1 00:00:00,080 --> 00:00:03,440 Speaker 1: I'm Barry Hults. You're listening to Master's in Business on 2 00:00:03,440 --> 00:00:07,360 Speaker 1: Bloomberg Radio. My extra special guest this week is Lynn Martin. 3 00:00:07,760 --> 00:00:10,960 Speaker 1: She is the president of the New York Stock Exchange, 4 00:00:11,360 --> 00:00:16,880 Speaker 1: the world's largest with over listed companies for a combined 5 00:00:17,000 --> 00:00:21,079 Speaker 1: market cap of about thirty six trillion dollars. She is 6 00:00:21,120 --> 00:00:24,239 Speaker 1: also chair of the Fixed Income and Data Services at 7 00:00:24,320 --> 00:00:28,680 Speaker 1: i c E Intercontinental Exchange. She began her career at 8 00:00:28,680 --> 00:00:33,159 Speaker 1: IBM in Global services and came to them with a b. S. 9 00:00:33,240 --> 00:00:37,520 Speaker 1: And Computer science and a master's degree in stats from Columbia. 10 00:00:37,920 --> 00:00:41,440 Speaker 1: Lynn Martin, Welcome to Bloomberg. Thanks so much for having me, 11 00:00:41,760 --> 00:00:44,640 Speaker 1: Thanks so much for joining us. I have so many 12 00:00:44,680 --> 00:00:47,600 Speaker 1: things to ask you about, but but I have to 13 00:00:47,640 --> 00:00:52,159 Speaker 1: start out with You're at IBM pretty much in its heyday. 14 00:00:52,680 --> 00:00:55,760 Speaker 1: Tell us what that experience was like. It was great, 15 00:00:56,360 --> 00:00:58,280 Speaker 1: And I'm going to date myself a little bit, but 16 00:00:58,400 --> 00:01:01,840 Speaker 1: I was there through the why two K crisis when 17 00:01:02,200 --> 00:01:07,280 Speaker 1: global Services was relied upon by customers around the globe 18 00:01:07,319 --> 00:01:12,800 Speaker 1: to get them through that crisis or near crisis or 19 00:01:12,880 --> 00:01:16,440 Speaker 1: almost crisis, one of those crisis. It wasn't your that's fair. 20 00:01:16,560 --> 00:01:20,080 Speaker 1: It wasn't a crisis in that I remember waking up 21 00:01:20,120 --> 00:01:24,560 Speaker 1: on New Year's Eve and going to work in a 22 00:01:24,600 --> 00:01:27,120 Speaker 1: call center because that's what that's what you do in 23 00:01:27,160 --> 00:01:30,360 Speaker 1: your twenties working at IBM, and we were watching the 24 00:01:30,400 --> 00:01:35,679 Speaker 1: Australia the clocks hitting in Australia, and everything was fine, 25 00:01:35,920 --> 00:01:41,679 Speaker 1: and we sort of knew that once Australia opened okay, 26 00:01:41,959 --> 00:01:44,160 Speaker 1: we were going to be okay. So was this like 27 00:01:44,200 --> 00:01:47,039 Speaker 1: a near miss or was this hey A lot was 28 00:01:47,160 --> 00:01:49,720 Speaker 1: made about something that turned out to be less of 29 00:01:49,840 --> 00:01:54,560 Speaker 1: I think the industry became very well prepared it. I 30 00:01:54,600 --> 00:01:57,480 Speaker 1: don't know that it was a near miss, but I 31 00:01:57,520 --> 00:02:00,920 Speaker 1: think we got ahead of a potential Channe challenge. There 32 00:02:01,040 --> 00:02:04,360 Speaker 1: was a technical challenge, and that computers only stood years 33 00:02:04,440 --> 00:02:08,680 Speaker 1: understood years uh in two digits as opposed to four digits, 34 00:02:08,880 --> 00:02:12,920 Speaker 1: so two thousand could have been and that could have 35 00:02:13,520 --> 00:02:17,080 Speaker 1: caused challenges. I don't know if those challenges would have 36 00:02:17,200 --> 00:02:21,880 Speaker 1: been as extreme as was forecasted or not, but I'm 37 00:02:21,880 --> 00:02:24,840 Speaker 1: really glad we didn't find out, to say the very least, 38 00:02:25,320 --> 00:02:28,760 Speaker 1: so so IBM. I'm sort of jumping ahead in the story. 39 00:02:28,840 --> 00:02:33,600 Speaker 1: You began your career in computer sciences coding on a 40 00:02:33,639 --> 00:02:36,359 Speaker 1: Commoto or sixty four. You would you like to explain 41 00:02:36,440 --> 00:02:39,320 Speaker 1: that for the you don't know what a Commodore six. 42 00:02:39,840 --> 00:02:43,880 Speaker 1: I remember when I came home from elementary school one 43 00:02:43,960 --> 00:02:47,160 Speaker 1: day and my dad, who was an electric electrical engineer 44 00:02:47,280 --> 00:02:52,560 Speaker 1: he used to design fuel gauges on on airplanes, came 45 00:02:52,600 --> 00:02:55,720 Speaker 1: home with this huge box. Uh. And it was a 46 00:02:55,720 --> 00:02:58,000 Speaker 1: Commodore sixty four. I said, what's that? He said, the 47 00:02:58,160 --> 00:03:01,640 Speaker 1: first home computer. And I said, at what's that? I 48 00:03:01,680 --> 00:03:04,280 Speaker 1: was a kid, I had no idea. Uh. And he 49 00:03:04,360 --> 00:03:08,120 Speaker 1: also brought home a stack of floppy disks, which were 50 00:03:08,200 --> 00:03:10,639 Speaker 1: quite large for the youth that don't remember, they were 51 00:03:10,760 --> 00:03:13,600 Speaker 1: literally floppy there. They were floppy. You could bend them, 52 00:03:13,600 --> 00:03:17,359 Speaker 1: twist them, whatever the case, though you shouldn't because your 53 00:03:17,480 --> 00:03:20,880 Speaker 1: program would not work if you did that. Um. And 54 00:03:20,919 --> 00:03:23,960 Speaker 1: I just became hooked on it. And as any good 55 00:03:24,040 --> 00:03:27,440 Speaker 1: kid does, you become hooked on video games first. So 56 00:03:28,440 --> 00:03:32,040 Speaker 1: that's really what got me into the Commodore sixty four. 57 00:03:32,360 --> 00:03:35,280 Speaker 1: So is that what led to a focus on data 58 00:03:35,360 --> 00:03:39,520 Speaker 1: service and computer science the simple C sixty four. Well, 59 00:03:39,640 --> 00:03:42,920 Speaker 1: what really led me in the path of studying for 60 00:03:43,040 --> 00:03:46,920 Speaker 1: my undergrad was my dad giving me really good advice 61 00:03:47,040 --> 00:03:50,040 Speaker 1: which was way ahead of its time in the early 62 00:03:50,120 --> 00:03:53,680 Speaker 1: nineties when I was applying to college where he suggested 63 00:03:53,720 --> 00:03:58,360 Speaker 1: I not go into the traditional engineering discipline but instead 64 00:03:58,400 --> 00:04:02,480 Speaker 1: go into computer science. It's mainly because not only was 65 00:04:02,520 --> 00:04:06,000 Speaker 1: I good at math and good at sciences, but he 66 00:04:06,040 --> 00:04:10,360 Speaker 1: said there will always be opportunities for women in computer science, 67 00:04:11,160 --> 00:04:14,160 Speaker 1: and it's something that was just way ahead of its time, 68 00:04:14,200 --> 00:04:17,320 Speaker 1: and it sounded good to a seventeen year old filling 69 00:04:17,320 --> 00:04:19,680 Speaker 1: out a bunch of college essays at the kitchen table. 70 00:04:20,400 --> 00:04:24,479 Speaker 1: So I ticked off computer science despite you know, not 71 00:04:24,560 --> 00:04:28,840 Speaker 1: having a tremendous amount of experience with a computer, aside 72 00:04:28,880 --> 00:04:32,960 Speaker 1: from playing with video games and fiddling around more as 73 00:04:33,000 --> 00:04:36,880 Speaker 1: a hobby than anything. And now you oversee a system 74 00:04:36,960 --> 00:04:41,599 Speaker 1: that is a combination of advanced data systems, lots of 75 00:04:41,720 --> 00:04:46,840 Speaker 1: hardware and software plumbing. You have to keep listed companies 76 00:04:46,920 --> 00:04:50,279 Speaker 1: up and running, trading as much as a billion shares 77 00:04:50,320 --> 00:04:53,520 Speaker 1: a day. How do you go from the common sixty 78 00:04:53,600 --> 00:04:59,960 Speaker 1: four to that? U Fortunately, technology has been significantly advanced, 79 00:05:00,000 --> 00:05:02,200 Speaker 1: and I think that's what's contributing to the volumes of 80 00:05:02,240 --> 00:05:04,919 Speaker 1: liquidity that you actually see in the markets, but also 81 00:05:05,839 --> 00:05:10,280 Speaker 1: the amount of incoming order messages that we see every day. 82 00:05:10,400 --> 00:05:13,040 Speaker 1: So when you say technology, it's the technology at the 83 00:05:13,080 --> 00:05:16,600 Speaker 1: New York Stock Exchange as well as the technology at 84 00:05:16,600 --> 00:05:20,440 Speaker 1: all the companies that are trading. Absolutely, and just macro technology. 85 00:05:20,480 --> 00:05:25,120 Speaker 1: I remember the first um classes I took in my 86 00:05:25,320 --> 00:05:28,880 Speaker 1: compsite major. I learned assembly language. I was coding on 87 00:05:28,880 --> 00:05:32,200 Speaker 1: a mainframe. I was moving from register one to register too, 88 00:05:32,360 --> 00:05:34,960 Speaker 1: and that's like the most basic form. And now mainframes 89 00:05:35,040 --> 00:05:40,520 Speaker 1: have become really thin machines. They've really moved into thin servers. 90 00:05:40,560 --> 00:05:45,520 Speaker 1: They've moved into storage capacity like the cloud. So that 91 00:05:45,760 --> 00:05:50,000 Speaker 1: the advancement of technology since I finished my degree in 92 00:05:50,040 --> 00:05:53,720 Speaker 1: the late nineties to where it is today has really 93 00:05:53,839 --> 00:05:59,440 Speaker 1: exploded into something that allows for more performance, more real 94 00:05:59,560 --> 00:06:03,960 Speaker 1: time type of interactions. There's a quote of yours I 95 00:06:04,000 --> 00:06:09,560 Speaker 1: really like, which which probably explains your career, which is 96 00:06:09,800 --> 00:06:12,560 Speaker 1: quote the amount of satisfaction I would get when I 97 00:06:12,600 --> 00:06:16,560 Speaker 1: compiled the program. In the program did exactly what I wanted. 98 00:06:16,560 --> 00:06:20,400 Speaker 1: It too, was beyond anything I had experienced previously. Explained 99 00:06:20,440 --> 00:06:25,800 Speaker 1: that absolutely, So I tend to view computer coding not 100 00:06:25,920 --> 00:06:30,160 Speaker 1: that dissimilar to communicating in a foreign language. A lot 101 00:06:30,200 --> 00:06:33,559 Speaker 1: of times you're learning your French Spanish to communicate with 102 00:06:33,720 --> 00:06:37,240 Speaker 1: someone in a different country. When you're learning computer coding, 103 00:06:37,240 --> 00:06:40,039 Speaker 1: you're just learning how to communicate with a machine. And 104 00:06:40,120 --> 00:06:43,000 Speaker 1: a lot of times when you're communicating with that machine, 105 00:06:43,120 --> 00:06:46,920 Speaker 1: you think you're telling them something really good and something 106 00:06:46,960 --> 00:06:50,480 Speaker 1: really positive, and what comes out is something really not positive. 107 00:06:51,080 --> 00:06:55,520 Speaker 1: So very frequently, maybe because I wasn't the best coder, 108 00:06:56,360 --> 00:06:59,880 Speaker 1: I would you know, come back with errors or stuff, 109 00:07:00,240 --> 00:07:03,120 Speaker 1: uh stuck in what is known as an infinite loop, 110 00:07:03,120 --> 00:07:05,239 Speaker 1: where the machine just keeps going and going and going 111 00:07:06,760 --> 00:07:11,480 Speaker 1: repeat exactly kept doing that. So on the days when 112 00:07:11,640 --> 00:07:14,280 Speaker 1: I would put you know, in code and the machine 113 00:07:14,320 --> 00:07:17,080 Speaker 1: would do exactly what I told it to, I was like, oh, wow, 114 00:07:17,280 --> 00:07:22,200 Speaker 1: that's great. So so you use the metaphor of a 115 00:07:22,240 --> 00:07:27,960 Speaker 1: foreign language, um verb, tense, syntax, punctuation, all these things 116 00:07:28,000 --> 00:07:31,960 Speaker 1: really matter when you code it sure does. Syntax absolutely matters. 117 00:07:32,000 --> 00:07:36,840 Speaker 1: Efficiency also absolutely matters. Think of the way I think 118 00:07:36,920 --> 00:07:39,560 Speaker 1: of efficiency around computer coding is not that different to 119 00:07:39,960 --> 00:07:43,360 Speaker 1: communicating with a local in in a in a foreign country. 120 00:07:43,400 --> 00:07:47,560 Speaker 1: They've got there, you've got your typical language, you know, 121 00:07:47,600 --> 00:07:49,440 Speaker 1: the stuff that you would learn in textbooks. But then 122 00:07:49,480 --> 00:07:52,440 Speaker 1: you've got you know, the more shorthand types of communication. 123 00:07:52,920 --> 00:07:55,320 Speaker 1: I kind of view efficient computer coding and the same 124 00:07:55,600 --> 00:07:59,080 Speaker 1: the same passion. Really quite interesting. Let's talk a little 125 00:07:59,080 --> 00:08:01,960 Speaker 1: bit about the I P O process. How does this work? 126 00:08:02,000 --> 00:08:04,920 Speaker 1: When a company decides they want to go public. So 127 00:08:04,920 --> 00:08:07,760 Speaker 1: a company will decide they want to go public, Typically 128 00:08:07,800 --> 00:08:12,280 Speaker 1: they will interview variety of exchanges. That could be domestic 129 00:08:12,360 --> 00:08:16,400 Speaker 1: US exchanges, it could be if they are a foreign company, um, 130 00:08:16,440 --> 00:08:20,600 Speaker 1: they will they will look at their home markets as well. Ultimately, 131 00:08:20,640 --> 00:08:23,080 Speaker 1: they have a certain objective in mind. Do they want 132 00:08:23,080 --> 00:08:25,640 Speaker 1: to raise capital, do they not want to raise capital? 133 00:08:25,720 --> 00:08:29,040 Speaker 1: If they want to raise capital, what investor base are 134 00:08:29,080 --> 00:08:32,720 Speaker 1: they really targeting? More often than not, a company will 135 00:08:32,800 --> 00:08:36,160 Speaker 1: select the US markets because we have the most diverse, 136 00:08:36,520 --> 00:08:41,520 Speaker 1: the deepest pools of liquidity, uh, the biggest access to investors, 137 00:08:41,559 --> 00:08:46,800 Speaker 1: the biggest opportunity for a company to gain a global following. 138 00:08:46,880 --> 00:08:50,079 Speaker 1: So typically they will they will select a US exchange. 139 00:08:50,440 --> 00:08:53,760 Speaker 1: So you guys obviously have to prep when a company 140 00:08:53,800 --> 00:08:56,960 Speaker 1: comes to you and says, hey, we're considering you and 141 00:08:57,000 --> 00:09:00,439 Speaker 1: some of your competitors. What is your process like to 142 00:09:00,520 --> 00:09:03,120 Speaker 1: prepare for. I don't know if we still use the 143 00:09:03,120 --> 00:09:06,480 Speaker 1: phrase beauty contest, but that that was the old investment 144 00:09:06,480 --> 00:09:10,679 Speaker 1: banking phrase. How do you gear up to say, here's 145 00:09:10,679 --> 00:09:13,439 Speaker 1: why you should list with the NYC and not our competitors. 146 00:09:13,440 --> 00:09:16,880 Speaker 1: So my philosophy is very focused on how can we 147 00:09:16,880 --> 00:09:19,920 Speaker 1: be a good partner to our listed companies and what 148 00:09:20,160 --> 00:09:24,640 Speaker 1: is that listed company seeking to achieve? And it's not 149 00:09:24,720 --> 00:09:27,000 Speaker 1: just about I P O day, I p O day, 150 00:09:27,120 --> 00:09:29,160 Speaker 1: I kind of equate to a wedding day. You're going 151 00:09:29,200 --> 00:09:34,600 Speaker 1: to have a great day, um hopefully, hopefully. But what 152 00:09:35,040 --> 00:09:37,320 Speaker 1: I what I tend to think about is what happens 153 00:09:37,320 --> 00:09:39,800 Speaker 1: a month after you go public? What happened six months 154 00:09:39,800 --> 00:09:41,640 Speaker 1: after you go public? And how can we be a 155 00:09:41,640 --> 00:09:44,400 Speaker 1: good partner to that firm and their public company journey. 156 00:09:45,040 --> 00:09:48,320 Speaker 1: I love the visual of this as a wedding day, 157 00:09:48,400 --> 00:09:51,520 Speaker 1: So so now I'm thinking of mother the bride, father 158 00:09:51,600 --> 00:09:55,120 Speaker 1: the bride. Who are you working more closely with? Is 159 00:09:55,120 --> 00:09:58,320 Speaker 1: it the investment bank? Is it management of the company? 160 00:09:58,720 --> 00:10:05,080 Speaker 1: Who you're working with? Both? Yeah, exactly exactly. You're working 161 00:10:05,120 --> 00:10:07,600 Speaker 1: with the banks who are underrating the deal, the mother 162 00:10:07,640 --> 00:10:11,160 Speaker 1: and father of the bride. To use your analogy, you're 163 00:10:11,160 --> 00:10:14,439 Speaker 1: also working very closely with the company because the company 164 00:10:14,480 --> 00:10:18,240 Speaker 1: has a vision. The company has been successful in that 165 00:10:18,280 --> 00:10:21,080 Speaker 1: they've gotten to the point where they're graduating to the 166 00:10:21,160 --> 00:10:25,160 Speaker 1: public markets, which is something that should be celebrated. But 167 00:10:25,520 --> 00:10:29,560 Speaker 1: the company also has a two year, three year, five 168 00:10:29,640 --> 00:10:33,360 Speaker 1: year strategy of what they are really seeking to achieve, 169 00:10:33,800 --> 00:10:37,680 Speaker 1: not just raising capital to fund operations or to fund 170 00:10:37,760 --> 00:10:41,199 Speaker 1: research and development. They have may have M and A targets. 171 00:10:41,240 --> 00:10:45,280 Speaker 1: They may want to expand their business through leveraging a 172 00:10:45,360 --> 00:10:49,719 Speaker 1: community that the listings market, particularly the ny s C 173 00:10:50,120 --> 00:10:54,560 Speaker 1: brings to the table. They may have specific concerns about 174 00:10:55,600 --> 00:10:59,960 Speaker 1: certain areas of the market. One one topic that see 175 00:11:00,000 --> 00:11:01,920 Speaker 1: eos are very focused on at the moment is E 176 00:11:02,120 --> 00:11:06,600 Speaker 1: S g UM Environmental, social and governance and how they 177 00:11:06,800 --> 00:11:10,240 Speaker 1: are bringing sustainable practices to the market. So they want 178 00:11:10,240 --> 00:11:13,240 Speaker 1: to tell that story. So everyone's got a different objective. 179 00:11:13,320 --> 00:11:16,520 Speaker 1: So we spent a lot of time with CEO, CFO, 180 00:11:16,760 --> 00:11:21,520 Speaker 1: UM I, r O, the whole team, CMO, Chief Marketing Officer, 181 00:11:21,640 --> 00:11:24,800 Speaker 1: because they're the ones that are, you know, orchestrating the story. 182 00:11:25,400 --> 00:11:27,800 Speaker 1: So let's talk about the story a little bit. I 183 00:11:27,880 --> 00:11:32,040 Speaker 1: just finished watching Apple iTunes we crashed, and what was 184 00:11:32,080 --> 00:11:36,280 Speaker 1: so interesting was as they're marching towards an I p O. 185 00:11:37,120 --> 00:11:39,440 Speaker 1: It has nothing to do with the exchange, It has 186 00:11:39,480 --> 00:11:43,679 Speaker 1: nothing to do with raising capital. The narrative just seemed 187 00:11:43,720 --> 00:11:47,760 Speaker 1: to have taken over from your perch. You must see 188 00:11:47,800 --> 00:11:50,880 Speaker 1: these things go by all the time. Maybe not so 189 00:11:50,960 --> 00:11:53,240 Speaker 1: much this year, which is a lighter I p O year, 190 00:11:53,520 --> 00:11:57,120 Speaker 1: but last year. How do you look at at these 191 00:11:57,280 --> 00:12:01,120 Speaker 1: new breaking news stories and all these um all the 192 00:12:01,160 --> 00:12:04,080 Speaker 1: buzz and mania around an I p O for both 193 00:12:04,120 --> 00:12:06,360 Speaker 1: good and bad, how does that affect your job? Our 194 00:12:06,440 --> 00:12:09,199 Speaker 1: job is ultimately to ensure that when a company comes 195 00:12:09,240 --> 00:12:12,160 Speaker 1: to the market, they get the best experience possible when 196 00:12:12,160 --> 00:12:15,120 Speaker 1: that stock opens and when that stock closes, first week, 197 00:12:15,240 --> 00:12:17,880 Speaker 1: first day, whatever the case may be, and that they're 198 00:12:17,880 --> 00:12:22,360 Speaker 1: happy with the experience. Ultimately, if there is news around 199 00:12:22,400 --> 00:12:26,720 Speaker 1: the company, it may influence their decision to go public 200 00:12:26,920 --> 00:12:32,119 Speaker 1: at a certain time. Ultimately, the company that you're referencing 201 00:12:32,120 --> 00:12:33,880 Speaker 1: did decide to go public, it just was at a 202 00:12:33,920 --> 00:12:37,040 Speaker 1: different different a little a little later, and and there 203 00:12:37,080 --> 00:12:40,280 Speaker 1: have been stories where that doesn't necessarily work out well. 204 00:12:41,000 --> 00:12:45,160 Speaker 1: Or a company like Facebook goes public, the initial rollout 205 00:12:45,520 --> 00:12:47,840 Speaker 1: is a little dicey. They announced something about mobile and 206 00:12:47,880 --> 00:12:51,120 Speaker 1: suddenly the stock takes off. So when you talk about 207 00:12:51,440 --> 00:12:54,560 Speaker 1: the month or the year after the wedding, these stories 208 00:12:54,600 --> 00:12:57,640 Speaker 1: really very much change. It's not just about the I 209 00:12:57,720 --> 00:12:59,520 Speaker 1: p O day, is it. It's not just about the 210 00:12:59,520 --> 00:13:03,040 Speaker 1: IPO it, but that's about the CEO articulating their strategy 211 00:13:03,120 --> 00:13:06,040 Speaker 1: and executing on that strategy. And that's what's going to 212 00:13:06,200 --> 00:13:10,400 Speaker 1: give the CEO and the public company the next group 213 00:13:10,480 --> 00:13:12,560 Speaker 1: of investors. They're going to get a group of investors 214 00:13:12,559 --> 00:13:13,920 Speaker 1: on i p O Day, They're going to do their 215 00:13:14,000 --> 00:13:16,320 Speaker 1: road show right before the i p O s They're 216 00:13:16,320 --> 00:13:19,800 Speaker 1: going to garner the initial set of interest, and a 217 00:13:19,800 --> 00:13:23,640 Speaker 1: lot of times companies will start that process even in 218 00:13:23,640 --> 00:13:26,679 Speaker 1: a soft manner, even before they're on the road for 219 00:13:26,760 --> 00:13:30,880 Speaker 1: the i p O. But post i p O day, 220 00:13:30,920 --> 00:13:36,000 Speaker 1: it's about execution. And when they have really exciting news 221 00:13:36,040 --> 00:13:39,840 Speaker 1: to share, the market tends to reward it, you know, 222 00:13:39,920 --> 00:13:42,960 Speaker 1: more people come into the stock. Let's talk about some 223 00:13:43,040 --> 00:13:47,200 Speaker 1: other ways some people take their companies public. Um, we've 224 00:13:47,200 --> 00:13:50,760 Speaker 1: watched SPACs super popular last year. They all seem to 225 00:13:50,760 --> 00:13:54,600 Speaker 1: have blown up and done fairly poorly this year. How 226 00:13:54,600 --> 00:13:58,400 Speaker 1: does the NYC look at a product like a spack 227 00:13:58,559 --> 00:14:03,319 Speaker 1: as an alternative method did for a private company going public? Ultimately, 228 00:14:03,360 --> 00:14:06,960 Speaker 1: we think SPACs are still a viable form for companies 229 00:14:06,960 --> 00:14:09,280 Speaker 1: to go public. What you saw was a flood of 230 00:14:09,320 --> 00:14:13,079 Speaker 1: SPACs coming to the market at the same time. So 231 00:14:13,160 --> 00:14:16,560 Speaker 1: that may have contributed to some of the challenges that 232 00:14:16,600 --> 00:14:20,680 Speaker 1: have now have now happened given the time horizon that 233 00:14:20,960 --> 00:14:24,440 Speaker 1: is associated with SPACs. Uh. But ultimately we see as 234 00:14:24,480 --> 00:14:27,040 Speaker 1: a very viable form for companies to continue to come 235 00:14:27,080 --> 00:14:30,040 Speaker 1: to market. Spacks have been around for probably fifteen to 236 00:14:30,080 --> 00:14:35,200 Speaker 1: twenty years, and that's what those people forget is that 237 00:14:35,240 --> 00:14:39,680 Speaker 1: this was a form that companies were using to go 238 00:14:39,880 --> 00:14:44,600 Speaker 1: public way before the last two years. They just became 239 00:14:45,640 --> 00:14:48,240 Speaker 1: much more popular in the last couple of years, which 240 00:14:48,240 --> 00:14:51,360 Speaker 1: is why you saw saw the flood, to say the 241 00:14:51,440 --> 00:14:54,320 Speaker 1: very least. Uh, there's been a little bit of agitation 242 00:14:54,480 --> 00:14:58,160 Speaker 1: towards direct listings where there seems to be a decent 243 00:14:58,200 --> 00:15:00,600 Speaker 1: amount of controversy on both sides. How do you guys 244 00:15:00,680 --> 00:15:03,040 Speaker 1: look at direct listings as opposed to the I p 245 00:15:03,120 --> 00:15:07,000 Speaker 1: O process. But we pioneered the direct listing. We pioneered it, 246 00:15:07,280 --> 00:15:11,000 Speaker 1: I believe three three or four years ago, UM, and 247 00:15:12,360 --> 00:15:15,720 Speaker 1: we are quite proud of that innovation. It's just another 248 00:15:15,840 --> 00:15:20,120 Speaker 1: innovation allowing for private companies in this case that didn't 249 00:15:20,160 --> 00:15:23,360 Speaker 1: want to raise capital, that didn't need to raise capital 250 00:15:23,720 --> 00:15:28,600 Speaker 1: to become public companies, to have that public currency, to 251 00:15:28,760 --> 00:15:32,000 Speaker 1: be able to do to fund their operations and or 252 00:15:32,080 --> 00:15:33,920 Speaker 1: to do M and A and or all the other 253 00:15:33,920 --> 00:15:36,560 Speaker 1: great things that come out along with with being a 254 00:15:36,600 --> 00:15:43,080 Speaker 1: public company, including providing investors the opportunity to participate in 255 00:15:43,160 --> 00:15:47,880 Speaker 1: the upside associated with the company. What about the circumstances 256 00:15:47,880 --> 00:15:52,800 Speaker 1: where investors can participate in the upside? Specifically, a lot 257 00:15:52,880 --> 00:15:57,480 Speaker 1: of these venture backed companies have stayed private for much longer. 258 00:15:57,520 --> 00:16:01,520 Speaker 1: They kept doing rounds and have grown to sizes that 259 00:16:01,560 --> 00:16:03,920 Speaker 1: we previously would think of as hey, they should have 260 00:16:03,960 --> 00:16:07,280 Speaker 1: gone public years ago. How do you guys look at that? 261 00:16:07,440 --> 00:16:11,040 Speaker 1: Is this something that you pay attention to? Where do 262 00:16:11,080 --> 00:16:12,600 Speaker 1: you think this go? Yeah, I mean, we believe in 263 00:16:12,640 --> 00:16:18,000 Speaker 1: the power of public markets. We believe in the upside 264 00:16:18,000 --> 00:16:23,520 Speaker 1: that comes along with being a public company. Transparency, good governance, 265 00:16:23,600 --> 00:16:26,280 Speaker 1: you get, you're able to reward your employees, You're able 266 00:16:26,320 --> 00:16:30,480 Speaker 1: to reward shareholders, allow a diverse group of shareholders to 267 00:16:30,520 --> 00:16:34,200 Speaker 1: participate in the upside. And based on the feedback that 268 00:16:34,280 --> 00:16:38,320 Speaker 1: we hear from companies who are private, the public currency 269 00:16:38,560 --> 00:16:42,560 Speaker 1: is still very strong. There is, despite the fact that 270 00:16:42,600 --> 00:16:45,960 Speaker 1: there's volatility in the market. There is still demand for 271 00:16:46,160 --> 00:16:48,480 Speaker 1: companies to go public. They're just trying to figure out 272 00:16:48,560 --> 00:16:53,520 Speaker 1: what time makes sense for them. Interesting. My extra special 273 00:16:53,520 --> 00:16:56,320 Speaker 1: guest this week is Lynn Martin. She is the president 274 00:16:56,480 --> 00:16:59,600 Speaker 1: of the world's largest stock exchange, the n y s C. 275 00:17:00,600 --> 00:17:05,159 Speaker 1: They host listed companies with a market cap somewhere in 276 00:17:05,200 --> 00:17:08,960 Speaker 1: the neighborhood of thirty six trillion dollars doing more than 277 00:17:09,000 --> 00:17:13,119 Speaker 1: a billion shares on a good day. That sounds like 278 00:17:13,200 --> 00:17:17,760 Speaker 1: a pretty complex situation just to begin with. What's it 279 00:17:17,880 --> 00:17:23,560 Speaker 1: like managing something with so many moving parts? There are 280 00:17:23,680 --> 00:17:26,240 Speaker 1: a lot of moving parts, But because I'm a technologist, 281 00:17:27,000 --> 00:17:30,560 Speaker 1: I feel really good about the service that we provide. 282 00:17:30,600 --> 00:17:33,119 Speaker 1: You know. One of the first things when I hopped 283 00:17:33,119 --> 00:17:37,680 Speaker 1: into this role in January, unsurprisingly that I focused on 284 00:17:37,720 --> 00:17:42,600 Speaker 1: was system capacity and thinking about how what what's our 285 00:17:42,640 --> 00:17:47,600 Speaker 1: average response times? What what sort of capacity do we 286 00:17:47,640 --> 00:17:50,760 Speaker 1: have in the system to handle peak days. I'm glad 287 00:17:50,800 --> 00:17:53,399 Speaker 1: I did that because a couple of weeks after that, 288 00:17:53,480 --> 00:17:57,440 Speaker 1: we had tremendous volatility. The week of January around then, 289 00:17:57,840 --> 00:18:01,400 Speaker 1: which is pretty funny because the prior year was almost 290 00:18:01,440 --> 00:18:04,760 Speaker 1: no volatility. Was the quietest year in alone. Started to 291 00:18:04,920 --> 00:18:07,639 Speaker 1: see it a bit in December. So we saw the 292 00:18:07,800 --> 00:18:10,920 Speaker 1: signs in December that volatility was starting to creep into 293 00:18:10,920 --> 00:18:12,840 Speaker 1: the market that we hadn't seen that to your point, 294 00:18:13,359 --> 00:18:15,879 Speaker 1: you know, really since the pandemic um the way we 295 00:18:15,960 --> 00:18:19,800 Speaker 1: look at capacity is incoming order messages for those listening 296 00:18:19,800 --> 00:18:23,399 Speaker 1: and coming order messages is buys coming into the system, 297 00:18:23,400 --> 00:18:28,960 Speaker 1: cells coming into the system, trades uh happening in the system. 298 00:18:29,000 --> 00:18:32,560 Speaker 1: And very quickly we started to see days that were 299 00:18:32,600 --> 00:18:37,840 Speaker 1: in excess of above pandemic levels from a messaging standpoint, 300 00:18:37,920 --> 00:18:42,200 Speaker 1: and equated to half a trillion messages being processed by 301 00:18:42,240 --> 00:18:47,800 Speaker 1: our systems every day, So have a trillion and by messages, 302 00:18:47,840 --> 00:18:52,280 Speaker 1: it's by sell trade buy cell trade. That is an 303 00:18:52,320 --> 00:18:55,879 Speaker 1: incoming order messages, which is tremendous. And the fact that 304 00:18:55,960 --> 00:18:59,639 Speaker 1: we were processing those with average response times in the 305 00:19:00,000 --> 00:19:07,560 Speaker 1: box of about thirty micro seconds micro MicroC that's incredibly incredible. Yeah, 306 00:19:07,800 --> 00:19:10,480 Speaker 1: and you know that really has continued. It's been something 307 00:19:10,560 --> 00:19:13,280 Speaker 1: I've had my eye on throughout the year. But our 308 00:19:13,320 --> 00:19:16,320 Speaker 1: technologists have done a great job. We've recently upgraded our 309 00:19:16,359 --> 00:19:21,000 Speaker 1: systems to our next generation matching engine technology and our 310 00:19:21,040 --> 00:19:25,600 Speaker 1: systems have Touchwood held up beautifully from a response time standpoint. 311 00:19:25,840 --> 00:19:28,760 Speaker 1: So when all these things go right, we never hear 312 00:19:28,800 --> 00:19:31,440 Speaker 1: about it. But when there's a little snaff who it's 313 00:19:31,560 --> 00:19:34,960 Speaker 1: front page of the Wall Street Journal. Um, let's let's 314 00:19:34,960 --> 00:19:37,320 Speaker 1: talk about some of those. Let's talk about what took 315 00:19:37,359 --> 00:19:41,480 Speaker 1: place on the flash crash back in Do we really 316 00:19:41,520 --> 00:19:45,160 Speaker 1: know whatever happened to that or did just And I'm 317 00:19:45,160 --> 00:19:48,520 Speaker 1: gonna give you guys credit. These are all old data systems. 318 00:19:48,560 --> 00:19:52,960 Speaker 1: Everything that existed then have more or less been replaced upgraded. Well, 319 00:19:53,000 --> 00:19:57,720 Speaker 1: I think in a situation like that, you have seen 320 00:19:57,840 --> 00:20:02,000 Speaker 1: a market structure evolve two to the point where there 321 00:20:02,040 --> 00:20:05,480 Speaker 1: have been system safeguards from a market structure standpoint put 322 00:20:05,520 --> 00:20:10,320 Speaker 1: in place around volatility halts. Let me before you go there, 323 00:20:10,359 --> 00:20:12,960 Speaker 1: let me just back up a little bit. So this 324 00:20:13,080 --> 00:20:17,119 Speaker 1: used to be a fairly manual system, with individual specialists, 325 00:20:17,440 --> 00:20:21,440 Speaker 1: human beings at different posts on the floor for each 326 00:20:21,480 --> 00:20:24,679 Speaker 1: individual stock. I kind of forget, having grown up with that, 327 00:20:24,760 --> 00:20:26,520 Speaker 1: I kind of forget. A lot of people are wholly 328 00:20:26,600 --> 00:20:31,159 Speaker 1: unfamiliar with that. And there was a transition process where 329 00:20:31,320 --> 00:20:36,240 Speaker 1: a lot of the manual processes were replaced with electronics 330 00:20:36,359 --> 00:20:41,320 Speaker 1: and and automated computers. There's still humans involved, but much 331 00:20:41,400 --> 00:20:44,560 Speaker 1: less than they once were. What was that part of 332 00:20:44,600 --> 00:20:48,120 Speaker 1: the impact in in the flash crash? And how has 333 00:20:48,160 --> 00:20:51,160 Speaker 1: that transition happened A lot of which took place long 334 00:20:51,200 --> 00:20:55,320 Speaker 1: before you got there. Yeah. Not, It wasn't necessarily as 335 00:20:55,359 --> 00:20:59,159 Speaker 1: a result of any one particular area other than just 336 00:20:59,200 --> 00:21:01,840 Speaker 1: an evolution of the market. What I like to say 337 00:21:01,880 --> 00:21:09,399 Speaker 1: is the most technologically advanced companies employ humans um and 338 00:21:09,440 --> 00:21:13,520 Speaker 1: employ human interaction. Humans are there to make sense of 339 00:21:13,840 --> 00:21:17,280 Speaker 1: what's going on in the market, apply human judgment, remove 340 00:21:17,400 --> 00:21:19,400 Speaker 1: noise from the system. It goes back to what we're 341 00:21:19,400 --> 00:21:25,080 Speaker 1: talking about very early on during our conversation today, which 342 00:21:25,160 --> 00:21:28,000 Speaker 1: is when you're writing code, frequently what's going to come 343 00:21:28,040 --> 00:21:32,639 Speaker 1: back is an error that because that is just the 344 00:21:32,680 --> 00:21:37,359 Speaker 1: computer reacting automatically to something. If you don't have the 345 00:21:37,440 --> 00:21:40,880 Speaker 1: human who can go in and fix the error, you're 346 00:21:40,920 --> 00:21:44,560 Speaker 1: going to remain in an error state. So the humans 347 00:21:44,680 --> 00:21:48,600 Speaker 1: job is really to remove the noise from the system, 348 00:21:48,720 --> 00:21:51,320 Speaker 1: is to remove the volatility from the system. It's something 349 00:21:51,359 --> 00:21:56,399 Speaker 1: that I employed in my previous role where we value 350 00:21:56,480 --> 00:21:59,639 Speaker 1: to point eight million securities and the fixed income opaque 351 00:21:59,760 --> 00:22:03,479 Speaker 1: so out of the market. Uh. Using a lot of 352 00:22:03,480 --> 00:22:06,600 Speaker 1: great systems, a lot of great mathematical mouths and also 353 00:22:06,880 --> 00:22:09,960 Speaker 1: a couple hundred humans. And the reason why we have 354 00:22:10,080 --> 00:22:13,119 Speaker 1: the best data set out there is because I have 355 00:22:13,280 --> 00:22:16,680 Speaker 1: those humans who are all former bond traders and former 356 00:22:17,400 --> 00:22:21,520 Speaker 1: uh MUNI specialists who can make noise of what's coming 357 00:22:21,560 --> 00:22:24,000 Speaker 1: into the system. I think the floor model is the 358 00:22:24,040 --> 00:22:28,200 Speaker 1: exact same thing. During really volatile days, you saw the 359 00:22:28,320 --> 00:22:31,440 Speaker 1: human element really come into play. We saw two times 360 00:22:31,520 --> 00:22:34,720 Speaker 1: less volatility on n y C issued stocks at the open, 361 00:22:35,200 --> 00:22:39,480 Speaker 1: three times less volatility on n y C issued stocks 362 00:22:39,520 --> 00:22:41,800 Speaker 1: at the close, and there's a hundred percent because of 363 00:22:41,840 --> 00:22:46,160 Speaker 1: the job of the floor Really interesting. So let's talk 364 00:22:46,200 --> 00:22:49,320 Speaker 1: about So you have stocks that are listed and some 365 00:22:49,400 --> 00:22:51,879 Speaker 1: of this is NYC and some of this is into 366 00:22:51,880 --> 00:22:56,399 Speaker 1: Continental Exchange. So they do stocks, they do bonds, they 367 00:22:56,440 --> 00:23:00,399 Speaker 1: do options, they do derivatives. What else I don't know 368 00:23:00,400 --> 00:23:03,679 Speaker 1: if I left anything out futures? What what else is 369 00:23:03,720 --> 00:23:09,520 Speaker 1: traded at either ny s C or I sees family 370 00:23:09,560 --> 00:23:12,800 Speaker 1: of exchanges. We also have six clearing houses globally that 371 00:23:12,880 --> 00:23:16,119 Speaker 1: cleared the bulk of the credit to false swaps market. 372 00:23:17,400 --> 00:23:21,880 Speaker 1: Where are where are those six around the around? Our 373 00:23:21,960 --> 00:23:26,440 Speaker 1: largest clearing house is based in Europe. Is uk f 374 00:23:26,560 --> 00:23:29,639 Speaker 1: C a registered We've got a clearing house based in 375 00:23:29,640 --> 00:23:32,960 Speaker 1: the US. We have a clearinghouse based in Singapore UH 376 00:23:33,080 --> 00:23:35,639 Speaker 1: as well as one in the MIDA one in Canada. 377 00:23:35,720 --> 00:23:38,439 Speaker 1: So we've we've got We've got them sprinkled throughout the globe. 378 00:23:38,560 --> 00:23:40,439 Speaker 1: And some of this is regional and some of this 379 00:23:40,520 --> 00:23:43,120 Speaker 1: is redundancy and back up, and it makes it makes 380 00:23:43,119 --> 00:23:45,639 Speaker 1: a whole lot of sense. So I've been talking. I 381 00:23:45,680 --> 00:23:47,680 Speaker 1: keep talking about the n y s C like it's 382 00:23:47,800 --> 00:23:51,320 Speaker 1: just the exchange. Let's talk about the n y C group. 383 00:23:51,800 --> 00:23:56,480 Speaker 1: That's four electronic exchanges UH NYC ARCA, which is the 384 00:23:56,600 --> 00:24:00,600 Speaker 1: leader in et f S NYS American Exchange of Exchange, 385 00:24:00,720 --> 00:24:05,359 Speaker 1: National Exchange, plus two options exchanges, the American Options and 386 00:24:05,520 --> 00:24:07,840 Speaker 1: our options which I think one is the New York 387 00:24:07,840 --> 00:24:10,199 Speaker 1: one is in San Francisco? Was that right? The floor 388 00:24:10,680 --> 00:24:15,399 Speaker 1: is in San Francisco. So how do all I just 389 00:24:15,520 --> 00:24:19,560 Speaker 1: mentioned four electronic exchanges to options changes? How do all 390 00:24:19,640 --> 00:24:23,480 Speaker 1: of these integrate with the n y c S operations? 391 00:24:23,480 --> 00:24:26,840 Speaker 1: So common technology UM is REELI what pulls all of 392 00:24:26,880 --> 00:24:29,199 Speaker 1: the exchanges together. The different medallions are really there to 393 00:24:29,200 --> 00:24:32,760 Speaker 1: try different market models, different matching algorithms on the on 394 00:24:32,840 --> 00:24:37,879 Speaker 1: the options side of the business, um different UH market 395 00:24:37,960 --> 00:24:40,960 Speaker 1: models from the equity side of the business. It gives 396 00:24:41,040 --> 00:24:44,600 Speaker 1: us more flexibility to have to be responsive to our customers. 397 00:24:44,960 --> 00:24:50,680 Speaker 1: Quite fascinating. So I mentioned was sort of aberrational. At 398 00:24:50,720 --> 00:24:54,720 Speaker 1: no point in the year was the market less than 399 00:24:54,840 --> 00:24:58,400 Speaker 1: five percent from all time highs. That led to very 400 00:24:58,560 --> 00:25:02,359 Speaker 1: very little volatility and a year UM. How does a 401 00:25:02,560 --> 00:25:07,280 Speaker 1: lack of volatility affect your daily work or UH? Really 402 00:25:07,359 --> 00:25:10,120 Speaker 1: the right way to ask that is, when volatility spikes 403 00:25:10,160 --> 00:25:12,560 Speaker 1: like we saw it this year, does that make your 404 00:25:12,640 --> 00:25:16,960 Speaker 1: job harder? It makes your job different. It makes your 405 00:25:17,040 --> 00:25:21,280 Speaker 1: job focused more on thinking about things like system capacity 406 00:25:21,400 --> 00:25:24,640 Speaker 1: response times. You're looking at that super closely because you've 407 00:25:24,640 --> 00:25:28,720 Speaker 1: always want to have a very responsive matching engine. You 408 00:25:28,760 --> 00:25:31,520 Speaker 1: spend a little bit less time though, welcoming I p 409 00:25:31,640 --> 00:25:35,399 Speaker 1: o s to the market because many companies are not 410 00:25:35,480 --> 00:25:39,800 Speaker 1: going to want to go out in a very volatile environment. 411 00:25:40,200 --> 00:25:44,280 Speaker 1: So this raises an interesting question, what can you guys 412 00:25:44,359 --> 00:25:47,560 Speaker 1: do to I don't know if you can end volatility, 413 00:25:47,560 --> 00:25:49,440 Speaker 1: but what can you do to tame it or make 414 00:25:49,480 --> 00:25:53,560 Speaker 1: it more manageable? Is there anything in your trading process 415 00:25:53,720 --> 00:25:59,200 Speaker 1: that can facilitate taking some of the spikes and volatility well? 416 00:25:59,240 --> 00:26:02,640 Speaker 1: That's where that's where our market maker model really resonates. 417 00:26:02,600 --> 00:26:06,200 Speaker 1: Sounds really resonating with those companies who still believe in 418 00:26:06,240 --> 00:26:10,359 Speaker 1: the public current public market currency, which there's many of them, 419 00:26:10,840 --> 00:26:13,960 Speaker 1: when they're thinking about coming to the market. Because you 420 00:26:14,000 --> 00:26:17,199 Speaker 1: can't predict volatility. No one can control volatilly. No one 421 00:26:17,200 --> 00:26:19,560 Speaker 1: can predict volatilly, but we can do things because of 422 00:26:19,560 --> 00:26:24,040 Speaker 1: our market model to help the companies that are listed 423 00:26:24,200 --> 00:26:27,800 Speaker 1: on us have a less volatile experience. So our market 424 00:26:27,800 --> 00:26:32,040 Speaker 1: model requires a designated market maker UM whose job is 425 00:26:32,119 --> 00:26:36,720 Speaker 1: to trade that stock from the floor and they create 426 00:26:36,840 --> 00:26:40,520 Speaker 1: an orderly market, correct correct, and they smooth out volatility, 427 00:26:40,560 --> 00:26:43,320 Speaker 1: not just intra day, but also the open and the clothes, 428 00:26:43,320 --> 00:26:46,840 Speaker 1: the open end, the closure incredibly important moments in time 429 00:26:47,440 --> 00:26:50,920 Speaker 1: for a company, particularly if you think of a company 430 00:26:51,119 --> 00:26:55,120 Speaker 1: having quarter end or they're having share repurchases or whatever 431 00:26:55,160 --> 00:26:58,480 Speaker 1: the case may be. So that's actually meaningful dollars even 432 00:26:58,520 --> 00:27:01,399 Speaker 1: post an I p O in a CFOs mine when 433 00:27:01,400 --> 00:27:04,879 Speaker 1: they're doing shared by backs, things of that nature. So 434 00:27:04,960 --> 00:27:08,679 Speaker 1: that's where our market model really resonates, particularly in times 435 00:27:08,720 --> 00:27:10,960 Speaker 1: like this when you see the volatility in the market, 436 00:27:11,000 --> 00:27:13,720 Speaker 1: you see the VIX over twenty but you know that 437 00:27:13,800 --> 00:27:16,439 Speaker 1: companies still want to go out in the public market. 438 00:27:16,720 --> 00:27:20,120 Speaker 1: You know, I think the public is probably less aware 439 00:27:20,359 --> 00:27:23,679 Speaker 1: of some of the institutional order flow like buy on 440 00:27:23,840 --> 00:27:27,919 Speaker 1: open or sell on clothes, which it doesn't hurt to 441 00:27:27,960 --> 00:27:32,400 Speaker 1: have a professional overseeing that process so it doesn't get 442 00:27:32,440 --> 00:27:34,840 Speaker 1: too well and also smooth at any imbalances because you're 443 00:27:34,880 --> 00:27:37,119 Speaker 1: not necessarily going to have a balance book at the 444 00:27:37,240 --> 00:27:40,800 Speaker 1: end of which means they're literally taking positions longer short 445 00:27:40,880 --> 00:27:44,800 Speaker 1: in order to satisfy those those orders at um. So, 446 00:27:44,800 --> 00:27:49,760 Speaker 1: so let's talk about some some imbalances. Uh, And I'm 447 00:27:49,800 --> 00:27:53,720 Speaker 1: thinking about the sort of meme stock mania that began 448 00:27:54,040 --> 00:27:57,720 Speaker 1: in when everybody was stuck at home during the pandemic 449 00:27:57,760 --> 00:28:01,960 Speaker 1: and just exploded in one. It was really a very 450 00:28:02,080 --> 00:28:07,159 Speaker 1: unexpectedly wild ride, especially the companies involved. What was that 451 00:28:07,240 --> 00:28:11,080 Speaker 1: experience like for you watching this You weren't yet president 452 00:28:11,200 --> 00:28:16,200 Speaker 1: of the NYC in one, but you were still there. 453 00:28:16,600 --> 00:28:19,560 Speaker 1: Tell us what that experience was like. I mean, it 454 00:28:19,600 --> 00:28:25,800 Speaker 1: was incredibly interesting to watch the the new retail interest 455 00:28:26,280 --> 00:28:30,280 Speaker 1: in certain stocks and why they had picked certain stocks. 456 00:28:30,720 --> 00:28:33,359 Speaker 1: And I think it's just still something that is is 457 00:28:33,480 --> 00:28:37,879 Speaker 1: fascinating intellectually more than anything. I can't really comment on 458 00:28:37,920 --> 00:28:41,240 Speaker 1: any of their decisions, but it's it's been interesting to 459 00:28:41,280 --> 00:28:46,480 Speaker 1: watch how social media has really emboldened a new class 460 00:28:46,480 --> 00:28:50,240 Speaker 1: of trader. My favorite moment of that was the young, 461 00:28:50,400 --> 00:28:53,680 Speaker 1: pretty handsome, young couple. And the way we subsidize our 462 00:28:53,720 --> 00:28:57,240 Speaker 1: lifestyle is we buy stocks. We only buy the stocks 463 00:28:57,240 --> 00:28:59,200 Speaker 1: that are going up, and when they go up, we 464 00:28:59,320 --> 00:29:01,120 Speaker 1: sell them, and and we just do that over and 465 00:29:01,160 --> 00:29:03,360 Speaker 1: over again from home. And I'm like, oh, I had 466 00:29:03,400 --> 00:29:06,520 Speaker 1: no idea. Was that easy to get rich trading stocks? 467 00:29:07,000 --> 00:29:09,160 Speaker 1: Why did someone tell me that thirty years ago? I 468 00:29:09,200 --> 00:29:12,640 Speaker 1: tend to take the view that having a very balanced 469 00:29:12,720 --> 00:29:15,680 Speaker 1: portfolio and knowing what you invest in, investing for the 470 00:29:15,760 --> 00:29:18,640 Speaker 1: long term is probably nine times out of tend, the 471 00:29:19,560 --> 00:29:21,200 Speaker 1: maybe nine and a half times out of tend, the 472 00:29:21,960 --> 00:29:25,800 Speaker 1: right philosophy to have. I think Warren Buffett wouldn't find 473 00:29:25,800 --> 00:29:30,400 Speaker 1: anything to disagree with with that. Um And yet we 474 00:29:30,480 --> 00:29:37,880 Speaker 1: see people pilings companies of questionable potential. My favorite example was, um, 475 00:29:38,040 --> 00:29:40,520 Speaker 1: was it Hurts that was bankrupt and everybody decided to 476 00:29:40,560 --> 00:29:44,000 Speaker 1: buy Hurt Since then, so so as you're observing, this 477 00:29:44,520 --> 00:29:46,400 Speaker 1: is part of your brain saying we have to do 478 00:29:46,440 --> 00:29:49,680 Speaker 1: excellent Y and Z to stop this or is it Well, 479 00:29:50,080 --> 00:29:54,640 Speaker 1: that's gonna be an interesting uh end when when that all? 480 00:29:54,720 --> 00:29:57,040 Speaker 1: When that train stopped. So our job is to make 481 00:29:57,080 --> 00:30:00,920 Speaker 1: sure the markets are open and are a vali to 482 00:30:01,280 --> 00:30:04,960 Speaker 1: the most diverse set of investors. No paternalism, you just 483 00:30:05,160 --> 00:30:10,800 Speaker 1: exactly here's the platform. Ultimately, if there is questionable behavior, 484 00:30:10,960 --> 00:30:14,000 Speaker 1: we police. That are red group. Who is a separate 485 00:30:14,000 --> 00:30:16,680 Speaker 1: group police is that works closely with the regulator. So 486 00:30:16,760 --> 00:30:19,080 Speaker 1: let's talk a little bit about that. You see behavior 487 00:30:19,240 --> 00:30:23,800 Speaker 1: that sometimes it's just that looks pretty stupid, and sometimes 488 00:30:23,800 --> 00:30:26,920 Speaker 1: it's like, hey, this is looking a little suspicious. Something 489 00:30:26,960 --> 00:30:31,440 Speaker 1: doesn't smell right here. What happens when your systems start 490 00:30:31,680 --> 00:30:35,000 Speaker 1: flashing little alerts, Hey look at this stock. Something seems 491 00:30:35,000 --> 00:30:37,280 Speaker 1: to be on kosher here. So that would be the 492 00:30:37,360 --> 00:30:42,040 Speaker 1: job of reg to to look at various trades internally regulation. Yeah, 493 00:30:42,040 --> 00:30:45,240 Speaker 1: they are a separate organization from from the business, but 494 00:30:45,440 --> 00:30:48,880 Speaker 1: they they are an internal organization and then you know 495 00:30:48,920 --> 00:30:55,240 Speaker 1: they would either take enforcement action if it was suspicious activity. Um, 496 00:30:55,280 --> 00:30:58,440 Speaker 1: not stupid, not stupid. It's not our job again to 497 00:30:58,520 --> 00:31:01,600 Speaker 1: take views on whether not a stock is worth something 498 00:31:01,680 --> 00:31:05,280 Speaker 1: that's for the market right aside? Uh And then if 499 00:31:05,320 --> 00:31:09,280 Speaker 1: appropriate refer it to the regulators. So I would assume 500 00:31:09,400 --> 00:31:13,360 Speaker 1: the NYC has a fairly close relationship with the SEC, 501 00:31:13,520 --> 00:31:16,000 Speaker 1: and there's probably a lot of back and forth on 502 00:31:16,000 --> 00:31:19,200 Speaker 1: on a regular basis. Uh, tell us a little bit 503 00:31:19,240 --> 00:31:22,280 Speaker 1: about that. How does they are a regulator? We're an 504 00:31:22,320 --> 00:31:25,680 Speaker 1: s R. Oh, so we did have a very close 505 00:31:25,720 --> 00:31:30,760 Speaker 1: working relationship with them. UM so your self regulating organization. 506 00:31:31,280 --> 00:31:36,560 Speaker 1: But you also have a relationship with the government regulator. Absolutely, 507 00:31:36,840 --> 00:31:41,040 Speaker 1: and I would imagine that's a fairly productive relationship. It is, 508 00:31:41,280 --> 00:31:44,280 Speaker 1: it is. Obviously we're have a very strong rule book. 509 00:31:44,760 --> 00:31:48,840 Speaker 1: We any time we make a change from market structure standpoint, 510 00:31:49,040 --> 00:31:52,120 Speaker 1: from an order type standpoint, that has to be fully 511 00:31:52,120 --> 00:31:54,840 Speaker 1: approved by the Commission. So we spend a lot of 512 00:31:54,880 --> 00:31:58,680 Speaker 1: time with the SEC going through various rule changes. We 513 00:31:58,720 --> 00:32:00,640 Speaker 1: want to introduce a new order type, we want to 514 00:32:00,680 --> 00:32:04,280 Speaker 1: introduce a new different fee. Uh, there's a variety of 515 00:32:04,280 --> 00:32:07,840 Speaker 1: reasons why we need to do so. Let's use a 516 00:32:08,080 --> 00:32:11,680 Speaker 1: let's use an example. I'm always again. Now I'm gonna 517 00:32:11,720 --> 00:32:14,960 Speaker 1: show my age. The circuit breakers from the eighties and 518 00:32:15,040 --> 00:32:18,920 Speaker 1: nineties were pretty modest, and things really had to go 519 00:32:19,000 --> 00:32:22,640 Speaker 1: off the rails before they kicked in. UM circuit breakers 520 00:32:22,640 --> 00:32:26,080 Speaker 1: have very much been brought up to speed, both on 521 00:32:26,360 --> 00:32:30,520 Speaker 1: the broad market and individual companies. Tell us about the 522 00:32:30,560 --> 00:32:32,880 Speaker 1: circuit breakers, Is that coming from the NYC? Is that 523 00:32:32,960 --> 00:32:35,080 Speaker 1: coming from this? So that is something in the wake 524 00:32:35,120 --> 00:32:38,520 Speaker 1: of the volatility that has occurred at various points, various 525 00:32:38,680 --> 00:32:42,960 Speaker 1: instances of stress in the market. I mean this goes 526 00:32:43,040 --> 00:32:48,040 Speaker 1: back to an absolutely pandemic um. The market has really 527 00:32:48,120 --> 00:32:50,840 Speaker 1: the positive of every time there has been a challenge, 528 00:32:50,880 --> 00:32:55,080 Speaker 1: the market has developed system safeguards for lack of their description, 529 00:32:55,200 --> 00:32:59,480 Speaker 1: so and they apply to all of the exchanges. So 530 00:33:00,040 --> 00:33:04,280 Speaker 1: utility halts for an example, we have volatility halts for 531 00:33:04,480 --> 00:33:08,080 Speaker 1: securities individual securities, but then we also have system halts 532 00:33:08,280 --> 00:33:13,000 Speaker 1: when the entirety of the market has a certain drop. 533 00:33:13,120 --> 00:33:16,280 Speaker 1: For example, you saw the market wide circuit breakers kicking 534 00:33:16,320 --> 00:33:19,920 Speaker 1: I believe four times during the pandemic, really during the 535 00:33:19,920 --> 00:33:22,560 Speaker 1: height of the pandemic. And that Yeah, And the way 536 00:33:22,600 --> 00:33:26,600 Speaker 1: that works is if the SMP is trading seven percent 537 00:33:26,840 --> 00:33:32,080 Speaker 1: below uh, it's it's opening level, it'll automatically hall opening 538 00:33:32,200 --> 00:33:37,920 Speaker 1: level of previous close. So we close at three thousand, 539 00:33:37,960 --> 00:33:43,840 Speaker 1: and we open points below that. There's a whole price there. Yeah, 540 00:33:44,200 --> 00:33:47,360 Speaker 1: makes sense. And individual companies. What are those circuit breakers 541 00:33:47,400 --> 00:33:53,320 Speaker 1: like it's I believe five um upper upper down. That's 542 00:33:53,320 --> 00:33:55,920 Speaker 1: the tempt and the first halt. Actually, to be fair, 543 00:33:55,960 --> 00:33:58,600 Speaker 1: it depends on the liquidity in the stock. It could 544 00:33:58,600 --> 00:34:00,920 Speaker 1: be five percent, could be why are depending on the 545 00:34:01,040 --> 00:34:03,800 Speaker 1: overall liquidity and market. But when we when we see 546 00:34:03,840 --> 00:34:06,760 Speaker 1: a liquid stock take a five or an eight or 547 00:34:06,800 --> 00:34:10,359 Speaker 1: a ten percent haircut, they tend to keep trading. You'll 548 00:34:10,360 --> 00:34:13,160 Speaker 1: have a very short halt and then just to let 549 00:34:13,200 --> 00:34:16,760 Speaker 1: the book sort of re rejigger. So the first halt 550 00:34:17,040 --> 00:34:21,480 Speaker 1: is how how many minutes? Uh? Five minutes? I think 551 00:34:21,480 --> 00:34:24,440 Speaker 1: it's ten minutes, all right, And then the second halt 552 00:34:24,520 --> 00:34:27,640 Speaker 1: is longer, and then if this continues to be a 553 00:34:27,719 --> 00:34:31,440 Speaker 1: mess it's halted for the day. So the first second, 554 00:34:31,480 --> 00:34:34,360 Speaker 1: third strike, they're they're out. We haven't seen that in 555 00:34:34,480 --> 00:34:37,080 Speaker 1: quite a while. What happens the next day when we 556 00:34:37,160 --> 00:34:41,160 Speaker 1: when we reopen, how is that priced? Is it? Is 557 00:34:41,160 --> 00:34:44,360 Speaker 1: it just the messages and orders or is there a 558 00:34:44,400 --> 00:34:47,759 Speaker 1: specialist trying to special If that's where the open, that's 559 00:34:47,760 --> 00:34:50,400 Speaker 1: where our market model shine. You have the opening auction 560 00:34:50,440 --> 00:34:54,040 Speaker 1: and the closing auction, which again perform that function I 561 00:34:54,080 --> 00:34:58,200 Speaker 1: mentioned earlier of smoothing at any imbalances, whatever the case 562 00:34:58,239 --> 00:35:01,879 Speaker 1: may be, to make for us smoother open and or 563 00:35:02,000 --> 00:35:05,880 Speaker 1: a smoother clothes And that's why when I mentioned earlier 564 00:35:05,880 --> 00:35:09,560 Speaker 1: that we've seen two two times less volatility at the 565 00:35:09,680 --> 00:35:12,319 Speaker 1: open and three times less volatility at the close this year. 566 00:35:13,040 --> 00:35:15,080 Speaker 1: That's what I'm talking about, the opening and the closing 567 00:35:15,080 --> 00:35:18,080 Speaker 1: off because the person is essentially a person is trying 568 00:35:18,120 --> 00:35:20,680 Speaker 1: to make sense, smoothing that out and make neat a 569 00:35:20,719 --> 00:35:23,640 Speaker 1: little more balance that might have been. And that means 570 00:35:23,680 --> 00:35:29,000 Speaker 1: they're also going at risk and taking positions to facilitate that. Um. So, 571 00:35:29,000 --> 00:35:31,160 Speaker 1: so you mentioned a couple of things I didn't get to. 572 00:35:31,280 --> 00:35:34,840 Speaker 1: I want to follow up on. One is um the 573 00:35:34,920 --> 00:35:38,279 Speaker 1: dual listing? So when a company is listed here and 574 00:35:38,440 --> 00:35:41,680 Speaker 1: overseas or is that the only reason to be a 575 00:35:41,760 --> 00:35:44,319 Speaker 1: dual listening? How often? What are the other reasons to 576 00:35:44,400 --> 00:35:48,319 Speaker 1: be besides geographic to be dual listed? A lot of 577 00:35:48,360 --> 00:35:53,719 Speaker 1: times it's geographic. Very infrequently. There are some securities that 578 00:35:53,719 --> 00:35:59,040 Speaker 1: are dual listed on US and our closest competitor in 579 00:35:59,120 --> 00:36:03,120 Speaker 1: the US, but that's very infrequent. So it's typically to 580 00:36:03,160 --> 00:36:06,840 Speaker 1: get access to a different group of of investors. A 581 00:36:06,840 --> 00:36:09,440 Speaker 1: lot of times. You'll also see a primary listing and 582 00:36:09,480 --> 00:36:12,360 Speaker 1: then something called an a d R be listed in 583 00:36:12,400 --> 00:36:15,400 Speaker 1: the US. What we'll do the primary that's more foreign 584 00:36:15,480 --> 00:36:19,000 Speaker 1: issuers that will want to have their primary listing on 585 00:36:19,680 --> 00:36:23,120 Speaker 1: the home market, but then tap the liquidity in the 586 00:36:23,239 --> 00:36:27,480 Speaker 1: US market, so they'll issue uh a d R. And 587 00:36:27,560 --> 00:36:31,680 Speaker 1: then what about additions and subtractions? I know, we occasionally 588 00:36:31,719 --> 00:36:35,759 Speaker 1: see companies that were once smaller companies listed it more 589 00:36:35,800 --> 00:36:40,080 Speaker 1: region what used to be considered regional exchanges, graduate to 590 00:36:40,120 --> 00:36:43,520 Speaker 1: the NYC. And then every now and then somebody, uh 591 00:36:44,480 --> 00:36:46,480 Speaker 1: you know, is past their cell by date and they 592 00:36:46,480 --> 00:36:48,960 Speaker 1: get delisted. Tell us a little bit about what that 593 00:36:49,040 --> 00:36:51,440 Speaker 1: process is like, Yeah, I mean the de listing process. 594 00:36:51,560 --> 00:36:53,000 Speaker 1: You know, there's a lot of things that go into 595 00:36:53,000 --> 00:36:56,400 Speaker 1: the d listing to it's pretty mathematical. Check these boxes. 596 00:36:57,000 --> 00:36:59,200 Speaker 1: It is, we've got a regg has a variety of 597 00:36:59,320 --> 00:37:04,960 Speaker 1: requirement to maintain your listing. It could be certain financial 598 00:37:05,239 --> 00:37:09,480 Speaker 1: wherewithal it could be the amount of shareholders and have 599 00:37:09,560 --> 00:37:12,799 Speaker 1: individual shareholders that are participating in your stock. So there 600 00:37:13,000 --> 00:37:16,160 Speaker 1: is a formula that that gets followed. And what about 601 00:37:16,160 --> 00:37:20,040 Speaker 1: the opposite, what about somebody graduating a better phrase to 602 00:37:20,120 --> 00:37:23,880 Speaker 1: the n Y yeah, and we've seen actually quite a 603 00:37:23,880 --> 00:37:27,680 Speaker 1: few companies graduate to the n y C um this 604 00:37:27,800 --> 00:37:30,240 Speaker 1: year alone. We've actually seen quite a few companies transfer 605 00:37:30,520 --> 00:37:32,600 Speaker 1: to the n y C this year. I think we've 606 00:37:32,600 --> 00:37:35,439 Speaker 1: had fourteen so far to date at which is which 607 00:37:35,480 --> 00:37:40,200 Speaker 1: is our best year on record. But um, we've we've 608 00:37:40,200 --> 00:37:42,600 Speaker 1: seen we have a smaller listings venue called n y 609 00:37:42,760 --> 00:37:46,640 Speaker 1: C American which for the smaller cap companies. And you know, 610 00:37:46,640 --> 00:37:49,239 Speaker 1: we're really happy when we see them graduate to the 611 00:37:49,280 --> 00:37:51,840 Speaker 1: big board for lack of a better description, because it 612 00:37:51,840 --> 00:37:53,960 Speaker 1: means they're having success, They're having a tremendous amount of 613 00:37:53,960 --> 00:37:56,279 Speaker 1: success in the public markets. All right, let me throw 614 00:37:56,280 --> 00:37:57,680 Speaker 1: you a little bit of a curve ball. I'm going 615 00:37:57,719 --> 00:38:00,480 Speaker 1: to ask you a question you can't possibly answer, but 616 00:38:00,840 --> 00:38:04,280 Speaker 1: I feel compelled to ask it. So I remember getting 617 00:38:04,320 --> 00:38:07,720 Speaker 1: a tour of the floor of the Exchange a million 618 00:38:07,800 --> 00:38:11,560 Speaker 1: years ago, and it was giants, room after room after 619 00:38:11,640 --> 00:38:15,080 Speaker 1: room down on Wall Street and Broad and literally where 620 00:38:15,120 --> 00:38:20,760 Speaker 1: physical um chairs were being traded, traded physically person in person. 621 00:38:21,880 --> 00:38:27,800 Speaker 1: That has slowly been computerized, that's slowly been um morphed 622 00:38:27,840 --> 00:38:31,719 Speaker 1: into the modern market structure. But I have very fond 623 00:38:31,800 --> 00:38:34,880 Speaker 1: memories of that massive building then it takes up like 624 00:38:34,960 --> 00:38:37,719 Speaker 1: two city blocks. Is there always going to be a 625 00:38:37,719 --> 00:38:40,759 Speaker 1: physical exchange? This is the question that I don't know 626 00:38:40,800 --> 00:38:43,360 Speaker 1: if anybody can answer, But is there always going to 627 00:38:43,400 --> 00:38:46,880 Speaker 1: be a physical exchange on Wall Street? At what point 628 00:38:46,920 --> 00:38:49,520 Speaker 1: does that just you know, become a venue for for 629 00:38:50,040 --> 00:38:52,839 Speaker 1: you know, aftermarket I p O parties and things like that. 630 00:38:53,280 --> 00:38:55,560 Speaker 1: There is always going to be the New York Stock 631 00:38:55,640 --> 00:38:57,920 Speaker 1: Exchange at the corner of Wall Abroad. We've been here 632 00:38:57,920 --> 00:39:00,600 Speaker 1: for two hundred thirty years count and not as being 633 00:39:00,600 --> 00:39:03,360 Speaker 1: here for the next two hundred thirty years. We've survived 634 00:39:03,480 --> 00:39:09,640 Speaker 1: many a war, pandemic, volatility, crisis, all sorts of all 635 00:39:09,680 --> 00:39:12,919 Speaker 1: sorts of unfortunate events. Um, so there will always be 636 00:39:13,120 --> 00:39:17,200 Speaker 1: a exchange at the corner of Wallombrod. That's really good 637 00:39:17,200 --> 00:39:20,400 Speaker 1: to hear. I have very fond memories of that, and 638 00:39:20,480 --> 00:39:26,160 Speaker 1: not too far from there, the tour of the Federal Reserve. Right, 639 00:39:26,520 --> 00:39:28,840 Speaker 1: So those were I think I'm trying to remember that 640 00:39:28,880 --> 00:39:31,520 Speaker 1: was a high school teacher or a college teacher. It 641 00:39:31,560 --> 00:39:34,200 Speaker 1: was it was a ways ago. Um, all right, so 642 00:39:34,280 --> 00:39:36,279 Speaker 1: I know only have you for a few minutes more. 643 00:39:36,360 --> 00:39:39,319 Speaker 1: Let me jump to all my favorite questions. We ask 644 00:39:39,440 --> 00:39:42,000 Speaker 1: all of our guests starting with what did you do 645 00:39:42,080 --> 00:39:45,160 Speaker 1: to keep yourself entertained during the pandemic? What were you 646 00:39:45,680 --> 00:39:49,359 Speaker 1: watching or listening to? Well? During the pandemic, I had 647 00:39:49,480 --> 00:39:53,600 Speaker 1: the unique privilege of homeschooling to children in addition to 648 00:39:53,680 --> 00:39:57,560 Speaker 1: doing my day job, which was focused on keeping the 649 00:39:57,560 --> 00:40:03,120 Speaker 1: fixed income markets moving forward. UM. So that was that 650 00:40:03,200 --> 00:40:06,960 Speaker 1: was a challenge. UM, But I have to say I 651 00:40:07,040 --> 00:40:10,960 Speaker 1: look back on it with fond memories, not just because 652 00:40:11,400 --> 00:40:15,840 Speaker 1: our fixed income business provided a lot of transparency in 653 00:40:15,880 --> 00:40:18,840 Speaker 1: a really opaque market, but also I got to spend 654 00:40:18,840 --> 00:40:21,640 Speaker 1: some time, a lot of time with my kids. That 655 00:40:21,640 --> 00:40:26,120 Speaker 1: that sounds like fun. Um. Let's talk about your mentors 656 00:40:26,160 --> 00:40:30,520 Speaker 1: who helped to shape your career. I would say that 657 00:40:30,600 --> 00:40:34,479 Speaker 1: I've had the opportunity to have mentors that have been 658 00:40:34,600 --> 00:40:39,080 Speaker 1: bosses all throughout my career. It really started with my 659 00:40:39,360 --> 00:40:44,680 Speaker 1: first project executive who was helping guide me through IBM, 660 00:40:44,800 --> 00:40:47,440 Speaker 1: who taught me a lot of really important lessons that 661 00:40:47,560 --> 00:40:50,520 Speaker 1: I still stay true to, one of which is you 662 00:40:50,520 --> 00:40:55,759 Speaker 1: can never over communicate. Um. But throughout my career, I've 663 00:40:55,800 --> 00:40:59,839 Speaker 1: been lucky fortunate that my bosses have always given me 664 00:41:00,000 --> 00:41:05,600 Speaker 1: stretch jobs where they give me an opportunity to do 665 00:41:06,280 --> 00:41:09,360 Speaker 1: a job that maybe I didn't have the background for, 666 00:41:09,719 --> 00:41:11,200 Speaker 1: or I didn't think I have the background for, but 667 00:41:11,239 --> 00:41:13,640 Speaker 1: they thought I was the right person for that job. 668 00:41:13,960 --> 00:41:17,200 Speaker 1: Sounds interesting. Um, what are some of your favorite books? 669 00:41:17,200 --> 00:41:20,640 Speaker 1: What are you reading right now? Books? Are books are 670 00:41:20,640 --> 00:41:24,600 Speaker 1: a challenge mainly because uh, I have I have a 671 00:41:24,640 --> 00:41:29,040 Speaker 1: finite amount of time in my day right now, you know, 672 00:41:29,200 --> 00:41:32,719 Speaker 1: continuing to read a couple of interesting business books. Like 673 00:41:32,840 --> 00:41:34,600 Speaker 1: you know, I always go back to the Michael Lewis 674 00:41:34,640 --> 00:41:38,879 Speaker 1: books because they're just a good read on on top 675 00:41:38,960 --> 00:41:42,800 Speaker 1: of anything. Um, they're they're they have They're a unique 676 00:41:42,880 --> 00:41:46,279 Speaker 1: mix of storytelling and business, so it kind of scratches 677 00:41:46,760 --> 00:41:51,400 Speaker 1: both itches. For lack of a better description, I just 678 00:41:51,520 --> 00:41:56,600 Speaker 1: reread Liars Poker on its thirtieth anniversary. It holds up 679 00:41:56,640 --> 00:42:00,319 Speaker 1: surprisingly Yeah, and you could see you could see the 680 00:42:00,320 --> 00:42:04,080 Speaker 1: outlines of oh, it's not quite a full Michael Lewis book, 681 00:42:04,320 --> 00:42:06,239 Speaker 1: but there are hints of the right he's about to 682 00:42:06,280 --> 00:42:09,320 Speaker 1: become really quite Also, like Moneyball, Moneyball is one of 683 00:42:09,360 --> 00:42:12,040 Speaker 1: my favorites. I mean, like that's hard to be in 684 00:42:12,080 --> 00:42:16,360 Speaker 1: baseball season. I'm a baseball fan, so therefore I'm gonna, 685 00:42:16,560 --> 00:42:20,120 Speaker 1: you know, focus on we may we may see the 686 00:42:20,200 --> 00:42:25,359 Speaker 1: Mets go this year. I'm not gonna I'm gonna hold 687 00:42:25,400 --> 00:42:28,560 Speaker 1: my breath entering September. And you know what usually happens 688 00:42:28,600 --> 00:42:33,719 Speaker 1: to to our boys from flashing in September. Right, they 689 00:42:33,840 --> 00:42:37,000 Speaker 1: seem to be a little different team this year under 690 00:42:38,040 --> 00:42:44,279 Speaker 1: another market participants, Stevie Cohen. Um, you gotta believe, right, 691 00:42:44,680 --> 00:42:47,880 Speaker 1: I listen, I grew up with you know, the Lenny 692 00:42:47,920 --> 00:42:52,400 Speaker 1: distra of of you. It's so easy to get to 693 00:42:52,600 --> 00:42:55,399 Speaker 1: say stadium. We used to go to games all the time, 694 00:42:55,440 --> 00:42:59,680 Speaker 1: and frequently to be disappointed, but so far, all I 695 00:42:59,719 --> 00:43:04,279 Speaker 1: remember my first games were six, I mean, and that's 696 00:43:04,320 --> 00:43:08,080 Speaker 1: when I just fell in love with them. The little 697 00:43:08,160 --> 00:43:11,200 Speaker 1: dribbler through the legs. That was it, exactly. I can't 698 00:43:11,239 --> 00:43:14,359 Speaker 1: Bill Buckner and his name will go down and infamy, 699 00:43:14,120 --> 00:43:18,360 Speaker 1: I assume in Boston. But man, that felt good in 700 00:43:18,400 --> 00:43:21,719 Speaker 1: New York, right, So uh, And watching that team was 701 00:43:21,760 --> 00:43:25,399 Speaker 1: a lot of fun with Strawberry and Ron Darling and 702 00:43:25,600 --> 00:43:27,640 Speaker 1: who I think is a great broadcaster by the way, 703 00:43:27,640 --> 00:43:31,680 Speaker 1: I turned into a great broadcaster. Um. You know, they 704 00:43:31,719 --> 00:43:34,959 Speaker 1: were always an interesting team, even if they didn't bring 705 00:43:35,000 --> 00:43:40,000 Speaker 1: home as many they were they were. I remember going 706 00:43:40,040 --> 00:43:42,239 Speaker 1: to the Subway Series between them and the Yankees in 707 00:43:42,239 --> 00:43:45,080 Speaker 1: the World Series in two thousand. I think that was 708 00:43:46,160 --> 00:43:49,600 Speaker 1: UH and is that a playoff for you know? There 709 00:43:49,680 --> 00:43:52,440 Speaker 1: was the they were in the World Series together. Really 710 00:43:54,600 --> 00:43:58,840 Speaker 1: kind of a blur to me that that was a 711 00:43:59,400 --> 00:44:01,600 Speaker 1: like O eight nine that that year was a little 712 00:44:01,600 --> 00:44:05,640 Speaker 1: bit of a blur um our. Last two questions, what 713 00:44:05,800 --> 00:44:08,360 Speaker 1: sort of advice would you give to a recent college 714 00:44:08,360 --> 00:44:14,280 Speaker 1: grad who was interested in a career involving data services, 715 00:44:14,320 --> 00:44:19,680 Speaker 1: listed stocks, any sort of training or exchange based work. 716 00:44:21,320 --> 00:44:24,040 Speaker 1: The advice that I would give is to, in some 717 00:44:24,239 --> 00:44:27,359 Speaker 1: respects expect the unexpected. And what I mean by that 718 00:44:27,680 --> 00:44:32,480 Speaker 1: is your traditional degrees aren't necessarily going to be what's 719 00:44:32,520 --> 00:44:36,280 Speaker 1: going to make you successful. So be intellectually curious about 720 00:44:36,400 --> 00:44:41,400 Speaker 1: the things not just involved in finance. Be intellectually curious 721 00:44:41,480 --> 00:44:47,480 Speaker 1: about the technology under that underpins the systems. And clearly 722 00:44:47,560 --> 00:44:50,160 Speaker 1: never be afraid to speak up if you want an 723 00:44:50,160 --> 00:44:54,799 Speaker 1: opportunity or take on an additional project that may not 724 00:44:54,880 --> 00:44:58,480 Speaker 1: be in your day to day but maybe something that 725 00:44:58,480 --> 00:45:01,640 Speaker 1: that is just an area that interest to you interesting. 726 00:45:01,760 --> 00:45:04,480 Speaker 1: And our final question, what do you know about the 727 00:45:04,520 --> 00:45:11,520 Speaker 1: world of data services, exchanges, market training, UH, and public 728 00:45:11,520 --> 00:45:16,160 Speaker 1: companies today. You wish you knew twenty or so years ago. 729 00:45:18,520 --> 00:45:22,080 Speaker 1: That is a great question. We saved it for last 730 00:45:22,120 --> 00:45:28,360 Speaker 1: for a reason. I guess I wish I knew how 731 00:45:28,440 --> 00:45:32,000 Speaker 1: important I wish I knew how important the role of 732 00:45:32,040 --> 00:45:36,560 Speaker 1: the programmer was going to become in financial markets. I 733 00:45:36,760 --> 00:45:43,920 Speaker 1: understood then that effectively fair value was determined by a 734 00:45:44,040 --> 00:45:47,600 Speaker 1: variety of mathematics about a bunch of math for lack 735 00:45:47,640 --> 00:45:51,680 Speaker 1: of better description. Those mathematical models became much more sophisticated 736 00:45:51,760 --> 00:45:55,680 Speaker 1: over time, But I don't know that I fully appreciated 737 00:45:55,760 --> 00:45:58,120 Speaker 1: that the guy who or girl who was writing the 738 00:45:58,239 --> 00:46:00,839 Speaker 1: code was going to be the one that was interacting 739 00:46:01,080 --> 00:46:05,720 Speaker 1: with the systems twentysomething years ago, and the importance of 740 00:46:05,760 --> 00:46:11,359 Speaker 1: efficient interaction quite fascinating. We have been speaking with Lynn Martin. 741 00:46:11,520 --> 00:46:15,279 Speaker 1: She is President of the New York Stock Exchange. Thank you, 742 00:46:15,400 --> 00:46:18,080 Speaker 1: Lynn for being so generous with your time. If you 743 00:46:18,200 --> 00:46:21,440 Speaker 1: enjoy this conversation, be shure and check out all of 744 00:46:21,480 --> 00:46:24,919 Speaker 1: our previous four hundred or so podcasts we've done over 745 00:46:24,960 --> 00:46:28,279 Speaker 1: the past eight years. You can find those at iTunes 746 00:46:28,360 --> 00:46:32,520 Speaker 1: or Spotify, or wherever you get your favorite podcasts from. 747 00:46:32,600 --> 00:46:36,000 Speaker 1: We love your comments, feedback, and suggestions right to us 748 00:46:36,080 --> 00:46:40,200 Speaker 1: at m IB podcast at Bloomberg dot net. Sign up 749 00:46:40,239 --> 00:46:42,960 Speaker 1: from my daily reading list at Rid Halts dot com. 750 00:46:43,040 --> 00:46:45,920 Speaker 1: Follow me on Twitter at rid Halts. I would be 751 00:46:46,000 --> 00:46:48,719 Speaker 1: remiss if I did not think the crack team who 752 00:46:48,719 --> 00:46:52,360 Speaker 1: helps put these conversations together each week. Bob Bragg is 753 00:46:52,400 --> 00:46:56,719 Speaker 1: my audio engineer, Paris Walden is my producer. Atika val 754 00:46:56,760 --> 00:47:00,400 Speaker 1: Bron is my project manager. Sean rue So is my 755 00:47:00,440 --> 00:47:04,080 Speaker 1: head of research. I'm Barry Ritolts. You've been listening to 756 00:47:04,160 --> 00:47:06,880 Speaker 1: Masters in Business on Bloomberg Radio.