1 00:00:10,119 --> 00:00:14,120 Speaker 1: Hello, and welcome to another episode of the Odd Loves Podcast. 2 00:00:14,160 --> 00:00:17,000 Speaker 1: I'm Joe wasn't All, and I'm Tracy al went Tracy. 3 00:00:17,079 --> 00:00:18,759 Speaker 1: You know what I just found out. You know, people 4 00:00:18,800 --> 00:00:20,400 Speaker 1: have asked me when did odd Lots start? And I 5 00:00:20,400 --> 00:00:23,600 Speaker 1: didn't know the date up until just now. The exact date. Yeah, 6 00:00:23,640 --> 00:00:28,880 Speaker 1: I know, roughly in our first episode November. I think 7 00:00:28,920 --> 00:00:31,479 Speaker 1: I would have said a little bit earlier, but October, 8 00:00:31,880 --> 00:00:35,879 Speaker 1: like summer or something. But we've been doing this, so 9 00:00:35,960 --> 00:00:39,760 Speaker 1: that's crazy. So it's like seven years more than seven years. Wow, 10 00:00:40,240 --> 00:00:43,720 Speaker 1: time time flies when you're having fun. That's a long time. 11 00:00:43,800 --> 00:00:47,000 Speaker 1: And when we first started, you were in New York, yes, 12 00:00:47,120 --> 00:00:49,400 Speaker 1: and then you went to I went to Abby Dhabi 13 00:00:49,640 --> 00:00:52,280 Speaker 1: and I remember I remember sitting on my living room 14 00:00:52,360 --> 00:00:56,400 Speaker 1: floor on this big Oriental carpet with like a Moroccan 15 00:00:56,480 --> 00:01:00,000 Speaker 1: poof in front of me and recording all thoughts episode 16 00:01:00,120 --> 00:01:03,000 Speaker 1: at six pm in the evening. And then and then 17 00:01:03,000 --> 00:01:04,760 Speaker 1: I went to Hong Kong, and then you made your 18 00:01:04,800 --> 00:01:06,760 Speaker 1: life even then you made it even more. And then 19 00:01:06,800 --> 00:01:10,040 Speaker 1: it was like ten or eleven pm at night, and 20 00:01:10,240 --> 00:01:13,560 Speaker 1: I was recording these episodes from an apartment that was 21 00:01:13,640 --> 00:01:16,559 Speaker 1: mostly made out of cement, which means that the sound 22 00:01:16,640 --> 00:01:19,000 Speaker 1: just echoed off of the walls and it was a 23 00:01:19,080 --> 00:01:22,960 Speaker 1: complete nightmare from an audio perspective. When in that process 24 00:01:23,000 --> 00:01:25,959 Speaker 1: did you get your dog? Oh? That was so he 25 00:01:26,000 --> 00:01:29,080 Speaker 1: was a pandemic puppy, and so he would have been 26 00:01:29,120 --> 00:01:32,000 Speaker 1: making noise in the background while I was huddled under 27 00:01:32,040 --> 00:01:34,600 Speaker 1: a blanket. And I used to cut the air conditioner 28 00:01:34,640 --> 00:01:36,560 Speaker 1: in Hong Kong as well because it was really noisy. 29 00:01:36,640 --> 00:01:40,160 Speaker 1: So I'd be in a cement apartment under a blanket 30 00:01:40,280 --> 00:01:42,800 Speaker 1: without any air conditioning, and the humidity is like a 31 00:01:42,840 --> 00:01:46,000 Speaker 1: hud percent and it's you know, ninety degrees out and 32 00:01:46,160 --> 00:01:48,960 Speaker 1: the puppy is winding behind me. So I'm glad to 33 00:01:49,000 --> 00:01:53,280 Speaker 1: say that, at least from my perspective, the recording options 34 00:01:53,360 --> 00:01:57,560 Speaker 1: have improved vastly since then. Yeah, I'm glad you're back. 35 00:01:57,640 --> 00:02:00,080 Speaker 1: I felt bad by the end, like you, having to 36 00:02:00,120 --> 00:02:03,360 Speaker 1: do these episodes there one that I did it like 37 00:02:03,600 --> 00:02:05,920 Speaker 1: three in the morning one. But it must have been 38 00:02:05,920 --> 00:02:09,560 Speaker 1: a really good guest. Yes, it was all worth it. 39 00:02:09,560 --> 00:02:11,400 Speaker 1: It was all worth it, and it led us to 40 00:02:11,720 --> 00:02:15,400 Speaker 1: um this moment here where we're reunited in the New 41 00:02:15,480 --> 00:02:18,680 Speaker 1: York office. We're in a proper recording studio and we 42 00:02:18,720 --> 00:02:21,400 Speaker 1: get to talk about our first ever episode. So I 43 00:02:21,440 --> 00:02:24,400 Speaker 1: think we actually recorded that episode in the exact studio 44 00:02:24,400 --> 00:02:26,120 Speaker 1: that we're in right now. We've recorded it in a 45 00:02:26,120 --> 00:02:28,520 Speaker 1: few different places, but I think it was literally in 46 00:02:28,600 --> 00:02:33,280 Speaker 1: this room right So for this festive holiday season, we 47 00:02:33,360 --> 00:02:37,200 Speaker 1: are re releasing our first ever episode. Our guest for 48 00:02:37,240 --> 00:02:40,720 Speaker 1: that episode was Tom Keane, who is our colleague over 49 00:02:40,760 --> 00:02:44,240 Speaker 1: here at Bloomberg News. You might recognize him from Bloomberg Television. 50 00:02:44,280 --> 00:02:48,440 Speaker 1: He hosts the Surveillance Morning Show, and when you listen 51 00:02:48,520 --> 00:02:51,200 Speaker 1: to it, you might notice that a few things are different, 52 00:02:51,800 --> 00:02:53,960 Speaker 1: a few different here and there. You know, we probably 53 00:02:54,120 --> 00:02:56,840 Speaker 1: sound a little bit different as well. We didn't know 54 00:02:56,880 --> 00:02:58,959 Speaker 1: what we were doing. We didn't know what Odds was 55 00:02:59,000 --> 00:03:00,600 Speaker 1: going to be. I mean, I still don't know what 56 00:03:00,639 --> 00:03:02,799 Speaker 1: it is, but we definitely didn't know at the time, 57 00:03:03,440 --> 00:03:04,919 Speaker 1: and so it was one of these things that were 58 00:03:04,919 --> 00:03:07,600 Speaker 1: like whoa podcast seemed fun, A lot of people are 59 00:03:07,600 --> 00:03:11,120 Speaker 1: listening to them, and you know, before we joined Bloomberg, 60 00:03:11,240 --> 00:03:15,160 Speaker 1: like Tom Keane sort of this legendary figure at the company, 61 00:03:15,280 --> 00:03:18,400 Speaker 1: sort of like someone that many of us idolized or 62 00:03:18,440 --> 00:03:21,760 Speaker 1: I certainly did, and his interview style and his intelligence 63 00:03:21,760 --> 00:03:24,160 Speaker 1: and just the sort of like general flair and esthetic 64 00:03:24,200 --> 00:03:26,440 Speaker 1: of Tom Keene for those who know him, a very 65 00:03:26,440 --> 00:03:29,120 Speaker 1: distinctive figure. So it's like, I gotta talk to Tom, 66 00:03:29,120 --> 00:03:31,919 Speaker 1: you said. Distinctive figure. For those who don't know Tom 67 00:03:32,040 --> 00:03:36,640 Speaker 1: is like what six ft seven or something. He's big. 68 00:03:36,720 --> 00:03:38,360 Speaker 1: He wears a bow tie. I think he used to 69 00:03:38,360 --> 00:03:42,720 Speaker 1: play hockey, and he just kind of strides the corridors 70 00:03:42,760 --> 00:03:46,800 Speaker 1: of Bloomberg's office, throwing out one liners like slope matters, 71 00:03:47,840 --> 00:03:50,080 Speaker 1: and if you get him going, he's really into talk 72 00:03:50,080 --> 00:03:54,440 Speaker 1: about musical instruments, like his banjo, got some some guitar 73 00:03:54,520 --> 00:03:56,880 Speaker 1: strings that he bought off a guy who lives in 74 00:03:56,880 --> 00:04:01,640 Speaker 1: Europe that he's always wanted amps. He can talk about anything. Yeah, 75 00:04:01,680 --> 00:04:03,560 Speaker 1: So I think this is one of the reasons why 76 00:04:03,600 --> 00:04:05,840 Speaker 1: we had him on. He was sort of our like 77 00:04:06,120 --> 00:04:10,000 Speaker 1: initial test subject, just to see what it would be 78 00:04:10,040 --> 00:04:12,640 Speaker 1: like recording a podcast, but we also wanted to talk 79 00:04:12,680 --> 00:04:15,200 Speaker 1: about things with him, So I think on this episode 80 00:04:15,200 --> 00:04:18,239 Speaker 1: we spoke about music, as you just mentioned, We talked 81 00:04:18,279 --> 00:04:20,680 Speaker 1: about Matt Winkler, who was sort of one of the 82 00:04:20,720 --> 00:04:24,240 Speaker 1: founding fathers of Bloomberg News. We talked about both ties 83 00:04:24,960 --> 00:04:28,039 Speaker 1: and his wardrobe. I think we talked about how his mom, 84 00:04:28,279 --> 00:04:31,400 Speaker 1: Tom's mom taught him how to make stock charge. Oh, yes, 85 00:04:31,680 --> 00:04:35,160 Speaker 1: very important. This is where the whole Slope Matters thing 86 00:04:35,279 --> 00:04:38,320 Speaker 1: comes from. So there's you know, it was one of 87 00:04:38,320 --> 00:04:42,200 Speaker 1: our first interviews. But it's also very very so it's 88 00:04:42,200 --> 00:04:45,120 Speaker 1: interesting to listen to just from from that perspective to 89 00:04:45,120 --> 00:04:47,600 Speaker 1: see how far we've come. But it's also with Tom Keane, 90 00:04:47,640 --> 00:04:50,440 Speaker 1: who's an interesting guy, and uh, we should we should 91 00:04:50,440 --> 00:04:53,039 Speaker 1: listen to it again. I am really excited that we 92 00:04:53,080 --> 00:04:55,920 Speaker 1: are digging this one from the vaults, both because I'm 93 00:04:55,960 --> 00:04:59,159 Speaker 1: still such a big fan of Tom and also sort 94 00:04:59,160 --> 00:05:01,920 Speaker 1: of take stock of the last seven years, how far 95 00:05:01,960 --> 00:05:04,800 Speaker 1: we've come figured out this format. And you know, one 96 00:05:04,880 --> 00:05:08,120 Speaker 1: day in the year I guess twenty nine, we'll be 97 00:05:08,160 --> 00:05:11,720 Speaker 1: listening to this episode and we're like, Wow, we've come along. 98 00:05:11,760 --> 00:05:14,880 Speaker 1: Hopefully we've come along basin our production values. Have it 99 00:05:15,160 --> 00:05:19,279 Speaker 1: improved immensely? Um? Okay, Well, without further ado, here is 100 00:05:19,360 --> 00:05:23,400 Speaker 1: the rerun of our very first Odd Lots episode featuring 101 00:05:23,560 --> 00:05:32,880 Speaker 1: Bloomberg's Tom Keane. Enjoy Hello, and welcome to the first 102 00:05:32,960 --> 00:05:36,080 Speaker 1: episode of Odd Lots. I'm Joe Wisnthal, co host of 103 00:05:36,120 --> 00:05:38,920 Speaker 1: What Do You miss and editor of Bloomberg Markets. Now, 104 00:05:38,920 --> 00:05:42,520 Speaker 1: I'm Tracy Alloway, executive editor of Bloomberg Markets. Here at 105 00:05:42,520 --> 00:05:45,920 Speaker 1: Odd Lots, we want to have a discussion every week 106 00:05:45,960 --> 00:05:52,320 Speaker 1: about economics, finance, markets, market structure, which Tracy loved, maybe 107 00:05:52,400 --> 00:05:56,800 Speaker 1: some politics and culture thrown in stuff that doesn't necessarily 108 00:05:56,880 --> 00:05:59,800 Speaker 1: fit into the normal day to day conversation. And we 109 00:06:00,000 --> 00:06:03,119 Speaker 1: couldn't think of any guest better to have than Tom 110 00:06:03,200 --> 00:06:09,119 Speaker 1: Keen in my Guinea Pig, You're the guinea pig. Tom Keane, 111 00:06:09,320 --> 00:06:12,680 Speaker 1: as everyone should know, is the host of Bloomberg Surveillance 112 00:06:12,760 --> 00:06:16,839 Speaker 1: on TV and radio here Bloomberg. He knows more about 113 00:06:16,920 --> 00:06:20,640 Speaker 1: markets and economics and world events than just about anyone 114 00:06:20,680 --> 00:06:23,800 Speaker 1: else in the room. He's a very eclectic background in 115 00:06:23,960 --> 00:06:27,400 Speaker 1: music and mathematics. And I wanted to have I wanted 116 00:06:27,440 --> 00:06:29,200 Speaker 1: to have Tom Keene on the show because Tom is 117 00:06:29,200 --> 00:06:33,040 Speaker 1: always interviewing people, but he's never answering questions, never behind, 118 00:06:33,960 --> 00:06:38,080 Speaker 1: try to avoid it. So we're gonna doing this for you, Joe. 119 00:06:38,080 --> 00:06:42,120 Speaker 1: I'm doing this for Tracy. Everybody knows that. But who 120 00:06:42,160 --> 00:06:44,440 Speaker 1: are you Tom? Why are you here? How did you 121 00:06:44,520 --> 00:06:48,080 Speaker 1: get to be Tom Keane who everybody knows and love? 122 00:06:48,360 --> 00:06:51,080 Speaker 1: I get it a lot, And what I would suggest 123 00:06:51,480 --> 00:06:55,800 Speaker 1: is it's one part short term stuff, one part long 124 00:06:55,920 --> 00:06:59,080 Speaker 1: term stuff, and one part blind luck. The long term 125 00:06:59,120 --> 00:07:01,760 Speaker 1: thing is is being acutely aware when I was a 126 00:07:01,839 --> 00:07:06,680 Speaker 1: kid and evermore every day knowing how twisted the early 127 00:07:06,760 --> 00:07:10,040 Speaker 1: years where I found something met Stan Freeburg record and 128 00:07:10,160 --> 00:07:12,360 Speaker 1: you don't know Stan Freeburg is he was a great comedian. 129 00:07:12,400 --> 00:07:15,680 Speaker 1: He just died this year. And then recently it was 130 00:07:15,760 --> 00:07:19,640 Speaker 1: about the privilege of running into Matt Winkler and basically 131 00:07:19,680 --> 00:07:22,440 Speaker 1: Matt and I with the support of l Mayers who 132 00:07:22,520 --> 00:07:26,160 Speaker 1: runs Bloomberg Media, and Ted Fine who runs TV, they're 133 00:07:26,240 --> 00:07:28,640 Speaker 1: the ones that made all this happen. Matt win Clair, 134 00:07:28,840 --> 00:07:31,600 Speaker 1: for our listeners who don't know, is the guy who 135 00:07:31,640 --> 00:07:35,160 Speaker 1: founded Bloomberg News essentially, so you got lucky met Matt 136 00:07:35,160 --> 00:07:37,520 Speaker 1: win Clair. He just hired me because the bow tie. 137 00:07:37,560 --> 00:07:41,480 Speaker 1: But to make a long story short, you get lucky 138 00:07:41,680 --> 00:07:46,000 Speaker 1: and I met Matt and we basically invented what you see. 139 00:07:46,480 --> 00:07:48,800 Speaker 1: That's that's a safe statement. What were you doing before then? 140 00:07:48,840 --> 00:07:51,680 Speaker 1: Because when I think of you, I think you projected 141 00:07:51,880 --> 00:07:55,320 Speaker 1: or of someone who's been doing radio for decades. But 142 00:07:55,440 --> 00:07:57,920 Speaker 1: what were you doing before you did radio and TV? 143 00:07:58,160 --> 00:08:00,800 Speaker 1: Well before the media thing, I was in the investment business. 144 00:08:00,800 --> 00:08:03,680 Speaker 1: But there's a whole sidecar thing in music and an 145 00:08:03,800 --> 00:08:08,640 Speaker 1: entertainment um, for example. And I gauge it off my 146 00:08:08,920 --> 00:08:12,320 Speaker 1: oldest child's age. I used to hold him in my 147 00:08:12,480 --> 00:08:15,800 Speaker 1: arms into the stock report in Boston. And this is 148 00:08:15,840 --> 00:08:19,000 Speaker 1: the vanilla days, not cross asset, the dal Jones industrial 149 00:08:19,000 --> 00:08:22,040 Speaker 1: average up forty two points today, eight forty two blah 150 00:08:22,040 --> 00:08:25,360 Speaker 1: blah blah. You know that. And so I did a 151 00:08:25,400 --> 00:08:27,720 Speaker 1: little bit of investment stuff back then, but a lot 152 00:08:27,760 --> 00:08:30,120 Speaker 1: of it was just the music business as well, which 153 00:08:30,160 --> 00:08:33,240 Speaker 1: is the show business aspect which a lot of people 154 00:08:33,280 --> 00:08:36,480 Speaker 1: in business media try to ignore every day. And they're wrong. 155 00:08:36,880 --> 00:08:39,280 Speaker 1: I mean, the f T s pink. It's pink for 156 00:08:39,320 --> 00:08:42,160 Speaker 1: a reason. It's pink. It's like what you did. I mean, 157 00:08:42,160 --> 00:08:47,959 Speaker 1: you invented the modern headline in modern business journalism. So 158 00:08:48,240 --> 00:08:51,200 Speaker 1: it's just you know that Wisnthal wrote that headliner. You 159 00:08:51,240 --> 00:09:12,319 Speaker 1: just know that. How has her investment experience informed your career? 160 00:09:12,679 --> 00:09:16,400 Speaker 1: Huge huge um. It's a massive type two and that 161 00:09:16,559 --> 00:09:21,440 Speaker 1: you learn so much enjoying losing money. It's it's for 162 00:09:21,480 --> 00:09:24,320 Speaker 1: those of you gaus see and it's log normal. Uh, 163 00:09:24,360 --> 00:09:26,680 Speaker 1: you learn way more on the downside and the upside. 164 00:09:26,960 --> 00:09:29,600 Speaker 1: Lots of fat tail risks. There's fat tail risk, but 165 00:09:29,640 --> 00:09:32,960 Speaker 1: that's not that that's over emphasized. It's the joy of 166 00:09:33,160 --> 00:09:38,679 Speaker 1: losing money within the fat tails, which is I think 167 00:09:38,720 --> 00:09:43,439 Speaker 1: that's when you learn factors more losing money than making 168 00:09:43,480 --> 00:09:46,480 Speaker 1: money factors. But I can tell you that the way 169 00:09:46,520 --> 00:09:49,199 Speaker 1: I learned to lose money was enjoying losing money in 170 00:09:49,240 --> 00:09:52,720 Speaker 1: the options market. And then so when you're doing the show, 171 00:09:52,720 --> 00:09:55,160 Speaker 1: whether on radio or TV, how do you apply that 172 00:09:55,640 --> 00:09:58,280 Speaker 1: the fact that you learned so much more when you 173 00:09:58,360 --> 00:10:01,319 Speaker 1: lost money. When you think about it, humility show, it's 174 00:10:01,320 --> 00:10:06,040 Speaker 1: a it's a humility thing of knowing every day how 175 00:10:06,120 --> 00:10:10,120 Speaker 1: dumb you are and trying to always work at getting smarter, 176 00:10:11,120 --> 00:10:15,000 Speaker 1: laughing at your mistakes. There's a there's a lot there's 177 00:10:15,080 --> 00:10:18,360 Speaker 1: less now after the financial crisis, but there's lots of 178 00:10:18,400 --> 00:10:25,160 Speaker 1: people strutting around with a certain intellectual arrogance about economics, finance, investment. 179 00:10:26,080 --> 00:10:29,080 Speaker 1: Right now nobody has arrogance and international relations. Did you 180 00:10:29,160 --> 00:10:31,880 Speaker 1: have to learn how much you don't know? Like, what's 181 00:10:31,920 --> 00:10:33,679 Speaker 1: the point earlier in your career where you thought you 182 00:10:33,760 --> 00:10:38,600 Speaker 1: knew it all and then you know certitude of yeah, 183 00:10:38,640 --> 00:10:42,640 Speaker 1: you you you learn from a wide set of mistakes 184 00:10:42,679 --> 00:10:48,160 Speaker 1: and cycles, which gives you a humility which forces you 185 00:10:48,640 --> 00:10:50,560 Speaker 1: to get smarter. For example, I went to a wedding 186 00:10:50,559 --> 00:10:54,720 Speaker 1: this weekend and half the wedding was from Uruguay. I 187 00:10:54,840 --> 00:10:58,840 Speaker 1: know nothing about Mona Badeo except one of my kids. 188 00:10:58,840 --> 00:11:01,480 Speaker 1: Friends all went down there because it couldn't get a 189 00:11:01,559 --> 00:11:04,920 Speaker 1: job in America. And I read seven articles this weekend 190 00:11:04,960 --> 00:11:09,520 Speaker 1: in Uruguay just to to begin to I have no 191 00:11:09,600 --> 00:11:14,600 Speaker 1: clue about Uruguay. There's that kind of madness, but compounded 192 00:11:14,600 --> 00:11:17,280 Speaker 1: over time. I know nothing about Uruguay. I know nothing 193 00:11:17,280 --> 00:11:19,520 Speaker 1: about Monte Videt was a great humble bred because you 194 00:11:19,600 --> 00:11:22,040 Speaker 1: slide in how easily you knew the capital. Now I 195 00:11:22,080 --> 00:11:25,840 Speaker 1: have to ask you talk about certitude on Wall Street 196 00:11:26,440 --> 00:11:29,040 Speaker 1: And in addition to having a musical background, you also 197 00:11:29,080 --> 00:11:32,280 Speaker 1: have a mathematical background. And it seems like one of 198 00:11:32,280 --> 00:11:35,360 Speaker 1: the areas in markets where people start to get really 199 00:11:35,480 --> 00:11:39,040 Speaker 1: certain and have that certain mathematical swagger is when it 200 00:11:39,080 --> 00:11:42,400 Speaker 1: comes to models, and you love talking about models. How 201 00:11:42,400 --> 00:11:45,280 Speaker 1: does your experience in mathematics feed into yeah that the 202 00:11:46,000 --> 00:11:48,679 Speaker 1: background there was growing up with it. I have the 203 00:11:49,280 --> 00:11:53,079 Speaker 1: clearest memories of gooding up on my tiptoes and looking 204 00:11:53,120 --> 00:11:57,320 Speaker 1: over my father's desk. Is he did very sophisticated triple 205 00:11:57,360 --> 00:12:01,120 Speaker 1: integration of space satellites and I would literally play on 206 00:12:01,160 --> 00:12:03,480 Speaker 1: the floor with the French curves is a million years 207 00:12:03,520 --> 00:12:06,280 Speaker 1: ago and like like think spot Nick and I g 208 00:12:06,480 --> 00:12:09,800 Speaker 1: y and all that, and all of that became a 209 00:12:09,960 --> 00:12:15,240 Speaker 1: mathiness which culminated in Max peters fabulous program at the 210 00:12:15,320 --> 00:12:20,079 Speaker 1: University of Colorado. Max Peters was a highly decorated Italian 211 00:12:20,120 --> 00:12:23,160 Speaker 1: infantryman up the spine of Italy and World War Two, 212 00:12:23,520 --> 00:12:26,240 Speaker 1: and he went out to Colorado and put together, uh, 213 00:12:26,280 --> 00:12:30,959 Speaker 1: the mother of all grinds in engineering academics. And I 214 00:12:31,040 --> 00:12:34,720 Speaker 1: was extremely fortunate to parachute into that for a couple 215 00:12:34,760 --> 00:12:38,000 Speaker 1: of years. So you take, you know what I get 216 00:12:38,000 --> 00:12:39,679 Speaker 1: in math and what I don't get, and trust me, 217 00:12:39,760 --> 00:12:42,520 Speaker 1: is a lot I don't get. And then you overlay 218 00:12:42,600 --> 00:12:48,040 Speaker 1: that into some of the certitude of quantitative finance and 219 00:12:48,120 --> 00:12:52,520 Speaker 1: you get a massive humility. I think the math overlay 220 00:12:52,880 --> 00:12:55,439 Speaker 1: is a it's a massive type one. You've got it, 221 00:12:55,960 --> 00:12:58,560 Speaker 1: But what it really is, and I see it every 222 00:12:58,640 --> 00:13:00,960 Speaker 1: day and it's getting worse. It's a little better in 223 00:13:00,960 --> 00:13:03,600 Speaker 1: a couple of last couple of years. Is the math 224 00:13:03,679 --> 00:13:10,160 Speaker 1: phobia within economics, finance, investments just stunning. It's just breathtaking 225 00:13:10,240 --> 00:13:14,120 Speaker 1: that you see rampant math phobia, because other people have 226 00:13:14,320 --> 00:13:17,640 Speaker 1: argued that it's just the opposite, that economics and finances 227 00:13:17,679 --> 00:13:21,079 Speaker 1: become too mathy to the point where people can't explain 228 00:13:21,240 --> 00:13:24,040 Speaker 1: in clear English what they're talking about. Yeah, well, let 229 00:13:24,040 --> 00:13:26,600 Speaker 1: me part that debate. You're absolutely right, Joe. The basic 230 00:13:26,679 --> 00:13:29,720 Speaker 1: idea is there was an era, particularly coming out of 231 00:13:29,720 --> 00:13:34,440 Speaker 1: World War Two, of math, too much math, math anxiety, etcetera, etcetera. 232 00:13:35,120 --> 00:13:38,319 Speaker 1: And then at the undergraduate level, not at the PhD, 233 00:13:38,440 --> 00:13:40,599 Speaker 1: not at the doctor trak level, the graduate level, but 234 00:13:40,640 --> 00:13:45,440 Speaker 1: at the undergraduate level, a vast majority of people don't 235 00:13:45,760 --> 00:13:49,480 Speaker 1: have the dynamics in their head to do even basic 236 00:13:49,559 --> 00:13:53,280 Speaker 1: martiality and microeconomics or you know, name the flavor of 237 00:13:53,320 --> 00:13:56,920 Speaker 1: macro you want to do. The British are very different. 238 00:13:56,960 --> 00:13:59,640 Speaker 1: In the French, they have much better, as a rule 239 00:13:59,640 --> 00:14:03,360 Speaker 1: of thumb, undergraduate mathematics than we do. If I, if 240 00:14:03,400 --> 00:14:06,720 Speaker 1: I talked to British students, their knowledge of first order 241 00:14:06,760 --> 00:14:11,400 Speaker 1: difference equations off the chart honors undergraduate programs in the US. 242 00:14:11,480 --> 00:14:13,880 Speaker 1: Some of them. They have no clue what what that is. 243 00:14:14,280 --> 00:14:16,800 Speaker 1: I'm pleased to say I've I've forgotten almost all the 244 00:14:16,840 --> 00:14:21,520 Speaker 1: mathematics I learned interversity and college. However, however, I want 245 00:14:21,520 --> 00:14:24,280 Speaker 1: to know. So when you see something like the events 246 00:14:24,600 --> 00:14:27,080 Speaker 1: of August when the market sold off and a lot 247 00:14:27,120 --> 00:14:31,480 Speaker 1: of people were talking about mathematical formulas and model based 248 00:14:31,480 --> 00:14:34,840 Speaker 1: equations and risk parody at the center of that sell off, 249 00:14:35,400 --> 00:14:37,800 Speaker 1: what do you think. I think some of it was 250 00:14:37,840 --> 00:14:43,080 Speaker 1: extremely valid this time around. Uh I, I think that 251 00:14:43,120 --> 00:14:47,480 Speaker 1: the model fatigue is much more in the macro area. 252 00:14:47,560 --> 00:14:49,640 Speaker 1: The work Olivia Blanchard did at the i m F 253 00:14:49,640 --> 00:14:55,840 Speaker 1: with Joe Stiglson others really important to ask the non 254 00:14:55,880 --> 00:15:00,560 Speaker 1: sophisticated and the very sophisticated differential equation models that pro 255 00:15:00,680 --> 00:15:04,520 Speaker 1: PhDs use, and I don't pretend to be fluent in them. 256 00:15:04,520 --> 00:15:07,040 Speaker 1: They're very suspect after what we went through in August 257 00:15:07,040 --> 00:15:10,880 Speaker 1: of O seven. Stepping back, so you have this interest 258 00:15:10,920 --> 00:15:14,160 Speaker 1: in lifelong interest in mathematics in music, which I was 259 00:15:14,360 --> 00:15:16,400 Speaker 1: also want to get to. But then how did that? 260 00:15:16,560 --> 00:15:19,800 Speaker 1: When did it click that markets and investments when you 261 00:15:19,880 --> 00:15:22,840 Speaker 1: were in the earliest memories, earliest it was permeating in 262 00:15:22,920 --> 00:15:26,200 Speaker 1: my house. My my grandfather knew a w CO and 263 00:15:26,240 --> 00:15:29,200 Speaker 1: the point and figure guy. He did point and figure charts. 264 00:15:29,280 --> 00:15:32,480 Speaker 1: My mother did point and figure charts. Comes from a 265 00:15:32,520 --> 00:15:40,680 Speaker 1: family totally totally permeating investment theory and investment analysis, you know, 266 00:15:40,800 --> 00:15:44,520 Speaker 1: just original Graham, Dodd and Coddle up. And in addition 267 00:15:44,520 --> 00:15:46,720 Speaker 1: to everything else, you were always interested in. You always 268 00:15:46,800 --> 00:15:49,760 Speaker 1: knew that this was something you wanted to know. You know, 269 00:15:50,080 --> 00:15:52,680 Speaker 1: you didn't know it just was there, just there, It 270 00:15:52,800 --> 00:15:55,720 Speaker 1: was just there kind of thing. I'm also, I don't 271 00:15:55,720 --> 00:15:58,520 Speaker 1: think a lot of people know about your musical background, 272 00:15:58,560 --> 00:16:02,800 Speaker 1: but why do you give us the six Here's Tom 273 00:16:02,840 --> 00:16:05,520 Speaker 1: King's music. The ninety second version is real simple. At 274 00:16:05,560 --> 00:16:08,880 Speaker 1: eight years old, I walked into a place called Stutsman's, 275 00:16:08,960 --> 00:16:12,640 Speaker 1: which is legendary and the acoustic music business with my father. 276 00:16:12,720 --> 00:16:16,520 Speaker 1: There were six Grutch White Falcons lined up and Rochester, 277 00:16:16,600 --> 00:16:19,800 Speaker 1: New York Kodak, and my father bought me a forty 278 00:16:19,800 --> 00:16:23,080 Speaker 1: two dollar you know, acoustic guitar and then I just 279 00:16:23,120 --> 00:16:28,160 Speaker 1: began grinding away and there were three or four iterations 280 00:16:28,200 --> 00:16:31,000 Speaker 1: of it. But to make a long story short, I 281 00:16:31,160 --> 00:16:34,720 Speaker 1: ended up doing the New England singer songwriter thing, juggling 282 00:16:34,720 --> 00:16:37,560 Speaker 1: a bunch of other stuff. There's a place in Nashville 283 00:16:37,600 --> 00:16:44,160 Speaker 1: called the Blueberg Cafe, which is magical. You know. It 284 00:16:44,240 --> 00:16:45,920 Speaker 1: was just the New England folks and it was sort 285 00:16:45,920 --> 00:16:50,200 Speaker 1: of you know in terms of artists around it. Uh, 286 00:16:50,240 --> 00:16:54,840 Speaker 1: it was post Tracy Chapman, Um Susanne Vega was really 287 00:16:54,840 --> 00:16:58,800 Speaker 1: happening with Luca and Solitude standing and then a whole 288 00:16:58,800 --> 00:17:01,760 Speaker 1: host of people came on, really jumped, started by a 289 00:17:01,760 --> 00:17:04,760 Speaker 1: guy named David Wilcox who did an album called Either 290 00:17:04,880 --> 00:17:08,000 Speaker 1: Hurricane for A and M Records, which just there was 291 00:17:08,040 --> 00:17:10,879 Speaker 1: like this mini folk boom and what was so cool? 292 00:17:11,520 --> 00:17:13,880 Speaker 1: We knew when we this is before the internet. That's 293 00:17:13,880 --> 00:17:17,320 Speaker 1: a key statement. Even we had no idea what was 294 00:17:17,359 --> 00:17:20,680 Speaker 1: coming in digital, but we knew how lucky we were 295 00:17:20,800 --> 00:17:24,600 Speaker 1: to do it. When we were doing it, we knew 296 00:17:24,640 --> 00:17:28,160 Speaker 1: it couldn't last. What was the greatest guitar you've ever owned? 297 00:17:29,280 --> 00:17:31,639 Speaker 1: The one I got now with the greatest guitar my 298 00:17:31,640 --> 00:17:35,400 Speaker 1: my concert Gibson J one hundred, which was picked out 299 00:17:35,400 --> 00:17:39,159 Speaker 1: by Eric Schoenberg up in Boston, was stolen and I 300 00:17:39,200 --> 00:17:41,240 Speaker 1: got it back four years ago. I told Dave Drummond 301 00:17:41,240 --> 00:17:44,119 Speaker 1: and Google. I got it off Google Images. There was 302 00:17:44,160 --> 00:17:47,080 Speaker 1: my guitar and Google images. But that and I've got 303 00:17:47,080 --> 00:17:50,600 Speaker 1: some others now, but I think that's you know, I 304 00:17:50,600 --> 00:17:53,160 Speaker 1: guess the best ones that when my father had, but 305 00:17:53,320 --> 00:17:58,280 Speaker 1: that's been lost. So with your very very idiosyncratic background 306 00:17:58,800 --> 00:18:04,240 Speaker 1: in mathematics, so it's like Joe Wisenthals an investment. When 307 00:18:04,240 --> 00:18:06,680 Speaker 1: you do your show today at Bloomberg and you look 308 00:18:06,720 --> 00:18:08,720 Speaker 1: around the world, what do you see as the most 309 00:18:08,760 --> 00:18:13,240 Speaker 1: important story going on right now? Um, I think the 310 00:18:13,320 --> 00:18:15,760 Speaker 1: number one stories. One of my kids said to me, Daddy, 311 00:18:15,760 --> 00:18:18,919 Speaker 1: when does this get got to be fun? And I 312 00:18:18,960 --> 00:18:24,520 Speaker 1: think there's a massive understanding by people of a certain 313 00:18:24,640 --> 00:18:29,720 Speaker 1: vintage that the kids don't have. They have lots of 314 00:18:30,000 --> 00:18:35,000 Speaker 1: wonderful digital stuff and medical stuff, etcetera, etcetera, but the 315 00:18:35,119 --> 00:18:41,199 Speaker 1: optimism has been shattered. And the answer Jeff emmel I 316 00:18:41,240 --> 00:18:43,720 Speaker 1: was with two years ago, I'm guessing and he said, look, 317 00:18:43,760 --> 00:18:45,560 Speaker 1: all we need is three point two percent g d 318 00:18:45,680 --> 00:18:48,439 Speaker 1: P and that solves a lot of problems. That's a 319 00:18:48,560 --> 00:18:52,960 Speaker 1: very smart comment by the applied methematician from Dartmouth. We 320 00:18:53,040 --> 00:18:57,520 Speaker 1: don't have that. The the younger people, people under about 321 00:18:57,640 --> 00:19:02,240 Speaker 1: thirty two, have never no own normal so when you 322 00:19:02,280 --> 00:19:06,000 Speaker 1: look at the world, you don't necessarily see problems of inequality, 323 00:19:06,119 --> 00:19:09,720 Speaker 1: you see generational problems. No, I think they're both there, 324 00:19:09,760 --> 00:19:14,480 Speaker 1: but I think in two thousand and fifteen that the 325 00:19:14,640 --> 00:19:19,240 Speaker 1: generational issues are less spoken, which to an extent speaks 326 00:19:19,280 --> 00:19:21,760 Speaker 1: of the anger in the politics today. When do you 327 00:19:21,800 --> 00:19:24,119 Speaker 1: think it wasn't normal or when was it was? Well, 328 00:19:24,359 --> 00:19:26,119 Speaker 1: you know, you stand on the floor of the Republican 329 00:19:26,160 --> 00:19:28,800 Speaker 1: convention ex conventions ago and you go, well, this is 330 00:19:28,840 --> 00:19:33,359 Speaker 1: surreal or the Democratic doesn't matter which part party, But 331 00:19:33,520 --> 00:19:38,000 Speaker 1: the answer is we are programmed for certain nominally real 332 00:19:38,040 --> 00:19:42,120 Speaker 1: GDP that ain't happened. There's a quarter here at quarter there, 333 00:19:42,560 --> 00:19:46,760 Speaker 1: Macroeconomic Advisors right now is third quarter at one. The 334 00:19:46,800 --> 00:19:49,879 Speaker 1: next quarter is a little bit better. But we we 335 00:19:49,960 --> 00:19:54,359 Speaker 1: have not had the run rate of GDP that provides 336 00:19:55,200 --> 00:19:59,880 Speaker 1: base psychological comfort to a lot of people, whether it's 337 00:20:00,359 --> 00:20:04,119 Speaker 1: over educated torps like my kids, or you know, people 338 00:20:04,200 --> 00:20:07,040 Speaker 1: really struggling millions of Americas. Do you think I mean, 339 00:20:07,040 --> 00:20:11,359 Speaker 1: I remember thinking in and when we had the Raging 340 00:20:11,400 --> 00:20:13,480 Speaker 1: Dead ceiling debate, and I think that was the first 341 00:20:13,480 --> 00:20:16,560 Speaker 1: time that we saw this huge I would say the 342 00:20:16,600 --> 00:20:19,800 Speaker 1: crisis and the economy seeming to really spill over into 343 00:20:19,840 --> 00:20:22,600 Speaker 1: politics and we had to start division the Tea Party 344 00:20:22,680 --> 00:20:25,679 Speaker 1: and leadership. But it hasn't faded as much as I 345 00:20:25,680 --> 00:20:30,119 Speaker 1: would have guessed, given how far unemployment has dropped. I mean, 346 00:20:31,200 --> 00:20:33,359 Speaker 1: the economy is much better than it was in eleven, 347 00:20:33,400 --> 00:20:36,000 Speaker 1: but we still have and look at Donald Trump and 348 00:20:36,440 --> 00:20:38,639 Speaker 1: leading in the polls, and you have a rise of 349 00:20:38,680 --> 00:20:42,159 Speaker 1: more radical politicians everywhere. Do you think it's something beyond economics. 350 00:20:42,240 --> 00:20:45,320 Speaker 1: It has to do with the media environment, the Internet. 351 00:20:45,600 --> 00:20:47,440 Speaker 1: It has to do the media, and it has to 352 00:20:47,480 --> 00:20:49,439 Speaker 1: do with speed of trans Twitter and all. I mean 353 00:20:49,480 --> 00:20:53,320 Speaker 1: the cypress crisis alone with Twitter was surreal that Saturday 354 00:20:53,320 --> 00:20:58,879 Speaker 1: morning when how the Twitter dialogue changed the discussion. But 355 00:20:59,040 --> 00:21:01,919 Speaker 1: what I would what is under emphasized from a Newtonian 356 00:21:02,000 --> 00:21:06,439 Speaker 1: mechanics standpoint is inertial force and the word chronic. And 357 00:21:06,520 --> 00:21:09,520 Speaker 1: the answer is you totally right about ten and eleven. 358 00:21:10,280 --> 00:21:12,760 Speaker 1: And what's different now is it may not be force 359 00:21:12,880 --> 00:21:16,520 Speaker 1: masure like it was then, but there's just this chronic 360 00:21:16,960 --> 00:21:21,600 Speaker 1: weight of gridlock in Washington, this chronic sense of g 361 00:21:21,760 --> 00:21:26,080 Speaker 1: d P under performing even while unemployment supposedly gets better. 362 00:21:26,160 --> 00:21:29,880 Speaker 1: And that goes back to productivity. You know, we could board. 363 00:21:29,920 --> 00:21:37,200 Speaker 1: Everybody was three ratio productivity analysis. Productivity in the media 364 00:21:37,320 --> 00:21:43,119 Speaker 1: is appallingly reported. There's capital, there's labor, and there's a 365 00:21:43,160 --> 00:21:47,600 Speaker 1: separate ratio wrapped around something called total factor productivity or TFP. 366 00:21:48,680 --> 00:21:52,639 Speaker 1: And the pros know all that and they sort of just, 367 00:21:52,840 --> 00:21:55,520 Speaker 1: you know, when we talk about productivity, gloss over it. 368 00:21:55,960 --> 00:21:59,439 Speaker 1: But the answers labor ain't happening. And certainly for a 369 00:21:59,560 --> 00:22:02,520 Speaker 1: part of America, this Angus Deaton winning the Nobel Prize 370 00:22:02,520 --> 00:22:05,400 Speaker 1: a big deal, big big deal. This is the death 371 00:22:05,440 --> 00:22:10,160 Speaker 1: of aggregate aggregation, of summoning everything together, and that that's 372 00:22:10,200 --> 00:22:13,160 Speaker 1: a really big deal that I talked to Shower tonight 373 00:22:13,160 --> 00:22:15,560 Speaker 1: and I'm a chance to talk about it. Well, that 374 00:22:15,640 --> 00:22:18,199 Speaker 1: brings us to again to your show. When you go 375 00:22:18,240 --> 00:22:21,840 Speaker 1: out and talk to people, what makes some good interviews 376 00:22:21,920 --> 00:22:24,760 Speaker 1: and who are the best interviewees that you think you've had, 377 00:22:25,000 --> 00:22:28,040 Speaker 1: it's a it's a chemistry. It's a mixture, uh, And 378 00:22:28,080 --> 00:22:32,879 Speaker 1: there's always exceptions. There's hyper academic people that fail, and 379 00:22:32,880 --> 00:22:35,000 Speaker 1: and that I do think it's a chemistry. We keep 380 00:22:35,119 --> 00:22:37,199 Speaker 1: very careful track of who we like and who we 381 00:22:37,280 --> 00:22:41,119 Speaker 1: don't like. And I would say the third rail is 382 00:22:41,160 --> 00:22:44,480 Speaker 1: we don't want people that are scripted or consulted. That 383 00:22:44,680 --> 00:22:47,639 Speaker 1: was the rage to three five years ago. There's less 384 00:22:47,680 --> 00:22:50,560 Speaker 1: of that now. We have less and less people on 385 00:22:50,720 --> 00:22:53,159 Speaker 1: talking points, which is where a consultant comes in and 386 00:22:53,160 --> 00:22:55,919 Speaker 1: tells them four things to say. That's going away. But 387 00:22:56,160 --> 00:22:58,680 Speaker 1: mostly it's you know, it's a media phrase. Pop. They've 388 00:22:58,680 --> 00:23:18,320 Speaker 1: got to have pop, particularly radio is critical. So we 389 00:23:18,400 --> 00:23:22,119 Speaker 1: talked about these sort of big general generational issues that 390 00:23:22,160 --> 00:23:25,080 Speaker 1: you see is the main thing? What about this moment 391 00:23:25,200 --> 00:23:28,320 Speaker 1: specifically when you look at financial markets? What are the 392 00:23:28,359 --> 00:23:31,840 Speaker 1: big things that you're watching. We're gonna get into prediction 393 00:23:31,920 --> 00:23:35,240 Speaker 1: season soon for what do you? What do you what 394 00:23:35,280 --> 00:23:37,280 Speaker 1: do you have your on? What would you what are 395 00:23:37,320 --> 00:23:39,200 Speaker 1: the charts that you look at first thing in the morning. 396 00:23:39,359 --> 00:23:41,679 Speaker 1: My chart of the year's inflation adjusted come out of 397 00:23:41,680 --> 00:23:44,160 Speaker 1: these back sixty seventy years. I've shown it in TV 398 00:23:44,280 --> 00:23:46,800 Speaker 1: probably ten times. You can still it's a great chart, 399 00:23:46,880 --> 00:23:53,360 Speaker 1: great great chart, and it looks like a persistent decline 400 00:23:53,560 --> 00:23:57,480 Speaker 1: in commodity prices over many years. And then there's a 401 00:23:57,600 --> 00:24:00,800 Speaker 1: China aberration and we are, are, off the top of 402 00:24:00,840 --> 00:24:03,960 Speaker 1: my head, two thirds to three quarters of our way 403 00:24:04,040 --> 00:24:08,800 Speaker 1: back to normal, which is commodity long term commodity deflation, 404 00:24:09,040 --> 00:24:11,480 Speaker 1: So you don't think the long term, we're not it's 405 00:24:11,520 --> 00:24:13,840 Speaker 1: not over yet, if we're going to return to normal, 406 00:24:14,040 --> 00:24:16,080 Speaker 1: I would suggest not that it's not over. I'm not 407 00:24:16,119 --> 00:24:18,359 Speaker 1: going to make a prediction. I would say the people 408 00:24:18,400 --> 00:24:21,719 Speaker 1: predicting it is over ore on tenuous ground. That's Thomas 409 00:24:21,800 --> 00:24:24,960 Speaker 1: media experience coming through that, refusing to make a prediction. 410 00:24:25,160 --> 00:24:27,680 Speaker 1: Do you think one thing I feel is like everyone 411 00:24:27,800 --> 00:24:31,240 Speaker 1: is talking about deflation in central banks around the world, 412 00:24:31,359 --> 00:24:34,239 Speaker 1: failing to hit their policies, and how are they going 413 00:24:34,280 --> 00:24:37,040 Speaker 1: to reflate? They can't do it? And then you see 414 00:24:37,080 --> 00:24:40,600 Speaker 1: these conversations the Phillips curve, this idea that the employment 415 00:24:40,840 --> 00:24:44,200 Speaker 1: and the inflation ratear inversely related, and how that's dead 416 00:24:44,240 --> 00:24:46,800 Speaker 1: and broken. Do you think we could be getting to 417 00:24:47,600 --> 00:24:51,199 Speaker 1: an extreme in the other direction where everyone has just 418 00:24:51,240 --> 00:24:54,040 Speaker 1: thrown in the towel on any sort of inflation coming 419 00:24:54,080 --> 00:24:57,800 Speaker 1: back and anything like that, and no to get wonky 420 00:24:57,920 --> 00:25:02,680 Speaker 1: on you within a class hi SLM matrix Johnny Hicks 421 00:25:02,720 --> 00:25:06,000 Speaker 1: thirty nine and Krugman's written about this beautifully. What I 422 00:25:06,040 --> 00:25:10,040 Speaker 1: would suggest is there's a total underestimation of real economy effects. 423 00:25:10,600 --> 00:25:12,960 Speaker 1: Everybody's over in the bank. What's yelling gonna do, what's 424 00:25:13,000 --> 00:25:15,560 Speaker 1: karneiy gonna do? Which is fine, I mean, that's what 425 00:25:15,640 --> 00:25:18,920 Speaker 1: keeps us in quite all rio's but the real economy 426 00:25:18,960 --> 00:25:21,960 Speaker 1: effects have been grossly underrated from day one of the 427 00:25:22,000 --> 00:25:25,320 Speaker 1: crisis August of oh seven. And the other thing I 428 00:25:25,320 --> 00:25:29,600 Speaker 1: would suggest is the interest rate transmission between the real 429 00:25:29,600 --> 00:25:32,680 Speaker 1: economy and the bank side of things. The l M 430 00:25:32,760 --> 00:25:36,560 Speaker 1: curve is totally broken at the zero bound, and these 431 00:25:37,080 --> 00:25:40,760 Speaker 1: there's things we don't understand that are going on in 432 00:25:40,800 --> 00:25:44,439 Speaker 1: the interest rate sphere right now that there there there's 433 00:25:44,480 --> 00:25:48,240 Speaker 1: a mystery here. I can't believe we've gotten this far 434 00:25:48,320 --> 00:25:53,240 Speaker 1: in the segment without talking about your bow ties and 435 00:25:53,280 --> 00:25:56,960 Speaker 1: the fact that the boat's high was almost entirely responsible 436 00:25:57,040 --> 00:25:59,919 Speaker 1: for bringing you to Bloomberg, since Mr Matt Winkler all 437 00:26:00,040 --> 00:26:04,720 Speaker 1: so enjoys wearing those ties. I found a picture of 438 00:26:04,760 --> 00:26:08,880 Speaker 1: my grandfather, my mother's father, five years ago, holding me, 439 00:26:09,480 --> 00:26:10,919 Speaker 1: and he had a bow tie on. I have no 440 00:26:11,040 --> 00:26:14,359 Speaker 1: recollection of my grandfather having a bow tech um. It 441 00:26:14,520 --> 00:26:16,959 Speaker 1: started when I was sort of sort of kind of 442 00:26:16,960 --> 00:26:20,359 Speaker 1: like premed and I was in emergency rooms and they 443 00:26:20,400 --> 00:26:22,880 Speaker 1: wouldn't let you wear a normal tie. Because they're afraid 444 00:26:22,880 --> 00:26:25,600 Speaker 1: the patient will grab you with a regular tie, so 445 00:26:25,640 --> 00:26:28,320 Speaker 1: I was forced to wear a bow tech uh, doing 446 00:26:28,480 --> 00:26:30,560 Speaker 1: what was called extern This is a million years ago. 447 00:26:30,800 --> 00:26:34,960 Speaker 1: This is before anesthesia and uh uh, you know it 448 00:26:35,080 --> 00:26:37,600 Speaker 1: sort of started with that. I'm assuming back then they 449 00:26:37,640 --> 00:26:40,600 Speaker 1: weren't a maze ties though as they they were not. No, 450 00:26:40,720 --> 00:26:43,600 Speaker 1: they weren't. We did the clip on thing minimally. I 451 00:26:43,720 --> 00:26:46,040 Speaker 1: must admit that that was like, I don't want to 452 00:26:46,040 --> 00:26:50,200 Speaker 1: do that. Do you have a favorite tie? Doesn't mean something? No, 453 00:26:50,240 --> 00:26:52,959 Speaker 1: not really. This one's actually very old. This is like 454 00:26:53,000 --> 00:26:55,560 Speaker 1: one of the original It's an orange tie for those 455 00:26:55,600 --> 00:26:59,280 Speaker 1: we Thank you very much for joining us. That was 456 00:26:59,320 --> 00:27:02,080 Speaker 1: a phenomenal discussion. I learned a lot about you. And 457 00:27:02,760 --> 00:27:04,640 Speaker 1: podcast is really cool. You know, we did them years 458 00:27:04,640 --> 00:27:11,200 Speaker 1: ago and I totally agree with a new um enthusiasm podcast. 459 00:27:11,600 --> 00:27:13,760 Speaker 1: Thank you for being our guinea pig. Tom, Thank you, 460 00:27:15,760 --> 00:27:17,840 Speaker 1: thank you for joining us on the first episode of 461 00:27:17,880 --> 00:27:20,679 Speaker 1: Odd Lots. Will be doing this every week and you 462 00:27:20,680 --> 00:27:23,960 Speaker 1: can find it on Bloomberg dot com, iTunes, SoundCloud, and 463 00:27:24,000 --> 00:27:27,199 Speaker 1: just about anywhere else. You can follow us on Twitter 464 00:27:27,600 --> 00:27:31,159 Speaker 1: at at the Stalwart for me, at Tracy Ellaway for 465 00:27:31,359 --> 00:27:34,840 Speaker 1: Tracy will obviously be tweeting out the links. And thanks 466 00:27:34,880 --> 00:27:37,199 Speaker 1: again to Tom Keene for joining us and being our 467 00:27:37,240 --> 00:27:39,879 Speaker 1: guinea pig on this first episode, and thank you for 468 00:27:39,920 --> 00:27:40,280 Speaker 1: listening