1 00:00:02,000 --> 00:00:07,160 Speaker 1: This is mesters in Business with Very Results on Bloomberg Radio. 2 00:00:09,320 --> 00:00:12,560 Speaker 1: This week on the podcast, I have an extra special guest. 3 00:00:12,720 --> 00:00:16,239 Speaker 1: Gerard O'Riley is a double threat. He is the chief 4 00:00:16,320 --> 00:00:21,919 Speaker 1: investment officer as well as the co CEO of Dimensional 5 00:00:21,960 --> 00:00:26,560 Speaker 1: Funds UH. They are a factor giant, managing about six 6 00:00:26,920 --> 00:00:31,560 Speaker 1: and fifty billion dollars and total assets. This is really 7 00:00:31,560 --> 00:00:36,960 Speaker 1: a master class in how to think about investing, how 8 00:00:37,000 --> 00:00:41,360 Speaker 1: to be systematic, how to approach it from a evidence 9 00:00:41,440 --> 00:00:48,240 Speaker 1: based scientific basis, how to incorporate the best of academic 10 00:00:48,320 --> 00:00:51,720 Speaker 1: research into your process. One of the things that I 11 00:00:52,000 --> 00:00:56,560 Speaker 1: found really interesting was the d F A focus on costs, 12 00:00:57,040 --> 00:01:02,880 Speaker 1: convenience and customization. Not every giant investment firm UH takes 13 00:01:02,880 --> 00:01:07,600 Speaker 1: that approach. Really, I've interviewed a number of folks from 14 00:01:07,680 --> 00:01:12,240 Speaker 1: d F a UM, from David Booth to Gene Parma 15 00:01:12,400 --> 00:01:16,720 Speaker 1: and throughout the rest of the organization. I think you 16 00:01:16,760 --> 00:01:22,039 Speaker 1: will find this to be absolutely fascinating and and really informative. So, 17 00:01:22,120 --> 00:01:25,720 Speaker 1: with no further ado, my conversation with d F as 18 00:01:25,880 --> 00:01:32,520 Speaker 1: Gerard O'Reilly, this is mesters in Business with Very Redults 19 00:01:33,000 --> 00:01:38,920 Speaker 1: on Bloomberg Radio. My extra special guest this week is 20 00:01:38,959 --> 00:01:43,120 Speaker 1: Gerard O'Reilly. He is the Chief Investment Officer and co 21 00:01:43,360 --> 00:01:47,800 Speaker 1: CEO of Dimensional Fund Advisors, a leader and factor based 22 00:01:47,840 --> 00:01:51,280 Speaker 1: investing for the past forty years. D f A has 23 00:01:51,320 --> 00:01:56,960 Speaker 1: about employees across thirteen offices globally and full disclosure my 24 00:01:57,120 --> 00:02:00,880 Speaker 1: firm Results. Wealth Management is a client of Dimensional Funds 25 00:02:00,880 --> 00:02:04,480 Speaker 1: and we manage a substantial chunk of our assets with 26 00:02:04,600 --> 00:02:08,680 Speaker 1: their products. They manage six hundred and fifty billion dollars 27 00:02:08,680 --> 00:02:13,040 Speaker 1: in assets, about eight percent of that is equity. Girard O'Reilly, 28 00:02:13,520 --> 00:02:17,360 Speaker 1: Welcome to Bloomberg. Thank you, Barry, and thanks for the invitation. 29 00:02:17,400 --> 00:02:19,239 Speaker 1: I've been looking forward to speaking with you for some time, 30 00:02:19,480 --> 00:02:22,360 Speaker 1: so so have I. You have such an interesting background. 31 00:02:22,760 --> 00:02:26,000 Speaker 1: I was really excited to talk to you, especially given 32 00:02:26,760 --> 00:02:30,280 Speaker 1: you have a PhD in aeronautics from the California Institute 33 00:02:30,320 --> 00:02:34,440 Speaker 1: of Technology. What what were your original career plans. Well, 34 00:02:35,080 --> 00:02:40,079 Speaker 1: I've always liked mathematics, and as an undergrad in Ireland 35 00:02:41,080 --> 00:02:44,600 Speaker 1: studied mathematics and physics and so on extensively. I was 36 00:02:44,639 --> 00:02:47,560 Speaker 1: thinking about what to do next and said, well, cal 37 00:02:47,639 --> 00:02:50,720 Speaker 1: Tech does a lot of great stuff in fluid mechanics 38 00:02:51,200 --> 00:02:53,360 Speaker 1: and particular in aeronautics and So I didn't have a 39 00:02:53,400 --> 00:02:56,480 Speaker 1: specific set of career plans. I just know that's the 40 00:02:56,480 --> 00:02:58,480 Speaker 1: subject that I wanted to study and that I enjoyed. 41 00:02:58,800 --> 00:03:02,600 Speaker 1: So I set off or cal Tech and and I 42 00:03:02,680 --> 00:03:06,760 Speaker 1: really enjoyed my time. They're working on various different projects, 43 00:03:06,840 --> 00:03:11,480 Speaker 1: many theoretical in nature, but very mathematically oriented. So when 44 00:03:11,520 --> 00:03:14,600 Speaker 1: you're looking at aeronautics in the United States, there aren't 45 00:03:14,600 --> 00:03:16,760 Speaker 1: a whole lot of career paths out of that other 46 00:03:16,840 --> 00:03:20,600 Speaker 1: than academia or going to Nassau or one of the 47 00:03:20,639 --> 00:03:25,519 Speaker 1: defense um companies. What led the shift from aeronautics to 48 00:03:25,680 --> 00:03:29,520 Speaker 1: finance as a career, Well, I wanted to learn some 49 00:03:29,560 --> 00:03:32,519 Speaker 1: more about finance. Number one. I hadn't taken a finance 50 00:03:32,560 --> 00:03:36,040 Speaker 1: course ever in my life before joining Dimensional, and Dimensional 51 00:03:36,120 --> 00:03:39,640 Speaker 1: was the firm that I joined straight out of college. Also, 52 00:03:40,120 --> 00:03:43,120 Speaker 1: you know, the academic path wasn't one that appealed to me. 53 00:03:43,160 --> 00:03:47,480 Speaker 1: I really enjoyed grad school, but I preferred to tackle 54 00:03:47,640 --> 00:03:50,040 Speaker 1: something that was I would say more than here and now, 55 00:03:50,280 --> 00:03:54,839 Speaker 1: where your projects that you're working on have impact very 56 00:03:54,960 --> 00:03:58,080 Speaker 1: quickly on you know, the end customer, the end consumer, 57 00:03:59,160 --> 00:04:02,800 Speaker 1: and then also where on the engineering side. You know, 58 00:04:02,840 --> 00:04:04,400 Speaker 1: you mentioned in the U S well, I'm not a 59 00:04:04,480 --> 00:04:06,840 Speaker 1: US citizens, so it's hard for non US citizen to 60 00:04:06,840 --> 00:04:08,760 Speaker 1: work in the aeronautics field here in the US because 61 00:04:09,200 --> 00:04:13,520 Speaker 1: largely required security clearance. So it's looking around. A friend 62 00:04:13,520 --> 00:04:16,920 Speaker 1: of mine was working for Dimensional, knew that person at Caltech, 63 00:04:17,560 --> 00:04:21,480 Speaker 1: and that person was talking about you know, Dimensional has 64 00:04:21,520 --> 00:04:25,800 Speaker 1: all these great academic connections. They really take finance from 65 00:04:25,800 --> 00:04:29,400 Speaker 1: a scientific perspective. Went down, checked it out and said, 66 00:04:29,920 --> 00:04:31,640 Speaker 1: this sounds interesting. I really want to give this a 67 00:04:31,680 --> 00:04:33,880 Speaker 1: shot for a period of time. So so let's talk 68 00:04:33,920 --> 00:04:38,920 Speaker 1: a little bit about those academic um connections. Ken French 69 00:04:39,000 --> 00:04:41,799 Speaker 1: has been at Dartmouth for a long time. His colleague 70 00:04:42,160 --> 00:04:45,919 Speaker 1: Gene Farmer, Nobel Prize winner at University of Chicago. Another 71 00:04:46,160 --> 00:04:50,520 Speaker 1: Nobel Prize winner, Robert Merton, also at Dimensional funds. What's 72 00:04:50,560 --> 00:04:55,039 Speaker 1: it like working with all these Nobel Prize winning economists 73 00:04:55,440 --> 00:04:58,560 Speaker 1: could be a little intimidating to some folks. It's always 74 00:04:58,560 --> 00:05:01,520 Speaker 1: intimidating when you start off working with somebody who's very, 75 00:05:01,600 --> 00:05:03,960 Speaker 1: very talented and you're getting to know them for the 76 00:05:04,000 --> 00:05:07,360 Speaker 1: first time. But it's a privilege and it's great fun 77 00:05:07,760 --> 00:05:12,680 Speaker 1: because those folks, you know, have have worked incredibly hard 78 00:05:12,880 --> 00:05:15,960 Speaker 1: to hone their craft, hone their skills, and when you 79 00:05:16,400 --> 00:05:18,880 Speaker 1: think about Ken or Gene or Bob or Meer and 80 00:05:18,960 --> 00:05:21,520 Speaker 1: any of those folks, they're very, very generous with their 81 00:05:21,520 --> 00:05:25,040 Speaker 1: time and so they're willing to teach because they're in academia, 82 00:05:25,600 --> 00:05:27,840 Speaker 1: and if you're willing to work hard, they're willing to 83 00:05:27,880 --> 00:05:30,000 Speaker 1: put the time and effort into you. So I started 84 00:05:30,000 --> 00:05:34,200 Speaker 1: off with no background in finance and got to learn 85 00:05:34,279 --> 00:05:37,520 Speaker 1: finance from some of the most amazing minds in the field. 86 00:05:37,640 --> 00:05:41,640 Speaker 1: So it was just it was great Ken, Jean and Bob. 87 00:05:41,920 --> 00:05:45,000 Speaker 1: I've never heard of those three gentlemen referred to quite 88 00:05:45,000 --> 00:05:47,200 Speaker 1: in that way, but I guess when you work with 89 00:05:47,279 --> 00:05:50,400 Speaker 1: them as frequently as you do UM, it becomes Ken, 90 00:05:50,480 --> 00:05:54,560 Speaker 1: Jean and Bob. So, so what are the parallels between 91 00:05:54,640 --> 00:06:00,919 Speaker 1: academia and UM working in finance professionally? And then I 92 00:06:00,960 --> 00:06:04,359 Speaker 1: have to ask what are the parallels between aeronautics and 93 00:06:04,400 --> 00:06:09,680 Speaker 1: fluid dynamics and finance and investment. Well, working in academia, 94 00:06:10,279 --> 00:06:12,560 Speaker 1: you know, you're always trying to solve a problem. You're 95 00:06:12,560 --> 00:06:16,480 Speaker 1: looking for interesting problems to solve that haven't really been 96 00:06:16,480 --> 00:06:19,440 Speaker 1: tackled before, or an aspect that you're working on hasn't 97 00:06:19,440 --> 00:06:22,479 Speaker 1: been tackled before, and you're saying, well, can I bring 98 00:06:22,600 --> 00:06:26,719 Speaker 1: something new to the table, something innovative, and that's incredibly 99 00:06:26,760 --> 00:06:30,599 Speaker 1: rewarding and incredibly interesting. Working in finance is no different. 100 00:06:31,000 --> 00:06:35,880 Speaker 1: You're looking for new problems to solve. Those problems are 101 00:06:35,960 --> 00:06:39,200 Speaker 1: largely driven by what it is your clients are looking for, 102 00:06:39,320 --> 00:06:44,400 Speaker 1: what types of investment solutions do they require to solve 103 00:06:44,440 --> 00:06:47,480 Speaker 1: the investment problems that they have, and then you're coming 104 00:06:47,520 --> 00:06:49,880 Speaker 1: up with innovative ways to solve those problems. So in 105 00:06:49,880 --> 00:06:53,240 Speaker 1: that respect, there's a lot of similarities. The time scale 106 00:06:53,240 --> 00:06:55,400 Speaker 1: and the time frames are a little bit tighter and 107 00:06:55,480 --> 00:06:58,960 Speaker 1: faster when it comes to finance than in academia. In 108 00:06:59,000 --> 00:07:02,320 Speaker 1: academia it may be multi years, and there's multi year 109 00:07:02,440 --> 00:07:04,560 Speaker 1: projects that happen in finance, but you want to be 110 00:07:04,600 --> 00:07:07,320 Speaker 1: able to live or something for your clients in shorter 111 00:07:07,360 --> 00:07:10,760 Speaker 1: time frames than that. When you think about engineering or 112 00:07:11,360 --> 00:07:15,120 Speaker 1: mathematics or physics, and then how does how do those 113 00:07:15,160 --> 00:07:17,920 Speaker 1: skill sets translate over to finance. Well, again, it's all 114 00:07:17,960 --> 00:07:22,320 Speaker 1: about problem solving and what you're looking for is how 115 00:07:22,360 --> 00:07:27,640 Speaker 1: do I number one pose the question correctly? How do 116 00:07:27,680 --> 00:07:30,680 Speaker 1: I ask the right question? Because that's as important as 117 00:07:30,720 --> 00:07:32,960 Speaker 1: trying to solve the problem, you have to set it 118 00:07:33,040 --> 00:07:35,280 Speaker 1: up in the right way. And that's true whether it's 119 00:07:35,320 --> 00:07:38,120 Speaker 1: in mathematics or physics, or engineering, or it's in finance. 120 00:07:38,560 --> 00:07:42,440 Speaker 1: Then how do I gather data to help me address 121 00:07:42,480 --> 00:07:45,280 Speaker 1: and find the answer to this question? And that's true 122 00:07:45,440 --> 00:07:48,160 Speaker 1: of both fields. And then how do I interpret the data? 123 00:07:48,400 --> 00:07:50,280 Speaker 1: What are the tools and the models that I can 124 00:07:50,400 --> 00:07:52,520 Speaker 1: use such that I'm going to be able to organize 125 00:07:52,560 --> 00:07:55,280 Speaker 1: these data in such a way to draw inferences about 126 00:07:55,720 --> 00:07:58,720 Speaker 1: how I want to act going forward. And that's true 127 00:07:58,720 --> 00:08:02,640 Speaker 1: of both, Madam, physics, engineering, and finance. I think the 128 00:08:02,640 --> 00:08:06,640 Speaker 1: big differences are the laws of physics tend not to 129 00:08:06,720 --> 00:08:11,840 Speaker 1: change over time, but the laws of that government finance 130 00:08:11,880 --> 00:08:16,760 Speaker 1: can change through time. There are many repeatable experiments in physics, 131 00:08:17,080 --> 00:08:20,720 Speaker 1: there are no repeatable experiments in finance. But there is 132 00:08:20,800 --> 00:08:23,080 Speaker 1: a kind of a common truth in both, which is 133 00:08:23,120 --> 00:08:28,120 Speaker 1: that in finance and investing, people demand return for bearing uncertainty. 134 00:08:28,200 --> 00:08:30,840 Speaker 1: That doesn't change through time, but how you go about 135 00:08:30,880 --> 00:08:33,840 Speaker 1: implementing that can change through time because the laws are changing. 136 00:08:35,000 --> 00:08:37,960 Speaker 1: On behalf of Isaac Newton, I'm going to raise an 137 00:08:37,960 --> 00:08:41,120 Speaker 1: objection that at least our understanding of the laws of 138 00:08:41,120 --> 00:08:46,360 Speaker 1: physics have changed over time. So so maybe the underlying 139 00:08:46,440 --> 00:08:49,360 Speaker 1: laws themselves are the same, but our perception seems to 140 00:08:49,400 --> 00:08:52,600 Speaker 1: have evolved. I think that's that's a good way to 141 00:08:52,600 --> 00:08:54,720 Speaker 1: put it, in a nice precise way to put it, 142 00:08:54,760 --> 00:08:59,640 Speaker 1: is that the underlying drivers don't change, but our perception changes. 143 00:08:59,679 --> 00:09:02,760 Speaker 1: And that's it's an interesting observation because their perceptions change 144 00:09:03,160 --> 00:09:07,439 Speaker 1: because the models that we use to explain and understand 145 00:09:07,640 --> 00:09:11,079 Speaker 1: those underlying drivers evolve over time. All models are incomplete, 146 00:09:11,400 --> 00:09:13,120 Speaker 1: none of them are true, none of them are perfect 147 00:09:13,120 --> 00:09:15,800 Speaker 1: descriptions of reality. And that's true of physics and it's 148 00:09:15,840 --> 00:09:19,600 Speaker 1: true of finance. But you can improve those models over time. 149 00:09:20,000 --> 00:09:22,120 Speaker 1: You can improve the data that you can collect over time, 150 00:09:22,400 --> 00:09:25,200 Speaker 1: and that enhances your understanding over time. I'm a big 151 00:09:25,200 --> 00:09:28,160 Speaker 1: fan of George Box. I love the quote all models 152 00:09:28,160 --> 00:09:31,040 Speaker 1: are wrong, but some are useful, and it sounds like 153 00:09:31,160 --> 00:09:35,240 Speaker 1: you very much embrace that philosophy as well. And it's 154 00:09:35,280 --> 00:09:38,720 Speaker 1: an important philosophy to embrace when you're, you know, working 155 00:09:38,720 --> 00:09:42,120 Speaker 1: in the field of finance, because ultimately, what you're doing 156 00:09:42,240 --> 00:09:45,720 Speaker 1: is you're investing money on behalf of others. It's their 157 00:09:45,720 --> 00:09:49,439 Speaker 1: life savings. Often so it's what they've made sacrifice to 158 00:09:49,880 --> 00:09:52,880 Speaker 1: put together so they can afford a better retirement or 159 00:09:52,960 --> 00:09:56,319 Speaker 1: something that's important to them in their future. And if 160 00:09:56,320 --> 00:09:59,240 Speaker 1: you ever believe that the model is reality, you're probably 161 00:09:59,240 --> 00:10:01,880 Speaker 1: going to build non robust solutions and do them a disservice. 162 00:10:02,120 --> 00:10:07,480 Speaker 1: So having a healthy skepticism around all models and basically 163 00:10:07,559 --> 00:10:11,280 Speaker 1: all data sources that you see is important because it 164 00:10:11,360 --> 00:10:14,760 Speaker 1: leads you to, well, what if I'm wrong, do I 165 00:10:14,800 --> 00:10:17,120 Speaker 1: still have a good in solution even if this model 166 00:10:17,679 --> 00:10:20,880 Speaker 1: it turns out to be incorrect? And I think that's 167 00:10:20,880 --> 00:10:22,960 Speaker 1: that's a good way of looking at the world. So 168 00:10:23,080 --> 00:10:25,640 Speaker 1: let's talk a little bit about your career. You began 169 00:10:25,679 --> 00:10:28,120 Speaker 1: at d f A in two thousand four in the 170 00:10:28,160 --> 00:10:32,120 Speaker 1: research department. A little more than a decade later your 171 00:10:32,160 --> 00:10:36,120 Speaker 1: chief investment officer, and not that many years after that 172 00:10:36,200 --> 00:10:41,400 Speaker 1: you become co chief executive officer. That's a pretty rapid 173 00:10:41,480 --> 00:10:44,559 Speaker 1: career path. Explain to us, if you would, the concept 174 00:10:44,640 --> 00:10:46,959 Speaker 1: of co CEO S or co c i O S 175 00:10:47,320 --> 00:10:52,440 Speaker 1: and how you managed to um advance so rapidly in 176 00:10:52,480 --> 00:10:56,320 Speaker 1: a firm that was led by David Booth for so 177 00:10:56,360 --> 00:10:59,160 Speaker 1: many decades. Yeah, So let me let me start with 178 00:10:59,200 --> 00:11:02,520 Speaker 1: the ladder. How how how do you advance? And my 179 00:11:02,600 --> 00:11:05,880 Speaker 1: viewpoint on success is there's a combination of three things, 180 00:11:05,920 --> 00:11:08,520 Speaker 1: and I'm not sure which one is most important, but 181 00:11:08,559 --> 00:11:12,200 Speaker 1: they probably all are equally important at different stages. One 182 00:11:12,559 --> 00:11:14,640 Speaker 1: is a little bit of luck, a little bit of 183 00:11:14,720 --> 00:11:19,000 Speaker 1: luck in the things that you've learned up to that 184 00:11:19,040 --> 00:11:22,080 Speaker 1: point in time when the opportunity comes, a little bit 185 00:11:22,120 --> 00:11:25,680 Speaker 1: of luck. For example, finding Dimensional was well suited to 186 00:11:26,280 --> 00:11:28,679 Speaker 1: the way that I thought about the world. And then 187 00:11:28,720 --> 00:11:32,559 Speaker 1: there's some talent. Do you have the right skill set 188 00:11:33,160 --> 00:11:36,880 Speaker 1: that will be helpful in that particular organization? And it 189 00:11:36,920 --> 00:11:40,240 Speaker 1: turns out that a quantitative and analytical type skill set 190 00:11:40,440 --> 00:11:44,320 Speaker 1: was very helpful for an organization like Dimensional in our clients. 191 00:11:44,880 --> 00:11:47,960 Speaker 1: And then hard work. Are you willing to do whatever 192 00:11:48,000 --> 00:11:53,319 Speaker 1: it takes to complete projects to move the ball forward 193 00:11:53,559 --> 00:11:57,440 Speaker 1: to help your clients succeed. And when you have all 194 00:11:57,480 --> 00:11:59,840 Speaker 1: three of those, I think good things can happen. And 195 00:12:00,120 --> 00:12:02,440 Speaker 1: was fortunate that had a little bit of each one 196 00:12:02,480 --> 00:12:06,200 Speaker 1: of those when I came to Dimensional, and Dimensional has 197 00:12:06,240 --> 00:12:09,200 Speaker 1: been a growing firm for money money decades and when 198 00:12:09,200 --> 00:12:11,920 Speaker 1: I came in two thousand or four, we had about 199 00:12:11,960 --> 00:12:15,960 Speaker 1: fifty billion under management and you know, that grew rapidly, 200 00:12:16,000 --> 00:12:17,960 Speaker 1: so there was a lot of opportunities for those folks 201 00:12:17,960 --> 00:12:20,679 Speaker 1: that were willing willing to step up. And so I 202 00:12:20,800 --> 00:12:24,320 Speaker 1: consider myself fortunate and very happy by how that's turned out, 203 00:12:24,320 --> 00:12:26,440 Speaker 1: because I've had a blast doing it and it's been 204 00:12:26,520 --> 00:12:29,280 Speaker 1: it's been rewarding. And then in terms of the co 205 00:12:29,520 --> 00:12:31,280 Speaker 1: c i O s and co CEOs, we do a 206 00:12:31,320 --> 00:12:34,479 Speaker 1: lot of CODs. We have cos of different department heads. 207 00:12:34,480 --> 00:12:39,839 Speaker 1: From my particular case, Dave Butler is the other co CEO. 208 00:12:40,480 --> 00:12:43,800 Speaker 1: And it tends to work well when you have people 209 00:12:43,840 --> 00:12:46,920 Speaker 1: who number one get along well with each other, they 210 00:12:46,960 --> 00:12:50,280 Speaker 1: respect each other and each other's ideas, and then they 211 00:12:50,280 --> 00:12:53,600 Speaker 1: have maybe complementary skill sets. And so the way that 212 00:12:53,720 --> 00:12:57,920 Speaker 1: Dave and I have worked in that job together I 213 00:12:57,960 --> 00:13:00,960 Speaker 1: think has been much more all my preference I would have. 214 00:13:01,080 --> 00:13:02,880 Speaker 1: I'm much preferred to have done it with him then 215 00:13:02,920 --> 00:13:06,880 Speaker 1: without them, because you can do some dividing and conquering. 216 00:13:07,559 --> 00:13:10,280 Speaker 1: But also what I find is that as you get promotions, 217 00:13:10,320 --> 00:13:13,320 Speaker 1: and this is a little bit facetious, but you tend 218 00:13:13,320 --> 00:13:16,280 Speaker 1: to become, at least if you judge it by the 219 00:13:16,320 --> 00:13:21,080 Speaker 1: input that you get from your peers, smarter and funnier 220 00:13:21,600 --> 00:13:24,760 Speaker 1: in that the input that you get from your peers 221 00:13:24,800 --> 00:13:28,439 Speaker 1: becomes less informationally rich. But when you have a troop 222 00:13:28,480 --> 00:13:31,840 Speaker 1: heer like Dave and I are are troop peers, anything goes. 223 00:13:31,960 --> 00:13:35,360 Speaker 1: We can have robust, open, honest conversations and with David 224 00:13:35,400 --> 00:13:38,080 Speaker 1: as well, which read us a lot of pressure test 225 00:13:38,160 --> 00:13:41,280 Speaker 1: things before we have to go and talk about them 226 00:13:41,320 --> 00:13:43,880 Speaker 1: with the rest of the firm. And that really, you know, 227 00:13:43,880 --> 00:13:46,480 Speaker 1: I always think iron sharpens iron, that you have to 228 00:13:46,559 --> 00:13:49,120 Speaker 1: have people who you can, you know, spar with on 229 00:13:49,160 --> 00:13:52,040 Speaker 1: a daily basis test your ideas. They'll push you, you 230 00:13:52,080 --> 00:13:56,439 Speaker 1: will push them so that you can improve every day. 231 00:13:56,520 --> 00:13:59,040 Speaker 1: So it's it's worked very very well. We do a 232 00:13:59,080 --> 00:14:02,760 Speaker 1: divide and conquered. We've thirteen global departments at dimensional. Four 233 00:14:02,800 --> 00:14:06,320 Speaker 1: comes straight to me, four go straight to him, and 234 00:14:06,360 --> 00:14:08,920 Speaker 1: then the five in the middle kind of go to 235 00:14:08,960 --> 00:14:11,000 Speaker 1: both of us either through the CEO we have a 236 00:14:11,040 --> 00:14:14,320 Speaker 1: CEO Lisa Dalmer, are directly like legal and compliance come 237 00:14:14,360 --> 00:14:17,920 Speaker 1: to both of us directly, and that way that it's 238 00:14:18,000 --> 00:14:21,080 Speaker 1: it's just worked well. We've been very pleased with what 239 00:14:21,160 --> 00:14:23,120 Speaker 1: we've been able to accomplish over the past five years 240 00:14:23,120 --> 00:14:26,080 Speaker 1: working together. It's I guess you each keep each other 241 00:14:26,160 --> 00:14:29,960 Speaker 1: sharp and keep each other honest. That's right, really interesting. 242 00:14:30,480 --> 00:14:33,960 Speaker 1: So so let's talk about factors a little bit um. 243 00:14:34,000 --> 00:14:38,880 Speaker 1: How did the academic research that that Rex and David, 244 00:14:39,000 --> 00:14:43,160 Speaker 1: the two co founders of d f A, How did 245 00:14:43,200 --> 00:14:46,160 Speaker 1: that become part of the investment process. So I guess 246 00:14:46,200 --> 00:14:49,560 Speaker 1: there's a couple of salient points there. One is factor 247 00:14:49,640 --> 00:14:52,920 Speaker 1: research in itself, and we talked a little bit earlier 248 00:14:52,920 --> 00:14:55,880 Speaker 1: on about models and what they're useful for and how 249 00:14:55,920 --> 00:14:58,520 Speaker 1: you draw inferences from them. I really look on factor 250 00:14:58,560 --> 00:15:01,920 Speaker 1: models as way to organize historical data so you can 251 00:15:01,960 --> 00:15:06,360 Speaker 1: try to understand better what really drove differences and returns 252 00:15:06,400 --> 00:15:09,880 Speaker 1: across different groups of securities, different groups of stocks, different 253 00:15:09,920 --> 00:15:13,960 Speaker 1: group of bonds, and from those you can glean very 254 00:15:13,960 --> 00:15:19,560 Speaker 1: important insights about the drivers of expected returns, the drivers 255 00:15:19,720 --> 00:15:24,760 Speaker 1: of differences and risk across different asset categories. And so 256 00:15:24,840 --> 00:15:29,000 Speaker 1: I think that's the important aspect of factor models. So 257 00:15:29,040 --> 00:15:32,760 Speaker 1: when you put them dimensional and its founding in context 258 00:15:32,840 --> 00:15:35,360 Speaker 1: of kind of a burgeoning field in the eighties and 259 00:15:35,400 --> 00:15:38,360 Speaker 1: in the nineties, when more and more factor models were 260 00:15:38,400 --> 00:15:43,480 Speaker 1: being developed and tested and so on. The founding was 261 00:15:44,560 --> 00:15:48,600 Speaker 1: two I would say, address an institutional need that David 262 00:15:48,720 --> 00:15:53,440 Speaker 1: had identified, which was there weren't many systematic strategies that 263 00:15:53,480 --> 00:15:58,040 Speaker 1: targeted the returns of small cap stocks, and he found 264 00:15:58,040 --> 00:16:01,840 Speaker 1: that that that was a hole in many institutional investor portfolios. 265 00:16:02,360 --> 00:16:05,160 Speaker 1: And along the around the same time, because David had 266 00:16:05,200 --> 00:16:08,200 Speaker 1: done his MBA at the University Chicago now Both School 267 00:16:08,240 --> 00:16:12,160 Speaker 1: of Business, around that same time, there was evidence coming 268 00:16:12,200 --> 00:16:15,480 Speaker 1: out that smaller cap stocks also had higher average returns 269 00:16:15,520 --> 00:16:18,800 Speaker 1: historically and reasons you know, promoted about why that would 270 00:16:18,840 --> 00:16:22,120 Speaker 1: be higher expected return is going forward, and so around 271 00:16:22,160 --> 00:16:24,720 Speaker 1: that time was kind of when those factor models were developing. 272 00:16:24,720 --> 00:16:27,320 Speaker 1: So I started with the client need, and then it 273 00:16:27,440 --> 00:16:30,840 Speaker 1: was well, let me go to the academics and understand, 274 00:16:31,600 --> 00:16:35,080 Speaker 1: what are the research around this client need. Am I 275 00:16:35,160 --> 00:16:37,160 Speaker 1: going to do something here that makes sense or not 276 00:16:37,200 --> 00:16:40,320 Speaker 1: makes sense from an academic perspective? And then how do 277 00:16:40,360 --> 00:16:45,040 Speaker 1: I build a good robust solution to address that client need. 278 00:16:45,400 --> 00:16:47,040 Speaker 1: And then, of course, in the nineties you had the 279 00:16:47,080 --> 00:16:49,240 Speaker 1: three factor model come along, and then in the mid 280 00:16:49,320 --> 00:16:51,520 Speaker 1: nineties you had momentum come along, and in the two 281 00:16:51,520 --> 00:16:54,800 Speaker 1: thousands you had things like profitability and investment come along. 282 00:16:55,160 --> 00:16:57,800 Speaker 1: So we had lots of different factors uncovered over time. 283 00:16:58,240 --> 00:16:59,480 Speaker 1: But the way that we look on each one of 284 00:16:59,520 --> 00:17:02,680 Speaker 1: those is their models. They give us insights from the data. 285 00:17:03,000 --> 00:17:05,840 Speaker 1: How do you use that to build robust portfolios? And 286 00:17:05,880 --> 00:17:08,000 Speaker 1: I would say that's been kind of part of our 287 00:17:08,040 --> 00:17:11,560 Speaker 1: heritage for forty years. How do we build portfolios that 288 00:17:11,640 --> 00:17:16,280 Speaker 1: can target these premiums but be robust regardless of the 289 00:17:16,320 --> 00:17:19,280 Speaker 1: market environment. And we've been through many different market crises 290 00:17:19,640 --> 00:17:22,080 Speaker 1: with a broad range of investment strategies that have come 291 00:17:22,080 --> 00:17:26,000 Speaker 1: out quite well the other side. So we're we're pretty 292 00:17:26,080 --> 00:17:30,520 Speaker 1: familiar in modern times with small cap indices like the 293 00:17:30,600 --> 00:17:33,919 Speaker 1: Russell two thousand, or the S and P six hundred 294 00:17:34,000 --> 00:17:37,520 Speaker 1: or whatever it happens to be. But when Sinkfeld and 295 00:17:37,760 --> 00:17:41,760 Speaker 1: Booth were forming um D f A in the early eighties, 296 00:17:42,560 --> 00:17:46,920 Speaker 1: these weren't really household names, if they even existed at all. 297 00:17:48,240 --> 00:17:51,320 Speaker 1: It's amazing to think that there was a period where 298 00:17:52,040 --> 00:17:55,280 Speaker 1: small caps weren't their own category. Tell us a little 299 00:17:55,320 --> 00:17:58,400 Speaker 1: bit about how that evolved. Yeah, if you go back 300 00:17:58,400 --> 00:18:00,640 Speaker 1: even further, so dimension was found in than eighty one, 301 00:18:00,680 --> 00:18:04,080 Speaker 1: But if you go back a decade earlier, and I'll 302 00:18:04,119 --> 00:18:06,399 Speaker 1: focus on David a little bit and his work with 303 00:18:06,480 --> 00:18:09,880 Speaker 1: mc McCown, who was at Wells Fargo at the time, 304 00:18:09,920 --> 00:18:14,000 Speaker 1: and he's a director of the firm, and so David 305 00:18:14,040 --> 00:18:18,600 Speaker 1: and Mac were working on indexes. So in the very 306 00:18:18,600 --> 00:18:23,440 Speaker 1: early seventies, the Max team with David created the first 307 00:18:23,480 --> 00:18:26,119 Speaker 1: index fund. It wasn't for retail, it was for an 308 00:18:26,119 --> 00:18:29,480 Speaker 1: institutional client, and it was based on US large cap stocks, 309 00:18:29,520 --> 00:18:33,000 Speaker 1: So he's very familiar with index based approaches. Then David 310 00:18:33,040 --> 00:18:36,600 Speaker 1: subsequently left and worked a gibecker for a while, understood 311 00:18:36,640 --> 00:18:40,320 Speaker 1: more about what clients were interested in looking for required 312 00:18:41,080 --> 00:18:44,480 Speaker 1: and so there wasn't a Russell two thousand available when 313 00:18:44,600 --> 00:18:47,000 Speaker 1: he was building the firm, so there wasn't an index 314 00:18:47,040 --> 00:18:50,560 Speaker 1: to attach the strategy to. The Other thing that was 315 00:18:51,240 --> 00:18:54,520 Speaker 1: kind of feedback from academia is yes, small cap investing 316 00:18:54,560 --> 00:18:56,760 Speaker 1: makes sense, but you're going to get killed on trading costs. 317 00:18:57,440 --> 00:19:00,280 Speaker 1: And so then you have this kind of environment where 318 00:19:00,600 --> 00:19:03,280 Speaker 1: there wasn't an index, it wasn't a household name. To 319 00:19:03,359 --> 00:19:06,240 Speaker 1: your point, you know small cap stocks as an asset category, 320 00:19:06,600 --> 00:19:09,800 Speaker 1: so you kind of have a blank canvas. If I know, 321 00:19:09,800 --> 00:19:12,280 Speaker 1: knowing everything that I know, what's the right way to 322 00:19:12,320 --> 00:19:16,399 Speaker 1: build a small cap strategy that hopefully then will be 323 00:19:16,440 --> 00:19:20,159 Speaker 1: efficient and won't suffer too greatly from trading costs and 324 00:19:20,200 --> 00:19:24,199 Speaker 1: implementing and investing client flows. So I think that it 325 00:19:24,280 --> 00:19:29,159 Speaker 1: was in some respects a very big advantage starting with 326 00:19:29,200 --> 00:19:32,359 Speaker 1: that blank canvas of how do you design the best portfolio, 327 00:19:32,400 --> 00:19:36,240 Speaker 1: you know how, with as few constraints as possible, because 328 00:19:36,400 --> 00:19:39,200 Speaker 1: you weren't worried about an index. And then subsequently Russell 329 00:19:39,920 --> 00:19:42,159 Speaker 1: had the Russell two thousand, and then of course in 330 00:19:42,200 --> 00:19:46,080 Speaker 1: the nineties, value versus growth became, you know, well established 331 00:19:46,080 --> 00:19:49,200 Speaker 1: asset categories, and so asset categories have been added over time. 332 00:19:49,920 --> 00:19:53,120 Speaker 1: So so let's talk a little bit about Gene Parma 333 00:19:53,359 --> 00:19:56,520 Speaker 1: and Ken French is what started out as a three 334 00:19:56,520 --> 00:19:59,440 Speaker 1: factor model, it eventually became five and seven. Now they're 335 00:19:59,480 --> 00:20:05,000 Speaker 1: a hundred of factors, many of which um don't really 336 00:20:05,040 --> 00:20:07,880 Speaker 1: add a whole lot of alpha or not consistent enough 337 00:20:07,920 --> 00:20:12,320 Speaker 1: alpha to justify their complications and costs. Tell us a 338 00:20:12,359 --> 00:20:15,760 Speaker 1: little bit about the Farmer French factor model. Yeah, so 339 00:20:16,160 --> 00:20:17,800 Speaker 1: you know, when you when you go to the eighties, 340 00:20:17,840 --> 00:20:21,320 Speaker 1: there was a lot of empirical evidence being uncovered that 341 00:20:21,400 --> 00:20:24,040 Speaker 1: the prevailing model from the sixties and the seventies, the 342 00:20:24,080 --> 00:20:28,800 Speaker 1: capital asset pricing model, didn't explain the data very well, 343 00:20:29,280 --> 00:20:30,639 Speaker 1: so when you look at it, it was it was 344 00:20:30,680 --> 00:20:34,480 Speaker 1: a beautiful model. It was very you know, intuitive, but 345 00:20:34,560 --> 00:20:37,400 Speaker 1: it didn't explain the data all that well. And so 346 00:20:37,840 --> 00:20:41,040 Speaker 1: Ken and Gene in the early nineties started to organize 347 00:20:41,040 --> 00:20:43,000 Speaker 1: all the data to say, can we put some of 348 00:20:43,000 --> 00:20:47,240 Speaker 1: these observations in one kind of unified viewpoint of the 349 00:20:47,320 --> 00:20:52,720 Speaker 1: historical data. And from that, you know, exercise came a 350 00:20:52,880 --> 00:20:57,000 Speaker 1: better model in the sense that it could explain the 351 00:20:57,080 --> 00:21:00,560 Speaker 1: returns that you saw among stocks are better than the 352 00:21:00,600 --> 00:21:03,080 Speaker 1: capital as sur pricing models, so explain more of the returns, 353 00:21:03,119 --> 00:21:05,560 Speaker 1: more of the variation that you saw on the returns 354 00:21:05,600 --> 00:21:09,400 Speaker 1: across stocks, and so that so subsequently came the three 355 00:21:09,440 --> 00:21:12,480 Speaker 1: factor model. Then to your point, lots of factors have 356 00:21:12,520 --> 00:21:14,600 Speaker 1: been added. If you look at family frenches are even 357 00:21:14,680 --> 00:21:18,360 Speaker 1: Ken's website, now you'll see a profitability factor, you'll see 358 00:21:18,359 --> 00:21:22,560 Speaker 1: an investment factor, you'll see momentum factors. You'll see all 359 00:21:22,560 --> 00:21:25,520 Speaker 1: different types of factors. And as I mentioned earlier, factors 360 00:21:25,520 --> 00:21:28,479 Speaker 1: are really great to help you organize the historical data. 361 00:21:29,400 --> 00:21:31,880 Speaker 1: But you don't want to get kind of two stereoid 362 00:21:31,960 --> 00:21:35,320 Speaker 1: about the latest factor model. I kind of view a 363 00:21:35,320 --> 00:21:37,840 Speaker 1: lot of the academic research over the past thirty years 364 00:21:38,200 --> 00:21:41,719 Speaker 1: as doing variance on a theme, and so it's not 365 00:21:41,840 --> 00:21:44,760 Speaker 1: that kind of a have brand new discovery, but it 366 00:21:44,800 --> 00:21:48,760 Speaker 1: refines your understanding of existing factors. So there's probably twenty 367 00:21:48,840 --> 00:21:51,359 Speaker 1: or thirty or forty different value factors out there, but 368 00:21:51,440 --> 00:21:53,600 Speaker 1: you don't need all twenty or thirty or forty when 369 00:21:53,600 --> 00:21:56,760 Speaker 1: you're managing a strategy. But you can get insights from 370 00:21:56,760 --> 00:21:59,720 Speaker 1: the different factors on how to manage a strategy effectively. 371 00:22:00,359 --> 00:22:03,000 Speaker 1: And so what I mean by that is if you 372 00:22:03,240 --> 00:22:05,639 Speaker 1: if if you think about what datas are are available. 373 00:22:06,080 --> 00:22:09,400 Speaker 1: You have security prices, you have data from income statements, 374 00:22:09,480 --> 00:22:12,960 Speaker 1: so things like income or profits or revenues or expenses, 375 00:22:13,359 --> 00:22:15,880 Speaker 1: and you have data from balance sheets, assets and liabilities. 376 00:22:16,040 --> 00:22:19,320 Speaker 1: They're the broadly the data that are available to go test. 377 00:22:20,160 --> 00:22:22,600 Speaker 1: And when you look at all of those factor models 378 00:22:22,600 --> 00:22:24,679 Speaker 1: their variants on the theme, the right are looking at 379 00:22:24,680 --> 00:22:28,760 Speaker 1: current values of those variables, whether it's current income or 380 00:22:28,840 --> 00:22:31,639 Speaker 1: current price to book ratios or price earnings ratios, are 381 00:22:31,640 --> 00:22:34,400 Speaker 1: they're looking at how they've changed, How to have prices 382 00:22:34,480 --> 00:22:37,480 Speaker 1: changed over the past number of months, How have assets 383 00:22:37,480 --> 00:22:40,359 Speaker 1: grown over the past number of months, How is profitability 384 00:22:40,440 --> 00:22:42,360 Speaker 1: changed over the past number of months. So there's three 385 00:22:42,440 --> 00:22:45,159 Speaker 1: data sources and people do two things with them, so 386 00:22:45,200 --> 00:22:48,120 Speaker 1: there's actually really kind of six that you can think 387 00:22:48,160 --> 00:22:51,679 Speaker 1: about that kind of encompass most of the hundreds of 388 00:22:51,720 --> 00:22:54,080 Speaker 1: factors that you see out there. And I think that 389 00:22:54,119 --> 00:22:57,560 Speaker 1: if you have coverage of those six current prices, current 390 00:22:57,600 --> 00:23:00,359 Speaker 1: balance sheet items, current income statement items, and then how 391 00:23:00,359 --> 00:23:03,240 Speaker 1: each one of those have changed in recent past, you 392 00:23:03,320 --> 00:23:07,480 Speaker 1: have pretty broad coverage of all the various different factor 393 00:23:07,560 --> 00:23:10,960 Speaker 1: literature that's available. And that's what we do at Dimensional. 394 00:23:11,320 --> 00:23:14,440 Speaker 1: So so let's for the lay person get a little 395 00:23:14,440 --> 00:23:19,439 Speaker 1: more granular with some of the more popular and effective 396 00:23:19,960 --> 00:23:26,560 Speaker 1: factors um. The four biggest ones I think are size, value, quality, 397 00:23:26,600 --> 00:23:29,080 Speaker 1: and momentum. Is there anything you would add to that 398 00:23:29,280 --> 00:23:32,600 Speaker 1: beyond beta which is just a given? So there's five? 399 00:23:33,000 --> 00:23:35,720 Speaker 1: What else would you add to that list? I would 400 00:23:35,720 --> 00:23:39,240 Speaker 1: add probably investment and proxy for investment is how a 401 00:23:39,320 --> 00:23:42,879 Speaker 1: firm is growing their assets over time. And when you 402 00:23:42,920 --> 00:23:45,840 Speaker 1: think about all of the ones that you just listed, Barry, 403 00:23:46,040 --> 00:23:49,280 Speaker 1: all of them are momentum, have something in common, and 404 00:23:49,320 --> 00:23:51,960 Speaker 1: what's that that they have in common? They're basically picking 405 00:23:52,040 --> 00:23:55,920 Speaker 1: up differences and discount rates that the market has applied 406 00:23:56,359 --> 00:23:59,480 Speaker 1: to different investment opportunities. So when you think about something 407 00:23:59,520 --> 00:24:02,159 Speaker 1: like value, you you're taking price and you're dividing it 408 00:24:02,160 --> 00:24:06,200 Speaker 1: by some company fundamental so some fundamental measure of firm size, 409 00:24:06,920 --> 00:24:08,800 Speaker 1: and you're saying, why do you want to do that? 410 00:24:08,840 --> 00:24:10,919 Speaker 1: Because you want to see who has low price today 411 00:24:11,480 --> 00:24:14,320 Speaker 1: relative to who has high price today. So there's firms 412 00:24:14,320 --> 00:24:16,359 Speaker 1: in the marketplace, some of them will trade at low prices, 413 00:24:16,400 --> 00:24:18,159 Speaker 1: some of them will trade at high prices. You need 414 00:24:18,200 --> 00:24:20,880 Speaker 1: to scale price, normalize price to be able to make 415 00:24:20,880 --> 00:24:24,920 Speaker 1: that determination. When you say quality, quality often comes down 416 00:24:24,920 --> 00:24:29,280 Speaker 1: to profitability. And what we know from the historical data 417 00:24:29,520 --> 00:24:31,840 Speaker 1: is the firms that have the highest profits or the 418 00:24:31,880 --> 00:24:35,200 Speaker 1: highest profitability, so profits divided by assets or profits divided 419 00:24:35,200 --> 00:24:40,399 Speaker 1: by book value in the marketplace tend to continue to 420 00:24:40,520 --> 00:24:43,560 Speaker 1: have that high profitability over the next year, two, three, four, 421 00:24:43,640 --> 00:24:46,679 Speaker 1: or five years. But what do those profits lead to? 422 00:24:46,920 --> 00:24:50,639 Speaker 1: Those profits lead to client cash flows or investor clash flows. 423 00:24:50,680 --> 00:24:52,840 Speaker 1: I should say the higher the profits, the more cash 424 00:24:52,840 --> 00:24:56,480 Speaker 1: flows investors can expect to get from their investments. So 425 00:24:56,520 --> 00:24:59,040 Speaker 1: it's telling you something about expected cash flows from that 426 00:24:59,119 --> 00:25:03,479 Speaker 1: investment in the future. Here I say investment because asset growth. 427 00:25:03,920 --> 00:25:08,000 Speaker 1: Let's imagine a company has to retain a lot of earnings, 428 00:25:08,080 --> 00:25:10,320 Speaker 1: or has to issue a lot of debt, or has 429 00:25:10,359 --> 00:25:12,480 Speaker 1: to issue a lot of stock in order to drive 430 00:25:12,520 --> 00:25:16,720 Speaker 1: those profits going forward. That leads fewer cash flows for investors. 431 00:25:16,720 --> 00:25:20,400 Speaker 1: So that also tells you something about expected cash flows. 432 00:25:20,440 --> 00:25:25,919 Speaker 1: So when you talk size, value, profitability, or quality and investment, 433 00:25:26,280 --> 00:25:29,359 Speaker 1: they're all telling you something about expecting cash flows. Are 434 00:25:29,359 --> 00:25:31,480 Speaker 1: the prices people are willing to pay. It's a discount 435 00:25:31,520 --> 00:25:37,200 Speaker 1: rate effect. Momentum is the outlier. There's no equally simple, 436 00:25:37,480 --> 00:25:41,360 Speaker 1: compelling story that lets you know why should you expect 437 00:25:41,640 --> 00:25:43,800 Speaker 1: that firms that have outperformed the market in the past 438 00:25:43,800 --> 00:25:46,280 Speaker 1: three the twelve months to continue to outperform the market 439 00:25:46,280 --> 00:25:48,320 Speaker 1: in the next three the twelve months, and vice versa. 440 00:25:49,200 --> 00:25:51,600 Speaker 1: But it's there, loud and clear in the historical data, 441 00:25:52,040 --> 00:25:53,879 Speaker 1: and so the question we ask ourselves is how do 442 00:25:53,920 --> 00:25:57,800 Speaker 1: we use that information with as low opportunity costs as 443 00:25:57,800 --> 00:25:59,560 Speaker 1: possible because we don't know why it's there, so we 444 00:25:59,600 --> 00:26:01,080 Speaker 1: don't know if it will be there in the future. 445 00:26:01,480 --> 00:26:03,840 Speaker 1: But if it's not there in the future, we don't 446 00:26:03,840 --> 00:26:09,119 Speaker 1: want to have incurred unnecessary costs on behalf of investors 447 00:26:09,160 --> 00:26:11,879 Speaker 1: pursuing something that we don't know why it exists in 448 00:26:11,880 --> 00:26:15,320 Speaker 1: the data to begin with. Really really interesting, when when 449 00:26:15,400 --> 00:26:18,879 Speaker 1: I think of momentum, I have I tend to think 450 00:26:19,000 --> 00:26:25,800 Speaker 1: of a persistency because either fund managers or investors have 451 00:26:25,960 --> 00:26:29,760 Speaker 1: gone through the whole process of selecting that stock, and 452 00:26:29,800 --> 00:26:32,800 Speaker 1: as long as it's working out, trending in the right 453 00:26:32,840 --> 00:26:37,000 Speaker 1: direction at market um returns or better, there's no reason 454 00:26:37,080 --> 00:26:39,480 Speaker 1: to remove it. So it becomes a little bit of 455 00:26:39,480 --> 00:26:45,160 Speaker 1: a self fulfilling prophecy until there's a substantial enough misstep 456 00:26:45,520 --> 00:26:48,080 Speaker 1: and then throw in all of the four oh one 457 00:26:48,160 --> 00:26:52,320 Speaker 1: k regular contributions. If that if you're in fund X 458 00:26:52,880 --> 00:26:56,040 Speaker 1: and it owns company A, B, S and C, and 459 00:26:56,080 --> 00:26:58,919 Speaker 1: all three of those are doing well, money continues to 460 00:26:58,960 --> 00:27:02,520 Speaker 1: flow to those funds automatically, and those funds tend to 461 00:27:02,560 --> 00:27:06,879 Speaker 1: buy their top performers. It's almost like a virtuous cycle. 462 00:27:07,440 --> 00:27:09,920 Speaker 1: You know, that's a possible explanation, and that it's certainly 463 00:27:09,960 --> 00:27:14,919 Speaker 1: it's certainly a little bit of narrative fallacy and hindsight bias. 464 00:27:15,000 --> 00:27:17,600 Speaker 1: To say the least, it's been tested. I mean, academics 465 00:27:17,600 --> 00:27:21,320 Speaker 1: have looked at you know, overreaction, under reaction, and why 466 00:27:21,560 --> 00:27:25,120 Speaker 1: is there continuation in returns. There's an interesting area of 467 00:27:25,200 --> 00:27:28,640 Speaker 1: research going on right now, and Professor Novi Marx had 468 00:27:28,640 --> 00:27:31,480 Speaker 1: one of the kind of first, well not one of 469 00:27:31,520 --> 00:27:33,359 Speaker 1: the first, but I kind of I would say, an 470 00:27:33,359 --> 00:27:37,679 Speaker 1: instrumental paper on on this recently that looks at profitability growth. 471 00:27:38,119 --> 00:27:43,080 Speaker 1: So how have affirms profits grown are declined over the 472 00:27:43,160 --> 00:27:47,080 Speaker 1: past three months to a year and does that explain 473 00:27:47,560 --> 00:27:50,639 Speaker 1: the returns pattern that you see related to momentum? And 474 00:27:50,680 --> 00:27:53,720 Speaker 1: that seems like a promising area of research if there 475 00:27:53,760 --> 00:27:57,200 Speaker 1: is a lot of explanatory power in how affirms profits 476 00:27:57,240 --> 00:28:00,840 Speaker 1: have changed or how their profitability has changed, and that 477 00:28:00,920 --> 00:28:03,800 Speaker 1: has the power to predict future profitability i e. Firms 478 00:28:03,800 --> 00:28:06,160 Speaker 1: that have grown their profits more quickly than other firms 479 00:28:06,440 --> 00:28:09,199 Speaker 1: may continue to grow their profits more quickly than other firms. 480 00:28:09,800 --> 00:28:12,760 Speaker 1: Then if that explains momentum, then you start to get 481 00:28:12,880 --> 00:28:18,520 Speaker 1: momentum back into that field of differences in discount rates, 482 00:28:18,600 --> 00:28:22,560 Speaker 1: and then that becomes a much more easy story to 483 00:28:22,640 --> 00:28:27,560 Speaker 1: understand in the sense that firm characteristics are much more 484 00:28:27,600 --> 00:28:31,639 Speaker 1: straightforward to predict than future private prices. Well run firms 485 00:28:31,640 --> 00:28:33,960 Speaker 1: tend to remain well run firms for some period of time. 486 00:28:34,280 --> 00:28:36,800 Speaker 1: But given that their well run firms when you think 487 00:28:36,840 --> 00:28:39,680 Speaker 1: about the price, that's said in the stock market, that's 488 00:28:39,720 --> 00:28:44,440 Speaker 1: the aggregive view of what expected return people require to 489 00:28:44,520 --> 00:28:46,640 Speaker 1: hold that investment. So they already understand it's a well 490 00:28:46,720 --> 00:28:49,800 Speaker 1: run firm, and so we think that it's priced fairly 491 00:28:50,320 --> 00:28:53,240 Speaker 1: given all that information. So it may have information about 492 00:28:53,440 --> 00:28:56,080 Speaker 1: how well run that firm has been over the past 493 00:28:56,160 --> 00:28:58,720 Speaker 1: number of quarters, and that has predictive power on how 494 00:28:58,720 --> 00:29:01,600 Speaker 1: well run that firm is expected to be over the 495 00:29:01,640 --> 00:29:04,040 Speaker 1: next few quarters. So so let's get into the weeds 496 00:29:04,080 --> 00:29:08,360 Speaker 1: a little bit. How can you distinguish between factor research 497 00:29:08,480 --> 00:29:13,480 Speaker 1: that's significant and factor work that's either statistical noise or 498 00:29:14,040 --> 00:29:18,320 Speaker 1: backwards looking form fitting, Because it seems like everybody has 499 00:29:18,360 --> 00:29:21,760 Speaker 1: developed a new model of their own which looks great 500 00:29:21,800 --> 00:29:25,560 Speaker 1: on paper. Um, the back tests are always wonderful, but 501 00:29:25,640 --> 00:29:28,720 Speaker 1: then in reality it doesn't seem to work. So so 502 00:29:28,760 --> 00:29:32,040 Speaker 1: how do you draw the line between hey, this really 503 00:29:32,200 --> 00:29:36,200 Speaker 1: is substantial versus just a just a good backdest. Yeah, 504 00:29:36,480 --> 00:29:38,640 Speaker 1: you hit on it perfectly. Very You're never going to 505 00:29:38,640 --> 00:29:42,360 Speaker 1: see a bad back test, in particular from an asset manager. Well, 506 00:29:42,400 --> 00:29:44,520 Speaker 1: because that's where they all go to diet. It's all 507 00:29:44,560 --> 00:29:47,400 Speaker 1: survivorship by it's all survivorship by it. So it is 508 00:29:47,400 --> 00:29:50,000 Speaker 1: a real challenge, and that's true even of the academic work, 509 00:29:50,560 --> 00:29:55,040 Speaker 1: because in academia, how do you get tenure? You published papers. 510 00:29:55,680 --> 00:29:57,960 Speaker 1: The types of papers that get published are those with 511 00:29:58,040 --> 00:30:02,920 Speaker 1: startling empirical observation, and so the hundred experiments that were 512 00:30:03,000 --> 00:30:06,360 Speaker 1: run that didn't lead to a startling empirical observation are 513 00:30:06,400 --> 00:30:09,000 Speaker 1: never published and the one that did is published. So 514 00:30:09,040 --> 00:30:14,280 Speaker 1: you have that bias when it comes to academic and 515 00:30:14,360 --> 00:30:17,200 Speaker 1: practitioner work. The way that we think about it is 516 00:30:18,360 --> 00:30:21,520 Speaker 1: kind of nuanced. First off, we start with the broader 517 00:30:21,560 --> 00:30:24,040 Speaker 1: view of the academic literature, what's the latest and greatest 518 00:30:24,040 --> 00:30:28,280 Speaker 1: out there in academia. Then at Dimensional, we've developed a 519 00:30:28,320 --> 00:30:31,680 Speaker 1: lot of in house proprietary data sets that go back 520 00:30:31,800 --> 00:30:37,040 Speaker 1: many many decades that include data with a level of tendiness, 521 00:30:37,040 --> 00:30:40,560 Speaker 1: I would say, and precision that's probably kind of second 522 00:30:40,600 --> 00:30:44,200 Speaker 1: to none and with respect to all the data sets 523 00:30:44,200 --> 00:30:47,800 Speaker 1: available out there. And of course you know we're here 524 00:30:47,840 --> 00:30:51,080 Speaker 1: at Bloomberg Studios who love data and we love data too. 525 00:30:51,760 --> 00:30:54,960 Speaker 1: You guys um were involved in the early days of 526 00:30:55,000 --> 00:30:57,880 Speaker 1: the CRISP data set. Let's talk a little bit about 527 00:30:58,320 --> 00:31:02,600 Speaker 1: what an advantage it was having not only access to that, 528 00:31:02,640 --> 00:31:05,920 Speaker 1: but the ability to really do a deep dive and 529 00:31:05,960 --> 00:31:09,760 Speaker 1: manipulate that data. Tell us a little bit about Chris. Yeah, 530 00:31:09,880 --> 00:31:14,040 Speaker 1: CRISP was started back in the sixties and it was 531 00:31:14,080 --> 00:31:20,400 Speaker 1: basically an effort by University Chicago and folks there to 532 00:31:20,440 --> 00:31:23,880 Speaker 1: gather all the stock price data and dividend data and 533 00:31:24,000 --> 00:31:26,840 Speaker 1: corporate action data to say, can we were computer return 534 00:31:26,880 --> 00:31:30,600 Speaker 1: on the U S stock market? Because pre nineteen sixties 535 00:31:30,640 --> 00:31:33,080 Speaker 1: you couldn't get that with a great deal of precision. 536 00:31:33,360 --> 00:31:36,760 Speaker 1: It's amazing, it really is amazing. And so so then 537 00:31:37,440 --> 00:31:40,200 Speaker 1: over time you know you had CRISP, and then you 538 00:31:40,320 --> 00:31:44,000 Speaker 1: had other supplements were company financials were added to the 539 00:31:44,080 --> 00:31:47,080 Speaker 1: data set and all joined and linked up together so 540 00:31:47,120 --> 00:31:49,920 Speaker 1: effectively you could test things well. And the way that 541 00:31:49,960 --> 00:31:53,160 Speaker 1: we think about testing things well is number one, do 542 00:31:53,240 --> 00:31:55,280 Speaker 1: you expect to see this in the data before you look? 543 00:31:55,600 --> 00:31:58,760 Speaker 1: Why are you looking for this for this thing? And 544 00:31:58,840 --> 00:32:02,720 Speaker 1: so that kind of juices some of them, the issues 545 00:32:02,760 --> 00:32:05,520 Speaker 1: with biases and back tests. You expect it before you 546 00:32:05,560 --> 00:32:08,080 Speaker 1: go see, and then you see the data tells you 547 00:32:08,120 --> 00:32:10,840 Speaker 1: how strong it has been or hasn't been. Then you 548 00:32:10,840 --> 00:32:12,960 Speaker 1: want to do a lot of robustness checks because robustness 549 00:32:13,000 --> 00:32:15,840 Speaker 1: is the name of the game. So you've tested it 550 00:32:15,920 --> 00:32:18,760 Speaker 1: in one data sample, can you test it in multiple 551 00:32:18,840 --> 00:32:21,560 Speaker 1: data samples? Can you test it out of sample? So 552 00:32:21,600 --> 00:32:24,120 Speaker 1: I'll give you I'll give you an example, and I 553 00:32:24,160 --> 00:32:26,840 Speaker 1: think this experiment is kind of unique when it comes 554 00:32:26,880 --> 00:32:29,920 Speaker 1: to academia. When you look at Famine French in their 555 00:32:29,960 --> 00:32:33,320 Speaker 1: ninety two paper, they used US stock data from the 556 00:32:33,360 --> 00:32:37,440 Speaker 1: sixties to the nineties and they tested value, premiums and 557 00:32:37,560 --> 00:32:39,920 Speaker 1: leverage and all sorts of things in that paper over 558 00:32:39,960 --> 00:32:43,640 Speaker 1: that data sample and produced the three factor model. Then 559 00:32:43,760 --> 00:32:45,719 Speaker 1: they came up with a prescription or a kind of 560 00:32:45,800 --> 00:32:49,880 Speaker 1: like almost a list of ingredients. Here's how you create 561 00:32:49,920 --> 00:32:53,200 Speaker 1: a factor model. And that's been used by most academic 562 00:32:53,240 --> 00:32:55,760 Speaker 1: since so the formula that they used has been used 563 00:32:55,760 --> 00:32:59,360 Speaker 1: by most academics. Sins. So, then later on in the nineties, 564 00:33:00,240 --> 00:33:03,600 Speaker 1: with Jim Davis who used to work at Dimensional, he 565 00:33:03,640 --> 00:33:06,960 Speaker 1: gathered a whole bunch of pre nineteen sixties data, so 566 00:33:07,000 --> 00:33:10,400 Speaker 1: he was able to extend the original family French analysis 567 00:33:10,400 --> 00:33:13,640 Speaker 1: to completely out of sample test and that went from 568 00:33:13,680 --> 00:33:17,400 Speaker 1: the twenties to the sixties. Then non US developed market 569 00:33:17,480 --> 00:33:21,440 Speaker 1: data were collected and the same tests that Feminine French 570 00:33:21,480 --> 00:33:24,040 Speaker 1: had round on. Their original sample was run on non 571 00:33:24,120 --> 00:33:27,480 Speaker 1: US developed markets, and then it was run on emerging 572 00:33:27,520 --> 00:33:30,840 Speaker 1: market data because that was collected. And now we're thirty 573 00:33:30,920 --> 00:33:33,800 Speaker 1: years past the family French original experiment. So now we 574 00:33:33,840 --> 00:33:36,160 Speaker 1: have another out of sample test. And so you have 575 00:33:36,280 --> 00:33:39,800 Speaker 1: five out of sample tests, and in four of those 576 00:33:39,840 --> 00:33:44,320 Speaker 1: five you see very very strong and reliable value premiums, 577 00:33:44,480 --> 00:33:46,600 Speaker 1: and you can't actually tell the difference between any of 578 00:33:46,600 --> 00:33:50,440 Speaker 1: those five about the magnitude statistically speaking, between the realization 579 00:33:50,440 --> 00:33:54,480 Speaker 1: of those premiums. That's robustness. You've seen it in sample 580 00:33:54,880 --> 00:33:57,640 Speaker 1: and you've seen it in many out of sample tests. 581 00:33:58,120 --> 00:34:01,320 Speaker 1: That gives you high confidence that what you're observing in 582 00:34:01,360 --> 00:34:04,680 Speaker 1: the data happened by more than just chance. It's something 583 00:34:04,800 --> 00:34:08,120 Speaker 1: real and you should expect to see it going forward. 584 00:34:08,520 --> 00:34:11,680 Speaker 1: But that's the type of rigorous analysis that we're able 585 00:34:11,719 --> 00:34:14,839 Speaker 1: to apply to new observations because now we have so 586 00:34:14,920 --> 00:34:17,520 Speaker 1: many different data sets that we can test the observation on, 587 00:34:17,880 --> 00:34:20,000 Speaker 1: we can shape up the experiment, we can find out 588 00:34:20,000 --> 00:34:23,080 Speaker 1: where the bodies are buried, how robust it is, and 589 00:34:23,160 --> 00:34:26,920 Speaker 1: that gives us confidence in the in the patterns that 590 00:34:26,960 --> 00:34:29,560 Speaker 1: were observing in the data, whether they're real or it's 591 00:34:29,600 --> 00:34:34,120 Speaker 1: just noise, really really interesting stuff. So so let's talk 592 00:34:34,160 --> 00:34:36,279 Speaker 1: a little bit about the growth of d f A 593 00:34:36,680 --> 00:34:40,040 Speaker 1: and and your role there. Um, you're a bit younger 594 00:34:40,080 --> 00:34:44,200 Speaker 1: than the typical member of your management team. How does 595 00:34:44,200 --> 00:34:47,080 Speaker 1: that affect how you do your job? What do you 596 00:34:47,120 --> 00:34:49,640 Speaker 1: bring to the table that some of the more senior 597 00:34:49,960 --> 00:34:54,120 Speaker 1: managers might be missing. So I've never really thought about it, 598 00:34:54,120 --> 00:34:56,800 Speaker 1: to be perfectly honest, And maybe that's in part because 599 00:34:57,719 --> 00:34:59,800 Speaker 1: I've always been on the younger side, whether it was 600 00:34:59,840 --> 00:35:01,720 Speaker 1: in high school relative to the rest of the folks 601 00:35:01,719 --> 00:35:03,680 Speaker 1: in my class. I went to college when I was sixteen, 602 00:35:04,360 --> 00:35:06,399 Speaker 1: and so it was a little younger than the other 603 00:35:06,440 --> 00:35:09,600 Speaker 1: folks in my class. And then when I started working 604 00:35:09,640 --> 00:35:12,200 Speaker 1: at Dimensional after doing a PhD, was younger than some 605 00:35:12,239 --> 00:35:14,000 Speaker 1: of the other folks in the research team. So it's 606 00:35:14,000 --> 00:35:15,960 Speaker 1: always been kind of the state of play. So I 607 00:35:16,000 --> 00:35:19,320 Speaker 1: don't think about it too much. I would say, a 608 00:35:19,400 --> 00:35:23,080 Speaker 1: Dimensional we have a very academic view of how to 609 00:35:23,160 --> 00:35:26,320 Speaker 1: interact with each other. So interact with each other with respect, 610 00:35:27,080 --> 00:35:31,320 Speaker 1: but challenge and argue the facts and the issues, and 611 00:35:32,640 --> 00:35:35,600 Speaker 1: the best ideas win. And so I think that when 612 00:35:35,719 --> 00:35:38,640 Speaker 1: it comes to how to interact with colleagues, whether they're 613 00:35:38,680 --> 00:35:43,120 Speaker 1: younger or they're older. It's exactly under that formula. You 614 00:35:43,200 --> 00:35:47,000 Speaker 1: have to operate with respect, listen to the ideas, and 615 00:35:47,040 --> 00:35:50,280 Speaker 1: then the best idea wins. Our view is, don't defend 616 00:35:50,440 --> 00:35:53,440 Speaker 1: the idea just because it's your idea. Embrace the best 617 00:35:53,480 --> 00:35:56,799 Speaker 1: idea and the right idea because ultimately, long term, that's 618 00:35:56,800 --> 00:35:58,719 Speaker 1: going to be better for the clients. And if you 619 00:35:58,719 --> 00:36:00,680 Speaker 1: make it better for the clients, you're going to have 620 00:36:00,719 --> 00:36:04,560 Speaker 1: a better business. So you know, when it comes to business, 621 00:36:04,719 --> 00:36:07,799 Speaker 1: clients first, makes business very straightforward on how to make 622 00:36:07,840 --> 00:36:10,560 Speaker 1: decisions and what decisions to make. And I think that 623 00:36:10,800 --> 00:36:14,279 Speaker 1: at that atmosphere, I've always enjoyed a dimensional and so 624 00:36:14,360 --> 00:36:18,040 Speaker 1: therefore age has never been, never been an important ingredient. 625 00:36:18,239 --> 00:36:21,640 Speaker 1: So let me flip that question around and ask what 626 00:36:21,800 --> 00:36:25,280 Speaker 1: advantages do you find when you're working with some older, 627 00:36:25,360 --> 00:36:28,280 Speaker 1: more experienced folks. What if they bring to the table 628 00:36:28,360 --> 00:36:32,120 Speaker 1: for you. Some of the things that come, in my view, 629 00:36:32,320 --> 00:36:37,279 Speaker 1: with wisdom and wisdom comes with experience, I believe, is 630 00:36:37,400 --> 00:36:41,520 Speaker 1: how to communicate, how to message, and how to help 631 00:36:41,560 --> 00:36:44,960 Speaker 1: people understand your point of view without alienating those folks. 632 00:36:45,480 --> 00:36:48,600 Speaker 1: And I think that's something that has been very helpful 633 00:36:48,640 --> 00:36:52,840 Speaker 1: for me in working with my colleagues at Dimensional Butler. 634 00:36:53,320 --> 00:36:56,880 Speaker 1: Dave Butler is a master of that, of course, and so, Okay, 635 00:36:56,920 --> 00:36:59,640 Speaker 1: you have a great idea, but if you can communicate 636 00:36:59,680 --> 00:37:04,080 Speaker 1: that great idea and you can't help people understand why 637 00:37:04,120 --> 00:37:06,560 Speaker 1: it's a great idea, it's going to die on the vine. 638 00:37:07,160 --> 00:37:09,920 Speaker 1: You really need to have the great idea and also 639 00:37:10,160 --> 00:37:14,600 Speaker 1: have an understanding of how people receive the information. And 640 00:37:14,640 --> 00:37:17,319 Speaker 1: I think that's something that I've always tried to pay 641 00:37:17,360 --> 00:37:21,080 Speaker 1: close attention to how my colleagues do that, In the 642 00:37:21,080 --> 00:37:23,360 Speaker 1: colleagues that do it effectively, how do they do it effectively? 643 00:37:24,239 --> 00:37:27,920 Speaker 1: Because ultimately, the best ideas win, but only those ideas 644 00:37:27,920 --> 00:37:30,680 Speaker 1: that can be communicated can be considered the best ideas. 645 00:37:31,360 --> 00:37:35,640 Speaker 1: So I mentioned earlier the trillion dollar club. You mentioned 646 00:37:36,400 --> 00:37:38,640 Speaker 1: uh in an interview. I think it was the Financial 647 00:37:38,640 --> 00:37:43,120 Speaker 1: Times that you think Dimensional Funds should be a member 648 00:37:43,160 --> 00:37:46,880 Speaker 1: of that rarefied club that is managing a trillion dollars 649 00:37:46,880 --> 00:37:50,560 Speaker 1: in client assets. Tell us a little bit about how 650 00:37:50,640 --> 00:37:54,879 Speaker 1: you're going to achieve that fairly lofty goal. Yeah, we 651 00:37:54,880 --> 00:37:57,719 Speaker 1: we definitely feel that Dimensional has a lot of run 652 00:37:57,719 --> 00:38:01,080 Speaker 1: way for growth and there's a few different reasons behind that. One. 653 00:38:01,560 --> 00:38:04,919 Speaker 1: We view that many different investors and managers have come 654 00:38:04,960 --> 00:38:09,120 Speaker 1: around to our point of view that systematic strategies are very, 655 00:38:09,239 --> 00:38:13,360 Speaker 1: very beneficial for the end investor. And by systematic I 656 00:38:13,400 --> 00:38:18,640 Speaker 1: mean more rules based approaches, approaches where you can communicate 657 00:38:18,719 --> 00:38:21,560 Speaker 1: up front, here's what you can expect from this strategy, 658 00:38:21,600 --> 00:38:24,520 Speaker 1: and then validate after the fact that you got and 659 00:38:24,560 --> 00:38:26,799 Speaker 1: delivered what you said you were going to deliver. And 660 00:38:26,840 --> 00:38:31,160 Speaker 1: I think that's incredibly important for investors to build trust 661 00:38:31,160 --> 00:38:33,399 Speaker 1: and confidence in the strategies over time, and Eventual has 662 00:38:33,400 --> 00:38:36,319 Speaker 1: been doing that for forty years. So I think that's 663 00:38:36,560 --> 00:38:40,520 Speaker 1: one reason that best ideas win, and we have some 664 00:38:40,560 --> 00:38:44,080 Speaker 1: of the best ideas in my view, and therefore that 665 00:38:44,160 --> 00:38:46,840 Speaker 1: will serve clients well. And if you're serving your clients 666 00:38:46,840 --> 00:38:50,240 Speaker 1: while you'll grow. Second kind of component there is exactly 667 00:38:50,280 --> 00:38:53,319 Speaker 1: what I said, serving clients well. It's clients first. We 668 00:38:53,360 --> 00:38:55,799 Speaker 1: think that if we deliver a great client experience, the 669 00:38:55,840 --> 00:38:59,280 Speaker 1: great support for that systematic approach so clients can understand 670 00:38:59,440 --> 00:39:02,080 Speaker 1: know what to expect to be able to have conversations. 671 00:39:02,120 --> 00:39:03,719 Speaker 1: We work with financial professionals, so they have to have 672 00:39:04,000 --> 00:39:09,279 Speaker 1: conversations with their constituencies and who they're accountable to. We 673 00:39:09,360 --> 00:39:12,440 Speaker 1: think that that will also help us grow. And then 674 00:39:12,440 --> 00:39:15,160 Speaker 1: in terms of the tactics to get there, Dave and 675 00:39:15,200 --> 00:39:17,680 Speaker 1: I have really discussed this over the past number of 676 00:39:17,760 --> 00:39:20,560 Speaker 1: years and we think that our investment philosophy is very, 677 00:39:20,640 --> 00:39:24,120 Speaker 1: very powerful and I can get into that in a moment. However, 678 00:39:24,440 --> 00:39:28,320 Speaker 1: the means for delivering that investment philosophy have evolved over time, 679 00:39:29,239 --> 00:39:31,320 Speaker 1: and our view is you get to learn our investment 680 00:39:31,320 --> 00:39:33,759 Speaker 1: philosophy one time, but then choose your own adventure on 681 00:39:33,800 --> 00:39:36,880 Speaker 1: what vehicle you like to consume that under. So you 682 00:39:36,920 --> 00:39:39,759 Speaker 1: know that we've launched e t F s recently and 683 00:39:39,800 --> 00:39:41,520 Speaker 1: we've had what I would view as a lot of 684 00:39:41,560 --> 00:39:43,880 Speaker 1: success on the e t F space. Our first e 685 00:39:43,960 --> 00:39:49,600 Speaker 1: t F went live in November of and we're around 686 00:39:49,600 --> 00:39:53,200 Speaker 1: forty eight billion in e t F assets over the 687 00:39:53,200 --> 00:39:56,640 Speaker 1: course of that time period, and so I think that's 688 00:39:56,680 --> 00:39:59,080 Speaker 1: been a good outcome. So same investment philosophy is what 689 00:39:59,080 --> 00:40:01,680 Speaker 1: we've had in commingled mutual funds, but now an ETS 690 00:40:01,760 --> 00:40:04,560 Speaker 1: separately managed accounts. How do we use new technology to 691 00:40:04,560 --> 00:40:07,080 Speaker 1: take that minimum down to a half million dollars from 692 00:40:07,080 --> 00:40:08,880 Speaker 1: where we used to be twenty million dollar minimum for 693 00:40:08,880 --> 00:40:12,279 Speaker 1: our separately managed accounts, and we've built that technology, a 694 00:40:12,360 --> 00:40:15,560 Speaker 1: true fintech solution to that problem, so that we can 695 00:40:15,600 --> 00:40:18,839 Speaker 1: serve those types of clients as well. So how we'll 696 00:40:18,880 --> 00:40:21,960 Speaker 1: get there is by identifying the needs that our clients 697 00:40:21,960 --> 00:40:25,920 Speaker 1: have and keeping in mind the three c's, which is, 698 00:40:26,200 --> 00:40:28,800 Speaker 1: there's a lot of complexity in the world that requires 699 00:40:28,840 --> 00:40:32,600 Speaker 1: customization to come with good solutions, but people want it conveniently. 700 00:40:33,480 --> 00:40:37,080 Speaker 1: So can we identify the complexity, can we provide the 701 00:40:37,080 --> 00:40:39,480 Speaker 1: tools so that people can customize the right solution, and 702 00:40:39,480 --> 00:40:41,960 Speaker 1: can we do all that very conveniently for our customers? 703 00:40:42,200 --> 00:40:44,000 Speaker 1: And if we do that, I think we'll be successful. 704 00:40:44,160 --> 00:40:47,759 Speaker 1: So full disclosure, My firm is a client of Dimensional Funds. 705 00:40:47,800 --> 00:40:52,120 Speaker 1: Redults Wealth Management uses Dimensional Funds as one of our 706 00:40:52,160 --> 00:40:56,720 Speaker 1: primary asset managers along with Vanguard, Black Rock, et cetera. 707 00:40:56,880 --> 00:41:00,319 Speaker 1: But Dimensional is definitely one of our UM large JR. 708 00:41:01,120 --> 00:41:05,600 Speaker 1: Fun providers, and I'm very aware of the process that 709 00:41:05,680 --> 00:41:10,360 Speaker 1: Dimensional goes through in order to make sure that their 710 00:41:10,440 --> 00:41:15,120 Speaker 1: clients understand the philosophy. You understand the model with an 711 00:41:15,160 --> 00:41:19,919 Speaker 1: eye towards avoiding the sort of flavor of the month. Hey, 712 00:41:19,960 --> 00:41:24,359 Speaker 1: I'm chasing this hot manager. No, now I'm chasing that 713 00:41:24,760 --> 00:41:28,279 Speaker 1: hot fun family. E t f s are very much 714 00:41:28,320 --> 00:41:33,440 Speaker 1: a break from that prior um embrace of of working 715 00:41:33,640 --> 00:41:36,840 Speaker 1: very closely with clients. Tell us a little bit about 716 00:41:36,920 --> 00:41:41,200 Speaker 1: the internal discussions that must have taken place before you 717 00:41:41,239 --> 00:41:44,200 Speaker 1: switch to e t f s, which, hey, anybody could 718 00:41:44,200 --> 00:41:47,000 Speaker 1: go to their online training account or robin Hood or 719 00:41:47,040 --> 00:41:49,000 Speaker 1: whatever it is and and by the e t F 720 00:41:49,680 --> 00:41:52,680 Speaker 1: how have you managed around that? So there was two 721 00:41:52,719 --> 00:41:56,920 Speaker 1: big drivers of that decision. The first was input from clients. 722 00:41:56,920 --> 00:41:58,960 Speaker 1: And as I mentioned around, we work with financial professionals, 723 00:41:59,040 --> 00:42:03,200 Speaker 1: so we don't work the end retail consumer. We work 724 00:42:03,280 --> 00:42:07,120 Speaker 1: with financial advisors like firms like yourself, who can get 725 00:42:07,160 --> 00:42:09,920 Speaker 1: that level of understanding and knowledge and experience so they 726 00:42:09,960 --> 00:42:14,319 Speaker 1: understand what we're what we're trying to accomplish. A lot 727 00:42:14,360 --> 00:42:16,400 Speaker 1: of those firms were saying, we're using e t f 728 00:42:16,480 --> 00:42:19,520 Speaker 1: s more and more frequently on behalf of our clients, 729 00:42:20,040 --> 00:42:21,960 Speaker 1: and we'd like to be able to use dimensional ETFs. 730 00:42:23,080 --> 00:42:26,160 Speaker 1: Could you launch ETFs please? And so we took that away. 731 00:42:26,200 --> 00:42:28,799 Speaker 1: We thought a lot about it, and that was kind 732 00:42:28,800 --> 00:42:32,560 Speaker 1: of twenty eighteen time frame, and on the books with 733 00:42:32,719 --> 00:42:36,239 Speaker 1: the SEC back then was a new proposed e t 734 00:42:36,440 --> 00:42:39,960 Speaker 1: F rule, and what that rule effectively did was it 735 00:42:40,719 --> 00:42:42,600 Speaker 1: made e t f s much more straightforward to bring 736 00:42:42,680 --> 00:42:45,560 Speaker 1: to the market, much more straightforward for the end investor 737 00:42:45,640 --> 00:42:49,120 Speaker 1: to evaluate, but then also clarified some things around the 738 00:42:49,160 --> 00:42:51,320 Speaker 1: inner workings of e t f s that were important 739 00:42:51,360 --> 00:42:55,520 Speaker 1: to us because we're not an index manager. We have 740 00:42:55,719 --> 00:42:58,000 Speaker 1: a lot of the benefits of an index based approach 741 00:42:58,080 --> 00:43:02,000 Speaker 1: that include broad diversification, load and over low costs, but 742 00:43:02,160 --> 00:43:04,960 Speaker 1: we have an active implementation and so those rules got 743 00:43:05,040 --> 00:43:08,399 Speaker 1: passed in the fourth quarter of is when the SEC 744 00:43:08,480 --> 00:43:11,720 Speaker 1: adopted those rules Rule six C eleven for anybody who's 745 00:43:11,760 --> 00:43:14,680 Speaker 1: nerdy enough to want to look into them, and that 746 00:43:14,960 --> 00:43:16,560 Speaker 1: was a bit of a game changer for us. We 747 00:43:16,600 --> 00:43:18,760 Speaker 1: could do now what we had done in our mutual 748 00:43:18,840 --> 00:43:22,160 Speaker 1: funds for decades in an e t F rapper, so 749 00:43:22,280 --> 00:43:25,120 Speaker 1: there was no give up on the investment proposition. As 750 00:43:25,160 --> 00:43:28,080 Speaker 1: soon as that rule was passed, we went into full 751 00:43:28,680 --> 00:43:31,719 Speaker 1: launch mode. By June, we had announced that we were 752 00:43:31,840 --> 00:43:34,840 Speaker 1: going to launch. By November of twenty so almost a 753 00:43:34,960 --> 00:43:38,319 Speaker 1: year after the rule came out, we had launched. Those 754 00:43:38,360 --> 00:43:41,919 Speaker 1: were the two big drivers on the tax efficiency side 755 00:43:42,239 --> 00:43:45,480 Speaker 1: that wasn't as big a driver for us largely because, 756 00:43:45,520 --> 00:43:47,440 Speaker 1: and you're familiar with this, our mutual funds tend to 757 00:43:47,480 --> 00:43:51,040 Speaker 1: be highly tax efficient, and we had tax managed mutual 758 00:43:51,080 --> 00:43:54,600 Speaker 1: funds that had similar tax efficiency ratios to e t 759 00:43:54,760 --> 00:43:58,240 Speaker 1: f s, So we had very very tax efficient approach. 760 00:43:58,640 --> 00:44:00,400 Speaker 1: E t f s taken up a bit our e 761 00:44:00,480 --> 00:44:03,600 Speaker 1: T S two, but it was more what our clients 762 00:44:03,640 --> 00:44:07,120 Speaker 1: were asking for, and the rules changed such that we 763 00:44:07,200 --> 00:44:11,000 Speaker 1: could deliver a investment proposition that was on par with 764 00:44:11,080 --> 00:44:14,839 Speaker 1: our mutual fund investment proposition. And your turnover in your 765 00:44:14,920 --> 00:44:19,480 Speaker 1: various funds is relatively low compared to the average mutual 766 00:44:19,560 --> 00:44:21,880 Speaker 1: funds at a fair statement. That's a fair statement on 767 00:44:21,920 --> 00:44:24,040 Speaker 1: the equity side, for sure, on the fixed income side, 768 00:44:24,400 --> 00:44:27,280 Speaker 1: where we do things that lead to slightly higher turnover 769 00:44:27,400 --> 00:44:29,600 Speaker 1: because of the information that you can take out of 770 00:44:29,680 --> 00:44:31,920 Speaker 1: yield curves at any point in time. But on the 771 00:44:32,000 --> 00:44:35,480 Speaker 1: equity side, you know, a core strategy has ten percent turnover, 772 00:44:35,760 --> 00:44:38,799 Speaker 1: value strategy twenty turnover in a given year. And how 773 00:44:38,880 --> 00:44:40,680 Speaker 1: to think about that is like in the value strategy, 774 00:44:40,920 --> 00:44:42,800 Speaker 1: when you buy a stock, you expect to hold it 775 00:44:42,840 --> 00:44:45,800 Speaker 1: for about five years at turnover. That's how how you 776 00:44:45,840 --> 00:44:49,040 Speaker 1: can kind of translate that into holding period on fixed income? 777 00:44:49,600 --> 00:44:53,040 Speaker 1: Is it primarily duration versus credit risk that that the 778 00:44:53,160 --> 00:44:56,960 Speaker 1: activity comes from. It's a combination of duration, it's a 779 00:44:56,960 --> 00:44:59,560 Speaker 1: combination of credit, and then it's also a combination of 780 00:44:59,640 --> 00:45:02,800 Speaker 1: currency of issuance. When you think about fixed income, a 781 00:45:02,880 --> 00:45:04,440 Speaker 1: lot of people focus on the FED and what the 782 00:45:04,480 --> 00:45:07,400 Speaker 1: FED is going to do. That's one rate among hundreds 783 00:45:07,440 --> 00:45:09,880 Speaker 1: of rates out there, because there's different currency of issuance, 784 00:45:09,960 --> 00:45:13,120 Speaker 1: different durations, different credit qualities. And what we do is 785 00:45:13,160 --> 00:45:16,560 Speaker 1: we take in five six hundred different interest rates from 786 00:45:16,600 --> 00:45:19,640 Speaker 1: around the world and we use that information every day 787 00:45:19,680 --> 00:45:22,920 Speaker 1: to say, how do we increase expected returns the return 788 00:45:22,960 --> 00:45:26,719 Speaker 1: of this portfolio, but manage risk very very robustly. So 789 00:45:26,800 --> 00:45:29,759 Speaker 1: again it's has an index feel, but it goes beyond 790 00:45:29,880 --> 00:45:33,439 Speaker 1: indexing with an active implementation to add value and manage risk. 791 00:45:34,120 --> 00:45:37,399 Speaker 1: Really interesting. So let's talk a little bit about what's 792 00:45:37,440 --> 00:45:40,480 Speaker 1: going on in the market this year. Pretty tough start. 793 00:45:41,320 --> 00:45:44,120 Speaker 1: First quarter was a bit shaky. It was a little 794 00:45:44,239 --> 00:45:49,040 Speaker 1: carry over from the end of uh SO growth investors 795 00:45:49,120 --> 00:45:53,200 Speaker 1: have been doing so well for so long, hasn't hasn't 796 00:45:53,200 --> 00:45:55,600 Speaker 1: been a great couple of quarters for them. How is 797 00:45:55,719 --> 00:45:59,120 Speaker 1: the f A navigating this volatility? Yeah, you're right. It 798 00:45:59,160 --> 00:46:01,640 Speaker 1: has been a rocky started the year in absolute terms, 799 00:46:01,719 --> 00:46:03,760 Speaker 1: and when you look at the first quarter of two 800 00:46:03,800 --> 00:46:06,320 Speaker 1: a lot of the major indicries, whether that's US or 801 00:46:06,719 --> 00:46:09,280 Speaker 1: non US, developed or emerging or in the negative territory, 802 00:46:10,239 --> 00:46:13,040 Speaker 1: You're right, Value has continued on it's good run, and 803 00:46:13,200 --> 00:46:16,400 Speaker 1: value has been having almost like a two year a 804 00:46:16,560 --> 00:46:19,120 Speaker 1: good relative performance, which is more what we expect from 805 00:46:19,160 --> 00:46:21,600 Speaker 1: the world, and that continued on in the in the 806 00:46:21,719 --> 00:46:25,600 Speaker 1: in the first quarter for sure, where value stocks help 807 00:46:25,600 --> 00:46:28,239 Speaker 1: performed growth stocks by as much as ten percentage points 808 00:46:28,280 --> 00:46:32,040 Speaker 1: and lots of different regions around the world. So that's 809 00:46:32,080 --> 00:46:35,840 Speaker 1: been good for the investors in dimensional strategies because a 810 00:46:35,880 --> 00:46:38,240 Speaker 1: lot of our strategies on the equity side overweight value 811 00:46:38,280 --> 00:46:41,120 Speaker 1: stocks and stocks with high profitability and so on. In 812 00:46:41,280 --> 00:46:44,120 Speaker 1: terms of navigating the volatility. You know, when you go 813 00:46:44,200 --> 00:46:46,880 Speaker 1: back to our investment principles, there's probably three that I 814 00:46:46,880 --> 00:46:51,080 Speaker 1: would highlight. One systematic approach is a good approach for 815 00:46:51,160 --> 00:46:55,000 Speaker 1: investors with the right support, the right continued information, innovation, 816 00:46:55,120 --> 00:46:58,800 Speaker 1: and the right price point. So that's one one basic principle. 817 00:46:59,320 --> 00:47:02,560 Speaker 1: The other two are that prices are predictions of the future. 818 00:47:02,760 --> 00:47:06,160 Speaker 1: Market prices are forward looking, how do you use those 819 00:47:06,239 --> 00:47:10,120 Speaker 1: prices to manage risk and increase expected returns? And the 820 00:47:10,200 --> 00:47:12,920 Speaker 1: third is that optionality has value. We should capture on 821 00:47:13,000 --> 00:47:15,160 Speaker 1: behalf of our clients. So when you go through a 822 00:47:15,239 --> 00:47:17,880 Speaker 1: time period like what we've just been through, where you 823 00:47:18,000 --> 00:47:22,960 Speaker 1: have Russia invading Ukraine, all the sanctions that then subsequently 824 00:47:23,080 --> 00:47:27,960 Speaker 1: came on Russian companies, Russian stocks, Russian individuals, that flexibility 825 00:47:28,040 --> 00:47:30,719 Speaker 1: or optionality is critical. Because what we were able to 826 00:47:30,840 --> 00:47:34,719 Speaker 1: do was in January, when you know, there was a 827 00:47:34,760 --> 00:47:40,400 Speaker 1: lot of talk of sanctions versus various different companies and individuals, 828 00:47:41,200 --> 00:47:45,200 Speaker 1: that we were able to freeze purchases on all Russian securities, 829 00:47:45,200 --> 00:47:47,359 Speaker 1: which was an important part of our process. We said, okay, 830 00:47:47,400 --> 00:47:49,800 Speaker 1: let's take a weight and see approach. And that was 831 00:47:49,840 --> 00:47:53,319 Speaker 1: in part because if you go back to and when 832 00:47:53,800 --> 00:47:56,279 Speaker 1: the annexation of the Crimea by Russia, at that point, 833 00:47:56,520 --> 00:47:58,160 Speaker 1: we have a set of criteria that we go through 834 00:47:58,440 --> 00:48:01,920 Speaker 1: rule of law, you know, how our foreigners treated versus locals, 835 00:48:02,719 --> 00:48:05,560 Speaker 1: the local infrastructure, and we said, you know what, that 836 00:48:05,719 --> 00:48:08,759 Speaker 1: criteria for that country right now is not quite being 837 00:48:09,160 --> 00:48:11,319 Speaker 1: perfectly well met. So we reduced Russia to a half 838 00:48:11,360 --> 00:48:14,759 Speaker 1: weight in ten. So we already had that flexibility built in, 839 00:48:15,800 --> 00:48:18,200 Speaker 1: but that's very helpful when you go through time periods 840 00:48:18,280 --> 00:48:21,760 Speaker 1: like this because you have a systematic approach that's largely 841 00:48:21,840 --> 00:48:24,880 Speaker 1: rules based. But you can't come with a set of 842 00:48:25,000 --> 00:48:28,120 Speaker 1: rules that will contemplate every state of the world. So 843 00:48:28,239 --> 00:48:31,480 Speaker 1: you need to have people who have pragmatic and practical 844 00:48:31,520 --> 00:48:35,040 Speaker 1: experience to say, well, what can we actually implement in 845 00:48:35,120 --> 00:48:38,200 Speaker 1: the real world, and then how does that citizen overlay 846 00:48:38,680 --> 00:48:41,520 Speaker 1: on top of what we do. So I think that 847 00:48:41,680 --> 00:48:44,640 Speaker 1: this year that has been helpful in our strategies and 848 00:48:44,719 --> 00:48:47,600 Speaker 1: how do we stay flexible to adapt to what's going 849 00:48:47,680 --> 00:48:50,200 Speaker 1: on in the world and in markets around the world. 850 00:48:50,840 --> 00:48:52,920 Speaker 1: So so let's talk a little bit more about the 851 00:48:53,160 --> 00:48:59,480 Speaker 1: value versus growth um relative performance. Growth has really had 852 00:48:59,520 --> 00:49:03,640 Speaker 1: a great decade. The growth was beating value. That started 853 00:49:03,680 --> 00:49:06,560 Speaker 1: to change last year. What do you attribute that too? 854 00:49:07,280 --> 00:49:10,759 Speaker 1: Is an inflation the end of quantitative easing and zero 855 00:49:10,840 --> 00:49:15,160 Speaker 1: interest rate policy, or or something else. And I'm sure 856 00:49:15,239 --> 00:49:17,120 Speaker 1: the investors who are listening are going to want to 857 00:49:17,200 --> 00:49:20,239 Speaker 1: know and how long can this last? Yeah, it's a 858 00:49:20,280 --> 00:49:22,520 Speaker 1: it's a very interesting question. I'm gonna flip it around 859 00:49:22,560 --> 00:49:25,200 Speaker 1: on your barry, which is why did we have such 860 00:49:25,239 --> 00:49:29,279 Speaker 1: a long run of growth out performing value over the 861 00:49:30,440 --> 00:49:33,759 Speaker 1: because that's the unexpected outcome. Value out performing growth is 862 00:49:33,800 --> 00:49:36,920 Speaker 1: not the unexpected outcome because when you think about value stocks, 863 00:49:37,520 --> 00:49:40,480 Speaker 1: there are stocks that have lower prices and higher expected 864 00:49:40,520 --> 00:49:43,839 Speaker 1: cash flows. So by definition, investors have applied a higher 865 00:49:43,880 --> 00:49:46,719 Speaker 1: discount rate to them, and that's every day, and so 866 00:49:46,840 --> 00:49:49,840 Speaker 1: you expect them to outperform growth stocks. When growth outperforms, 867 00:49:49,880 --> 00:49:53,840 Speaker 1: that's the unexpected outcome, and that happens plenty, because returns 868 00:49:53,880 --> 00:49:57,239 Speaker 1: over the short pull are driven by the unexpected things 869 00:49:57,320 --> 00:49:59,640 Speaker 1: that happened, not they expected. When you look over the 870 00:49:59,680 --> 00:50:04,480 Speaker 1: past decade, there was probably unexpectedly good outcomes for the 871 00:50:04,600 --> 00:50:07,120 Speaker 1: facebooks and the Amazons and the Netflix. If you go back, 872 00:50:07,560 --> 00:50:10,560 Speaker 1: you know, fifteen years and say do you expect this 873 00:50:10,680 --> 00:50:13,520 Speaker 1: group of fang stocks or whoever to have an annulyzed 874 00:50:13,560 --> 00:50:15,680 Speaker 1: compound rate of return of thirty percent a year for 875 00:50:15,719 --> 00:50:18,000 Speaker 1: the next decade. Not many people would have said yes. 876 00:50:18,520 --> 00:50:21,520 Speaker 1: But they did very very well. They improved their earnings 877 00:50:21,560 --> 00:50:25,200 Speaker 1: profile quite dramatically over that period and were rewarded when 878 00:50:25,239 --> 00:50:29,440 Speaker 1: you go then into the later time period. Um, you know, 879 00:50:29,920 --> 00:50:33,360 Speaker 1: those value stocks in particular in the US, when you 880 00:50:33,400 --> 00:50:35,759 Speaker 1: look at the price to earnings or price to book 881 00:50:37,480 --> 00:50:41,120 Speaker 1: ratios of value stocks for versus growth those ratios and 882 00:50:41,200 --> 00:50:46,000 Speaker 1: those differences had grown dramatically large. So growth had become higher, higher, higher, 883 00:50:46,120 --> 00:50:49,520 Speaker 1: higher in terms of their valuations, whereas value had stayed 884 00:50:49,600 --> 00:50:52,040 Speaker 1: kind of right around where it was because value had 885 00:50:52,080 --> 00:50:54,480 Speaker 1: come in kind of like it's long term average, but 886 00:50:54,600 --> 00:50:56,319 Speaker 1: growth had come in well ahead of its long term 887 00:50:56,360 --> 00:51:00,200 Speaker 1: average in terms of returns, and so value was still 888 00:51:00,200 --> 00:51:02,960 Speaker 1: in the same position to deliver those good returns going forward, 889 00:51:03,239 --> 00:51:05,680 Speaker 1: whereas the expected returns on growth stocks had probably dropped 890 00:51:06,280 --> 00:51:09,960 Speaker 1: given those higher valuations. So so let me phrase my 891 00:51:10,160 --> 00:51:13,440 Speaker 1: hindsight bias and in the form of a question, which is, 892 00:51:14,680 --> 00:51:20,160 Speaker 1: isn't it obvious today that post financial crisis the financials 893 00:51:20,239 --> 00:51:22,560 Speaker 1: would lag for quite a while, and there they tend 894 00:51:22,640 --> 00:51:25,440 Speaker 1: to be big value stocks. And then when we look 895 00:51:25,480 --> 00:51:29,839 Speaker 1: at the growth side, Hey, this was a societal transformation, 896 00:51:29,920 --> 00:51:37,120 Speaker 1: a generational shift, uh, towards mobile, towards internet, towards technology. Again, 897 00:51:37,280 --> 00:51:40,279 Speaker 1: with the benefit of hindsight, how did we not see? 898 00:51:40,360 --> 00:51:43,200 Speaker 1: Why was this a surprise? It's perfectly obvious after the 899 00:51:43,280 --> 00:51:47,840 Speaker 1: fact that this massive change was taking place. It's obvious 900 00:51:47,880 --> 00:51:50,040 Speaker 1: after the fact that in the middle of it. You 901 00:51:50,160 --> 00:51:52,720 Speaker 1: never know exactly what's going to happen because there's always 902 00:51:52,760 --> 00:51:55,680 Speaker 1: new technologies. People often talk about the new normal, and 903 00:51:55,760 --> 00:51:58,840 Speaker 1: there is no new normal because technologies have been developed 904 00:51:59,200 --> 00:52:03,520 Speaker 1: persistently a decade by decade for the past hundred years, 905 00:52:04,120 --> 00:52:07,319 Speaker 1: and those technologies give rise to uncertainty about who will 906 00:52:07,400 --> 00:52:10,000 Speaker 1: adapt and use them in the best manner, and who 907 00:52:10,040 --> 00:52:11,720 Speaker 1: will be the winners and who will be the users 908 00:52:12,160 --> 00:52:14,880 Speaker 1: once that new technology comes into place. So there's always 909 00:52:14,920 --> 00:52:17,120 Speaker 1: a massive amount of uncertain It existed a decade ago 910 00:52:17,160 --> 00:52:20,200 Speaker 1: and exists today. And what we look to markets to 911 00:52:20,239 --> 00:52:23,880 Speaker 1: do is process that information to say, given that uncertainty, 912 00:52:24,000 --> 00:52:25,680 Speaker 1: who am I going to demand the higher return to 913 00:52:25,800 --> 00:52:28,120 Speaker 1: hold or a lower returned to hold? So I think 914 00:52:28,160 --> 00:52:31,600 Speaker 1: that's the state of the world. And but even things 915 00:52:31,680 --> 00:52:34,080 Speaker 1: by you know, like who's going to predict that COVID 916 00:52:34,120 --> 00:52:37,000 Speaker 1: would come along and be such a boon to the 917 00:52:37,080 --> 00:52:40,080 Speaker 1: Amazons and the netflix of the world because everybody who 918 00:52:40,160 --> 00:52:42,600 Speaker 1: was locked in their house for some period of time 919 00:52:42,800 --> 00:52:46,560 Speaker 1: that is unexpected. That's an unexpectedly good outcome, not for 920 00:52:46,680 --> 00:52:51,560 Speaker 1: society but for the firms that were well positioned to 921 00:52:51,800 --> 00:52:56,040 Speaker 1: meet the needs of society. When that unexpected event began 922 00:52:56,160 --> 00:53:00,960 Speaker 1: to unfold. So so let's talk about another surprise in return, 923 00:53:01,160 --> 00:53:05,520 Speaker 1: which has been since the financial crisis. The US has 924 00:53:05,640 --> 00:53:10,640 Speaker 1: just trounced international returns for far longer than I think 925 00:53:10,800 --> 00:53:15,200 Speaker 1: even the most ardent US investor expected. How do we 926 00:53:15,360 --> 00:53:19,880 Speaker 1: explain the dominance of US equities versus either developed x 927 00:53:20,080 --> 00:53:23,120 Speaker 1: US or emerging markets. And there, I'd point to you 928 00:53:23,360 --> 00:53:27,680 Speaker 1: to the last decade, which was the previous decade, where 929 00:53:28,480 --> 00:53:31,920 Speaker 1: you know, small cap stocks, non U S stocks, emerging 930 00:53:31,960 --> 00:53:36,640 Speaker 1: market stocks greatly outward outpaced US large cap stocks. And 931 00:53:36,800 --> 00:53:39,040 Speaker 1: then in the decade that you're referring to, it flipped 932 00:53:39,440 --> 00:53:43,320 Speaker 1: completely and US large cap stocks outpaced everybody else, in 933 00:53:43,400 --> 00:53:46,560 Speaker 1: particular US large cap growth stocks. Again, i'd put that 934 00:53:46,680 --> 00:53:49,759 Speaker 1: down there's an unexpected component to that, and I'd put 935 00:53:49,840 --> 00:53:51,719 Speaker 1: it down to the success of some of those U 936 00:53:51,920 --> 00:53:54,360 Speaker 1: S firms that are now the largest firms in the 937 00:53:54,520 --> 00:53:57,279 Speaker 1: US marketplace. That doesn't mean they will continue to be 938 00:53:57,360 --> 00:53:59,600 Speaker 1: the largest firms in the US marketplace, because what we've 939 00:53:59,760 --> 00:54:02,879 Speaker 1: seen over time, the largest firms tend to get there 940 00:54:02,920 --> 00:54:07,239 Speaker 1: by outperforming everybody else. And in the global marketplace. Now 941 00:54:07,880 --> 00:54:10,799 Speaker 1: the US has many of those largest firms and then 942 00:54:10,840 --> 00:54:13,120 Speaker 1: in the you know, one to five years after they 943 00:54:13,160 --> 00:54:14,759 Speaker 1: become the largest firms in the world, they tend to 944 00:54:14,840 --> 00:54:19,359 Speaker 1: underperform everybody else's other firms innovate and try to take 945 00:54:19,440 --> 00:54:23,280 Speaker 1: that top spot. So there it's just you know, success 946 00:54:23,520 --> 00:54:30,200 Speaker 1: of those companies, and that's driven the investor demand for 947 00:54:30,239 --> 00:54:32,399 Speaker 1: those companies because they've been able to satisfy so much 948 00:54:32,480 --> 00:54:37,399 Speaker 1: client demand. Those are well run companies, and investors see 949 00:54:37,680 --> 00:54:39,960 Speaker 1: high cash flows from those companies and they're willing to 950 00:54:40,000 --> 00:54:42,680 Speaker 1: build up the prices. So so let's talk about a 951 00:54:42,719 --> 00:54:46,200 Speaker 1: couple of things that are in the midst of changing 952 00:54:46,800 --> 00:54:50,400 Speaker 1: and what you guys are doing about it. And I 953 00:54:50,480 --> 00:54:53,120 Speaker 1: guess I have to start with volatility. We saw a 954 00:54:53,200 --> 00:54:56,840 Speaker 1: giant spike in O eight or nine during the financial crisis, 955 00:54:57,400 --> 00:55:01,800 Speaker 1: another big spike in during the pain endemic, and the 956 00:55:02,239 --> 00:55:06,880 Speaker 1: VIX the measure of volatility was high thirties, and just 957 00:55:07,239 --> 00:55:09,680 Speaker 1: just a month or so ago that seems to be 958 00:55:09,880 --> 00:55:13,840 Speaker 1: rolling over and coming back down. First, what have we 959 00:55:14,000 --> 00:55:17,719 Speaker 1: learned about volatility and how can investors use it to 960 00:55:17,760 --> 00:55:21,880 Speaker 1: their advantage? And second, what do you think this softening 961 00:55:21,960 --> 00:55:25,600 Speaker 1: of volatility today might imply for the rest at least 962 00:55:25,640 --> 00:55:28,480 Speaker 1: of this calendar year. So what we've learned over time 963 00:55:28,520 --> 00:55:33,840 Speaker 1: about volatility is that when there's a market crisis, and 964 00:55:33,960 --> 00:55:37,600 Speaker 1: this goes without saying volatility increases. Why because uncertainty increases. 965 00:55:38,160 --> 00:55:40,360 Speaker 1: There's a lot more uncertainty about what the range of 966 00:55:40,400 --> 00:55:43,799 Speaker 1: outcomes maybe, and that uncertainty leads to a few different things. 967 00:55:44,360 --> 00:55:47,680 Speaker 1: Increases in the volume of stocks and bonds that are traded, 968 00:55:48,440 --> 00:55:51,400 Speaker 1: increases in bid off or spread, so the cost to 969 00:55:51,560 --> 00:55:56,320 Speaker 1: trade those stocks and bonds, increases in volatility. All of 970 00:55:56,360 --> 00:55:59,960 Speaker 1: those things come in a crisis. We had a crisis 971 00:56:00,040 --> 00:56:05,360 Speaker 1: in March of when Russia invaded Ukraine. We had another crisis, 972 00:56:05,800 --> 00:56:08,720 Speaker 1: how would that translate into global markets? And volatility tends 973 00:56:08,760 --> 00:56:12,200 Speaker 1: to spike. But we've also learned over time is that 974 00:56:12,600 --> 00:56:16,440 Speaker 1: spikes and volatility are unpredictable. So it's a shock, it's 975 00:56:16,520 --> 00:56:20,799 Speaker 1: unexpected for a reason because it's unpredictable. And then once 976 00:56:20,880 --> 00:56:24,360 Speaker 1: it's spikes, it tends to decay slowly unless there's another 977 00:56:24,440 --> 00:56:26,759 Speaker 1: big shock that comes along to spike it back up. 978 00:56:27,320 --> 00:56:29,840 Speaker 1: So it tends to decay over the over course of 979 00:56:29,880 --> 00:56:32,600 Speaker 1: three to six months, goes back down to normal levels, 980 00:56:32,760 --> 00:56:35,680 Speaker 1: and you can actually see that from market prices there's 981 00:56:35,920 --> 00:56:38,680 Speaker 1: different market prices that tell you about the implied volatility 982 00:56:38,760 --> 00:56:41,480 Speaker 1: of markets over the next thirty days, over the next 983 00:56:41,600 --> 00:56:43,719 Speaker 1: thirty days following that, the thirty days following that, and 984 00:56:43,760 --> 00:56:45,879 Speaker 1: so on the forth. And what you see from market 985 00:56:45,920 --> 00:56:48,480 Speaker 1: prices is that when you get a big spike, it 986 00:56:49,600 --> 00:56:53,040 Speaker 1: from market prices is expected to decline over you know, 987 00:56:53,160 --> 00:56:55,719 Speaker 1: the next subsequent months. And we saw that clearly in March. 988 00:56:56,719 --> 00:57:00,600 Speaker 1: Volatility spiked, but the markets told you that it expects 989 00:57:00,640 --> 00:57:03,600 Speaker 1: the decline over the next a few months. It's the 990 00:57:03,640 --> 00:57:05,839 Speaker 1: same with inflation. Right now, you can look at break 991 00:57:05,880 --> 00:57:08,880 Speaker 1: even inflation and it's expected to be about six percent 992 00:57:09,360 --> 00:57:13,320 Speaker 1: as of the end of Q two, But if you 993 00:57:13,360 --> 00:57:15,839 Speaker 1: look at over five years, it's expected to be six 994 00:57:15,880 --> 00:57:18,560 Speaker 1: percent over the next twelve months and then declined to 995 00:57:18,640 --> 00:57:21,440 Speaker 1: something sub three in the subsequent four years. Right, So 996 00:57:21,560 --> 00:57:24,400 Speaker 1: markets always tell you something about what's expected right now 997 00:57:24,920 --> 00:57:27,400 Speaker 1: and what's expected in the future. So since you brought 998 00:57:27,440 --> 00:57:30,480 Speaker 1: up inflation, let's talk a little bit about that. Um, 999 00:57:30,880 --> 00:57:34,920 Speaker 1: what is the f A doing in preparation for higher 1000 00:57:35,000 --> 00:57:38,600 Speaker 1: interest rates? If the Fed keeps raising rates and if 1001 00:57:38,800 --> 00:57:42,880 Speaker 1: bond investors keep selling short duration holdings, how are you 1002 00:57:42,920 --> 00:57:44,720 Speaker 1: going to adjust to that, what do you think about 1003 00:57:44,800 --> 00:57:49,040 Speaker 1: things like high grade corporates and tips versus high yield 1004 00:57:49,240 --> 00:57:53,120 Speaker 1: and and risk of your bonds, your inflation and interest rates. 1005 00:57:53,160 --> 00:57:55,800 Speaker 1: Inflation has been high. Everybody knows that over the past while. 1006 00:57:56,960 --> 00:57:59,240 Speaker 1: And the way that we view inflation is there's two 1007 00:57:59,320 --> 00:58:02,040 Speaker 1: things that you can do the markets. You can look 1008 00:58:02,080 --> 00:58:04,840 Speaker 1: at get understanding of what the market expects. But the 1009 00:58:05,000 --> 00:58:09,120 Speaker 1: unexpected often happens. Nobody can predict the unexpected, so therefore 1010 00:58:09,160 --> 00:58:11,560 Speaker 1: you can but you can plan for the unexpected, and 1011 00:58:11,640 --> 00:58:14,120 Speaker 1: you can plan to outpace it or to hedge it. 1012 00:58:14,560 --> 00:58:17,480 Speaker 1: And so if you want to outpace things like what 1013 00:58:17,640 --> 00:58:22,240 Speaker 1: you mentioned, corporate bonds, globally diversified bond strategies, equities and 1014 00:58:22,320 --> 00:58:25,440 Speaker 1: so on over time have had positive real returns, so 1015 00:58:25,600 --> 00:58:29,560 Speaker 1: returns in excessive inflation, in high inflationary environments and low 1016 00:58:29,600 --> 00:58:31,760 Speaker 1: inflationary environments. And if you look back the thirty past 1017 00:58:31,760 --> 00:58:34,320 Speaker 1: thirty four years, you see that if you want to 1018 00:58:34,520 --> 00:58:37,680 Speaker 1: hedge it, you can use treasury inflation protective bonds, and 1019 00:58:37,720 --> 00:58:39,840 Speaker 1: we think that they're a good solution. You can also 1020 00:58:39,920 --> 00:58:41,160 Speaker 1: then if you don't want to give up so much 1021 00:58:41,200 --> 00:58:45,200 Speaker 1: expected return by corporates, are bonds like that and then 1022 00:58:45,280 --> 00:58:47,880 Speaker 1: hedge it with different types of instruments like inflation swaps 1023 00:58:47,920 --> 00:58:50,600 Speaker 1: and so on that can hedge out your inflation exposure. 1024 00:58:50,640 --> 00:58:53,960 Speaker 1: They're the two ways to deal with inflation in our view. 1025 00:58:54,000 --> 00:58:55,960 Speaker 1: You can plan for it. You can't predict when you're 1026 00:58:55,960 --> 00:58:58,000 Speaker 1: getting the spike, but you can plan for it. When 1027 00:58:58,080 --> 00:59:00,600 Speaker 1: it comes to interest rates and increasing interest rates, again, 1028 00:59:01,000 --> 00:59:04,600 Speaker 1: you can't predict when they're going to shoot up. That's 1029 00:59:04,720 --> 00:59:06,400 Speaker 1: not a something that you can predict, but you can 1030 00:59:06,480 --> 00:59:09,040 Speaker 1: plan for it. How do you plan for it? Well, 1031 00:59:09,200 --> 00:59:11,320 Speaker 1: we mentioned earlier on that there's an obsession over the 1032 00:59:11,400 --> 00:59:13,800 Speaker 1: Fed Funds rate. But if you look over the past 1033 00:59:13,840 --> 00:59:17,600 Speaker 1: thirty years, thirty to forty years, the Fed has increased 1034 00:59:17,600 --> 00:59:20,680 Speaker 1: the Fed Funds rate one month out of six, has 1035 00:59:20,760 --> 00:59:23,240 Speaker 1: decreased the Fed Funds rate one month out of six, 1036 00:59:23,520 --> 00:59:26,120 Speaker 1: and has left it flat in the other four months 1037 00:59:26,160 --> 00:59:27,920 Speaker 1: out of six. That's been about the pattern over the 1038 00:59:28,000 --> 00:59:31,240 Speaker 1: past forty years. And when you look at the months 1039 00:59:31,280 --> 00:59:33,959 Speaker 1: in which has increased the Fed funds rate, about half 1040 00:59:34,040 --> 00:59:36,120 Speaker 1: the time the third year rate has gone up and 1041 00:59:36,200 --> 00:59:38,200 Speaker 1: about half the time the third year rate has gone down. 1042 00:59:38,320 --> 00:59:40,880 Speaker 1: So what does that tell you? It tells you that 1043 00:59:41,280 --> 00:59:43,960 Speaker 1: other rates out there other interest rates don't move in 1044 00:59:44,040 --> 00:59:47,480 Speaker 1: lockstep with what the FED is doing. So if you 1045 00:59:47,560 --> 00:59:50,520 Speaker 1: think about that and you extrapolate, you have interest rates 1046 00:59:50,560 --> 00:59:52,720 Speaker 1: on the short end, the intermediate end, the long end. 1047 00:59:53,200 --> 00:59:55,520 Speaker 1: You have interest rates as they applied to corporate bonds 1048 00:59:56,000 --> 00:59:58,040 Speaker 1: from triple A s down the double B s. You 1049 00:59:58,080 --> 01:00:00,920 Speaker 1: have interest rates from a current from bonds issued in 1050 01:00:01,000 --> 01:00:04,480 Speaker 1: euros and British pounds in Assie dollars and so and 1051 01:00:04,560 --> 01:00:06,600 Speaker 1: so forth, and none of them move in lockstep with 1052 01:00:06,680 --> 01:00:10,000 Speaker 1: this FED. So you can diversify. That's how you plan. 1053 01:00:10,480 --> 01:00:12,200 Speaker 1: The FED may do what it's going to do, but 1054 01:00:12,280 --> 01:00:15,640 Speaker 1: it's one interest rate among money, and that's going all 1055 01:00:15,720 --> 01:00:17,440 Speaker 1: of those other interest rates. You wanted to drive the 1056 01:00:17,600 --> 01:00:19,600 Speaker 1: returns of your probably diversity by portfolio because if you 1057 01:00:19,600 --> 01:00:21,880 Speaker 1: look from oh eight on the subsequent ten years, the 1058 01:00:21,920 --> 01:00:24,160 Speaker 1: FED funds rate was basically at zero for a decade, 1059 01:00:24,680 --> 01:00:28,520 Speaker 1: but it globally diversified portfolio stocks and bonds returned about four. 1060 01:00:29,760 --> 01:00:32,840 Speaker 1: So in a zero FED funds rate, you've got about 1061 01:00:32,880 --> 01:00:36,640 Speaker 1: a four return. So again it goes back to you 1062 01:00:36,720 --> 01:00:39,240 Speaker 1: don't have to be able to predict the unexpected. You 1063 01:00:39,400 --> 01:00:41,200 Speaker 1: just have to be able to plan for it and 1064 01:00:41,280 --> 01:00:45,200 Speaker 1: then stick with that plan, regardless of what the unexpected 1065 01:00:45,280 --> 01:00:48,560 Speaker 1: brings brings the past. So let's talk a little bit 1066 01:00:48,600 --> 01:00:53,160 Speaker 1: about your career. Uh, pretty much, since you've been in 1067 01:00:53,200 --> 01:00:56,920 Speaker 1: the world of finance, we've only seen low rates and 1068 01:00:57,000 --> 01:01:02,320 Speaker 1: we've only seen mostly low inflation. Does that impact you're thinking, 1069 01:01:02,400 --> 01:01:06,680 Speaker 1: there's a color your perspective having lived um as of 1070 01:01:06,800 --> 01:01:12,040 Speaker 1: financial professional in this somewhat aberrational environment, or are you 1071 01:01:12,200 --> 01:01:15,560 Speaker 1: looking at the academic research and able to pull yourself 1072 01:01:15,640 --> 01:01:18,760 Speaker 1: out of it. So I would say it's a little 1073 01:01:18,760 --> 01:01:21,080 Speaker 1: bit of yes, a little bit of no. Um in 1074 01:01:21,560 --> 01:01:25,520 Speaker 1: the yes category is that certainly, after the financial crisis, 1075 01:01:25,600 --> 01:01:28,919 Speaker 1: the global financial crisis, there were a lot of client 1076 01:01:29,080 --> 01:01:32,160 Speaker 1: questions about the role of fixed income in a portfolio 1077 01:01:33,200 --> 01:01:36,160 Speaker 1: because if you're used to headier times when interest rates 1078 01:01:36,280 --> 01:01:39,520 Speaker 1: were higher, you might have a different perspective on how 1079 01:01:39,560 --> 01:01:42,280 Speaker 1: to use that strategy than when you know interest rates 1080 01:01:42,320 --> 01:01:45,640 Speaker 1: are low. And so that has informed Okay, what are 1081 01:01:45,680 --> 01:01:49,000 Speaker 1: the things that our clients are caring about and what 1082 01:01:49,240 --> 01:01:52,040 Speaker 1: is it that we need to deliver to clients given 1083 01:01:52,080 --> 01:01:55,640 Speaker 1: that those are the concerns and these are the problems 1084 01:01:55,680 --> 01:01:57,840 Speaker 1: that they're trying to solve in a low interest rate environment. 1085 01:01:57,840 --> 01:01:59,760 Speaker 1: So that's a little bit of yes because it's been 1086 01:01:59,800 --> 01:02:02,640 Speaker 1: on client's minds. The little bit of no is that 1087 01:02:03,280 --> 01:02:07,080 Speaker 1: we've had We have decades upon decades, fifty sixty years 1088 01:02:07,760 --> 01:02:12,880 Speaker 1: and longer of data on the returns of bonds, both 1089 01:02:12,960 --> 01:02:16,600 Speaker 1: here in the US of corporates and of other bonds 1090 01:02:16,640 --> 01:02:19,400 Speaker 1: around the world issued in different currencies, and so we 1091 01:02:19,440 --> 01:02:22,200 Speaker 1: can look at lots of different high interest rate low 1092 01:02:22,280 --> 01:02:26,960 Speaker 1: interest rate environments, transitions between those when the interest rates 1093 01:02:27,000 --> 01:02:29,720 Speaker 1: were had gone up or gone down, and so we 1094 01:02:29,800 --> 01:02:32,720 Speaker 1: can understand are there certain strategies that work better or 1095 01:02:32,720 --> 01:02:34,960 Speaker 1: worse than each of those environments, and we can then 1096 01:02:35,000 --> 01:02:37,520 Speaker 1: we can design strategies that work well for both environments. 1097 01:02:37,560 --> 01:02:40,560 Speaker 1: So that long term view is something that we always 1098 01:02:40,640 --> 01:02:42,680 Speaker 1: keep in mind, which means that you know something that 1099 01:02:42,680 --> 01:02:45,760 Speaker 1: happens over a decade or fifteen years. It does give 1100 01:02:45,840 --> 01:02:49,840 Speaker 1: us new information, but doesn't necessarily change dramatically our investment priors. 1101 01:02:50,880 --> 01:02:54,720 Speaker 1: Really really interesting. Before I get to my favorite questions, 1102 01:02:54,800 --> 01:02:57,760 Speaker 1: I just have to throw a curveball at you. So, 1103 01:02:58,320 --> 01:03:05,320 Speaker 1: in your bachelor's in theoretical physics from Trinity College, what 1104 01:03:05,440 --> 01:03:08,400 Speaker 1: were you studying in theoretical physics? What areas did you 1105 01:03:08,680 --> 01:03:12,959 Speaker 1: concentrate in because I'm familiar with that space and find 1106 01:03:13,040 --> 01:03:16,560 Speaker 1: it absolutely fascinating. Yeah, it is really a very very 1107 01:03:16,640 --> 01:03:20,000 Speaker 1: interesting space. And you know, when I have was a 1108 01:03:20,560 --> 01:03:23,040 Speaker 1: it was a kid, I like to read Stephen Hawkings 1109 01:03:23,200 --> 01:03:27,240 Speaker 1: and those types of books. So I was very interested 1110 01:03:27,680 --> 01:03:31,960 Speaker 1: in relativity and so kind of that that side of 1111 01:03:32,120 --> 01:03:35,560 Speaker 1: what Einstein worked on, and I found that very interesting. 1112 01:03:35,600 --> 01:03:38,640 Speaker 1: We had a lot of we have courses on relativity 1113 01:03:38,720 --> 01:03:43,240 Speaker 1: when we were in in university in theoretical physics. The 1114 01:03:43,320 --> 01:03:46,360 Speaker 1: other side is quantum mechanics. And quantum mechanics is very 1115 01:03:46,480 --> 01:03:50,120 Speaker 1: very interesting because you never know anything with certainty, so 1116 01:03:50,280 --> 01:03:53,280 Speaker 1: it kind of has parallels to to the real world. 1117 01:03:53,680 --> 01:03:57,080 Speaker 1: You can't know something's position and its speed at the 1118 01:03:57,160 --> 01:04:01,360 Speaker 1: same time, you can only know one perfectly, or you 1119 01:04:01,400 --> 01:04:04,800 Speaker 1: can know both in a with a lot of uncertainty. 1120 01:04:05,600 --> 01:04:09,880 Speaker 1: But quantum mechanics is also incredibly interesting because everything has 1121 01:04:10,000 --> 01:04:12,160 Speaker 1: multiple states of the world is and as in those 1122 01:04:12,240 --> 01:04:15,400 Speaker 1: multiple states all the time with some set of probability. 1123 01:04:15,520 --> 01:04:19,480 Speaker 1: So that's also a very fascinating field of study. And 1124 01:04:19,520 --> 01:04:22,360 Speaker 1: I enjoyed those quite a lot when when I was 1125 01:04:22,520 --> 01:04:25,840 Speaker 1: working on them back in Trinity College in Dublin. So 1126 01:04:26,160 --> 01:04:29,800 Speaker 1: so if you're a fan of um Hawk Professor Hawkings 1127 01:04:30,040 --> 01:04:33,160 Speaker 1: and some of his work. Can we all admit that 1128 01:04:33,560 --> 01:04:36,160 Speaker 1: dark matter and dark energy is a cheat and we 1129 01:04:36,280 --> 01:04:39,680 Speaker 1: really have no idea what's going on with the expansion 1130 01:04:39,680 --> 01:04:43,440 Speaker 1: in the universe, because every explanation I've heard from various 1131 01:04:43,520 --> 01:04:47,080 Speaker 1: theoretical physicists have been, well, we're not sure, but we've 1132 01:04:47,240 --> 01:04:49,880 Speaker 1: made up this thing that we hope to figure out 1133 01:04:50,000 --> 01:04:53,480 Speaker 1: one day. It seems like it seems like it's um 1134 01:04:54,320 --> 01:04:58,080 Speaker 1: you know, a short cut. You know it may be 1135 01:04:58,200 --> 01:05:01,200 Speaker 1: a short cut. But I'd go back to your earlier 1136 01:05:01,280 --> 01:05:06,320 Speaker 1: statement was, which is around how our models evolve over time, 1137 01:05:06,680 --> 01:05:09,040 Speaker 1: our data evolves over time. Like you saw from a 1138 01:05:09,120 --> 01:05:11,880 Speaker 1: couple of weeks ago there was a new discovery from 1139 01:05:12,360 --> 01:05:16,280 Speaker 1: the Hubble Telescope of the oldest star yet which is 1140 01:05:16,400 --> 01:05:20,360 Speaker 1: older than the universe, which is which seems to be 1141 01:05:20,400 --> 01:05:24,120 Speaker 1: a little confused, a little confusing, and so new data 1142 01:05:24,200 --> 01:05:26,760 Speaker 1: emergence all the time, and then you create models to 1143 01:05:26,800 --> 01:05:29,680 Speaker 1: try and understand those data, but you know it's not 1144 01:05:29,760 --> 01:05:32,680 Speaker 1: what understood yet are not I would say it's what 1145 01:05:32,800 --> 01:05:36,600 Speaker 1: understood not completely understood, and there's a lot left that's 1146 01:05:37,560 --> 01:05:40,960 Speaker 1: not known yet for people to Discover fair enough. So 1147 01:05:41,200 --> 01:05:44,280 Speaker 1: so let's jump to a little less heavy material and 1148 01:05:44,480 --> 01:05:48,800 Speaker 1: talk about our favorite questions, starting with tell us, what 1149 01:05:48,920 --> 01:05:51,760 Speaker 1: you've been streaming during the past couple of years of 1150 01:05:52,320 --> 01:05:57,760 Speaker 1: Lockdown and Pandemic, either podcast or Amazon and Netflix. What's 1151 01:05:57,760 --> 01:06:01,320 Speaker 1: been keeping you entertained? Yeah, couple of different shows have 1152 01:06:01,480 --> 01:06:03,680 Speaker 1: been keeping me entertained. So it was in a board meeting, 1153 01:06:04,200 --> 01:06:06,040 Speaker 1: one of the Advisor board meetings, and one of the 1154 01:06:06,080 --> 01:06:08,920 Speaker 1: board members mc McCown said that he had been watching 1155 01:06:09,120 --> 01:06:11,640 Speaker 1: a documentary series called The Prize, and The Prize is 1156 01:06:11,720 --> 01:06:14,320 Speaker 1: from a while ago. It's it's about the kind of 1157 01:06:14,360 --> 01:06:17,880 Speaker 1: the history of oil and you know how it started 1158 01:06:18,000 --> 01:06:21,040 Speaker 1: and where it evolved two and all the various different 1159 01:06:21,080 --> 01:06:23,760 Speaker 1: issues that have arisen as a result. So that was 1160 01:06:23,880 --> 01:06:28,000 Speaker 1: super interesting and I'd recommend that to anybody who's kind 1161 01:06:28,000 --> 01:06:33,240 Speaker 1: of interested in those types of historical shows. Other things 1162 01:06:33,800 --> 01:06:37,000 Speaker 1: that I find interesting over the past few years, I've 1163 01:06:37,120 --> 01:06:40,320 Speaker 1: watched a lot of documentaries about you know, World War two, 1164 01:06:40,360 --> 01:06:44,280 Speaker 1: World War One, Vietnam War. Ken Burns has some great 1165 01:06:44,320 --> 01:06:47,560 Speaker 1: stuff even on the US Civil War that have been 1166 01:06:47,760 --> 01:06:51,280 Speaker 1: very interesting. The Fog of War that that was another 1167 01:06:51,400 --> 01:06:56,520 Speaker 1: interesting show. I find those particularly interesting, just how do 1168 01:06:56,600 --> 01:06:59,040 Speaker 1: you ever get there? Because war is in a rational act, 1169 01:06:59,400 --> 01:07:02,280 Speaker 1: so what what are the things that have to happen 1170 01:07:02,360 --> 01:07:04,560 Speaker 1: in order to get there? Because it's much more rational 1171 01:07:04,760 --> 01:07:07,240 Speaker 1: to cooperate and to trade than it is to go 1172 01:07:07,360 --> 01:07:09,480 Speaker 1: to Everybody will be better off in the former and 1173 01:07:09,600 --> 01:07:11,520 Speaker 1: worse off in the latter, So how do you actually 1174 01:07:11,640 --> 01:07:14,560 Speaker 1: get to that state of the world? Is interesting. I 1175 01:07:14,640 --> 01:07:17,040 Speaker 1: have a six year old daughter and so we watch 1176 01:07:17,120 --> 01:07:21,320 Speaker 1: shows together and that also keeps me entertained. She loves 1177 01:07:22,080 --> 01:07:23,720 Speaker 1: If I were an Animal. I don't know if you've 1178 01:07:23,720 --> 01:07:27,480 Speaker 1: seen that show on Netflix, but that's a goody. And 1179 01:07:27,680 --> 01:07:30,000 Speaker 1: then another one that came out recently a Netflix is 1180 01:07:30,080 --> 01:07:31,560 Speaker 1: Old Enough. I don't know if you've seen this as 1181 01:07:31,600 --> 01:07:35,320 Speaker 1: a Japanese show, and they have like little three year olds, 1182 01:07:35,360 --> 01:07:37,800 Speaker 1: four year olds, five year olds, and their parents give 1183 01:07:37,800 --> 01:07:39,800 Speaker 1: them a task to do and then they have to 1184 01:07:39,880 --> 01:07:41,840 Speaker 1: go off into the round town into the shop and 1185 01:07:41,840 --> 01:07:44,520 Speaker 1: they're followed by a camera coup by themselves, and they 1186 01:07:44,720 --> 01:07:48,040 Speaker 1: accomplished this task. It's hilarious. It's it's really really fun 1187 01:07:48,120 --> 01:07:51,800 Speaker 1: to old Enough to check. That's a fun one. Let's 1188 01:07:51,840 --> 01:07:54,520 Speaker 1: talk about some of your mentors, who were some of 1189 01:07:54,600 --> 01:07:57,680 Speaker 1: the folks who helped shape your career. Yeah, I would 1190 01:07:57,720 --> 01:08:00,640 Speaker 1: say that in terms of folks that have my career. 1191 01:08:01,320 --> 01:08:05,080 Speaker 1: Some of the names that you mentioned, whether it's a Fama, French, Martin, 1192 01:08:05,160 --> 01:08:09,440 Speaker 1: have all been very helpful to me over time. David 1193 01:08:09,680 --> 01:08:12,720 Speaker 1: of course, has been very very helpful to me over time. 1194 01:08:13,680 --> 01:08:16,240 Speaker 1: EDWARDO used to work at Dimensional, has been very helpful 1195 01:08:16,280 --> 01:08:19,479 Speaker 1: to me over time. And then I'd be remiss if 1196 01:08:19,479 --> 01:08:22,800 Speaker 1: I didn't say my parents, because there you know, up 1197 01:08:22,880 --> 01:08:26,479 Speaker 1: until the time that you leave the home, and they're 1198 01:08:26,960 --> 01:08:30,799 Speaker 1: your ultimate mentors in terms of shaping how you approach problems, 1199 01:08:30,840 --> 01:08:35,160 Speaker 1: how you view the world, what you prioritize. My parents 1200 01:08:35,280 --> 01:08:40,880 Speaker 1: have always emphasized education and the importance of keeping your 1201 01:08:40,960 --> 01:08:43,600 Speaker 1: mind active and trying to better yourself. How do you 1202 01:08:43,840 --> 01:08:46,639 Speaker 1: become better than your were the day before? And that's 1203 01:08:46,680 --> 01:08:49,680 Speaker 1: a spirit that I think it's important for anybody to 1204 01:08:49,800 --> 01:08:53,720 Speaker 1: keep kind of pulling towards for as long as they're 1205 01:08:53,720 --> 01:08:55,920 Speaker 1: on this planet, because what else is there to do 1206 01:08:56,200 --> 01:08:59,479 Speaker 1: but try to improve your skills and and how you 1207 01:08:59,560 --> 01:09:02,920 Speaker 1: interact with the world. So let's talk about books. This 1208 01:09:03,120 --> 01:09:05,840 Speaker 1: is everybody's favorite questions. What are you reading right now 1209 01:09:06,240 --> 01:09:09,280 Speaker 1: and what are some of your favorites. You know, I 1210 01:09:09,400 --> 01:09:14,280 Speaker 1: am not reading any book right now. I've been consumed 1211 01:09:14,520 --> 01:09:18,080 Speaker 1: with work over the past few years and by reading 1212 01:09:18,160 --> 01:09:21,960 Speaker 1: for pleasure has taken a back seat, unfortunately. But some 1213 01:09:22,080 --> 01:09:26,240 Speaker 1: of my favorite books over time, I would say one 1214 01:09:26,600 --> 01:09:28,320 Speaker 1: Freedom to Choose. I don't know if you've read that 1215 01:09:28,439 --> 01:09:31,960 Speaker 1: book by Milton Freeman. I think is a great book 1216 01:09:32,160 --> 01:09:36,560 Speaker 1: and timeless, I mean written many decades ago, but but 1217 01:09:36,880 --> 01:09:40,439 Speaker 1: very very timeless. They wro't deserved them. I think is 1218 01:09:40,479 --> 01:09:43,600 Speaker 1: one of the all time classics as well by you 1219 01:09:43,680 --> 01:09:47,280 Speaker 1: know That's uh is an all time classic. So you're 1220 01:09:47,280 --> 01:09:51,000 Speaker 1: going to get my idea from I like books about markets, 1221 01:09:52,120 --> 01:09:56,559 Speaker 1: about how to organize people and how do you get 1222 01:09:56,800 --> 01:09:59,360 Speaker 1: to a state of affairs where you're making the most 1223 01:09:59,439 --> 01:10:02,920 Speaker 1: efficient use of the resources, where people have freedom to 1224 01:10:03,920 --> 01:10:07,719 Speaker 1: pursue what interests them. I find that an interesting area 1225 01:10:07,760 --> 01:10:10,240 Speaker 1: of reading. What sort of advice would you give to 1226 01:10:10,320 --> 01:10:14,080 Speaker 1: a recent college grad or someone who was interested in 1227 01:10:14,240 --> 01:10:19,240 Speaker 1: a career in investing and finance? So two big areas 1228 01:10:19,960 --> 01:10:22,560 Speaker 1: one and this is something that is kind of I 1229 01:10:22,640 --> 01:10:26,240 Speaker 1: call it a dimensional motto, and it's do the right thing, 1230 01:10:26,479 --> 01:10:28,080 Speaker 1: do it the right way, and do it right now. 1231 01:10:29,160 --> 01:10:32,759 Speaker 1: And so when you're pursuing a career in any field, 1232 01:10:33,560 --> 01:10:35,280 Speaker 1: you want to feel good about what you're doing. You 1233 01:10:35,320 --> 01:10:39,080 Speaker 1: want to feel that you're helping people. Do you want 1234 01:10:39,120 --> 01:10:41,600 Speaker 1: to do well while you're helping people. But that's the 1235 01:10:41,760 --> 01:10:44,439 Speaker 1: right thing, And then do it the right way is 1236 01:10:44,760 --> 01:10:49,519 Speaker 1: how do you come with a path to make a 1237 01:10:49,560 --> 01:10:53,040 Speaker 1: decision that uses as much of the information that's available 1238 01:10:53,120 --> 01:10:55,080 Speaker 1: to you. There's gonna be a lot of noise in 1239 01:10:55,120 --> 01:10:56,560 Speaker 1: the outcome, but you want to be proud of the 1240 01:10:56,600 --> 01:10:58,920 Speaker 1: decision that you made given the information that you had 1241 01:10:58,960 --> 01:11:01,040 Speaker 1: at the time. I think that's doing things in the 1242 01:11:01,160 --> 01:11:03,519 Speaker 1: right way. And then do it right now. Never sit 1243 01:11:03,600 --> 01:11:08,400 Speaker 1: on your hands, be proactive, get after it, close projects. 1244 01:11:08,520 --> 01:11:11,240 Speaker 1: If you can't close it, move on, ask for help, 1245 01:11:11,800 --> 01:11:14,400 Speaker 1: and don't sit on your hands, go out and get 1246 01:11:14,439 --> 01:11:17,200 Speaker 1: it done. Then, when it comes to finance in particular, 1247 01:11:18,200 --> 01:11:22,160 Speaker 1: remember what you're doing. You're taking people's life savings and 1248 01:11:22,520 --> 01:11:25,760 Speaker 1: you're trying to help them achieve objectives and goals, and 1249 01:11:25,800 --> 01:11:27,960 Speaker 1: they're taking risk to achieve these oblection and goals that 1250 01:11:28,000 --> 01:11:32,000 Speaker 1: they couldn't achieve without taking those risks. And that's a very, 1251 01:11:32,280 --> 01:11:37,160 Speaker 1: very meaningful responsibility. So don't take it lightly. And you're 1252 01:11:37,200 --> 01:11:39,840 Speaker 1: moving into a field that you can really help people 1253 01:11:40,439 --> 01:11:44,439 Speaker 1: have a better life, but you can also harm people 1254 01:11:44,680 --> 01:11:47,080 Speaker 1: if you do things in the wrong way. So I 1255 01:11:47,160 --> 01:11:49,680 Speaker 1: think that that's a something that you've got to keep 1256 01:11:49,720 --> 01:11:53,479 Speaker 1: in mind when it comes to finance. It's not your money, 1257 01:11:53,600 --> 01:11:57,960 Speaker 1: it's somebody else's money. Be fiduciary, be prudent, and then 1258 01:11:58,040 --> 01:12:01,679 Speaker 1: you can really help people. Be it off really interesting 1259 01:12:01,760 --> 01:12:05,040 Speaker 1: answer and our final question, what do you know about 1260 01:12:05,080 --> 01:12:08,679 Speaker 1: the world of investing today? You wish you knew about 1261 01:12:08,760 --> 01:12:11,879 Speaker 1: twenty years ago or so when you were first getting started. 1262 01:12:12,720 --> 01:12:15,160 Speaker 1: When I was first getting started, I had this view 1263 01:12:15,240 --> 01:12:16,800 Speaker 1: of the world because I had never taken a course 1264 01:12:16,840 --> 01:12:20,799 Speaker 1: in finance before a dimensional which and I didn't understand 1265 01:12:20,840 --> 01:12:23,160 Speaker 1: markets that well. I had the view of the world 1266 01:12:23,840 --> 01:12:26,479 Speaker 1: that all you had to come was with was a 1267 01:12:26,560 --> 01:12:30,280 Speaker 1: better mathematical model than anybody else out there, and then 1268 01:12:30,360 --> 01:12:32,240 Speaker 1: that would be able to predict where prices were going 1269 01:12:32,280 --> 01:12:34,880 Speaker 1: to go. And of course I was quickly disabused of 1270 01:12:34,960 --> 01:12:39,759 Speaker 1: that notion after having conversations with Ken and Geene and Bob, 1271 01:12:39,840 --> 01:12:44,280 Speaker 1: and you just need a better model, And so I 1272 01:12:44,360 --> 01:12:47,400 Speaker 1: wish I had known that then, but now I certainly 1273 01:12:47,479 --> 01:12:50,920 Speaker 1: know it, and it's really helped shape how I view 1274 01:12:51,080 --> 01:12:55,080 Speaker 1: what good investment solutions are for clients and what really 1275 01:12:55,160 --> 01:12:59,080 Speaker 1: the power of markets are and can be really really 1276 01:12:59,200 --> 01:13:03,880 Speaker 1: interesting uh stuff. We have been speaking with Gerardo Riley. 1277 01:13:04,000 --> 01:13:07,640 Speaker 1: He is the c i O and co CEO of 1278 01:13:07,760 --> 01:13:12,120 Speaker 1: Dimensional Funds. If you enjoy this conversation, well, be sure 1279 01:13:12,120 --> 01:13:14,439 Speaker 1: and check out any of our four hundred or so 1280 01:13:14,720 --> 01:13:18,920 Speaker 1: previous interviews. You can find those at iTunes or Spotify 1281 01:13:19,120 --> 01:13:22,799 Speaker 1: or wherever you get your podcasts. We love your comments, 1282 01:13:22,880 --> 01:13:26,760 Speaker 1: feedback and suggestions right to us at m IB podcast 1283 01:13:26,840 --> 01:13:29,720 Speaker 1: at Bloomberg dot net. You can sign up from my 1284 01:13:29,880 --> 01:13:33,120 Speaker 1: Daily reads at Ritalts dot com. Follow me on Twitter 1285 01:13:33,320 --> 01:13:36,320 Speaker 1: at ritlts. I would be remiss if I did not 1286 01:13:36,479 --> 01:13:39,479 Speaker 1: thank our crack staff that helps put these conversations together 1287 01:13:40,160 --> 01:13:45,439 Speaker 1: each week. Mohammed Remaui is my audio engineer. Attica val 1288 01:13:45,520 --> 01:13:49,640 Speaker 1: Bron is my product manager, Paris Wald is my producer. 1289 01:13:49,800 --> 01:13:53,439 Speaker 1: Sean Russo is my head of research. I'm Barry Ritalts. 1290 01:13:53,920 --> 01:13:57,559 Speaker 1: You've been listening to Masters in Business on Bloomberg Radio.