1 00:00:03,160 --> 00:00:06,560 Speaker 1: Global business news twenty four hours a day at Bloomberg 2 00:00:06,600 --> 00:00:09,680 Speaker 1: dot Com, the radio, plus Globolat and on your radio. 3 00:00:09,920 --> 00:00:14,120 Speaker 1: This is a Bloomberg Business Flash from Bloomberg World Headquarters. 4 00:00:14,160 --> 00:00:17,000 Speaker 1: I'm Katherine Calorie. Wall Street is joining the global rally 5 00:00:17,040 --> 00:00:19,400 Speaker 1: for a second day, and the dollar is weakening their 6 00:00:19,440 --> 00:00:23,360 Speaker 1: speculation that policymakers will move to prevent the UK's European 7 00:00:23,400 --> 00:00:27,080 Speaker 1: secession from hampering global growth. The SMP five hundred has 8 00:00:27,080 --> 00:00:29,560 Speaker 1: erased its loss for the year, a Goldman Sachs index 9 00:00:29,560 --> 00:00:31,520 Speaker 1: of the most shortage shares is up the most since 10 00:00:32,680 --> 00:00:35,560 Speaker 1: and Britain's foot SEE one hundred erased its post brexit 11 00:00:35,640 --> 00:00:38,840 Speaker 1: losses with the six point three percent gain over two days. 12 00:00:39,240 --> 00:00:41,520 Speaker 1: We talk the markets every fifteen minutes throughout the trading 13 00:00:41,560 --> 00:00:44,240 Speaker 1: day on Bloomberg Radio. Dow Industrial Average is currently have 14 00:00:44,320 --> 00:00:47,440 Speaker 1: two hundred seventy seven points one point six percent trading 15 00:00:47,479 --> 00:00:50,920 Speaker 1: at seventeen thousand, six hundred eighty seven. SMP five funded 16 00:00:51,000 --> 00:00:53,279 Speaker 1: up thirty four points one point seven percent to two 17 00:00:53,320 --> 00:00:56,600 Speaker 1: thousand seventy NASTACK up eighty eight points one point nine 18 00:00:56,600 --> 00:00:59,920 Speaker 1: percent to forty seven eighty West Texas Intermedia crude oil 19 00:01:00,040 --> 00:01:04,200 Speaker 1: but dollar fifty eight to barrel three point three spout 20 00:01:04,200 --> 00:01:08,640 Speaker 1: gold up five dollars ounce did seventy ten year treasury 21 00:01:08,640 --> 00:01:10,520 Speaker 1: down twelve thirty seconds with the yield of one point 22 00:01:10,600 --> 00:01:14,440 Speaker 1: five zero percent. And that's a bloomberg business flash. Catherine Cownderie, 23 00:01:14,440 --> 00:01:17,600 Speaker 1: thank you so very much. A sharp sell off on Friday, 24 00:01:17,640 --> 00:01:21,680 Speaker 1: three days of gains. Now this week, as markets bounce 25 00:01:21,760 --> 00:01:24,840 Speaker 1: back from the breggsit concerns, is it time to put 26 00:01:24,880 --> 00:01:27,480 Speaker 1: some money into e t s and if so, which kind? 27 00:01:27,560 --> 00:01:30,560 Speaker 1: Let's go back to Katherine County for today's e t 28 00:01:30,760 --> 00:01:33,000 Speaker 1: F report, brought to you by National Realty Providers of 29 00:01:33,800 --> 00:01:37,000 Speaker 1: Satisfaction Guaranteed New York City Realty Investments see them at 30 00:01:37,120 --> 00:01:39,600 Speaker 1: n r i A dot net. Here's Catherine Cowndry now, 31 00:01:40,319 --> 00:01:44,440 Speaker 1: market turmoil can create some opportunities. It did for David Kotak, 32 00:01:44,560 --> 00:01:47,080 Speaker 1: the chairman of Camerland Advisers, in the wake of the 33 00:01:47,200 --> 00:01:50,240 Speaker 1: UK vote to withdraw from the European Union. What do 34 00:01:50,320 --> 00:01:53,840 Speaker 1: we know that will now happen for a long time. 35 00:01:54,120 --> 00:01:57,760 Speaker 1: We're gonna have low, low interest rates longer, longer, longer 36 00:01:57,800 --> 00:02:01,120 Speaker 1: than going to be zero in Japan, zero in Europe. 37 00:02:01,240 --> 00:02:04,120 Speaker 1: After the Brexit vote, investors pushed back. That's on federal 38 00:02:04,200 --> 00:02:07,360 Speaker 1: reserve interest rate increases, pricing in just a ten percent 39 00:02:07,480 --> 00:02:11,680 Speaker 1: chance for higher borrowing costs by February. What does that 40 00:02:11,800 --> 00:02:16,960 Speaker 1: say for housing in the US that's not impacted by Brexit. 41 00:02:17,240 --> 00:02:19,960 Speaker 1: I we took up our weight in the cell off 42 00:02:20,400 --> 00:02:24,040 Speaker 1: in sectors like consumer discretionary in housing. You can be 43 00:02:24,080 --> 00:02:27,800 Speaker 1: opportunistical one side. On the other side, k Talk says 44 00:02:27,840 --> 00:02:30,160 Speaker 1: it's time to reassess other holdings in light of the 45 00:02:30,240 --> 00:02:33,639 Speaker 1: volatility caused by Brexit. Co tox for am uses et 46 00:02:33,919 --> 00:02:37,320 Speaker 1: s to invest in equities. That's your Bloomberg ETF report. 47 00:02:37,480 --> 00:02:42,080 Speaker 1: I'm Catherine Cowdery. You're listening to Taking Stock with Kathleen 48 00:02:42,120 --> 00:02:45,520 Speaker 1: Hayes and Pim Fox on Bloomberg Radio. Brexit took a 49 00:02:45,520 --> 00:02:49,520 Speaker 1: big toll on the stock market on Friday and actually 50 00:02:49,560 --> 00:02:52,840 Speaker 1: also on Monday, two days down now two days up 51 00:02:52,840 --> 00:02:55,760 Speaker 1: for the U S stock market sn P five hundreds 52 00:02:55,760 --> 00:02:59,040 Speaker 1: still though, however, stuck between two thousand, one hundred. It 53 00:02:59,120 --> 00:03:01,359 Speaker 1: seems too many people. So where do we go next? 54 00:03:01,840 --> 00:03:04,160 Speaker 1: Is the Brexit impact going to fade? And what about 55 00:03:04,200 --> 00:03:06,840 Speaker 1: those polls? If you like to watch numbers and stocks 56 00:03:06,840 --> 00:03:09,240 Speaker 1: and think about statistics, why did they go so wrong. 57 00:03:09,280 --> 00:03:12,000 Speaker 1: Scott Rothbort is here to answer all of these questions 58 00:03:12,000 --> 00:03:15,480 Speaker 1: and more, President and founder like you ask that management 59 00:03:15,520 --> 00:03:19,720 Speaker 1: also teaching at Satan University. Niversity, that's right university. So 60 00:03:19,800 --> 00:03:21,640 Speaker 1: we're really glad to have your bas Scott. Let's start 61 00:03:21,639 --> 00:03:25,239 Speaker 1: with Brexit. First of all, you had raised some cash 62 00:03:25,240 --> 00:03:28,240 Speaker 1: prior to the Brexit vote. Why a little bit? Um, 63 00:03:28,280 --> 00:03:30,080 Speaker 1: We just felt that some of our stocks were just 64 00:03:30,320 --> 00:03:33,200 Speaker 1: over extended, and um, we paired back a little bit, 65 00:03:33,240 --> 00:03:35,760 Speaker 1: not a tremendous amount. Do you have any concern about Brexit? 66 00:03:35,840 --> 00:03:38,960 Speaker 1: You know, I really didn't and I still don't have 67 00:03:39,000 --> 00:03:42,600 Speaker 1: any concern about Brexit. And and the reason is because, frankly, 68 00:03:43,280 --> 00:03:46,920 Speaker 1: I've been in this business long enough, and I've seen dislocations, 69 00:03:46,960 --> 00:03:50,400 Speaker 1: and I've gone through things like the Russian coup and 70 00:03:50,440 --> 00:03:54,720 Speaker 1: the invasion of Afghanistan and flash crashes, and we know 71 00:03:54,800 --> 00:03:57,800 Speaker 1: what happens in the long run is that the market, 72 00:03:57,840 --> 00:04:00,280 Speaker 1: while it may correct, it's also self correct in the 73 00:04:00,280 --> 00:04:04,560 Speaker 1: other the other direction, and it'll return to normal. And 74 00:04:04,600 --> 00:04:07,920 Speaker 1: so we expected that once the Brexit vote took place, 75 00:04:07,920 --> 00:04:11,200 Speaker 1: whether it was to remain or to leave, that markets 76 00:04:11,200 --> 00:04:13,440 Speaker 1: would have a reaction and then that reaction would then 77 00:04:13,480 --> 00:04:16,880 Speaker 1: be reversed. So, uh, you teach at Seton Hall University. 78 00:04:17,200 --> 00:04:20,400 Speaker 1: Uh in over there in South Orange, New Jersey, just 79 00:04:20,680 --> 00:04:22,640 Speaker 1: part of the tri state area, the Greater New York 80 00:04:22,640 --> 00:04:24,280 Speaker 1: City metro area. I like to think of it as 81 00:04:25,040 --> 00:04:28,760 Speaker 1: statist statistics. And you're in the world of business and finance. 82 00:04:29,839 --> 00:04:32,240 Speaker 1: What what do you what have you made of the 83 00:04:32,240 --> 00:04:35,880 Speaker 1: wrongness of the polls? Okay? Um? Well, first of all, 84 00:04:35,880 --> 00:04:39,040 Speaker 1: Seaton Hall has a fabulous polling center, sports polling center, 85 00:04:39,400 --> 00:04:43,160 Speaker 1: which I've had the opportunity to participate in from this 86 00:04:43,360 --> 00:04:46,520 Speaker 1: point of view of being able to pull people, and 87 00:04:46,880 --> 00:04:49,200 Speaker 1: of course they we do it based upon some sort 88 00:04:49,240 --> 00:04:54,320 Speaker 1: of you know, sports themed um question. For instance, we 89 00:04:54,400 --> 00:04:57,280 Speaker 1: did a sports poll I think a few months back 90 00:04:57,600 --> 00:05:01,960 Speaker 1: talking about all of these online gambling sites. Um. And 91 00:05:02,000 --> 00:05:04,680 Speaker 1: so I I understand what it is like to put 92 00:05:04,720 --> 00:05:07,279 Speaker 1: together a pole and be on the phone and trying 93 00:05:07,320 --> 00:05:09,839 Speaker 1: to ask people questions and and how you get the 94 00:05:09,880 --> 00:05:13,200 Speaker 1: sampled data. But I don't think that people quite understand 95 00:05:13,200 --> 00:05:16,320 Speaker 1: when they look at political polls these days, that these 96 00:05:16,320 --> 00:05:19,680 Speaker 1: political polls can be crafted in such a way that 97 00:05:19,720 --> 00:05:22,640 Speaker 1: they are pre biased. And there's something else in which 98 00:05:22,680 --> 00:05:25,719 Speaker 1: we have we call observer bias, where actually the person 99 00:05:25,760 --> 00:05:29,080 Speaker 1: who was taking the poll, who's making the observations, can 100 00:05:29,120 --> 00:05:34,000 Speaker 1: actually skew the data and information by the way which 101 00:05:34,000 --> 00:05:37,159 Speaker 1: they ask questions other people they select to answer. I'll 102 00:05:37,160 --> 00:05:40,000 Speaker 1: give you an example. My son UH, and we live 103 00:05:40,080 --> 00:05:45,400 Speaker 1: in Nevada, was phoned up and asked about Uh was 104 00:05:45,440 --> 00:05:48,800 Speaker 1: asked to participate in the poll. He said who he 105 00:05:48,839 --> 00:05:50,919 Speaker 1: would vote for president as being the first question, and 106 00:05:50,920 --> 00:05:53,600 Speaker 1: then he hung up on him didn't complete the poll. 107 00:05:54,800 --> 00:05:59,760 Speaker 1: Now this wasn't a bad line disconnection. Clearly. What happened 108 00:05:59,760 --> 00:06:01,719 Speaker 1: was they didn't like the answer and they were looking 109 00:06:01,760 --> 00:06:06,240 Speaker 1: to get some sort of predetermined response. Another way that 110 00:06:06,320 --> 00:06:09,080 Speaker 1: you can kind of fix the polls, so to speak, 111 00:06:09,920 --> 00:06:14,719 Speaker 1: is you can limit the population of telephone numbers you 112 00:06:14,760 --> 00:06:19,240 Speaker 1: get to certain area codes. So and the other problem 113 00:06:19,279 --> 00:06:21,800 Speaker 1: is that people don't understand that. You know, if you 114 00:06:21,880 --> 00:06:25,360 Speaker 1: do a sample of people and you get them to respond, 115 00:06:26,040 --> 00:06:32,400 Speaker 1: you have a margin of era of three for that. Um. Well, um, 116 00:06:32,440 --> 00:06:36,559 Speaker 1: that may seem like very little, but when you're doing 117 00:06:36,600 --> 00:06:39,800 Speaker 1: a poll for the entire United Kingdom in which thirty 118 00:06:39,839 --> 00:06:43,719 Speaker 1: some odd million people we're going to vote on a 119 00:06:43,960 --> 00:06:48,520 Speaker 1: very important issue, trying to set up a series of 120 00:06:48,560 --> 00:06:51,640 Speaker 1: poles just for a thousand people or or so. It's 121 00:06:51,800 --> 00:06:54,400 Speaker 1: very hard. So the vote is in though, regardless of 122 00:06:54,400 --> 00:06:56,640 Speaker 1: the polls. And you say in a recent piece there's 123 00:06:56,640 --> 00:06:59,520 Speaker 1: a new normal the world will have to face. People 124 00:06:59,600 --> 00:07:04,279 Speaker 1: need to change their premise as investors. What does that mean? Well, 125 00:07:04,320 --> 00:07:07,119 Speaker 1: as investors means that if you have a bias going 126 00:07:07,200 --> 00:07:09,760 Speaker 1: in if you think that, for instance, if your if 127 00:07:09,760 --> 00:07:11,960 Speaker 1: your bias was that the world is going to fall 128 00:07:12,000 --> 00:07:15,320 Speaker 1: apart because of brexit um, and it's not going to 129 00:07:15,440 --> 00:07:17,440 Speaker 1: fall apart, well, then maybe able to take a step 130 00:07:17,440 --> 00:07:21,880 Speaker 1: back and say, well, perhaps I was biased going into that, 131 00:07:22,240 --> 00:07:24,920 Speaker 1: and I didn't do my research, and I've got to 132 00:07:25,560 --> 00:07:28,120 Speaker 1: look at the world a little bit differently. Now. I 133 00:07:28,200 --> 00:07:31,720 Speaker 1: have the advantage of I grew up on the East Coast. 134 00:07:31,840 --> 00:07:34,560 Speaker 1: I grew up in Brooklyn, live in Manhattan, I lived 135 00:07:34,560 --> 00:07:37,760 Speaker 1: in Tokyo, I lived in London. We now live most 136 00:07:37,840 --> 00:07:41,400 Speaker 1: of the time in Henderson, Nevada. UM. So I get 137 00:07:41,400 --> 00:07:43,800 Speaker 1: to speak to people from all around the world. And 138 00:07:43,840 --> 00:07:46,400 Speaker 1: if you speak to people, just for instance, on the coasts, 139 00:07:47,440 --> 00:07:52,320 Speaker 1: they think differently than people who maybe in the Midwest. UM. 140 00:07:52,360 --> 00:07:54,720 Speaker 1: And unfortunately what happens is that people who live on 141 00:07:54,760 --> 00:07:59,360 Speaker 1: the coasts. All right, Well, um, project their opinions about 142 00:07:59,440 --> 00:08:00,880 Speaker 1: the rest of the country and the rest of the 143 00:08:00,880 --> 00:08:03,160 Speaker 1: world based upon how they think. Well that that may 144 00:08:03,160 --> 00:08:05,679 Speaker 1: not necessarily the same. And we're seeing this take place, 145 00:08:05,720 --> 00:08:09,000 Speaker 1: whether it's in terms of the elections or gun control 146 00:08:09,720 --> 00:08:12,320 Speaker 1: or when' that we should have a sugar at tax 147 00:08:12,840 --> 00:08:16,200 Speaker 1: and and so I think what people need to do 148 00:08:16,200 --> 00:08:19,240 Speaker 1: when I say change your premise is understand both sides 149 00:08:19,280 --> 00:08:21,840 Speaker 1: of the argument. Well, that makes sense because this presidential 150 00:08:21,880 --> 00:08:24,360 Speaker 1: election to the United States is going to be potentially 151 00:08:24,400 --> 00:08:26,400 Speaker 1: a very big deal. I want to ask about a 152 00:08:26,400 --> 00:08:30,160 Speaker 1: couple of individual stocks though, Um, you also said this 153 00:08:30,240 --> 00:08:32,800 Speaker 1: note that you took advantage of the panic to buy 154 00:08:32,800 --> 00:08:36,040 Speaker 1: some shares of Glaxo smith Klin. Of course that's a 155 00:08:36,080 --> 00:08:39,360 Speaker 1: big British pharmaceuticals company. Is that something other people should 156 00:08:39,400 --> 00:08:40,920 Speaker 1: be looking at or is it more just a post 157 00:08:40,920 --> 00:08:43,360 Speaker 1: Brexit vote trade. Well, we had a wonderful opportunity to bide, 158 00:08:43,440 --> 00:08:47,600 Speaker 1: especially in the pre market on on the Brexit panic day, um, 159 00:08:47,600 --> 00:08:51,200 Speaker 1: which was Friday. Uh and uh. We saw that the 160 00:08:51,240 --> 00:08:54,600 Speaker 1: price of the A d r s went down because 161 00:08:54,640 --> 00:08:57,679 Speaker 1: the price of Glaxo stock in the UK went down, 162 00:08:57,760 --> 00:08:59,760 Speaker 1: but also because the strength and dollars, so you've got 163 00:08:59,800 --> 00:09:02,160 Speaker 1: a will benefit, so to speak, if you're out there 164 00:09:02,200 --> 00:09:06,040 Speaker 1: buying stock. Um. But we thought the stock was cheap enough. 165 00:09:06,480 --> 00:09:09,040 Speaker 1: We liked the dividend, so we bought it both for 166 00:09:09,120 --> 00:09:13,080 Speaker 1: our growth portfolios but also for our dividend value portfolios. 167 00:09:13,200 --> 00:09:17,439 Speaker 1: And if anything, UM, when I look at a stock 168 00:09:17,520 --> 00:09:20,640 Speaker 1: like glack Cell, which makes pharmaceuticals, I think that that 169 00:09:21,200 --> 00:09:27,000 Speaker 1: is somewhat more protected than from the potential Breggxit problem 170 00:09:27,040 --> 00:09:31,000 Speaker 1: because everybody needs pharmaceuticals around the world, and they also 171 00:09:31,040 --> 00:09:35,120 Speaker 1: have patents. If we're talking about an industrial company, UM 172 00:09:35,160 --> 00:09:37,880 Speaker 1: that's maybe making widgets, let's say, as we say in academia, 173 00:09:38,280 --> 00:09:41,320 Speaker 1: then maybe I wouldn't want to buy a British widget company. 174 00:09:42,200 --> 00:09:44,800 Speaker 1: Buy amongst the briggs it rubble. You wrote about this 175 00:09:44,960 --> 00:09:48,600 Speaker 1: yesterday Whole Foods. Why and if there's no the brigsit 176 00:09:48,640 --> 00:09:50,600 Speaker 1: rubble clears up a bit, is a whole food still 177 00:09:50,600 --> 00:09:53,400 Speaker 1: a buy? Well, Well, we saw a break and with 178 00:09:53,480 --> 00:09:55,880 Speaker 1: whole foods and few things and and and as you 179 00:09:55,920 --> 00:09:58,120 Speaker 1: know I follow food and restaurant stocks. We talked about 180 00:09:58,120 --> 00:10:01,160 Speaker 1: this quite often on Bloomberg and by you talked about 181 00:10:01,160 --> 00:10:04,000 Speaker 1: my commentary of people can get my commentary for free 182 00:10:04,480 --> 00:10:07,840 Speaker 1: on www dot Lakeview Asset dot com and we call 183 00:10:07,880 --> 00:10:10,440 Speaker 1: it my gut feeling, uh, and I published it a 184 00:10:10,440 --> 00:10:13,240 Speaker 1: few times a week. But I've had my eyron Whole Foods. 185 00:10:13,240 --> 00:10:15,240 Speaker 1: I've been out of the stock over the years, and 186 00:10:15,240 --> 00:10:20,480 Speaker 1: it's got clawbered recently, really clobert um. They've had some problems, uh, 187 00:10:20,720 --> 00:10:25,280 Speaker 1: some operational problems. Their margins have contracted, Uh, growth slowed 188 00:10:25,280 --> 00:10:27,040 Speaker 1: down a little bit, and the stock has come down 189 00:10:27,040 --> 00:10:31,360 Speaker 1: tremendously this year. Uh. The stock went x dividend this week. 190 00:10:31,360 --> 00:10:33,600 Speaker 1: We start an opportunity by it before it went ex dividend. 191 00:10:33,640 --> 00:10:36,320 Speaker 1: And also what we noticed was that when all this 192 00:10:36,440 --> 00:10:40,560 Speaker 1: fallout from the brigit was taking place, that actually Whole 193 00:10:40,640 --> 00:10:44,120 Speaker 1: Foods was going up, was doing much better. So I 194 00:10:44,200 --> 00:10:46,600 Speaker 1: was being a little contrayed and said, well, people need 195 00:10:46,640 --> 00:10:48,920 Speaker 1: still need to buy food. We heard about the sugar 196 00:10:48,960 --> 00:10:51,600 Speaker 1: tacks in Philadelphia. Let's step up and buy some cheap 197 00:10:51,640 --> 00:10:56,360 Speaker 1: Whole foods. Well, congratulations Mr Rockport, the finance professor, and 198 00:10:56,360 --> 00:10:58,160 Speaker 1: he just told you how you can follow his very 199 00:10:58,200 --> 00:11:02,800 Speaker 1: interesting commentary. He's present Founder lake Usset Management professor at 200 00:11:02,800 --> 00:11:06,559 Speaker 1: Seton Hall University in New Jersey. I'm Kathleen Hayes, this 201 00:11:06,679 --> 00:11:13,240 Speaker 1: is taking stock on Bloomberg Radio. Yeah,