1 00:00:05,800 --> 00:00:08,360 Speaker 1: Welcome to the Bloomberg P and L Podcast. I'm Pim 2 00:00:08,400 --> 00:00:11,440 Speaker 1: Fox along with my co host Lisa Abramowitz. Each day 3 00:00:11,480 --> 00:00:15,000 Speaker 1: we bring you the most important, noteworthy, and useful interviews 4 00:00:15,040 --> 00:00:17,520 Speaker 1: for you and your money, whether you're at the grocery 5 00:00:17,560 --> 00:00:20,560 Speaker 1: store or the trading floor. Find the Bloomberg P and 6 00:00:20,680 --> 00:00:32,240 Speaker 1: L Podcast on Apple Podcasts, SoundCloud and Bloomberg dot Com. Well, 7 00:00:32,280 --> 00:00:34,240 Speaker 1: we want to talk about one of the biggest deals 8 00:00:34,479 --> 00:00:37,440 Speaker 1: in the food service and the food industry because there's 9 00:00:37,520 --> 00:00:40,360 Speaker 1: distribution that may be involved, and to help us understand 10 00:00:40,440 --> 00:00:43,680 Speaker 1: what's going on, We've got Bert Flickinger. And Bert, of 11 00:00:43,760 --> 00:00:46,159 Speaker 1: course is an expert when it comes to retail. He's 12 00:00:46,200 --> 00:00:50,360 Speaker 1: the managing director at Strategic Resource Group. Bert tell us 13 00:00:50,520 --> 00:00:54,400 Speaker 1: about the companies involved, admonished me for how poorly I 14 00:00:54,480 --> 00:00:58,360 Speaker 1: pronounced their names, and uh tell us about who the 15 00:00:58,400 --> 00:01:03,960 Speaker 1: protagonists are Uhi. This this will be If McCormick is 16 00:01:04,040 --> 00:01:07,520 Speaker 1: able to buy Record Record ben Kaiser Food division, which 17 00:01:07,840 --> 00:01:11,399 Speaker 1: the crown jewels are French As Mustard and Frank's Hot Sauce, 18 00:01:12,200 --> 00:01:14,720 Speaker 1: there will be one of the great acquisitions in the 19 00:01:14,800 --> 00:01:18,840 Speaker 1: last fifteen years. Fast footnote. I've worked with mccormicks CEO 20 00:01:19,040 --> 00:01:23,240 Speaker 1: Frank Curzy is since I started at zat Iran sleepy 21 00:01:23,240 --> 00:01:27,000 Speaker 1: little New Orleans brand for years with Steve's eighties Millenn 22 00:01:27,120 --> 00:01:31,760 Speaker 1: Pollock and colleagues at IPG. Lawrence and his team takes 23 00:01:31,760 --> 00:01:36,000 Speaker 1: at Iran's from multi regional to national to international, and 24 00:01:36,160 --> 00:01:39,280 Speaker 1: McCormick has done a lot of great things with with 25 00:01:39,480 --> 00:01:44,560 Speaker 1: mayonnaise obviously spices, but with what they can do with 26 00:01:44,640 --> 00:01:49,320 Speaker 1: Frank's hot sauce and UH taking French as mustard from 27 00:01:49,440 --> 00:01:54,640 Speaker 1: UH distant almost non entity in major markets to a powerhouse. 28 00:01:55,200 --> 00:01:59,720 Speaker 1: It's gonna be Kraft Heinz's worst nightmare. And it's people 29 00:01:59,760 --> 00:02:03,400 Speaker 1: say it's overpaying, their underpaying that that's that's bold. And 30 00:02:03,480 --> 00:02:05,480 Speaker 1: you say the people say that they're overpaying, I want 31 00:02:05,520 --> 00:02:08,160 Speaker 1: to home in on that because they are paying four 32 00:02:08,240 --> 00:02:12,560 Speaker 1: point two billion dollars for record UH Right now, McCormick 33 00:02:12,639 --> 00:02:15,480 Speaker 1: shares down about five and a half percent, So indeed, 34 00:02:15,520 --> 00:02:19,240 Speaker 1: shareholders do not think UH that they're going to UH 35 00:02:19,400 --> 00:02:22,360 Speaker 1: compete with Kraft Heinz perhaps in the same level that 36 00:02:22,440 --> 00:02:24,160 Speaker 1: you do. What gives you faith that you're right in 37 00:02:24,200 --> 00:02:29,040 Speaker 1: the market's wrong. Because Laurence Lawrence Curzy has has been 38 00:02:29,080 --> 00:02:33,000 Speaker 1: a brilliant brand builder and marketers his whole life. Lisa, 39 00:02:33,120 --> 00:02:36,760 Speaker 1: see you look at French as um, you know, good 40 00:02:36,880 --> 00:02:40,919 Speaker 1: good brand in Rochester where Wegmans is, but really hasn't 41 00:02:40,960 --> 00:02:45,360 Speaker 1: achieved size and scale anywhere else. Lawrence Uh and other 42 00:02:45,440 --> 00:02:48,000 Speaker 1: than the northeast, and Lawrence Curzy is will will make 43 00:02:48,080 --> 00:02:50,320 Speaker 1: French as a power brand. All right, hang on, hang 44 00:02:50,360 --> 00:02:53,520 Speaker 1: on Bird, hang on, that's all in the future. Can 45 00:02:53,560 --> 00:02:56,480 Speaker 1: you just explain to me a little bit about the 46 00:02:56,560 --> 00:03:00,160 Speaker 1: stock because at one time, I mean, this was just 47 00:03:00,440 --> 00:03:02,959 Speaker 1: you know, a hundred and five bucks of share and 48 00:03:04,280 --> 00:03:07,600 Speaker 1: the McCormick I mean, and unless I'm reading it wrong, 49 00:03:07,600 --> 00:03:10,160 Speaker 1: I mean it has not been a pretty ride down 50 00:03:10,240 --> 00:03:15,080 Speaker 1: to nine. Full disclosure, Pim. I've I've been a McCormick 51 00:03:15,120 --> 00:03:18,280 Speaker 1: shareholder halfway to forever and I'm ecstatic about the deal. 52 00:03:18,639 --> 00:03:21,639 Speaker 1: I've seen the stock drop in recent years as low 53 00:03:21,680 --> 00:03:26,800 Speaker 1: as sixty eight. But mc McCormick needs more size and 54 00:03:26,880 --> 00:03:29,880 Speaker 1: scale record Ben Kaiser never had. At the combination of 55 00:03:29,919 --> 00:03:34,120 Speaker 1: the two companies gives it. Uh provides that size and scale, 56 00:03:34,560 --> 00:03:38,600 Speaker 1: and they'll help the retailers profitably grow the sales in 57 00:03:38,640 --> 00:03:44,040 Speaker 1: the departments and with the consumers, particularly millennials there in 58 00:03:44,040 --> 00:03:46,760 Speaker 1: the sweet spot of categories that will take it from 59 00:03:47,600 --> 00:03:50,800 Speaker 1: a routine category to a growth what the f Stein 60 00:03:50,880 --> 00:03:54,920 Speaker 1: calls expandable consumption that the more people buy, they'll quickly 61 00:03:54,960 --> 00:03:58,960 Speaker 1: consume it and and buy more. So you're you're portraying 62 00:03:58,960 --> 00:04:02,480 Speaker 1: a world where just a wash in mustard and hot sauce, 63 00:04:03,040 --> 00:04:05,080 Speaker 1: as well as perhaps some allspice. You know, I have 64 00:04:05,200 --> 00:04:08,160 Speaker 1: to wonder given the fact that grocery chains have had 65 00:04:08,800 --> 00:04:12,360 Speaker 1: quite a bit of trouble, given the depressed pricing on 66 00:04:12,600 --> 00:04:17,719 Speaker 1: commodities on different food staples, as well as distribution challenges, 67 00:04:17,800 --> 00:04:20,680 Speaker 1: right because we have Amazon buying whole foods, you know, 68 00:04:21,040 --> 00:04:23,839 Speaker 1: how does this play into that and what challenges do 69 00:04:23,920 --> 00:04:26,360 Speaker 1: they face that may uh you know, be a little 70 00:04:26,360 --> 00:04:31,080 Speaker 1: bit more complicated than past challenges. Lisa, to your present points, 71 00:04:31,160 --> 00:04:35,360 Speaker 1: let the key retailers in Texas or the fastest growing 72 00:04:35,400 --> 00:04:39,200 Speaker 1: selling the Latino, African, American and Caucasian and Asian communities 73 00:04:39,240 --> 00:04:43,360 Speaker 1: answer your question. McCormick's Mayonnaise is one of the fastest 74 00:04:43,360 --> 00:04:47,159 Speaker 1: growing mayonnaisees uh craft both for miracle whip and craft 75 00:04:47,200 --> 00:04:50,600 Speaker 1: mayonnaisees uh cut quality and being able to do a 76 00:04:50,640 --> 00:04:56,040 Speaker 1: combination of mayo in In addition to spice product all 77 00:04:56,080 --> 00:05:00,720 Speaker 1: the way across salet, snacks, condiments, uh, pickles, a lot 78 00:05:00,760 --> 00:05:04,919 Speaker 1: of other things that McCormick can move quickly on. Retailers 79 00:05:05,320 --> 00:05:09,039 Speaker 1: want this, They're excited about it. Consumer consumers will be 80 00:05:09,160 --> 00:05:10,680 Speaker 1: very all right, hang on, hang on, hang on, let 81 00:05:10,720 --> 00:05:12,600 Speaker 1: me ask, let me just let let let's just get 82 00:05:12,640 --> 00:05:15,880 Speaker 1: your your detail. On another topic, I happen to do 83 00:05:15,960 --> 00:05:20,560 Speaker 1: with Lawrence Curtis, right, he is the chief executive of McCormick. 84 00:05:20,839 --> 00:05:23,240 Speaker 1: But he hasn't been in that role for very long, 85 00:05:23,360 --> 00:05:28,719 Speaker 1: or has he. He's he's been for a few fist goals, 86 00:05:29,000 --> 00:05:33,280 Speaker 1: and but Lawrence was in charge of marketing, uh, president 87 00:05:33,279 --> 00:05:36,080 Speaker 1: and CEO for a long time. Okay, So this is 88 00:05:36,120 --> 00:05:39,080 Speaker 1: something that that that a veteran because if he is 89 00:05:39,120 --> 00:05:42,520 Speaker 1: a veteran of McCormick, I mean he's spent many many 90 00:05:42,600 --> 00:05:45,560 Speaker 1: years there. Uh yeah, PIM and LI. So you could, 91 00:05:45,600 --> 00:05:49,560 Speaker 1: you could, You can take any CpG company nationally internationally. 92 00:05:49,880 --> 00:05:54,560 Speaker 1: Lawrence is one of the best marketers, executive leaders, and 93 00:05:54,640 --> 00:05:58,919 Speaker 1: business builders anywhere any country. He can make this work. 94 00:05:59,839 --> 00:06:03,480 Speaker 1: I sorry, can he make this deal work? Because I mean, 95 00:06:03,560 --> 00:06:06,400 Speaker 1: right now you have a situation where their stock is 96 00:06:06,480 --> 00:06:09,120 Speaker 1: down five and a McCormick stock is down five and 97 00:06:09,120 --> 00:06:13,760 Speaker 1: a half percent. The record stock is up about one uh. 98 00:06:14,120 --> 00:06:17,440 Speaker 1: And you know, you look at that chart of McCormick, 99 00:06:17,760 --> 00:06:21,320 Speaker 1: it's not good by one. Courtys should make it. Make 100 00:06:21,360 --> 00:06:24,520 Speaker 1: it work too. This will be this will be a 101 00:06:24,600 --> 00:06:28,039 Speaker 1: stock even in an overvalued market. Uh. There will be 102 00:06:28,120 --> 00:06:32,599 Speaker 1: up in two years. And it's an individual investor, I'd 103 00:06:32,640 --> 00:06:34,960 Speaker 1: be buying more. And I don't say that lightly because 104 00:06:34,960 --> 00:06:37,760 Speaker 1: there are very few stocks that that I'd recommend at 105 00:06:37,760 --> 00:06:43,040 Speaker 1: this point. All right, Bert Flickinger is absolutely bullish on 106 00:06:43,440 --> 00:06:47,719 Speaker 1: McCormack after this acquisition. Shareholders not so much right now, 107 00:06:47,760 --> 00:06:50,760 Speaker 1: but we'll see if they joined Burnt in his enthusiasm 108 00:06:50,880 --> 00:06:54,359 Speaker 1: for hot sauce and Mannise. Bert flickinjer managing director at 109 00:06:54,360 --> 00:07:10,400 Speaker 1: Strategic Resource Group, coming to us on the phone. This 110 00:07:10,560 --> 00:07:13,280 Speaker 1: is the era of data that just is more and 111 00:07:13,320 --> 00:07:16,840 Speaker 1: more of it being churned out by huge companies. You're 112 00:07:16,840 --> 00:07:20,440 Speaker 1: talking from Amazon to Google to IBM uh and it's 113 00:07:20,480 --> 00:07:25,160 Speaker 1: getting more tricky to stored. All enter company called Looker, 114 00:07:25,400 --> 00:07:29,520 Speaker 1: which is trying to assemble data from the variety of 115 00:07:29,560 --> 00:07:33,280 Speaker 1: different places that companies have stored uh stored it without 116 00:07:33,520 --> 00:07:35,840 Speaker 1: having to extract it from those platforms to give us 117 00:07:35,840 --> 00:07:37,440 Speaker 1: more of a sense of what really this means, because 118 00:07:37,440 --> 00:07:39,680 Speaker 1: I'm sure I butchered it. Frank b. N He is 119 00:07:39,760 --> 00:07:43,920 Speaker 1: chief executive officer of Looker, which is based in Santa Cruz, California, 120 00:07:44,080 --> 00:07:47,480 Speaker 1: and also joining us as man Deep Singh industry analysts 121 00:07:47,480 --> 00:07:51,400 Speaker 1: at Bloomberg Intelligence, who's based Who's in Chicago today? Frank, 122 00:07:51,680 --> 00:07:54,640 Speaker 1: did I totally butcher that? Can you explain a little 123 00:07:54,640 --> 00:07:59,320 Speaker 1: bit better what it is that Looker is seeking to accomplish? Sure? 124 00:07:59,440 --> 00:08:02,880 Speaker 1: Thanks a lot, I appreciate you having me on this morning. Um. Yeah, 125 00:08:02,880 --> 00:08:04,840 Speaker 1: you know, look, there's a software product, I mean where 126 00:08:04,840 --> 00:08:07,760 Speaker 1: a software as a service cloud product that helps companies. 127 00:08:08,360 --> 00:08:10,760 Speaker 1: Think of it as putting data into the hands of 128 00:08:10,880 --> 00:08:14,160 Speaker 1: everyone to make better decisions. You know, in the past, 129 00:08:14,240 --> 00:08:16,680 Speaker 1: I think we served the c suite, or we we 130 00:08:16,760 --> 00:08:19,280 Speaker 1: gave data to the CFO, or we gave data to 131 00:08:19,400 --> 00:08:21,960 Speaker 1: the important people. And now what we're seeing is we're 132 00:08:21,960 --> 00:08:25,559 Speaker 1: seeing companies who are who are disrupting their industries really 133 00:08:25,600 --> 00:08:28,120 Speaker 1: put data into the hands of everyone so they can 134 00:08:28,160 --> 00:08:30,880 Speaker 1: make better decisions. And and I mean we're doing like 135 00:08:30,920 --> 00:08:32,920 Speaker 1: you said, we're doing it at interesting companies, you know, 136 00:08:32,960 --> 00:08:37,200 Speaker 1: places like Amazon or the Economist or Sony Gaming, you know, 137 00:08:37,240 --> 00:08:40,000 Speaker 1: Blue Apron, Twilio. So so it's the companies who are 138 00:08:40,000 --> 00:08:42,120 Speaker 1: disrupting on data or finding if they if they give, 139 00:08:42,400 --> 00:08:44,760 Speaker 1: if they give better information to the people in their 140 00:08:44,880 --> 00:08:47,840 Speaker 1: organizations to make better decisions that they can they can 141 00:08:47,880 --> 00:08:50,840 Speaker 1: actually you know, work more on on you know, fact 142 00:08:50,960 --> 00:08:55,160 Speaker 1: rather than just intuition. Man Deep maybe just explained to 143 00:08:55,280 --> 00:08:57,480 Speaker 1: us exactly where this fits in and what kind of 144 00:08:57,520 --> 00:09:01,600 Speaker 1: growth the industry is seeing. We think bi analytics is 145 00:09:01,640 --> 00:09:05,880 Speaker 1: about a fifteen to twenty billion dollar market growing at 146 00:09:05,920 --> 00:09:09,319 Speaker 1: ten percent, and part of the growth, like Frank said, 147 00:09:09,520 --> 00:09:13,400 Speaker 1: is coming from this wave around a self service analytics 148 00:09:13,480 --> 00:09:18,200 Speaker 1: where companies are now letting their line of business users 149 00:09:18,320 --> 00:09:22,520 Speaker 1: to leverage analytics and really do it on their own 150 00:09:22,640 --> 00:09:25,240 Speaker 1: raw than giving it to an I T. Department to 151 00:09:25,400 --> 00:09:29,840 Speaker 1: crank out reports. So that has really driven the market 152 00:09:30,080 --> 00:09:32,800 Speaker 1: and I think it's it's there's a lot of upside 153 00:09:32,800 --> 00:09:36,480 Speaker 1: to you know, just the growth Outlook, Frank, I'm trying 154 00:09:36,520 --> 00:09:40,040 Speaker 1: to wrap my head around what exactly it means to 155 00:09:40,160 --> 00:09:43,680 Speaker 1: give the consumer, the little guy, uh, the access to 156 00:09:43,840 --> 00:09:47,160 Speaker 1: leverage data. Does that mean when somebody is looking up 157 00:09:47,160 --> 00:09:49,360 Speaker 1: their Twitter statistics to find out how many of their 158 00:09:49,440 --> 00:09:52,079 Speaker 1: users are fake or does it mean, uh, they can 159 00:09:52,200 --> 00:09:56,320 Speaker 1: use Amazon data that they've accumulated to give them a 160 00:09:56,400 --> 00:10:00,560 Speaker 1: personality profile of themselves. Yeah. It. You know, it doesn't 161 00:10:00,559 --> 00:10:03,880 Speaker 1: even have to be that complex. You know, companies you 162 00:10:03,920 --> 00:10:07,160 Speaker 1: know are operating, you know, with lots of systems. You know, 163 00:10:07,200 --> 00:10:10,520 Speaker 1: they have customer support systems, and they have finance systems, 164 00:10:10,480 --> 00:10:12,800 Speaker 1: and they have all of these things. But think think 165 00:10:12,840 --> 00:10:15,000 Speaker 1: if I'm just a customer support person on the phone 166 00:10:15,000 --> 00:10:17,760 Speaker 1: and I'm helping I'm helping people every day, I want 167 00:10:17,760 --> 00:10:20,240 Speaker 1: to have access to exactly what's happening. I want to 168 00:10:20,280 --> 00:10:23,200 Speaker 1: have access is the person I'm talking to? Are they 169 00:10:23,320 --> 00:10:26,360 Speaker 1: not paying their bills? Are they behind? Are they you know, 170 00:10:26,480 --> 00:10:29,440 Speaker 1: have they filed a lot of customer support tickets? What's 171 00:10:29,480 --> 00:10:32,000 Speaker 1: their overall health? How should I be you know, kind 172 00:10:32,000 --> 00:10:34,360 Speaker 1: of thinking about it when I'm talking to them in 173 00:10:34,480 --> 00:10:36,880 Speaker 1: real time, you know, at that moment. And I think, 174 00:10:37,160 --> 00:10:39,320 Speaker 1: you know, data has done well to serve the big 175 00:10:39,320 --> 00:10:41,880 Speaker 1: science projects that you mentioned, you know, like what's happening 176 00:10:41,880 --> 00:10:45,920 Speaker 1: on Twitter things like that, but it hasn't really been democratized, right, 177 00:10:46,000 --> 00:10:48,359 Speaker 1: It hasn't really been put into the hands of everybody, 178 00:10:48,559 --> 00:10:50,080 Speaker 1: and what we're trying to do a looker is put 179 00:10:50,080 --> 00:10:52,360 Speaker 1: it into the hands of everybody, like those customer support 180 00:10:52,440 --> 00:10:55,680 Speaker 1: people or or people on the warehouse floor taking pictures 181 00:10:55,679 --> 00:10:58,840 Speaker 1: of merchandise. How are those pictures, you know, performing on 182 00:10:58,880 --> 00:11:00,920 Speaker 1: the web really put it in the hands of everyone 183 00:11:00,920 --> 00:11:03,120 Speaker 1: so they can they can you know, make better decisions 184 00:11:03,120 --> 00:11:06,120 Speaker 1: every day. Frank, there's a tension here because on one level, 185 00:11:06,320 --> 00:11:09,520 Speaker 1: it's important to have a certain democratization of information, and 186 00:11:09,840 --> 00:11:12,440 Speaker 1: on the other hand there's some pretty big privacy concerns 187 00:11:12,440 --> 00:11:15,200 Speaker 1: that this race is as well. How do you address that? Yeah, 188 00:11:15,240 --> 00:11:19,200 Speaker 1: that's an interesting question. So data has been really decentralized, 189 00:11:19,280 --> 00:11:22,439 Speaker 1: and there's been this proliferation of lots of like spreadsheet 190 00:11:22,480 --> 00:11:25,760 Speaker 1: tools and workbook tools and and things that people carry 191 00:11:25,760 --> 00:11:27,800 Speaker 1: around on their on their laptops. And that's a big 192 00:11:27,840 --> 00:11:31,760 Speaker 1: security problem because you know, people go around with patient, private, 193 00:11:32,000 --> 00:11:34,680 Speaker 1: private information on their laptops or things like that because 194 00:11:34,679 --> 00:11:37,439 Speaker 1: they're downloading these spreadsheets. And what we're trying to do 195 00:11:37,480 --> 00:11:39,679 Speaker 1: and look or is sort of bring control over all 196 00:11:39,720 --> 00:11:42,199 Speaker 1: of that so that there's this single source of truth, 197 00:11:42,240 --> 00:11:45,400 Speaker 1: but it's also managed and governed in their security around 198 00:11:45,400 --> 00:11:47,480 Speaker 1: and I think that is a huge issue because we've 199 00:11:47,480 --> 00:11:49,360 Speaker 1: really been suffering over the last ten years of a 200 00:11:49,400 --> 00:11:53,080 Speaker 1: proliferation of small tools that let people walk around with 201 00:11:53,120 --> 00:11:56,679 Speaker 1: stuff on on their desktops and laptops. Tell us about 202 00:11:56,720 --> 00:12:01,000 Speaker 1: this language that you've developed, and why people should now 203 00:12:01,040 --> 00:12:05,480 Speaker 1: go and have to learn another computer language, not more 204 00:12:05,559 --> 00:12:08,520 Speaker 1: middleware please. Yeah, no, we're not. We're not, you know, 205 00:12:08,600 --> 00:12:11,160 Speaker 1: saying that that business users would learn any kind of 206 00:12:11,240 --> 00:12:14,080 Speaker 1: data language. But but you know, people coming out of 207 00:12:14,120 --> 00:12:16,199 Speaker 1: school today who want to get into tech are often 208 00:12:16,240 --> 00:12:18,640 Speaker 1: looking at data as a career. And you know, you 209 00:12:18,640 --> 00:12:21,040 Speaker 1: have a lot of people are majoring in economics or 210 00:12:21,120 --> 00:12:23,720 Speaker 1: business and and they're doing data stuff. It's one of 211 00:12:23,720 --> 00:12:26,960 Speaker 1: the fastest growing career paths. Yeah, you end up taking 212 00:12:27,000 --> 00:12:30,439 Speaker 1: that sequel server course right exactly. And what we want 213 00:12:30,440 --> 00:12:33,280 Speaker 1: to do is we want to give those people interesting 214 00:12:33,360 --> 00:12:36,120 Speaker 1: tools so they can better serve their companies. So the 215 00:12:36,160 --> 00:12:38,240 Speaker 1: people in the company and the support team or the 216 00:12:38,559 --> 00:12:40,800 Speaker 1: you know, the warehouse, they're not you know, learning a 217 00:12:40,880 --> 00:12:43,920 Speaker 1: data language by any means. But we're sort of leveraging 218 00:12:43,960 --> 00:12:45,800 Speaker 1: these people who are coming out of school with a 219 00:12:45,840 --> 00:12:50,040 Speaker 1: bit of data background and allowing them to curate an experience, right, 220 00:12:50,120 --> 00:12:53,440 Speaker 1: curate you know, a better application for those for those 221 00:12:53,520 --> 00:12:56,840 Speaker 1: end business users, Man Deep, what's the barrier to entry here? 222 00:12:56,920 --> 00:12:59,520 Speaker 1: Why aren't some of the behemoth tech companies coming up 223 00:12:59,559 --> 00:13:03,120 Speaker 1: with their own analytics systems and UH and using them 224 00:13:03,240 --> 00:13:07,000 Speaker 1: to democratize their offerings. I think right now, this is 225 00:13:07,160 --> 00:13:10,360 Speaker 1: coming up bottom up, So you're you're seeing a lot 226 00:13:10,400 --> 00:13:13,320 Speaker 1: of the startups, I mean a Tableau and Click are 227 00:13:13,360 --> 00:13:17,000 Speaker 1: not startups anymore. But they really came up with a 228 00:13:17,040 --> 00:13:20,280 Speaker 1: new way to think outside of the data warehouse realm. 229 00:13:20,480 --> 00:13:23,000 Speaker 1: So a lot of this kind of functionality was done 230 00:13:23,000 --> 00:13:26,600 Speaker 1: in a dataware housing platform. What these guys did was 231 00:13:27,040 --> 00:13:30,480 Speaker 1: came up with this self service analytics platform where, like 232 00:13:30,640 --> 00:13:35,199 Speaker 1: Frank said, you you can have the users crankout reports, 233 00:13:35,280 --> 00:13:38,800 Speaker 1: analyzed the data. And this whole proliferation of cloud and 234 00:13:38,920 --> 00:13:43,640 Speaker 1: AI is actually a tail wind for for this analytics wave. 235 00:13:43,760 --> 00:13:47,240 Speaker 1: So the large behemoths are still catching up, you know, 236 00:13:47,480 --> 00:13:51,080 Speaker 1: just integrating the new technologies, and they haven't really focused 237 00:13:51,080 --> 00:13:54,040 Speaker 1: on the analytics aspect that much. Man Deep, can you 238 00:13:54,080 --> 00:13:57,520 Speaker 1: just give us a little more detail about artificial intelligence? 239 00:13:57,559 --> 00:14:01,200 Speaker 1: Give you about thirty seconds here, sure, So AI is 240 00:14:01,320 --> 00:14:05,600 Speaker 1: really coming together in the sense that the basis of 241 00:14:06,120 --> 00:14:10,400 Speaker 1: performing AI analytics is data and once you have data 242 00:14:10,440 --> 00:14:14,000 Speaker 1: aggregated and you can process it in real time through cloud, 243 00:14:14,400 --> 00:14:17,400 Speaker 1: it enables a lot of things in terms of doing 244 00:14:17,440 --> 00:14:20,360 Speaker 1: things real time, which can drive our o I for 245 00:14:20,440 --> 00:14:23,480 Speaker 1: a lot of industries. So that's really the genesis of 246 00:14:23,520 --> 00:14:26,360 Speaker 1: this wave of self service analytics. Frank, I'm only going 247 00:14:26,400 --> 00:14:30,120 Speaker 1: to give you ten seconds. Ai. Yeah, No, I think 248 00:14:30,120 --> 00:14:32,280 Speaker 1: that you know, we're finally starting to see, you know, 249 00:14:32,320 --> 00:14:35,360 Speaker 1: out of the benefit of the cloud platforms at Amazon 250 00:14:35,400 --> 00:14:37,880 Speaker 1: and Google and whatnot, the ability for everyone to be 251 00:14:38,040 --> 00:14:40,920 Speaker 1: like those organizations. And I think you'll see those kind 252 00:14:40,920 --> 00:14:43,480 Speaker 1: of technologies go into the hands of smaller you know, 253 00:14:43,720 --> 00:14:46,880 Speaker 1: or smaller companies. Thank you very much, Frank Bien. He 254 00:14:47,040 --> 00:14:49,640 Speaker 1: is the chief executive of Looker. They are based in 255 00:14:49,720 --> 00:14:52,600 Speaker 1: Santa Cruz and our thanks to man Deep Singh, industry 256 00:14:52,640 --> 00:15:09,240 Speaker 1: analysts for Bloomberg Intelligence. One of the biggest movers today 257 00:15:09,240 --> 00:15:13,120 Speaker 1: in the stock market is Scripts Networks Interactive, with a 258 00:15:13,240 --> 00:15:16,600 Speaker 1: gain in its shares of about fourteen and a half percent. 259 00:15:16,680 --> 00:15:20,520 Speaker 1: This comes after Discovery and via commercead to be talking 260 00:15:20,560 --> 00:15:24,000 Speaker 1: about combining with this network and just for anyone who 261 00:15:24,040 --> 00:15:28,240 Speaker 1: doesn't know, we're talking HDTV and food network. People want uh, 262 00:15:28,480 --> 00:15:31,760 Speaker 1: some some food throwdowns and some renovations of their homes. 263 00:15:31,760 --> 00:15:33,000 Speaker 1: Here to give us a little bit more of a 264 00:15:33,040 --> 00:15:35,840 Speaker 1: sense of how realistic a deal is is Alex Sherman, 265 00:15:35,920 --> 00:15:39,760 Speaker 1: Technology Media and Telecom Mergers and Acquisitions reporter for bloom 266 00:15:39,760 --> 00:15:43,120 Speaker 1: Brick News. Alex, do you think, first of all, who 267 00:15:43,120 --> 00:15:46,360 Speaker 1: do you think has the upper hand? Discovery or Viacom. 268 00:15:46,440 --> 00:15:49,640 Speaker 1: So this is a tricky one because I think there 269 00:15:49,640 --> 00:15:52,800 Speaker 1: are problems with both buyers um and that, and therefore, 270 00:15:52,920 --> 00:15:55,080 Speaker 1: while the market seems to be pretty confident that a 271 00:15:55,120 --> 00:15:57,600 Speaker 1: deal is going to happen here, I am less confident. 272 00:15:58,200 --> 00:16:00,680 Speaker 1: I can look, I reported this story, so I can 273 00:16:00,720 --> 00:16:03,480 Speaker 1: say that there is a sales process going on. But 274 00:16:03,560 --> 00:16:07,240 Speaker 1: Scripts has been perennially for sale. Three years ago. Discovery 275 00:16:07,240 --> 00:16:10,280 Speaker 1: almost bought Scripts and that deal fell apart. So there 276 00:16:10,360 --> 00:16:12,840 Speaker 1: is some logic there that Discovery is at least interested. 277 00:16:13,280 --> 00:16:15,640 Speaker 1: And Discovery has a lot of programming that is geared 278 00:16:15,640 --> 00:16:18,480 Speaker 1: towards men in general, and Scripts has a lot of 279 00:16:18,520 --> 00:16:21,320 Speaker 1: programming that is geared toward women in general. So there 280 00:16:21,360 --> 00:16:23,840 Speaker 1: there is sort of a natural fit there. But that 281 00:16:23,880 --> 00:16:26,880 Speaker 1: deal didn't get done for a reason, in part because 282 00:16:27,320 --> 00:16:31,000 Speaker 1: there was a bid ask difference. And now if if 283 00:16:31,080 --> 00:16:34,680 Speaker 1: Scripts is running a sales process, you'd imagine again Discovery 284 00:16:34,680 --> 00:16:37,200 Speaker 1: would have to pay up. And Discovery is controlled or 285 00:16:37,200 --> 00:16:39,960 Speaker 1: at least not quite controlled. But let's say thirty of 286 00:16:39,960 --> 00:16:42,600 Speaker 1: the votes are controlled by John Malone, who is not 287 00:16:42,760 --> 00:16:45,320 Speaker 1: known to bid up in auctions, So that is a 288 00:16:45,360 --> 00:16:48,880 Speaker 1: problem with Discovery. Viacom's problem is if you take a 289 00:16:48,880 --> 00:16:52,200 Speaker 1: look at Viacom shares, you don't You only have to 290 00:16:52,200 --> 00:16:55,360 Speaker 1: go back until about March or so when Viacom was 291 00:16:55,360 --> 00:16:57,960 Speaker 1: trading out about forty six dollars a share. They're now 292 00:16:58,000 --> 00:17:00,880 Speaker 1: trading at thirty six dollars a share. So any deal 293 00:17:00,920 --> 00:17:03,600 Speaker 1: with Viacom would probably be at least some component of 294 00:17:03,600 --> 00:17:05,920 Speaker 1: it would be stock, and they sort of missed their 295 00:17:06,040 --> 00:17:10,360 Speaker 1: chance to use their currency. Well uh, and now their 296 00:17:10,520 --> 00:17:13,640 Speaker 1: company is about fourteen fourteen and a half billion dollar 297 00:17:13,760 --> 00:17:17,879 Speaker 1: market cap company. Scripts is about let's say a nine 298 00:17:17,920 --> 00:17:21,200 Speaker 1: point eight nine point nine is almost ten billion dollar 299 00:17:21,280 --> 00:17:24,720 Speaker 1: market cap company. It would be a huge deal for Viacom, 300 00:17:24,960 --> 00:17:28,159 Speaker 1: and they wouldn't be using their stock at its high price, 301 00:17:28,320 --> 00:17:31,199 Speaker 1: So that's probably the problem with Viacom. They're just from 302 00:17:31,240 --> 00:17:34,640 Speaker 1: a financial standpoint, this would be an enormous bet. Viacom 303 00:17:34,680 --> 00:17:38,080 Speaker 1: almost merged with CBS. Is Scripts really the right bet? 304 00:17:38,280 --> 00:17:41,919 Speaker 1: I don't know. Doesn't this also lead to Sherry Redstone, 305 00:17:42,920 --> 00:17:45,720 Speaker 1: So there's control issues on both front to your point, 306 00:17:45,760 --> 00:17:49,800 Speaker 1: because say you need a genealogical assistant here, because there 307 00:17:49,800 --> 00:17:52,639 Speaker 1: are a lot of tangled right. So Summer Redstone is 308 00:17:52,640 --> 00:17:55,480 Speaker 1: still alive, but not the only family, right. But ya, exactly, 309 00:17:55,480 --> 00:17:57,480 Speaker 1: They're all run by families, which is, by the way, 310 00:17:57,520 --> 00:18:01,680 Speaker 1: a bigger reason, even beyond Viacom, why any of these 311 00:18:01,720 --> 00:18:04,439 Speaker 1: media deals haven't happened yet. So let me take a 312 00:18:04,480 --> 00:18:07,800 Speaker 1: step back here. All of these media companies probably should 313 00:18:07,800 --> 00:18:12,440 Speaker 1: consolidate with each other, and by all I mean a MC, Scripts, Discovery, Viacom. 314 00:18:12,480 --> 00:18:16,960 Speaker 1: They're all challenged right now because cable providers are offering 315 00:18:17,119 --> 00:18:20,399 Speaker 1: skinnier bundles where they for years and years and years, 316 00:18:20,640 --> 00:18:23,640 Speaker 1: these companies have done phenomenally well because when it came 317 00:18:23,640 --> 00:18:27,640 Speaker 1: time to negotiating contracts with the PayTV providers I mean 318 00:18:27,680 --> 00:18:30,880 Speaker 1: the Comcast, Direct TV, Dish Network, etcetera of the world, 319 00:18:30,880 --> 00:18:33,320 Speaker 1: how you get your TV. When it came down to 320 00:18:33,359 --> 00:18:36,960 Speaker 1: negotiating contracts with them, the deal was you either take 321 00:18:37,000 --> 00:18:40,639 Speaker 1: all of our channels or none. That is starting to 322 00:18:40,840 --> 00:18:44,960 Speaker 1: happen less and less these days because the leverage has 323 00:18:45,040 --> 00:18:48,000 Speaker 1: moved towards the PayTV providers who have basically said, we'll 324 00:18:48,000 --> 00:18:51,040 Speaker 1: take none and then what are you gonna do about it? Uh? 325 00:18:51,240 --> 00:18:53,080 Speaker 1: And the reason they're being that you can get a 326 00:18:53,080 --> 00:18:56,560 Speaker 1: lot of these programming, the taped programming in other ways. 327 00:18:56,600 --> 00:18:58,359 Speaker 1: You can get some on Netflix, you can get some 328 00:18:58,680 --> 00:19:01,520 Speaker 1: on Hulu. Uh. You know, I want to just go 329 00:19:01,560 --> 00:19:04,480 Speaker 1: a little bit broader. And when we're talking about h 330 00:19:04,560 --> 00:19:07,800 Speaker 1: g TV, we're talking reality television shows, and I'm wondering 331 00:19:08,080 --> 00:19:11,240 Speaker 1: how much their business model is being challenged by YouTube 332 00:19:11,400 --> 00:19:13,600 Speaker 1: and the fact that the barrier to entry has just 333 00:19:13,680 --> 00:19:16,840 Speaker 1: gone down so dramatically, So a lot of the programming 334 00:19:16,880 --> 00:19:19,440 Speaker 1: has been challenged. The programming that has not been challenged 335 00:19:19,520 --> 00:19:22,400 Speaker 1: is live programming because you can't get that on YouTube. 336 00:19:22,680 --> 00:19:26,800 Speaker 1: But Discovery and and viacommon scripts they don't have live programming. 337 00:19:27,000 --> 00:19:30,280 Speaker 1: So they are really in the crosshairs of how people 338 00:19:30,320 --> 00:19:34,560 Speaker 1: watch TV changing and and and it's it's not it's 339 00:19:34,680 --> 00:19:38,280 Speaker 1: it's not good for these companies, which is why theoretically 340 00:19:38,880 --> 00:19:42,879 Speaker 1: they could merge and then they could sort of have 341 00:19:43,000 --> 00:19:44,800 Speaker 1: a little bit more leverage because they could be like, well, 342 00:19:44,800 --> 00:19:48,240 Speaker 1: we have these three channels that are still good, like HDTV, 343 00:19:48,320 --> 00:19:50,359 Speaker 1: A lot of people still watch that. And you know, 344 00:19:50,400 --> 00:19:53,400 Speaker 1: Discovery has a few channels that people still watch. And yeah, 345 00:19:53,440 --> 00:19:55,240 Speaker 1: we have a bunch of these channels that nobody still watches. 346 00:19:55,280 --> 00:19:58,120 Speaker 1: But if we put our combined forces together, we come 347 00:19:58,160 --> 00:20:00,359 Speaker 1: up with a company that at least still has some average. 348 00:20:00,400 --> 00:20:02,959 Speaker 1: Of course, you end up then with a huge company 349 00:20:03,000 --> 00:20:06,280 Speaker 1: that has a lot of channels that nobody still watches too, 350 00:20:06,400 --> 00:20:09,000 Speaker 1: So this runs both ways, where yeah, they get a 351 00:20:09,000 --> 00:20:10,680 Speaker 1: little bit more leverage, But then what are you gonna 352 00:20:10,680 --> 00:20:14,399 Speaker 1: do with the fourteen channels that no one's watching anymore? Wow? 353 00:20:14,440 --> 00:20:17,200 Speaker 1: I don't know what I know. That is the challenge, 354 00:20:17,240 --> 00:20:19,040 Speaker 1: I guess. But but Alex, can you just do a 355 00:20:19,080 --> 00:20:21,879 Speaker 1: little family feud for us? I was on a family 356 00:20:21,880 --> 00:20:25,240 Speaker 1: feud by the way, like literally on family. But we 357 00:20:25,320 --> 00:20:28,879 Speaker 1: got to talk about that, all right, family, I love it, alright. 358 00:20:29,040 --> 00:20:31,639 Speaker 1: So did you get to meet Richard Dawson. It was 359 00:20:31,640 --> 00:20:35,320 Speaker 1: not Richard Dawson. It was Louis Anderson. I was not 360 00:20:35,400 --> 00:20:38,360 Speaker 1: kissed on the cheek by Richard Dawson. All right, So 361 00:20:39,080 --> 00:20:41,800 Speaker 1: who are the families involved here? So the families are 362 00:20:41,800 --> 00:20:45,320 Speaker 1: the Redstone family, runs Viacom. Sumner Redstone is still alive. 363 00:20:45,600 --> 00:20:48,760 Speaker 1: He's very ill. Sherry Redstone has basically been running the 364 00:20:48,760 --> 00:20:52,920 Speaker 1: company and in his stead for years now. Scripts is 365 00:20:52,960 --> 00:20:58,600 Speaker 1: also owned by a family, and it's the idea behind 366 00:20:59,280 --> 00:21:02,920 Speaker 1: whether or not Scripts would sell has always been like, well, 367 00:21:02,960 --> 00:21:05,040 Speaker 1: the family is on board if the price is right. 368 00:21:05,720 --> 00:21:08,840 Speaker 1: Discovery is owned by John Malone. A MC is owned 369 00:21:08,840 --> 00:21:14,240 Speaker 1: by the Dolan family. There are you know. CBS is 370 00:21:14,280 --> 00:21:17,040 Speaker 1: sort of a free radical. That's the Deaths John Malone's 371 00:21:17,119 --> 00:21:19,560 Speaker 1: term out there, but but only in the sense that 372 00:21:19,600 --> 00:21:22,439 Speaker 1: the Redstones of course controls CBS two. They're only a 373 00:21:22,480 --> 00:21:25,480 Speaker 1: free radical in the sense that less Moonvez maybe has 374 00:21:25,480 --> 00:21:28,439 Speaker 1: more control than than most CEOs with that company. So 375 00:21:28,720 --> 00:21:30,840 Speaker 1: some people think that CBS might be able to do 376 00:21:30,960 --> 00:21:34,480 Speaker 1: something on the pure force of less Moonvez, the CEO 377 00:21:34,560 --> 00:21:36,840 Speaker 1: of that company, even though he doesn't own it. So 378 00:21:36,920 --> 00:21:39,040 Speaker 1: each of these families is owned. Each of these companies 379 00:21:39,080 --> 00:21:42,240 Speaker 1: is owned by family, and yes they're publicly traded companies, 380 00:21:42,600 --> 00:21:45,399 Speaker 1: but whether or not they actually trade depends really on 381 00:21:45,440 --> 00:21:47,320 Speaker 1: whether or not the families want to keep owning the 382 00:21:47,359 --> 00:21:50,600 Speaker 1: company or whether they're game to do a deal. As always, 383 00:21:50,640 --> 00:21:53,120 Speaker 1: thank you very much, Alex Sherman. He is our mergers 384 00:21:53,119 --> 00:22:08,080 Speaker 1: and acquisitions reporter for Bloomberg. Well, I want to turn 385 00:22:08,160 --> 00:22:11,840 Speaker 1: to Ksey Matthews, an economist and the chief investment officer 386 00:22:11,960 --> 00:22:15,440 Speaker 1: for U m B Bank with the responsibility for over 387 00:22:15,480 --> 00:22:20,280 Speaker 1: eight billion dollars. They're based in Kansas City, and Casey Matthews, 388 00:22:20,280 --> 00:22:23,080 Speaker 1: thanks very much for coming into our Bloombrook studio. Good morning, 389 00:22:23,080 --> 00:22:25,800 Speaker 1: Good to see you. I'm wondering if you could. First 390 00:22:25,800 --> 00:22:29,199 Speaker 1: of all, I'm outgunned here because I understand that there 391 00:22:29,200 --> 00:22:32,800 Speaker 1: are two people in the studio who have connections to Wisconsin. 392 00:22:33,359 --> 00:22:37,520 Speaker 1: That's right, grew up just outside Milwaukee, Wisconsin, all right, 393 00:22:37,640 --> 00:22:42,199 Speaker 1: and Lisa Abrahmwitz. Come on. My mother's family is from Wiscon, Okay. 394 00:22:42,240 --> 00:22:44,960 Speaker 1: So that reason I ask is what is the view 395 00:22:45,080 --> 00:22:49,199 Speaker 1: of what is going on, not only with the politics 396 00:22:49,200 --> 00:22:54,040 Speaker 1: and song, but with money and people's relationship. What do 397 00:22:54,040 --> 00:22:57,159 Speaker 1: they think to feel confident that the stock market is 398 00:22:57,160 --> 00:23:00,439 Speaker 1: going to stay moving higher? Well, yeah, let me let 399 00:23:00,480 --> 00:23:02,560 Speaker 1: me start with maybe confidence in the business sector. I'm 400 00:23:02,600 --> 00:23:05,680 Speaker 1: more of a mackerel stage, and we'll get into the micro. 401 00:23:06,280 --> 00:23:08,560 Speaker 1: But it's interesting that when you look at some of 402 00:23:08,600 --> 00:23:12,720 Speaker 1: the surveys the n F I b small business optimism. Clearly, 403 00:23:12,960 --> 00:23:16,120 Speaker 1: business owners and executives feel good about things, but when 404 00:23:16,160 --> 00:23:17,960 Speaker 1: you ask them, what are you gonna do about it, 405 00:23:18,600 --> 00:23:21,119 Speaker 1: they're sitting on their hands because they're saying, tell me 406 00:23:21,160 --> 00:23:23,199 Speaker 1: what my tax rate is going to be, tell me 407 00:23:23,280 --> 00:23:25,760 Speaker 1: what my regulatory environment is going to be. So they 408 00:23:25,760 --> 00:23:28,359 Speaker 1: feel good that there's a light at the end of 409 00:23:28,359 --> 00:23:32,399 Speaker 1: the tunnel, but until they get some concrete evidence that 410 00:23:32,520 --> 00:23:36,000 Speaker 1: confidence the lack of uncertainty, they're not going to take 411 00:23:36,080 --> 00:23:39,520 Speaker 1: any action. And that's why you see a mediocre economy. Well, 412 00:23:39,560 --> 00:23:41,760 Speaker 1: you know, I'm interested in in the fact that you 413 00:23:41,760 --> 00:23:44,720 Speaker 1: start on this note because we were just talking about frankly, 414 00:23:44,960 --> 00:23:47,520 Speaker 1: how the bump that we've seen in stocks has been 415 00:23:47,800 --> 00:23:52,920 Speaker 1: less due to President Trump's election and the expected changes politically, 416 00:23:54,040 --> 00:23:57,200 Speaker 1: but more due to the earnings growth that we've seen 417 00:23:57,400 --> 00:24:00,960 Speaker 1: and the boom there. So it's sort of trusts with 418 00:24:01,000 --> 00:24:03,560 Speaker 1: this sitting on the hands wondering what's going to happen. 419 00:24:03,880 --> 00:24:07,520 Speaker 1: Mediocrity that you're describing when you're talking about the expected 420 00:24:07,560 --> 00:24:09,919 Speaker 1: eight percent growth that you are looking for in the 421 00:24:09,920 --> 00:24:12,960 Speaker 1: second quarter earnings and earnings growth in the second quarter, 422 00:24:13,000 --> 00:24:15,760 Speaker 1: that's right. Well, if you go you have to go 423 00:24:15,840 --> 00:24:18,080 Speaker 1: back to end of two thousand and fourteen, where we 424 00:24:18,080 --> 00:24:21,360 Speaker 1: saw an earnings recession. In corporate America, we had seven 425 00:24:21,480 --> 00:24:26,080 Speaker 1: quarters of earnings contraction. It's interesting the timing. It wasn't 426 00:24:26,160 --> 00:24:29,440 Speaker 1: until the third quarter of two thousand and sixteen when 427 00:24:29,480 --> 00:24:32,560 Speaker 1: we got the data, was middle of October through November. 428 00:24:32,800 --> 00:24:35,640 Speaker 1: The election, right, all of a sudden, we saw this 429 00:24:35,800 --> 00:24:39,320 Speaker 1: ray of sunshine. Earnings turned positive at whopping two percent 430 00:24:39,600 --> 00:24:41,399 Speaker 1: in the fourth quarter they were eight or nine the 431 00:24:41,440 --> 00:24:44,680 Speaker 1: first quarter of this year eighteen percent, and all of 432 00:24:44,720 --> 00:24:47,160 Speaker 1: a sudden, stocks moved and a lot of people label 433 00:24:47,200 --> 00:24:50,800 Speaker 1: at the Trump rally. Well, I call it an earnings rally. 434 00:24:50,840 --> 00:24:53,639 Speaker 1: So do you think that people are conflating the election 435 00:24:53,920 --> 00:24:56,840 Speaker 1: with the earnings growth that we saw really accelerate at 436 00:24:56,840 --> 00:25:00,720 Speaker 1: the beginning of the year. So just translate this into 437 00:25:01,080 --> 00:25:05,159 Speaker 1: market positioning. Does this mean that you are bullish on 438 00:25:05,520 --> 00:25:08,679 Speaker 1: US stocks? And if so, which sectors in particular? And 439 00:25:08,720 --> 00:25:11,920 Speaker 1: how much longer can this rally continue? Yeah, we are 440 00:25:12,080 --> 00:25:15,199 Speaker 1: bullish on stocks. Were overweight risk based assets for a 441 00:25:15,280 --> 00:25:19,280 Speaker 1: number of reasons. The macro data points to an improving economy. 442 00:25:19,320 --> 00:25:21,240 Speaker 1: One thing I look at is the I s M 443 00:25:21,400 --> 00:25:25,359 Speaker 1: new orders, very leading indicator when business owners and executives 444 00:25:25,400 --> 00:25:28,600 Speaker 1: tell me they're placing orders, it tells me their business 445 00:25:28,680 --> 00:25:31,760 Speaker 1: is good. Um, so we're at you know, two year 446 00:25:31,840 --> 00:25:35,399 Speaker 1: highs on those numbers. The bond market gives us clues 447 00:25:35,760 --> 00:25:37,480 Speaker 1: when you look at the shape of the yield curve. 448 00:25:37,560 --> 00:25:40,800 Speaker 1: We have at a ninety basis point a slope between 449 00:25:40,800 --> 00:25:44,520 Speaker 1: twos and tens. And typically when you start to see 450 00:25:44,520 --> 00:25:47,400 Speaker 1: the yield curve flatten, which we started to see. Even 451 00:25:47,440 --> 00:25:50,920 Speaker 1: when it gets flat, you still have twelve to twenty 452 00:25:50,960 --> 00:25:55,480 Speaker 1: four months before the stock market reacts. So I believe 453 00:25:55,680 --> 00:25:59,639 Speaker 1: that earnings will continue to support stock prices. That's not 454 00:25:59,680 --> 00:26:01,240 Speaker 1: to say we won't see a correction. We haven't seen 455 00:26:01,240 --> 00:26:03,800 Speaker 1: a correction a long time. They're normal and healthy. But 456 00:26:03,960 --> 00:26:07,680 Speaker 1: longer term, I think stocks will outperform fixed income. In 457 00:26:07,720 --> 00:26:11,520 Speaker 1: cash sectors, we like you know, you've seen some weakness 458 00:26:11,520 --> 00:26:15,040 Speaker 1: in the dollar that will help exports those industrials. We 459 00:26:15,119 --> 00:26:18,680 Speaker 1: do like finance finances sitting here waiting for a steep 460 00:26:18,760 --> 00:26:22,960 Speaker 1: yield curve, higher interest rates, and regulation reform. So we're 461 00:26:23,000 --> 00:26:26,240 Speaker 1: pretty broad based as far as our sector allocation. I 462 00:26:26,280 --> 00:26:28,880 Speaker 1: want to ask you turned back to something you said 463 00:26:28,920 --> 00:26:33,840 Speaker 1: about the business feels right that was the macro picture. Um, 464 00:26:34,200 --> 00:26:41,879 Speaker 1: what if the macro picture is about ever increasing uncertainty. 465 00:26:42,160 --> 00:26:44,240 Speaker 1: In other words, you're waiting for the dust to settle, 466 00:26:44,320 --> 00:26:48,080 Speaker 1: and the companies that are making new strides and whatever 467 00:26:48,080 --> 00:26:50,480 Speaker 1: it is, they're not waiting for the dust to settle 468 00:26:50,520 --> 00:26:53,199 Speaker 1: because they realize that it doesn't settle anymore. You have 469 00:26:53,240 --> 00:26:56,480 Speaker 1: too many market participants. You have too many uh companies 470 00:26:56,520 --> 00:26:59,199 Speaker 1: that can come in with a laptop and you know, 471 00:27:00,119 --> 00:27:04,960 Speaker 1: genius mind and disrupt the entire transportation industry like Uber 472 00:27:05,119 --> 00:27:09,119 Speaker 1: or hotels like Airbnb. Well, I think what happens is 473 00:27:09,160 --> 00:27:12,360 Speaker 1: if you don't get clarity on some of these issues, 474 00:27:12,480 --> 00:27:15,639 Speaker 1: you're stuck in the mud at that two percent GDP growth. 475 00:27:16,960 --> 00:27:18,639 Speaker 1: So yeah, but they're not going to do it for 476 00:27:18,680 --> 00:27:20,359 Speaker 1: the health of the GDP grow They're going to do 477 00:27:20,359 --> 00:27:22,040 Speaker 1: it because they know that they can make more money. 478 00:27:22,040 --> 00:27:24,000 Speaker 1: That it's good for their business. Right. But so if 479 00:27:24,040 --> 00:27:26,399 Speaker 1: the competition comes in, you know the guy down the 480 00:27:26,400 --> 00:27:30,560 Speaker 1: street says, well, no, I'm gonna offer it better, faster, cheaper. Well, 481 00:27:30,600 --> 00:27:33,479 Speaker 1: those are disruptors that are in the marketplace today. Amazon. 482 00:27:33,600 --> 00:27:35,359 Speaker 1: Does that need to happen more? I mean, so I'm 483 00:27:35,400 --> 00:27:37,879 Speaker 1: wondering why you would you be investing in, you know, 484 00:27:37,920 --> 00:27:41,679 Speaker 1: like healthcare and biotechnology and technology. Is that not an 485 00:27:42,000 --> 00:27:45,720 Speaker 1: area of where you want to put some of your chicks. Sure? Absolutely. 486 00:27:45,720 --> 00:27:49,359 Speaker 1: Like I said, we're pretty much diversified across broad sectors 487 00:27:50,119 --> 00:27:53,119 Speaker 1: because of that situation. But I think that's that healthy 488 00:27:53,200 --> 00:27:57,080 Speaker 1: competition makes us better, makes corporations better, more efficient. It 489 00:27:57,080 --> 00:28:00,680 Speaker 1: will increase earnings and of course revenue growth. But you've 490 00:28:00,720 --> 00:28:02,919 Speaker 1: got the disruptors like Amazon, and you've got some of 491 00:28:02,920 --> 00:28:06,359 Speaker 1: these other companies that um like Nelson Pelts to try 492 00:28:06,400 --> 00:28:08,440 Speaker 1: in a blue apron. You know, some of these things 493 00:28:08,520 --> 00:28:12,400 Speaker 1: show up and almost disappear. Well you know, uh not yet. 494 00:28:12,440 --> 00:28:16,240 Speaker 1: It hasn't disappeared yet, but but it definitely is track. Yeah, 495 00:28:16,240 --> 00:28:18,399 Speaker 1: it's on that track, you know. Casey. I want to 496 00:28:18,440 --> 00:28:20,480 Speaker 1: just talk about how you're saying that the fundamentals look 497 00:28:20,480 --> 00:28:23,280 Speaker 1: good and so the rally will continue. One problem that 498 00:28:23,359 --> 00:28:27,520 Speaker 1: people raises that the markets have become broadly public markets anyway, 499 00:28:27,560 --> 00:28:31,720 Speaker 1: have become broadly detached from fundamentals due to central banks stimulus. 500 00:28:31,800 --> 00:28:34,960 Speaker 1: And now that the economy is improving, as central banks 501 00:28:34,960 --> 00:28:37,600 Speaker 1: withdraw the stimulus, it doesn't matter that fundamentals are improving. 502 00:28:37,840 --> 00:28:40,320 Speaker 1: It's going to cause a sell off that could potentially 503 00:28:40,400 --> 00:28:42,800 Speaker 1: be more sustained or at least cap any future gains. 504 00:28:43,000 --> 00:28:45,840 Speaker 1: I mean, are you concerned about that? Does that matter 505 00:28:45,920 --> 00:28:48,520 Speaker 1: to you? I'm not. It's interesting some things we think 506 00:28:48,560 --> 00:28:51,880 Speaker 1: about is does the FED really matter? And when you 507 00:28:51,920 --> 00:28:55,760 Speaker 1: think about it, well, I would assume most CFOs have 508 00:28:56,040 --> 00:28:59,400 Speaker 1: termed out their debt if they have access to either 509 00:28:59,480 --> 00:29:01,600 Speaker 1: capital more keats, or if there's a lot of pressure 510 00:29:01,640 --> 00:29:04,280 Speaker 1: on banks to term out their debt. Same thing with 511 00:29:04,360 --> 00:29:07,760 Speaker 1: the consumer now today, something like ninety plus percent of 512 00:29:07,800 --> 00:29:10,560 Speaker 1: mortgages are a fixed rate. Mortgages are not veritable rate 513 00:29:10,640 --> 00:29:12,440 Speaker 1: like the housing crisis, So I think you're in a 514 00:29:12,480 --> 00:29:16,720 Speaker 1: different environment. So I'm not sure the FED today with this, 515 00:29:16,800 --> 00:29:20,680 Speaker 1: with seven years of monetary stimulus, really matters if rates, 516 00:29:21,200 --> 00:29:23,240 Speaker 1: you know, move up slightly, I don't know if it 517 00:29:23,320 --> 00:29:27,600 Speaker 1: really matters dramatically to corporate runnings. Really interesting, especially because 518 00:29:27,840 --> 00:29:30,360 Speaker 1: for so long there was is there is no alternative 519 00:29:30,440 --> 00:29:33,680 Speaker 1: trade where basically people weren't getting an yield in bonds, 520 00:29:33,720 --> 00:29:35,320 Speaker 1: you might as well go to stocks. You know, there's 521 00:29:35,320 --> 00:29:36,720 Speaker 1: a concern that people are going to rotate out of 522 00:29:36,720 --> 00:29:38,960 Speaker 1: stocks and go back into bonds if yields get high enough. 523 00:29:39,080 --> 00:29:41,920 Speaker 1: Especially because I mean realistically, if if yields can remain 524 00:29:41,960 --> 00:29:43,960 Speaker 1: this low, that means that growth will not pick up 525 00:29:44,440 --> 00:29:47,120 Speaker 1: that much. But you've got a long ways to go 526 00:29:48,040 --> 00:29:50,400 Speaker 1: before people go out of stocks and into bonds because 527 00:29:50,440 --> 00:29:53,200 Speaker 1: of the yield. Kasey Matthews, thank you so much for 528 00:29:53,240 --> 00:29:55,640 Speaker 1: joining us. Truly a pleasure having you in the studio here. 529 00:29:55,720 --> 00:29:58,760 Speaker 1: Casey Matthews is economist and chief investment officer. You m 530 00:29:58,800 --> 00:30:01,880 Speaker 1: B Bank, which is based in Kansas City, Missouri, UH 531 00:30:01,880 --> 00:30:04,920 Speaker 1: and manages over eight billion dollars need joins us here 532 00:30:04,960 --> 00:30:11,080 Speaker 1: in our Bloomberg eleven three oh studios. Thanks for listening 533 00:30:11,160 --> 00:30:14,040 Speaker 1: to the Bloomberg P and L podcast. You can subscribe 534 00:30:14,080 --> 00:30:17,640 Speaker 1: and listen to interviews at Apple Podcasts, SoundCloud, or whatever 535 00:30:17,720 --> 00:30:21,200 Speaker 1: podcast platform you prefer. I'm pim Fox. I'm on Twitter 536 00:30:21,480 --> 00:30:25,240 Speaker 1: at pim Fox. I'm on Twitter at Lisa abramoids one. 537 00:30:25,480 --> 00:30:28,160 Speaker 1: Before the podcast, you can always catch us worldwide on 538 00:30:28,200 --> 00:30:29,040 Speaker 1: Bloomberg Radio