1 00:00:01,360 --> 00:00:04,120 Speaker 1: Welcome to the Bloomberg Markets Podcast. I'm Paul Sweeney. Along 2 00:00:04,120 --> 00:00:06,200 Speaker 1: with my co host of Bonnie Quinn. Every business day 3 00:00:06,240 --> 00:00:10,360 Speaker 1: we bring you interviews from CEOs, market pros, and Bloomberg experts, 4 00:00:10,400 --> 00:00:13,600 Speaker 1: along with essential market moving news. Find the Bloomberg Markets 5 00:00:13,600 --> 00:00:17,000 Speaker 1: Podcast on Apple Podcasts or wherever you listen to podcasts, 6 00:00:17,000 --> 00:00:20,799 Speaker 1: and on Bloomberg dot com. With President elected Biden just 7 00:00:20,960 --> 00:00:24,720 Speaker 1: weeks away from entering the White House, markets are discounting 8 00:00:24,760 --> 00:00:27,640 Speaker 1: what it means for financial assets, particularly from a tax 9 00:00:27,760 --> 00:00:30,639 Speaker 1: adjusted basis, and that is obviously very important for the 10 00:00:30,720 --> 00:00:33,559 Speaker 1: wealthy and ultra wealthy in the market to get a 11 00:00:33,560 --> 00:00:36,600 Speaker 1: sense of kind of what they're thinking. We're fortunate to 12 00:00:36,640 --> 00:00:40,040 Speaker 1: welcome back to the show Michael Son and Felt, chairman 13 00:00:40,120 --> 00:00:44,040 Speaker 1: and founder of Tiger One. Tiger twenty one is a 14 00:00:44,080 --> 00:00:49,360 Speaker 1: peer membership organization for high net worth creators and preservers 15 00:00:49,479 --> 00:00:53,040 Speaker 1: of wealth, helping to navigate through the challenges and opportunities 16 00:00:53,080 --> 00:00:55,920 Speaker 1: that success that success creates. Michael, thanks so much for 17 00:00:56,000 --> 00:00:59,120 Speaker 1: joining us again here. Uh you know, we're now back 18 00:00:59,160 --> 00:01:02,320 Speaker 1: to having a Democrat in the White House. One of 19 00:01:02,360 --> 00:01:06,039 Speaker 1: the assumptions is that taxes maybe going up. What are 20 00:01:06,040 --> 00:01:09,039 Speaker 1: some of your members Uh, what are they reacting? What 21 00:01:09,080 --> 00:01:12,959 Speaker 1: are they saying about a Biden presidency. Well, first of all, 22 00:01:13,000 --> 00:01:15,120 Speaker 1: thanks for having me back. It's great to be with you. 23 00:01:15,959 --> 00:01:21,080 Speaker 1: Our members are very focused on taxes historically, though what 24 00:01:21,160 --> 00:01:23,600 Speaker 1: they said is it was more about whether the money 25 00:01:23,720 --> 00:01:27,240 Speaker 1: was being well spent by the government, uh, and less 26 00:01:27,280 --> 00:01:30,800 Speaker 1: about the absolute level of taxes. We just did a 27 00:01:30,800 --> 00:01:36,840 Speaker 1: recent survey across our membership and hum, a difference of 28 00:01:36,959 --> 00:01:39,680 Speaker 1: as much as five or ten as little as five 29 00:01:39,760 --> 00:01:42,800 Speaker 1: or ten percent in the tax rate would get some 30 00:01:42,880 --> 00:01:46,760 Speaker 1: of our members to move to lower tax states, a 31 00:01:46,800 --> 00:01:49,920 Speaker 1: lot of people moving out of California and New York. 32 00:01:50,000 --> 00:01:52,760 Speaker 1: So it's really a warning that there are limits on 33 00:01:52,800 --> 00:01:56,680 Speaker 1: a state basis and on the federal basis. Most of 34 00:01:56,720 --> 00:01:59,880 Speaker 1: our members think that if you have a divided government, 35 00:02:00,080 --> 00:02:05,559 Speaker 1: if the Republicans maintained control of the Senate, the most 36 00:02:05,600 --> 00:02:10,760 Speaker 1: sweeping tax changes are a little less likely. Why do 37 00:02:10,840 --> 00:02:14,880 Speaker 1: they a certain point stop trusting government so they'll play 38 00:02:14,880 --> 00:02:18,880 Speaker 1: a certain amount of tax But then suddenly they've decided 39 00:02:18,880 --> 00:02:21,160 Speaker 1: that what the government was on the rest of the 40 00:02:21,200 --> 00:02:23,760 Speaker 1: extra tax on is not what they would want the 41 00:02:23,760 --> 00:02:26,840 Speaker 1: government to spend it on. Is that is? So there's 42 00:02:26,840 --> 00:02:31,960 Speaker 1: obviously a wide range of opinion among our membership. Most 43 00:02:32,000 --> 00:02:36,080 Speaker 1: of our members come from very middle and lower middle 44 00:02:36,120 --> 00:02:40,239 Speaker 1: class backgrounds and have started with very little and bootstrapped 45 00:02:40,280 --> 00:02:45,120 Speaker 1: into great success. These are some of the great entrepreneurs, 46 00:02:45,160 --> 00:02:50,320 Speaker 1: so there is a sense of individualism and wanting to 47 00:02:50,919 --> 00:02:55,320 Speaker 1: create incentives. So the topic that comes up the most is, 48 00:02:55,400 --> 00:02:58,680 Speaker 1: of course, a government needs to pave the roads and 49 00:02:58,760 --> 00:03:03,800 Speaker 1: have a military. But when taxes start becoming instruments of 50 00:03:03,919 --> 00:03:08,680 Speaker 1: policy of wealth redistribution, some of our members think that 51 00:03:08,760 --> 00:03:12,760 Speaker 1: goes beyond what prudent tax policy should be. But others 52 00:03:13,040 --> 00:03:16,919 Speaker 1: are frankly quite aware of the inequality in our society 53 00:03:17,400 --> 00:03:22,200 Speaker 1: and are struggling to think about how the dispersion of 54 00:03:22,280 --> 00:03:28,720 Speaker 1: wealth the inequality can best be addressed. So, Michael, given 55 00:03:28,960 --> 00:03:33,200 Speaker 1: the pandemic uncertainty, given the political uncertainty which now seems 56 00:03:33,240 --> 00:03:36,760 Speaker 1: to be more certain, how have your members been preparing 57 00:03:36,880 --> 00:03:43,600 Speaker 1: their portfolios for beyond sure um. Earlier in the year, 58 00:03:44,240 --> 00:03:49,720 Speaker 1: we had a historic rise in cash. Over the last decade, 59 00:03:50,080 --> 00:03:53,800 Speaker 1: our members have generally held about twelve percent in cash, 60 00:03:54,400 --> 00:03:58,960 Speaker 1: never more than percent and never less than eleven percent, 61 00:03:59,080 --> 00:04:01,040 Speaker 1: And all of a sudden and in the first quarter 62 00:04:01,680 --> 00:04:06,080 Speaker 1: of this year, members rose cash two levels close to 63 00:04:07,040 --> 00:04:11,800 Speaker 1: high teams. It was not only the largest shift, but 64 00:04:11,960 --> 00:04:16,080 Speaker 1: the fastest shift we ever recorded. So once the pandemic hit, 65 00:04:16,480 --> 00:04:19,719 Speaker 1: people were really battening down the hatches. And when you 66 00:04:19,800 --> 00:04:24,920 Speaker 1: have complicated portfolios, making sure you have enough cash becomes 67 00:04:25,000 --> 00:04:28,840 Speaker 1: far more important than what your returns are because you 68 00:04:28,880 --> 00:04:31,600 Speaker 1: don't want to default on obligations and you don't want 69 00:04:31,600 --> 00:04:35,960 Speaker 1: to miss opportunities. UM. You know, as the year has progressed, obviously, 70 00:04:36,000 --> 00:04:40,760 Speaker 1: the markets have been extraordinary and our members have recorded 71 00:04:41,440 --> 00:04:45,160 Speaker 1: really amazing returns and that was before November. November returns 72 00:04:45,160 --> 00:04:48,119 Speaker 1: are coming in. Uh. Just this morning, I was looking 73 00:04:48,160 --> 00:04:51,400 Speaker 1: at the first three hedge funds to report in my 74 00:04:51,440 --> 00:04:55,560 Speaker 1: own portfolio with ten plus percent returns. UH. So our 75 00:04:55,600 --> 00:04:59,839 Speaker 1: members are reporting over ten percent returns for the year. 76 00:05:00,040 --> 00:05:02,520 Speaker 1: My guess is they'll end up at mid teens depending 77 00:05:02,560 --> 00:05:07,720 Speaker 1: on December, and they're back to technology and healthcare on 78 00:05:07,800 --> 00:05:13,480 Speaker 1: the public sector side, technology particularly as the long term 79 00:05:13,560 --> 00:05:16,640 Speaker 1: best bet UM. I think we've talked about in prior 80 00:05:16,680 --> 00:05:20,560 Speaker 1: shows that our members have had a bias towards technology 81 00:05:20,640 --> 00:05:22,800 Speaker 1: for the last couple of years as the number one 82 00:05:23,360 --> 00:05:29,960 Speaker 1: area of focus intensified with alternative UM UH with AI 83 00:05:30,120 --> 00:05:33,719 Speaker 1: artificial intelligence, and that was that turned out to be 84 00:05:33,760 --> 00:05:38,159 Speaker 1: an amazing bet. Our single largest stock has historically been Apple, 85 00:05:38,240 --> 00:05:42,039 Speaker 1: but all of the fangs have been favorites among our members. 86 00:05:42,080 --> 00:05:45,160 Speaker 1: And what a year they've had, real quick Michael, But 87 00:05:45,400 --> 00:05:48,880 Speaker 1: what was the hard asset of choice for people deploying 88 00:05:48,920 --> 00:05:50,559 Speaker 1: some of that cash that they raised at the beginning 89 00:05:50,560 --> 00:05:54,559 Speaker 1: of year. Uh So, you know, real estate is still 90 00:05:54,680 --> 00:05:58,800 Speaker 1: the largest asset class, but there's no asset class that's 91 00:05:58,800 --> 00:06:03,040 Speaker 1: had more turmoil within real estate. On the industrial side, 92 00:06:03,440 --> 00:06:07,320 Speaker 1: it's just been on fire. The entire shift to online 93 00:06:07,400 --> 00:06:11,559 Speaker 1: sales has created needs for warehousing from which all those 94 00:06:11,640 --> 00:06:16,800 Speaker 1: online purchases get delivered. So that's been the top page. Well, 95 00:06:16,800 --> 00:06:18,520 Speaker 1: it is funny you should mention that because we just 96 00:06:18,600 --> 00:06:20,680 Speaker 1: had a headline costs in the Bloomberg that kk R 97 00:06:20,800 --> 00:06:24,000 Speaker 1: is nearing a deal for a portfolio of US warehouses. 98 00:06:24,120 --> 00:06:26,960 Speaker 1: It would be an eight hundred million dollar deal. Michael. 99 00:06:27,000 --> 00:06:28,880 Speaker 1: We're out of time, but thank you for joining. Always 100 00:06:28,880 --> 00:06:32,720 Speaker 1: a pleasure. Michael Sonontelt is chairman and founder of Tiger 101 00:06:32,839 --> 00:06:35,520 Speaker 1: twenty one and as a group, it has seventies seven 102 00:06:35,560 --> 00:06:38,359 Speaker 1: billion dollars in assets under management. Taking care of the 103 00:06:38,400 --> 00:06:42,400 Speaker 1: old toy, wealthy and really just chatting among one another 104 00:06:42,440 --> 00:06:46,359 Speaker 1: about how things should get done on on all sides 105 00:06:46,520 --> 00:06:52,000 Speaker 1: of the table. So we thank Michael very much. Let's 106 00:06:52,000 --> 00:06:53,880 Speaker 1: bring in somebody who was listening to that interview now 107 00:06:53,880 --> 00:06:56,320 Speaker 1: and who knows all about this, Sam Fazali, senior pharmaceutical 108 00:06:56,320 --> 00:06:59,280 Speaker 1: analysts for Bloomberg Intelligence. And Sam, we were listening to 109 00:06:59,320 --> 00:07:03,120 Speaker 1: the BioNTech guests who talked about about this wonderful news 110 00:07:03,200 --> 00:07:07,520 Speaker 1: that Britain's will start getting shot. Why Britain first? This 111 00:07:07,720 --> 00:07:10,600 Speaker 1: this stac scene was made in the US and in Germany, 112 00:07:10,600 --> 00:07:13,320 Speaker 1: and what it's great that anybody is getting it. This 113 00:07:13,400 --> 00:07:14,960 Speaker 1: is this is the beginning of the argument. You know, 114 00:07:15,000 --> 00:07:19,560 Speaker 1: who goes first? Yeah, so, um, I mean, I mean 115 00:07:19,960 --> 00:07:23,600 Speaker 1: the speed with which regulators wanting to get to look 116 00:07:23,640 --> 00:07:27,160 Speaker 1: at drugs and and and make the decisions is different. 117 00:07:27,320 --> 00:07:32,360 Speaker 1: Some have more, um layers of approval requirements, etcetera. So 118 00:07:32,400 --> 00:07:34,400 Speaker 1: there's a lot of arguments and discussion going on about 119 00:07:34,440 --> 00:07:37,120 Speaker 1: why the UK went first and got there first before 120 00:07:37,160 --> 00:07:41,000 Speaker 1: the EMA. When it comes to distributing, you know, the press. 121 00:07:41,080 --> 00:07:43,520 Speaker 1: The press conference that the UK had was pretty clear 122 00:07:43,560 --> 00:07:47,120 Speaker 1: about the direction they're going, and that is healthcare workers 123 00:07:47,160 --> 00:07:51,680 Speaker 1: and then m at risk people, which are usually the 124 00:07:51,760 --> 00:07:55,520 Speaker 1: all the folks who live in often in the care homes, etcetera. 125 00:07:55,560 --> 00:07:56,960 Speaker 1: So it will be by age, and I think it 126 00:07:56,960 --> 00:07:58,520 Speaker 1: will be the same in the United States. And I 127 00:07:58,600 --> 00:08:01,400 Speaker 1: suspect you were pretty much him as in Europe. I 128 00:08:01,400 --> 00:08:04,520 Speaker 1: think everyone's kind of made up their minds on that one. Hey, Sam, 129 00:08:04,560 --> 00:08:06,640 Speaker 1: here in the United States we have a fairly large 130 00:08:07,000 --> 00:08:10,520 Speaker 1: percentage of the population that is generally anti vaccine for 131 00:08:10,640 --> 00:08:13,440 Speaker 1: a variety of reasons. Is that is that a typical 132 00:08:13,520 --> 00:08:16,200 Speaker 1: is that you have a similar issue in the UK 133 00:08:16,320 --> 00:08:20,560 Speaker 1: and across Europe. UM. Yeah, you know, Paul, I think 134 00:08:20,600 --> 00:08:24,680 Speaker 1: it varies depending on geography and even within a country. 135 00:08:24,720 --> 00:08:28,760 Speaker 1: So I've even heard, for instance, eastern Massachusetts being likely 136 00:08:28,840 --> 00:08:32,360 Speaker 1: to go into the level, but then you go further 137 00:08:32,640 --> 00:08:36,080 Speaker 1: west and the numbers dropped massively. We've also got the 138 00:08:36,120 --> 00:08:39,840 Speaker 1: situation in UM in France that after the European countries, 139 00:08:39,880 --> 00:08:42,840 Speaker 1: it appears to be in surveys at least one of 140 00:08:42,880 --> 00:08:48,160 Speaker 1: the more reticent countries. So it will vary country by country. 141 00:08:48,280 --> 00:08:52,160 Speaker 1: We basically need about fifty to six people to take 142 00:08:52,200 --> 00:08:56,079 Speaker 1: this vaccine UM, given that there's ten to tent who 143 00:08:56,080 --> 00:08:58,600 Speaker 1: would have been infected along the way anyway to get 144 00:08:58,679 --> 00:09:00,680 Speaker 1: us to that HERD immunity number. So it's not like 145 00:09:00,760 --> 00:09:04,120 Speaker 1: we need to vaccinate everybody. Yeah, and it's great news 146 00:09:04,160 --> 00:09:07,640 Speaker 1: that it's starting quickly. Though on the regulators approving this. 147 00:09:07,760 --> 00:09:10,160 Speaker 1: Did the European Union regulators not have to get this 148 00:09:10,240 --> 00:09:12,920 Speaker 1: approved as well? Explained? Was how weird managed to get 149 00:09:12,960 --> 00:09:17,160 Speaker 1: us all done sort of unilaterally? Yeah, but the UK 150 00:09:17,400 --> 00:09:20,720 Speaker 1: is UM you know, within within the rules of their 151 00:09:22,000 --> 00:09:24,640 Speaker 1: during emergencies etcetera. They do have the option to go 152 00:09:25,760 --> 00:09:29,680 Speaker 1: away from the European unions e m A European Medicine 153 00:09:29,679 --> 00:09:34,320 Speaker 1: Association which is UM, which is what they've done. So 154 00:09:34,360 --> 00:09:39,280 Speaker 1: this is and I think what the process was and 155 00:09:39,360 --> 00:09:42,959 Speaker 1: why the UK was faster than europe versus the s 156 00:09:43,040 --> 00:09:45,400 Speaker 1: d A. I think a lot of that comes down 157 00:09:45,440 --> 00:09:48,520 Speaker 1: to how much resources you throw at it. I can't 158 00:09:48,520 --> 00:09:51,880 Speaker 1: I cannot accept that the European that the UK regulator 159 00:09:52,360 --> 00:09:56,280 Speaker 1: cut corners to get to this UM. They've been engaged 160 00:09:56,320 --> 00:10:01,840 Speaker 1: with FIGHTER and other regulators since June looking at manufacturing 161 00:10:01,920 --> 00:10:03,679 Speaker 1: all the other work that needs to be done. So 162 00:10:03,960 --> 00:10:07,120 Speaker 1: I just can't believe that this one week difference will 163 00:10:07,200 --> 00:10:09,440 Speaker 1: make an enormous amount of difference to people in terms 164 00:10:09,440 --> 00:10:11,360 Speaker 1: of what data they see. Suddenly they're going to see 165 00:10:11,360 --> 00:10:14,160 Speaker 1: a lot more data than the UK has. Hey Sam, 166 00:10:14,240 --> 00:10:17,120 Speaker 1: you're you've been covering as pharmaceutical game for decades. Here 167 00:10:17,160 --> 00:10:20,400 Speaker 1: you actually have a PhD in the crazy science behind 168 00:10:20,480 --> 00:10:24,640 Speaker 1: all this. Do you personally care and you think patients 169 00:10:24,640 --> 00:10:27,439 Speaker 1: in general should care which vaccine they get? Or are 170 00:10:27,440 --> 00:10:29,560 Speaker 1: you going to take just the first thing that gets 171 00:10:29,559 --> 00:10:34,120 Speaker 1: offered to san Fazelli? Yeah, so I think Paul, that 172 00:10:34,160 --> 00:10:37,440 Speaker 1: will come down to the efficacy. But but and so 173 00:10:37,520 --> 00:10:39,079 Speaker 1: we need to see the data, the data I've seen 174 00:10:39,160 --> 00:10:43,240 Speaker 1: so far from fiser BioNTech versus moderna. You know, I 175 00:10:43,280 --> 00:10:45,080 Speaker 1: will take either one of them that's offered to me. 176 00:10:45,240 --> 00:10:48,360 Speaker 1: The fier BioNTech one appears to be. And those are 177 00:10:48,520 --> 00:10:52,720 Speaker 1: carefully chosen words until we see more data, more tolerable 178 00:10:52,800 --> 00:10:55,360 Speaker 1: in terms of you get less fever, you get less 179 00:10:55,360 --> 00:10:58,120 Speaker 1: body aches. But frankly, if it comes down to it, 180 00:10:58,200 --> 00:10:59,880 Speaker 1: and says Sam, you're gonna have to wait a year 181 00:11:00,120 --> 00:11:01,840 Speaker 1: that one, but we can give you more there no now, 182 00:11:02,520 --> 00:11:04,400 Speaker 1: and I think I'll just that would be a very 183 00:11:04,400 --> 00:11:06,520 Speaker 1: easy question to answer. Of course I'll say yes to that. 184 00:11:07,320 --> 00:11:10,760 Speaker 1: So this is then honestly just fantastic news. We're beginning 185 00:11:10,760 --> 00:11:12,960 Speaker 1: to get the roll out of the vaccine. The vaccine works, 186 00:11:13,160 --> 00:11:16,240 Speaker 1: and you know, can we all go home and rest 187 00:11:16,280 --> 00:11:19,440 Speaker 1: peacefully or are there things that still stick out and 188 00:11:19,880 --> 00:11:22,280 Speaker 1: bother you like, do we know, for example, if they're 189 00:11:22,280 --> 00:11:24,160 Speaker 1: going to be longer term effects, we know we're going 190 00:11:24,160 --> 00:11:28,280 Speaker 1: to need another vaccine in nine months. Yes, so there 191 00:11:28,280 --> 00:11:31,720 Speaker 1: are what what does bother me? One is is not? 192 00:11:32,320 --> 00:11:35,320 Speaker 1: Is not long term side effects and all that. I 193 00:11:35,320 --> 00:11:38,160 Speaker 1: think you know, We've had a lot of vaccines developed 194 00:11:38,360 --> 00:11:41,800 Speaker 1: and it's essentially they have pretty similar, very rare side 195 00:11:41,840 --> 00:11:44,439 Speaker 1: effect issues which are related to the immune system, but 196 00:11:44,760 --> 00:11:47,240 Speaker 1: very rare. So I'm not worried about that. What I 197 00:11:47,280 --> 00:11:49,760 Speaker 1: am worried about is that the virus as soon as 198 00:11:49,760 --> 00:11:53,120 Speaker 1: we start putting pressure on it, I pushing people to 199 00:11:53,160 --> 00:11:56,080 Speaker 1: be vaccinated, and therefore they have an immune response that 200 00:11:56,440 --> 00:11:59,640 Speaker 1: we end up what's called colonal selection. We end up 201 00:11:59,679 --> 00:12:04,240 Speaker 1: selecting a version of the virus that's less susceptible to 202 00:12:04,280 --> 00:12:09,320 Speaker 1: our vaccine. Now, as you just heard biotic Um, Alex 203 00:12:09,920 --> 00:12:13,720 Speaker 1: and Guy were asking the Biotic executive about mutations. They 204 00:12:13,720 --> 00:12:18,680 Speaker 1: said they've studied ten mutations and the vaccines equally efficacious 205 00:12:18,920 --> 00:12:22,760 Speaker 1: in those settings. I don't know, he didn't really answer 206 00:12:22,760 --> 00:12:25,520 Speaker 1: the question about that particular mutation I asked about, But 207 00:12:25,640 --> 00:12:27,920 Speaker 1: it doesn't really matter. There are others that will come 208 00:12:27,960 --> 00:12:32,760 Speaker 1: along that might escape our vaccine. But remember that we've 209 00:12:32,800 --> 00:12:34,960 Speaker 1: got a technology that you can go back and change 210 00:12:35,000 --> 00:12:37,800 Speaker 1: the sequence, change the RNA that you give people and 211 00:12:37,840 --> 00:12:39,800 Speaker 1: get a new vaccine out. I mean, it would take time, 212 00:12:40,240 --> 00:12:43,400 Speaker 1: but we have to watch out for that. Hey, Sam, 213 00:12:43,440 --> 00:12:46,160 Speaker 1: thank you so much for joining us. We always appreciate 214 00:12:46,240 --> 00:12:50,600 Speaker 1: your perspective and your experience Here. Santa Zelli senior pharmaceutical analyst, 215 00:12:50,960 --> 00:12:54,079 Speaker 1: and it's also the head of the entire Bloomberg Intelligence 216 00:12:54,120 --> 00:12:57,480 Speaker 1: European research operations. So who's a busy person these days? 217 00:12:57,480 --> 00:13:01,320 Speaker 1: Giving us his latest thoughts on the vaccine. Well, let's 218 00:13:01,320 --> 00:13:04,360 Speaker 1: welcome in now a very special guest joining us from 219 00:13:04,440 --> 00:13:08,160 Speaker 1: the University of Missouri's Law school. Frank Moment as professor 220 00:13:08,400 --> 00:13:13,120 Speaker 1: at missour Law and has written a book on well, 221 00:13:13,200 --> 00:13:15,320 Speaker 1: let's give the title, High Crimes and Misdemeanor is a 222 00:13:15,400 --> 00:13:17,920 Speaker 1: History of impeachment for the Age of Trump. Thank you 223 00:13:18,000 --> 00:13:21,559 Speaker 1: for so much joining us, Frank. Frank, you know a 224 00:13:21,640 --> 00:13:24,160 Speaker 1: lot of talk about presidential pardons these days, and the 225 00:13:24,160 --> 00:13:26,760 Speaker 1: potential for the president to pardon really anybody in his 226 00:13:26,800 --> 00:13:32,400 Speaker 1: family or even himself. Can he do that? We have 227 00:13:32,440 --> 00:13:35,960 Speaker 1: to distinguish between two possible types of partners, one pardon 228 00:13:36,040 --> 00:13:39,480 Speaker 1: of himself and the other pardon of other people. In 229 00:13:39,520 --> 00:13:45,160 Speaker 1: my view, a president cannot constitutionally pardoned himself. Um. There's 230 00:13:45,160 --> 00:13:50,240 Speaker 1: a complex array of reasons why that's so, Um, although 231 00:13:50,240 --> 00:13:54,600 Speaker 1: it's never mentested. Um. Now with respect to partnering members 232 00:13:54,640 --> 00:13:57,079 Speaker 1: of his family, are people around him that I think 233 00:13:57,200 --> 00:13:59,840 Speaker 1: is clearer. I think he can do that, although it's 234 00:14:00,000 --> 00:14:04,160 Speaker 1: possible that um, depending on the circumstances of such a pardon, 235 00:14:04,760 --> 00:14:11,360 Speaker 1: the pardon itself might be a crime. So, Professor, for 236 00:14:11,440 --> 00:14:13,280 Speaker 1: pardons to be issued, don't they have to be issued 237 00:14:13,320 --> 00:14:16,599 Speaker 1: against someone who's been convicted of something as opposed to 238 00:14:16,760 --> 00:14:23,400 Speaker 1: kind of preemptively issuing blanket pardons. No, Um, Actually, a 239 00:14:23,480 --> 00:14:28,560 Speaker 1: pardon can be issued for any conduct that occurred prior 240 00:14:28,680 --> 00:14:31,520 Speaker 1: to the insurance of the pardon, whether it was it 241 00:14:31,600 --> 00:14:36,960 Speaker 1: has been investigated or indicted. We have several very significant 242 00:14:37,400 --> 00:14:40,160 Speaker 1: instances about occurring in our own history. For example, after 243 00:14:40,160 --> 00:14:43,960 Speaker 1: the Civil War, Andrew Johnson pardoned many thousands of former 244 00:14:44,000 --> 00:14:48,080 Speaker 1: Confederates who would potentially have been liable for the very 245 00:14:48,120 --> 00:14:50,400 Speaker 1: serious crime of treason, but who had never been charged 246 00:14:50,440 --> 00:14:53,800 Speaker 1: for it. Similarly, after Vietnam War, first President Ford and 247 00:14:53,840 --> 00:14:58,080 Speaker 1: then President Carter gave a series of amnesties or pardons 248 00:14:58,120 --> 00:15:01,400 Speaker 1: to people who would have been esecutable for and sometimes 249 00:15:01,480 --> 00:15:07,240 Speaker 1: had been prosecuted for draftivation. So there's plenty of precedent 250 00:15:07,320 --> 00:15:09,880 Speaker 1: for that. Now. One thing that can't be done is 251 00:15:09,920 --> 00:15:13,160 Speaker 1: you can't pardon somebody for a crime that hasn't yet 252 00:15:13,200 --> 00:15:17,480 Speaker 1: been committed. Um nor I think can you pardon somebody 253 00:15:17,600 --> 00:15:20,760 Speaker 1: or a crime which is ongoing. So imagine, just for 254 00:15:20,760 --> 00:15:23,560 Speaker 1: the sake of argument, that someone in the Trump orbit 255 00:15:23,800 --> 00:15:28,040 Speaker 1: was currently engaging in some kind of crime that continued 256 00:15:28,120 --> 00:15:31,000 Speaker 1: after he left office. He wouldn't be able to pardon 257 00:15:31,080 --> 00:15:35,120 Speaker 1: that person for a crime that that continued, yeah, or 258 00:15:35,360 --> 00:15:38,960 Speaker 1: hasn't started yet. We're getting very minority report here. But Frank, 259 00:15:39,440 --> 00:15:42,720 Speaker 1: you mentioned earlier that the pardon itself might be a crime. 260 00:15:42,800 --> 00:15:46,800 Speaker 1: I'm interest in that because if that wasn't a deterrent, 261 00:15:47,600 --> 00:15:53,080 Speaker 1: then why wouldn't an outgoing president who doesn't really you know, mind, 262 00:15:53,600 --> 00:15:56,000 Speaker 1: just issue a blanket pardon for anybody that he thinks 263 00:15:56,120 --> 00:16:00,280 Speaker 1: might ever have or you know, be implicated in some 264 00:16:00,360 --> 00:16:02,800 Speaker 1: kind of claim that you know, might have to deal 265 00:16:02,840 --> 00:16:05,960 Speaker 1: with that after he's left the presidency. So why would 266 00:16:06,000 --> 00:16:11,240 Speaker 1: a pardoner itself be a clime Potentially, Well, it's only 267 00:16:11,280 --> 00:16:13,320 Speaker 1: going to be a crime in a very limited set 268 00:16:13,320 --> 00:16:18,480 Speaker 1: of circumstances. Actually, although we don't yet know. There of course, 269 00:16:18,520 --> 00:16:20,880 Speaker 1: as a report about the details. But there's of course 270 00:16:20,880 --> 00:16:23,760 Speaker 1: a report out of Washington's at the Justice Department is 271 00:16:23,840 --> 00:16:30,640 Speaker 1: investigating a essentially country campaign contributions for pardon bribery scheme, 272 00:16:31,560 --> 00:16:35,080 Speaker 1: and anybody involved in an exchange like that, up to 273 00:16:35,120 --> 00:16:38,520 Speaker 1: and including the president would be guilty of the separate 274 00:16:38,560 --> 00:16:41,440 Speaker 1: crime of bribery. Now there's we don't have any of 275 00:16:41,440 --> 00:16:44,240 Speaker 1: the details, We have no indication whatsoever who's involved in 276 00:16:44,280 --> 00:16:47,440 Speaker 1: certainly no indication that the president himself is involved in 277 00:16:47,480 --> 00:16:50,800 Speaker 1: such a thing. But just assume hypothetically that a president 278 00:16:50,840 --> 00:16:55,320 Speaker 1: were to agree to take a bribe in response to 279 00:16:55,360 --> 00:16:57,280 Speaker 1: which he would issue a pardon, that would be a 280 00:16:57,280 --> 00:17:02,400 Speaker 1: freestanding crime. Now, simply issuing a pardon that is broadly 281 00:17:02,480 --> 00:17:06,520 Speaker 1: self interested and unseemly is not going to be criminal. 282 00:17:06,560 --> 00:17:09,920 Speaker 1: And lots of presidents, unfortunately have done that kind of thing. 283 00:17:10,520 --> 00:17:13,439 Speaker 1: I mean, the President Clinton, for example, issues a series 284 00:17:13,480 --> 00:17:16,720 Speaker 1: of pardons at the end of his presidency that we're 285 00:17:16,840 --> 00:17:19,760 Speaker 1: very ugly. Indeed, indeed, at least one of them, the 286 00:17:19,760 --> 00:17:21,919 Speaker 1: Mark rich pardon, I suppose, could have been argued to 287 00:17:21,920 --> 00:17:25,679 Speaker 1: have been overtly corrupt because Richard made contributions to the Clintons. 288 00:17:26,320 --> 00:17:32,640 Speaker 1: But generally speaking, even unseemly pardons are going to stand 289 00:17:33,119 --> 00:17:37,680 Speaker 1: and aren't going to be separately prosecutable. Well, can pardons 290 00:17:37,880 --> 00:17:42,520 Speaker 1: be challenged in any scenario there, Professor, or they pretty 291 00:17:42,600 --> 00:17:46,159 Speaker 1: much uh you know etgten Stone once they are in 292 00:17:46,200 --> 00:17:50,520 Speaker 1: fact issued. There's a little disagreement about that. My own 293 00:17:50,640 --> 00:17:52,919 Speaker 1: view is once the pardon is issued, regardless of the 294 00:17:52,920 --> 00:17:56,719 Speaker 1: reason where it's having been issued, then the pardon itself stands, 295 00:17:57,280 --> 00:18:00,439 Speaker 1: at least as to the person pardoned. Now are there 296 00:18:00,440 --> 00:18:04,240 Speaker 1: are some people who disagree with that, who suggests that, um, 297 00:18:04,280 --> 00:18:08,119 Speaker 1: there are certain ways or certain occasions on which what 298 00:18:08,240 --> 00:18:12,479 Speaker 1: they characterize is really self interested pardons, pardons that in 299 00:18:12,560 --> 00:18:18,199 Speaker 1: some way benefit the president, that denigrate from his his 300 00:18:18,280 --> 00:18:22,399 Speaker 1: obligation to ensure that the laws be faithfully executed, that 301 00:18:22,520 --> 00:18:25,480 Speaker 1: those might be challengeable in court. I very much doubt that. 302 00:18:26,160 --> 00:18:28,240 Speaker 1: I think once the pardon is issued, it is almost 303 00:18:28,280 --> 00:18:31,159 Speaker 1: certainly going to stand. The President has suggested that they 304 00:18:31,240 --> 00:18:35,959 Speaker 1: might want to again in four years. Would any history 305 00:18:36,000 --> 00:18:42,119 Speaker 1: of pardons impact any kind of potential future run well, 306 00:18:42,160 --> 00:18:46,080 Speaker 1: I think. I think in a prior universe, in in 307 00:18:46,520 --> 00:18:50,760 Speaker 1: the political universe, that we inherit and inhabited you know, 308 00:18:50,880 --> 00:18:53,440 Speaker 1: eight times twenty years ago for the for the remainder 309 00:18:53,520 --> 00:18:57,080 Speaker 1: of our history. Back to Yeah, I think there would 310 00:18:57,080 --> 00:18:59,919 Speaker 1: be a problem if a president issued a bunch of 311 00:19:00,000 --> 00:19:03,000 Speaker 1: obviously self interested partons, including pardons of himself. I think 312 00:19:03,000 --> 00:19:06,320 Speaker 1: that would be almost automatically disqualifying. But in our current 313 00:19:06,359 --> 00:19:10,320 Speaker 1: media environment, wherever where both sides are so heavily siloed, 314 00:19:10,600 --> 00:19:14,359 Speaker 1: you've already got people within what I might call that 315 00:19:14,480 --> 00:19:19,200 Speaker 1: the Trump universe, Fox News and other places actively suggesting 316 00:19:19,280 --> 00:19:22,600 Speaker 1: that Trump pardoned himself in order to, you know, protect 317 00:19:22,640 --> 00:19:25,800 Speaker 1: himself against the evil liberals and the operation of the 318 00:19:25,840 --> 00:19:29,480 Speaker 1: deep state. I think, at least among Trump's face, a 319 00:19:29,560 --> 00:19:33,919 Speaker 1: series of grotesquely self interested partons, including one of himself, 320 00:19:34,000 --> 00:19:38,960 Speaker 1: might not be disqualifying. Whether you know that would be 321 00:19:39,000 --> 00:19:43,600 Speaker 1: true for the larger Electorate's another question, Professor, what gives 322 00:19:43,600 --> 00:19:45,760 Speaker 1: a sense of timing here. Should we expecting some of 323 00:19:45,800 --> 00:19:48,479 Speaker 1: these pardons really at the last minute, or can they 324 00:19:48,480 --> 00:19:54,280 Speaker 1: happen at any time? Well, because a pardon only covers 325 00:19:55,480 --> 00:20:00,000 Speaker 1: conduct that's already occurred, the safest time are the most 326 00:20:00,040 --> 00:20:02,879 Speaker 1: inclusive times usha pardon is at the very end of 327 00:20:02,920 --> 00:20:05,960 Speaker 1: the president's term. Certainly, if you pardons himself, I would 328 00:20:05,960 --> 00:20:08,159 Speaker 1: expect he'll do that on the very last day, pactly 329 00:20:08,280 --> 00:20:12,840 Speaker 1: the very last minute. Um. But you know, some other cases, 330 00:20:12,960 --> 00:20:17,479 Speaker 1: as we've already seen with the Mr Flynn, are not 331 00:20:17,840 --> 00:20:21,000 Speaker 1: I don't seem to have that kind of time angle. 332 00:20:21,320 --> 00:20:23,800 Speaker 1: And uh, you know, we might see a series of 333 00:20:23,840 --> 00:20:26,720 Speaker 1: pardons leading up the last day. But I think it's 334 00:20:26,880 --> 00:20:31,240 Speaker 1: not unreasonable to at least imagine that you would get 335 00:20:31,320 --> 00:20:34,639 Speaker 1: a series of pardons from Mr Trump issued on the 336 00:20:34,680 --> 00:20:38,320 Speaker 1: morning of January. Frank, thanks so much for joining us. 337 00:20:38,320 --> 00:20:41,640 Speaker 1: We really appreciate it. Frank Bowman the Floyd Art Gibson 338 00:20:41,640 --> 00:20:45,600 Speaker 1: Missouri and dowed Professor of Law at the University Missouri 339 00:20:45,800 --> 00:20:49,800 Speaker 1: School of Law based in Colombia, Missouri, talking to us 340 00:20:49,840 --> 00:20:54,359 Speaker 1: about the pardons, and that certainly presidents have done that 341 00:20:54,560 --> 00:20:59,600 Speaker 1: pardons made pardons in the past rather routinely. The expectation 342 00:20:59,760 --> 00:21:03,760 Speaker 1: is that President Trump will continue to make them as 343 00:21:03,800 --> 00:21:05,679 Speaker 1: he winds his way down to the last days of 344 00:21:05,880 --> 00:21:11,920 Speaker 1: his presidency. So what can we expect for the holiday 345 00:21:11,920 --> 00:21:14,280 Speaker 1: shopping season. We've been talking about it now for a 346 00:21:14,280 --> 00:21:17,760 Speaker 1: few weeks pre Black Friday, but of course typically it 347 00:21:17,760 --> 00:21:19,560 Speaker 1: would remain hot right through the end of the year. 348 00:21:19,640 --> 00:21:22,320 Speaker 1: Let's bring in Craig Johnson, president of Customer Growth Partners, 349 00:21:22,320 --> 00:21:25,680 Speaker 1: to tell us exactly what his surveys are telling him. Craig, 350 00:21:25,720 --> 00:21:27,960 Speaker 1: you've been doing this for a couple of decades now, 351 00:21:28,920 --> 00:21:31,159 Speaker 1: how things grown to a hold this year? Or do 352 00:21:31,359 --> 00:21:34,040 Speaker 1: anticipate that people will spend even if it's on their 353 00:21:34,080 --> 00:21:38,720 Speaker 1: credit cards? Well, people are spending. U They may not 354 00:21:38,800 --> 00:21:41,840 Speaker 1: be spending on Black Friday or Cyber Monday Monday as 355 00:21:41,920 --> 00:21:44,520 Speaker 1: much as some people thought, but they are spending. So 356 00:21:44,880 --> 00:21:48,560 Speaker 1: we went into the season with our nineties annual now 357 00:21:49,200 --> 00:21:53,760 Speaker 1: preseason forecast up five, which is above consensus and based 358 00:21:53,800 --> 00:21:55,600 Speaker 1: on what we saw for November as a hole. I'm 359 00:21:55,640 --> 00:21:58,760 Speaker 1: not talking about Friday, but for November is an entirety 360 00:21:59,400 --> 00:22:04,520 Speaker 1: through through Monday, sales were up about seven UM and 361 00:22:04,600 --> 00:22:07,640 Speaker 1: for November they week three hundred and three billion dollars, 362 00:22:07,880 --> 00:22:11,000 Speaker 1: and that's entirely consistent with our forecast to five eight. 363 00:22:11,040 --> 00:22:13,640 Speaker 1: In fact, the season overall may come in a little 364 00:22:13,680 --> 00:22:18,440 Speaker 1: bit above six. So those numbers are craict Those numbers 365 00:22:18,480 --> 00:22:21,879 Speaker 1: are really really interesting. And um, given that when you 366 00:22:21,920 --> 00:22:24,840 Speaker 1: were so much unemployment out there and so much angst 367 00:22:24,840 --> 00:22:26,720 Speaker 1: in the marketplace and so much uncertainty, what do you 368 00:22:26,760 --> 00:22:32,080 Speaker 1: attribute uh those growth numbers? Two? Well, they it's a 369 00:22:32,080 --> 00:22:33,800 Speaker 1: couple of things going on, but the most important is 370 00:22:33,840 --> 00:22:38,600 Speaker 1: the consumer fundamentals are overall are very very strong. Disposable 371 00:22:38,640 --> 00:22:41,200 Speaker 1: personal income. I'm not talking about the people have a job, 372 00:22:41,240 --> 00:22:44,960 Speaker 1: but as a heart, as a whole growth and disposedment 373 00:22:45,040 --> 00:22:48,080 Speaker 1: coming as the single biggest particular retail sales, it's up 374 00:22:48,240 --> 00:22:51,680 Speaker 1: about six percent year of a year. UM, that's very strong. 375 00:22:52,480 --> 00:22:57,320 Speaker 1: Consumer household financials balance sheets are the healthiest ever with 376 00:22:57,440 --> 00:23:00,280 Speaker 1: an eight point seven percent household debt service a show 377 00:23:00,359 --> 00:23:03,240 Speaker 1: for the FED. And the personal savings rate is about 378 00:23:04,480 --> 00:23:08,320 Speaker 1: and that personal savings is key because consumers have dry 379 00:23:08,440 --> 00:23:11,960 Speaker 1: powder of two point four trillion dollars on their balance 380 00:23:11,960 --> 00:23:14,240 Speaker 1: sheets right now, so they don't have to tap into 381 00:23:14,280 --> 00:23:17,080 Speaker 1: credit cards. Uh, they can buy just out of current 382 00:23:17,119 --> 00:23:21,000 Speaker 1: cash flow and that's what's really driving the growth. Are 383 00:23:21,000 --> 00:23:25,200 Speaker 1: we talking about particular strata of customers though, Craig, surely 384 00:23:25,200 --> 00:23:30,960 Speaker 1: those on food lines and visiting countries can't be doing this. Um. Again, 385 00:23:31,080 --> 00:23:34,400 Speaker 1: it's it doesn't extend to everybody. Obviously, if you're if 386 00:23:34,440 --> 00:23:37,200 Speaker 1: you're in a household where nobody has a job, you're 387 00:23:37,280 --> 00:23:42,280 Speaker 1: focusing just on needs and not wants. But for the 388 00:23:42,400 --> 00:23:47,840 Speaker 1: ninety plus percent of the American public, uh, that is 389 00:23:47,880 --> 00:23:52,639 Speaker 1: financially healthy. Um, they are spending and uh and again 390 00:23:52,680 --> 00:23:56,320 Speaker 1: they have a lot of available cash on hand to spend. UM. 391 00:23:56,600 --> 00:23:58,560 Speaker 1: And you know, we're all hoping that you know, some 392 00:23:58,600 --> 00:24:04,080 Speaker 1: of the financial aid coming out of Washington actually eventuates. 393 00:24:04,320 --> 00:24:07,080 Speaker 1: But in the meantime, the consumers, you know, in the 394 00:24:07,400 --> 00:24:09,840 Speaker 1: up four of the upper five, of the of the 395 00:24:09,880 --> 00:24:14,159 Speaker 1: five quintiles, Uh, they are spending very sharply increase year 396 00:24:14,200 --> 00:24:17,800 Speaker 1: of the year. So it's interesting. Correct, we've seen obviously 397 00:24:17,840 --> 00:24:23,600 Speaker 1: a big pullback in UH, travel, hospitality spending by consumers. 398 00:24:23,600 --> 00:24:26,000 Speaker 1: Do you think some of that's getting redirected into just 399 00:24:26,119 --> 00:24:30,680 Speaker 1: buying stuff? Well, Uh, there is, there is a major 400 00:24:30,800 --> 00:24:35,320 Speaker 1: rotation and spending and um. And with at the margin 401 00:24:35,520 --> 00:24:38,760 Speaker 1: from the categories you mention entertainment, travel, hospitality, et cetera. 402 00:24:39,280 --> 00:24:43,959 Speaker 1: UH is clearly down UM and about two hundred and 403 00:24:43,960 --> 00:24:49,640 Speaker 1: sixty billion dollars UH has been shifted from those services 404 00:24:49,680 --> 00:24:51,920 Speaker 1: into the good sectors. And that's a that's a big 405 00:24:51,960 --> 00:24:55,240 Speaker 1: that's a big number. Now, it's obviously not as big 406 00:24:55,280 --> 00:24:57,400 Speaker 1: as the anywhere near as big as the two point 407 00:24:57,480 --> 00:25:01,119 Speaker 1: four trillion dollars they already have you on hand at 408 00:25:01,160 --> 00:25:04,080 Speaker 1: home UM, which is about a doubling of what it 409 00:25:04,160 --> 00:25:05,800 Speaker 1: was last year at one point to a trillion, So 410 00:25:05,840 --> 00:25:08,119 Speaker 1: they have an intriminal one to point to a trillion. 411 00:25:08,320 --> 00:25:10,280 Speaker 1: And then when you add in the two sixty billion 412 00:25:10,280 --> 00:25:14,480 Speaker 1: dollars from from the services from the discussionary services sector UM, 413 00:25:14,560 --> 00:25:18,119 Speaker 1: that is an additional boost UM, as is lower gasoline 414 00:25:18,119 --> 00:25:22,280 Speaker 1: prices which are around, you know, abouts last year. So 415 00:25:22,280 --> 00:25:26,240 Speaker 1: are you're getting a variety of favorable tail winds behind 416 00:25:26,280 --> 00:25:29,040 Speaker 1: the spending. So, Craig, do you dive in to see 417 00:25:29,080 --> 00:25:32,440 Speaker 1: exactly what they're spending on? Are they buying codes? Are 418 00:25:32,440 --> 00:25:37,760 Speaker 1: they replenishing durable goods? Are they buying toys? Well, yes 419 00:25:37,800 --> 00:25:41,440 Speaker 1: to all the but but this is overwhelmingly a hardlines Christmas. 420 00:25:41,680 --> 00:25:44,840 Speaker 1: Soft lines means you know apparel and you know, and 421 00:25:45,960 --> 00:25:48,440 Speaker 1: Lenen and so forth. But the hard lines, and this 422 00:25:48,560 --> 00:25:51,800 Speaker 1: is you know, whether consumer electronics Nintendo switched the other, 423 00:25:51,960 --> 00:25:54,960 Speaker 1: you know, the other new video gainst the Sony PS four, 424 00:25:55,359 --> 00:26:00,919 Speaker 1: T vs iPhone, twelves. Um household appliance to either major appliances, 425 00:26:01,720 --> 00:26:05,240 Speaker 1: refrigerators are sold out a lot of places until delivery 426 00:26:05,240 --> 00:26:09,000 Speaker 1: in January, if not February. UM, So it's mainly herdlines 427 00:26:09,040 --> 00:26:13,360 Speaker 1: and that means toys, means games, exercise gears, major appliances 428 00:26:14,280 --> 00:26:18,800 Speaker 1: out a. Propane heaters, patio heaters are impossible to find, UM, 429 00:26:19,160 --> 00:26:21,960 Speaker 1: and all those are getting sold, long air fryers, etcetera. 430 00:26:22,359 --> 00:26:24,639 Speaker 1: You know, there is some you know, some apparel is 431 00:26:24,680 --> 00:26:27,439 Speaker 1: getting sold, but hot product, but it's mainly in the 432 00:26:27,440 --> 00:26:31,080 Speaker 1: footwear categories. You know, the Nike Airmax through seventy of 433 00:26:31,080 --> 00:26:35,680 Speaker 1: the air vapor match, etcetera. Those are still very very hot. So, Craig, 434 00:26:35,720 --> 00:26:39,199 Speaker 1: this is at digital Christmas buying online. Is this just 435 00:26:39,440 --> 00:26:41,760 Speaker 1: accelerating a trend that we've kind of been seeing here 436 00:26:41,800 --> 00:26:45,679 Speaker 1: for you know, parts of a decade. Absolutely for for 437 00:26:45,720 --> 00:26:49,919 Speaker 1: the last ten years, every year the penetration of digital 438 00:26:49,920 --> 00:26:53,119 Speaker 1: penetration of overall sales has gone up about a point. 439 00:26:53,400 --> 00:26:57,960 Speaker 1: So in other words, last year penetration was about eight. Well, 440 00:26:58,000 --> 00:27:00,520 Speaker 1: the COVID jumped in immediately up to you know, to 441 00:27:02,240 --> 00:27:06,199 Speaker 1: penetration as of uh, you know, the March April May periade, 442 00:27:06,440 --> 00:27:08,480 Speaker 1: and now it's going up another two or three points. 443 00:27:08,480 --> 00:27:13,639 Speaker 1: So we have basically almost a decade of increase in 444 00:27:14,280 --> 00:27:17,840 Speaker 1: online penetration all squeezed into less than a year, which 445 00:27:17,960 --> 00:27:21,960 Speaker 1: is an interesting Yeah, just extraordinary numbers, uh that we're 446 00:27:21,960 --> 00:27:23,919 Speaker 1: seeing on the retail sales fronts. Looks like it's going 447 00:27:23,960 --> 00:27:26,960 Speaker 1: to be a pretty solid holiday sales despite all the 448 00:27:27,040 --> 00:27:30,719 Speaker 1: uncertainty out there. Craig Johnson, President of Customer Growth Partners, 449 00:27:30,720 --> 00:27:33,720 Speaker 1: we thank you for joining us here sharing that data again. 450 00:27:33,920 --> 00:27:38,000 Speaker 1: Strong strong November sales looks like again bringing in a 451 00:27:38,040 --> 00:27:42,760 Speaker 1: pretty solid holiday shopping season. Thanks for listening to Bloomberg 452 00:27:42,840 --> 00:27:46,239 Speaker 1: Markets podcast. You can subscribe and listen to interviews at 453 00:27:46,240 --> 00:27:50,639 Speaker 1: Apple Podcasts or whatever podcast platform you prefer. I'm Bonnie Quinn. 454 00:27:50,760 --> 00:27:53,439 Speaker 1: I'm on Twitter at Bonnie Quinn and on Paul Sweeney. 455 00:27:53,440 --> 00:27:56,080 Speaker 1: I'm on Twitter at pt Sweeney. Before the podcast, you 456 00:27:56,119 --> 00:28:02,879 Speaker 1: can always catch us worldwide at Bloomberg Radio. Oh oh