1 00:00:06,559 --> 00:00:11,840 Speaker 1: Welcome to trillions. I'm Joel Webber and I'm Eric bel Tunis. Eric. 2 00:00:11,880 --> 00:00:14,360 Speaker 1: We've been doing this for a little bit now, but 3 00:00:14,400 --> 00:00:18,240 Speaker 1: we haven't spent that much time talking about what's basically 4 00:00:18,280 --> 00:00:21,880 Speaker 1: the hottest trend in ETFs, which is smart beta. Yeah, 5 00:00:21,960 --> 00:00:24,320 Speaker 1: it's one of them. I think people forget that smart 6 00:00:24,360 --> 00:00:27,200 Speaker 1: beta is of e t F assets, so it's a 7 00:00:27,240 --> 00:00:31,400 Speaker 1: big chunk um. The other virtually is all what we 8 00:00:31,480 --> 00:00:34,400 Speaker 1: call cheap beta, which is you know, the smp F 9 00:00:35,000 --> 00:00:37,280 Speaker 1: t F and we called the plane vanilla side. But 10 00:00:37,320 --> 00:00:41,080 Speaker 1: this is up from nothing ten years ago, so it 11 00:00:41,240 --> 00:00:45,400 Speaker 1: is a it's growing faster on a percentage basis than 12 00:00:45,479 --> 00:00:47,240 Speaker 1: e t F as a whole, and it's a really 13 00:00:47,280 --> 00:00:49,600 Speaker 1: interesting dynamic area. This is where a lot of the 14 00:00:49,600 --> 00:00:53,000 Speaker 1: innovation and new products are being launched. So how are 15 00:00:53,000 --> 00:00:57,320 Speaker 1: we supposed to think about this thing called smart beta. Well, 16 00:00:57,360 --> 00:00:59,720 Speaker 1: it's very it can be confusing a lot of times. 17 00:00:59,720 --> 00:01:01,320 Speaker 1: People think it sounds like something you get at the 18 00:01:01,320 --> 00:01:03,279 Speaker 1: g n C store. Yeah, I mean right, I know, 19 00:01:03,360 --> 00:01:08,880 Speaker 1: you're like a way container little double chocolate smart beta. 20 00:01:09,040 --> 00:01:11,560 Speaker 1: It's definitely jargon e um and it's got a buzzword 21 00:01:11,600 --> 00:01:14,600 Speaker 1: type feel to it. But um, you know. Essentially, a 22 00:01:14,640 --> 00:01:16,840 Speaker 1: lot of people will give you different definitions of it. 23 00:01:17,200 --> 00:01:20,039 Speaker 1: To me, you know, the metaphor I use is the 24 00:01:20,080 --> 00:01:23,200 Speaker 1: way people describe different religions, right they there's this thing 25 00:01:23,240 --> 00:01:26,280 Speaker 1: that they say, it's like a bunch of people touching 26 00:01:26,319 --> 00:01:28,760 Speaker 1: an elephant in a dark room, in the way different 27 00:01:28,760 --> 00:01:31,800 Speaker 1: religions describing God. It's the same thing, but it's just 28 00:01:31,840 --> 00:01:34,520 Speaker 1: being Uh, they're feeling a different part of it, but 29 00:01:34,600 --> 00:01:38,160 Speaker 1: ultimately it's the same thing to me. Smarting Right now, 30 00:01:38,440 --> 00:01:41,560 Speaker 1: what is that I'm in a dark room? You don't 31 00:01:41,560 --> 00:01:44,160 Speaker 1: want to know. No, let's say you're touching the trunk. 32 00:01:44,280 --> 00:01:46,600 Speaker 1: Somebody else is touching the side. My point is that 33 00:01:46,840 --> 00:01:49,920 Speaker 1: smart beata kind of can mean different things to different people. Uh, 34 00:01:49,920 --> 00:01:53,200 Speaker 1: it's controversial, and I think that a lot of people 35 00:01:53,760 --> 00:01:57,160 Speaker 1: instead of thinking you need to like capture it and 36 00:01:57,240 --> 00:01:59,520 Speaker 1: be done with understanding it in two seconds, I think 37 00:01:59,560 --> 00:02:02,000 Speaker 1: you need to just accept that it's a little bit 38 00:02:02,000 --> 00:02:04,680 Speaker 1: of a mystery to some people. There's a complexity to 39 00:02:04,720 --> 00:02:07,120 Speaker 1: it and there's differing opinions. As long as you embrace 40 00:02:07,240 --> 00:02:10,400 Speaker 1: that and sort of go into this conversation we're about 41 00:02:10,400 --> 00:02:12,880 Speaker 1: to have knowing that you're gonna understand a little more. 42 00:02:13,360 --> 00:02:14,959 Speaker 1: I think that's the way to go. You gotta lower 43 00:02:15,000 --> 00:02:18,200 Speaker 1: the bar on this because you can't really just get 44 00:02:18,240 --> 00:02:21,200 Speaker 1: it done in like two or three sentences. If I 45 00:02:21,200 --> 00:02:23,840 Speaker 1: had to do it in one sentence, what would you 46 00:02:23,880 --> 00:02:26,519 Speaker 1: how would you describe it? I would say smart Beta 47 00:02:26,960 --> 00:02:29,160 Speaker 1: is trying to take Peter Lynch's brain and put it 48 00:02:29,160 --> 00:02:34,359 Speaker 1: into our two D two's think. In other words, you're 49 00:02:34,360 --> 00:02:36,840 Speaker 1: trying to take the best of active management, but putting 50 00:02:36,880 --> 00:02:39,720 Speaker 1: it into a rules based index that an e t 51 00:02:39,880 --> 00:02:44,600 Speaker 1: F track. So it's basically a conversion of active and 52 00:02:44,639 --> 00:02:47,960 Speaker 1: trying to make it better and more efficient and cheaper 53 00:02:48,440 --> 00:02:52,360 Speaker 1: and deliver it in a better structure. This week, Gun Trillion, 54 00:02:53,720 --> 00:02:59,040 Speaker 1: the Mystery of smart Beta. Okay, Eric, we've got some guests. 55 00:02:59,280 --> 00:03:01,360 Speaker 1: This is really important because I think every one of 56 00:03:01,400 --> 00:03:04,320 Speaker 1: these guests, including you and me, we'll have a different 57 00:03:04,320 --> 00:03:06,600 Speaker 1: perspective on smart Beta. So we're all sort of touching 58 00:03:06,600 --> 00:03:09,320 Speaker 1: the elephant in your dark room, your creepy dark room 59 00:03:09,600 --> 00:03:12,240 Speaker 1: filled with an elephant and us. Yeah, it's almost akin 60 00:03:12,320 --> 00:03:14,440 Speaker 1: to when you go to a conference. So an ETF conferences, 61 00:03:14,639 --> 00:03:17,040 Speaker 1: there's a panel, you tend to have people from different lenses. 62 00:03:17,080 --> 00:03:19,120 Speaker 1: So we're trying to sort of capture that so that 63 00:03:19,200 --> 00:03:21,639 Speaker 1: you can get all of the viewpoints and then you 64 00:03:21,680 --> 00:03:23,280 Speaker 1: can sort of come up with your own sort of 65 00:03:23,480 --> 00:03:25,239 Speaker 1: view on it. One of them is remote, so he's 66 00:03:25,240 --> 00:03:27,519 Speaker 1: in his own dark room touching a different elephant. Near 67 00:03:27,600 --> 00:03:31,360 Speaker 1: case are a columnst with Bloomberg gad fly high near Hello, 68 00:03:31,960 --> 00:03:35,200 Speaker 1: he's in d C. Joining us in the booth is 69 00:03:35,240 --> 00:03:39,360 Speaker 1: Tom Seraphagus, who is a colleague of yours at Bloomberg Intelligence, 70 00:03:39,360 --> 00:03:44,040 Speaker 1: where he's an et F analyst. Also came from Oppenheimer 71 00:03:44,600 --> 00:03:47,360 Speaker 1: before he was at Bloomberg uh and has some experience 72 00:03:47,600 --> 00:03:51,080 Speaker 1: with smart beata et F. Sah, how are you. I'm 73 00:03:51,120 --> 00:03:53,920 Speaker 1: doing great, Thanks for joining. So Tom, can you help 74 00:03:54,040 --> 00:03:57,480 Speaker 1: start this discussion around what is smart beata. Let's definance 75 00:03:57,520 --> 00:03:59,880 Speaker 1: so that you know, before people get in the deep 76 00:04:00,040 --> 00:04:01,800 Speaker 1: to the pool with us, we have an understanding of 77 00:04:01,840 --> 00:04:04,400 Speaker 1: what we're talking about. Yeah. Sure. And so before I 78 00:04:04,680 --> 00:04:07,160 Speaker 1: joined here, I was at on the other side of 79 00:04:07,200 --> 00:04:09,200 Speaker 1: the table, so on the issue side. And you have 80 00:04:09,240 --> 00:04:11,680 Speaker 1: to remember that a lot of these active shops have 81 00:04:11,920 --> 00:04:14,840 Speaker 1: a tremendous amount of resources, right They've got portfolio managers 82 00:04:14,880 --> 00:04:18,279 Speaker 1: and analysts. So I think in a very brief way, 83 00:04:18,600 --> 00:04:20,800 Speaker 1: what they were trying to do is take the mind 84 00:04:20,960 --> 00:04:25,760 Speaker 1: of the portfolio manager Peter Rearle and sure someone else saying, Hey, 85 00:04:25,880 --> 00:04:28,000 Speaker 1: when you're picking stocks, what are things you're looking at? 86 00:04:28,080 --> 00:04:30,440 Speaker 1: How are you running your screens? What are you looking at? 87 00:04:30,960 --> 00:04:33,720 Speaker 1: Let's take your approach, put it, write it down, put 88 00:04:33,760 --> 00:04:35,520 Speaker 1: it in an index, and launch an e t F 89 00:04:35,600 --> 00:04:38,720 Speaker 1: over it. So, um, you know, the the definition of 90 00:04:38,800 --> 00:04:41,160 Speaker 1: smart data. It's not so clear, right, it's a very 91 00:04:41,200 --> 00:04:43,040 Speaker 1: gray area. But it's really trying to take a lot 92 00:04:43,120 --> 00:04:46,440 Speaker 1: of these approaches, democratize it, put in e TF and 93 00:04:46,520 --> 00:04:48,680 Speaker 1: make it available to the masses as a product, as 94 00:04:48,680 --> 00:04:51,040 Speaker 1: a problem, buy off the shelf, right, how do you 95 00:04:51,080 --> 00:04:54,160 Speaker 1: think about it? I think those are both good. Both 96 00:04:54,320 --> 00:04:56,960 Speaker 1: Eric and Tom's definitions I think are both good. I'll 97 00:04:57,000 --> 00:04:58,960 Speaker 1: just try to simplify it even further because I think 98 00:04:58,960 --> 00:05:02,480 Speaker 1: it's really important to convey to to investors that this 99 00:05:02,640 --> 00:05:05,840 Speaker 1: is actually very simple in concept. All this is is 100 00:05:05,880 --> 00:05:08,840 Speaker 1: traditional active management. All that all the strategies that they've 101 00:05:08,880 --> 00:05:11,320 Speaker 1: always loved and have been used for decades by every 102 00:05:11,360 --> 00:05:15,440 Speaker 1: active manager ever. Value you know, buying cheaper companies, buying 103 00:05:15,839 --> 00:05:19,040 Speaker 1: you know, high growth companies, buying smaller companies, whatever the 104 00:05:19,080 --> 00:05:22,280 Speaker 1: strategy that you like is effectively giving it to a 105 00:05:22,320 --> 00:05:24,920 Speaker 1: computer and letting a computer run it. That's really all 106 00:05:25,040 --> 00:05:27,680 Speaker 1: it is. And the reason that's important to convey is 107 00:05:28,240 --> 00:05:30,600 Speaker 1: the the the act of taking it away from the 108 00:05:30,960 --> 00:05:33,800 Speaker 1: from the human and giving it to the computer gives 109 00:05:33,839 --> 00:05:37,320 Speaker 1: the investor several advantages that are really important. One, it 110 00:05:37,400 --> 00:05:39,360 Speaker 1: makes it a lot cheaper because you can you know, 111 00:05:39,440 --> 00:05:41,400 Speaker 1: the computer doesn't cost as much as the human. That's 112 00:05:41,400 --> 00:05:43,920 Speaker 1: the first thing that's you know, that's a good thing. 113 00:05:43,960 --> 00:05:46,719 Speaker 1: The second thing is they're more transparent because you've turned 114 00:05:46,800 --> 00:05:49,360 Speaker 1: it over to a computer. You've had to write rules 115 00:05:49,440 --> 00:05:52,320 Speaker 1: about how this thing will look. And investors can read 116 00:05:52,400 --> 00:05:54,920 Speaker 1: these rules and they can see exactly what they're buying, 117 00:05:55,080 --> 00:05:57,480 Speaker 1: So that's useful to And the other thing is that 118 00:05:57,600 --> 00:06:00,400 Speaker 1: it allows them. It allows us to to um to 119 00:06:00,520 --> 00:06:03,560 Speaker 1: fine tune how we think about these various styles. So 120 00:06:03,680 --> 00:06:06,720 Speaker 1: for example, me and you, we might both like value, right, 121 00:06:06,760 --> 00:06:08,840 Speaker 1: but you might like it more than me, So I 122 00:06:08,880 --> 00:06:10,720 Speaker 1: can write a computer program for you that gives you 123 00:06:10,800 --> 00:06:12,600 Speaker 1: more value and less value for me, and so on. 124 00:06:12,880 --> 00:06:14,760 Speaker 1: And these are things that are more difficult to do 125 00:06:14,839 --> 00:06:17,560 Speaker 1: with human beings. So that's all it is. That sounds simple. 126 00:06:18,200 --> 00:06:20,720 Speaker 1: Let me add in here to dovetail to my original 127 00:06:20,760 --> 00:06:23,800 Speaker 1: definition and just go and how the term came about, 128 00:06:23,920 --> 00:06:27,200 Speaker 1: because this probably will help. A consulting firm named Towers 129 00:06:27,240 --> 00:06:30,400 Speaker 1: Watson is who coined the phrase smart beta, and this 130 00:06:30,600 --> 00:06:32,800 Speaker 1: is exactly how they defined it in their literature in 131 00:06:32,880 --> 00:06:35,880 Speaker 1: your two thousand. Smart beta is simply about trying to 132 00:06:35,960 --> 00:06:39,040 Speaker 1: identify good investment ideas that can be structured better. So 133 00:06:39,200 --> 00:06:41,120 Speaker 1: think about it. What do people not like about an 134 00:06:41,160 --> 00:06:45,400 Speaker 1: active mutual fund? Their high cost uh they have the 135 00:06:45,720 --> 00:06:48,480 Speaker 1: manager could be emotional and make decisions based on the 136 00:06:48,560 --> 00:06:52,680 Speaker 1: current trends, and they have distribute capital gains regardless of 137 00:06:52,720 --> 00:06:54,880 Speaker 1: whether you if even if you stayed invested, you've got 138 00:06:54,960 --> 00:06:58,720 Speaker 1: tax bill. Now, if you take that active mutual fund 139 00:06:59,000 --> 00:07:01,919 Speaker 1: strategy and you put it into a rules based index, 140 00:07:02,320 --> 00:07:05,960 Speaker 1: you essentially eliminate the emotion, right because it's gonna rebalance 141 00:07:06,000 --> 00:07:09,000 Speaker 1: at least us said it's a robot. You're gonna lower 142 00:07:09,040 --> 00:07:10,960 Speaker 1: the feet because there's less overhead. And the ETF structure 143 00:07:10,960 --> 00:07:13,120 Speaker 1: so usually like a third as cheap as or a 144 00:07:13,160 --> 00:07:15,840 Speaker 1: third as expensive as an active mutual fund, and they're 145 00:07:16,040 --> 00:07:17,960 Speaker 1: et f s are very tax efficients. You can have 146 00:07:18,120 --> 00:07:21,120 Speaker 1: some decent turnover inside the fund, and yet no capital 147 00:07:21,160 --> 00:07:24,640 Speaker 1: gains get distributed, so you eliminate three of the baggage 148 00:07:25,120 --> 00:07:27,040 Speaker 1: of the active mutual fund when you cross it over. So, 149 00:07:27,360 --> 00:07:30,800 Speaker 1: in a way, Towers Watson definition is true. It's just 150 00:07:30,920 --> 00:07:33,840 Speaker 1: been mutated over the years and piss people off because 151 00:07:33,880 --> 00:07:36,560 Speaker 1: they think that it means smart means better, and it's 152 00:07:36,600 --> 00:07:39,559 Speaker 1: not necessarily better. It's better, a better way to deliver something, 153 00:07:40,040 --> 00:07:43,360 Speaker 1: not you're gonna definitely outperform unless we're clear. There's also 154 00:07:43,760 --> 00:07:46,720 Speaker 1: different names for smart beta. It's not like smart beta 155 00:07:47,040 --> 00:07:48,760 Speaker 1: is the only one, right Tom, Like, what other what 156 00:07:48,880 --> 00:07:52,040 Speaker 1: other terms can mean smart beta? Yeah, I mean there's 157 00:07:52,040 --> 00:07:54,120 Speaker 1: a lot of terminology that's been thrown out. People call 158 00:07:54,120 --> 00:07:58,880 Speaker 1: it strategic beta, tarnative beta, active beta, um, you know. 159 00:07:58,880 --> 00:08:00,720 Speaker 1: And a lot of people will get caught on, get 160 00:08:00,800 --> 00:08:02,720 Speaker 1: caught up in the semantics because they think that, oh, 161 00:08:02,800 --> 00:08:05,560 Speaker 1: if it's not smart beta, it's dumb beta. Right. It's not. 162 00:08:05,720 --> 00:08:07,920 Speaker 1: It's not so binary that it's just one or the other. 163 00:08:08,040 --> 00:08:10,360 Speaker 1: So a lot of people send they get caught up 164 00:08:10,360 --> 00:08:12,360 Speaker 1: in the semantics and kind of forget, like, what is 165 00:08:12,440 --> 00:08:14,760 Speaker 1: the bigger theme here? What are these products giving you? 166 00:08:14,840 --> 00:08:16,680 Speaker 1: What are they offering you? Can you help me embrace 167 00:08:16,760 --> 00:08:18,640 Speaker 1: the complicated a little bit here, because one of the 168 00:08:19,320 --> 00:08:22,080 Speaker 1: main differences with smart beta is with a with a 169 00:08:22,160 --> 00:08:24,880 Speaker 1: normal ETF the most popular ones to play vanilla ones, 170 00:08:25,080 --> 00:08:29,080 Speaker 1: those are all market cap weighted, right, So help me 171 00:08:29,160 --> 00:08:33,040 Speaker 1: better understand what sometimes the differences with smart beta. Right 172 00:08:33,080 --> 00:08:35,120 Speaker 1: when the Towers Watson definition, they would call all that 173 00:08:35,240 --> 00:08:37,800 Speaker 1: bulk beta. So there's just two bulk beta, which is 174 00:08:37,840 --> 00:08:40,240 Speaker 1: market cap weighted. This I think a lot of people 175 00:08:40,400 --> 00:08:42,959 Speaker 1: just called beta. So just tell me what market cap 176 00:08:43,080 --> 00:08:45,000 Speaker 1: is right like that? Right, So the SMP five hundred, 177 00:08:45,520 --> 00:08:48,240 Speaker 1: the bigger the stocks market cap, the bigger the waiting, right, 178 00:08:48,320 --> 00:08:50,800 Speaker 1: so as stocks get bigger, that sort the top waitings 179 00:08:50,840 --> 00:08:54,400 Speaker 1: in the Sapier, Apple, Microsoft, Amazon, Right, you got it. Now, 180 00:08:55,120 --> 00:08:56,800 Speaker 1: that is another way to view smart beta and how 181 00:08:56,880 --> 00:08:59,760 Speaker 1: some people do it. If you're in the index fund 182 00:08:59,800 --> 00:09:01,959 Speaker 1: were world or the vanguard world and you're used to 183 00:09:02,040 --> 00:09:06,439 Speaker 1: market cap weighted products which are very popular, smart beta 184 00:09:06,520 --> 00:09:09,520 Speaker 1: to you seems like a mutation or a twist on 185 00:09:09,640 --> 00:09:12,199 Speaker 1: the market cap waiting structure. So you could actually view 186 00:09:12,240 --> 00:09:14,640 Speaker 1: smart beta from that lens and either look at it 187 00:09:14,720 --> 00:09:17,320 Speaker 1: as you know, a way to try to do better 188 00:09:17,400 --> 00:09:19,640 Speaker 1: than the market cap waited next, because you're gonna change 189 00:09:19,679 --> 00:09:21,520 Speaker 1: the way the index works. You're gonna maybe wait by 190 00:09:21,559 --> 00:09:24,040 Speaker 1: dividends instead of market cap. You're gonna wait by size. 191 00:09:24,480 --> 00:09:27,760 Speaker 1: You could wait by a collection of fundamentals. That is 192 00:09:27,800 --> 00:09:29,360 Speaker 1: another way to view it. So you can see you 193 00:09:29,440 --> 00:09:32,040 Speaker 1: can see smart beta from the active side or from 194 00:09:32,080 --> 00:09:34,959 Speaker 1: the passive side and describe it different ways. But it 195 00:09:35,720 --> 00:09:38,840 Speaker 1: sounds kind of controversial, right, Well, it is. The word 196 00:09:38,960 --> 00:09:42,280 Speaker 1: smart is the controversial part. But I equated to Obamacare, 197 00:09:42,400 --> 00:09:44,160 Speaker 1: right remember that what was the real name of the bill? 198 00:09:44,160 --> 00:09:46,760 Speaker 1: I don't even remember the patient and affordable care branding 199 00:09:46,840 --> 00:09:49,640 Speaker 1: right there, right, so even Obama started calling it Obamacare. 200 00:09:49,920 --> 00:09:53,040 Speaker 1: Smart beta is just a buzzword that you cannot beat down. 201 00:09:53,480 --> 00:09:55,880 Speaker 1: People have come along, like Tom said morning Star Targic 202 00:09:55,880 --> 00:10:00,640 Speaker 1: called a strategic beta, scientific beta, alternative beta, quantiment indexes. 203 00:10:00,640 --> 00:10:03,600 Speaker 1: How about that one anti benchmark is the France calls 204 00:10:03,640 --> 00:10:05,679 Speaker 1: it that active betas You just keep coming back to 205 00:10:05,760 --> 00:10:08,400 Speaker 1: smart beta. Smart beta sticks. It's sticks. But the words 206 00:10:08,440 --> 00:10:11,520 Speaker 1: smart is what has made it controversial because at the 207 00:10:11,600 --> 00:10:13,960 Speaker 1: end of the day, you may or may not outperform. 208 00:10:14,440 --> 00:10:16,880 Speaker 1: You're just going to have a different return stream than 209 00:10:17,200 --> 00:10:25,520 Speaker 1: a market cap weighted index. Okay, Eric, can we actually 210 00:10:25,880 --> 00:10:29,120 Speaker 1: break this thing down and talk about smart beta in 211 00:10:29,280 --> 00:10:32,240 Speaker 1: real life. Let's call it a case study. Do you 212 00:10:32,440 --> 00:10:36,079 Speaker 1: have an example of a smart beta e t F 213 00:10:36,679 --> 00:10:38,600 Speaker 1: and can you walk me through how how it did 214 00:10:38,640 --> 00:10:40,800 Speaker 1: what it did? Right? So, here's a classic one came 215 00:10:40,840 --> 00:10:43,199 Speaker 1: out in two thousand five. So this is like early 216 00:10:43,320 --> 00:10:47,120 Speaker 1: settler smart beta product, right, This was the Power Shares FO. 217 00:10:47,520 --> 00:10:49,679 Speaker 1: I can picture it out in the homestead it Well, 218 00:10:49,720 --> 00:10:52,960 Speaker 1: back then it was a lot of open frontier people 219 00:10:53,000 --> 00:10:54,920 Speaker 1: put flags everywhere. Now that's why the t flants. He 220 00:10:55,000 --> 00:10:56,439 Speaker 1: is very crowded, hard to come up with new ideas. 221 00:10:56,679 --> 00:10:58,800 Speaker 1: There's not much white space left, so to speak. But 222 00:10:58,920 --> 00:11:00,400 Speaker 1: this really was one of the first ones. It's the 223 00:11:00,440 --> 00:11:04,240 Speaker 1: Power Shares Foutsie Raffie US one thousand portfolio tik R 224 00:11:04,280 --> 00:11:08,720 Speaker 1: PRF can see that again. No, Um, here's what it does. 225 00:11:08,800 --> 00:11:11,600 Speaker 1: It's pretty simple. It tracks stocks based on four fundamental 226 00:11:11,679 --> 00:11:15,480 Speaker 1: measures right book value, cash flow, sales, and dividends. So 227 00:11:16,000 --> 00:11:19,240 Speaker 1: it just screens stocks a thousand stocks by those metrics, 228 00:11:19,480 --> 00:11:22,559 Speaker 1: comes up with a score, sorts them by that, and 229 00:11:22,640 --> 00:11:25,319 Speaker 1: then every quarter it looks for which ones go to 230 00:11:25,400 --> 00:11:27,040 Speaker 1: the top, and then it waits them by the score, 231 00:11:27,120 --> 00:11:29,800 Speaker 1: and that's the that's how the index works, and that's 232 00:11:29,840 --> 00:11:32,040 Speaker 1: what the e t F tracks. It just that's all 233 00:11:32,080 --> 00:11:34,319 Speaker 1: it does. And it it goes on like a little robot. 234 00:11:34,520 --> 00:11:37,280 Speaker 1: It is. It's a total how often does it rebalance quarterly? 235 00:11:37,880 --> 00:11:40,439 Speaker 1: So it is it's almost like a robotic version of 236 00:11:40,480 --> 00:11:42,400 Speaker 1: a stock analyst, right, because it's doing things that a 237 00:11:42,480 --> 00:11:45,680 Speaker 1: researcher would look for. Those are definitely popular fundamental metrics. 238 00:11:46,280 --> 00:11:49,240 Speaker 1: Here's where I think this is a great example of 239 00:11:49,320 --> 00:11:51,560 Speaker 1: how it works, especially on the lack of emotion side. 240 00:11:51,920 --> 00:11:54,559 Speaker 1: In two thousand and eight, right bank stocks were hated. 241 00:11:55,160 --> 00:11:58,040 Speaker 1: Nobody wanted to buy City Group at any of these stocks. Well, 242 00:11:58,080 --> 00:12:00,240 Speaker 1: as you know, they got cheaper on a value ation 243 00:12:00,280 --> 00:12:03,800 Speaker 1: basis because everybody sold them. Well, this thing is a robot. 244 00:12:03,840 --> 00:12:05,839 Speaker 1: It doesn't it doesn't read the news, it doesn't know 245 00:12:05,880 --> 00:12:07,840 Speaker 1: what's going on. It isn't not getting yelled at by clients. 246 00:12:08,120 --> 00:12:11,480 Speaker 1: So it rebalanced into financials in a big way. It 247 00:12:11,559 --> 00:12:14,880 Speaker 1: had fifty financials in the beginning of two thousand nine. 248 00:12:15,280 --> 00:12:17,840 Speaker 1: Doubled down on it, doubled down it went. It went 249 00:12:17,920 --> 00:12:20,920 Speaker 1: into the burning building, as Warren Buffett would say, big 250 00:12:21,000 --> 00:12:26,320 Speaker 1: time with with a suit of gasoline. And on top 251 00:12:26,360 --> 00:12:28,439 Speaker 1: of that, I mean, like, I assume maybe the guy 252 00:12:28,520 --> 00:12:31,280 Speaker 1: in charge of this who is whom so the person 253 00:12:31,320 --> 00:12:33,839 Speaker 1: who made the indexes Rob or not, and I'm guessing 254 00:12:33,960 --> 00:12:36,079 Speaker 1: Rob maybe got some heat for that he did. So 255 00:12:36,360 --> 00:12:38,319 Speaker 1: I interviewed him from my book and he said that 256 00:12:38,760 --> 00:12:41,840 Speaker 1: institutional funds that tracked that index on a separate account 257 00:12:41,840 --> 00:12:44,360 Speaker 1: basis called up and said, do not do the rebalance. 258 00:12:44,480 --> 00:12:47,880 Speaker 1: We do not want to do this exactly, and in 259 00:12:47,960 --> 00:12:49,439 Speaker 1: separate account you can do that. The e t F 260 00:12:49,559 --> 00:12:53,199 Speaker 1: cannot well guess who won after five years. Basically, that 261 00:12:53,320 --> 00:12:58,680 Speaker 1: rebalance accounted for almost all of the firm's out performance 262 00:12:58,720 --> 00:13:05,400 Speaker 1: over the next five years and came from the financials 263 00:13:05,679 --> 00:13:08,959 Speaker 1: read that one rebalance. Now Ben Johnson the Morning Star 264 00:13:09,040 --> 00:13:11,560 Speaker 1: calls it the immaculate rebalance. He thinks that I'm bringing 265 00:13:11,640 --> 00:13:14,280 Speaker 1: up a extreme situation. He also brings up that a 266 00:13:14,320 --> 00:13:16,280 Speaker 1: lot of investors in the e t F left too 267 00:13:16,720 --> 00:13:19,360 Speaker 1: because they couldn't handle it, and so part of smart beta, 268 00:13:19,400 --> 00:13:22,000 Speaker 1: this brings up the behavior if you really want to 269 00:13:22,040 --> 00:13:24,760 Speaker 1: benefit from it, and the lack of emotion, you have 270 00:13:24,880 --> 00:13:26,760 Speaker 1: to hang in there. So the e t F did 271 00:13:26,800 --> 00:13:28,400 Speaker 1: its job going the burning building, but a lot of 272 00:13:28,440 --> 00:13:31,640 Speaker 1: investors couldn't handle the heat either. So that's why all 273 00:13:31,720 --> 00:13:34,439 Speaker 1: of that is important to point out there. But that 274 00:13:34,600 --> 00:13:37,480 Speaker 1: is an example of how the smart beta can work 275 00:13:37,559 --> 00:13:40,520 Speaker 1: for you and arguably be better than a human being manager. 276 00:13:41,200 --> 00:13:45,280 Speaker 1: Near do you have a case study that rivals erics? Well, 277 00:13:45,360 --> 00:13:47,199 Speaker 1: and I don't know if it rivals erics, but it's 278 00:13:47,200 --> 00:13:51,199 Speaker 1: a little bit different. Okay, why not? Okay, yeah, let's 279 00:13:51,280 --> 00:13:53,880 Speaker 1: let's let's let's get it on. Um. There are two 280 00:13:53,880 --> 00:13:55,439 Speaker 1: et f s that I wanted to bring up that 281 00:13:55,600 --> 00:13:58,520 Speaker 1: illustrate the point that I made earlier about giving you 282 00:13:59,120 --> 00:14:03,040 Speaker 1: different types different depths of exposure. Um. So, for example, 283 00:14:03,120 --> 00:14:06,160 Speaker 1: I looked at value at the at value as as 284 00:14:06,160 --> 00:14:09,240 Speaker 1: an active strategy, and one value e t F, which 285 00:14:09,280 --> 00:14:11,959 Speaker 1: is basically a factor, right, like to simplify that, like 286 00:14:12,400 --> 00:14:15,680 Speaker 1: value and there are many different types of factors. Value 287 00:14:15,840 --> 00:14:19,040 Speaker 1: is basically probably the most famous one, right, Yeah, it's 288 00:14:19,120 --> 00:14:20,920 Speaker 1: most famous, and for a good reason, by the way, 289 00:14:21,000 --> 00:14:25,160 Speaker 1: because in the it has it works very often. I mean, 290 00:14:25,160 --> 00:14:26,760 Speaker 1: in other words, if if you looked, if you back 291 00:14:26,800 --> 00:14:29,880 Speaker 1: tested all these various types of factors value and quality 292 00:14:29,960 --> 00:14:33,400 Speaker 1: and momentum and so on, the batting the success rate 293 00:14:33,480 --> 00:14:36,560 Speaker 1: for value over rolling periods is exceedingly high, so it's 294 00:14:36,680 --> 00:14:41,400 Speaker 1: very popular over time, that's right. And it's also intuitively appealing, 295 00:14:41,520 --> 00:14:43,880 Speaker 1: right because it makes sense that if you buy a 296 00:14:44,000 --> 00:14:46,240 Speaker 1: company that's beaten down, you should get paid more than 297 00:14:46,280 --> 00:14:48,520 Speaker 1: a company that's not. Right. It just that has intuitive 298 00:14:48,800 --> 00:14:50,960 Speaker 1: appeal and the and the you know, and the numbers 299 00:14:51,040 --> 00:14:53,560 Speaker 1: check out. So that's why it's so popular. And there 300 00:14:53,600 --> 00:14:55,880 Speaker 1: are there are two different ETFs that you can buy 301 00:14:56,160 --> 00:14:58,400 Speaker 1: that give you different types of exposure to it. One 302 00:14:58,640 --> 00:15:01,800 Speaker 1: is VTV, which is the Vanguard Value e t F, 303 00:15:02,400 --> 00:15:05,160 Speaker 1: and that E t F sorts stocks based on a 304 00:15:05,320 --> 00:15:09,200 Speaker 1: value uh based on a value sort, and then awaits 305 00:15:09,240 --> 00:15:11,640 Speaker 1: them by market cap. So that's one way that you 306 00:15:11,680 --> 00:15:14,360 Speaker 1: can get value. But by contrast, you can also buy 307 00:15:14,400 --> 00:15:16,640 Speaker 1: a different value t F that's v l U E, 308 00:15:16,880 --> 00:15:19,200 Speaker 1: which is the I Shares Edge m S C I 309 00:15:19,800 --> 00:15:22,560 Speaker 1: U S a value factor E t F. Don't ask 310 00:15:22,600 --> 00:15:24,360 Speaker 1: me why these names have to be this long. And 311 00:15:24,480 --> 00:15:27,720 Speaker 1: the difference here is that this two sorts by value, 312 00:15:27,800 --> 00:15:30,640 Speaker 1: but then it also waits by value, and so the 313 00:15:30,720 --> 00:15:33,400 Speaker 1: difference is you get more value exposure in the Eye 314 00:15:33,440 --> 00:15:35,640 Speaker 1: Shares Fund than you do in the Vanguard fund. That 315 00:15:35,800 --> 00:15:38,240 Speaker 1: that doesn't make the Eye Shares fund better, but it 316 00:15:38,320 --> 00:15:41,120 Speaker 1: allows investors to express a preference as to how much 317 00:15:41,240 --> 00:15:43,600 Speaker 1: value they want in their portfolio. If they want more, 318 00:15:43,720 --> 00:15:45,360 Speaker 1: they go for the eye shares. If they want less, 319 00:15:45,400 --> 00:15:47,080 Speaker 1: they go for the vangord Um. I would like to 320 00:15:47,160 --> 00:15:49,640 Speaker 1: use a metaphor we Tom and I we use that. 321 00:15:49,720 --> 00:15:51,360 Speaker 1: We have this thing called the smart data spectrum and 322 00:15:51,400 --> 00:15:53,600 Speaker 1: what Near just described. If you could picture a spectrum. 323 00:15:54,120 --> 00:15:55,840 Speaker 1: On one side, you have v t V. It's got 324 00:15:56,280 --> 00:15:57,640 Speaker 1: just a little bit of value. It's going to do 325 00:15:57,720 --> 00:16:00,960 Speaker 1: better if the regular market to doing well because it's 326 00:16:01,040 --> 00:16:04,200 Speaker 1: mostly bulk beta. V l u E is sort of 327 00:16:04,240 --> 00:16:06,760 Speaker 1: in the middle um and the value factor in the 328 00:16:06,840 --> 00:16:08,960 Speaker 1: name is important because when you see that, that means 329 00:16:09,000 --> 00:16:11,200 Speaker 1: it's waiting by the factor that's going to give you 330 00:16:11,320 --> 00:16:13,320 Speaker 1: more juice. It's almost like v t V is o 331 00:16:13,440 --> 00:16:17,960 Speaker 1: duels and vlu E barely any alcohol, and we have 332 00:16:18,040 --> 00:16:20,080 Speaker 1: to think like like two cases to get you know, 333 00:16:20,200 --> 00:16:23,600 Speaker 1: buzz the middle. There is vlu E that's going to 334 00:16:23,640 --> 00:16:25,760 Speaker 1: be more like just a regular beer like a Budweiser. 335 00:16:26,240 --> 00:16:29,600 Speaker 1: But then there's even ones that go further that are 336 00:16:29,760 --> 00:16:32,400 Speaker 1: more hardcore value. Because v l u E and Near 337 00:16:32,480 --> 00:16:35,720 Speaker 1: probably knows this has a volatility screen, So it's got 338 00:16:35,800 --> 00:16:40,040 Speaker 1: this like screen to kick out really hardcore value stocks 339 00:16:40,120 --> 00:16:43,000 Speaker 1: because it's trying to sell it to advisors who don't 340 00:16:43,000 --> 00:16:45,920 Speaker 1: want that crazy of a ride. But there are more 341 00:16:46,120 --> 00:16:50,480 Speaker 1: pure value like west Gray's q val the quant shares 342 00:16:50,800 --> 00:16:52,360 Speaker 1: that are on the far end, which would be like 343 00:16:52,440 --> 00:16:58,120 Speaker 1: that German beer that has exactly So I think this 344 00:16:58,360 --> 00:17:02,200 Speaker 1: is a fascinating development in the E t F world. 345 00:17:02,280 --> 00:17:05,080 Speaker 1: In this could make smart beta confusing to people, but 346 00:17:05,359 --> 00:17:07,640 Speaker 1: really what you got to figure out is how much 347 00:17:07,800 --> 00:17:11,720 Speaker 1: juice or factor content, how much aggressiveness do you want 348 00:17:11,840 --> 00:17:13,480 Speaker 1: in this smart beta e t F because they have 349 00:17:13,600 --> 00:17:16,560 Speaker 1: all flavors, Tom, can you give me a case study 350 00:17:16,680 --> 00:17:19,680 Speaker 1: the tops erics. Yeah, so mine's a little bit different 351 00:17:19,800 --> 00:17:22,080 Speaker 1: in that, you know what the example that Eric gave 352 00:17:22,200 --> 00:17:24,320 Speaker 1: is more takes the mind of the act of manager 353 00:17:24,400 --> 00:17:27,520 Speaker 1: and it kicks out the emotion. Right. This the product 354 00:17:27,560 --> 00:17:28,919 Speaker 1: I want to talk about is a little bit more 355 00:17:28,960 --> 00:17:32,320 Speaker 1: about targeting a specific outcome or helping somebody targeted outcomes. 356 00:17:32,359 --> 00:17:33,879 Speaker 1: So let's just say you want to own the market, 357 00:17:34,400 --> 00:17:36,040 Speaker 1: but you want to own it in the least vollowed 358 00:17:36,040 --> 00:17:38,080 Speaker 1: a way. You don't want to own the riskiest stocks. 359 00:17:38,320 --> 00:17:40,840 Speaker 1: So I want to bring up SPLV, which is the 360 00:17:41,040 --> 00:17:45,639 Speaker 1: SMP five Low Volatility et F by Power Shares. The 361 00:17:45,760 --> 00:17:47,800 Speaker 1: name long enough. I was gonna say, Eric, we should 362 00:17:47,800 --> 00:17:50,119 Speaker 1: do a whole episode about the longest names in the 363 00:17:50,200 --> 00:17:51,840 Speaker 1: e t F in the et F world. A new 364 00:17:51,880 --> 00:17:53,960 Speaker 1: one was just filed that broke the record. Really Yeah, 365 00:17:54,000 --> 00:17:57,639 Speaker 1: the goobelly pet parents, Pet companions and their parents and 366 00:17:57,760 --> 00:18:02,200 Speaker 1: pet Ecosystem TMF. What I know, there's a multiple reasons 367 00:18:02,240 --> 00:18:05,960 Speaker 1: why that's insanely amazing. We don't know yet, but it's 368 00:18:06,000 --> 00:18:08,600 Speaker 1: gotta have five. I think it's gonna be kitty or doggie, 369 00:18:08,640 --> 00:18:11,520 Speaker 1: but um, that's a whole different story. A couple of 370 00:18:11,520 --> 00:18:14,719 Speaker 1: east also have thirteen fourteen words in the name, but um, 371 00:18:14,840 --> 00:18:17,600 Speaker 1: that one's long. Okay, back to SPLV, uh sure, And 372 00:18:17,760 --> 00:18:20,320 Speaker 1: why I wanted to highlight this one is because it's 373 00:18:20,359 --> 00:18:22,880 Speaker 1: in a smart bit of category that's gotten pretty popular. 374 00:18:23,440 --> 00:18:25,520 Speaker 1: It's just it's low volatility, right, so it's owning the 375 00:18:25,560 --> 00:18:28,720 Speaker 1: market in the least volatial way. So this launched back 376 00:18:28,760 --> 00:18:31,480 Speaker 1: in two thousand and eleven. And if you remember what 377 00:18:31,600 --> 00:18:33,520 Speaker 1: happened in the summer of two thousand eleven was the 378 00:18:33,640 --> 00:18:36,119 Speaker 1: U S got downgraded by the SMP. There's a lot 379 00:18:36,160 --> 00:18:38,000 Speaker 1: of market volatility, and this thing kind of got put 380 00:18:38,080 --> 00:18:40,879 Speaker 1: on the map like, hey, it's down less than the market. 381 00:18:41,600 --> 00:18:45,520 Speaker 1: So it's very simple messogy it is and it's very simple, 382 00:18:45,520 --> 00:18:47,160 Speaker 1: and I think this is why it's had some success 383 00:18:47,280 --> 00:18:49,680 Speaker 1: is it takes the SMP five hundred and it says 384 00:18:49,800 --> 00:18:51,720 Speaker 1: we are only going to take the one hundred least 385 00:18:51,800 --> 00:18:55,160 Speaker 1: vottle stocks. It's very simple, right, so you're gonna own 386 00:18:55,200 --> 00:18:58,280 Speaker 1: on hundred least bottle stocks in the SMP. So when 387 00:18:58,320 --> 00:19:00,440 Speaker 1: you look at you know, some big draw sounds in 388 00:19:00,520 --> 00:19:03,400 Speaker 1: the SMP, it's down less, holds up a little bit better. 389 00:19:04,040 --> 00:19:06,040 Speaker 1: So it's kind of maybe like a little bit of 390 00:19:06,080 --> 00:19:07,639 Speaker 1: a foam O E t F that you still want 391 00:19:07,680 --> 00:19:10,600 Speaker 1: to own the market, but you want to do it 392 00:19:10,640 --> 00:19:13,360 Speaker 1: in at least volowed away um. And it's twenty five 393 00:19:13,359 --> 00:19:15,080 Speaker 1: basis points. It's been out for a lot. It's gotten 394 00:19:15,080 --> 00:19:18,440 Speaker 1: pretty big, about six six or seven billion. Also like 395 00:19:18,520 --> 00:19:21,359 Speaker 1: that you didn't uh, you didn't pampa product that you 396 00:19:21,440 --> 00:19:23,520 Speaker 1: used to work on. That's that's classic of you. And 397 00:19:23,840 --> 00:19:25,720 Speaker 1: let me talk. I want to add on to this 398 00:19:25,720 --> 00:19:28,639 Speaker 1: because it's interesting. When SPLV came out, it tracks just 399 00:19:28,800 --> 00:19:31,879 Speaker 1: those one stocks, so it has no sector. It doesn't 400 00:19:31,880 --> 00:19:34,040 Speaker 1: care where it just goes where it ends up, having 401 00:19:34,359 --> 00:19:37,560 Speaker 1: typically overweight to utilities and staples, which are always usually 402 00:19:37,600 --> 00:19:41,600 Speaker 1: the less volatile stocks. So some investors think, oh, well 403 00:19:41,800 --> 00:19:44,920 Speaker 1: those are more susceptible to rising rates, or they don't 404 00:19:44,920 --> 00:19:47,639 Speaker 1: want to be overweight these sectors. So our Shares came 405 00:19:47,640 --> 00:19:50,560 Speaker 1: out with a version that has sector bands, and then 406 00:19:50,600 --> 00:19:52,560 Speaker 1: there's multiple products that do a little of both and 407 00:19:52,640 --> 00:19:55,200 Speaker 1: so but SPLV was like sort of the speaking of 408 00:19:55,240 --> 00:19:57,040 Speaker 1: the frontier. That was like the first one that nailed 409 00:19:57,080 --> 00:20:00,320 Speaker 1: down low vall. Now there's probably forty five produc that 410 00:20:00,440 --> 00:20:03,280 Speaker 1: do some twist on that to sort of again it's 411 00:20:03,280 --> 00:20:05,119 Speaker 1: like soda flavors or whatever, and they serve it up 412 00:20:05,119 --> 00:20:08,480 Speaker 1: in different ways. But SPLV was the first. How often 413 00:20:08,560 --> 00:20:11,560 Speaker 1: does it change those one stocks that it holds. It 414 00:20:11,600 --> 00:20:13,680 Speaker 1: does it every quarter. Every every quarter, it's going to 415 00:20:13,760 --> 00:20:15,720 Speaker 1: rank the stocks and it's going to take the one hundred, 416 00:20:16,680 --> 00:20:19,800 Speaker 1: which can be tough because let's say utilities got creamed, right, 417 00:20:20,320 --> 00:20:22,199 Speaker 1: You're gonna have to take some down performance until let 418 00:20:22,280 --> 00:20:25,120 Speaker 1: thing rebalances into wherever the next low ball is. That's 419 00:20:25,160 --> 00:20:33,680 Speaker 1: why these things take a stomach Okay, I love these 420 00:20:33,760 --> 00:20:36,000 Speaker 1: case studies that really helped me figure out sort of 421 00:20:36,320 --> 00:20:40,360 Speaker 1: how these products can perform nearer. I'm gonna come back 422 00:20:40,359 --> 00:20:42,840 Speaker 1: to you because in addition to writing for Bloomberg, Godfly, 423 00:20:43,359 --> 00:20:46,440 Speaker 1: you also touch other people's money right right Well, in 424 00:20:46,680 --> 00:20:49,280 Speaker 1: addition to writing for Bloomberg, I also run an asset 425 00:20:49,320 --> 00:20:52,359 Speaker 1: management firm. Um, so you know, I manage money for 426 00:20:52,440 --> 00:20:55,840 Speaker 1: other people, and um, I am. I've always been a 427 00:20:55,920 --> 00:20:59,080 Speaker 1: big believer in active management. I think that the evidence 428 00:20:59,640 --> 00:21:03,120 Speaker 1: for are certain types. There's a lot of quackery out there, 429 00:21:03,400 --> 00:21:06,720 Speaker 1: without question, but I think, um, the evidence for the 430 00:21:06,960 --> 00:21:10,000 Speaker 1: types of factors that we're talking about today, Um, you know, 431 00:21:10,240 --> 00:21:13,440 Speaker 1: value and some of the other's quality, momentum and so on, 432 00:21:13,920 --> 00:21:16,879 Speaker 1: I think is is well documented in the academic literature, 433 00:21:16,920 --> 00:21:19,520 Speaker 1: and I think not controversial. Everyone agrees about it and 434 00:21:19,600 --> 00:21:22,520 Speaker 1: so on, um and so um. And so I've I've 435 00:21:22,560 --> 00:21:25,440 Speaker 1: been sort of an early adopter of these smart beta 436 00:21:25,480 --> 00:21:28,520 Speaker 1: funds just because their active management more cheaply. So, if 437 00:21:28,560 --> 00:21:30,800 Speaker 1: you believe in active management, and you should pay as 438 00:21:30,840 --> 00:21:32,879 Speaker 1: little as possible for it, and so smart data is 439 00:21:32,920 --> 00:21:35,040 Speaker 1: perfect for that. The question is, Okay, now you've got 440 00:21:35,080 --> 00:21:37,080 Speaker 1: all these options, what do you do with them, And 441 00:21:37,280 --> 00:21:39,080 Speaker 1: this is where it's early days, and there's a lot 442 00:21:39,119 --> 00:21:40,960 Speaker 1: of confusion about what to do. But what I would 443 00:21:41,000 --> 00:21:44,120 Speaker 1: propose and what I actually do is I use them 444 00:21:44,160 --> 00:21:47,639 Speaker 1: for simple diversification. You know, it stands to reason that 445 00:21:47,680 --> 00:21:49,600 Speaker 1: if you don't know which strategy is going to do 446 00:21:49,680 --> 00:21:51,840 Speaker 1: well over the next period, then you just want to 447 00:21:51,880 --> 00:21:55,040 Speaker 1: diversify across these strategies. In my opinion, it just makes 448 00:21:55,080 --> 00:21:57,480 Speaker 1: sense to instead of putting all your eggs in the 449 00:21:57,560 --> 00:22:00,760 Speaker 1: market cap index or value index or whatever, have exposure 450 00:22:00,800 --> 00:22:03,800 Speaker 1: to various types of things. And these smart data products 451 00:22:04,119 --> 00:22:06,680 Speaker 1: allow you to do that relatively cheaply, and I think 452 00:22:06,720 --> 00:22:08,680 Speaker 1: that's the first step on the bus. If investors take 453 00:22:08,680 --> 00:22:11,359 Speaker 1: away anything from this podcast, in my opinion, what they 454 00:22:11,400 --> 00:22:14,080 Speaker 1: ought to take is look at your portfolio, see what 455 00:22:14,200 --> 00:22:16,360 Speaker 1: kind of exposures you have. If you have too much 456 00:22:16,440 --> 00:22:19,439 Speaker 1: in one particular type or style of investing or factor, 457 00:22:19,760 --> 00:22:22,160 Speaker 1: however you want to call it, then look at look 458 00:22:22,200 --> 00:22:25,080 Speaker 1: at smart data funds and diversify across them so that 459 00:22:25,160 --> 00:22:27,600 Speaker 1: your eggs are in different baskets. Which puts me back 460 00:22:27,640 --> 00:22:32,960 Speaker 1: in a dark room touching an elephant um tom when 461 00:22:33,040 --> 00:22:35,679 Speaker 1: you are on the other side of the table. How 462 00:22:35,760 --> 00:22:38,520 Speaker 1: are you guys seeing investors use this both from like 463 00:22:38,600 --> 00:22:41,480 Speaker 1: a retail perspective but also like the institutional Yeah, so 464 00:22:41,520 --> 00:22:43,800 Speaker 1: I think it really comes down to, now the ets 465 00:22:43,880 --> 00:22:46,119 Speaker 1: that are coming out, they're trying to solve a problem, right, 466 00:22:46,160 --> 00:22:47,560 Speaker 1: So it really comes down to what are you trying 467 00:22:47,600 --> 00:22:50,840 Speaker 1: to solve? So are you using an active manager that 468 00:22:50,920 --> 00:22:53,280 Speaker 1: maybe you're not happy with, like you have a value manager, 469 00:22:53,440 --> 00:22:55,600 Speaker 1: Maybe you might want to look at a value smart 470 00:22:55,600 --> 00:22:57,520 Speaker 1: beta et F Do you want to be in the market, 471 00:22:57,640 --> 00:23:00,680 Speaker 1: but you want to be in the lowest vault as possible. 472 00:23:00,880 --> 00:23:03,159 Speaker 1: So it's about talking to your clients, are talking to 473 00:23:03,200 --> 00:23:06,560 Speaker 1: investors and finding a problem. It's not just all the 474 00:23:06,640 --> 00:23:09,920 Speaker 1: tools are now out there, right, the toolbox has gotten 475 00:23:10,200 --> 00:23:12,320 Speaker 1: really big. It's just about figuring out what are you 476 00:23:12,359 --> 00:23:14,680 Speaker 1: trying to accomplish. And that's the conversation we constantly have 477 00:23:14,760 --> 00:23:16,560 Speaker 1: with clients that this is the problem that I have. 478 00:23:16,720 --> 00:23:19,399 Speaker 1: I want income, but I also want low vall income. 479 00:23:19,520 --> 00:23:21,080 Speaker 1: So let's build a product that will give to you, 480 00:23:21,280 --> 00:23:24,080 Speaker 1: let's say, dividend paying stocks, but also with like a 481 00:23:24,160 --> 00:23:28,080 Speaker 1: low vall component to it. Solutions, Yeah, it has them exactly. 482 00:23:29,680 --> 00:23:34,440 Speaker 1: Heyntroll here now that I'm the editor of Bloomberg business Week. 483 00:23:34,880 --> 00:23:37,800 Speaker 1: I pulled a few strings for our loyal podcast listeners. 484 00:23:38,520 --> 00:23:43,360 Speaker 1: Go to business Week mag dot com slash trillions right 485 00:23:43,440 --> 00:23:47,600 Speaker 1: now for a thirty day free trial. If you like 486 00:23:47,720 --> 00:23:50,399 Speaker 1: what we're doing here on trillions, you're gonna love the 487 00:23:50,600 --> 00:23:54,520 Speaker 1: stories that we published in Bloomberg business Week. Here's that 488 00:23:54,640 --> 00:23:58,120 Speaker 1: special offer one more time. Go to business Week mag 489 00:23:58,480 --> 00:24:07,879 Speaker 1: dot com slash trillions for your free thirty day trial. Thanks. Okay, 490 00:24:08,040 --> 00:24:12,119 Speaker 1: last topic, what kind of due diligence should investors be 491 00:24:12,240 --> 00:24:17,080 Speaker 1: doing before they pick up these tools? Um, okay, I'll 492 00:24:17,119 --> 00:24:19,040 Speaker 1: take a crack at this. So when I look at 493 00:24:19,080 --> 00:24:21,680 Speaker 1: smart beta again, we have this spectrum and the way 494 00:24:21,800 --> 00:24:24,480 Speaker 1: we determine where on the spectrum it goes. Again, the 495 00:24:24,600 --> 00:24:27,240 Speaker 1: more quote beta that is in it, the more it 496 00:24:27,280 --> 00:24:31,000 Speaker 1: could just be a replacement. I think. The more volatile 497 00:24:31,080 --> 00:24:33,159 Speaker 1: it is, the more you could add it on as 498 00:24:33,240 --> 00:24:34,760 Speaker 1: like a little bit of hot sauce. But anyway to 499 00:24:35,840 --> 00:24:39,360 Speaker 1: determine what you're looking at, I would suggest a few things. 500 00:24:39,480 --> 00:24:42,040 Speaker 1: One is the index waiting methodology. Does it wait by 501 00:24:42,080 --> 00:24:45,760 Speaker 1: market cap? If so, it's probably gonna be not that volatile, 502 00:24:45,840 --> 00:24:48,320 Speaker 1: not that much exposure to any factor or anything. But 503 00:24:48,520 --> 00:24:51,479 Speaker 1: you get a little is it weighted towards the factor itself? 504 00:24:51,600 --> 00:24:54,240 Speaker 1: That's important. That's number one. Number two is the standard deviation, 505 00:24:54,359 --> 00:24:57,320 Speaker 1: which it's a simple term. If you fell asleep and 506 00:24:57,359 --> 00:24:59,320 Speaker 1: stats class like I did, I had to relearn it, 507 00:24:59,720 --> 00:25:05,200 Speaker 1: but so useful. It's just basically looking at past volatility 508 00:25:05,280 --> 00:25:07,560 Speaker 1: of this fund or stock or whatever it could be, 509 00:25:07,600 --> 00:25:09,920 Speaker 1: use for anything, and just telling you how bumpy the 510 00:25:10,000 --> 00:25:12,480 Speaker 1: ride will be. It's like almost like a ski slopes line, 511 00:25:12,560 --> 00:25:15,440 Speaker 1: like is this a black diamond or a blue square? 512 00:25:15,920 --> 00:25:19,680 Speaker 1: So standard deviation is basically how bumpy the ride will be. 513 00:25:20,000 --> 00:25:21,960 Speaker 1: But you need it relative to something. So I said, 514 00:25:22,000 --> 00:25:24,119 Speaker 1: just taking the standard deviation of s P Y or 515 00:25:24,200 --> 00:25:26,639 Speaker 1: the SMP fire, which I believe right now might be 516 00:25:26,680 --> 00:25:29,920 Speaker 1: around nine. That means there's a two thirds chance that 517 00:25:30,000 --> 00:25:32,119 Speaker 1: the annual return of the SMP over the next year 518 00:25:32,200 --> 00:25:35,320 Speaker 1: will be one way or the other, up or down. 519 00:25:35,720 --> 00:25:39,159 Speaker 1: So where is it versus nine percent? If it's you 520 00:25:39,359 --> 00:25:41,480 Speaker 1: are going to have a real volatile smart beta product. 521 00:25:41,800 --> 00:25:43,760 Speaker 1: If it's nine, it's about the same. And there's some 522 00:25:43,880 --> 00:25:46,280 Speaker 1: that are lower, like the low volley t F might 523 00:25:46,320 --> 00:25:49,879 Speaker 1: be more like eight percent, So figure that out. That 524 00:25:50,080 --> 00:25:52,080 Speaker 1: is huge because you don't want to buy something that's 525 00:25:52,119 --> 00:25:55,280 Speaker 1: too spicy for you or too lame or you know, 526 00:25:55,359 --> 00:25:57,280 Speaker 1: doesn't move around enough. So I think those are two 527 00:25:57,320 --> 00:25:59,800 Speaker 1: important stats. And then of course the fee I think 528 00:26:00,000 --> 00:26:02,000 Speaker 1: ast matter here and now you can get a smart 529 00:26:02,040 --> 00:26:05,239 Speaker 1: beta e t F for under twenty basis points. Uh, 530 00:26:05,359 --> 00:26:08,159 Speaker 1: so you should shop around a little bit. Although when 531 00:26:08,240 --> 00:26:10,880 Speaker 1: you're swinging for the fences, the fee isn't quite as important. 532 00:26:10,920 --> 00:26:13,280 Speaker 1: But I think expense ratio is interesting because the range 533 00:26:13,359 --> 00:26:16,880 Speaker 1: now is from a D to nine. There's a big range, 534 00:26:16,920 --> 00:26:19,080 Speaker 1: and there's a lot of cost pressure going on in 535 00:26:19,160 --> 00:26:22,000 Speaker 1: this space. So near when when our two D two 536 00:26:22,240 --> 00:26:24,200 Speaker 1: with the head of Peter Lynch shows up at your door, 537 00:26:24,280 --> 00:26:27,119 Speaker 1: what what do you do? Well? I think the I 538 00:26:27,560 --> 00:26:30,040 Speaker 1: think as an investor, you want to look at UM 539 00:26:30,280 --> 00:26:31,960 Speaker 1: if you look at nothing else, you want to look 540 00:26:32,000 --> 00:26:34,399 Speaker 1: at what's what's the factor that I'm buying and how 541 00:26:34,480 --> 00:26:36,000 Speaker 1: much am I paying for it? I think those are 542 00:26:36,080 --> 00:26:38,840 Speaker 1: one and two. But if I if I can UM, 543 00:26:39,040 --> 00:26:40,840 Speaker 1: let me, let me just use the time I have 544 00:26:41,040 --> 00:26:44,400 Speaker 1: for just just a little bit of advocacy for this industry. 545 00:26:44,840 --> 00:26:47,560 Speaker 1: I think I think there's something very important that we 546 00:26:47,680 --> 00:26:49,520 Speaker 1: need to solve for UM and I think we need 547 00:26:49,560 --> 00:26:52,040 Speaker 1: to solve for it very quickly if if smart beta 548 00:26:52,119 --> 00:26:54,159 Speaker 1: is going to take off, and that is, we need 549 00:26:54,240 --> 00:26:56,680 Speaker 1: to have some sort of labeling system to continue the 550 00:26:56,880 --> 00:27:00,000 Speaker 1: analogy from earlier UM that tells you how much alcohol 551 00:27:00,200 --> 00:27:03,520 Speaker 1: content is in the e t F is in this factor. So, 552 00:27:03,520 --> 00:27:05,600 Speaker 1: in other words, you need you if you're looking at 553 00:27:05,640 --> 00:27:09,000 Speaker 1: five value value factory t s. Let's just say you 554 00:27:09,119 --> 00:27:11,640 Speaker 1: have no idea how much value is in one versus 555 00:27:11,680 --> 00:27:13,800 Speaker 1: the other, and so as a consumer you can't make 556 00:27:13,840 --> 00:27:16,000 Speaker 1: it a smart decision about which one to buy. And 557 00:27:16,080 --> 00:27:18,040 Speaker 1: so we need to come up with a standardized system 558 00:27:18,280 --> 00:27:20,719 Speaker 1: to be able to say, right there almost like an 559 00:27:20,840 --> 00:27:23,240 Speaker 1: expense ratio, like you know what it costs, right It's 560 00:27:23,240 --> 00:27:25,840 Speaker 1: almost like an expense ratio that tells you this has 561 00:27:26,040 --> 00:27:28,520 Speaker 1: this percentage of value in it. And we can do 562 00:27:28,600 --> 00:27:30,280 Speaker 1: in any We can do it on a ten point scale, 563 00:27:30,359 --> 00:27:32,639 Speaker 1: we can do it on a D scale, we can 564 00:27:32,680 --> 00:27:34,520 Speaker 1: do it in any way you want. But we have 565 00:27:34,720 --> 00:27:36,159 Speaker 1: to do it, and there's a way to measure it. 566 00:27:36,200 --> 00:27:38,280 Speaker 1: It would be very easy to do. The industry is 567 00:27:38,280 --> 00:27:40,440 Speaker 1: going to push back against it because I don't think 568 00:27:40,600 --> 00:27:43,560 Speaker 1: ultimately I think they want this to be UM somewhat 569 00:27:43,600 --> 00:27:47,520 Speaker 1: confusing because it's very difficult to differentiate um if you 570 00:27:47,600 --> 00:27:50,280 Speaker 1: make a lot of information available to investors. But I 571 00:27:50,320 --> 00:27:51,960 Speaker 1: think we have to insist on it, and we have 572 00:27:52,080 --> 00:27:54,439 Speaker 1: to give investors that tool so they can they can 573 00:27:54,480 --> 00:27:58,040 Speaker 1: discriminate among the products. Spoken like a true Bloomberg Gadfly 574 00:27:58,119 --> 00:28:03,399 Speaker 1: columnist Tom, we set out to explore the mystery of 575 00:28:03,440 --> 00:28:07,000 Speaker 1: smart beta. Have we explored it? I think it's going 576 00:28:07,040 --> 00:28:10,080 Speaker 1: to be an ongoing debate, not just you know, beyond 577 00:28:10,160 --> 00:28:11,800 Speaker 1: this room, but it's going to be something that's going 578 00:28:11,840 --> 00:28:14,560 Speaker 1: to keep evolving in the ETF industry because the lines 579 00:28:14,600 --> 00:28:17,399 Speaker 1: are getting very, very blurry. Even the new players atting 580 00:28:17,440 --> 00:28:19,679 Speaker 1: in into the market, their traditional active shops that are 581 00:28:19,680 --> 00:28:23,120 Speaker 1: now launching ETFs. Now there's actively managed ETFs. The lines 582 00:28:23,160 --> 00:28:25,920 Speaker 1: getting very blurry. Well, you just heard a lot of 583 00:28:25,960 --> 00:28:28,960 Speaker 1: different takes on it, and it really depends on where 584 00:28:29,080 --> 00:28:31,439 Speaker 1: you're coming from. And you may find smart beta as 585 00:28:31,480 --> 00:28:34,199 Speaker 1: a solution to a boring core passive portfolio. You want 586 00:28:34,200 --> 00:28:36,720 Speaker 1: a little spice in your life, or you may have 587 00:28:36,840 --> 00:28:39,880 Speaker 1: too much spice and paying too much for the active managers. 588 00:28:39,880 --> 00:28:42,160 Speaker 1: So you think underperforming the mutual fund structure, and you 589 00:28:42,240 --> 00:28:45,000 Speaker 1: want to basically lower your fees and make it a 590 00:28:45,160 --> 00:28:48,720 Speaker 1: more clean, um cheaper version of what you were getting before. 591 00:28:48,800 --> 00:28:51,800 Speaker 1: So there's a lot of angles to this. Yeah, I 592 00:28:51,920 --> 00:28:54,840 Speaker 1: like the hot sauce. Hot sauce. Hot sauce is better 593 00:28:54,920 --> 00:28:59,360 Speaker 1: than the elephant. Alright near case are Tom Saraphagus. Thank 594 00:28:59,400 --> 00:29:01,640 Speaker 1: you so much for us for Trillians, Thanks, thanks for 595 00:29:01,680 --> 00:29:08,520 Speaker 1: having me, Thanks for listening to Trillions until next time. 596 00:29:08,640 --> 00:29:11,440 Speaker 1: You can find us on the Bloomberg terminal, Bloomberg dot com, 597 00:29:12,000 --> 00:29:15,880 Speaker 1: Apple podcasts, and whether else you listen to podcasts. We'd 598 00:29:15,880 --> 00:29:18,720 Speaker 1: love to hear from you. We're on Twitter, I'm at 599 00:29:18,880 --> 00:29:22,959 Speaker 1: Joel Weber Show. He's at Eric fall Tunas. You can 600 00:29:23,000 --> 00:29:26,560 Speaker 1: find near at near Casar and good luck spot on 601 00:29:26,600 --> 00:29:30,960 Speaker 1: this one, but Tom's at at T Sarah Vegas. Trillions 602 00:29:31,160 --> 00:29:34,600 Speaker 1: is produced by Magnus Hendrickson. Francesca Leedy is the head 603 00:29:34,600 --> 00:29:36,280 Speaker 1: of Bloomberg podcast Bye.