1 00:00:14,280 --> 00:00:17,440 Speaker 1: Hello, and welcome to What Goes Up, a Bloomberg weekly 2 00:00:17,520 --> 00:00:21,000 Speaker 1: market podcast. I'm Sarah Pontac, a market supporter on the 3 00:00:21,000 --> 00:00:24,120 Speaker 1: Cross Asset team, and I'm Mike Reagan, a senior editor 4 00:00:24,239 --> 00:00:26,799 Speaker 1: on the Markets team here at Bloomberg. This week on 5 00:00:26,840 --> 00:00:30,159 Speaker 1: the show or session, worries are receding, but what is 6 00:00:30,160 --> 00:00:32,559 Speaker 1: the yield curve telling us? We'll hear from one of 7 00:00:32,560 --> 00:00:36,400 Speaker 1: the topics research pioneers and when it comes to factor investing, 8 00:00:36,640 --> 00:00:39,600 Speaker 1: a strategy that has really grown in popularity over the 9 00:00:39,680 --> 00:00:44,840 Speaker 1: years and focuses on stock characteristics you may know like growth, value, momentum, 10 00:00:44,840 --> 00:00:48,640 Speaker 1: and volatility. You may have to be careful and don't worry. 11 00:00:48,680 --> 00:00:51,160 Speaker 1: We will close out the episode with our tradition, which 12 00:00:51,200 --> 00:00:54,360 Speaker 1: is the craziest thing I saw in markets this week. 13 00:00:55,280 --> 00:00:58,480 Speaker 1: But for Sarah, we've got some very smart guests today. 14 00:00:58,640 --> 00:01:01,640 Speaker 1: We were very lucky yant Alden guests. And uh, they 15 00:01:01,640 --> 00:01:03,880 Speaker 1: both have one thing in common. They have a connection 16 00:01:03,920 --> 00:01:07,400 Speaker 1: to Duke University. So don't bring up basketball. Whatever you do, 17 00:01:07,520 --> 00:01:09,520 Speaker 1: we'll never hear the end of it. If that happens. 18 00:01:09,600 --> 00:01:12,080 Speaker 1: I thought you were going to say they have another connection, 19 00:01:12,240 --> 00:01:15,800 Speaker 1: the one other connection there. We can both call them Cam, 20 00:01:15,840 --> 00:01:18,160 Speaker 1: So we might get confusing here, but we'll start with 21 00:01:18,280 --> 00:01:22,720 Speaker 1: our guest here from outside of Bloomberg, Campbell Harvey and sorry, 22 00:01:22,720 --> 00:01:24,800 Speaker 1: this guy's got a resume. That's we would need a 23 00:01:24,880 --> 00:01:28,800 Speaker 1: whole separate podcast to go through it all next episode. 24 00:01:28,959 --> 00:01:30,840 Speaker 1: But I think the highlights and correct me if I'm 25 00:01:30,840 --> 00:01:35,319 Speaker 1: wrong here, Campbell uh finance professor at Duke Approximately a 26 00:01:35,360 --> 00:01:40,080 Speaker 1: gazillion published research reports. I think currently a senior advisor 27 00:01:40,160 --> 00:01:43,000 Speaker 1: with rob ar Nots Research affiliates. Is that the is 28 00:01:43,000 --> 00:01:46,880 Speaker 1: that the current highlights That's approximately right, It's a gazillion 29 00:01:47,080 --> 00:01:51,400 Speaker 1: and one one okay, well the notes on that and 30 00:01:51,440 --> 00:01:56,080 Speaker 1: then the other cam Our own Macroman, a longtime trader 31 00:01:56,160 --> 00:02:00,120 Speaker 1: in the markets, traded rates, currencies, all manner of macro 32 00:02:00,240 --> 00:02:04,639 Speaker 1: products Cameron christ He now writes are Bloomberg column called 33 00:02:04,680 --> 00:02:07,760 Speaker 1: the Macroman column. Cameron, Welcome to the show. Thank you 34 00:02:07,880 --> 00:02:11,080 Speaker 1: very much. So we're really excited to have you here, Cam, 35 00:02:11,280 --> 00:02:14,240 Speaker 1: because we know that you are one of the first 36 00:02:14,400 --> 00:02:17,120 Speaker 1: to really think about the idea of the yield curve 37 00:02:17,200 --> 00:02:20,079 Speaker 1: and it's predictive power, about how it relates to a recession. 38 00:02:20,560 --> 00:02:23,639 Speaker 1: And from my standpoint, there are many people who are 39 00:02:23,639 --> 00:02:27,320 Speaker 1: talking about recession fears going away. Now people are looking 40 00:02:27,440 --> 00:02:30,200 Speaker 1: at the spread between the two year yield and the 41 00:02:30,280 --> 00:02:32,680 Speaker 1: ten ure yield, or the three month yield and the 42 00:02:32,720 --> 00:02:36,040 Speaker 1: tenure yield. But you actually looked at the spread between 43 00:02:36,080 --> 00:02:38,680 Speaker 1: the five year yield and the three month yield. Why 44 00:02:38,680 --> 00:02:41,240 Speaker 1: do you look there? Okay? So this is based on 45 00:02:41,280 --> 00:02:46,440 Speaker 1: my dissertation in six at the University Chicago, and I 46 00:02:46,520 --> 00:02:49,240 Speaker 1: looked at a longer term rate minus a shorter term 47 00:02:49,320 --> 00:02:52,880 Speaker 1: rate for particular reason because that is implied by the 48 00:02:52,919 --> 00:02:57,360 Speaker 1: economic theory. So what we're looking for is a forecast 49 00:02:57,400 --> 00:03:01,480 Speaker 1: of GDP GDPs measured quarterly, so a three month rate 50 00:03:01,520 --> 00:03:05,760 Speaker 1: to anchor the short is very important. Since my dissertation, 51 00:03:06,440 --> 00:03:09,520 Speaker 1: other people have looked at different pieces of the yield curve, 52 00:03:09,960 --> 00:03:12,440 Speaker 1: so they get stressed about the nine and a half 53 00:03:12,520 --> 00:03:15,360 Speaker 1: year minus the eight and a half year, and a 54 00:03:15,400 --> 00:03:18,040 Speaker 1: lot of data mining has gone on the yield curve, 55 00:03:18,480 --> 00:03:23,480 Speaker 1: so it's fit's slightly better historically using a different piece 56 00:03:23,520 --> 00:03:27,880 Speaker 1: of the curve. However, in my opinion, and of course 57 00:03:27,919 --> 00:03:31,320 Speaker 1: i'm biased on this, UM want to use the original 58 00:03:31,360 --> 00:03:35,560 Speaker 1: idea from six. It's got a long track record out 59 00:03:35,600 --> 00:03:40,280 Speaker 1: of sample. It's three for three in terms of predicting recessions, 60 00:03:40,320 --> 00:03:44,640 Speaker 1: including the global financial crisis. So it's not obvious that's broken. 61 00:03:44,960 --> 00:03:47,440 Speaker 1: So why do you want to go to some niche 62 00:03:47,840 --> 00:03:50,080 Speaker 1: spread in the yield curve? Doesn't make any sense to me. 63 00:03:50,400 --> 00:03:52,040 Speaker 1: I was gonna say, you must in some ways feel 64 00:03:52,080 --> 00:03:55,120 Speaker 1: like the doctor Frankenstein of the yield curve recession signal. 65 00:03:55,160 --> 00:03:56,880 Speaker 1: You know, in a way, you know, it created this 66 00:03:56,920 --> 00:03:59,760 Speaker 1: monster that's taken on a life and it doesn't go away. 67 00:04:00,040 --> 00:04:05,240 Speaker 1: Usually dissertations collect dust somewhere, and I'm talking about it 68 00:04:05,320 --> 00:04:08,920 Speaker 1: after so many years. You know, I am always curious 69 00:04:09,080 --> 00:04:13,040 Speaker 1: about the causing effect relationship between the yield curve and 70 00:04:13,320 --> 00:04:17,600 Speaker 1: the inevitable recession. Is it just that, uh you know, 71 00:04:18,040 --> 00:04:21,839 Speaker 1: fixed income investors are great at telling the future, or 72 00:04:21,920 --> 00:04:25,680 Speaker 1: is it more um practical? Say, you know, banks obviously 73 00:04:26,080 --> 00:04:30,320 Speaker 1: have shrinking profit margins in a scenario where short term 74 00:04:30,400 --> 00:04:33,280 Speaker 1: rates go above long term rates, and therefore they might 75 00:04:33,360 --> 00:04:35,640 Speaker 1: rain rain in lending a little bit. What what do 76 00:04:35,680 --> 00:04:38,160 Speaker 1: you have you done much work to to sort of 77 00:04:38,160 --> 00:04:40,680 Speaker 1: suss out what the causing effect is? Yeah, okay, so 78 00:04:40,920 --> 00:04:44,520 Speaker 1: this is very important. Uh. The economic model I propose 79 00:04:44,880 --> 00:04:47,520 Speaker 1: is not a causal model. So it's not like the 80 00:04:47,600 --> 00:04:51,320 Speaker 1: yield curve in version is causing a recession. The yield 81 00:04:51,360 --> 00:04:56,280 Speaker 1: curve is capturing in a very elegant way expectations. It 82 00:04:56,360 --> 00:05:01,320 Speaker 1: is all about uh, future cash flow. And if you 83 00:05:01,360 --> 00:05:05,440 Speaker 1: think of other financial assets like a stock, that's also 84 00:05:05,480 --> 00:05:10,720 Speaker 1: about future cash flows, but it's different. So a yield curve, 85 00:05:10,760 --> 00:05:15,520 Speaker 1: a government yield curve, it is less risky. You know, 86 00:05:15,960 --> 00:05:20,800 Speaker 1: the US tenure is perhaps the safest asset in the world. UH, 87 00:05:20,839 --> 00:05:25,719 Speaker 1: it's got cash flows that are very predictable. So the 88 00:05:25,760 --> 00:05:28,960 Speaker 1: coupon you know exactly when you're gonna get it, and 89 00:05:29,720 --> 00:05:32,360 Speaker 1: you actually know the maturity. With the stock, you don't 90 00:05:32,360 --> 00:05:34,080 Speaker 1: know what the dividends, what are they going to be? 91 00:05:34,640 --> 00:05:39,479 Speaker 1: And the maturity is not obvious. So this captures expectations 92 00:05:40,320 --> 00:05:45,280 Speaker 1: and it is basically we're able to extract a forecast. 93 00:05:45,400 --> 00:05:48,919 Speaker 1: That forecast turns out to be very accurate. But and 94 00:05:49,000 --> 00:05:52,479 Speaker 1: this is a very important but that's only relevant recently. 95 00:05:53,080 --> 00:05:56,000 Speaker 1: In the past, I give my yield curve forecast and 96 00:05:56,040 --> 00:06:01,240 Speaker 1: not many people noticed. But after it hit and forecast 97 00:06:01,320 --> 00:06:05,400 Speaker 1: accurately the global financial crisis, now people are talking about it. 98 00:06:06,080 --> 00:06:10,760 Speaker 1: So is there a possibility that people see well yield 99 00:06:10,760 --> 00:06:13,720 Speaker 1: curves inverted or very flat. I'm not going to pull 100 00:06:13,760 --> 00:06:16,760 Speaker 1: a trigger on this capital investment. I'm not gonna hire 101 00:06:17,120 --> 00:06:21,520 Speaker 1: um this extra like number of people. So this is 102 00:06:21,600 --> 00:06:25,080 Speaker 1: this all called self fulfilling prophecy. So if there's causality, 103 00:06:25,480 --> 00:06:29,200 Speaker 1: the causality is with the self fulfilling prophecy. But that's 104 00:06:29,240 --> 00:06:32,560 Speaker 1: only a recent phenomenon. That's it's become sort of I think, 105 00:06:32,560 --> 00:06:35,039 Speaker 1: as Cameron would call, it's gone tabloid almost that the 106 00:06:35,080 --> 00:06:38,839 Speaker 1: yield curve signal. So Cameron, you've you've sort of straddled 107 00:06:38,960 --> 00:06:42,760 Speaker 1: the research end of markets and the actual practitioner trading 108 00:06:42,880 --> 00:06:46,080 Speaker 1: end of markets. You know, put your trader hat on, 109 00:06:46,240 --> 00:06:48,960 Speaker 1: and what do you do with this information that the 110 00:06:49,040 --> 00:06:53,279 Speaker 1: yield curve. Yeah, it's potentially predicting a recession, but it 111 00:06:53,360 --> 00:06:56,920 Speaker 1: could be twelve months, eighteen months, maybe longer. Um. As 112 00:06:56,960 --> 00:06:59,320 Speaker 1: a trader, how would you sort of you know, put 113 00:06:59,360 --> 00:07:03,200 Speaker 1: that information work. Well, the the irony is looking at 114 00:07:03,240 --> 00:07:06,800 Speaker 1: it from say, from an equity perspective, is that equity 115 00:07:06,800 --> 00:07:10,960 Speaker 1: returns contemporaneously or generally pretty good when the yield curve 116 00:07:11,040 --> 00:07:13,840 Speaker 1: is flat or even when it inverts. The real sort 117 00:07:13,880 --> 00:07:16,640 Speaker 1: of clacks in the danger signals when it rest deepens 118 00:07:17,120 --> 00:07:21,120 Speaker 1: after flattening and then inverting, because that is the sign 119 00:07:21,440 --> 00:07:24,200 Speaker 1: that money is pouring in to the short end. People 120 00:07:24,320 --> 00:07:27,000 Speaker 1: are worried much more about preserve it, you know, about 121 00:07:27,080 --> 00:07:31,040 Speaker 1: return of capital rather than return on capital. And that's 122 00:07:31,080 --> 00:07:35,560 Speaker 1: when sort of the economic cycle, the economic um expansion 123 00:07:35,600 --> 00:07:38,280 Speaker 1: walks out the front door and let's in, you know, 124 00:07:38,360 --> 00:07:43,320 Speaker 1: the grim reaper of of of recession. So clearly we 125 00:07:43,320 --> 00:07:46,360 Speaker 1: were sort of flirting with that, where we were flirting 126 00:07:46,360 --> 00:07:49,480 Speaker 1: with inversion a few months ago. We've now sort of 127 00:07:49,520 --> 00:07:55,679 Speaker 1: stabilized at uh flat but positive slope for the yield curve, 128 00:07:55,880 --> 00:07:58,800 Speaker 1: which is ironically very or maybe not ironically, but it's 129 00:07:58,840 --> 00:08:02,360 Speaker 1: a very very similar um environment to what we observed 130 00:08:02,400 --> 00:08:06,640 Speaker 1: in the mid nineteen nineties, which interestingly enough, was the 131 00:08:06,760 --> 00:08:10,880 Speaker 1: last time the Fed really managed to engineer a soft 132 00:08:11,000 --> 00:08:13,680 Speaker 1: landing for the economy. And I will add that especially 133 00:08:13,720 --> 00:08:16,080 Speaker 1: this past week, we've seen a bit of a bowl steepening, 134 00:08:16,120 --> 00:08:19,520 Speaker 1: so short term rates falling faster than longer term rates. 135 00:08:19,520 --> 00:08:21,880 Speaker 1: And now you look at the twos tends spread and 136 00:08:21,920 --> 00:08:25,040 Speaker 1: it's at the highest levels, the widest levels of the year. 137 00:08:25,200 --> 00:08:27,680 Speaker 1: Cam I want to ask you for the people who 138 00:08:27,680 --> 00:08:30,680 Speaker 1: are looking at the spread between three month yields and 139 00:08:30,720 --> 00:08:33,600 Speaker 1: ten yere yields or two year yields and ten year yields, 140 00:08:33,600 --> 00:08:37,880 Speaker 1: and saying that we're seeing a steepening. Any negativity, any 141 00:08:37,920 --> 00:08:40,760 Speaker 1: inversion that we saw was transitory. Yet you look at 142 00:08:40,800 --> 00:08:43,840 Speaker 1: the spread between the three month yield and the five 143 00:08:43,920 --> 00:08:45,880 Speaker 1: year yield A turned negative in March and it is 144 00:08:45,920 --> 00:08:50,600 Speaker 1: still negative. Are some people potentially missing out on the implications? Okay, 145 00:08:50,600 --> 00:08:55,000 Speaker 1: so this is important my economic model. Uh, it's not 146 00:08:55,080 --> 00:08:59,439 Speaker 1: just predicting recessions, it's predicting economic growth. So if you're 147 00:08:59,480 --> 00:09:03,320 Speaker 1: flat or if you're inverted, it's saying the same thing 148 00:09:03,720 --> 00:09:08,280 Speaker 1: that growth will slow. But I do want to qualify 149 00:09:08,360 --> 00:09:11,520 Speaker 1: this on a couple of dimensions. So number one, my 150 00:09:11,600 --> 00:09:15,160 Speaker 1: model relates to an inversion that happens for a full 151 00:09:15,240 --> 00:09:21,480 Speaker 1: quarter and a few days that doesn't count. Nevertheless, flat 152 00:09:21,640 --> 00:09:26,160 Speaker 1: means lower growth. The other qualification is that this model 153 00:09:26,240 --> 00:09:29,760 Speaker 1: is a very simple model. It's like one variable. So 154 00:09:29,920 --> 00:09:33,360 Speaker 1: if I'm a trader or if I'm a professional economic forecaster, 155 00:09:33,440 --> 00:09:36,719 Speaker 1: I'm going to use that information along with other information 156 00:09:37,000 --> 00:09:41,000 Speaker 1: in the market. So that's the second point. That third 157 00:09:41,080 --> 00:09:45,720 Speaker 1: point I think is really important and dangerous for traders 158 00:09:45,760 --> 00:09:50,440 Speaker 1: it's dangerous for ceo s and CFOs were a hundred 159 00:09:50,480 --> 00:09:54,960 Speaker 1: and twenty months of recovery since the last economic rough, 160 00:09:55,640 --> 00:09:59,840 Speaker 1: and usually a cycle last post of World War two 161 00:10:00,080 --> 00:10:04,160 Speaker 1: fifty eight months, So this is a very long period, 162 00:10:04,440 --> 00:10:08,200 Speaker 1: and I think that there's a behavioral bias here that 163 00:10:08,520 --> 00:10:12,800 Speaker 1: when you're in this long period of recovery, people tend 164 00:10:12,880 --> 00:10:14,360 Speaker 1: to look at the good news and put a lot 165 00:10:14,400 --> 00:10:16,800 Speaker 1: of weight on it and ignore the bad news. And 166 00:10:17,200 --> 00:10:22,040 Speaker 1: it operates the oppositely um when you're in a recession, 167 00:10:22,240 --> 00:10:26,000 Speaker 1: that everything's bad news and the green shoots are ignored. 168 00:10:26,559 --> 00:10:30,200 Speaker 1: So I think that this is exactly the time where 169 00:10:30,840 --> 00:10:36,560 Speaker 1: UM a trader or CEO or CFO re earns their 170 00:10:36,679 --> 00:10:41,600 Speaker 1: keep because a turning point is much more likely today 171 00:10:41,720 --> 00:10:44,280 Speaker 1: than it was a few years ago. Now, you mentioned 172 00:10:44,280 --> 00:10:47,160 Speaker 1: these other factors that people should look at, and I 173 00:10:47,160 --> 00:10:49,680 Speaker 1: believe you've referred to them as the four horsemen of 174 00:10:49,800 --> 00:10:53,160 Speaker 1: an impending recession, which is timely being with the Kentucky 175 00:10:53,200 --> 00:10:56,800 Speaker 1: Derby coming up, that's what everyone's pick. So maybe later 176 00:10:57,120 --> 00:10:59,440 Speaker 1: and correct me if I'm wrong, But if I remember correctly, 177 00:10:59,600 --> 00:11:02,800 Speaker 1: the you'l rbs one of them. Volatility and markets is 178 00:11:02,840 --> 00:11:06,920 Speaker 1: another sort of protectionist trade policy that we've seen is one. 179 00:11:07,600 --> 00:11:11,400 Speaker 1: And uh, the really interesting one you brought up was this, uh, 180 00:11:11,600 --> 00:11:15,080 Speaker 1: the Duke CFO survey. So Duke every quarter conducts a 181 00:11:15,360 --> 00:11:19,200 Speaker 1: survey of was it like four CFOs chief financial officers 182 00:11:19,640 --> 00:11:22,319 Speaker 1: around the country And you started this right, I was 183 00:11:22,360 --> 00:11:25,400 Speaker 1: the co founder of the survey years ago. Really interesting 184 00:11:25,440 --> 00:11:27,800 Speaker 1: and what I find fascinating about it is so it's 185 00:11:27,800 --> 00:11:31,719 Speaker 1: done quarterly, So it's a very robust sample of important 186 00:11:31,840 --> 00:11:34,840 Speaker 1: risk managers around the country. But the quarterly thing, I 187 00:11:34,880 --> 00:11:37,160 Speaker 1: think is interesting because if you survey them in this 188 00:11:37,320 --> 00:11:41,439 Speaker 1: past December, um, you know, the middle of this nasty 189 00:11:41,480 --> 00:11:45,160 Speaker 1: stock market, everyone's talking about an impending recession. I did 190 00:11:45,240 --> 00:11:48,160 Speaker 1: notice that it's snapped back some of the the reading 191 00:11:48,240 --> 00:11:51,760 Speaker 1: snapped back at least pushed the probability of a recession 192 00:11:51,800 --> 00:11:54,480 Speaker 1: a little further into the future. I mean, are are 193 00:11:54,520 --> 00:11:58,280 Speaker 1: the and the other horseman? Volatility has tamed down a 194 00:11:58,360 --> 00:12:00,160 Speaker 1: little bit. So I guess the question is you have 195 00:12:00,280 --> 00:12:02,160 Speaker 1: the have the horseman turned around or are they just 196 00:12:02,200 --> 00:12:06,520 Speaker 1: stopping for lunch somewhere? Do you think? So? It is true, um, 197 00:12:06,679 --> 00:12:10,800 Speaker 1: that the Duke's CFO survey has got predictive ability, and 198 00:12:10,840 --> 00:12:15,719 Speaker 1: it's documented, So it makes sense because these CFOs, they 199 00:12:15,760 --> 00:12:17,560 Speaker 1: know what the spending plans are, they know what the 200 00:12:17,600 --> 00:12:20,880 Speaker 1: hiring plants are in advance. So we've got a lot 201 00:12:20,920 --> 00:12:25,480 Speaker 1: of evidence that shows that UH this indicator is a 202 00:12:25,559 --> 00:12:32,320 Speaker 1: leading indicator of the usual leading indicators. So believe that 203 00:12:32,400 --> 00:12:36,720 Speaker 1: a recession will start by the beginning of two thousand 204 00:12:36,840 --> 00:12:40,360 Speaker 1: twenty one. In the previous survey it was the end 205 00:12:40,440 --> 00:12:45,559 Speaker 1: of two thousand twenty, so the push is one quarter um, 206 00:12:45,720 --> 00:12:49,840 Speaker 1: so that probability is still shockingly high. It needs to 207 00:12:49,880 --> 00:12:53,280 Speaker 1: be taken into account. As for one of the other horsemen, 208 00:12:53,360 --> 00:12:56,960 Speaker 1: volatility is actually a little more subtled and volatility. It 209 00:12:57,040 --> 00:13:01,480 Speaker 1: has to do with uncertainty, which is core related with volatility. 210 00:13:01,559 --> 00:13:07,280 Speaker 1: But nevertheless, if there's uncertainty in the economy, then UH 211 00:13:07,520 --> 00:13:12,360 Speaker 1: managers are are hesitant to make these investments that lead 212 00:13:12,400 --> 00:13:16,840 Speaker 1: to economic growth. So it might be that there's uncertainty, 213 00:13:16,880 --> 00:13:20,520 Speaker 1: but market volatility could be low, So don't be spoofed 214 00:13:20,520 --> 00:13:23,120 Speaker 1: by the lower market volatility. Cameron, I want to come 215 00:13:23,120 --> 00:13:26,520 Speaker 1: to you because as we speak about upper management and 216 00:13:26,559 --> 00:13:29,240 Speaker 1: company executives, one of the best times to hear from 217 00:13:29,280 --> 00:13:32,200 Speaker 1: them is an earning season. So we are now about 218 00:13:32,200 --> 00:13:34,760 Speaker 1: halfway through when it comes to guidance or what the 219 00:13:34,800 --> 00:13:39,360 Speaker 1: executives are actually saying anecdotally about what the macro environment 220 00:13:39,440 --> 00:13:42,400 Speaker 1: looks like. What has been the takeaway so far? Well, 221 00:13:42,440 --> 00:13:44,240 Speaker 1: I think it's been pretty mixed, hasn't it. I mean, 222 00:13:44,280 --> 00:13:48,280 Speaker 1: you're this is more your wheelhouse than than mine. My 223 00:13:48,679 --> 00:13:52,920 Speaker 1: afternoons have I spend them differently than listening to two 224 00:13:52,960 --> 00:13:55,640 Speaker 1: conference calls and whatever? Um? You know. I think what 225 00:13:55,679 --> 00:13:58,920 Speaker 1: we can say is that last year we had earnings 226 00:13:59,080 --> 00:14:03,520 Speaker 1: goose tremend just lee by the corporate tax cut. The 227 00:14:03,520 --> 00:14:06,920 Speaker 1: base effects of that have have waned. The same time, 228 00:14:07,240 --> 00:14:09,760 Speaker 1: growth and the rest of the world maybe stabilizing, but 229 00:14:09,800 --> 00:14:12,400 Speaker 1: I think you can hardly call it robust. So a 230 00:14:12,400 --> 00:14:15,240 Speaker 1: lot of the comparisons are going to continue to look 231 00:14:15,720 --> 00:14:19,800 Speaker 1: pretty tough. At the same time, it certainly seems as 232 00:14:19,840 --> 00:14:24,040 Speaker 1: if the economy is in need of going through an 233 00:14:24,040 --> 00:14:25,920 Speaker 1: inventory cycle in the in the sense that we've had 234 00:14:25,960 --> 00:14:29,720 Speaker 1: inventories built up over the last quarter or two. I 235 00:14:29,760 --> 00:14:31,680 Speaker 1: think we're now at the point where those are gonna 236 00:14:31,680 --> 00:14:34,400 Speaker 1: need to be drawn down. We've seen some evidence of 237 00:14:34,440 --> 00:14:38,720 Speaker 1: this in Wednesday's Manufacturing I s M survey. So that 238 00:14:38,840 --> 00:14:42,760 Speaker 1: all elos being equal, should UH lead to a deceleration 239 00:14:43,280 --> 00:14:46,120 Speaker 1: in activity. And the problem with an inventory cycle is 240 00:14:46,120 --> 00:14:48,120 Speaker 1: if you have an inventory draw down at the same 241 00:14:48,120 --> 00:14:50,720 Speaker 1: time as you have another shock to the economy, that's 242 00:14:50,720 --> 00:14:54,040 Speaker 1: the sort of thing that can spur UM a bit 243 00:14:54,040 --> 00:14:57,240 Speaker 1: of a recession. Kim Harve you uh oh, do you 244 00:14:57,320 --> 00:14:59,880 Speaker 1: have something to say? Yeah, I just want to emphasize 245 00:15:00,040 --> 00:15:03,160 Speaker 1: that I think that too much way is put on 246 00:15:03,200 --> 00:15:07,800 Speaker 1: earnings UM. Earnings are what happened in the past, and 247 00:15:08,320 --> 00:15:12,320 Speaker 1: earnings are also managed, heavily managed, so we need to 248 00:15:12,320 --> 00:15:18,880 Speaker 1: be so we need to be careful about that. And 249 00:15:19,280 --> 00:15:23,520 Speaker 1: you know, guidance is more important. But again I prefer 250 00:15:23,880 --> 00:15:28,440 Speaker 1: something where where people are making forecasts of the fundamental stuff, 251 00:15:28,640 --> 00:15:32,480 Speaker 1: the investment they're actually gonna make, or they're hiring plans, 252 00:15:32,840 --> 00:15:37,040 Speaker 1: rather than these numbers that are unreliable. And you've done 253 00:15:37,040 --> 00:15:40,000 Speaker 1: some research on earnings misrepresentation, I believe yes, I do 254 00:15:40,120 --> 00:15:43,680 Speaker 1: have a number of papers UM, one called the Misrepresentation 255 00:15:43,720 --> 00:16:04,040 Speaker 1: of Earnings. I would like to shift gears, Cam Harvey 256 00:16:04,120 --> 00:16:07,080 Speaker 1: to some other really fascinating research that you've been involved 257 00:16:07,120 --> 00:16:10,600 Speaker 1: with UH lately, and that's factor investing along with Rob 258 00:16:10,680 --> 00:16:14,280 Speaker 1: or Not at research affiliates. Now this latest paper you 259 00:16:14,280 --> 00:16:17,800 Speaker 1: guys have is pretty critical of factor investing as a whole, 260 00:16:17,840 --> 00:16:21,440 Speaker 1: which is kind of surprising coming from you know, research affiliates, 261 00:16:21,480 --> 00:16:24,800 Speaker 1: one of the pioneers of of factory investing. But I 262 00:16:24,840 --> 00:16:28,640 Speaker 1: guess it gets back to that idea of a Frankenstein monster. 263 00:16:28,720 --> 00:16:30,680 Speaker 1: You know that you know are not an other sort 264 00:16:30,720 --> 00:16:33,360 Speaker 1: of pioneer this. But it's just gone into a million 265 00:16:33,360 --> 00:16:37,240 Speaker 1: different directions. There's hundreds of factors. Now what it's Sarah, 266 00:16:37,240 --> 00:16:40,480 Speaker 1: I think you said seven hundred and fiftys that can 267 00:16:40,480 --> 00:16:42,880 Speaker 1: be described as smart beta, which in e T f 268 00:16:42,960 --> 00:16:45,440 Speaker 1: Land is code for factors. So I counted not just 269 00:16:45,520 --> 00:16:49,800 Speaker 1: e T s, but mutual funds and one point three trillion. 270 00:16:50,160 --> 00:16:55,360 Speaker 1: So it is basically there were some pioneering papers, but 271 00:16:55,440 --> 00:16:58,560 Speaker 1: now it's how to control just in the academic arena, 272 00:16:59,000 --> 00:17:03,400 Speaker 1: a document for four hundred published factors. These factors are 273 00:17:03,440 --> 00:17:06,400 Speaker 1: designed to beat the market, so it's it is a 274 00:17:06,520 --> 00:17:11,680 Speaker 1: data mining expedition to find stuff that supposedly beats the market. 275 00:17:12,080 --> 00:17:15,960 Speaker 1: Yet when you actually go to live trading, very disappointing 276 00:17:16,720 --> 00:17:19,560 Speaker 1: for one reason because of the actual cost of making 277 00:17:19,600 --> 00:17:23,040 Speaker 1: all these transactions to balance these So in this paper 278 00:17:23,680 --> 00:17:28,760 Speaker 1: we talked about three blunders that are made by factor investors. 279 00:17:28,800 --> 00:17:35,000 Speaker 1: So number one is exaggerated expectations, and the exaggeration is 280 00:17:35,080 --> 00:17:39,479 Speaker 1: because the expectations are based upon these back tests. So 281 00:17:39,520 --> 00:17:41,960 Speaker 1: if I said I had this great factor where I 282 00:17:41,960 --> 00:17:45,479 Speaker 1: look at the last twenty years and pick one hundred 283 00:17:45,520 --> 00:17:49,680 Speaker 1: stocks that did the best and then equally wait them, 284 00:17:49,760 --> 00:17:52,560 Speaker 1: would you have any confidence that that is going to 285 00:17:52,600 --> 00:17:57,240 Speaker 1: obtain in the future. Probably not. So that's effectively what's happened. 286 00:17:57,359 --> 00:18:01,680 Speaker 1: There's been extreme overfitting and people base their expectations upon 287 00:18:02,840 --> 00:18:07,400 Speaker 1: this back test and they're disappointed when you go forward, 288 00:18:07,560 --> 00:18:09,879 Speaker 1: and in fact that the research talks about how once 289 00:18:10,040 --> 00:18:13,600 Speaker 1: the factor is published in a in a journal, the 290 00:18:14,080 --> 00:18:16,000 Speaker 1: out performance tends to go away. So I guess the 291 00:18:16,359 --> 00:18:18,720 Speaker 1: secret is if you discover one never published it in 292 00:18:18,720 --> 00:18:22,520 Speaker 1: a journal, is that I guess so. So the haircut 293 00:18:22,560 --> 00:18:27,560 Speaker 1: is massive after publication. It's also massive after let's a 294 00:18:27,680 --> 00:18:32,199 Speaker 1: launch of of ETF. So so those expectations are just 295 00:18:32,320 --> 00:18:37,000 Speaker 1: playing fault. And it's even worse than this because things 296 00:18:37,040 --> 00:18:43,120 Speaker 1: like crowding, not taking into account. The academic papers, it's surprising, 297 00:18:43,240 --> 00:18:48,399 Speaker 1: but they're published without any reference to transactions costs. Cameron, 298 00:18:48,440 --> 00:18:50,520 Speaker 1: I have to come to you and put your trading 299 00:18:50,560 --> 00:18:55,639 Speaker 1: hat back on. Do you ever consider factors when you 300 00:18:55,680 --> 00:18:58,320 Speaker 1: were making trades back in my day or making trades now? 301 00:18:59,160 --> 00:19:02,440 Speaker 1: The short answer is yes, but the somewhat nuanced ants 302 00:19:02,520 --> 00:19:06,320 Speaker 1: or is not as they are typically articulated in the market. Now. 303 00:19:06,359 --> 00:19:08,840 Speaker 1: My understanding of most of these factor strategies as they 304 00:19:08,880 --> 00:19:13,480 Speaker 1: take a population and say, well, the top that meet X, Y, 305 00:19:13,480 --> 00:19:17,040 Speaker 1: and Z criteria, I'm going to implement in my strategy 306 00:19:17,080 --> 00:19:20,919 Speaker 1: with no reference to whether the absolute attraction of the 307 00:19:21,080 --> 00:19:24,920 Speaker 1: of these criteria, so of the of these securities. So 308 00:19:25,359 --> 00:19:28,639 Speaker 1: I actually do uh, in my personal finances use a 309 00:19:28,680 --> 00:19:31,400 Speaker 1: few basic criteria, but they're based on absolute and there's 310 00:19:31,440 --> 00:19:33,359 Speaker 1: there's actually quite a bit of insight to be gleaned 311 00:19:33,440 --> 00:19:36,320 Speaker 1: because you can observe how many stocks in the universe 312 00:19:36,880 --> 00:19:39,920 Speaker 1: actually meet an absolute level of criteria and it actually 313 00:19:39,960 --> 00:19:43,439 Speaker 1: gives you, I think, some insight into the ex anti 314 00:19:44,080 --> 00:19:48,320 Speaker 1: level of attractiveness of returns. Moving forward, Cam Harvey, Uh, 315 00:19:48,440 --> 00:19:50,600 Speaker 1: you know I mentioned a few colleagues that you were 316 00:19:50,680 --> 00:19:52,560 Speaker 1: coming on the podcast, So I've got about a thousand 317 00:19:52,680 --> 00:19:55,399 Speaker 1: questions for you, for different people working on different stories, 318 00:19:55,440 --> 00:19:58,080 Speaker 1: but I'm gonna limit it to just one very interesting 319 00:19:58,080 --> 00:20:02,439 Speaker 1: You've done some research on political risk, and it's obviously 320 00:20:02,480 --> 00:20:05,560 Speaker 1: a hot topic now with Brexit, with the you know, 321 00:20:05,600 --> 00:20:09,520 Speaker 1: Donald Trump's surprise election. I'm curious, how does one go 322 00:20:09,640 --> 00:20:13,119 Speaker 1: about quantifying political risk? Cannot be done and sort of 323 00:20:13,640 --> 00:20:16,239 Speaker 1: just take the temperature of political risk, uh in the 324 00:20:16,280 --> 00:20:18,240 Speaker 1: markets for us right now? What are you sort of 325 00:20:18,280 --> 00:20:22,399 Speaker 1: looking at and thinking about in that topic? So we 326 00:20:22,520 --> 00:20:25,520 Speaker 1: come back to one of the horsemen. So remember I 327 00:20:25,560 --> 00:20:29,000 Speaker 1: said it was uncertainty. It wasn't just market volatility. So 328 00:20:29,080 --> 00:20:33,280 Speaker 1: what are the fundamental drivers of uncertainty in the market? 329 00:20:33,880 --> 00:20:38,879 Speaker 1: And again sometimes you see high volatility, sometimes lower. But 330 00:20:39,200 --> 00:20:43,639 Speaker 1: the way to measure political risk, UM, there's many different 331 00:20:43,680 --> 00:20:49,119 Speaker 1: ways to do it. So one way is basically asking experts, 332 00:20:49,200 --> 00:20:53,240 Speaker 1: and there's many different ratings, so we've got different organizations 333 00:20:53,240 --> 00:20:57,320 Speaker 1: to provide ratings for countries about that. UM. The sort 334 00:20:57,320 --> 00:21:00,919 Speaker 1: of research that I've been involved in that's not actually 335 00:21:00,920 --> 00:21:06,800 Speaker 1: published yet UM uses natural language processing tools to scrape 336 00:21:06,840 --> 00:21:11,679 Speaker 1: through enormous amount of data within each country, and not 337 00:21:11,840 --> 00:21:17,119 Speaker 1: just English sort of data, but many different languages and 338 00:21:17,560 --> 00:21:23,400 Speaker 1: essentially developing uncertainty measures that reflect what's actually going on 339 00:21:24,000 --> 00:21:27,160 Speaker 1: in that country at that time. Using all of the data. 340 00:21:27,280 --> 00:21:30,000 Speaker 1: So it is a great application of machine learning. And 341 00:21:30,000 --> 00:21:33,840 Speaker 1: we're just seeing some some of the early applications of 342 00:21:33,880 --> 00:21:36,200 Speaker 1: machine learning in finance, and this is a really good one. 343 00:21:36,280 --> 00:21:38,719 Speaker 1: So this sounds like it goes beyond the Baker Bloom 344 00:21:38,880 --> 00:21:44,520 Speaker 1: data's type of yes. So um, that is uh, this 345 00:21:44,680 --> 00:21:49,359 Speaker 1: new sentiment, it's new sentiment. They also have like volatility measures. 346 00:21:49,400 --> 00:21:52,719 Speaker 1: This is much much deeper and that we use uh 347 00:21:53,160 --> 00:21:55,560 Speaker 1: different languages. I also have to ask you, and I 348 00:21:55,600 --> 00:21:57,879 Speaker 1: know it might be difficult to quantify, but when we 349 00:21:57,920 --> 00:22:00,359 Speaker 1: think about crowding risks when it comes to fact so 350 00:22:00,400 --> 00:22:04,280 Speaker 1: a lot of investors all chasing the same characteristics within 351 00:22:04,400 --> 00:22:07,040 Speaker 1: stocks or whatever it may be. Is it possible to 352 00:22:07,119 --> 00:22:10,320 Speaker 1: look at one area of the market right now and say, wow, 353 00:22:10,520 --> 00:22:14,040 Speaker 1: that could be a crowded, risky area to be in. Yeah. 354 00:22:14,160 --> 00:22:17,520 Speaker 1: So again, when you do factor investing, you need to 355 00:22:17,520 --> 00:22:21,159 Speaker 1: be very careful. So I talked about essentially a lot 356 00:22:21,200 --> 00:22:24,080 Speaker 1: of these factors are are fake, so they were just 357 00:22:24,200 --> 00:22:27,639 Speaker 1: purely data mind. So there's another set of factors that 358 00:22:27,640 --> 00:22:30,000 Speaker 1: are not fake, they're real. So the real source of 359 00:22:30,280 --> 00:22:33,480 Speaker 1: premium what are they? But they generally have a more 360 00:22:33,520 --> 00:22:39,320 Speaker 1: economic reason behind them. So if you've got something, for example, 361 00:22:39,600 --> 00:22:44,399 Speaker 1: that's got a big negative tail, then there's gonna be 362 00:22:44,400 --> 00:22:46,919 Speaker 1: a reward for taking on that kind of risk. But 363 00:22:47,040 --> 00:22:50,080 Speaker 1: people don't like downside risk, so that's not going to 364 00:22:50,200 --> 00:22:53,920 Speaker 1: go away in the long term. But if there's enough 365 00:22:53,960 --> 00:22:58,760 Speaker 1: people reaching for yield, so to speak, driving the prices 366 00:22:58,880 --> 00:23:03,240 Speaker 1: up and you expect returned down, then it gets crowded 367 00:23:03,920 --> 00:23:06,520 Speaker 1: and you expect to return becomes very low. So you 368 00:23:06,600 --> 00:23:11,040 Speaker 1: need to take that into account. And it's actually reasonably straightforward, 369 00:23:11,040 --> 00:23:14,280 Speaker 1: and it's kind of linked to another concept that um 370 00:23:14,680 --> 00:23:17,639 Speaker 1: rob Arnon is pioneered in this idea of revaluation. So 371 00:23:17,680 --> 00:23:20,359 Speaker 1: you look at a factor, and these factors are not independent, 372 00:23:20,400 --> 00:23:23,480 Speaker 1: they're a linked together. So you might look at a 373 00:23:23,680 --> 00:23:27,439 Speaker 1: historical back test of a particular factor. But at the 374 00:23:27,520 --> 00:23:34,080 Speaker 1: beginning of the sample, the stocks were relatively cheap in 375 00:23:34,160 --> 00:23:37,199 Speaker 1: terms of value. At the end of the sample they're expensive. 376 00:23:37,720 --> 00:23:41,159 Speaker 1: So the factor looks like a great trading strategy that 377 00:23:41,200 --> 00:23:45,680 Speaker 1: beats the market, but it's nothing other than value. And 378 00:23:45,800 --> 00:23:49,000 Speaker 1: given that it's expense of right now, if you go 379 00:23:49,160 --> 00:23:52,000 Speaker 1: in and put your money into that factor, you're gonna 380 00:23:52,000 --> 00:23:55,560 Speaker 1: be disappointed because the expected returns are low and is 381 00:23:55,600 --> 00:23:58,240 Speaker 1: the crowding sort of the flip side of the coin 382 00:23:58,440 --> 00:24:00,879 Speaker 1: of what you also talk about in research, which is 383 00:24:00,920 --> 00:24:03,480 Speaker 1: these bigger than expected draw downs. You know, to me, 384 00:24:03,560 --> 00:24:06,320 Speaker 1: it's like when you got Zion Williamson square and all 385 00:24:06,320 --> 00:24:08,160 Speaker 1: your points and all of a sudden shoe falls off. 386 00:24:08,160 --> 00:24:10,480 Speaker 1: You know, is it are those sort of two I know, 387 00:24:10,520 --> 00:24:15,800 Speaker 1: I'm but are you know? Are those two related? The 388 00:24:15,840 --> 00:24:19,480 Speaker 1: crowding and the the sort of oversized draw downs in 389 00:24:19,520 --> 00:24:22,960 Speaker 1: many factors. So there is a link. Uh, It's a 390 00:24:23,000 --> 00:24:28,639 Speaker 1: subtle link. But it is remarkable to me that people 391 00:24:28,720 --> 00:24:34,960 Speaker 1: believe that these factor returns are so normally distributed that 392 00:24:35,040 --> 00:24:38,440 Speaker 1: they don't have they don't believe these factors have these 393 00:24:38,520 --> 00:24:43,400 Speaker 1: giant tails and and it leads to this naive view 394 00:24:43,840 --> 00:24:47,960 Speaker 1: of risk management. And we document in our paper some 395 00:24:48,040 --> 00:24:52,639 Speaker 1: of these factors, if they were normally distributed bell curve 396 00:24:53,320 --> 00:24:56,520 Speaker 1: that some of these realizations are so unlikely that that 397 00:24:56,560 --> 00:25:01,600 Speaker 1: what happened once every four point one trillion years. So 398 00:25:02,000 --> 00:25:05,680 Speaker 1: these factors have tails that needs to be taken into account. 399 00:25:05,880 --> 00:25:09,919 Speaker 1: And it's also important to realize that, uh, if you 400 00:25:09,920 --> 00:25:13,680 Speaker 1: have a portfolio factors, often people think, well, we can 401 00:25:13,720 --> 00:25:17,560 Speaker 1: diversify that tail risk away by having a portfolio. Well, 402 00:25:17,560 --> 00:25:20,640 Speaker 1: that's totally wrong. So we show in our paper even 403 00:25:20,640 --> 00:25:23,879 Speaker 1: if you put a portfolio together, it's got giant downside 404 00:25:23,880 --> 00:25:29,400 Speaker 1: brisk and again it reflects a naive risk management mentality 405 00:25:29,760 --> 00:25:33,359 Speaker 1: amongst factor investors. At one point, for trillion years is 406 00:25:33,359 --> 00:25:36,120 Speaker 1: approximately my retirement horizon too. So I'm glad, I'm glad 407 00:25:36,119 --> 00:25:40,359 Speaker 1: you brought that up. Optimistic, Yes, optimistically, Sarah's that time 408 00:25:42,040 --> 00:25:45,560 Speaker 1: is the craziest thing I ever saw in markets parentheses 409 00:25:45,800 --> 00:25:48,560 Speaker 1: this week? All right, Cameron, what's the craziest thing you 410 00:25:48,560 --> 00:25:53,320 Speaker 1: saw in markets? Ever? This week? Ever? This week the 411 00:25:53,640 --> 00:25:57,480 Speaker 1: Philadelphia semi Conductor Index make a new dingdong hi when 412 00:25:57,480 --> 00:26:04,280 Speaker 1: we're semi conductor de ram prizes are unbelievable. So I'm 413 00:26:04,280 --> 00:26:06,520 Speaker 1: going to bring you up Pure One Imports, you know, 414 00:26:06,560 --> 00:26:10,520 Speaker 1: the home to Core furniture company. So just last week 415 00:26:10,680 --> 00:26:14,719 Speaker 1: SMP actually downgraded them triple ce not looking too hot. 416 00:26:14,760 --> 00:26:17,200 Speaker 1: But in the past five days or in the past 417 00:26:17,440 --> 00:26:19,720 Speaker 1: week or so, the company is up a hundred percent. 418 00:26:19,760 --> 00:26:21,560 Speaker 1: It's doubled in price. But you also have to take 419 00:26:21,600 --> 00:26:25,200 Speaker 1: onto account. I mean it's about fifty cents, so it's 420 00:26:25,200 --> 00:26:28,840 Speaker 1: easy to double per Peer point five Imports right now, Kam, 421 00:26:28,880 --> 00:26:31,680 Speaker 1: you got something for us. I got something totally different. 422 00:26:32,160 --> 00:26:35,720 Speaker 1: So what really shocked me this week was the launch 423 00:26:36,040 --> 00:26:42,600 Speaker 1: of Crypto Kicks by Nike. So Nike is going into 424 00:26:42,640 --> 00:26:47,600 Speaker 1: the cryptocurrency business, and it's very interesting. Their ambitions are 425 00:26:47,640 --> 00:26:50,639 Speaker 1: not to use it for apparel or footwear, but for 426 00:26:50,720 --> 00:26:53,360 Speaker 1: the trading of digital assets. So they're going to leverage 427 00:26:53,400 --> 00:26:57,760 Speaker 1: their brand to do something completely different. What are crypto kicks, 428 00:26:57,840 --> 00:27:02,600 Speaker 1: I have to ask. It is the currency, So yet 429 00:27:02,640 --> 00:27:05,359 Speaker 1: another example of crypto being a solution in search of 430 00:27:05,400 --> 00:27:07,760 Speaker 1: a problem. All Right, we'll see. I'll tell you what. 431 00:27:08,600 --> 00:27:12,080 Speaker 1: Usually I I word myself the award for craziest things 432 00:27:12,119 --> 00:27:13,760 Speaker 1: I've ever seen in markets this week, but I think 433 00:27:13,760 --> 00:27:16,720 Speaker 1: I'm gonna give it to you, Cam Hard, but I'll 434 00:27:16,760 --> 00:27:19,119 Speaker 1: give you mine anyway. Okay, Sarah. As you know, my 435 00:27:19,200 --> 00:27:22,840 Speaker 1: favorite personal market is the predictions market, and with a 436 00:27:22,880 --> 00:27:27,000 Speaker 1: million people entering the race for president in there's some 437 00:27:27,240 --> 00:27:30,720 Speaker 1: really fun betting action going on in the predictions market. 438 00:27:31,080 --> 00:27:33,520 Speaker 1: I'm gonna name you five celebrities and everyone tell me 439 00:27:33,560 --> 00:27:37,720 Speaker 1: who they think betters are placing the best chance of 440 00:27:37,760 --> 00:27:44,160 Speaker 1: actually running in Alright, Oprah Winfrey, Mark Zuckerberg, the Rock, 441 00:27:45,200 --> 00:27:48,160 Speaker 1: Mark Cuban or Kanye West. Who do you think people 442 00:27:48,160 --> 00:27:50,080 Speaker 1: are putting the best probability on a ventor in the ring? 443 00:27:52,119 --> 00:27:53,560 Speaker 1: I want to say, oh broh, but I'll go with 444 00:27:53,600 --> 00:27:58,760 Speaker 1: the Rock. Kanye Kanie is pretty good. He's second at chance, 445 00:27:59,000 --> 00:28:02,640 Speaker 1: Mark Cuban actually highest, uh, followed by Oprah at seven 446 00:28:03,160 --> 00:28:05,840 Speaker 1: and Zuckerberg and the Rock at I'm really rooting for 447 00:28:05,880 --> 00:28:07,840 Speaker 1: the Rock. I'm open. I thought Mark Cuban would be 448 00:28:07,840 --> 00:28:10,560 Speaker 1: the obvious what it couldn't be the who would vote 449 00:28:10,560 --> 00:28:16,120 Speaker 1: for Zuck and silence? Well, that's all the time after 450 00:28:16,200 --> 00:28:19,800 Speaker 1: this week. That is all the time this week actually, 451 00:28:20,200 --> 00:28:23,040 Speaker 1: Kevin Christ, Cam Harvey, thank you so much for coming 452 00:28:23,040 --> 00:28:32,879 Speaker 1: in and joining us today What Goes Up. We'll be 453 00:28:32,920 --> 00:28:35,800 Speaker 1: back next week. Until then, you can find us on 454 00:28:35,840 --> 00:28:39,320 Speaker 1: the Bloomberg Terminal website and app or wherever you get 455 00:28:39,360 --> 00:28:41,680 Speaker 1: your podcasts. We'd love it if you took the time 456 00:28:41,720 --> 00:28:44,120 Speaker 1: to rate and review the show so more listeners can 457 00:28:44,160 --> 00:28:47,320 Speaker 1: find us, And you can find us on Twitter. Follow 458 00:28:47,400 --> 00:28:51,360 Speaker 1: me at at Sarah Pontzack Mike is at re Anonymous, 459 00:28:51,360 --> 00:28:54,560 Speaker 1: our guest Cam Harvey is at Cam Harvey, and Cameron 460 00:28:54,680 --> 00:28:58,000 Speaker 1: Christ is at Fifth Rule. What Goes Up is produced 461 00:28:58,000 --> 00:29:01,800 Speaker 1: by topurh foreheads ahead of Boomer podcast is francesco Leavie. 462 00:29:02,080 --> 00:29:03,840 Speaker 1: Thanks for listening, See you next time.