1 00:00:12,640 --> 00:00:15,880 Speaker 1: Hello, and welcome to What Goes Up, a Bloomberg weekly 2 00:00:15,920 --> 00:00:19,560 Speaker 1: market podcast. I'm Sarah Ponzek, a reporter on the Cross 3 00:00:19,560 --> 00:00:22,480 Speaker 1: Asset team, and I'm Mike Reagan, a senior editor on 4 00:00:22,520 --> 00:00:26,240 Speaker 1: the Markets Team. This week on the show, two guests, 5 00:00:26,440 --> 00:00:30,960 Speaker 1: two topics. First, this month marks the tenure anniversary of 6 00:00:31,000 --> 00:00:34,000 Speaker 1: the infamous flash Crash. We're joined by the author of 7 00:00:34,000 --> 00:00:37,120 Speaker 1: a new book. It's called Flash Crash, A Trading Savant, 8 00:00:37,200 --> 00:00:40,640 Speaker 1: a global man hunt, and the most mysterious market crash 9 00:00:40,680 --> 00:00:44,239 Speaker 1: in history. Also, later on, small cap companies have been 10 00:00:44,280 --> 00:00:46,960 Speaker 1: pressured even more than the market as a whole through 11 00:00:47,120 --> 00:00:51,279 Speaker 1: the coronavirus crash. Joh for Rizvi, a portfolio manager over 12 00:00:51,320 --> 00:00:55,400 Speaker 1: at Harding Lovener, explains how to navigate the space, and 13 00:00:55,520 --> 00:00:58,120 Speaker 1: as always, we will close out the episode with the 14 00:00:58,280 --> 00:01:01,720 Speaker 1: craziest things we saw in markets this week. So if 15 00:01:01,760 --> 00:01:04,120 Speaker 1: you saw something crazy, please give us a call on 16 00:01:04,160 --> 00:01:07,840 Speaker 1: the hotline at six four six three two four three 17 00:01:07,920 --> 00:01:10,920 Speaker 1: four nine. Oh, and maybe we'll play your voicemail on 18 00:01:10,959 --> 00:01:13,520 Speaker 1: the show. Of course, you can tweet at us with 19 00:01:13,600 --> 00:01:17,000 Speaker 1: your crazy market observations. Just tweet to the handle at 20 00:01:17,080 --> 00:01:20,520 Speaker 1: Podcasts and let us know what you're looking at. And uh, 21 00:01:20,800 --> 00:01:24,319 Speaker 1: Sarah obviously, as you know, the craziest thing is uh, 22 00:01:24,360 --> 00:01:27,160 Speaker 1: my favorite part of the show. We're kind of lucky 23 00:01:27,240 --> 00:01:30,240 Speaker 1: this week though, in that we don't get to talk 24 00:01:30,280 --> 00:01:33,280 Speaker 1: about the just the craziest things we saw in markets. 25 00:01:33,280 --> 00:01:35,800 Speaker 1: This week, we literally get to talk about the craziest 26 00:01:35,800 --> 00:01:38,640 Speaker 1: thing I think I've ever seen in my career. And 27 00:01:38,680 --> 00:01:43,039 Speaker 1: that was ten years ago this month. Um, I was 28 00:01:43,080 --> 00:01:46,319 Speaker 1: the editor handling the daily markets wrap that I know 29 00:01:46,400 --> 00:01:52,320 Speaker 1: you've you've written a million times. And as you know, 30 00:01:52,440 --> 00:01:55,200 Speaker 1: some some days the market wrap is very easy to write. 31 00:01:55,280 --> 00:01:58,320 Speaker 1: There's a clear explanation for why the the markets did 32 00:01:58,320 --> 00:02:00,960 Speaker 1: what they did. Sometimes it's not so easy, and this 33 00:02:01,040 --> 00:02:03,160 Speaker 1: was one of those days. You know. It was, um, 34 00:02:03,360 --> 00:02:05,600 Speaker 1: probably a year after the market a little bit more 35 00:02:05,640 --> 00:02:08,360 Speaker 1: than a year after the market bottom from the financial crisis, 36 00:02:08,720 --> 00:02:11,480 Speaker 1: so people were still very much on edge, right, and 37 00:02:11,520 --> 00:02:15,120 Speaker 1: all of a sudden the European debt crisis had just 38 00:02:15,280 --> 00:02:20,040 Speaker 1: sprung into life and really caused another bout of volatility 39 00:02:20,080 --> 00:02:23,840 Speaker 1: that that was worrying too too many people, um and 40 00:02:23,960 --> 00:02:27,080 Speaker 1: causing some some swings in the market. Um. This swing 41 00:02:27,160 --> 00:02:30,800 Speaker 1: was something else though, Uh, you know, I remember distinctly 42 00:02:30,919 --> 00:02:35,520 Speaker 1: that the TV News channels, the cable news channels broadcasting 43 00:02:35,800 --> 00:02:40,880 Speaker 1: these massive demonstrations in Greece, in Athens and other parts 44 00:02:40,880 --> 00:02:43,440 Speaker 1: of Greece as people sort of rebelled against the austerity 45 00:02:43,480 --> 00:02:46,520 Speaker 1: that was being imposed on Greece as as part of 46 00:02:46,560 --> 00:02:51,880 Speaker 1: their bailout conditions from the European Union. Um. And so 47 00:02:51,919 --> 00:02:54,800 Speaker 1: the market was understandably weak because of that. But then 48 00:02:54,800 --> 00:02:57,120 Speaker 1: all of a sudden, the bottom just dropped out, and 49 00:02:57,200 --> 00:03:01,040 Speaker 1: I mean within minutes, uh, every X was crashing. I 50 00:03:01,120 --> 00:03:03,520 Speaker 1: think at the low point that Dow in the SNP 51 00:03:03,600 --> 00:03:07,280 Speaker 1: had both dropped more than nine percent during the day. 52 00:03:07,560 --> 00:03:09,760 Speaker 1: It was insane and and all of that within a 53 00:03:09,760 --> 00:03:13,320 Speaker 1: matter of minutes, some blue chip company stocks were trading 54 00:03:13,360 --> 00:03:17,639 Speaker 1: for literally a penny apiece. Um. So so needles to say, 55 00:03:17,680 --> 00:03:22,240 Speaker 1: we didn't quite have a great explanation that day in 56 00:03:22,280 --> 00:03:25,480 Speaker 1: the in the Market's wrap. It's ten years later though, 57 00:03:25,560 --> 00:03:28,120 Speaker 1: and we're very lucky to have on the show someone 58 00:03:28,200 --> 00:03:31,200 Speaker 1: who has uh an excellent book out on this topic 59 00:03:31,720 --> 00:03:34,519 Speaker 1: and on the role of sort of one loner trader 60 00:03:34,520 --> 00:03:38,400 Speaker 1: outside of London, the role he played on that day 61 00:03:39,320 --> 00:03:42,080 Speaker 1: when the markets just went crazy. So very happy to 62 00:03:42,120 --> 00:03:44,080 Speaker 1: have him on the show. His name is Liam Vaughan. 63 00:03:44,280 --> 00:03:48,760 Speaker 1: He's an investigative reporter with Bloomberg News in London and, 64 00:03:48,840 --> 00:03:51,760 Speaker 1: as you said, author of that new book flash Crash, 65 00:03:51,960 --> 00:03:54,440 Speaker 1: which I know I'm a little biased, he's one of us, 66 00:03:54,480 --> 00:03:56,400 Speaker 1: but honestly, I think this is gonna be one of 67 00:03:56,400 --> 00:03:59,280 Speaker 1: those finance books that is going to be a must 68 00:03:59,320 --> 00:04:01,040 Speaker 1: read for every on. It's going to be on everyone's 69 00:04:01,040 --> 00:04:03,720 Speaker 1: bookshelf soon because from what I've read of it so far, 70 00:04:03,800 --> 00:04:09,160 Speaker 1: it's just so excellently well written and well reported. Uh So, Liam, 71 00:04:09,200 --> 00:04:11,000 Speaker 1: I hope you're not blushing too much, but welcome to 72 00:04:11,000 --> 00:04:18,320 Speaker 1: the show. I can hardly thank you. Michael, no problem. 73 00:04:18,360 --> 00:04:21,440 Speaker 1: So tell us briefly, I know, I mean I could 74 00:04:21,480 --> 00:04:25,200 Speaker 1: talk about this for hours, but just briefly, sort of 75 00:04:25,240 --> 00:04:29,040 Speaker 1: give us a portrait of uh this character nav Saro, 76 00:04:29,120 --> 00:04:33,080 Speaker 1: who was uh this loner trader outside of London that 77 00:04:33,240 --> 00:04:36,560 Speaker 1: the US prosecutor sort of zeroed in on for his 78 00:04:36,680 --> 00:04:38,760 Speaker 1: role in this. But how did he get to that 79 00:04:38,839 --> 00:04:43,000 Speaker 1: point where he's a guy that uh could at least 80 00:04:43,040 --> 00:04:47,640 Speaker 1: play some role in what happened on the markets that day? Yes, so, 81 00:04:48,040 --> 00:04:53,880 Speaker 1: Novenda Sings Around is a fairly ordinary in some ways 82 00:04:54,000 --> 00:04:57,520 Speaker 1: kid from a pretty working class area of West London 83 00:04:57,600 --> 00:05:02,000 Speaker 1: called Hounslow. He lived with his parents. They live in 84 00:05:02,040 --> 00:05:06,200 Speaker 1: a pretty modest semi detached house which is directly below 85 00:05:06,240 --> 00:05:09,320 Speaker 1: the Heathrow flight path. So if you go to his 86 00:05:09,520 --> 00:05:12,239 Speaker 1: road you can literally look up at every five minutes 87 00:05:12,279 --> 00:05:15,080 Speaker 1: you can see the planes fly by so close that 88 00:05:15,120 --> 00:05:20,560 Speaker 1: you can count the windows and all conversations stops because 89 00:05:20,600 --> 00:05:22,560 Speaker 1: you are you know, because you are that close to 90 00:05:22,600 --> 00:05:24,000 Speaker 1: the flight path. So that gives you a sort of 91 00:05:24,040 --> 00:05:28,600 Speaker 1: sense of of where he's coming from. Not a partial neighborhood, 92 00:05:28,600 --> 00:05:33,400 Speaker 1: in other words, absolutely not. No. Um And he was 93 00:05:33,440 --> 00:05:37,560 Speaker 1: a gifted, you know, kind of mathematician at school. He 94 00:05:37,640 --> 00:05:41,440 Speaker 1: had um, you know, some academic chops and he found 95 00:05:41,440 --> 00:05:45,440 Speaker 1: that he could multiply figures in his head with great ease. 96 00:05:45,520 --> 00:05:47,720 Speaker 1: But he wasn't a great scholar. He was a you know, 97 00:05:47,760 --> 00:05:49,720 Speaker 1: a bit of a sort of cheeky lad if you like, 98 00:05:50,040 --> 00:05:54,039 Speaker 1: who loved playing football with his mates. Um And he 99 00:05:54,160 --> 00:05:57,320 Speaker 1: was also a keen computer gamer, so he used to 100 00:05:57,360 --> 00:06:00,240 Speaker 1: play the game Fifa, which is a soccer game, and 101 00:06:00,279 --> 00:06:02,920 Speaker 1: he ended up being in the top seven hundred out 102 00:06:02,920 --> 00:06:05,440 Speaker 1: of three million in the world. So he, you know, 103 00:06:05,480 --> 00:06:10,360 Speaker 1: he was pretty de extrous, good reflexes, pretty obsessive um. 104 00:06:10,520 --> 00:06:13,920 Speaker 1: And then after he'd finished university, he saw an advert 105 00:06:14,080 --> 00:06:20,240 Speaker 1: in the Evening Standard newspaper which literally said wanted futures 106 00:06:20,279 --> 00:06:24,920 Speaker 1: traders must work well under pressure, which is believable to 107 00:06:25,279 --> 00:06:33,160 Speaker 1: how Goldman Sacks hires their Yeah, amazing. And the interesting 108 00:06:33,240 --> 00:06:36,800 Speaker 1: thing about now is that he's kind of arrival into 109 00:06:36,800 --> 00:06:40,360 Speaker 1: the market's coincided with this kind of evolution from the 110 00:06:40,400 --> 00:06:45,479 Speaker 1: pits to the screens. So late nineties you had all 111 00:06:45,520 --> 00:06:48,520 Speaker 1: these pits closing, and you have the beginning of kind 112 00:06:48,520 --> 00:06:51,680 Speaker 1: of mascale or electronic trading, but you don't really have 113 00:06:52,240 --> 00:06:55,920 Speaker 1: high frequency trading at that point, so you have all 114 00:06:55,960 --> 00:06:59,000 Speaker 1: these kind of trading arcades that spring up there are 115 00:06:59,040 --> 00:07:02,480 Speaker 1: a little bit rough and ready, And basically the system 116 00:07:02,520 --> 00:07:06,360 Speaker 1: is that they will back you with money, um, and 117 00:07:06,400 --> 00:07:09,080 Speaker 1: if you don't do well, they'll kind of coach you, 118 00:07:09,200 --> 00:07:10,880 Speaker 1: give you some sort of tuition on how to use 119 00:07:10,920 --> 00:07:13,240 Speaker 1: the software, how to read the markets, a bit of 120 00:07:13,640 --> 00:07:17,240 Speaker 1: you know, economics, that kind of thing, um, and then 121 00:07:17,880 --> 00:07:19,520 Speaker 1: they'll kind of let you loose, and if you have 122 00:07:19,640 --> 00:07:21,880 Speaker 1: some potential, they'll keep giving you more money, and if 123 00:07:21,880 --> 00:07:25,120 Speaker 1: you don't, then they'll let you go. Um. And basically 124 00:07:25,160 --> 00:07:27,920 Speaker 1: the way that the business model works for those arcades 125 00:07:27,920 --> 00:07:30,240 Speaker 1: is that you know, all you need is a handful 126 00:07:30,280 --> 00:07:32,560 Speaker 1: of naves that are very good and they take a 127 00:07:32,600 --> 00:07:37,560 Speaker 1: percentage on everything they make. They charge um, you know, 128 00:07:37,680 --> 00:07:39,360 Speaker 1: per a round trip. And you know it was it 129 00:07:39,360 --> 00:07:41,240 Speaker 1: was a good business model for a while. But most 130 00:07:41,280 --> 00:07:43,040 Speaker 1: of those that walk through this the door of this 131 00:07:43,160 --> 00:07:46,440 Speaker 1: firm few text, which is above a supermarket, you know, 132 00:07:46,480 --> 00:07:49,840 Speaker 1: about forty five minutes outside and miles away from the 133 00:07:49,920 --> 00:07:55,080 Speaker 1: kind of city of London. Um. And so nav you know, 134 00:07:55,280 --> 00:07:58,120 Speaker 1: arrives there like all the other sort of rookies. Um. 135 00:07:58,600 --> 00:08:01,200 Speaker 1: And he's taught a style of tray aiding called scalping, 136 00:08:02,400 --> 00:08:05,560 Speaker 1: which has got nothing to do with kind of macroeconomics 137 00:08:05,680 --> 00:08:08,440 Speaker 1: or looking at interest rates. In the long term. It's 138 00:08:08,440 --> 00:08:13,920 Speaker 1: all about looking at orders coming into and leaving the marketplace, um, 139 00:08:13,960 --> 00:08:17,000 Speaker 1: and looking for clues as to whether the markets about 140 00:08:17,040 --> 00:08:20,240 Speaker 1: to fall or about to rise, and then taking very 141 00:08:20,280 --> 00:08:23,360 Speaker 1: short term positions in and out and in a lot 142 00:08:23,360 --> 00:08:25,720 Speaker 1: of ways it is a weirdly like a kind of 143 00:08:25,760 --> 00:08:28,840 Speaker 1: computer game, you know, where you're reacting very quickly to 144 00:08:28,960 --> 00:08:32,640 Speaker 1: information and data that comes your way. UM. So now 145 00:08:32,679 --> 00:08:36,640 Speaker 1: have you know inevitably turns out to be incredibly gifted 146 00:08:36,800 --> 00:08:40,120 Speaker 1: at this style of trading, and he goes from literally 147 00:08:40,160 --> 00:08:44,280 Speaker 1: having no money at all to having two million pounds 148 00:08:44,640 --> 00:08:47,480 Speaker 1: by the time he leaves few texts five years later, 149 00:08:48,000 --> 00:08:51,160 Speaker 1: which might sound like small fryer, but this is a 150 00:08:51,200 --> 00:08:54,559 Speaker 1: guy who's still living at home with his parents, um, 151 00:08:54,600 --> 00:08:56,800 Speaker 1: and compared to most of the other traders on the floor, 152 00:08:56,840 --> 00:09:00,839 Speaker 1: he was a complete start. So two thousand and eight 153 00:09:00,840 --> 00:09:02,920 Speaker 1: he decides to leave. He's sick of sort of giving 154 00:09:02,920 --> 00:09:06,160 Speaker 1: away his profits to the managers a few texts, so 155 00:09:06,240 --> 00:09:09,480 Speaker 1: he goes home to his parents place, um, and he 156 00:09:09,520 --> 00:09:13,720 Speaker 1: starts trading from there um. But something happens along the 157 00:09:13,760 --> 00:09:18,040 Speaker 1: way which kind of changes I guess his destiny in life. 158 00:09:18,120 --> 00:09:21,679 Speaker 1: He starts to become increasingly fed up with the arrival 159 00:09:21,679 --> 00:09:25,840 Speaker 1: of high frequency trading firms because essentially, you know, what 160 00:09:26,040 --> 00:09:28,120 Speaker 1: h f T or a certain type of HFT does 161 00:09:28,280 --> 00:09:30,600 Speaker 1: is very similar to what navs doing. It's kind of 162 00:09:30,640 --> 00:09:34,360 Speaker 1: looking at orders entering and leaving the market, looking at 163 00:09:34,360 --> 00:09:37,320 Speaker 1: the queue for different price levels, and tries to jump 164 00:09:37,360 --> 00:09:40,760 Speaker 1: ahead of you know, short term price moves. So people 165 00:09:40,840 --> 00:09:45,920 Speaker 1: like navs suddenly found themselves really struggling to compete, and 166 00:09:46,040 --> 00:09:48,280 Speaker 1: rather than quit the market, which is what a lot 167 00:09:48,320 --> 00:09:52,240 Speaker 1: of his friends did. Nev decided that he would get 168 00:09:52,240 --> 00:09:55,080 Speaker 1: in touch with a computer programmer and he would build 169 00:09:56,000 --> 00:10:00,920 Speaker 1: essentially a kind of sophisticated spoofing mach shame, which he 170 00:10:00,960 --> 00:10:03,880 Speaker 1: could not. Yeah, let me just set up because because 171 00:10:03,920 --> 00:10:06,880 Speaker 1: I think that's that's sort of the plot twist where 172 00:10:06,880 --> 00:10:10,559 Speaker 1: it gets really interesting. Because scalping is one thing, and 173 00:10:10,720 --> 00:10:12,840 Speaker 1: correct me if I'm wrong, but scalping is a type 174 00:10:12,880 --> 00:10:18,559 Speaker 1: of trading that's basically legal spoofing. Spoofing is when you 175 00:10:18,559 --> 00:10:22,840 Speaker 1: you intentionally put try to flood the market with orders, 176 00:10:23,000 --> 00:10:25,559 Speaker 1: either by orders to sort of send that futures price 177 00:10:25,679 --> 00:10:28,600 Speaker 1: up or flood the market with sell orders to bring 178 00:10:28,640 --> 00:10:32,160 Speaker 1: it down. Uh, And you don't really intend those orders 179 00:10:32,200 --> 00:10:34,800 Speaker 1: to be executed. You put them in as as sort 180 00:10:34,800 --> 00:10:36,840 Speaker 1: of a way to trick the other traders, whether they 181 00:10:36,880 --> 00:10:40,240 Speaker 1: be humans or computer algorithms, into thinking that there is 182 00:10:40,280 --> 00:10:44,600 Speaker 1: a supply demand imbalance going on. So I'm curious when 183 00:10:45,160 --> 00:10:48,400 Speaker 1: was it when exactly did not make the leap from 184 00:10:48,400 --> 00:10:51,400 Speaker 1: scalping to to actually spoofing. Did he do that as 185 00:10:51,440 --> 00:10:54,240 Speaker 1: sort of a hand trader or was it not until 186 00:10:54,320 --> 00:10:57,520 Speaker 1: he developed this, uh, this computer program that you're talking about. 187 00:10:58,080 --> 00:11:00,960 Speaker 1: So that is a very good question at and I 188 00:11:01,000 --> 00:11:04,320 Speaker 1: guess one that I'm saying I don't know entirely because 189 00:11:05,480 --> 00:11:12,000 Speaker 1: the first time that he um sort of is on 190 00:11:12,240 --> 00:11:15,920 Speaker 1: record as kind of getting involved with spoofing is in 191 00:11:15,960 --> 00:11:19,160 Speaker 1: two thousand and nine, which is when he writes to 192 00:11:19,240 --> 00:11:24,760 Speaker 1: this UM like developer and has a kind of blueprint 193 00:11:24,840 --> 00:11:28,400 Speaker 1: for this algorithm program that he wants to build, and 194 00:11:28,400 --> 00:11:30,640 Speaker 1: it's at that point in time where he's quite specific 195 00:11:30,679 --> 00:11:33,920 Speaker 1: about wanting to exactly, as you say, have a machine 196 00:11:33,960 --> 00:11:36,880 Speaker 1: that will be able to flood the market with said 197 00:11:37,000 --> 00:11:40,640 Speaker 1: orders specifically and then make sure that they never get 198 00:11:40,760 --> 00:11:43,240 Speaker 1: hit so the orders will be canceled. But by then 199 00:11:44,400 --> 00:11:48,080 Speaker 1: other market participants, particularly algorithms that are making very short 200 00:11:48,200 --> 00:11:51,240 Speaker 1: term decisions, will have reacted to it, and the market 201 00:11:51,280 --> 00:11:53,199 Speaker 1: will have moved a couple of ticks, and that will 202 00:11:53,240 --> 00:11:58,480 Speaker 1: be simultaneously trading and trying to sort of benefit from that. UM. 203 00:11:58,520 --> 00:12:00,439 Speaker 1: I mean as to whether it was illegal, the actual 204 00:12:00,480 --> 00:12:03,800 Speaker 1: spoofing rules themselves didn't come in until later until two 205 00:12:03,880 --> 00:12:08,160 Speaker 1: thousand and eleven UM, but arguably what he was doing 206 00:12:08,520 --> 00:12:11,439 Speaker 1: would have been caught up in other kind of statutes 207 00:12:11,480 --> 00:12:17,040 Speaker 1: like fraud security. Yeah, exactly, so, so, Liam, can you 208 00:12:17,080 --> 00:12:19,800 Speaker 1: help explain to us the mechanics of what actually happens 209 00:12:20,160 --> 00:12:22,079 Speaker 1: during a flash crash. I mean, we constantly hear this, 210 00:12:22,160 --> 00:12:25,080 Speaker 1: and I'm sure the mechanics are different in each one 211 00:12:25,120 --> 00:12:26,880 Speaker 1: that has happened over history, But in the likes of 212 00:12:26,920 --> 00:12:29,440 Speaker 1: the one that happened in two thousen, what were the 213 00:12:29,440 --> 00:12:33,880 Speaker 1: actual mechanics underlying? And then how did nav actually get 214 00:12:33,920 --> 00:12:37,760 Speaker 1: pulled into all of this? What was his role in it? Sure? So, 215 00:12:37,880 --> 00:12:42,160 Speaker 1: May six, two thousand and ten, UM, as you was saying, Michael, 216 00:12:42,240 --> 00:12:44,920 Speaker 1: was a very volatile day anyway. It was the kind 217 00:12:44,920 --> 00:12:48,760 Speaker 1: of midst of the Eurozone crisis. Um. The kind of 218 00:12:48,760 --> 00:12:52,720 Speaker 1: fear index was was rising very quickly throughout that weekend, 219 00:12:52,800 --> 00:12:56,160 Speaker 1: increasingly on that day, and you just had prices that 220 00:12:56,240 --> 00:13:01,520 Speaker 1: were we're kind of increasingly fall links throughout the course 221 00:13:01,520 --> 00:13:04,800 Speaker 1: of the US morning, and up until about one thirty 222 00:13:05,520 --> 00:13:09,680 Speaker 1: the market was down about three or four percent already. UM. 223 00:13:09,760 --> 00:13:14,120 Speaker 1: And then you know, panned the camera to Kansas City 224 00:13:14,360 --> 00:13:18,920 Speaker 1: and there is a kind of large pension fund called 225 00:13:18,920 --> 00:13:24,520 Speaker 1: woodele and Reid that had been long stocks and had 226 00:13:24,520 --> 00:13:28,520 Speaker 1: been benefited from a kind of big ramp up in prices. 227 00:13:28,920 --> 00:13:31,440 Speaker 1: But on that day, the fund manager got skittish and 228 00:13:31,480 --> 00:13:33,840 Speaker 1: decided that he wanted to hedge his position, so he 229 00:13:33,960 --> 00:13:39,880 Speaker 1: so he he sold seventy five thousand e minis, which 230 00:13:39,920 --> 00:13:43,200 Speaker 1: is worth about four billion dollars at the time. So 231 00:13:43,640 --> 00:13:47,320 Speaker 1: this very large fund places this very large order. But 232 00:13:47,400 --> 00:13:50,840 Speaker 1: the issue, one of the issues arose in the way 233 00:13:50,840 --> 00:13:55,000 Speaker 1: that they executed the order because they used an algorithm, 234 00:13:55,000 --> 00:14:00,720 Speaker 1: but they didn't include any kind of um like fail 235 00:14:00,800 --> 00:14:03,880 Speaker 1: safe so for example, so the so the algorithm they 236 00:14:04,000 --> 00:14:08,000 Speaker 1: used was to sell at nine percent of the market 237 00:14:08,080 --> 00:14:12,240 Speaker 1: volume until the order was executed. And the issue arose 238 00:14:12,679 --> 00:14:15,040 Speaker 1: in the because the markets were so volatile at that 239 00:14:15,080 --> 00:14:19,440 Speaker 1: point in time, volumes actually spiked and this selling algorithm 240 00:14:19,520 --> 00:14:22,600 Speaker 1: went into overdrive and was just selling and selling and 241 00:14:22,640 --> 00:14:25,640 Speaker 1: selling and selling. There was no kind of price level, 242 00:14:25,720 --> 00:14:29,280 Speaker 1: no stop loss there. So that arrives when you have 243 00:14:29,360 --> 00:14:32,560 Speaker 1: this already highly volatile marketplace, and then there's a bunch 244 00:14:32,600 --> 00:14:36,479 Speaker 1: of other things, weird things that happen as well. So, um, 245 00:14:36,560 --> 00:14:39,880 Speaker 1: you know, this is happening in the the futures markets, 246 00:14:39,880 --> 00:14:44,440 Speaker 1: but meanwhile in the stock markets there is there's like 247 00:14:44,480 --> 00:14:46,800 Speaker 1: technical issues that day that just so happened to be 248 00:14:46,840 --> 00:14:48,760 Speaker 1: happening on the New York Stock Exchange, which means that 249 00:14:48,800 --> 00:14:52,040 Speaker 1: there's delays in prices that are arriving to people, and 250 00:14:52,120 --> 00:14:55,000 Speaker 1: all of this creates a situation where market participants are 251 00:14:55,040 --> 00:14:59,720 Speaker 1: just leaving in their droves um and then suddenly, at 252 00:14:59,760 --> 00:15:02,960 Speaker 1: one forty one PM, the bottom falls out of the 253 00:15:03,000 --> 00:15:07,440 Speaker 1: market and within the space of four minutes it's fallen 254 00:15:07,520 --> 00:15:11,400 Speaker 1: another five per cent um, and you know, there's a 255 00:15:11,440 --> 00:15:14,680 Speaker 1: complete and utter drought of liquidity at that point as 256 00:15:14,720 --> 00:15:16,920 Speaker 1: everyone is kind of pulling the plug, and then the 257 00:15:17,000 --> 00:15:22,320 Speaker 1: crmes stop mechanism kicks in because the pride, the volty, 258 00:15:22,440 --> 00:15:24,840 Speaker 1: the velocity of the movie is so great that it 259 00:15:25,040 --> 00:15:28,920 Speaker 1: pauses the market for five seconds and at that point, 260 00:15:29,000 --> 00:15:30,800 Speaker 1: you know, it's the longest five seconds in the world 261 00:15:30,800 --> 00:15:33,600 Speaker 1: while everyone's kind of waiting to see what happens. And 262 00:15:33,600 --> 00:15:39,080 Speaker 1: then miraculously and again kind of confusingly because nobody knew 263 00:15:39,080 --> 00:15:41,240 Speaker 1: really why at the time, but the market as soon 264 00:15:41,280 --> 00:15:43,760 Speaker 1: as as soon as the market reopens, prices starts to 265 00:15:43,880 --> 00:15:47,560 Speaker 1: rally again um and within half an hour they're back 266 00:15:47,560 --> 00:15:50,680 Speaker 1: at the level they were before the drop. But meanwhile, 267 00:15:50,720 --> 00:15:54,520 Speaker 1: in the US stock market, there are, as Michael mentioned, 268 00:15:54,520 --> 00:15:56,080 Speaker 1: you know, some stocks that are trading at like no 269 00:15:56,080 --> 00:15:59,480 Speaker 1: all point zero zero one cent others that are suddenly 270 00:15:59,480 --> 00:16:05,920 Speaker 1: trading at hundred thousand dollars um because there's no liquidity 271 00:16:05,920 --> 00:16:07,560 Speaker 1: in the market, so they've gone to these things called 272 00:16:07,600 --> 00:16:12,600 Speaker 1: stop stub quotes, and and so in the aftermath of 273 00:16:12,640 --> 00:16:16,680 Speaker 1: all this, there's a big period of introspection. There's an 274 00:16:16,680 --> 00:16:20,680 Speaker 1: investigation by the CFTC and the SECUM and they come 275 00:16:20,720 --> 00:16:24,760 Speaker 1: to the conclusion that a combination of the kind of 276 00:16:24,840 --> 00:16:28,680 Speaker 1: changing marketplace and the increasing preponderance of electronic trading and 277 00:16:28,720 --> 00:16:33,480 Speaker 1: high frequency trading, the extreme macro economic conditions, and this 278 00:16:33,720 --> 00:16:38,800 Speaker 1: pension fund had all combined to create the conditions for 279 00:16:38,840 --> 00:16:42,920 Speaker 1: this exceptional market event. Naver Sara is nowhere to be 280 00:16:42,960 --> 00:16:47,480 Speaker 1: seen at this point. It's only two years later when 281 00:16:47,720 --> 00:16:51,080 Speaker 1: another day trader happens to be kind of back testing 282 00:16:51,200 --> 00:16:54,600 Speaker 1: his trading system on May six, two and ten, just 283 00:16:54,680 --> 00:16:59,160 Speaker 1: randomly picks that day, and he notices that throughout that 284 00:16:59,240 --> 00:17:03,240 Speaker 1: day in the order book there are hundreds of millions 285 00:17:03,280 --> 00:17:06,399 Speaker 1: of dollars of sell orders that are just sitting there, 286 00:17:06,880 --> 00:17:09,080 Speaker 1: never get hit. As soon as the price moves, they 287 00:17:09,080 --> 00:17:13,719 Speaker 1: get canceled and reposted um. And to him it's just 288 00:17:13,800 --> 00:17:16,800 Speaker 1: like a complete shock and an eye opener like, what 289 00:17:16,880 --> 00:17:19,399 Speaker 1: the hell Like, you know, surely this had had a 290 00:17:19,400 --> 00:17:23,720 Speaker 1: contributing factor as well. So he blew the whistle essentially 291 00:17:24,320 --> 00:17:27,439 Speaker 1: to the CFTC. They've got a program now where if 292 00:17:27,480 --> 00:17:30,160 Speaker 1: you lead to a successful conviction, you know, you get 293 00:17:30,200 --> 00:17:32,760 Speaker 1: paid for it. So this guy who I interview in 294 00:17:32,760 --> 00:17:35,480 Speaker 1: the book and I call him Mr X, blows the whistle. 295 00:17:35,520 --> 00:17:37,760 Speaker 1: And that is the first time that Nav appears on 296 00:17:37,800 --> 00:17:41,240 Speaker 1: the radar of the authorities. He probably wouldn't have done 297 00:17:41,280 --> 00:17:44,320 Speaker 1: if he wasn't for this guy. And then fast forward 298 00:17:44,400 --> 00:17:47,719 Speaker 1: to two thousand and fifteen and and Nav, you know, 299 00:17:48,000 --> 00:17:51,359 Speaker 1: is asleep in his parents house, in his bedroom in 300 00:17:51,440 --> 00:17:53,120 Speaker 1: hound Slow, and he gets a knock on the door 301 00:17:53,840 --> 00:17:56,760 Speaker 1: and it's you know, a dozen FBI agents and police 302 00:17:56,800 --> 00:18:00,439 Speaker 1: officers and they arrest him for manipulation in the market 303 00:18:00,760 --> 00:18:03,640 Speaker 1: and for spoofing and over a period of a long 304 00:18:03,680 --> 00:18:07,600 Speaker 1: period of time, but specifically as well um on the 305 00:18:07,680 --> 00:18:12,920 Speaker 1: day of the flash crash um. So you know, one 306 00:18:12,920 --> 00:18:14,800 Speaker 1: of the questions I attempt to grapple with is the 307 00:18:14,800 --> 00:18:18,800 Speaker 1: extent to which Surround really had a significant impact on 308 00:18:18,920 --> 00:18:22,480 Speaker 1: what happened that day, whether he was just a contributory factor, 309 00:18:22,480 --> 00:18:25,840 Speaker 1: whether he had no impact at all, And there's people 310 00:18:25,840 --> 00:18:28,840 Speaker 1: with views across that whole spectrum eving to this day. 311 00:18:29,040 --> 00:18:30,760 Speaker 1: I know, and I think you and I had gotten 312 00:18:30,800 --> 00:18:33,560 Speaker 1: a little bit of a Twitter conversation about that with 313 00:18:33,640 --> 00:18:38,400 Speaker 1: some others. Um. And so it clearly, as you point out, 314 00:18:38,400 --> 00:18:41,439 Speaker 1: there were structural issues with the market. There was you 315 00:18:41,440 --> 00:18:45,080 Speaker 1: know what Ellen read a fund manager putting in a 316 00:18:45,160 --> 00:18:48,480 Speaker 1: very aggressive, you know, ill advised sal ord or into 317 00:18:48,520 --> 00:18:52,840 Speaker 1: the market. UM. I do think people sort of rallied 318 00:18:52,880 --> 00:18:56,240 Speaker 1: behind the character of Nav because he's very much a 319 00:18:56,280 --> 00:19:00,040 Speaker 1: sympathetic character, as you say, a blue collar kid. I 320 00:19:00,400 --> 00:19:03,560 Speaker 1: believe he has Asperger's right. Um. Uh, you know Matt 321 00:19:03,600 --> 00:19:07,399 Speaker 1: Wiz who sort of makes this small fortune on his own. Um. 322 00:19:07,640 --> 00:19:10,600 Speaker 1: And he's kind of, you know, treating the market like 323 00:19:10,600 --> 00:19:13,960 Speaker 1: a video game, playing at the way he best sees fit. 324 00:19:14,080 --> 00:19:17,240 Speaker 1: And I don't wonder though, Liam, Now, if if there 325 00:19:17,240 --> 00:19:20,720 Speaker 1: were some giant hedge funds, some multibillion dollar hedge fund 326 00:19:20,960 --> 00:19:27,200 Speaker 1: or some uh you know, emerging high frequency trading firm 327 00:19:27,560 --> 00:19:29,879 Speaker 1: that was doing what he was doing. I mean, he 328 00:19:29,960 --> 00:19:33,080 Speaker 1: was he was responsible for what was it like thirty 329 00:19:33,119 --> 00:19:36,960 Speaker 1: or forty of the seal orders on the spres that 330 00:19:37,040 --> 00:19:41,000 Speaker 1: day something like. One statistic is staggering is that he 331 00:19:41,080 --> 00:19:44,840 Speaker 1: was amongst the top five largest traders on the E Mini, 332 00:19:44,920 --> 00:19:47,119 Speaker 1: which is one of the biggest futures markets in the world, 333 00:19:47,240 --> 00:19:50,720 Speaker 1: for a period of five years. So crazy to consider 334 00:19:50,840 --> 00:19:54,240 Speaker 1: one person day trading at home. Yes, I just wonder, 335 00:19:54,320 --> 00:19:57,440 Speaker 1: what do you think, you know, Uh, would the sympathy 336 00:19:57,560 --> 00:20:00,240 Speaker 1: be there for what he was doing if if did 337 00:20:00,280 --> 00:20:02,960 Speaker 1: say it's been a ten billion dollar hedge fund, I 338 00:20:03,000 --> 00:20:07,800 Speaker 1: won't name any names, or you know, a prominent firm. 339 00:20:07,840 --> 00:20:09,919 Speaker 1: I think you're absolutely right. The thing about nav is 340 00:20:09,960 --> 00:20:13,320 Speaker 1: that he was an outsider. He was like David taking 341 00:20:13,320 --> 00:20:15,840 Speaker 1: on Golias. You had Michael Lewis's book that had just 342 00:20:15,840 --> 00:20:18,879 Speaker 1: come out about high frequency traders and now throughout his 343 00:20:18,920 --> 00:20:21,000 Speaker 1: whole career, and he put you know, there's all these 344 00:20:21,040 --> 00:20:23,080 Speaker 1: emails that came out afterwards. He used to call the 345 00:20:23,240 --> 00:20:27,280 Speaker 1: HRT firms nerds and geeks, and he used to complain 346 00:20:27,320 --> 00:20:30,000 Speaker 1: about them. So he became like almost like a folk hero. 347 00:20:30,240 --> 00:20:33,439 Speaker 1: So particularly so a lot of day traders. I do 348 00:20:33,600 --> 00:20:37,640 Speaker 1: think that you are right, you know, And and there 349 00:20:37,640 --> 00:20:42,000 Speaker 1: have been a bunch of both banks and hedge funds 350 00:20:42,160 --> 00:20:44,200 Speaker 1: and large h of T shops that have now been 351 00:20:44,200 --> 00:20:46,399 Speaker 1: done for spoofing, and that certainly doesn't seem to be 352 00:20:46,440 --> 00:20:51,080 Speaker 1: the same level of of sympathy around it. Um. My 353 00:20:51,200 --> 00:20:53,199 Speaker 1: view on the flash crash itself is it's, you know, 354 00:20:53,320 --> 00:20:58,040 Speaker 1: it's it's almost impossible to reverse engineer what happened in 355 00:20:58,119 --> 00:21:00,960 Speaker 1: such a complex and busy market. It's just sort of 356 00:21:01,000 --> 00:21:03,800 Speaker 1: suffice to say that he was a major player. He 357 00:21:03,880 --> 00:21:07,600 Speaker 1: was a major kind of seller, um, and other market 358 00:21:07,640 --> 00:21:10,440 Speaker 1: participants were reacted to his orders. So I don't think 359 00:21:10,680 --> 00:21:13,000 Speaker 1: you can really say that he had nothing to do 360 00:21:13,040 --> 00:21:16,800 Speaker 1: with it. Um. But again, would it have happened if 361 00:21:16,800 --> 00:21:20,080 Speaker 1: he wasn't there? Maybe maybe it would. So yeah, it's 362 00:21:20,080 --> 00:21:23,600 Speaker 1: hard to prove the you know, the hypothetical what would 363 00:21:23,640 --> 00:21:25,840 Speaker 1: have happened if he wasn't around? Hey, Liam, Like I said, 364 00:21:25,880 --> 00:21:28,040 Speaker 1: I wish we could talk about this for hours, um, 365 00:21:28,160 --> 00:21:30,399 Speaker 1: but I think that's all the time we have for today. 366 00:21:31,080 --> 00:21:33,480 Speaker 1: I encourage everyone check out this book. I and I 367 00:21:33,480 --> 00:21:35,359 Speaker 1: believe it's going to be made into a movie. Or 368 00:21:35,480 --> 00:21:39,199 Speaker 1: is that correct? It is? Yeah, the Dev Patel, the 369 00:21:39,640 --> 00:21:42,600 Speaker 1: you know, amazing Hollywood actor, has signed onto play nav 370 00:21:42,600 --> 00:21:46,880 Speaker 1: if you could believe that, So congratulations and yeah, everyone 371 00:21:47,040 --> 00:21:49,480 Speaker 1: everyone has to either download the book or purchase the 372 00:21:49,480 --> 00:21:52,480 Speaker 1: book somehow, because it's the perfect quarantine rate. And if 373 00:21:52,480 --> 00:21:55,840 Speaker 1: you need someone to play a frazzled stocks market editor 374 00:21:55,880 --> 00:21:57,280 Speaker 1: trying to keep up with all of it, you know, 375 00:21:57,320 --> 00:22:04,200 Speaker 1: I'm I'm I'm Availbule. I'm just saying, and the frazzle journalist, Liam, 376 00:22:04,240 --> 00:22:05,920 Speaker 1: thanks so much for coming on the show. We appreciate 377 00:22:05,960 --> 00:22:25,600 Speaker 1: it for having me, guys, it was a pleasure. All right. 378 00:22:25,600 --> 00:22:28,440 Speaker 1: Now we're going to switch gears a little bit from 379 00:22:28,480 --> 00:22:31,840 Speaker 1: the flash crash of two thousand and ten to an 380 00:22:31,840 --> 00:22:35,680 Speaker 1: area of the market that itself has been pretty volatile 381 00:22:35,840 --> 00:22:40,520 Speaker 1: amidst the coronavirus crash, right, Mike, Yeah, absolutely, Sarah, And 382 00:22:41,000 --> 00:22:44,439 Speaker 1: I'm fascinated to talk to our next guest because, as 383 00:22:44,520 --> 00:22:46,560 Speaker 1: we all know, the U S stock market is basically 384 00:22:46,600 --> 00:22:52,640 Speaker 1: become dominated by the big five stocks. You know, Microsoft, Alphabet, Amazon, Apple, 385 00:22:53,359 --> 00:22:58,560 Speaker 1: and Facebook makeup of the stock market right now. Um, 386 00:22:58,600 --> 00:23:01,480 Speaker 1: But there's this whole other world of companies out there 387 00:23:01,520 --> 00:23:04,280 Speaker 1: around the world, global small cap companies. And I think 388 00:23:04,280 --> 00:23:07,520 Speaker 1: a lot of investors maybe get scared at the notion 389 00:23:07,560 --> 00:23:11,200 Speaker 1: of global small caps just because it's unfamiliar. So our 390 00:23:11,240 --> 00:23:14,439 Speaker 1: guest is gonna walk us through how he goes about 391 00:23:15,040 --> 00:23:18,080 Speaker 1: UH screening through the universe of global small caps to 392 00:23:18,160 --> 00:23:21,960 Speaker 1: find value and to find growth potential in You know, again, 393 00:23:22,040 --> 00:23:25,600 Speaker 1: what I think is an area that most American investors 394 00:23:25,600 --> 00:23:28,439 Speaker 1: aren't very comfortable with. So so hopefully we'll make them 395 00:23:28,440 --> 00:23:31,840 Speaker 1: a little bit more comfortable, UH with some insights from 396 00:23:32,080 --> 00:23:36,159 Speaker 1: Joffer riz Vey. He's a portfolio manager of the Harding 397 00:23:36,280 --> 00:23:41,800 Speaker 1: Lovener Global Small Companies and International Small Companies Funds UH. Joffer, 398 00:23:41,880 --> 00:23:48,639 Speaker 1: welcome to the show. Let's let's start with that basic idea. 399 00:23:48,680 --> 00:23:51,879 Speaker 1: I mean, I think of the universe of small cap 400 00:23:51,960 --> 00:23:54,040 Speaker 1: stocks around the world, and I don't know what it 401 00:23:54,119 --> 00:23:56,959 Speaker 1: must be number in the thousands, if not tens of 402 00:23:57,000 --> 00:24:00,679 Speaker 1: thousands of potential stocks. So how do you sort of 403 00:24:00,920 --> 00:24:05,040 Speaker 1: um sift through that opportunity set? What is what's sort 404 00:24:05,080 --> 00:24:08,320 Speaker 1: of your process for screening the whole world of small 405 00:24:08,359 --> 00:24:12,160 Speaker 1: caps and and finding stuff that you like. Sure, so 406 00:24:12,840 --> 00:24:18,840 Speaker 1: the global universe is well or ten thousand companies. In fact, 407 00:24:19,240 --> 00:24:22,440 Speaker 1: some of the indusseries that we are benchmarked against, like 408 00:24:22,480 --> 00:24:26,120 Speaker 1: the m c I All Country World Index, has about 409 00:24:26,480 --> 00:24:30,720 Speaker 1: six thousand companies in it. So it's a very large universe. 410 00:24:31,520 --> 00:24:34,960 Speaker 1: If you even think about the US the Russell two thousand, 411 00:24:35,119 --> 00:24:38,720 Speaker 1: as the name suggests, is four times more than the 412 00:24:38,840 --> 00:24:41,800 Speaker 1: SMP five hun group, So this is a much much 413 00:24:41,920 --> 00:24:46,400 Speaker 1: larger universe than than large caps and on an absolute basis, 414 00:24:47,480 --> 00:24:52,520 Speaker 1: in terms of how we screen through it, we use 415 00:24:52,760 --> 00:24:56,280 Speaker 1: essentially four criteria and we are looking at companies about 416 00:24:56,320 --> 00:25:01,480 Speaker 1: what to invest in. Topmost is the competitive position of 417 00:25:01,520 --> 00:25:04,960 Speaker 1: the company. We look for a competitive advantage that's there 418 00:25:05,040 --> 00:25:09,280 Speaker 1: today and that's sustainable in the long term. We then 419 00:25:09,320 --> 00:25:12,199 Speaker 1: look at the quality of the management team, which is 420 00:25:12,200 --> 00:25:17,359 Speaker 1: particularly important in small caps. Incremental decisions by management have 421 00:25:17,480 --> 00:25:20,919 Speaker 1: a much bigger ripple effect in small caps versus large caps. 422 00:25:22,040 --> 00:25:25,600 Speaker 1: Then we look at sustainable growth, how big is the 423 00:25:25,640 --> 00:25:29,600 Speaker 1: market the company's addressing, can it take market share? And 424 00:25:29,640 --> 00:25:32,840 Speaker 1: then last we look at financial strength, which is the 425 00:25:32,920 --> 00:25:36,560 Speaker 1: quality of the underlying financials, the accounting, the balance sheet, 426 00:25:37,280 --> 00:25:40,439 Speaker 1: things like leverage. So those are the four things we 427 00:25:40,480 --> 00:25:43,800 Speaker 1: look at, and we could sometimes use a screen based 428 00:25:43,840 --> 00:25:48,159 Speaker 1: on those criteria, which would be a quantitative screen, and 429 00:25:48,240 --> 00:25:51,960 Speaker 1: sometimes it would be visiting these companies and figuring it 430 00:25:52,040 --> 00:25:55,400 Speaker 1: out by ruling of our sleeves and traveling to their 431 00:25:55,400 --> 00:25:59,959 Speaker 1: headquarters and things like that. Drop or are there any 432 00:26:00,160 --> 00:26:03,760 Speaker 1: qualities that you've maybe been looking for even more so 433 00:26:03,880 --> 00:26:07,280 Speaker 1: are placing more emphasis on in the past couple of 434 00:26:07,359 --> 00:26:11,000 Speaker 1: months as we face everything going on. I mean, seems 435 00:26:11,040 --> 00:26:13,720 Speaker 1: like measures of leverage and profitability have really come under 436 00:26:13,760 --> 00:26:15,880 Speaker 1: the microscope. And that's part of the reason that when 437 00:26:15,880 --> 00:26:19,320 Speaker 1: you do look at broad benchmarks of small cap funds, 438 00:26:19,359 --> 00:26:22,120 Speaker 1: they've underperformed. So I'm curious if there's anything in this 439 00:26:22,280 --> 00:26:25,399 Speaker 1: environment that you find to be even more important to 440 00:26:25,440 --> 00:26:30,680 Speaker 1: be aware of. Absolutely, so we you know, first principle 441 00:26:30,720 --> 00:26:33,240 Speaker 1: as we stick to our hinting, so we'll still look 442 00:26:33,280 --> 00:26:36,439 Speaker 1: at these four criteria that I that I outlined, but 443 00:26:36,560 --> 00:26:40,440 Speaker 1: as you mentioned, uh, financial leverage is very important. It's 444 00:26:40,440 --> 00:26:43,960 Speaker 1: coming under the microscope. In fact, we looked looked at 445 00:26:44,600 --> 00:26:48,840 Speaker 1: in quarter one which endined mark thirty one this year, 446 00:26:49,520 --> 00:26:54,919 Speaker 1: we looked at different quintiles of performance. When the quintiles 447 00:26:54,960 --> 00:26:59,240 Speaker 1: were based on interest coverage, which is a measure of 448 00:26:59,280 --> 00:27:03,200 Speaker 1: the average UM. So when we looked at inface coverage, 449 00:27:03,200 --> 00:27:07,280 Speaker 1: we looked at UM what did the most labored companies 450 00:27:07,440 --> 00:27:11,240 Speaker 1: perform versus the least labored companies by quintile, and so 451 00:27:11,280 --> 00:27:13,320 Speaker 1: we put them in five buckets and what we saw 452 00:27:13,440 --> 00:27:17,600 Speaker 1: us there was about a thousand basis point difference between 453 00:27:18,320 --> 00:27:21,840 Speaker 1: the least level companies, meaning that they underperformed much less. 454 00:27:22,080 --> 00:27:24,160 Speaker 1: Now everything was down, by the way, but they were 455 00:27:24,200 --> 00:27:27,239 Speaker 1: down much less than the most labored companies, and there 456 00:27:27,320 --> 00:27:30,119 Speaker 1: was a little over a thousand basis for in difference 457 00:27:30,160 --> 00:27:34,720 Speaker 1: between that. So that immediately makes us aware that this 458 00:27:34,800 --> 00:27:39,200 Speaker 1: has been an important factor sort of speak. But we've 459 00:27:39,200 --> 00:27:42,280 Speaker 1: always looked at it. As I mentioned that financial strength 460 00:27:42,520 --> 00:27:46,040 Speaker 1: is one of the criteria we look at. UH In 461 00:27:46,080 --> 00:27:50,640 Speaker 1: this environment, small caps particularly are going to be under 462 00:27:50,640 --> 00:27:54,760 Speaker 1: the microscope in terms of financial leverage because it's you know, 463 00:27:54,880 --> 00:27:59,399 Speaker 1: they don't have the flexibility of large caps. It's very 464 00:27:59,440 --> 00:28:01,840 Speaker 1: easy for them to lose business in such a way 465 00:28:01,880 --> 00:28:05,000 Speaker 1: that their free cash fows diminished significantly so that they 466 00:28:05,040 --> 00:28:10,160 Speaker 1: can pay back their interest. So in this environment, if 467 00:28:10,200 --> 00:28:12,720 Speaker 1: you flip it around and if a company does have 468 00:28:12,840 --> 00:28:16,800 Speaker 1: strong financial strength, then it's actually going to give it 469 00:28:16,960 --> 00:28:21,119 Speaker 1: competitive advantage as well. In other words, they can continue 470 00:28:21,160 --> 00:28:24,840 Speaker 1: to invest while others are scrambling for cash or maybe 471 00:28:24,840 --> 00:28:28,040 Speaker 1: even winding up. They can continue to invest in R 472 00:28:28,080 --> 00:28:31,440 Speaker 1: and D and new plans. So we think it it's 473 00:28:31,560 --> 00:28:34,240 Speaker 1: not just important in and of itself, but It's also 474 00:28:34,400 --> 00:28:37,800 Speaker 1: important because it gives the company competitive advantage. So that's 475 00:28:37,800 --> 00:28:40,600 Speaker 1: the single biggest thing. But again I would say that 476 00:28:40,720 --> 00:28:44,480 Speaker 1: competitive advantage of the company, this continues to be one 477 00:28:44,480 --> 00:28:47,520 Speaker 1: of the single biggest things we look at. You know what, 478 00:28:47,680 --> 00:28:51,560 Speaker 1: I hear small caps, and and even when I hear 479 00:28:51,600 --> 00:28:54,800 Speaker 1: international equities, I sort of I sort of think risk. 480 00:28:55,080 --> 00:28:57,280 Speaker 1: You know, I I feel like there's a touch hole 481 00:28:57,520 --> 00:29:00,080 Speaker 1: potentially more gains there. You know, small caps over the 482 00:29:00,120 --> 00:29:03,240 Speaker 1: long run famously do better than big caps, but they're 483 00:29:03,280 --> 00:29:06,680 Speaker 1: often is that trade off with sort of higher blatility? 484 00:29:07,080 --> 00:29:10,240 Speaker 1: Am I wrong in thinking that? Jeffers is the sort 485 00:29:10,240 --> 00:29:16,360 Speaker 1: of risk adjusted returns of small caps around the world? Um, 486 00:29:16,400 --> 00:29:19,160 Speaker 1: you know, how does that compare to the US? So 487 00:29:19,800 --> 00:29:24,160 Speaker 1: if we look at international small cap risk as measured 488 00:29:24,200 --> 00:29:28,720 Speaker 1: by standard deviation of returns, globally, it is higher. We 489 00:29:28,800 --> 00:29:31,840 Speaker 1: looked at the last twenty years and what we found 490 00:29:31,920 --> 00:29:35,880 Speaker 1: is if you take the excess returns in the numerator, 491 00:29:35,920 --> 00:29:38,360 Speaker 1: which is the access returns of small caps and believe 492 00:29:38,400 --> 00:29:42,040 Speaker 1: versus large caps, and then you divide that by the 493 00:29:42,160 --> 00:29:45,520 Speaker 1: access risk. In other words, this is the sharp ratio 494 00:29:46,600 --> 00:29:49,800 Speaker 1: how much access returns am I getting versus how much 495 00:29:49,920 --> 00:29:53,280 Speaker 1: excess risk am I taking taking and the net of 496 00:29:53,360 --> 00:29:58,560 Speaker 1: that ratio is still positive and and statistically significant, meaning 497 00:30:00,320 --> 00:30:03,880 Speaker 1: in simple English, and just hit for risk. I still 498 00:30:03,960 --> 00:30:08,240 Speaker 1: have better returns in small cap historically looking into the 499 00:30:08,360 --> 00:30:11,360 Speaker 1: last twenty years, which is a good basis of thinking 500 00:30:11,400 --> 00:30:15,240 Speaker 1: about our small caps because this is multiple cycles, multiple 501 00:30:15,360 --> 00:30:19,280 Speaker 1: market cycles. Well before we get to the craziest things. 502 00:30:19,320 --> 00:30:22,000 Speaker 1: I know Mike especially is really looking forward to it. 503 00:30:22,040 --> 00:30:25,640 Speaker 1: This week. I want to know, have there been any 504 00:30:25,720 --> 00:30:28,760 Speaker 1: companies within your portfolio in the last couple of months 505 00:30:29,200 --> 00:30:32,840 Speaker 1: where what's happened due to the coronavirus and people having 506 00:30:32,880 --> 00:30:35,320 Speaker 1: to stay at home where the risk profile has just 507 00:30:35,440 --> 00:30:38,000 Speaker 1: completely changed and you guys have had to go back 508 00:30:38,120 --> 00:30:43,040 Speaker 1: and just completely maybe reevaluate your outlook on that company 509 00:30:43,080 --> 00:30:47,000 Speaker 1: and the whole ding going forward. So so I give 510 00:30:47,040 --> 00:30:52,320 Speaker 1: a few different examples here, uh, companies where the risk 511 00:30:52,320 --> 00:30:56,920 Speaker 1: profile has changed unfavorably and in some cases, you know, 512 00:30:57,000 --> 00:31:00,360 Speaker 1: surprisingly the company has been a beneficiary of mean innars 513 00:31:00,400 --> 00:31:03,400 Speaker 1: of surprising but on the margin, you know, a little 514 00:31:03,440 --> 00:31:06,400 Speaker 1: bit different than what we were expecting originally. So an 515 00:31:06,440 --> 00:31:11,240 Speaker 1: example of a company that got directly impacted both because 516 00:31:11,240 --> 00:31:14,880 Speaker 1: of coronavirus as well as the oil shop is core 517 00:31:15,000 --> 00:31:21,400 Speaker 1: laps there and oil services company. They provide reservoir management 518 00:31:21,520 --> 00:31:27,440 Speaker 1: services to large, large oil majors. And this company suffering 519 00:31:27,480 --> 00:31:30,560 Speaker 1: is suffering from a lack of demand. And there's also 520 00:31:30,600 --> 00:31:34,880 Speaker 1: some labor editions as well. So in cases like these, 521 00:31:34,960 --> 00:31:40,480 Speaker 1: what we're constantly doing, and it's more acute in a 522 00:31:40,560 --> 00:31:43,960 Speaker 1: crisis like this, is we're re evaluating the fundamental thesis 523 00:31:44,000 --> 00:31:47,560 Speaker 1: that we had and against this again, against those four 524 00:31:48,040 --> 00:31:51,240 Speaker 1: benchmarks of work is a good company? What is a 525 00:31:51,320 --> 00:31:57,640 Speaker 1: quality growth company? If the company doesn't meet our criteria anymore, ideally, 526 00:31:57,760 --> 00:32:01,560 Speaker 1: the analysts and the PM is raising their hand saying, look, 527 00:32:01,600 --> 00:32:04,120 Speaker 1: we got the thesis wrong. One of the things we 528 00:32:04,200 --> 00:32:08,440 Speaker 1: do continuously and again is brought into sharp focus in 529 00:32:09,040 --> 00:32:13,480 Speaker 1: circumstances like these, is we have something called mileposts where 530 00:32:13,480 --> 00:32:17,280 Speaker 1: we are measuring a company's growth against a pre commitment. 531 00:32:17,360 --> 00:32:19,520 Speaker 1: So we say that we think this company is going 532 00:32:19,520 --> 00:32:21,840 Speaker 1: to grow about the industry, that industry is going se 533 00:32:22,920 --> 00:32:25,640 Speaker 1: we think this company will grow above the If it doesn't, 534 00:32:25,880 --> 00:32:29,520 Speaker 1: if it reports quarterly or six monthly growth that's not 535 00:32:30,680 --> 00:32:32,880 Speaker 1: in line with the milepost, and we will say, okay, 536 00:32:32,920 --> 00:32:37,000 Speaker 1: the smile post is it's broken or it's failed or 537 00:32:37,040 --> 00:32:40,160 Speaker 1: it's behind, and depending on that we revalue it. So 538 00:32:40,880 --> 00:32:44,560 Speaker 1: in some cases. Another case would be Senior, which is 539 00:32:44,560 --> 00:32:47,239 Speaker 1: a part supplier to the aerospace coming out of the 540 00:32:47,360 --> 00:32:50,480 Speaker 1: UK out of the air to the aerospace industry out 541 00:32:50,480 --> 00:32:56,480 Speaker 1: of the UK is again impacted because of lack of 542 00:32:56,520 --> 00:33:00,160 Speaker 1: demand and lack of travel in aerospace. So the the's 543 00:33:00,200 --> 00:33:05,120 Speaker 1: like these are examples where we'll either generically speaking, we'll 544 00:33:05,160 --> 00:33:08,440 Speaker 1: either say, look our thesis is broken, we shouldn't want 545 00:33:08,440 --> 00:33:11,800 Speaker 1: it anymore, or look everything is intact. It's just a shock. 546 00:33:12,880 --> 00:33:16,160 Speaker 1: The long term still looks good, in which case it's 547 00:33:16,200 --> 00:33:19,040 Speaker 1: become more attractive. Let's buy more of it, so the 548 00:33:19,120 --> 00:33:23,440 Speaker 1: outcomes would be salad or added to it as you 549 00:33:23,520 --> 00:33:27,080 Speaker 1: might imagine. The flip side is the flip side of 550 00:33:27,160 --> 00:33:31,920 Speaker 1: this is companies where we've been surprised, where one example 551 00:33:32,040 --> 00:33:35,760 Speaker 1: is one of the world's largest shipbrokers, again a company 552 00:33:35,760 --> 00:33:41,480 Speaker 1: out of the UK called Clark Since where because of 553 00:33:41,720 --> 00:33:45,640 Speaker 1: the demand for storage of oil. Because oil became cheap, 554 00:33:46,000 --> 00:33:50,920 Speaker 1: demand for storage went up. This company supplies big oil 555 00:33:50,960 --> 00:33:56,240 Speaker 1: tankers or pod carriers, and um it benefited from them. 556 00:33:56,280 --> 00:33:59,719 Speaker 1: We did not expect that when we initially started the 557 00:33:59,720 --> 00:34:02,640 Speaker 1: thesis is on this company, that was a boon for 558 00:34:02,680 --> 00:34:05,400 Speaker 1: this I mean you didn't expect jagative oil prices to 559 00:34:05,720 --> 00:34:08,200 Speaker 1: help the oil upstart. So I imagine that you know 560 00:34:08,320 --> 00:34:11,600 Speaker 1: that was that was a little bit unexpected. A more 561 00:34:11,680 --> 00:34:15,319 Speaker 1: expected outcome would be heavy. Like can Access which is 562 00:34:15,320 --> 00:34:19,280 Speaker 1: a Canadian supply chain management software company, demand for supply 563 00:34:19,400 --> 00:34:23,040 Speaker 1: chain management software went up because the companies are trying 564 00:34:23,080 --> 00:34:26,640 Speaker 1: to figure a new supply chains and this company provides 565 00:34:27,160 --> 00:34:31,600 Speaker 1: real time supply chain management software. You know where doing inventorieschool, 566 00:34:31,640 --> 00:34:35,000 Speaker 1: which warehouses should they go to and where should I 567 00:34:35,040 --> 00:34:39,080 Speaker 1: ship out from? And demand for these company's software went up. 568 00:34:39,160 --> 00:34:43,320 Speaker 1: So so surprises on on on on both sides. Companies 569 00:34:43,360 --> 00:34:47,360 Speaker 1: that we're negatively it to some companies that were positively 570 00:34:47,440 --> 00:34:49,839 Speaker 1: it as well. And in those cases where there where 571 00:34:49,920 --> 00:34:53,440 Speaker 1: where there's a where there's a positive outcome. What what 572 00:34:53,560 --> 00:34:56,440 Speaker 1: we're doing is we're saying, what is the valuation of 573 00:34:56,520 --> 00:34:59,480 Speaker 1: this company? Is the growth a little more attractive, can 574 00:34:59,520 --> 00:35:02,160 Speaker 1: we hold it for a little longer, should be trimmed 575 00:35:02,480 --> 00:35:06,680 Speaker 1: or should be selling evaluation? So again the food side 576 00:35:06,719 --> 00:35:12,719 Speaker 1: of the cell trim decision in these cases, well, Sarah, uh, 577 00:35:13,080 --> 00:35:15,680 Speaker 1: that's a great segue to the craziest things we saw, 578 00:35:15,840 --> 00:35:20,480 Speaker 1: because um, the negative oil price shock is just the 579 00:35:20,520 --> 00:35:23,839 Speaker 1: crazy thing that keeps on giving. And I'll start with mine, 580 00:35:23,880 --> 00:35:28,080 Speaker 1: which once again has to do with just the amazing 581 00:35:28,080 --> 00:35:30,600 Speaker 1: glut of oil around the world right now. So Wall 582 00:35:30,600 --> 00:35:35,279 Speaker 1: Street Journal story talking about the creative ways people are 583 00:35:36,400 --> 00:35:39,400 Speaker 1: lengths they're going to the store oil. Remember when this happened, 584 00:35:39,400 --> 00:35:41,040 Speaker 1: a lot of my friends were joking like, oh, you 585 00:35:41,040 --> 00:35:44,160 Speaker 1: can put it in my garage or fill my swimming pool. 586 00:35:44,719 --> 00:35:50,040 Speaker 1: Well guess what people are really doing that this this 587 00:35:50,160 --> 00:35:54,880 Speaker 1: journal story, it's nuts. There's there's uh, all these crazy 588 00:35:54,960 --> 00:35:57,880 Speaker 1: schemes people have to buy oil. Guys buy an oil 589 00:35:57,920 --> 00:36:02,040 Speaker 1: for like a dollar eighteen of barrel, uh, and they're 590 00:36:02,080 --> 00:36:06,040 Speaker 1: trying to stick it in caves, abandoned rock mines and 591 00:36:06,120 --> 00:36:08,400 Speaker 1: even giants swimming pools. There's and there's something called a 592 00:36:08,480 --> 00:36:11,360 Speaker 1: fuel bladder that's some guys selling that is basically just 593 00:36:11,400 --> 00:36:17,680 Speaker 1: a big giant rubber containment thing for for story storing oil. So, um, 594 00:36:17,800 --> 00:36:20,040 Speaker 1: good luck to all these people filling their swimming pools 595 00:36:20,040 --> 00:36:24,680 Speaker 1: with oil. I gotta say I hope that or else. 596 00:36:24,760 --> 00:36:26,320 Speaker 1: That's a lot of effort for a whole lot of 597 00:36:26,360 --> 00:36:31,279 Speaker 1: nothing and a good contribution from Twitter from a guy 598 00:36:31,400 --> 00:36:35,360 Speaker 1: at Financial Gambler. His craziest thing in markets is similar. 599 00:36:35,400 --> 00:36:39,239 Speaker 1: It's some how Goldman Sacks has been looking at the 600 00:36:39,360 --> 00:36:41,920 Speaker 1: use of Zoom and that the user base of Zoom 601 00:36:41,920 --> 00:36:45,040 Speaker 1: and other sort of video conferencing techniques to kind of 602 00:36:45,080 --> 00:36:48,200 Speaker 1: suss out what the future demand for oil is, with 603 00:36:48,239 --> 00:36:50,399 Speaker 1: the thinking obviously that if you're if you're doing more 604 00:36:50,480 --> 00:36:53,799 Speaker 1: virtual meetings, you know you're gonna be taking less car 605 00:36:53,880 --> 00:36:57,080 Speaker 1: trips and and business trips and air air trips and 606 00:36:57,080 --> 00:36:59,640 Speaker 1: that sort of thing. So, um, it is the crazy 607 00:36:59,680 --> 00:37:03,000 Speaker 1: thing that keeps on giving, for sure. It truly is 608 00:37:03,120 --> 00:37:07,480 Speaker 1: it truly is you mentioned uh using zoom as a 609 00:37:07,520 --> 00:37:11,399 Speaker 1: measure of looking at measures of oil in a way, 610 00:37:11,600 --> 00:37:13,360 Speaker 1: but a lot of people have been looking at alternative 611 00:37:13,440 --> 00:37:17,799 Speaker 1: data through what we're going through and my crazy thing, um, 612 00:37:18,000 --> 00:37:20,560 Speaker 1: it's it's a little bit sobering, I will I'll preface 613 00:37:20,600 --> 00:37:24,239 Speaker 1: it with that. Um. But there was data out of 614 00:37:24,280 --> 00:37:28,560 Speaker 1: open table this past week showing that their forecast, they're 615 00:37:28,560 --> 00:37:32,359 Speaker 1: calling for one in every four restaurants to go out 616 00:37:32,360 --> 00:37:35,480 Speaker 1: of business by the time this all ends, which is 617 00:37:35,480 --> 00:37:37,479 Speaker 1: really crazy to think about and it hard to wrap 618 00:37:37,520 --> 00:37:39,759 Speaker 1: your mind around, because I feel like there's so many 619 00:37:39,760 --> 00:37:42,959 Speaker 1: people thinking that once economy is reopen, we're just gonna 620 00:37:42,960 --> 00:37:45,799 Speaker 1: snap back to normal. But that just really highlights how 621 00:37:45,840 --> 00:37:49,799 Speaker 1: much everything might change. Right, So, Jeoffrey, I hope you 622 00:37:49,800 --> 00:37:57,120 Speaker 1: don't have any restaurant chains in your funds. Have you 623 00:37:57,160 --> 00:38:01,400 Speaker 1: seen any crazy things in the market this week? I 624 00:38:01,400 --> 00:38:08,399 Speaker 1: think for us, the the the most. The craziest thing 625 00:38:08,480 --> 00:38:13,640 Speaker 1: I would say is that despite value underperforming for so long, 626 00:38:14,320 --> 00:38:19,000 Speaker 1: including you know, this year, even this past week, the 627 00:38:19,080 --> 00:38:23,160 Speaker 1: discrepancy between the performance of small cap value versus small 628 00:38:23,200 --> 00:38:27,600 Speaker 1: cap growth and it's just huge. I'm looking at US 629 00:38:27,640 --> 00:38:33,360 Speaker 1: small caps for example, in the US value small caps 630 00:38:33,400 --> 00:38:36,000 Speaker 1: are down ten percent in the last week or so, 631 00:38:36,880 --> 00:38:43,759 Speaker 1: growth small caps are down five, so about that's a 632 00:38:43,840 --> 00:38:46,359 Speaker 1: huge difference. And and this is after if you look 633 00:38:46,400 --> 00:38:52,360 Speaker 1: globally small caps, small cap growth is down fourteen percent 634 00:38:52,480 --> 00:38:55,279 Speaker 1: and small cap values down thirty percent, so you know, 635 00:38:55,320 --> 00:38:58,399 Speaker 1: over twice as much. So despite the year to day, 636 00:38:58,480 --> 00:39:01,720 Speaker 1: it just continues. And there are so many people calling 637 00:39:01,840 --> 00:39:05,440 Speaker 1: for a turnaround in this situation with abridging change, where 638 00:39:06,000 --> 00:39:10,120 Speaker 1: value starts are performing, and week after week, including this 639 00:39:10,200 --> 00:39:14,840 Speaker 1: last week, it just it's the same story. Yeah, another 640 00:39:14,920 --> 00:39:18,719 Speaker 1: even before the virus, everyone was predicting that was right 641 00:39:18,760 --> 00:39:22,440 Speaker 1: around the corner. It's uh, someday, someday we'll get that rotation, 642 00:39:23,400 --> 00:39:26,719 Speaker 1: even even within the large cap arena. I mean, an 643 00:39:26,760 --> 00:39:29,680 Speaker 1: unbelievable statistic from this week. At one point on Thursday, 644 00:39:29,960 --> 00:39:32,200 Speaker 1: if you look at an equal weighted version of the SMP, 645 00:39:32,320 --> 00:39:34,440 Speaker 1: it was down eight and a half percent, whereas the 646 00:39:34,560 --> 00:39:37,399 Speaker 1: SMP was only down about five percent on the week, 647 00:39:37,440 --> 00:39:39,799 Speaker 1: And that was the largest weekly discrepancy in at least 648 00:39:39,800 --> 00:39:42,080 Speaker 1: thirty years. So it just shows you that we continue 649 00:39:42,120 --> 00:39:44,600 Speaker 1: to see, as you said at the beginning, mega caps 650 00:39:45,560 --> 00:39:48,799 Speaker 1: just ruling really in the stock market and anything, um 651 00:39:48,840 --> 00:39:53,439 Speaker 1: that's a bit smaller just it hasn't. Yeah, yeah, it's 652 00:39:53,480 --> 00:39:56,080 Speaker 1: really hard hard to see any of that reversing until 653 00:39:56,200 --> 00:39:59,760 Speaker 1: this sort of virus goes away or at least gets 654 00:40:00,320 --> 00:40:04,760 Speaker 1: under control and the cyclical value stock start performing again. 655 00:40:04,880 --> 00:40:06,520 Speaker 1: But um, oh sorry, you know, we did get a 656 00:40:06,520 --> 00:40:09,560 Speaker 1: call from the what Goes Up hotline with a pretty 657 00:40:09,560 --> 00:40:13,640 Speaker 1: good crazy thing. Let's let's take a listen, and my 658 00:40:13,760 --> 00:40:16,359 Speaker 1: name is John, And to me, what the craziest thing 659 00:40:16,480 --> 00:40:20,000 Speaker 1: is is that nobody seems to focus on the fact 660 00:40:20,040 --> 00:40:25,960 Speaker 1: that the FED and the Treasury have eliminated capitalism. So 661 00:40:26,120 --> 00:40:32,560 Speaker 1: these the bastillion of capitalism, the Federal Reserve has effectively 662 00:40:33,160 --> 00:40:38,719 Speaker 1: stopped capitalism from working. And your buy back the hypothication 663 00:40:39,120 --> 00:40:43,920 Speaker 1: of now obviously moving towards possibility that the FED is 664 00:40:43,920 --> 00:40:47,719 Speaker 1: going to buy equities is a perfect example of that. 665 00:40:48,480 --> 00:40:52,759 Speaker 1: The key component of capitalism is efficiency mechanism, which allows 666 00:40:52,840 --> 00:40:57,799 Speaker 1: businesses to fail and that capital then gets reallocated. So 667 00:40:58,080 --> 00:41:04,600 Speaker 1: not really sure why the society UM hasn't focused on that, 668 00:41:04,880 --> 00:41:11,280 Speaker 1: except if it's just obstruction of that information. UM. Last crisis, 669 00:41:11,440 --> 00:41:14,160 Speaker 1: Bloomberg had to sue the FED in order to get 670 00:41:14,160 --> 00:41:18,800 Speaker 1: the information. Now it seems I don't know what's crazier, 671 00:41:18,840 --> 00:41:20,520 Speaker 1: the fact that they're doing it or that no one 672 00:41:20,560 --> 00:41:25,799 Speaker 1: seems to care. But that's a little negative. But that's 673 00:41:25,880 --> 00:41:30,600 Speaker 1: my view of the insanity. Sorry, I thought a lot 674 00:41:30,640 --> 00:41:34,440 Speaker 1: about that voicemail. It's it's a pretty uh intriguing voicemail, 675 00:41:34,440 --> 00:41:36,120 Speaker 1: And at first I was thinking, well, that's a little 676 00:41:36,160 --> 00:41:39,000 Speaker 1: bit of hyperbole on this guy's part. The FED killed capitalism, 677 00:41:39,080 --> 00:41:42,120 Speaker 1: But yeah, when you think about it, I mean, the 678 00:41:42,200 --> 00:41:45,680 Speaker 1: cure for this virus really was to shut down capitalism, 679 00:41:46,560 --> 00:41:49,160 Speaker 1: or at least big chunks of it, huge spots of 680 00:41:49,320 --> 00:41:51,520 Speaker 1: industry in this country. So I'm not sure if the 681 00:41:51,560 --> 00:41:54,000 Speaker 1: FEDS the one who did it, or the actual government, 682 00:41:54,320 --> 00:41:57,120 Speaker 1: local government, state governments, and the FED just kind of 683 00:41:57,160 --> 00:41:59,880 Speaker 1: reacted to this new regime we're in. But I'm not 684 00:42:00,160 --> 00:42:01,840 Speaker 1: I'm not sure he's he's too far off with that. 685 00:42:02,360 --> 00:42:04,480 Speaker 1: And we did hear from Powell this past week who 686 00:42:04,560 --> 00:42:08,040 Speaker 1: even said, as it stands, there's really not much more 687 00:42:08,080 --> 00:42:10,840 Speaker 1: potentially that they can do, although they will do whatever 688 00:42:10,880 --> 00:42:13,520 Speaker 1: they need to do, but they're now calling on d 689 00:42:13,600 --> 00:42:17,479 Speaker 1: C and for more physical policy because at this point 690 00:42:17,480 --> 00:42:19,920 Speaker 1: in time, unless they go negative, which they said they 691 00:42:19,960 --> 00:42:21,960 Speaker 1: don't want to do at least for now. I mean, 692 00:42:22,719 --> 00:42:24,640 Speaker 1: what more is there there for the FED to do? 693 00:42:25,640 --> 00:42:29,279 Speaker 1: Crazy times? Indeed, uh Joffre, I don't know, what do 694 00:42:29,320 --> 00:42:34,759 Speaker 1: you think? Did the FED kill capitalism? I hope it's 695 00:42:34,800 --> 00:42:40,640 Speaker 1: not that easy to kill capitalism, that's right, very tough 696 00:42:40,680 --> 00:42:48,279 Speaker 1: to beast to kill. Indeed, I agree, interestingly, that's so 697 00:42:48,360 --> 00:42:50,480 Speaker 1: that voicemails a few days ago. I did see R 698 00:42:50,600 --> 00:42:55,319 Speaker 1: I P. Capitalism trending on Twitter yesterday, so I don't know, 699 00:42:55,600 --> 00:42:59,960 Speaker 1: I don't know, are catching on? Yeah? Yeah, but who 700 00:43:00,080 --> 00:43:02,800 Speaker 1: who would have ever believed we'd be discussing whether or 701 00:43:02,840 --> 00:43:06,279 Speaker 1: not capitalism was murdered in our time. Certainly not I, 702 00:43:06,480 --> 00:43:10,960 Speaker 1: not I. But with that said, Joffer Risby, so glad 703 00:43:11,000 --> 00:43:12,560 Speaker 1: that you were able to join us on the show 704 00:43:12,560 --> 00:43:24,239 Speaker 1: this week. Thank you, Sarah, Thank you Mike. What Goes Up. 705 00:43:24,320 --> 00:43:27,239 Speaker 1: We'll be back next week. Until then, you can find 706 00:43:27,280 --> 00:43:30,040 Speaker 1: us on the Bloomberg Terminal website and app, or wherever 707 00:43:30,080 --> 00:43:32,560 Speaker 1: you get your podcasts. We'd love it if you took 708 00:43:32,600 --> 00:43:35,279 Speaker 1: the time to rate interview the show on Apple Podcasts 709 00:43:35,520 --> 00:43:38,160 Speaker 1: so more listeners can find us, and you can find 710 00:43:38,200 --> 00:43:41,200 Speaker 1: us on Twitter, follow me at at Sara pont Zack, 711 00:43:41,480 --> 00:43:44,840 Speaker 1: Mike is that Reaganonymous, and you can also follow Bloomberg 712 00:43:44,880 --> 00:43:49,320 Speaker 1: Podcasts at podcasts. What Goes Up is produced by Toper Foreheads. 713 00:43:49,640 --> 00:43:53,040 Speaker 1: The head of Bloomberg podcast is Francesca Levie. Thanks for listening, 714 00:43:53,120 --> 00:44:08,280 Speaker 1: See you next time. Before