken.

315
00:18:44,880 --> 00:18:47,880
Speaker 1: Do you think we could be getting to an extreme

316
00:18:47,960 --> 00:18:51,240
Speaker 1: in the other direction where everyone is just thrown in

317
00:18:51,320 --> 00:18:54,159
Speaker 1: the towel on any sort of inflation coming back and

318
00:18:54,240 --> 00:18:57,920
Speaker 1: anything like that, and no to get wonky on you

319
00:18:58,800 --> 00:19:02,800
Speaker 1: within a classic I SLM matrix Johnny Hicks thirty nine

320
00:19:02,800 --> 00:19:06,360
Speaker 1: and Krugman's written about this beautifully. What I would suggest

321
00:19:06,480 --> 00:19:10,800
Speaker 1: is there's a total underestimation of real economy effects. Everybody's

322
00:19:10,840 --> 00:19:12,919
Speaker 1: over in the bank. What's yelling gonna do, what's karneiy

323
00:19:12,960 --> 00:19:15,440
Speaker 1: gonna do? Which is fine, I mean, that's what keeps

324
00:19:15,520 --> 00:19:18,919
Speaker 1: us some point, all rio's but the real economy effects

325
00:19:18,960 --> 00:19:22,040
Speaker 1: have been grossly underrated from day one of the crisis

326
00:19:22,119 --> 00:19:25,240
Speaker 1: August of oh seven. And the other thing I would

327
00:19:25,359 --> 00:19:29,800
Speaker 1: suggest is the interest rate transmission between the real economy

328
00:19:29,920 --> 00:19:32,600
Speaker 1: and the bank side of things. The l M curve

329
00:19:32,720 --> 00:19:36,920
Speaker 1: is totally broken at the zero bound. And these there's

330
00:19:36,960 --> 00:19:40,560
Speaker 1: things we don't understand that are going on in the

331
00:19:40,600 --> 00:19:44,199
Speaker 1: interest rate sphere right now that are there. There's a

332
00:19:44,280 --> 00:19:48,040
Speaker 1: mystery here. I can't believe we've gotten this far in

333
00:19:48,080 --> 00:19:52,960
Speaker 1: the segment without talking about your bow ties and the

334
00:19:53,040 --> 00:19:56,600
Speaker 1: fact that the bow t high was almost entirely responsible

335
00:19:56,680 --> 00:19:59,760
Speaker 1: for bringing you to Bloomberg. Since Mr Matt Winkler also

336
00:19:59,800 --> 00:20:05,080
Speaker 1: in choice wearing those. I found a picture of my grandfather,

337
00:20:05,200 --> 00:20:09,280
Speaker 1: my mother's father, five years ago, holding me and he

338
00:20:09,320 --> 00:20:11,439
Speaker 1: had a bow tie on. I have no recollection of

339
00:20:11,480 --> 00:20:15,040
Speaker 1: my grandfather having a bow tech um. It started when

340
00:20:15,040 --> 00:20:17,359
Speaker 1: I was sort of sort of kind of like premed

341
00:20:17,800 --> 00:20:20,600
Speaker 1: and I was in emergency rooms and they wouldn't let

342
00:20:20,600 --> 00:20:22,879
Speaker 1: you wear a normal tie because they're afraid the patient

343
00:20:22,920 --> 00:20:25,439
Speaker 1: will grab you with a regular tie. So I was

344
00:20:25,480 --> 00:20:28,359
Speaker 1: forced to wear a bow tech uh, doing what was

345
00:20:28,400 --> 00:20:30,680
Speaker 1: called extern This is a million years ago. This is

346
00:20:30,760 --> 00:20:34,960
Speaker 1: before anesthesia and uh uh you know it sort of

347
00:20:35,000 --> 00:20:38,080
Speaker 1: started with that. I'm assuming back then they weren't amazed

348
00:20:38,119 --> 00:20:40,760
Speaker 1: ties though as they they were not. No, they weren't.

349
00:20:40,760 --> 00:20:43,919
Speaker 1: We did the clip on thing minimally. I must admit

350
00:20:44,040 --> 00:20:46,040
Speaker 1: that that was like, no, I don't want to do that.

351
00:20:46,400 --> 00:20:49,800
Speaker 1: Do you have a favorite tie? Doesn't mean something? No,

352
00:20:49,840 --> 00:20:52,600
Speaker 1: not really. This one's actually very old. This is like

353
00:20:52,640 --> 00:20:55,160
Speaker 1: one of the original it's an orange tie for those.

354
00:20:57,119 --> 00:20:59,000
Speaker 1: Thank you very much for joining us. That was a

355
00:20:59,080 --> 00:21:01,719
Speaker 1: phenomenal Discuy Shin. I learned a lot about you and

356
00:21:02,359 --> 00:21:04,240
Speaker 1: podcasts is really cool. You know we did in years

357
00:21:04,240 --> 00:21:10,840
Speaker 1: ago and I totally agree with a new um enthusiasm podcast.

358
00:21:11,240 --> 00:21:13,399
Speaker 1: Thank you for being our guinea pig. Tom, Thank you,

359
00:21:15,359 --> 00:21:17,479
Speaker 1: thank you for joining us on the first episode of

360
00:21:17,480 --> 00:21:20,280
Speaker 1: Odd Lots. We'll be doing this every week and you

361
00:21:20,320 --> 00:21:23,600
Speaker 1: can find it on Bloomberg dot com, iTunes, SoundCloud and

362
00:21:23,640 --> 00:21:26,840
Speaker 1: just about anywhere else. You can follow us on Twitter

363
00:21:27,200 --> 00:21:30,800
Speaker 1: at at the Stalwart, for Me, at Tracy Ellaway. For

364
00:21:30,960 --> 00:21:34,440
Speaker 1: Tracy will obviously be tweeting out the links. And thanks

365
00:21:34,480 --> 00:21:36,840
Speaker 1: again to Tom Keene for joining us and being our

366
00:21:36,880 --> 00:21:39,480
Speaker 1: guinea pig on this first episode, and thank you for

367
00:21:39,520 --> 00:21:39,879
Speaker 1: listening