1 00:00:05,800 --> 00:00:08,720 Speaker 1: Welcome to the Bloomberg p m L Podcast. I'm Pim Fox. 2 00:00:08,760 --> 00:00:11,520 Speaker 1: Along with my co host Lisa Bramowitz. Each day we 3 00:00:11,640 --> 00:00:15,120 Speaker 1: bring you the most important, noteworthy, and useful interviews for 4 00:00:15,200 --> 00:00:17,840 Speaker 1: you and your money, whether you're at the grocery store 5 00:00:17,960 --> 00:00:20,720 Speaker 1: or the trading floor. Find the Bloomberg p m L 6 00:00:20,840 --> 00:00:31,479 Speaker 1: Podcast on Apple Podcasts, SoundCloud, and Bloomberg dot com. We 7 00:00:31,560 --> 00:00:34,680 Speaker 1: are here live from the Bloomberg Invest Conference at Bloomberg's 8 00:00:34,680 --> 00:00:37,440 Speaker 1: New York City headquarters. Coverage of the Bloomberg Invest Conference 9 00:00:37,640 --> 00:00:40,199 Speaker 1: on Bloomberg Radio is brought to you by s e I. 10 00:00:40,320 --> 00:00:42,440 Speaker 1: This is the big week, the week when we're finally 11 00:00:42,479 --> 00:00:45,440 Speaker 1: going to hear some of these details about President Trump's 12 00:00:45,440 --> 00:00:49,000 Speaker 1: infrastructure spending plan and to break down exactly what we 13 00:00:49,040 --> 00:00:53,240 Speaker 1: can expect and what potential opportunities there are for private investors. 14 00:00:53,560 --> 00:00:56,000 Speaker 1: Glenn young Ken is here to make it all makes 15 00:00:56,040 --> 00:00:58,720 Speaker 1: sense for me anyway, President and Chief operating Officer of 16 00:00:58,760 --> 00:01:01,080 Speaker 1: the Carlisle Group, and he joins us here in our 17 00:01:01,120 --> 00:01:05,479 Speaker 1: Bloomberg headquarters. Glenn, let's start with good morning, good morning, 18 00:01:05,520 --> 00:01:08,200 Speaker 1: thank you for joining me. Let's start with the idea 19 00:01:08,400 --> 00:01:12,319 Speaker 1: of this two hundred billion dollars of federal money and 20 00:01:12,480 --> 00:01:15,959 Speaker 1: eight hundred billion dollars of private money that comes in 21 00:01:16,360 --> 00:01:21,320 Speaker 1: because these private investors are promised incentives. What are these incentives? Yeah, 22 00:01:21,360 --> 00:01:24,360 Speaker 1: I actually think, um, the incentives are going to be 23 00:01:24,480 --> 00:01:28,720 Speaker 1: more for the jurisdictions that take on new projects. So 24 00:01:29,040 --> 00:01:32,640 Speaker 1: how's this really going to work? Um, First, the government's 25 00:01:32,640 --> 00:01:34,679 Speaker 1: incentives are going to be are going to be put 26 00:01:34,720 --> 00:01:39,399 Speaker 1: in place so that cities and states could actually earn 27 00:01:39,959 --> 00:01:43,959 Speaker 1: a bonus if they take on public private partnerships for 28 00:01:44,040 --> 00:01:47,319 Speaker 1: critical assets. So, for example, if you're the mayor of 29 00:01:47,319 --> 00:01:48,800 Speaker 1: a city and you have an airport, and you have 30 00:01:48,920 --> 00:01:51,800 Speaker 1: big plans for that airport, and you actually put it 31 00:01:51,840 --> 00:01:54,880 Speaker 1: into a public private partnership process and bring in private 32 00:01:54,920 --> 00:01:58,000 Speaker 1: capital to fund the expansion of that airport, and oh, 33 00:01:58,000 --> 00:02:01,480 Speaker 1: by the way, most likely receive a payment up front. Um, 34 00:02:01,560 --> 00:02:04,440 Speaker 1: the government will in fact provide an incremental incentive on 35 00:02:04,520 --> 00:02:07,480 Speaker 1: top of that for the city. The city then has 36 00:02:07,720 --> 00:02:11,320 Speaker 1: the capital that they liberated from that airport plus incremental 37 00:02:11,360 --> 00:02:13,960 Speaker 1: capital from the government to invest in new things. All right, 38 00:02:14,040 --> 00:02:16,200 Speaker 1: back up, when you say they get this extra incentive, 39 00:02:16,240 --> 00:02:18,519 Speaker 1: they get this extra bonus. That money is coming from somewhere. 40 00:02:18,520 --> 00:02:20,320 Speaker 1: Where does that come from? Does that mean higher tolls. 41 00:02:20,320 --> 00:02:23,520 Speaker 1: Does that mean higher taxes federally? Well, first of all, 42 00:02:23,560 --> 00:02:26,400 Speaker 1: the two hundred billion was in the president's plan that 43 00:02:26,440 --> 00:02:29,040 Speaker 1: they submitted two weeks ago, which was a ten year 44 00:02:29,120 --> 00:02:32,760 Speaker 1: a ten year a budget frame plus a ten year forecast. 45 00:02:33,120 --> 00:02:35,760 Speaker 1: And it's over the ten year period the two billion 46 00:02:36,280 --> 00:02:39,680 Speaker 1: um and they actually hope to liberate more than eight 47 00:02:39,720 --> 00:02:43,120 Speaker 1: hundred billion. They hope to actually attract a billion trillion 48 00:02:43,160 --> 00:02:44,960 Speaker 1: I'm sorry, they hope to attract a trillion into this 49 00:02:45,760 --> 00:02:48,240 Speaker 1: and so, which I actually think they will because the 50 00:02:48,280 --> 00:02:50,800 Speaker 1: asset base that the private sector has a chance to 51 00:02:50,840 --> 00:02:55,640 Speaker 1: invest in is already existing. Very interesting fact, just in 52 00:02:55,680 --> 00:02:58,400 Speaker 1: the airports alone in the United States today, which are 53 00:02:58,480 --> 00:03:01,720 Speaker 1: up and running commercial entities, right They get landing fees, 54 00:03:01,760 --> 00:03:05,519 Speaker 1: they have passenger traffic, they have restaurants and shopping in them. 55 00:03:05,560 --> 00:03:08,120 Speaker 1: There's there's an estimate that that's already a half a 56 00:03:08,200 --> 00:03:11,840 Speaker 1: trillion of value that exists in just airports in the 57 00:03:11,919 --> 00:03:14,400 Speaker 1: United States today. Okay, so I love the idea of 58 00:03:14,440 --> 00:03:16,960 Speaker 1: liberating cash. By the way that my mind is going wild, 59 00:03:17,040 --> 00:03:20,840 Speaker 1: but I'm trying to understand, uh, this idea of attracting 60 00:03:20,840 --> 00:03:23,680 Speaker 1: when trillion dollars in investment capital. It seems like private 61 00:03:23,680 --> 00:03:27,040 Speaker 1: equity firms in particular, which are despot for opportunities in 62 00:03:27,080 --> 00:03:31,160 Speaker 1: this overvalued market, at least ind of some. I'm surprised 63 00:03:31,200 --> 00:03:35,960 Speaker 1: that there haven't already been more investments made in infrastructure. 64 00:03:35,960 --> 00:03:38,200 Speaker 1: And if they're not, are there structural reasons as to 65 00:03:38,280 --> 00:03:41,240 Speaker 1: why not? For example, whether it's certain interests or whether 66 00:03:41,320 --> 00:03:44,600 Speaker 1: it's certain obstacles that are just physical. You can't construct 67 00:03:44,600 --> 00:03:46,720 Speaker 1: a new airport when there's one that needs to operate, 68 00:03:46,960 --> 00:03:48,760 Speaker 1: right at least that's It's a great question, and I 69 00:03:48,800 --> 00:03:52,040 Speaker 1: think there's two primary um factors to make sure we 70 00:03:52,080 --> 00:03:55,800 Speaker 1: look at first. Outside of the United States, this concept 71 00:03:55,880 --> 00:04:01,360 Speaker 1: has flourished so in Europe, in Canada, and particularly in Australia. 72 00:04:01,760 --> 00:04:06,920 Speaker 1: The concept of bringing private investors into infrastructure and have 73 00:04:07,080 --> 00:04:10,360 Speaker 1: them almost recapitalize that infrastructure in a partnership with the 74 00:04:10,400 --> 00:04:14,200 Speaker 1: government has flourished for years and years and years. It 75 00:04:14,240 --> 00:04:18,479 Speaker 1: hasn't happened here because there's been a hesitancy to actually 76 00:04:18,520 --> 00:04:22,320 Speaker 1: take on what is something new, because there's been this 77 00:04:22,400 --> 00:04:24,920 Speaker 1: expectation that the government is just gonna pay for it. 78 00:04:25,320 --> 00:04:28,039 Speaker 1: We'll don't worry, We're just gonna pay for it. And 79 00:04:28,080 --> 00:04:30,840 Speaker 1: the reality today is we can't afford to pay for 80 00:04:30,880 --> 00:04:34,080 Speaker 1: it any longer. There are critical infrastructure needs that the 81 00:04:34,120 --> 00:04:36,279 Speaker 1: government can pay for, but it can't pay for the 82 00:04:36,400 --> 00:04:41,960 Speaker 1: two trillion dollar gap that exists today in our infrastructure requirements. 83 00:04:41,960 --> 00:04:46,159 Speaker 1: And so it is the need to actually uncover this 84 00:04:46,440 --> 00:04:49,560 Speaker 1: new technique for this country. And it's not perfectly new, 85 00:04:49,560 --> 00:04:52,479 Speaker 1: because we've done a few already that in fact will 86 00:04:52,560 --> 00:04:55,400 Speaker 1: will gain some momentum and actually begin to start filling 87 00:04:55,400 --> 00:04:57,920 Speaker 1: that gap. So, Carlisle overseas about a hundred and sixty 88 00:04:57,920 --> 00:05:01,040 Speaker 1: two billion dollars of assets. How much money do you 89 00:05:01,080 --> 00:05:05,479 Speaker 1: expect Carlyle to invest should this plan come to fruition 90 00:05:05,720 --> 00:05:09,080 Speaker 1: in infrastructure. That's that's that's a very nice direct question, 91 00:05:09,120 --> 00:05:10,479 Speaker 1: and so I'm going to dodge it a little bit. 92 00:05:10,680 --> 00:05:12,920 Speaker 1: I imagine that you would find a way. But we 93 00:05:13,279 --> 00:05:16,520 Speaker 1: in fact, we in fact have funds and are raising funds, 94 00:05:16,800 --> 00:05:20,359 Speaker 1: and so in energy and infrastructure today we manage across 95 00:05:20,400 --> 00:05:24,320 Speaker 1: equity and debt capital about twenty eight billion dollars. Infrastructure 96 00:05:24,360 --> 00:05:28,640 Speaker 1: needs extend from energy infrastructure to airports, to toll roads, 97 00:05:28,839 --> 00:05:32,840 Speaker 1: to telecom infrastructure, and so the depth of this market 98 00:05:33,000 --> 00:05:35,839 Speaker 1: is big. It's big, and so what I think what 99 00:05:35,880 --> 00:05:38,600 Speaker 1: we're gonna end up seeing, however, is a few key 100 00:05:38,640 --> 00:05:43,400 Speaker 1: critical projects out front that blaze a trail. Airports. Airports 101 00:05:43,440 --> 00:05:45,919 Speaker 1: particularly because they're commercial entities that are up and running 102 00:05:46,120 --> 00:05:48,640 Speaker 1: there and as a result, people understand them. They've been 103 00:05:48,640 --> 00:05:51,600 Speaker 1: done in other countries, um, and that to me is 104 00:05:52,000 --> 00:05:54,840 Speaker 1: a decision that a mayor can make and the f 105 00:05:54,839 --> 00:05:57,440 Speaker 1: a A can make and they don't really need government 106 00:05:57,480 --> 00:06:00,200 Speaker 1: intervention from the from Washington to do this. And that's 107 00:06:00,240 --> 00:06:02,320 Speaker 1: the great part about A Reports today. This is a 108 00:06:02,360 --> 00:06:04,880 Speaker 1: fascinating conversation. I could speak with you all morning because 109 00:06:04,880 --> 00:06:07,680 Speaker 1: it really highlights a lot of interesting angles such as 110 00:06:07,800 --> 00:06:10,560 Speaker 1: if you do have airports that are successful at pairing 111 00:06:10,600 --> 00:06:14,720 Speaker 1: partner private and public money, then what does this lead 112 00:06:14,720 --> 00:06:17,200 Speaker 1: to next? But unfortunately have to leave their Glenn at Calfkin, 113 00:06:17,440 --> 00:06:19,440 Speaker 1: thank you so much for joining us at President and 114 00:06:19,520 --> 00:06:22,720 Speaker 1: chief operating Officer at the Carlyle Group. It's private equity 115 00:06:22,760 --> 00:06:25,719 Speaker 1: firm that oversees a hundred and sixty two billion dollars 116 00:06:25,880 --> 00:06:40,400 Speaker 1: in Washington, d C. We are broadcasting alive from the 117 00:06:40,400 --> 00:06:44,320 Speaker 1: Bloomberg Invest Conference at Bloomberg's New York City headquarters. Coverage 118 00:06:44,320 --> 00:06:46,599 Speaker 1: of the Bloomberg Invest Conference on Bloomberg Radio is brought 119 00:06:46,600 --> 00:06:49,680 Speaker 1: to you by s EI And of course no investing 120 00:06:49,880 --> 00:06:54,120 Speaker 1: conference or conversation would be complete unless we addressed the 121 00:06:54,160 --> 00:06:57,080 Speaker 1: big elephant in the room, which is passive investing and 122 00:06:57,120 --> 00:06:59,159 Speaker 1: the shift that's been going on. Moody's put out our 123 00:06:59,160 --> 00:07:02,320 Speaker 1: report earlier. This you're saying the passive investing will overtake 124 00:07:02,360 --> 00:07:06,400 Speaker 1: active management by twenty twenty four in the US. From 125 00:07:06,440 --> 00:07:08,760 Speaker 1: more perspective on this, I want to bring in Christie Mitchem. 126 00:07:08,800 --> 00:07:11,960 Speaker 1: She is chief executive officer of Will's Fargo Asset Management, 127 00:07:11,960 --> 00:07:14,560 Speaker 1: which has four hundred and eighty billion dollars of assets 128 00:07:14,600 --> 00:07:17,040 Speaker 1: under management and is based in San Francisco, which has 129 00:07:17,120 --> 00:07:19,239 Speaker 1: much better weather than there is in New York City. 130 00:07:19,440 --> 00:07:22,160 Speaker 1: Yet today and yet here she is and she joins 131 00:07:22,240 --> 00:07:24,520 Speaker 1: us here in New York. Christie, thank you so much. UM. 132 00:07:24,680 --> 00:07:27,760 Speaker 1: You know, we hear so much about this accelerating shift 133 00:07:27,920 --> 00:07:33,120 Speaker 1: to passive management, particularly with respect to equities. Uh is 134 00:07:33,160 --> 00:07:35,800 Speaker 1: now any different? I mean, it's time with such low volatility, 135 00:07:35,840 --> 00:07:39,320 Speaker 1: won't this just absolutely accelerate? You know? I think today 136 00:07:39,480 --> 00:07:41,120 Speaker 1: is different, and I think one of the things that 137 00:07:41,160 --> 00:07:44,800 Speaker 1: investors may be missing that really sits below overall low 138 00:07:44,880 --> 00:07:47,640 Speaker 1: volatility levels is a real c shift in the marketplace, 139 00:07:47,960 --> 00:07:51,880 Speaker 1: and that's a change in the para wise correlation between stocks. 140 00:07:52,280 --> 00:07:54,920 Speaker 1: You know, we saw that correlation actually hit a low 141 00:07:55,040 --> 00:07:57,800 Speaker 1: in January of this year after hitting a high back 142 00:07:57,800 --> 00:08:00,200 Speaker 1: in two thousand twelves. Hold on sec let's just back up. 143 00:08:00,200 --> 00:08:04,600 Speaker 1: In other words, the correlation between stocks, all stocks moving 144 00:08:04,600 --> 00:08:07,680 Speaker 1: in tandem broke down earlier this year reach the lowest 145 00:08:07,680 --> 00:08:10,320 Speaker 1: point ever. It reached the lowest points since two thousand 146 00:08:10,320 --> 00:08:12,760 Speaker 1: eight in the Lehman bankruptcy. So a really big shift 147 00:08:13,240 --> 00:08:15,640 Speaker 1: um and one of the things that's really characterized the 148 00:08:15,680 --> 00:08:18,720 Speaker 1: big market rally that we've seen since the global financial 149 00:08:18,760 --> 00:08:22,920 Speaker 1: crisis has been strong correlations that's changing and that presents 150 00:08:23,040 --> 00:08:26,200 Speaker 1: real opportunities for active managers. Let me just give you 151 00:08:26,240 --> 00:08:28,360 Speaker 1: a playbook from from what we're seeing in terms of 152 00:08:28,360 --> 00:08:30,920 Speaker 1: our own stable of managers. Since the beginning of the year, 153 00:08:31,040 --> 00:08:34,880 Speaker 1: seventy of our active managers are outperforming their respective benchmarks. 154 00:08:35,120 --> 00:08:37,600 Speaker 1: And just to put that in perspective, they're outperforming by 155 00:08:37,600 --> 00:08:41,800 Speaker 1: a lot. On average asset weighted outperforming respective benchmarks by 156 00:08:41,800 --> 00:08:44,880 Speaker 1: over seventy five basis points. Is that including fees or 157 00:08:44,920 --> 00:08:47,720 Speaker 1: not including that is after fees, that is after fees. Okay, 158 00:08:47,880 --> 00:08:49,800 Speaker 1: and this is the biggest out performance in a while 159 00:08:49,840 --> 00:08:52,400 Speaker 1: that you've seen. Absolutely, I mean, two thousand thirteen to 160 00:08:52,520 --> 00:08:56,280 Speaker 1: two thousand sixteen really difficult years for active managers. The 161 00:08:56,360 --> 00:08:59,800 Speaker 1: number of active managers out performing in the large caps 162 00:09:00,040 --> 00:09:04,480 Speaker 1: ace in two thousand sixteen, that's the lowest number since 163 00:09:04,600 --> 00:09:07,920 Speaker 1: n So if investors are looking for a place to 164 00:09:07,960 --> 00:09:11,400 Speaker 1: put money, I think that place today is active managers. 165 00:09:11,679 --> 00:09:14,760 Speaker 1: And active managers also performed better in a downturn, so 166 00:09:14,800 --> 00:09:17,320 Speaker 1: it's a great way to pick up excess return today 167 00:09:17,360 --> 00:09:20,720 Speaker 1: but also put a little insurance cushion inside your portfolio. Okay, 168 00:09:20,720 --> 00:09:24,200 Speaker 1: So you said that seventy of Wells Fargo asset managements 169 00:09:24,280 --> 00:09:31,400 Speaker 1: active fund management have been outperforming their perspective benchmarks. Have 170 00:09:31,520 --> 00:09:35,160 Speaker 1: they seen inflows? Have they seen flows follow that performance? 171 00:09:35,240 --> 00:09:37,559 Speaker 1: Or is is marketing still a really big issue. I 172 00:09:37,640 --> 00:09:39,520 Speaker 1: still think we're a little bit late to see I 173 00:09:39,520 --> 00:09:41,360 Speaker 1: mean we're a little bit early to seeing flows, you 174 00:09:41,360 --> 00:09:43,920 Speaker 1: know where I think people are still really focusing on 175 00:09:44,040 --> 00:09:47,240 Speaker 1: this sort of you know, um phenomena in terms of 176 00:09:47,240 --> 00:09:49,719 Speaker 1: a trend towards passive investing. And that's why I think 177 00:09:49,760 --> 00:09:53,040 Speaker 1: this is really an important story and a potentially important 178 00:09:53,120 --> 00:09:56,439 Speaker 1: inflection point for asset management. So is there a particular 179 00:09:56,520 --> 00:10:01,439 Speaker 1: equity market where you're seeing correlations breakdown even more than others? Well, 180 00:10:01,480 --> 00:10:05,040 Speaker 1: I think, you know, across the board we're seeing correlations breakdown, 181 00:10:05,040 --> 00:10:07,960 Speaker 1: but I think where it's particularly impactful is in the 182 00:10:08,040 --> 00:10:11,000 Speaker 1: sectors where it's been difficult to outperform, and that is 183 00:10:11,120 --> 00:10:13,680 Speaker 1: US large large cap and oh, by the way, that's 184 00:10:13,679 --> 00:10:15,920 Speaker 1: a big part of the investors portfolio in the U 185 00:10:16,120 --> 00:10:18,240 Speaker 1: S so UM. Just to sort of back up, the 186 00:10:18,280 --> 00:10:22,160 Speaker 1: correlations were tightly moving together just simply because of the 187 00:10:22,200 --> 00:10:24,120 Speaker 1: central banks and the fact that the Federal Reserve was 188 00:10:24,160 --> 00:10:28,559 Speaker 1: basically driving all of the risk on sentiment. Now, however, 189 00:10:28,880 --> 00:10:31,280 Speaker 1: it's unclear what's really driving it. And as we talk 190 00:10:31,360 --> 00:10:34,240 Speaker 1: about with Dave Wilson or Bloomberg Soocks editor and calumnist, 191 00:10:34,520 --> 00:10:37,200 Speaker 1: uh here uh you know, every day, you know you 192 00:10:37,280 --> 00:10:40,360 Speaker 1: have big losers and big winners. How long can we 193 00:10:40,400 --> 00:10:43,280 Speaker 1: continue with this type of activity and this type of 194 00:10:43,280 --> 00:10:46,240 Speaker 1: dispersion before something macro kind of gets in the way 195 00:10:46,320 --> 00:10:48,160 Speaker 1: and drives us all, especially with all the noise that 196 00:10:48,200 --> 00:10:50,600 Speaker 1: we're hearing on the geopolitical side. Listen, I do think 197 00:10:50,600 --> 00:10:53,320 Speaker 1: there is tons of noise on the geo political side. Obviously, 198 00:10:53,360 --> 00:10:55,480 Speaker 1: if we just look to tomorrow, there's a lot going on. 199 00:10:55,559 --> 00:10:58,680 Speaker 1: We've got Comely, we've got the ECB, we've got UK elections. 200 00:10:58,720 --> 00:11:00,960 Speaker 1: I mean, there's a ton of focus on. But at 201 00:11:00,960 --> 00:11:03,600 Speaker 1: the end of the day, stock market valuations and performance 202 00:11:03,640 --> 00:11:06,960 Speaker 1: comes down to, you know, how our corporate earnings performing. 203 00:11:07,280 --> 00:11:09,680 Speaker 1: And we've really seen, you know, really strong growth. So 204 00:11:09,720 --> 00:11:11,800 Speaker 1: we look at top line growth, we're looking at numbers 205 00:11:11,800 --> 00:11:14,920 Speaker 1: in the six to seven percent range um bottom line growth, 206 00:11:15,080 --> 00:11:18,679 Speaker 1: you know, similar numbers, so, you know, really outstanding growth 207 00:11:18,720 --> 00:11:20,920 Speaker 1: both in the top line and the bottom line compared 208 00:11:20,960 --> 00:11:22,839 Speaker 1: to last year. And I think that's what's supporting the 209 00:11:22,920 --> 00:11:25,000 Speaker 1: valuations that we see in the marketplace today. So a 210 00:11:25,000 --> 00:11:26,880 Speaker 1: couple of years ago, there was a lot of fear 211 00:11:26,960 --> 00:11:30,200 Speaker 1: among investors about rising rates and about a big sell 212 00:11:30,280 --> 00:11:34,280 Speaker 1: off in markets. And at that time, liquid alternatives and 213 00:11:34,360 --> 00:11:37,800 Speaker 1: other types of unconstrained funds were the rage. People piled 214 00:11:37,840 --> 00:11:40,400 Speaker 1: into these things, saying, please protect me in a downturn. 215 00:11:41,120 --> 00:11:45,480 Speaker 1: Not anymore? Are these things were going to be popular again? Oh? 216 00:11:45,520 --> 00:11:47,640 Speaker 1: I think so. I mean, you know, I think everything 217 00:11:47,679 --> 00:11:50,640 Speaker 1: has its space in the marketplace, and it has its time, 218 00:11:51,000 --> 00:11:53,240 Speaker 1: and I think, you know, those products in particular, did 219 00:11:53,240 --> 00:11:56,040 Speaker 1: not perform well in a market that was really doing, um, 220 00:11:56,080 --> 00:11:59,280 Speaker 1: you know, quite well. Um. So I think we'll continue 221 00:11:59,320 --> 00:12:02,760 Speaker 1: to see people put rainy day strategies in their portfolio portfolios, 222 00:12:02,760 --> 00:12:04,559 Speaker 1: and I think that's a good thing to do. So. Um. 223 00:12:04,600 --> 00:12:07,800 Speaker 1: With respect to Wells Fargo asset management, what area in 224 00:12:07,880 --> 00:12:11,400 Speaker 1: asset management, uh do you see as having the biggest 225 00:12:11,400 --> 00:12:15,599 Speaker 1: opportunity for potential expansion? Well, I think the biggest opportunity 226 00:12:15,640 --> 00:12:18,760 Speaker 1: is undeniably multi asset class solutions. I think as investors, 227 00:12:18,760 --> 00:12:22,000 Speaker 1: individual investors continue to get more sophisticated about what they 228 00:12:22,040 --> 00:12:24,079 Speaker 1: want and what they need, we're going to see a 229 00:12:24,120 --> 00:12:27,559 Speaker 1: world drive towards customization. You're not gonna want the market portfolio. 230 00:12:27,679 --> 00:12:30,320 Speaker 1: You're gonna want a portfolio that that actually matches your 231 00:12:30,440 --> 00:12:34,360 Speaker 1: specific needs, desires, and obviously liabilities, and that's going to 232 00:12:34,400 --> 00:12:37,280 Speaker 1: be a big shift. I think the undeniably the biggest 233 00:12:37,280 --> 00:12:40,800 Speaker 1: opportunity is multiasset class solutions and personalized investment. So what 234 00:12:41,000 --> 00:12:43,559 Speaker 1: will a computer be giving me that? I think it's 235 00:12:43,600 --> 00:12:46,840 Speaker 1: highly likely that a computer will be part of that solution, absolutely, 236 00:12:47,080 --> 00:12:49,200 Speaker 1: and that's not a bad thing. Actually, you know, what 237 00:12:49,240 --> 00:12:51,719 Speaker 1: I think, you know, robo advisory, and you know, as 238 00:12:51,760 --> 00:12:54,720 Speaker 1: we call it, is actually highly enabling, right. It allows 239 00:12:54,800 --> 00:12:57,160 Speaker 1: us to take the kind of insights and strategies that 240 00:12:57,200 --> 00:13:00,120 Speaker 1: have typically been only accessible to the largest institution to 241 00:13:00,200 --> 00:13:02,680 Speaker 1: the world and put them in the hands of individual investors. 242 00:13:02,679 --> 00:13:05,320 Speaker 1: And that's an incredibly important and valuable shift. Do you 243 00:13:05,320 --> 00:13:07,400 Speaker 1: think that there's any area of the market that's going 244 00:13:07,440 --> 00:13:10,840 Speaker 1: to continue to shrink and will eventually become obsolete with 245 00:13:10,880 --> 00:13:14,280 Speaker 1: respect to active asset management, Well, then I think we're 246 00:13:14,280 --> 00:13:16,680 Speaker 1: going to continue to face pressures in the large cap 247 00:13:17,000 --> 00:13:19,319 Speaker 1: marketplace as it relates to active management. I think we 248 00:13:19,400 --> 00:13:22,720 Speaker 1: do see opportunities or at least in more readily available 249 00:13:22,720 --> 00:13:25,960 Speaker 1: opportunities and less efficient spaces, so that small cap stocks, 250 00:13:26,040 --> 00:13:29,040 Speaker 1: mid cap stocks, international stocks, emerging markets. So you know, 251 00:13:29,080 --> 00:13:30,440 Speaker 1: I'm not saying it's we're not going to have to 252 00:13:30,440 --> 00:13:32,840 Speaker 1: fight the good fight in large cap I think we are, 253 00:13:33,280 --> 00:13:35,080 Speaker 1: and I think we'll be able to generate alpha more 254 00:13:35,120 --> 00:13:37,160 Speaker 1: easily in other spaces. But I still think it has 255 00:13:37,160 --> 00:13:39,480 Speaker 1: a real place in the portfolio. Christie Mitchum, thank you 256 00:13:39,480 --> 00:13:41,079 Speaker 1: so much for joining us to really a question to 257 00:13:41,080 --> 00:13:43,560 Speaker 1: speak with you. Christie Mitchum is chief executive officer of 258 00:13:43,559 --> 00:13:46,800 Speaker 1: Wells Fargo Asset Management UH in San Francisco. It overseas 259 00:13:46,880 --> 00:13:50,400 Speaker 1: four hundred and eighty billion dollars in assets and is 260 00:13:50,400 --> 00:13:55,119 Speaker 1: on the forefront of this transition, this changing investment landscape 261 00:13:55,120 --> 00:14:08,320 Speaker 1: that we have talked so much about. One of the 262 00:14:08,320 --> 00:14:10,760 Speaker 1: things that is being talked about is the revolution that 263 00:14:10,880 --> 00:14:13,960 Speaker 1: is going on in consumer lending, in particular the way 264 00:14:13,960 --> 00:14:17,880 Speaker 1: that people UH investors are mining big data to get 265 00:14:17,880 --> 00:14:20,880 Speaker 1: a better sense of what the potential for defaults and 266 00:14:21,040 --> 00:14:23,680 Speaker 1: delinquencies could be. To get more of a sense of 267 00:14:23,920 --> 00:14:27,360 Speaker 1: just where this is in the mortgage market in particular, 268 00:14:27,360 --> 00:14:30,040 Speaker 1: I want to bring Emmanuel Shireff. He is executive vice 269 00:14:30,040 --> 00:14:33,120 Speaker 1: president and a portfolio manager at PIMCO, which is based 270 00:14:33,160 --> 00:14:36,160 Speaker 1: in Newport Beach. Emmanuel, thank you so much for joining us. 271 00:14:36,400 --> 00:14:38,920 Speaker 1: So you know, we hear a lot about the ability 272 00:14:39,080 --> 00:14:43,120 Speaker 1: for online lenders to mind big data and understand people's 273 00:14:43,120 --> 00:14:46,720 Speaker 1: Facebook feeds and whatever else and credit scores to get 274 00:14:46,760 --> 00:14:49,640 Speaker 1: a an accurate sense of whether they will default. What 275 00:14:49,680 --> 00:14:50,880 Speaker 1: do you look at? What do you think is the 276 00:14:50,920 --> 00:14:54,840 Speaker 1: most important online data indicator that is a deciding factor 277 00:14:54,880 --> 00:14:57,040 Speaker 1: for whether somebody will make good on their bills? So 278 00:14:57,280 --> 00:14:59,120 Speaker 1: thank you for having me. First of all, at PIMCO, 279 00:14:59,160 --> 00:15:00,720 Speaker 1: we have been doing machine learn in big data for 280 00:15:00,760 --> 00:15:03,560 Speaker 1: a very long time in the mortgage and real estate space. UM. 281 00:15:03,680 --> 00:15:06,440 Speaker 1: I've been heavily involved in that. And UH We've built 282 00:15:06,440 --> 00:15:09,240 Speaker 1: out a fairly significant infrastructure that allows us to gather 283 00:15:09,360 --> 00:15:13,160 Speaker 1: information on local real estate markets, UM on all the 284 00:15:13,200 --> 00:15:16,680 Speaker 1: mortgage performance, on consumer credits, on local demographics and economics 285 00:15:16,840 --> 00:15:18,480 Speaker 1: and and and all of that, and we use that 286 00:15:18,480 --> 00:15:21,680 Speaker 1: in order to build up our mortgage lending models and 287 00:15:21,720 --> 00:15:26,120 Speaker 1: our mortgage performance models. UM. With with kind of Internet 288 00:15:26,120 --> 00:15:28,640 Speaker 1: related big data, UH, it's worth being a little bit 289 00:15:28,640 --> 00:15:30,560 Speaker 1: skeptical of some of the claims that are that are 290 00:15:30,600 --> 00:15:33,960 Speaker 1: being put out there. UM. Whenever we analyze data, we 291 00:15:34,000 --> 00:15:37,520 Speaker 1: spend a lot of time cleaning data, understanding the provenance 292 00:15:37,920 --> 00:15:39,880 Speaker 1: of of where it comes from, how it was collected, 293 00:15:40,160 --> 00:15:42,920 Speaker 1: adjusting for potential biases and other related issues that may 294 00:15:42,960 --> 00:15:45,240 Speaker 1: arise with the data in order to try and extract 295 00:15:45,360 --> 00:15:49,000 Speaker 1: correct UM signals from it and adjust for whatever biases 296 00:15:49,040 --> 00:15:52,600 Speaker 1: may exist. And and so with a lot of online data, 297 00:15:53,320 --> 00:15:56,200 Speaker 1: the collection methods are a little bit uncertain and subject 298 00:15:56,240 --> 00:15:58,760 Speaker 1: to change at any time. So, for example, if you 299 00:15:59,200 --> 00:16:01,160 Speaker 1: there there's a idea of vendors out there that have 300 00:16:01,200 --> 00:16:03,080 Speaker 1: sprung up over the last few years selling you know, 301 00:16:03,200 --> 00:16:07,440 Speaker 1: Twitter sentiments, location data, you know Facebook feed information and 302 00:16:07,720 --> 00:16:10,920 Speaker 1: all of that, and the populations can change very rapidly, 303 00:16:11,160 --> 00:16:13,600 Speaker 1: the methods of collection can change all the time. There's 304 00:16:13,640 --> 00:16:15,520 Speaker 1: not a whole lot of history for most of these things, 305 00:16:15,760 --> 00:16:19,239 Speaker 1: so it's difficult to calibrate what exactly all this information 306 00:16:19,280 --> 00:16:22,800 Speaker 1: means for future mortgage performance, especially for signals that have 307 00:16:22,880 --> 00:16:24,880 Speaker 1: only been around post crisis, that you have never even 308 00:16:24,920 --> 00:16:28,800 Speaker 1: observed in in a high stress situation. So, um, this 309 00:16:28,840 --> 00:16:32,120 Speaker 1: is fascinating. The amount of time and attention focusing on 310 00:16:32,440 --> 00:16:35,000 Speaker 1: exactly where the data is from, how it's been collected, 311 00:16:35,040 --> 00:16:38,520 Speaker 1: and it's track record of potential biases. I'm wondering, do 312 00:16:38,560 --> 00:16:42,080 Speaker 1: you have a track record with respect to how accurate 313 00:16:42,320 --> 00:16:46,240 Speaker 1: pimco's model is in predicting tolinquencies in the mortgage debt mark. Well, 314 00:16:46,240 --> 00:16:48,560 Speaker 1: when you're working with long term data that has existed 315 00:16:48,600 --> 00:16:51,480 Speaker 1: for some time, it's possible to back test it, to 316 00:16:51,520 --> 00:16:55,240 Speaker 1: build a variety of models surrounding it. Um with with 317 00:16:55,320 --> 00:16:57,800 Speaker 1: bigger data, it's much more challenging to do that, and 318 00:16:58,120 --> 00:16:59,920 Speaker 1: you have to kind of depend on whether or not 319 00:17:00,240 --> 00:17:04,159 Speaker 1: you've um correctly made those those adjustments. And whenever a 320 00:17:04,200 --> 00:17:06,040 Speaker 1: high stress period occurs, such as it is now, you 321 00:17:06,119 --> 00:17:09,320 Speaker 1: start seeing things potentially breaking down or you're forced to 322 00:17:10,000 --> 00:17:11,960 Speaker 1: high stress period of time like now now as a 323 00:17:12,040 --> 00:17:14,240 Speaker 1: high stress period of time, it's it's becoming a little 324 00:17:14,280 --> 00:17:19,960 Speaker 1: bit more stressful. Please explain, um, So, as I think 325 00:17:19,960 --> 00:17:22,840 Speaker 1: you mentioned, some of the online lenders are seeing a 326 00:17:22,840 --> 00:17:26,359 Speaker 1: little bit of increases in the faults and delinquencies. So 327 00:17:26,359 --> 00:17:30,400 Speaker 1: it's never been unexpected and unpleasant surprises in the prospers 328 00:17:30,400 --> 00:17:32,240 Speaker 1: of the world and the lending clubs of the world, 329 00:17:32,320 --> 00:17:36,160 Speaker 1: and there has been a certain wave of cynicism over 330 00:17:36,200 --> 00:17:38,520 Speaker 1: some of these models correct exactly. So, so I think 331 00:17:38,640 --> 00:17:41,480 Speaker 1: now they're seeing a little bit of um, maybe not 332 00:17:41,480 --> 00:17:43,280 Speaker 1: stressed in the world, but stressed to their models that 333 00:17:43,320 --> 00:17:45,680 Speaker 1: they have to make adjustments for. But are you seeing 334 00:17:45,680 --> 00:17:50,080 Speaker 1: stressed to the underlying US mortgage market. Uh, We're still 335 00:17:50,160 --> 00:17:52,719 Speaker 1: very constructive on the mortgage market, on the housing market overall. 336 00:17:52,840 --> 00:17:54,800 Speaker 1: But do we do we want to talk more about 337 00:17:54,840 --> 00:17:59,159 Speaker 1: big data machine learning? All right? Well, I mean with 338 00:17:59,240 --> 00:18:02,080 Speaker 1: big data and machine learning, what are the signs that 339 00:18:02,160 --> 00:18:04,840 Speaker 1: you look for, uh, to sort of lead up to 340 00:18:05,160 --> 00:18:10,000 Speaker 1: a potential deterioration of the market. UM. So we track 341 00:18:10,320 --> 00:18:13,280 Speaker 1: UM a lot of information on the local demographics and economics. 342 00:18:13,840 --> 00:18:19,840 Speaker 1: We track affordability ratios, UM, we track UM. The consumer 343 00:18:19,840 --> 00:18:22,639 Speaker 1: performance overall always tracked credit scores and so on. But 344 00:18:22,720 --> 00:18:24,600 Speaker 1: a lot of this is built into a variety of 345 00:18:25,480 --> 00:18:27,600 Speaker 1: nested models that rely on each other and build upon 346 00:18:27,600 --> 00:18:29,640 Speaker 1: each other to build a complete picture of the consumer 347 00:18:29,760 --> 00:18:33,440 Speaker 1: and projected mortgage performance. So in some cases the signals 348 00:18:33,440 --> 00:18:36,280 Speaker 1: interact in ways that with machine learning you don't always 349 00:18:36,400 --> 00:18:39,600 Speaker 1: entirely understand why a signal is showing what is showing? 350 00:18:40,000 --> 00:18:42,919 Speaker 1: So how much work is done after you get you know, 351 00:18:42,960 --> 00:18:45,280 Speaker 1: you put something into your model and it spits out 352 00:18:45,520 --> 00:18:47,320 Speaker 1: you know six, and you look at the six and 353 00:18:47,359 --> 00:18:48,399 Speaker 1: then what do you do with it? You know? I 354 00:18:48,400 --> 00:18:51,360 Speaker 1: mean do do people basically do portfolio managers take the 355 00:18:51,160 --> 00:18:54,639 Speaker 1: input from the models and just going directly put it 356 00:18:54,640 --> 00:18:57,119 Speaker 1: out there or is there analysis and a discussion about 357 00:18:57,400 --> 00:18:59,200 Speaker 1: what to do with that? The models with with any 358 00:18:59,240 --> 00:19:01,240 Speaker 1: model that we use is UM. The model is just 359 00:19:01,240 --> 00:19:04,760 Speaker 1: an additional tool in our toolbox that is given to 360 00:19:04,880 --> 00:19:07,320 Speaker 1: the portfolio manager and the trader. So the models allow 361 00:19:07,359 --> 00:19:10,320 Speaker 1: us to synthesize a huge amount of information UM in 362 00:19:10,400 --> 00:19:15,320 Speaker 1: order to help us make a better investment decision more quickly. UM. 363 00:19:15,320 --> 00:19:17,840 Speaker 1: But it's not making an investing decision by itself. And 364 00:19:17,960 --> 00:19:19,840 Speaker 1: maybe that's part of the issue that some of the 365 00:19:19,840 --> 00:19:23,600 Speaker 1: consumer lenders you mentioned are are facing. UM, that the 366 00:19:23,680 --> 00:19:26,880 Speaker 1: models are effectively making decisions by themselves without additional human 367 00:19:26,920 --> 00:19:29,320 Speaker 1: input or review. Do you think that social media is 368 00:19:29,480 --> 00:19:33,760 Speaker 1: a viable tool to understand somebody's ability to pay their debts? 369 00:19:34,720 --> 00:19:36,040 Speaker 1: I don't know. I haven't spent a lot of time 370 00:19:36,040 --> 00:19:39,040 Speaker 1: looking into social media effects because on on debt repayment 371 00:19:39,240 --> 00:19:40,919 Speaker 1: and some of some of them would say that, but 372 00:19:41,040 --> 00:19:45,040 Speaker 1: it might also be UM might also be completely unpredicted 373 00:19:45,080 --> 00:19:47,919 Speaker 1: depending on what it is that people post fascinating. I 374 00:19:47,960 --> 00:19:50,600 Speaker 1: honestly this is this is an important area and it's 375 00:19:50,640 --> 00:19:54,240 Speaker 1: one that an increasing number of investors are turning to UM. 376 00:19:54,320 --> 00:19:57,480 Speaker 1: And just real quick, how many people do you have 377 00:19:57,560 --> 00:20:02,840 Speaker 1: at PIMCO who are focusing on maintain these databases? Depending 378 00:20:02,840 --> 00:20:05,440 Speaker 1: on the sector we have UM, I don't actually know 379 00:20:05,480 --> 00:20:07,119 Speaker 1: how many we have in total at PIMCO. We have 380 00:20:07,720 --> 00:20:10,560 Speaker 1: many dozens of people working on these things, and UH, 381 00:20:10,600 --> 00:20:15,000 Speaker 1: depending on depending on the sector, there's more fewer Emmanuel Sharreff, 382 00:20:15,040 --> 00:20:17,280 Speaker 1: Thank you so much for joining. Emmanuel Sharreff is executive 383 00:20:17,359 --> 00:20:21,119 Speaker 1: vice president and portfolio manager at PIMCO, which is based 384 00:20:21,160 --> 00:20:23,600 Speaker 1: in Newport Beach, and we are broadcasting live from the 385 00:20:23,600 --> 00:20:28,400 Speaker 1: Bloomberg invest Conference at Bloomberg Bloomberg's New York City headquarters. 386 00:20:40,000 --> 00:20:43,119 Speaker 1: We had some breaking news this morning. President Donald Trump 387 00:20:43,240 --> 00:20:46,160 Speaker 1: tweeted I will be nominating Christopher A. Ray, a man 388 00:20:46,200 --> 00:20:49,560 Speaker 1: of impeccable credentials, to be the new Director of the FBI. 389 00:20:49,720 --> 00:20:52,359 Speaker 1: Details to follow. We are not going to wait for 390 00:20:52,440 --> 00:20:54,520 Speaker 1: him to give us some details before getting some of 391 00:20:54,560 --> 00:20:57,600 Speaker 1: our own from Larry Liebert, national Security Team editor for 392 00:20:57,680 --> 00:21:00,679 Speaker 1: Bloomberg in Washington, d C. Larry, what do we know 393 00:21:00,720 --> 00:21:04,720 Speaker 1: about Christopher Ray, the nominee for the new director of 394 00:21:04,840 --> 00:21:07,640 Speaker 1: the FBI. Well, he's quite well known. He's a white 395 00:21:07,680 --> 00:21:10,440 Speaker 1: color defense attorney now, but he served at the Justice 396 00:21:10,440 --> 00:21:15,119 Speaker 1: Department for a number of years, including prosecuting the infamous 397 00:21:15,119 --> 00:21:20,120 Speaker 1: in Ron uh financial scandal. UH. The initial response UH, 398 00:21:20,160 --> 00:21:23,480 Speaker 1: including from some critics of President Trump, has been that 399 00:21:23,560 --> 00:21:27,760 Speaker 1: he's a good, solid choice, although he didn't have specific 400 00:21:27,840 --> 00:21:32,680 Speaker 1: experience as some previous FBI directors heading the agency. So UM. 401 00:21:32,720 --> 00:21:36,440 Speaker 1: All in all, considered a respectable, solid choice. But uh, 402 00:21:36,480 --> 00:21:38,920 Speaker 1: there'll be confirmation here he is. Well, he'll be asked 403 00:21:38,960 --> 00:21:41,720 Speaker 1: a lot of tough questions, including whether um, he was 404 00:21:41,760 --> 00:21:47,399 Speaker 1: promised independence by President Trump or whether President Trump asked 405 00:21:47,480 --> 00:21:51,399 Speaker 1: him for loyalty as uh the fired FBI director Comey 406 00:21:52,080 --> 00:21:55,360 Speaker 1: apparently alleges, Yeah, this is this is important. The other 407 00:21:55,400 --> 00:21:58,400 Speaker 1: thing that I find interesting is the timing of President 408 00:21:58,440 --> 00:22:04,000 Speaker 1: Trump's announcement this day before fired FBI Director James Comey 409 00:22:04,160 --> 00:22:06,800 Speaker 1: is set to testify in front of Congress about his 410 00:22:06,840 --> 00:22:11,000 Speaker 1: discussions with President Trump. Uh, what is the significance of this? 411 00:22:11,080 --> 00:22:12,919 Speaker 1: I mean, is it just a coincidence? Has this been 412 00:22:12,960 --> 00:22:14,760 Speaker 1: a word in the works for a long time, or 413 00:22:14,920 --> 00:22:19,000 Speaker 1: is this a move to perhaps um pull attention away 414 00:22:19,000 --> 00:22:22,239 Speaker 1: from the hearings and towards the future. Well, there's no 415 00:22:22,400 --> 00:22:26,080 Speaker 1: pulling attention away for these theories where are fair enough dramatic. 416 00:22:26,240 --> 00:22:29,919 Speaker 1: But I think you're right. The the President's message is 417 00:22:29,960 --> 00:22:33,560 Speaker 1: returning the page. That's all history. We have chosen a 418 00:22:33,600 --> 00:22:39,280 Speaker 1: new uh appropriate FBI chief that we're nominating. Let's get 419 00:22:39,320 --> 00:22:42,399 Speaker 1: on with it. And that's certainly the best, politically speaking, 420 00:22:42,440 --> 00:22:44,920 Speaker 1: the best message he could send. Uh. You know, there's 421 00:22:44,960 --> 00:22:46,920 Speaker 1: been a lot of talk about whether he'll be able 422 00:22:46,960 --> 00:22:51,040 Speaker 1: to resist tweeting all day long tomorrow Comey's testifying, But 423 00:22:51,440 --> 00:22:54,480 Speaker 1: in terms of using his Twitter handle this morning, he 424 00:22:54,600 --> 00:22:59,040 Speaker 1: sent an effective message show going into tomorrow's hearing. Right, Um, Larry, 425 00:22:59,119 --> 00:23:01,439 Speaker 1: and before we get to getting a sense of what 426 00:23:01,480 --> 00:23:03,920 Speaker 1: the big issues are tomorrow, I really want to touch 427 00:23:04,040 --> 00:23:07,960 Speaker 1: on Attorney General Sessions because there are reports that he 428 00:23:08,119 --> 00:23:12,080 Speaker 1: offered to resign after getting some pressure from President Trump 429 00:23:12,240 --> 00:23:15,800 Speaker 1: based on his decision to accuse himself from the Russia inquiry. 430 00:23:16,400 --> 00:23:18,119 Speaker 1: Do you think that this is realistic the color that 431 00:23:18,160 --> 00:23:21,000 Speaker 1: you're getting from people in Washington, d C. Do they 432 00:23:21,080 --> 00:23:25,840 Speaker 1: really expect that Attorney General sessions proposal might be accepted 433 00:23:25,920 --> 00:23:30,440 Speaker 1: and is a realistic possibility? Well, the sets are reporters 434 00:23:30,520 --> 00:23:35,840 Speaker 1: Chris Stroman and others get is that, Uh, President Trump 435 00:23:35,880 --> 00:23:37,800 Speaker 1: tended to lash out at those around him when he 436 00:23:37,800 --> 00:23:40,360 Speaker 1: feels they haven't come through. In this case, Uh, he's 437 00:23:40,440 --> 00:23:46,480 Speaker 1: never forgiven, reportedly Attorney General Sessions for recusing himself and 438 00:23:46,800 --> 00:23:49,240 Speaker 1: which led to a whole series of events, including naming 439 00:23:49,760 --> 00:23:52,960 Speaker 1: a former FBI Chief of Special Prosecutor And with that 440 00:23:53,080 --> 00:23:57,560 Speaker 1: kind of festering um that Attorney General Sessions said, if 441 00:23:57,600 --> 00:23:59,720 Speaker 1: you'd like me to resign, I will. But I think 442 00:23:59,800 --> 00:24:02,280 Speaker 1: the sense was it was more a message to the president, 443 00:24:02,320 --> 00:24:05,200 Speaker 1: if I don't have your faith, I'll go, rather than 444 00:24:06,160 --> 00:24:09,240 Speaker 1: a resignation we should expect in the near future. Okay, 445 00:24:09,240 --> 00:24:11,200 Speaker 1: So people don't think it's realistic. They think it's more 446 00:24:11,240 --> 00:24:14,560 Speaker 1: sort of a an ultimatum. Look, you have you have, 447 00:24:14,680 --> 00:24:18,440 Speaker 1: you have carte launch here. Do you want me to go? Right? Exactly? 448 00:24:18,480 --> 00:24:21,760 Speaker 1: And of course you never know this. This president has proven, 449 00:24:21,920 --> 00:24:25,000 Speaker 1: uh if if nothing else, that he's done predictable, that 450 00:24:25,080 --> 00:24:27,720 Speaker 1: he goes his own way, uh, and that he has 451 00:24:27,760 --> 00:24:32,040 Speaker 1: been frustrated. He attacked the Justice Department for the revised 452 00:24:32,240 --> 00:24:36,639 Speaker 1: UH travel ban that he signed uh in a tweet. 453 00:24:36,680 --> 00:24:40,879 Speaker 1: And that's unprecedented. So we're always ready for surprising. All right, Larry, 454 00:24:40,920 --> 00:24:44,160 Speaker 1: now to turn to the center stage event tomorrow, James 455 00:24:44,240 --> 00:24:47,800 Speaker 1: Comey heading to Capitol Hill. What are the main issues 456 00:24:47,840 --> 00:24:49,639 Speaker 1: that he's going to be talking about. What are the 457 00:24:49,640 --> 00:24:52,280 Speaker 1: main things that people want to hear, Well, they really 458 00:24:52,320 --> 00:24:56,880 Speaker 1: want to hear what he and Donald Trump discussed, As 459 00:24:57,320 --> 00:25:02,879 Speaker 1: has been reported, did the President repeatedly ask him for loyalty, uh, 460 00:25:03,760 --> 00:25:08,879 Speaker 1: which he apparently sidestepped. Did the president ask him, can't 461 00:25:08,880 --> 00:25:13,760 Speaker 1: you let this go? After? Uh? General Flynn was fired 462 00:25:13,800 --> 00:25:18,320 Speaker 1: as National Security Advisor? Uh? And did he feel that 463 00:25:18,320 --> 00:25:22,439 Speaker 1: that was a clear effort in interference? Uh? And the 464 00:25:22,520 --> 00:25:27,040 Speaker 1: question is, will well we hear from people familiar with 465 00:25:27,240 --> 00:25:30,560 Speaker 1: UH with Comey who changed to speak? His mind is 466 00:25:30,600 --> 00:25:33,879 Speaker 1: that he will recount those discussions, but he'll leave it 467 00:25:33,960 --> 00:25:37,560 Speaker 1: to others to decide whether it was obstruction of justice 468 00:25:37,720 --> 00:25:42,159 Speaker 1: or otherwise deeply inappropriate. Larry, does it matter? Does it 469 00:25:42,280 --> 00:25:45,360 Speaker 1: matter what he says? Because let's say he says, uh, 470 00:25:45,400 --> 00:25:48,320 Speaker 1: you know, anything, everything, but that it was obstruction of justice, 471 00:25:48,320 --> 00:25:52,080 Speaker 1: and then it's clear that congressmen feel like it is. UM, 472 00:25:52,240 --> 00:25:56,439 Speaker 1: then what Well, that's always the question because practically speaking, 473 00:25:56,440 --> 00:26:00,639 Speaker 1: you know, prosecute a president but uh. And peachment is 474 00:26:00,680 --> 00:26:04,439 Speaker 1: not on anybody's lips really at this point. But that 475 00:26:04,480 --> 00:26:08,840 Speaker 1: would keep alive not only the investigation of Russian interference, 476 00:26:09,080 --> 00:26:11,560 Speaker 1: but whether those close to the present were involved and 477 00:26:11,600 --> 00:26:15,200 Speaker 1: whether there's any effort to suppress that information. So it's 478 00:26:15,200 --> 00:26:17,320 Speaker 1: it's a key moment, and I should add that right 479 00:26:17,359 --> 00:26:21,119 Speaker 1: now send an intelligence committee is hearing from Dan Coates, 480 00:26:21,119 --> 00:26:24,239 Speaker 1: the Director of National Intelligence, and others suppressing them on 481 00:26:24,280 --> 00:26:30,840 Speaker 1: their conversations with UH with President Trump, and so there's 482 00:26:30,880 --> 00:26:34,440 Speaker 1: quite a build up to tomorrow's Comy testimony. Larry, your 483 00:26:34,440 --> 00:26:38,280 Speaker 1: head must be spinning concertainly, Larry Liebert, thank you so 484 00:26:38,359 --> 00:26:40,760 Speaker 1: much for distilling all of the activities that are going 485 00:26:40,760 --> 00:26:42,639 Speaker 1: on in Washington, d C right now what we can 486 00:26:42,640 --> 00:26:46,400 Speaker 1: inspect or expect for tomorrow. Larry Leebert is National Security 487 00:26:46,440 --> 00:26:50,800 Speaker 1: Team editor for Bloomberg News and he is UH there 488 00:26:50,800 --> 00:26:53,639 Speaker 1: in the heat of the action in Washington, d C. 489 00:26:57,040 --> 00:26:59,560 Speaker 1: Thanks for listening to the Bloomberg p m L podcast. 490 00:27:00,040 --> 00:27:03,800 Speaker 1: Can subscribe and listen to interviews at Apple Podcasts, SoundCloud, 491 00:27:03,920 --> 00:27:07,400 Speaker 1: or whatever podcast platform you prefer. I'm pim Fox. I'm 492 00:27:07,400 --> 00:27:10,960 Speaker 1: on Twitter at pim Fox. I'm on Twitter at Lisa 493 00:27:11,000 --> 00:27:13,960 Speaker 1: Abramo wits one. Before the podcast, you can always catch 494 00:27:14,040 --> 00:27:15,760 Speaker 1: us worldwide on Bluebirg Radio.