1 00:00:00,080 --> 00:00:06,080 Speaker 1: M This is Masters in Business with Very Renaults on 2 00:00:06,240 --> 00:00:10,880 Speaker 1: Bluebird Radio. My special guest this week is Hornda Silva. 3 00:00:11,080 --> 00:00:15,480 Speaker 1: He is a fascinating quant a pioneer in low vall 4 00:00:15,760 --> 00:00:20,440 Speaker 1: and factor based investing. He runs the Analytic Investors Group 5 00:00:20,520 --> 00:00:24,840 Speaker 1: at Wells Fargo Asset Management. His team runs over twenty 6 00:00:24,880 --> 00:00:30,200 Speaker 1: billion dollars in an assortment of quantitative strategies and really 7 00:00:30,480 --> 00:00:34,639 Speaker 1: just a fascinating guy. UM who has spent a lot 8 00:00:34,680 --> 00:00:40,520 Speaker 1: of time studying factors, studying quantitative investing, thinking about what 9 00:00:40,720 --> 00:00:45,640 Speaker 1: does and doesn't work and when and why. I find 10 00:00:45,640 --> 00:00:49,840 Speaker 1: a lot of quants tend to be UM a little 11 00:00:49,880 --> 00:00:54,800 Speaker 1: more singularly focused, and he's a pretty broad based, holistic 12 00:00:54,880 --> 00:00:58,880 Speaker 1: sort of guy. He He's quite a fascinating background and 13 00:00:59,160 --> 00:01:03,080 Speaker 1: really interesting set of hobbies. UM. So, with no further 14 00:01:03,120 --> 00:01:10,440 Speaker 1: ado my conversation with Wells Fargoes horind Da Silva. This 15 00:01:10,880 --> 00:01:15,319 Speaker 1: is Mesters in Business with Very Reholts on Bloomberg Radio. 16 00:01:17,560 --> 00:01:21,000 Speaker 1: My special guest this week is Horirenda Silva. He is 17 00:01:21,160 --> 00:01:25,720 Speaker 1: a leader and portfolio manager at the Analytic Investors Group 18 00:01:25,800 --> 00:01:30,039 Speaker 1: of Wells Fargo Asset Management. His team runs a variety 19 00:01:30,080 --> 00:01:35,119 Speaker 1: of quantitative strategies with assets of over twenty billion dollars. 20 00:01:35,120 --> 00:01:39,360 Speaker 1: Harin is known as a pioneer in both low volatility 21 00:01:39,720 --> 00:01:44,400 Speaker 1: and factor based investing. He has won numerous awards, including 22 00:01:45,040 --> 00:01:48,800 Speaker 1: multiple CFA Institute, Graham and Dot Awards, as well as 23 00:01:49,560 --> 00:01:55,360 Speaker 1: multiple Bernstein Fabosi Awards from institutional investor Harendra da Silva. 24 00:01:55,760 --> 00:02:00,080 Speaker 1: Welcome to Bloomberg. Thanks to having me on, Barry. I 25 00:02:00,160 --> 00:02:02,400 Speaker 1: had to chat with you. Yep. I've been looking forward 26 00:02:02,400 --> 00:02:05,280 Speaker 1: to talking to you because you have such an interesting 27 00:02:05,400 --> 00:02:09,680 Speaker 1: background and your career has taken so many interesting paths. 28 00:02:10,320 --> 00:02:13,760 Speaker 1: Tell us about your life a bit. How did you 29 00:02:13,880 --> 00:02:16,960 Speaker 1: get into the financial services business? How do you go 30 00:02:17,120 --> 00:02:24,400 Speaker 1: from Sri Lanka to UC Irvine. Well, apart from watching 31 00:02:24,400 --> 00:02:26,400 Speaker 1: that episode of The Graduate as a kid, we had 32 00:02:26,440 --> 00:02:28,519 Speaker 1: the advice to the guy who was go West young 33 00:02:28,560 --> 00:02:33,000 Speaker 1: man um that was kind of the original inspiration. But 34 00:02:33,400 --> 00:02:36,480 Speaker 1: I really you know, growing up in Sri Lanka, it 35 00:02:36,560 --> 00:02:39,080 Speaker 1: was traditional for people to go to school or the 36 00:02:39,160 --> 00:02:43,680 Speaker 1: university in the UK. And so I studied as an 37 00:02:43,760 --> 00:02:51,400 Speaker 1: engineer undergrad and had the misfortune to graduate which was 38 00:02:51,440 --> 00:02:55,800 Speaker 1: the height of a recession. And uh, you know, at 39 00:02:55,840 --> 00:02:58,200 Speaker 1: the time, I had two choices. I could go onto 40 00:02:58,200 --> 00:03:01,680 Speaker 1: graduate school. Why could go back to Sri Lanka, and 41 00:03:02,360 --> 00:03:03,880 Speaker 1: you know, Sri Lanka was in the middle of a 42 00:03:03,960 --> 00:03:07,240 Speaker 1: civil war at the time, so the choice was not difficult, 43 00:03:09,080 --> 00:03:13,000 Speaker 1: and I made the decision to go to graduate school 44 00:03:13,000 --> 00:03:16,600 Speaker 1: at the University of Rochester to study finance. And I 45 00:03:16,680 --> 00:03:19,120 Speaker 1: was interested in finance because I saw a lot of 46 00:03:19,520 --> 00:03:24,800 Speaker 1: similarities between finance and engineering, which was, you know, the 47 00:03:24,840 --> 00:03:30,160 Speaker 1: ability to kind of design products, design strategies, and the 48 00:03:30,200 --> 00:03:33,320 Speaker 1: idea that you could build something and actually put it 49 00:03:33,360 --> 00:03:36,680 Speaker 1: to the test really appealed to me. So that's why 50 00:03:36,760 --> 00:03:39,000 Speaker 1: I initially went to the University of Rochester to study 51 00:03:39,040 --> 00:03:44,840 Speaker 1: finance and study finance for several years and then started 52 00:03:44,840 --> 00:03:50,119 Speaker 1: working for a consulting firm called Analysis Group, and I 53 00:03:50,160 --> 00:03:53,920 Speaker 1: was had by another stroke of good fortune and got 54 00:03:54,680 --> 00:03:58,839 Speaker 1: assigned to a project for Merrill Lynch in the mid 55 00:03:58,880 --> 00:04:01,720 Speaker 1: eighties where we were taking managers to go into the 56 00:04:01,800 --> 00:04:05,840 Speaker 1: Marylands Consults program and if you remember remember that program, 57 00:04:05,920 --> 00:04:09,080 Speaker 1: but it was one of the first rap programs. And 58 00:04:09,120 --> 00:04:11,160 Speaker 1: so I was twenty five years old and I got 59 00:04:11,200 --> 00:04:15,840 Speaker 1: to visit something like money managers in person. So you 60 00:04:15,920 --> 00:04:18,599 Speaker 1: go to the money manager, you'd listen to their story 61 00:04:18,920 --> 00:04:22,320 Speaker 1: and you try to capture in a quantitative way what 62 00:04:22,360 --> 00:04:26,320 Speaker 1: they were doing. And that's when I first got really 63 00:04:26,400 --> 00:04:30,000 Speaker 1: kind of interested in factor investing because I realized that, 64 00:04:30,160 --> 00:04:32,000 Speaker 1: you know, you go and talk to, for example, a 65 00:04:32,000 --> 00:04:36,120 Speaker 1: bunch of growth managers, and talking to them, you realized 66 00:04:36,120 --> 00:04:39,440 Speaker 1: that all the ones that were doing well at that 67 00:04:39,480 --> 00:04:44,080 Speaker 1: particular time well focused on a particular factors, and not 68 00:04:44,200 --> 00:04:47,320 Speaker 1: only were the growth focused then maybe for example, focused 69 00:04:47,360 --> 00:04:50,520 Speaker 1: on earning the acceleration, and those were the guys who 70 00:04:50,560 --> 00:04:52,680 Speaker 1: are doing well at that time. And then you'd go 71 00:04:52,800 --> 00:04:56,520 Speaker 1: talk to the value managers, for example, and you realize, well, 72 00:04:56,680 --> 00:04:59,040 Speaker 1: the only ones that are doing really well are the 73 00:04:59,080 --> 00:05:02,440 Speaker 1: ones who are f for example, divident deals. And that's 74 00:05:02,440 --> 00:05:05,279 Speaker 1: when I realized, wow, you know, the factors actually explained 75 00:05:05,360 --> 00:05:08,200 Speaker 1: a lot, and I kind of asked myself the question, 76 00:05:09,080 --> 00:05:12,360 Speaker 1: which obviously wasn't related to the project I was working on. 77 00:05:12,560 --> 00:05:15,080 Speaker 1: Is it the manager or is it the factor. I'm 78 00:05:15,120 --> 00:05:19,920 Speaker 1: hearing a parallel between what you described in your background 79 00:05:20,000 --> 00:05:24,080 Speaker 1: with mechanical engineering and the ability to test that in 80 00:05:24,120 --> 00:05:28,320 Speaker 1: the real world with factor investing and being able to 81 00:05:28,440 --> 00:05:33,040 Speaker 1: quantify what's driving returns. Am I reading too much into that? 82 00:05:33,279 --> 00:05:36,720 Speaker 1: Or is there some parallel there? No? I think there 83 00:05:36,760 --> 00:05:38,760 Speaker 1: is a parallel. I think it's the same idea of 84 00:05:38,839 --> 00:05:41,279 Speaker 1: kind of what makes this work. And you know it 85 00:05:41,400 --> 00:05:43,919 Speaker 1: maybe even a biased view because in an engineer, you 86 00:05:44,000 --> 00:05:47,520 Speaker 1: think there's always there's a way to there's a formula, 87 00:05:47,680 --> 00:05:51,080 Speaker 1: or there's a way to quantify something. So obviously if 88 00:05:51,080 --> 00:05:53,200 Speaker 1: I had a history background, that would not think are 89 00:05:53,240 --> 00:05:57,400 Speaker 1: you thinking was a manager or the process as opposed 90 00:05:57,440 --> 00:06:00,480 Speaker 1: to something they were doing that was focusing on the factor. 91 00:06:00,760 --> 00:06:04,360 Speaker 1: So I think the engineering aspect really kind of affected 92 00:06:04,440 --> 00:06:07,839 Speaker 1: the way I looked at the problem. Very very interesting. 93 00:06:07,880 --> 00:06:12,920 Speaker 1: And some of the factors that you cover include not 94 00:06:13,080 --> 00:06:16,520 Speaker 1: just the traditional factors um, but you spend a lot 95 00:06:16,560 --> 00:06:19,760 Speaker 1: of time focusing on low volatility. Tell us a little 96 00:06:19,760 --> 00:06:23,560 Speaker 1: bit why that factor is significant, what what are the 97 00:06:23,600 --> 00:06:27,320 Speaker 1: common elements in the factors that that you find intriguing. Well, 98 00:06:27,360 --> 00:06:30,719 Speaker 1: that factor, to me was probably the one that was 99 00:06:30,800 --> 00:06:34,520 Speaker 1: I call it the most neglected factor because I was 100 00:06:34,600 --> 00:06:40,640 Speaker 1: working on my PhD thesis in the early nineties when 101 00:06:41,160 --> 00:06:46,000 Speaker 1: Farmer and French came out with this paper that basically said, 102 00:06:46,839 --> 00:06:50,240 Speaker 1: if you want to describe what they what academics kind 103 00:06:50,240 --> 00:06:54,120 Speaker 1: of called the cross section of returns, In other words, 104 00:06:54,160 --> 00:06:57,080 Speaker 1: which stocks go up and which stocks to go. That 105 00:06:57,839 --> 00:07:01,680 Speaker 1: value book to market and far Camp were very good 106 00:07:01,680 --> 00:07:05,960 Speaker 1: at explaining it, and that data did a really poor job, 107 00:07:06,200 --> 00:07:10,880 Speaker 1: and that high data stocks had the same return as 108 00:07:10,920 --> 00:07:14,760 Speaker 1: low beata stocks. To me, everybody when they read the 109 00:07:14,800 --> 00:07:17,920 Speaker 1: paper in nine two focused on, oh yeah, the value 110 00:07:17,960 --> 00:07:21,080 Speaker 1: premium and a small cap premium. And what I found 111 00:07:21,160 --> 00:07:24,640 Speaker 1: really curious was, Wow, you can build a portfolio of 112 00:07:24,720 --> 00:07:28,160 Speaker 1: Lottle better stocks that's going to have a way better 113 00:07:28,280 --> 00:07:32,800 Speaker 1: returned risk profile than a portfolio of high data stocks 114 00:07:32,880 --> 00:07:36,360 Speaker 1: or beta one portfolio. And I was really fascinated by 115 00:07:36,480 --> 00:07:38,960 Speaker 1: why a no one focused on it, and most people 116 00:07:39,000 --> 00:07:43,040 Speaker 1: didn't think it was really that interesting. So what I call, 117 00:07:43,400 --> 00:07:46,040 Speaker 1: you know, what's now referred to in our team as 118 00:07:46,080 --> 00:07:49,040 Speaker 1: a low all anomaly is this idea that if you 119 00:07:49,120 --> 00:07:51,720 Speaker 1: build a portfolio a little beata stocks, you get a 120 00:07:51,800 --> 00:07:55,240 Speaker 1: much better shop ratio, a much better return risk ratio 121 00:07:55,360 --> 00:07:59,760 Speaker 1: than the market portfolio. Translate that for the lady listener, 122 00:08:00,640 --> 00:08:04,120 Speaker 1: what are loval what are low beta stocks? And how 123 00:08:04,160 --> 00:08:08,200 Speaker 1: do what is the significance of the sharp ratio? Typically 124 00:08:08,480 --> 00:08:11,520 Speaker 1: a little bit of stock is what we mean by 125 00:08:11,520 --> 00:08:14,000 Speaker 1: a little bit of stock is a stock that doesn't 126 00:08:14,080 --> 00:08:18,640 Speaker 1: move a lot with the market moves in the mark, 127 00:08:18,680 --> 00:08:20,200 Speaker 1: it will go up less in the market. It will 128 00:08:20,240 --> 00:08:22,040 Speaker 1: go down less in the morning, go down less in 129 00:08:22,080 --> 00:08:26,240 Speaker 1: the market exactly. And what if you put them in 130 00:08:26,280 --> 00:08:29,440 Speaker 1: a portfolio. The portfolio obviously, if you buy a portfolio 131 00:08:29,560 --> 00:08:31,080 Speaker 1: of a little bit of stocks and be it as 132 00:08:31,120 --> 00:08:35,840 Speaker 1: a point eight, they'll actually move less than the market. 133 00:08:36,120 --> 00:08:38,959 Speaker 1: So portfolio will go up less in the market and 134 00:08:39,000 --> 00:08:42,160 Speaker 1: go down lest in the market. But what it also 135 00:08:42,240 --> 00:08:49,560 Speaker 1: means is that the because you're invested in equities, you 136 00:08:49,679 --> 00:08:52,600 Speaker 1: get the equity of risk premium. So the long run 137 00:08:52,640 --> 00:08:55,880 Speaker 1: return of equities with a lot less volatility so on 138 00:08:55,920 --> 00:09:01,400 Speaker 1: a risk of the dropacious, it's better than traditional portfolios, 139 00:09:02,360 --> 00:09:05,120 Speaker 1: right right. And what the Farmer, all the work the 140 00:09:05,200 --> 00:09:09,480 Speaker 1: Farmer and French did showed was that low beta stocks 141 00:09:09,520 --> 00:09:12,760 Speaker 1: have actually the same return over the very long run 142 00:09:13,000 --> 00:09:16,920 Speaker 1: as HIGHBERDA stocks or better one stocks. And what's really 143 00:09:16,920 --> 00:09:19,160 Speaker 1: fascinating is if you look at a little bit of stocks. 144 00:09:19,160 --> 00:09:22,360 Speaker 1: You know, when you first tell people, they tell you, oh, yeah, 145 00:09:22,400 --> 00:09:26,840 Speaker 1: a little bit of stock. Oh yeah, that's just that's Amazon. Sorry, 146 00:09:26,840 --> 00:09:30,199 Speaker 1: that's that's electric utility, And you go no, no, no, no, 147 00:09:30,280 --> 00:09:33,120 Speaker 1: that's not electric utilities. A little bit of stock is 148 00:09:33,120 --> 00:09:35,960 Speaker 1: the stock that doesn't move with the market. So early 149 00:09:36,080 --> 00:09:40,520 Speaker 1: on in its cycle, Amazon was indeed a little bit 150 00:09:40,559 --> 00:09:44,040 Speaker 1: of stock because what was driving it was this idiot 151 00:09:44,040 --> 00:09:47,640 Speaker 1: think credit returned related to doing business in a very 152 00:09:47,720 --> 00:09:53,080 Speaker 1: unusual way. As the company evolved, it became increasingly a 153 00:09:53,120 --> 00:09:55,800 Speaker 1: Beta one stock, and now it's a bet at one 154 00:09:55,840 --> 00:09:58,320 Speaker 1: point to one point three stock because it's a very, 155 00:09:58,480 --> 00:10:01,240 Speaker 1: very large cap company. It's the very large component of 156 00:10:01,280 --> 00:10:06,240 Speaker 1: the economy, and it's no longer very geosyncratic Richard. So 157 00:10:06,280 --> 00:10:08,920 Speaker 1: if you look at our portfolios very you find that 158 00:10:09,000 --> 00:10:12,520 Speaker 1: companies like that are now very high data. For example, 159 00:10:12,720 --> 00:10:16,040 Speaker 1: even Tesler was actually in a little bit of portfolio 160 00:10:16,559 --> 00:10:19,600 Speaker 1: four years ago. It's no longer in a little bit 161 00:10:19,600 --> 00:10:23,160 Speaker 1: of portfolio because it's megacap and it moves so much 162 00:10:23,200 --> 00:10:25,640 Speaker 1: with the market. But when people think of a little 163 00:10:25,640 --> 00:10:27,680 Speaker 1: bit of portfolios, you know, the other thing you see 164 00:10:27,679 --> 00:10:29,480 Speaker 1: often in a little bit of portfolios that you've got 165 00:10:29,480 --> 00:10:33,600 Speaker 1: to be very careful with is biotech companies, right, because 166 00:10:33,600 --> 00:10:37,720 Speaker 1: the biotech company will often have two or three patterns, 167 00:10:38,800 --> 00:10:44,240 Speaker 1: and it's going through a process for regulatory approval after 168 00:10:44,280 --> 00:10:47,120 Speaker 1: the drug, so it's not really moving. The moving in 169 00:10:47,160 --> 00:10:50,960 Speaker 1: the market at all, So stuff like that very often 170 00:10:51,000 --> 00:10:53,440 Speaker 1: you see those in a little bit of portfolios. So 171 00:10:53,559 --> 00:10:56,120 Speaker 1: it really is kind of it's kind of fascinating to 172 00:10:56,440 --> 00:10:59,880 Speaker 1: to watch this and if you look at sectors, for example, 173 00:11:00,400 --> 00:11:03,520 Speaker 1: spent more of my life studying this anomaly and anything else. 174 00:11:03,559 --> 00:11:07,640 Speaker 1: But if you look in the sixties, energy was low BEATA. 175 00:11:08,080 --> 00:11:11,560 Speaker 1: Nobody really cared about it. It was almost utility. Went 176 00:11:11,600 --> 00:11:16,000 Speaker 1: through the oil crisis in the seventies, it became very 177 00:11:16,080 --> 00:11:19,959 Speaker 1: high data because it was the fact it sort of 178 00:11:20,280 --> 00:11:23,600 Speaker 1: started moving the market. Through the seventies and eighties, oil 179 00:11:23,640 --> 00:11:26,680 Speaker 1: companies were very HIGHBATA, you know, when the market was 180 00:11:26,720 --> 00:11:30,760 Speaker 1: dominated by the Seven Sisters. Then it became LOWBATA, and 181 00:11:30,800 --> 00:11:34,720 Speaker 1: as we went through this last crisis with energy consumption falling, 182 00:11:34,800 --> 00:11:38,560 Speaker 1: they became high BEATA once again. So it really is 183 00:11:39,040 --> 00:11:42,960 Speaker 1: sounding a little bit geek like, but it's fascinating to 184 00:11:43,080 --> 00:11:46,719 Speaker 1: watch the evolution of companies and industries because it's not 185 00:11:46,800 --> 00:11:48,760 Speaker 1: as simple as oh yeah, a little bit of stocks 186 00:11:48,800 --> 00:11:51,600 Speaker 1: are different paying utilities. So let's talk a little bit 187 00:11:51,720 --> 00:11:57,600 Speaker 1: about factor investing, and in general, we've seen many factors 188 00:11:58,200 --> 00:12:03,480 Speaker 1: significantly underperformed over the past decade. Why is that? What 189 00:12:03,520 --> 00:12:08,000 Speaker 1: are your thoughts here, Well, when you say factors under perform, 190 00:12:08,040 --> 00:12:10,720 Speaker 1: you know you're saying the return to the factor was 191 00:12:10,760 --> 00:12:16,840 Speaker 1: not the expected. Sign is that the well, you know, 192 00:12:16,960 --> 00:12:21,360 Speaker 1: the granddaddy of underperformance. The past decade has been value 193 00:12:21,520 --> 00:12:27,319 Speaker 1: versus growth, but there has been some other factor surprises 194 00:12:27,360 --> 00:12:30,920 Speaker 1: to the downside. I guess um when when we had 195 00:12:30,960 --> 00:12:34,080 Speaker 1: the big blow up in in volatility a couple of 196 00:12:34,120 --> 00:12:39,080 Speaker 1: years ago, that seemed to affect some factor performance and 197 00:12:40,160 --> 00:12:44,040 Speaker 1: small cap has dramatically lagged for for quite a while. 198 00:12:44,080 --> 00:12:46,720 Speaker 1: It's starting to catch up over the past year, but 199 00:12:46,920 --> 00:12:50,360 Speaker 1: the past decade was not kind two small caps. When 200 00:12:50,400 --> 00:12:52,720 Speaker 1: you look at the world of factors, and I know 201 00:12:53,400 --> 00:12:56,840 Speaker 1: there are many, many more factors than just you know, 202 00:12:57,000 --> 00:13:00,600 Speaker 1: the three or six that most people are familiar with, 203 00:13:01,040 --> 00:13:04,679 Speaker 1: how do you look at what's doing well, what's doing poorly? 204 00:13:04,760 --> 00:13:08,960 Speaker 1: And how do you contextualize that? Yes, so I really 205 00:13:09,000 --> 00:13:12,400 Speaker 1: look at it from two dimensions. One is the does 206 00:13:12,440 --> 00:13:17,959 Speaker 1: the factor matter? Right? So does the factor describe what's 207 00:13:17,960 --> 00:13:20,120 Speaker 1: going on in the market? And when I mean by describe, 208 00:13:20,160 --> 00:13:22,880 Speaker 1: if you look at the factor, can I tell you 209 00:13:22,960 --> 00:13:26,800 Speaker 1: whether a group of stocks either out performing on the performing? 210 00:13:26,880 --> 00:13:29,800 Speaker 1: So that to me the sign doesn't really matter when 211 00:13:30,040 --> 00:13:33,160 Speaker 1: in the first dimension is is this factor meaningful? So 212 00:13:33,360 --> 00:13:36,720 Speaker 1: just to pick up, you know, a random factor. You 213 00:13:36,720 --> 00:13:41,240 Speaker 1: could pick a factor as where the name of the 214 00:13:41,360 --> 00:13:46,840 Speaker 1: company is in the alphabet, right, that's a factor. You'll 215 00:13:46,880 --> 00:13:50,079 Speaker 1: find that that study is actually completely useless in explaining 216 00:13:50,080 --> 00:13:52,800 Speaker 1: whether a group of stocks went up or down, right, 217 00:13:52,920 --> 00:13:56,679 Speaker 1: because it's just random. On the other hand, small camp 218 00:13:56,720 --> 00:13:59,800 Speaker 1: as you mentioned, is actually really useful in terms of 219 00:14:00,280 --> 00:14:04,600 Speaker 1: people are running away from risk. Usually small cap companies 220 00:14:04,640 --> 00:14:07,960 Speaker 1: do really poorly if they're running towards risk, as they've 221 00:14:07,960 --> 00:14:10,040 Speaker 1: done a little bit in the last three to six months. 222 00:14:10,480 --> 00:14:14,720 Speaker 1: Small cap companies really generally do well. The correlation isn't one, 223 00:14:15,559 --> 00:14:20,360 Speaker 1: but generally, regardless of the environment, there is a difference 224 00:14:20,360 --> 00:14:23,800 Speaker 1: in performance in large cap companies with the small cap companies, 225 00:14:24,160 --> 00:14:27,840 Speaker 1: and the same versus value the same in sort of 226 00:14:27,920 --> 00:14:32,400 Speaker 1: quality related factors. Now, the expectation that people form on 227 00:14:32,480 --> 00:14:35,160 Speaker 1: these factors is they look at the long run return, 228 00:14:35,480 --> 00:14:38,160 Speaker 1: you know, whether it's from a Farmer French study or elsewhere, 229 00:14:38,440 --> 00:14:40,880 Speaker 1: and then they say, wow, you know, this factor on 230 00:14:41,000 --> 00:14:44,120 Speaker 1: average has been positive, so I expected to be positive 231 00:14:44,360 --> 00:14:48,040 Speaker 1: going forward, And that I think is one of the 232 00:14:48,080 --> 00:14:52,400 Speaker 1: misunderstandings with factor investing because you know when you when 233 00:14:52,400 --> 00:14:55,480 Speaker 1: you pace the quantitative back, the first thing that goes 234 00:14:55,520 --> 00:15:00,200 Speaker 1: through my head is what's the hit rate? Right? Because 235 00:15:00,200 --> 00:15:02,120 Speaker 1: when I when I tell you something is going to outperform, 236 00:15:02,200 --> 00:15:04,480 Speaker 1: the first thing you need to be doing, is an investor, 237 00:15:04,720 --> 00:15:07,160 Speaker 1: is to say, Okay, how often is that going to happen. 238 00:15:07,560 --> 00:15:12,080 Speaker 1: My conjecture is that even the best factors will outperform 239 00:15:12,320 --> 00:15:16,040 Speaker 1: six or seven out of out of ten times. So 240 00:15:16,080 --> 00:15:17,800 Speaker 1: you can think of that in meaning it's going to 241 00:15:17,880 --> 00:15:21,560 Speaker 1: miss three or four out of ten times. Yeah, so 242 00:15:21,600 --> 00:15:25,360 Speaker 1: the underperformed three or four at ten times. So you 243 00:15:25,400 --> 00:15:27,240 Speaker 1: think of a coin, if you have a sixty percent 244 00:15:27,320 --> 00:15:30,720 Speaker 1: chance of winning, you can get a bunch of classes 245 00:15:30,760 --> 00:15:33,240 Speaker 1: in a row even with a sixty percent chance of winning. 246 00:15:34,600 --> 00:15:37,080 Speaker 1: So so the degree under performance and the length of 247 00:15:37,160 --> 00:15:40,560 Speaker 1: periods and the performance can be really, really long. And 248 00:15:40,720 --> 00:15:44,560 Speaker 1: that I think has not been adequately communicated to people 249 00:15:44,560 --> 00:15:47,160 Speaker 1: who are investing in these strategies because the other things 250 00:15:47,280 --> 00:15:51,120 Speaker 1: is factors of momentum, and this is only recently coming 251 00:15:51,120 --> 00:15:53,520 Speaker 1: to the four where people are talking about it, and 252 00:15:53,720 --> 00:15:55,920 Speaker 1: this is something so you're not let me interrupt you 253 00:15:55,960 --> 00:15:58,640 Speaker 1: a second or and you're you're not talking about momentum 254 00:15:58,680 --> 00:16:03,160 Speaker 1: itself as a factor. You're talking about the momentum of 255 00:16:03,280 --> 00:16:08,920 Speaker 1: other factors impacting that factor exactly. And I think that 256 00:16:09,120 --> 00:16:12,160 Speaker 1: is probably probably one of the most least appreciated things 257 00:16:12,160 --> 00:16:15,160 Speaker 1: in investing, and it's something to be really really aware of. 258 00:16:15,640 --> 00:16:18,520 Speaker 1: And I noticed this when I first got into investing, 259 00:16:18,640 --> 00:16:22,000 Speaker 1: because I look at these investment firms and you know, 260 00:16:22,080 --> 00:16:25,640 Speaker 1: they'd look growing like gangbasters because their performance was great. 261 00:16:25,880 --> 00:16:27,880 Speaker 1: You know, the head of the firm was viewed as 262 00:16:27,880 --> 00:16:31,320 Speaker 1: a genius. And I look at it really closely as 263 00:16:31,360 --> 00:16:35,480 Speaker 1: a you know, ex engineer, and go, it's not what's 264 00:16:35,520 --> 00:16:39,760 Speaker 1: blading up on this one and this factor with its 265 00:16:39,800 --> 00:16:44,800 Speaker 1: earnings acceleration or undeliveraged companies, whatever. The fact is that's 266 00:16:44,840 --> 00:16:48,240 Speaker 1: been in favor for the last five years, and as 267 00:16:48,240 --> 00:16:50,560 Speaker 1: long as it's in favor, they're going to do really, 268 00:16:50,640 --> 00:16:53,800 Speaker 1: really well. And then suddenly the factor starts under performing 269 00:16:54,200 --> 00:16:56,720 Speaker 1: and the managers say, as well, you know, my style 270 00:16:56,840 --> 00:16:58,520 Speaker 1: is out of favor, but it's going to come back. 271 00:16:59,720 --> 00:17:01,920 Speaker 1: So cynically, you know, if you're an investor, you know 272 00:17:02,040 --> 00:17:05,560 Speaker 1: this right, because if you're doing well, you're a genius. 273 00:17:05,640 --> 00:17:08,399 Speaker 1: But if you're doing coolly your styles out of favor. Um, 274 00:17:09,960 --> 00:17:15,359 Speaker 1: it's an asymmetrical bet. Yeah, it's an asymmetrical bet. So 275 00:17:15,480 --> 00:17:22,640 Speaker 1: beyond momentum, factors have persistence that tends to continue to exist. 276 00:17:23,160 --> 00:17:25,800 Speaker 1: Are some factors more persistent than others that we can 277 00:17:25,800 --> 00:17:30,240 Speaker 1: to really get into wonky geek territory here, But how 278 00:17:30,320 --> 00:17:37,080 Speaker 1: consistent does persistence apply to different factors? Is it similar 279 00:17:37,240 --> 00:17:41,359 Speaker 1: or or some factors do they tend to enjoy that 280 00:17:41,480 --> 00:17:47,280 Speaker 1: momentum for longer periods than other factors. It's remarkably consistent. 281 00:17:47,800 --> 00:17:51,200 Speaker 1: I mean I in all the work I've done, the work, 282 00:17:51,320 --> 00:17:53,840 Speaker 1: the time and effort I put into modeling each factor 283 00:17:53,840 --> 00:17:59,520 Speaker 1: individually has not been fruitful. What I see very consistently 284 00:18:00,080 --> 00:18:03,840 Speaker 1: is that factors tend to the factors that I worked 285 00:18:03,880 --> 00:18:06,960 Speaker 1: in the last year tend to continue to work. The 286 00:18:07,000 --> 00:18:11,280 Speaker 1: ones that have worked in the last two to three years, 287 00:18:11,480 --> 00:18:14,160 Speaker 1: they tend to work well, but a little less. So. 288 00:18:14,200 --> 00:18:17,399 Speaker 1: The persistence is very strong over one year, less strong 289 00:18:17,440 --> 00:18:20,879 Speaker 1: over two to three years. And then after three years 290 00:18:20,920 --> 00:18:23,480 Speaker 1: you actually start to see some mini version. When you 291 00:18:23,480 --> 00:18:26,639 Speaker 1: look at manager cycles, you will really really stop saying that, 292 00:18:26,840 --> 00:18:29,000 Speaker 1: but I mean they managed cycles. Is the tendency for 293 00:18:29,080 --> 00:18:32,560 Speaker 1: certain types of managers to perform. And so I think 294 00:18:32,600 --> 00:18:36,080 Speaker 1: when you're when you're thinking about factor investing, it's really 295 00:18:36,240 --> 00:18:40,520 Speaker 1: important to actually weigh the factors that have been working well, 296 00:18:42,000 --> 00:18:45,800 Speaker 1: not only recently, but also think about the mean reversion factor. Right, 297 00:18:46,000 --> 00:18:49,760 Speaker 1: So if you think about, you know, value factors right now, 298 00:18:50,080 --> 00:18:52,160 Speaker 1: right now, it's kind of a nice time for value 299 00:18:52,320 --> 00:18:54,879 Speaker 1: right because the last six months have been very strong. 300 00:18:55,640 --> 00:18:59,240 Speaker 1: So the fact that has momentum not great momentum because 301 00:18:59,280 --> 00:19:01,560 Speaker 1: it's only been strong for three to four months, maybe 302 00:19:01,600 --> 00:19:04,480 Speaker 1: six months, but the mean reversion aspect of it is 303 00:19:04,480 --> 00:19:09,000 Speaker 1: actually quite strong. So what's really fascinating about this? When 304 00:19:09,000 --> 00:19:12,840 Speaker 1: I first started running money using this tract of momentum approach, 305 00:19:13,920 --> 00:19:17,520 Speaker 1: I was doing it for a uh the US client 306 00:19:17,720 --> 00:19:22,880 Speaker 1: using US stocks, and it was a client was based 307 00:19:22,880 --> 00:19:24,680 Speaker 1: out of Japan, and they came to me and said, hey, 308 00:19:24,720 --> 00:19:27,600 Speaker 1: what this work in Japan? And you know, being the 309 00:19:27,640 --> 00:19:30,240 Speaker 1: typical quant, I said, yeah, well, let me check, right, 310 00:19:30,240 --> 00:19:33,480 Speaker 1: So I collected all this data in Japan and I 311 00:19:33,560 --> 00:19:39,320 Speaker 1: found that the same fact exists for Japanese stocks that 312 00:19:39,440 --> 00:19:42,280 Speaker 1: you see this same factor momentum and then you know, 313 00:19:42,320 --> 00:19:46,800 Speaker 1: subsequently repested it in in emerging markets and in Europe 314 00:19:46,960 --> 00:19:50,480 Speaker 1: and you see the same. So I think what you're 315 00:19:50,520 --> 00:19:54,200 Speaker 1: seeing is really and you can depending on which camp 316 00:19:54,240 --> 00:19:57,280 Speaker 1: you fall into. I dated the economic cycle. Oh, it's 317 00:19:57,320 --> 00:20:00,000 Speaker 1: just human behavior, right. If you think about the way 318 00:20:00,080 --> 00:20:02,680 Speaker 1: our decision making process sense to work, is we tend 319 00:20:02,720 --> 00:20:04,840 Speaker 1: to like things, so we tend to focus on things 320 00:20:04,840 --> 00:20:08,040 Speaker 1: that are work recently. So that recentcly bias, I think, 321 00:20:08,160 --> 00:20:10,760 Speaker 1: is there in the way we buy stocks and the 322 00:20:10,800 --> 00:20:14,200 Speaker 1: way we think of fads coming in and out of favor, 323 00:20:15,040 --> 00:20:18,119 Speaker 1: and that's what's driving That's what something you need to 324 00:20:18,119 --> 00:20:21,359 Speaker 1: take into account when you're tractor investing, because I think 325 00:20:21,880 --> 00:20:25,879 Speaker 1: the idea that yeah, value work, small cap works, quality works. 326 00:20:25,920 --> 00:20:28,560 Speaker 1: So you take these you know, five words, six factor tilts, 327 00:20:28,560 --> 00:20:31,240 Speaker 1: and you're going to beat the market regardless of what happens. Well, 328 00:20:31,280 --> 00:20:33,080 Speaker 1: that's going to work if you have a very long 329 00:20:33,200 --> 00:20:36,000 Speaker 1: or a reason. But if those factors out of favor 330 00:20:36,080 --> 00:20:39,320 Speaker 1: for the last three years, you're in for a little 331 00:20:39,320 --> 00:20:41,600 Speaker 1: bit of pain for the next two or three years. 332 00:20:41,640 --> 00:20:44,840 Speaker 1: So Horne, let's talk a little bit about the idea 333 00:20:45,040 --> 00:20:51,159 Speaker 1: of managers expressing their philosophy in their portfolio. I I 334 00:20:51,240 --> 00:20:55,680 Speaker 1: can't help but think that you're a part lo val, 335 00:20:56,080 --> 00:21:02,879 Speaker 1: part value sort of quant. Is that a fair just ryption? Yeah, 336 00:21:02,920 --> 00:21:07,360 Speaker 1: I would say it's it's part global active quant active 337 00:21:07,440 --> 00:21:10,600 Speaker 1: quant because I really think, I mean, love all is 338 00:21:10,640 --> 00:21:14,760 Speaker 1: an anomaly. But as a manager, you can express a 339 00:21:14,800 --> 00:21:17,520 Speaker 1: lot more views than just having a low Boll view 340 00:21:17,560 --> 00:21:20,280 Speaker 1: in the portfolio. I want to talk to you about 341 00:21:20,320 --> 00:21:23,600 Speaker 1: some of the funds you guys specifically manage, but before 342 00:21:24,359 --> 00:21:27,320 Speaker 1: I do, I have to ask you a question. What 343 00:21:27,520 --> 00:21:32,840 Speaker 1: is the fundamental law of active management? Well, that is 344 00:21:32,880 --> 00:21:38,960 Speaker 1: actually a formula, and the idea behind the formula is 345 00:21:39,000 --> 00:21:44,760 Speaker 1: that you can relate your investing success to really three things. 346 00:21:46,400 --> 00:21:49,880 Speaker 1: So if you think about investing in US lodge cap stocks, 347 00:21:49,960 --> 00:21:52,119 Speaker 1: the first thing that's going to matter for you in 348 00:21:52,240 --> 00:21:55,840 Speaker 1: terms of your relative success of investing is how big 349 00:21:55,880 --> 00:21:59,320 Speaker 1: your universe is, so what we call brand. The second 350 00:21:59,480 --> 00:22:02,600 Speaker 1: is your bill lead forecast, which is as a quant, 351 00:22:02,680 --> 00:22:07,360 Speaker 1: measured by the correlation between your predictions and what actually happens. 352 00:22:07,400 --> 00:22:10,800 Speaker 1: The quants called that your information coefficient, how much information 353 00:22:10,880 --> 00:22:14,000 Speaker 1: you have. And then the third thing is how effectively 354 00:22:14,119 --> 00:22:19,240 Speaker 1: you transfer your information into your portfolio, So you overweight 355 00:22:19,320 --> 00:22:21,800 Speaker 1: the stocks you like, and are you underweight the stocks 356 00:22:21,920 --> 00:22:24,760 Speaker 1: don't like. And that's what quants referred to as the 357 00:22:24,800 --> 00:22:29,920 Speaker 1: transfer coefficient. So the three decisions that are the three 358 00:22:30,000 --> 00:22:33,080 Speaker 1: inputs to determining your success is one is how big 359 00:22:33,119 --> 00:22:36,159 Speaker 1: the universe? You know, what the breadth of your investment decisions. 360 00:22:36,640 --> 00:22:39,560 Speaker 1: The second is how well are you forecasting? And the 361 00:22:39,640 --> 00:22:43,399 Speaker 1: third is how effectively are you transferring all your information 362 00:22:43,720 --> 00:22:47,200 Speaker 1: into the portfolio? The transfer coefficient, and that's a formula 363 00:22:47,320 --> 00:22:50,359 Speaker 1: that was very useful when you think about how much 364 00:22:51,280 --> 00:22:55,040 Speaker 1: our performance you can get from a portfolio. And it's 365 00:22:55,080 --> 00:22:58,720 Speaker 1: a formula that my colleagues Steve Sali and Roger Clark 366 00:22:58,760 --> 00:23:03,760 Speaker 1: and I developed um in the nineties and then published 367 00:23:03,760 --> 00:23:08,359 Speaker 1: a paper in the two thousand timeframe. That's where that 368 00:23:08,440 --> 00:23:11,919 Speaker 1: question came from. So now let's talk about some of 369 00:23:11,920 --> 00:23:16,840 Speaker 1: the specific funds that that you manage over at Wells Fargo, 370 00:23:17,440 --> 00:23:22,240 Speaker 1: starting with the Global Dividends Opportunity Funds. From the name, 371 00:23:23,040 --> 00:23:26,840 Speaker 1: I'm going to guess that it is both global and 372 00:23:26,960 --> 00:23:30,879 Speaker 1: dividend focused. Um, what do we make of the trend 373 00:23:31,040 --> 00:23:35,639 Speaker 1: of falling dividends at least here in the US over 374 00:23:35,680 --> 00:23:40,600 Speaker 1: the past call it a few decades, Yeah, I mean 375 00:23:40,600 --> 00:23:43,520 Speaker 1: that's been kind of a corporate trend right because of 376 00:23:43,560 --> 00:23:49,480 Speaker 1: the tendency to have buy back as a methodology forgive 377 00:23:49,880 --> 00:23:53,399 Speaker 1: beturning money to to shareholders. So I mean it's a 378 00:23:53,520 --> 00:23:57,199 Speaker 1: challenge from a form divid and focused strategy in that 379 00:23:57,320 --> 00:24:01,639 Speaker 1: particular portfolio. The other thing we do is use covered calls. 380 00:24:02,119 --> 00:24:07,520 Speaker 1: So Analytic got it start as a firm in doing 381 00:24:07,600 --> 00:24:11,760 Speaker 1: covered call strategies. So we actually use a vault of 382 00:24:11,800 --> 00:24:16,360 Speaker 1: the forecasting model to identify overvalue call options and then 383 00:24:16,520 --> 00:24:21,480 Speaker 1: use that to generate additional income for the portfolio. Interesting, 384 00:24:21,560 --> 00:24:24,320 Speaker 1: it's a little bit unusual, but this is typical quant 385 00:24:24,960 --> 00:24:28,919 Speaker 1: approach in that you're not just using one way to 386 00:24:29,040 --> 00:24:32,680 Speaker 1: generate income. You're using dividends as well as covered calls 387 00:24:32,720 --> 00:24:36,760 Speaker 1: as a way to generate income for the portfolio. So historically, 388 00:24:36,960 --> 00:24:43,480 Speaker 1: covered call writing was always a challenge because you're balancing, um, 389 00:24:43,560 --> 00:24:46,879 Speaker 1: the risk of a stock that's working out getting cold 390 00:24:46,920 --> 00:24:54,080 Speaker 1: away versus the versus the income you get from selling 391 00:24:54,119 --> 00:24:57,119 Speaker 1: the calls. I'm going to assume giving your background and 392 00:24:57,280 --> 00:25:03,280 Speaker 1: low vall that um you managed to offset that because 393 00:25:03,840 --> 00:25:07,520 Speaker 1: you're going to have a lower beta group of names. 394 00:25:07,560 --> 00:25:10,000 Speaker 1: They're they're less likely to get called away when the 395 00:25:10,000 --> 00:25:12,920 Speaker 1: stock starts to run up or I should say less 396 00:25:12,960 --> 00:25:18,480 Speaker 1: likely to run up and therefore have the stock called away. Yeah, 397 00:25:18,600 --> 00:25:21,680 Speaker 1: so that I mean that portfolio is team managed, so 398 00:25:21,720 --> 00:25:25,119 Speaker 1: it's actually uses some of the other skills within Wells 399 00:25:25,119 --> 00:25:27,679 Speaker 1: frougu as well, So we're not the only manager. So 400 00:25:27,720 --> 00:25:31,919 Speaker 1: we just manage the option portflio. And the reason the 401 00:25:31,960 --> 00:25:36,720 Speaker 1: way we avoid the stop getting called away is by 402 00:25:36,840 --> 00:25:41,040 Speaker 1: using index calls. Because if you use the index calls, 403 00:25:41,160 --> 00:25:44,119 Speaker 1: all you're susceptible to the market run up, not the 404 00:25:44,160 --> 00:25:47,880 Speaker 1: individual stocks themselves going up, right, right, you still run 405 00:25:47,920 --> 00:25:52,080 Speaker 1: the risk of having to buy in to replace the 406 00:25:52,080 --> 00:25:55,880 Speaker 1: the the underlying right well, the underlying wouldn't get called 407 00:25:55,920 --> 00:25:59,719 Speaker 1: away because you're selling the calls on the SMPI index 408 00:25:59,800 --> 00:26:03,040 Speaker 1: very example, so you would have to be you'd be 409 00:26:03,119 --> 00:26:05,199 Speaker 1: on the hook for the payment. But the key with 410 00:26:05,359 --> 00:26:09,399 Speaker 1: call writing that most people don't think about is you 411 00:26:09,480 --> 00:26:12,280 Speaker 1: have to have a way to value the call. Right, 412 00:26:12,920 --> 00:26:16,360 Speaker 1: So if volatility is over priced, you know, as it 413 00:26:16,440 --> 00:26:19,320 Speaker 1: was at the start of this year, for example, that's 414 00:26:19,320 --> 00:26:22,600 Speaker 1: a great time to be selling calls. Then you're getting 415 00:26:22,600 --> 00:26:27,920 Speaker 1: paid for exactly getting paid for the risk. But having 416 00:26:27,920 --> 00:26:30,760 Speaker 1: a call writing strategy where you're always selling the same 417 00:26:30,800 --> 00:26:34,879 Speaker 1: call that is usually not going to be as successful 418 00:26:34,920 --> 00:26:39,040 Speaker 1: as something that is actually constantly looking at you know, 419 00:26:39,160 --> 00:26:42,280 Speaker 1: one month, two month, three month horizon and different strikes 420 00:26:42,359 --> 00:26:45,119 Speaker 1: and trying to figure out where's the most amount of 421 00:26:45,160 --> 00:26:50,360 Speaker 1: miss pricing coming in the marketplace. Now, for example, as 422 00:26:50,400 --> 00:26:53,200 Speaker 1: we are in sitting here in you know, in the 423 00:26:53,760 --> 00:26:57,280 Speaker 1: and the may longer dated calls actually more miss priced 424 00:26:57,280 --> 00:26:59,679 Speaker 1: than shot it calls. So you really need to be 425 00:26:59,680 --> 00:27:02,760 Speaker 1: thinking about increasing the tenor of your call if you're 426 00:27:02,760 --> 00:27:07,280 Speaker 1: in a call writing strategy. Very very interesting. What about 427 00:27:07,280 --> 00:27:11,119 Speaker 1: the Low Volatility US Equity Fund? What what's the philosophy 428 00:27:11,160 --> 00:27:17,040 Speaker 1: behind that? So that's a fairly vanilla fund um from 429 00:27:17,119 --> 00:27:19,400 Speaker 1: if you think about everything that we do, because it's 430 00:27:19,600 --> 00:27:24,119 Speaker 1: long US stock. The idea is to have a bottlatility 431 00:27:24,240 --> 00:27:27,480 Speaker 1: of about seventy percent of the equity market and and 432 00:27:27,560 --> 00:27:32,120 Speaker 1: a similar return. The fund itself is new, but we've 433 00:27:32,200 --> 00:27:35,520 Speaker 1: run that strategy now since the early two thousands. So 434 00:27:35,600 --> 00:27:38,359 Speaker 1: it's you know, I'm going to show my age because 435 00:27:38,359 --> 00:27:41,200 Speaker 1: it's I've been involved with that type of for sixteen 436 00:27:41,240 --> 00:27:46,360 Speaker 1: years and we're building a portfolio that has that has 437 00:27:46,400 --> 00:27:50,080 Speaker 1: a low beta, so the average beata for all the 438 00:27:50,119 --> 00:27:53,240 Speaker 1: stocks in the portfolio is around point six point seven. 439 00:27:53,760 --> 00:27:57,000 Speaker 1: But at the same time, we're tilting towards the characteristics 440 00:27:57,080 --> 00:27:59,359 Speaker 1: that are in favor. So if you look at the 441 00:27:59,400 --> 00:28:03,440 Speaker 1: portfolio right now, you'll see that it has a very 442 00:28:03,520 --> 00:28:07,720 Speaker 1: big loading on price to sales as a characteristic. That's 443 00:28:07,720 --> 00:28:11,159 Speaker 1: a factor that has really really been rewarded in the marketplace. 444 00:28:11,720 --> 00:28:15,360 Speaker 1: Investors are really focusing on that right now. Trading earnings 445 00:28:15,480 --> 00:28:21,040 Speaker 1: is not useful as because any trailings earnings number contains 446 00:28:21,080 --> 00:28:25,399 Speaker 1: the pandemic. So using forward looking numbers like forward pe 447 00:28:25,720 --> 00:28:29,960 Speaker 1: price to sales are really really important. Asset turnover is 448 00:28:30,000 --> 00:28:32,959 Speaker 1: a factor that is really important right now as well, 449 00:28:33,119 --> 00:28:36,320 Speaker 1: so you'll see a lot of stocks with high asset 450 00:28:36,359 --> 00:28:40,560 Speaker 1: turnover um in that portfolio. So we tend to look 451 00:28:40,560 --> 00:28:43,400 Speaker 1: at a very broad range of factors, not just sort 452 00:28:43,400 --> 00:28:47,760 Speaker 1: of you know, value, growth, quality, small cap, but really 453 00:28:47,800 --> 00:28:51,040 Speaker 1: kind of try to capture the pross section of factors 454 00:28:51,120 --> 00:28:54,760 Speaker 1: that fundamental investors look at. Let's talk a little bit 455 00:28:54,840 --> 00:28:58,800 Speaker 1: about where we are in this market cycle today. Do 456 00:28:58,920 --> 00:29:02,760 Speaker 1: you look at us as sort of late cycle or 457 00:29:03,000 --> 00:29:06,720 Speaker 1: was last year a reset and this is a relatively 458 00:29:06,800 --> 00:29:10,400 Speaker 1: young market, or do you not care about any of 459 00:29:10,440 --> 00:29:19,040 Speaker 1: those things I do care um from the perspective of 460 00:29:19,160 --> 00:29:23,360 Speaker 1: looking at things in a historical context, because that's often 461 00:29:23,440 --> 00:29:25,360 Speaker 1: a kind of a useful guide as you're trying to 462 00:29:25,360 --> 00:29:28,960 Speaker 1: figure out which factors too blobal weight and underweight, and 463 00:29:29,080 --> 00:29:31,680 Speaker 1: you know, does it make sense? And I would say, 464 00:29:31,840 --> 00:29:36,680 Speaker 1: given the way factors are behaving right now, it's much 465 00:29:36,800 --> 00:29:41,800 Speaker 1: more of a really early cycle. There's a huge focus 466 00:29:41,960 --> 00:29:46,440 Speaker 1: on in the US and globally on estimate revisions. So 467 00:29:46,600 --> 00:29:50,920 Speaker 1: stocks with high being revised upwards are doing incredibly well, 468 00:29:51,000 --> 00:29:53,560 Speaker 1: so people are really focused on that as a factor. 469 00:29:54,640 --> 00:29:58,080 Speaker 1: Small cap is doing well, Low price to sales stocks 470 00:29:58,080 --> 00:30:01,640 Speaker 1: are doing well. Those are all that associated early in 471 00:30:01,680 --> 00:30:05,600 Speaker 1: the fact in the cycle. If you look at interest 472 00:30:05,680 --> 00:30:09,160 Speaker 1: rate sensitivity as a factor, you know that is something 473 00:30:09,240 --> 00:30:12,440 Speaker 1: really people are really focused on because there's concerned that 474 00:30:12,760 --> 00:30:15,480 Speaker 1: there's going to be a rise in rates. And you 475 00:30:15,520 --> 00:30:19,240 Speaker 1: should think about equities having duration. Most people don't, but 476 00:30:19,360 --> 00:30:22,760 Speaker 1: equities do have duration, and different equities have different types 477 00:30:22,800 --> 00:30:26,640 Speaker 1: of different levels of duration. So that's a measure managing 478 00:30:26,680 --> 00:30:30,680 Speaker 1: newport fill. So when you say duration, harand let me 479 00:30:30,720 --> 00:30:34,080 Speaker 1: interrupt yourself. When you say duration, most people think in 480 00:30:34,240 --> 00:30:38,760 Speaker 1: terms of fixed income and bonds is having a duration. Hey, 481 00:30:38,760 --> 00:30:40,760 Speaker 1: this is a ten year bond, of twenty year bond 482 00:30:40,800 --> 00:30:44,000 Speaker 1: and nine bond, what have you. What does duration mean 483 00:30:44,080 --> 00:30:48,480 Speaker 1: when it comes to equity. I'm assuming there's some sensitivity 484 00:30:48,560 --> 00:30:52,160 Speaker 1: to changes in interest rate policy. What do you mean 485 00:30:52,160 --> 00:30:55,240 Speaker 1: by duration, he said to me. The duration of a 486 00:30:55,280 --> 00:31:00,360 Speaker 1: stock is how sensitive a stock is in the tenure yield. 487 00:31:00,720 --> 00:31:06,800 Speaker 1: So if it's insitive, then you'll see that it's it's insensitive. 488 00:31:07,080 --> 00:31:09,880 Speaker 1: You'll see that it's not affected by changes in rates. 489 00:31:11,520 --> 00:31:16,240 Speaker 1: And you know, when you look at individual companies, it's 490 00:31:16,280 --> 00:31:18,840 Speaker 1: really fascinating. You stop. When you look at them carefully 491 00:31:18,840 --> 00:31:21,800 Speaker 1: from that perspective, you'll see that some companies, for example, 492 00:31:22,520 --> 00:31:27,400 Speaker 1: have a lot of floating rate debt, so as rates 493 00:31:27,560 --> 00:31:31,720 Speaker 1: rise there interest payments are going to rise. So those 494 00:31:31,720 --> 00:31:35,520 Speaker 1: companies tend to be more interest rate sensitive than others. Huh, 495 00:31:35,800 --> 00:31:39,960 Speaker 1: that's really that's really interesting. So much has happened since 496 00:31:40,000 --> 00:31:44,360 Speaker 1: you started in the industry today. What do you think 497 00:31:44,400 --> 00:31:51,280 Speaker 1: are the biggest differences in asset management relative to years 498 00:31:51,280 --> 00:31:53,600 Speaker 1: ago or so? Well, there's a lot been a lot 499 00:31:53,680 --> 00:31:57,240 Speaker 1: of good developments, I think, you know, on the positive side, 500 00:31:57,800 --> 00:32:03,040 Speaker 1: um fees have come down, the trading costs have come down, 501 00:32:03,640 --> 00:32:09,160 Speaker 1: so your ability to trade a portfolio mo uh, you know, 502 00:32:09,240 --> 00:32:13,200 Speaker 1: to have higher turnover to capture factor rotation. That's become 503 00:32:13,240 --> 00:32:19,920 Speaker 1: a lot easier. The costs of data um have come down, 504 00:32:20,000 --> 00:32:24,600 Speaker 1: but at the same time, the amount of data available 505 00:32:24,640 --> 00:32:28,239 Speaker 1: for purchase has gone up. So if you look at 506 00:32:28,320 --> 00:32:31,320 Speaker 1: us as a group as a team, you know our 507 00:32:31,440 --> 00:32:36,320 Speaker 1: data costs is millions, but it seems to always go up, 508 00:32:36,400 --> 00:32:38,840 Speaker 1: not come down. What are your thoughts on some of 509 00:32:38,880 --> 00:32:43,959 Speaker 1: these alternative data sources. I know people are buying satellite 510 00:32:44,040 --> 00:32:49,840 Speaker 1: data where you can see movement of tankers and ships 511 00:32:49,880 --> 00:32:52,840 Speaker 1: that are I've even seen some people say we could 512 00:32:52,880 --> 00:32:56,520 Speaker 1: tell how loaded the ship is by how low it 513 00:32:56,560 --> 00:32:59,320 Speaker 1: sits in the water relative to its waterline. Do you 514 00:32:59,400 --> 00:33:04,680 Speaker 1: do you have anything it's on these alternative data sources? Yeah, 515 00:33:05,280 --> 00:33:08,120 Speaker 1: I think those that type of data is really useful 516 00:33:08,760 --> 00:33:13,040 Speaker 1: in terms of updating your earnings estimate forecasts, because that's 517 00:33:13,120 --> 00:33:16,600 Speaker 1: ultimately what it comes down to. The challenge with that 518 00:33:16,720 --> 00:33:21,080 Speaker 1: data is that it's really really time sensitive. So when 519 00:33:21,120 --> 00:33:23,880 Speaker 1: I think of data quality, I'm always trying to think 520 00:33:23,920 --> 00:33:28,600 Speaker 1: about what horizon do I need to invest with to 521 00:33:28,680 --> 00:33:31,800 Speaker 1: actually use that. Now that's really useful for me in 522 00:33:31,800 --> 00:33:34,520 Speaker 1: a short run trading model. But that's not the sweet 523 00:33:34,560 --> 00:33:37,240 Speaker 1: spot for from our size because we can't turn those 524 00:33:37,240 --> 00:33:42,600 Speaker 1: portfolios over enough to capture that. So I I prefer data, 525 00:33:42,680 --> 00:33:46,400 Speaker 1: for example that looks at you know, um. Just to 526 00:33:46,520 --> 00:33:49,200 Speaker 1: pick up example of something I'm working on now, is 527 00:33:50,040 --> 00:33:53,000 Speaker 1: what's the carbon footprint of a company and how can 528 00:33:53,040 --> 00:33:57,000 Speaker 1: it be measured? Right? Because we know the cost of 529 00:33:57,000 --> 00:33:59,240 Speaker 1: emitting carbon is going to go up in the future. 530 00:34:00,280 --> 00:34:02,800 Speaker 1: That's going to be a big factor in the profitability 531 00:34:02,800 --> 00:34:06,160 Speaker 1: of companies and their behavior, and they need to invest 532 00:34:07,160 --> 00:34:10,520 Speaker 1: in terms of new plants, encuipment. So what data can 533 00:34:10,600 --> 00:34:13,560 Speaker 1: be used to capture that. That's kind of a moment intermediate, 534 00:34:13,600 --> 00:34:17,120 Speaker 1: long horizon factor. And the more data I can collect 535 00:34:17,120 --> 00:34:19,319 Speaker 1: on that dimension, the better if I am. But it 536 00:34:19,360 --> 00:34:22,600 Speaker 1: doesn't rely on me getting to some information quicker than 537 00:34:23,440 --> 00:34:26,960 Speaker 1: somebody else and the information kind of dying at the 538 00:34:26,960 --> 00:34:31,040 Speaker 1: next learnings announcement that you know that makes a lot 539 00:34:31,080 --> 00:34:33,960 Speaker 1: of sense. In fact, since you mentioned low carbon I'm 540 00:34:33,960 --> 00:34:36,600 Speaker 1: going to jump ahead to another question. What are your 541 00:34:36,600 --> 00:34:40,920 Speaker 1: thoughts on E s G. On environmental, social and corporate 542 00:34:40,960 --> 00:34:45,640 Speaker 1: governance as potential factors. I've I've heard people describe them 543 00:34:45,680 --> 00:34:48,600 Speaker 1: as risk screens. What do you think of E s G. 544 00:34:50,400 --> 00:34:53,640 Speaker 1: I think they're really important as risk screens. I think 545 00:34:53,680 --> 00:34:56,799 Speaker 1: if you what we've found is that if you use 546 00:34:57,280 --> 00:35:03,080 Speaker 1: E s G related factors actually incredibly important in describing 547 00:35:03,160 --> 00:35:05,640 Speaker 1: the future return volatil the of a start. The thing 548 00:35:05,719 --> 00:35:11,000 Speaker 1: that's most important is governance, and you'll find that companies 549 00:35:11,040 --> 00:35:14,880 Speaker 1: with poor governance the returns are very very fat tailed 550 00:35:15,880 --> 00:35:18,279 Speaker 1: and as a portfolio manager, you need to account for 551 00:35:18,320 --> 00:35:23,000 Speaker 1: that because most risk models missed that. Right, a poorly 552 00:35:23,040 --> 00:35:28,400 Speaker 1: government company has a significant chance of a really negative return, 553 00:35:29,600 --> 00:35:32,320 Speaker 1: but it doesn't happen very often. It will happen once 554 00:35:32,360 --> 00:35:36,799 Speaker 1: every fifteen years, once every thirty years. So in a 555 00:35:36,840 --> 00:35:40,359 Speaker 1: typical risk model it actually doesn't show up, but it 556 00:35:40,400 --> 00:35:43,280 Speaker 1: does show up if you look at you know, long 557 00:35:45,080 --> 00:35:48,600 Speaker 1: time series of data and beef. I find these E 558 00:35:48,760 --> 00:35:53,920 Speaker 1: s G factors are really really important in risk forecasting. 559 00:35:54,080 --> 00:35:58,880 Speaker 1: They're not useful in return forecasting because I think you 560 00:35:58,920 --> 00:36:03,520 Speaker 1: can make the case that people like these stocks or 561 00:36:03,640 --> 00:36:05,799 Speaker 1: the like these stocks for other reasons, you know, just 562 00:36:05,880 --> 00:36:08,960 Speaker 1: like you have people like since some people like sin stocks, right, 563 00:36:09,000 --> 00:36:12,920 Speaker 1: that's where there's a sin stock etf um So I 564 00:36:12,920 --> 00:36:16,359 Speaker 1: think the return aspect for me, is less important than 565 00:36:16,400 --> 00:36:21,040 Speaker 1: the volatility aspect. Huh. That's really interesting. It's it's sort 566 00:36:21,080 --> 00:36:26,560 Speaker 1: of the Charlie Manger approach, which is, don't be more smart, 567 00:36:26,760 --> 00:36:31,000 Speaker 1: be less stupid. In other words, and I love phrases it, 568 00:36:31,040 --> 00:36:35,560 Speaker 1: but you're you're looking to screen out potential disasters with 569 00:36:35,640 --> 00:36:41,319 Speaker 1: E s G rather than screen in additional alpha. Right. 570 00:36:41,400 --> 00:36:43,600 Speaker 1: That's to me, that's exactly the way to think about 571 00:36:43,640 --> 00:36:47,760 Speaker 1: because these companies have something in their behavior where there's 572 00:36:47,800 --> 00:36:51,920 Speaker 1: going to be a chance that they have a bad 573 00:36:51,960 --> 00:36:56,279 Speaker 1: outcome in the next twenty years. Right. But if you 574 00:36:56,320 --> 00:36:59,160 Speaker 1: have a portfolio of poorly government companies and suppose you 575 00:36:59,200 --> 00:37:03,000 Speaker 1: have a hundred stocks in them, there's a significant chance 576 00:37:03,080 --> 00:37:04,879 Speaker 1: that one of them is going to have a bad 577 00:37:04,920 --> 00:37:08,520 Speaker 1: outcome next year. So that they is a really key 578 00:37:08,560 --> 00:37:14,560 Speaker 1: dimension in using E s G in a portfolio because 579 00:37:15,040 --> 00:37:17,520 Speaker 1: most people think don't think about it that way. But 580 00:37:17,920 --> 00:37:21,440 Speaker 1: I found that with D s G factors, and increasingly 581 00:37:21,440 --> 00:37:25,680 Speaker 1: with environmental factors like carbon or water pollution, or you know, 582 00:37:26,040 --> 00:37:30,360 Speaker 1: even something is like plastics, the company's plastic emissions, is 583 00:37:30,360 --> 00:37:33,279 Speaker 1: there a way to measure that and quantify and incorporate 584 00:37:33,280 --> 00:37:36,200 Speaker 1: in the portfolio Because that's increasingly going to be something 585 00:37:36,239 --> 00:37:40,080 Speaker 1: that investors care about and something that the company will 586 00:37:40,120 --> 00:37:43,239 Speaker 1: have to care about in the way people assess their 587 00:37:43,239 --> 00:37:50,080 Speaker 1: future profitability. That's really that's really kind of kind of intriguing. 588 00:37:50,239 --> 00:37:54,480 Speaker 1: So so here we are, the economy is just starting 589 00:37:54,520 --> 00:37:58,719 Speaker 1: to reopen. People are more concerned with inflation than they 590 00:37:58,719 --> 00:38:03,200 Speaker 1: are with unemployment. It's means, what factors do you see 591 00:38:03,760 --> 00:38:09,040 Speaker 1: really taking advantage of the post COVID reopening, anything stand 592 00:38:09,080 --> 00:38:13,280 Speaker 1: out in particular, and what do you think is um 593 00:38:13,360 --> 00:38:18,239 Speaker 1: the wrong factor for this phase of the recovery. Well, 594 00:38:18,360 --> 00:38:24,360 Speaker 1: I would stay away from anything that uses recent accounting data. 595 00:38:24,600 --> 00:38:27,319 Speaker 1: So just to just to give you something tangible, you know, 596 00:38:28,000 --> 00:38:30,400 Speaker 1: people focus on things like r o E and r 597 00:38:30,440 --> 00:38:33,560 Speaker 1: o A right return on equity return and affect those 598 00:38:33,640 --> 00:38:38,840 Speaker 1: numbers are really have been affected by the company's performance 599 00:38:38,880 --> 00:38:42,839 Speaker 1: in COVID. So that's those are factors I would focus on. 600 00:38:43,600 --> 00:38:46,560 Speaker 1: I mentioned trailing learning zeal as a fact that you 601 00:38:46,600 --> 00:38:50,200 Speaker 1: should not look at, so that anything that uses trailing 602 00:38:50,200 --> 00:38:53,160 Speaker 1: accounting data you've got to stay away from. But if 603 00:38:53,160 --> 00:38:55,359 Speaker 1: you want to look at the valuation, which I think 604 00:38:55,600 --> 00:38:59,680 Speaker 1: there's a lot of evaluation factors that are you should 605 00:38:59,680 --> 00:39:02,680 Speaker 1: be looking at looking at the ratio of price to 606 00:39:02,800 --> 00:39:06,520 Speaker 1: sales is a really excellent factor right now given where 607 00:39:06,520 --> 00:39:10,839 Speaker 1: we are in the cycle. Staying away from companies with 608 00:39:10,960 --> 00:39:15,359 Speaker 1: a lot of debt, especially debt that is floating rate 609 00:39:15,400 --> 00:39:19,000 Speaker 1: as opposed to fix rate, is something that's that you 610 00:39:19,080 --> 00:39:23,480 Speaker 1: should be looking at doing. Going towards companies that have 611 00:39:23,719 --> 00:39:27,399 Speaker 1: high operating margins in terms of their business model. That's 612 00:39:27,440 --> 00:39:29,719 Speaker 1: a little bit difficult to do because of the accounting 613 00:39:29,760 --> 00:39:33,680 Speaker 1: data problem for the last year. Uh, that's something that 614 00:39:33,760 --> 00:39:37,839 Speaker 1: you should be incorporating into your portfolio. And the other 615 00:39:37,920 --> 00:39:40,600 Speaker 1: thing that I would really emphasize in the current cycle 616 00:39:41,440 --> 00:39:45,280 Speaker 1: because of the change we're seeing in the way companies 617 00:39:45,320 --> 00:39:49,680 Speaker 1: do business, is staying away from companies where there's a 618 00:39:49,680 --> 00:39:54,440 Speaker 1: lot of disagreements about their future earnings. So one thing 619 00:39:54,480 --> 00:39:59,640 Speaker 1: people don't have attention to is looking at analyst dispersion. 620 00:40:00,320 --> 00:40:03,200 Speaker 1: So if you look at an earnings forecast, everybody looks 621 00:40:03,239 --> 00:40:05,759 Speaker 1: at the mean, but you should also look at is 622 00:40:05,840 --> 00:40:08,000 Speaker 1: look at the difference between the high and the lower 623 00:40:08,080 --> 00:40:11,680 Speaker 1: or the spread between analysts, because whenever they has a 624 00:40:11,719 --> 00:40:15,919 Speaker 1: big spread, that means there's a lot of disagreement as 625 00:40:15,920 --> 00:40:18,760 Speaker 1: to the future profitability of the company, and that factor 626 00:40:18,960 --> 00:40:21,839 Speaker 1: is something that's going to be really important in this 627 00:40:21,960 --> 00:40:25,800 Speaker 1: stage of the cycle. Really interesting. I have one curveball 628 00:40:26,400 --> 00:40:31,120 Speaker 1: question to h to throw at you. Um, what sort 629 00:40:31,120 --> 00:40:35,080 Speaker 1: of motorcycle do you like to ride? Oh? My goodness, 630 00:40:35,080 --> 00:40:38,759 Speaker 1: how much time do you have? Well I did read 631 00:40:38,840 --> 00:40:42,920 Speaker 1: that you have bite pretty much all over the world. 632 00:40:43,360 --> 00:40:47,239 Speaker 1: How much of an exaggeration is that? Uh? No, that 633 00:40:47,360 --> 00:40:51,120 Speaker 1: that that is true. I mean motorcycle is reatively speaking, 634 00:40:51,120 --> 00:40:55,200 Speaker 1: are cheap. Um. And one of my hobbies is exploring 635 00:40:55,239 --> 00:40:57,879 Speaker 1: the world on a motorcycle. So I like to keep 636 00:40:57,920 --> 00:41:01,440 Speaker 1: bikes at different out of the world. I can you know, 637 00:41:01,520 --> 00:41:03,640 Speaker 1: I can show up, I leave my clothes on the 638 00:41:03,680 --> 00:41:06,759 Speaker 1: bike and can hop on and ride. But my my 639 00:41:06,960 --> 00:41:12,520 Speaker 1: daily rider, because I ride to work, is an electric 640 00:41:12,600 --> 00:41:15,600 Speaker 1: Hollie Davidson, which is actually a wonderful bike. It's a 641 00:41:16,719 --> 00:41:24,160 Speaker 1: called Hallie Davidson Live Wire. Um. It's quiet, it's fast, um. 642 00:41:24,200 --> 00:41:26,200 Speaker 1: And when I leave early morning for work, I don't 643 00:41:26,239 --> 00:41:31,240 Speaker 1: disturb my neighbors. Um. And we have so solar panels 644 00:41:31,280 --> 00:41:36,480 Speaker 1: at home so it doesn't cost me anything to run. Um. 645 00:41:36,520 --> 00:41:40,920 Speaker 1: My favorite bike to ride on the weekends is to 646 00:41:40,960 --> 00:41:44,560 Speaker 1: have an obscure Italian bike called the bi motor, the Due, 647 00:41:45,600 --> 00:41:50,279 Speaker 1: which is a two stroke, very light sport bike. And 648 00:41:50,320 --> 00:41:54,640 Speaker 1: then my favorite bike for touring is you know BMWs 649 00:41:54,680 --> 00:41:57,200 Speaker 1: because you can get them service anywhere in the world, 650 00:41:57,320 --> 00:42:01,000 Speaker 1: and there it's almost like a train. They almost never takedown, right, 651 00:42:01,120 --> 00:42:03,759 Speaker 1: They're big, they're solid, they're comfortable, and they could go 652 00:42:04,640 --> 00:42:07,800 Speaker 1: on and on and on. Do you find I asked 653 00:42:07,800 --> 00:42:09,720 Speaker 1: this question as a kid who used to write dirt 654 00:42:09,719 --> 00:42:14,560 Speaker 1: bikes and and some of the smaller one fifties do 655 00:42:14,640 --> 00:42:18,520 Speaker 1: you find like traffic is so heavy these days and 656 00:42:18,680 --> 00:42:22,280 Speaker 1: people are just not paying attention. It's a little more 657 00:42:22,520 --> 00:42:29,600 Speaker 1: challenging to to be on a motorcycle. There is considerably 658 00:42:29,640 --> 00:42:35,279 Speaker 1: more distracted driving um in southern California. Actually, in California, 659 00:42:35,360 --> 00:42:39,080 Speaker 1: it's legal to filter or split lanes, So when you're 660 00:42:39,160 --> 00:42:42,040 Speaker 1: driving down the middle of lanes, what you'll often see 661 00:42:42,320 --> 00:42:44,480 Speaker 1: is the you know, the person next to you and 662 00:42:44,560 --> 00:42:47,920 Speaker 1: driving a car has their phone in their lap. And 663 00:42:47,960 --> 00:42:49,799 Speaker 1: I don't know why, there's a tendency if you're look 664 00:42:49,840 --> 00:42:51,759 Speaker 1: if you're texting, you always put the phone in your 665 00:42:51,840 --> 00:42:53,719 Speaker 1: lap and then you look down away from where you're 666 00:42:53,800 --> 00:42:56,840 Speaker 1: driving and you text, and I see that probably you 667 00:42:56,880 --> 00:42:59,520 Speaker 1: know the time in the in the morning, so that 668 00:43:00,239 --> 00:43:02,440 Speaker 1: it is a hazard that one has to deal with, 669 00:43:03,360 --> 00:43:07,640 Speaker 1: and it does make it um so that usually when 670 00:43:07,680 --> 00:43:10,160 Speaker 1: I get to work in the morning, I'm really awake 671 00:43:10,880 --> 00:43:14,960 Speaker 1: because my adrenaline is flowing to see, to say the 672 00:43:15,040 --> 00:43:19,720 Speaker 1: very least, And you have a trip planned later for 673 00:43:20,800 --> 00:43:25,480 Speaker 1: parts of Asia. Where are you heading to this year? Well, 674 00:43:25,520 --> 00:43:28,480 Speaker 1: this is actually a continuation of a trip that was 675 00:43:28,600 --> 00:43:33,399 Speaker 1: got canceled last year because of COVID. So last year 676 00:43:33,640 --> 00:43:38,200 Speaker 1: I left the motorcycle. I was writing in Riga in Latviere, 677 00:43:38,480 --> 00:43:41,120 Speaker 1: and I'm writing this year. The plan is to write 678 00:43:41,160 --> 00:43:45,680 Speaker 1: from Riga to Saint Petersburg to Moscow and then next 679 00:43:45,800 --> 00:43:49,960 Speaker 1: year to ride from Moscow all the way to the 680 00:43:50,000 --> 00:43:53,399 Speaker 1: east coast of Russia to Ladivostok, which is right next 681 00:43:53,440 --> 00:43:56,879 Speaker 1: to Tokyo. Right, I've never been to Moscow, but St. 682 00:43:56,880 --> 00:44:01,440 Speaker 1: Petersburg is an amazing city and you can spend weeks there. 683 00:44:01,680 --> 00:44:06,919 Speaker 1: It's just an unbelievable amount of things to see and do. Yeah, 684 00:44:06,960 --> 00:44:09,480 Speaker 1: I'm really looking forward to that. I've put lots of 685 00:44:09,560 --> 00:44:12,719 Speaker 1: wonderful things about that city. All right, So I know 686 00:44:12,800 --> 00:44:15,160 Speaker 1: I only have you for a couple more minutes. Let's 687 00:44:15,280 --> 00:44:18,680 Speaker 1: jump to our favorite questions that we ask all of 688 00:44:18,719 --> 00:44:21,720 Speaker 1: our guests, starting with what are you streaming these days? 689 00:44:21,800 --> 00:44:27,200 Speaker 1: Give us some of your favorite Netflix, Amazon Prime podcast entertainment. 690 00:44:27,880 --> 00:44:30,319 Speaker 1: So I'm I'm going to be a disappointment on that 691 00:44:30,440 --> 00:44:35,040 Speaker 1: dimension because I don't think I watched Netflix in over 692 00:44:35,080 --> 00:44:38,120 Speaker 1: a year, so I'm not. I didn't grow up with 693 00:44:38,160 --> 00:44:42,200 Speaker 1: televisions in Sri Lanka or Scream, so I'm I'm almost 694 00:44:42,239 --> 00:44:45,920 Speaker 1: never watched them. Well, all right, listen, I would be 695 00:44:45,960 --> 00:44:49,839 Speaker 1: more productive if I wasn't if I wasn't watching. I'm 696 00:44:49,840 --> 00:44:52,640 Speaker 1: a big reader. I mean I read more, I more 697 00:44:52,680 --> 00:44:54,960 Speaker 1: than anybody else I know, so I'm probably got the 698 00:44:54,960 --> 00:44:59,520 Speaker 1: world's biggest Amazon books built. So so let me let 699 00:44:59,520 --> 00:45:01,960 Speaker 1: me jump to that question. Then tell us about some 700 00:45:02,040 --> 00:45:04,120 Speaker 1: of your favorite books. What if some are your all 701 00:45:04,200 --> 00:45:08,920 Speaker 1: time favorites, and what are you reading now? Well, what 702 00:45:09,040 --> 00:45:12,360 Speaker 1: I'm reading now? There's two books that I'm really enjoying. 703 00:45:12,760 --> 00:45:18,960 Speaker 1: One is called The Well Gardened Mind, m Um. It's 704 00:45:19,000 --> 00:45:22,960 Speaker 1: a book on how gardening and nature affects the way 705 00:45:23,040 --> 00:45:26,520 Speaker 1: we think. And this book, I think, to an investor 706 00:45:26,719 --> 00:45:30,799 Speaker 1: is really fascinating because it talks about how you exposure 707 00:45:30,840 --> 00:45:35,120 Speaker 1: to nature affects your decision making. So if we take 708 00:45:35,160 --> 00:45:38,840 Speaker 1: two people, for example, or two groups of students and 709 00:45:38,880 --> 00:45:41,280 Speaker 1: they're about to take the exam, one of them books 710 00:45:41,280 --> 00:45:45,319 Speaker 1: through an urban environment, are the group box to an arboretum, 711 00:45:45,640 --> 00:45:48,319 Speaker 1: so they exposed to nature. The one that boxer an 712 00:45:48,360 --> 00:45:52,440 Speaker 1: arboretum will have higher test case. And that's because of 713 00:45:52,560 --> 00:45:57,480 Speaker 1: something that's called attention restoration. Because we are also focused 714 00:45:57,640 --> 00:46:00,959 Speaker 1: in such short time periods now that after a while 715 00:46:01,080 --> 00:46:03,319 Speaker 1: we get attention fatigue, so you have to figure out 716 00:46:03,400 --> 00:46:05,840 Speaker 1: where to restore that. And if you're involved in trading 717 00:46:05,960 --> 00:46:09,480 Speaker 1: or building portfolios, this is something that that's really really critical. 718 00:46:09,600 --> 00:46:13,120 Speaker 1: So this book is a really fascinating book from st 719 00:46:13,400 --> 00:46:16,920 Speaker 1: from that standpoint, because I think, especially in the COVID environment, 720 00:46:16,960 --> 00:46:19,560 Speaker 1: we're all working longer hours and you have to figure 721 00:46:19,600 --> 00:46:22,920 Speaker 1: out a way to restore your attention during the day. 722 00:46:23,200 --> 00:46:25,239 Speaker 1: And that's kind of one of the big things that 723 00:46:25,360 --> 00:46:28,439 Speaker 1: got out of this book. Um. But the other book 724 00:46:28,440 --> 00:46:32,840 Speaker 1: I'm reading is I'm a I'm a total Formula one fanatic. Um. 725 00:46:32,880 --> 00:46:35,320 Speaker 1: I'm reading this book by one of my favorite designers, 726 00:46:35,400 --> 00:46:39,200 Speaker 1: Adrian Newey, and he wrote a book last year called 727 00:46:39,239 --> 00:46:43,040 Speaker 1: How How to Build a Car and Adrian Nui, I 728 00:46:43,120 --> 00:46:45,000 Speaker 1: think is one of the b he's the design of 729 00:46:45,000 --> 00:46:47,120 Speaker 1: a red bull. If we didn't know, but he's one 730 00:46:47,120 --> 00:46:50,719 Speaker 1: of the best card designers ever to go through Formula one. 731 00:46:51,040 --> 00:46:57,600 Speaker 1: And the book goes through his designed philosophy and how 732 00:46:58,560 --> 00:47:01,840 Speaker 1: his philosophy evolved over time and all the different cars 733 00:47:01,840 --> 00:47:05,360 Speaker 1: that he designed, but it also decided describes his career. 734 00:47:05,920 --> 00:47:10,200 Speaker 1: And you know, if most people don't realize it, but 735 00:47:10,280 --> 00:47:13,640 Speaker 1: Adrian was, you know, one of the key partners that Williams, 736 00:47:13,640 --> 00:47:15,760 Speaker 1: which is now one of the worst Formula one teams, 737 00:47:16,200 --> 00:47:19,279 Speaker 1: and it was a disagreement with the owners of the 738 00:47:19,320 --> 00:47:23,080 Speaker 1: company that left him. They do him leaving, So if 739 00:47:23,080 --> 00:47:26,680 Speaker 1: not for that disagreement, Williams would probably be continue to 740 00:47:26,680 --> 00:47:29,600 Speaker 1: be the number one team in UH in Formula one. 741 00:47:29,680 --> 00:47:31,719 Speaker 1: So it kind of highlights to me one of the 742 00:47:31,760 --> 00:47:35,800 Speaker 1: importance of realizing that teams are really important to a 743 00:47:35,840 --> 00:47:40,719 Speaker 1: business and if you let key team members go UH, 744 00:47:40,920 --> 00:47:43,280 Speaker 1: that's going to have a big impact on your business. 745 00:47:43,320 --> 00:47:45,440 Speaker 1: So there is a really strong tide of how to 746 00:47:45,520 --> 00:47:49,640 Speaker 1: build an effective team in a very high performance environment 747 00:47:49,719 --> 00:47:53,480 Speaker 1: in this book Give us Another the last one m 748 00:47:54,320 --> 00:47:57,000 Speaker 1: The most other book I read more recently, which is 749 00:47:57,000 --> 00:48:03,000 Speaker 1: a little more academic, is annoyed. Sure, yeah, and I 750 00:48:03,040 --> 00:48:05,080 Speaker 1: think you've talked about that. I think I've seen that 751 00:48:05,120 --> 00:48:09,920 Speaker 1: in your podcast. Um. The other one that I really 752 00:48:09,960 --> 00:48:12,799 Speaker 1: liked that I read recently was actually by one of 753 00:48:12,800 --> 00:48:17,120 Speaker 1: the people who was one of the founders of Analytic, 754 00:48:17,800 --> 00:48:21,080 Speaker 1: were involved in its founding, which is a book called 755 00:48:21,120 --> 00:48:25,560 Speaker 1: A Man for All Markets. Sure, and I think it's 756 00:48:25,560 --> 00:48:29,239 Speaker 1: a book that anybody going into quantity financial really by 757 00:48:29,360 --> 00:48:32,080 Speaker 1: at Top was definitely one of the smart st guys 758 00:48:32,160 --> 00:48:38,080 Speaker 1: I've ever met, UM, and so that I really really 759 00:48:38,160 --> 00:48:44,640 Speaker 1: enjoyed reading. UM. So, I you know, pretty pretty wide array, 760 00:48:44,719 --> 00:48:49,560 Speaker 1: but I'm really fascinated by this interaction between the how, 761 00:48:51,000 --> 00:48:55,040 Speaker 1: what what we have faced with effects our decision making. 762 00:48:55,440 --> 00:48:59,520 Speaker 1: And another book I read recently that I really liked 763 00:49:00,120 --> 00:49:05,520 Speaker 1: is called The Nature of Fear about Survival Lessons in 764 00:49:05,600 --> 00:49:09,120 Speaker 1: the while that's how we what happens to us when 765 00:49:09,160 --> 00:49:13,359 Speaker 1: we are fearful, because if we think about investors, you know, 766 00:49:14,480 --> 00:49:17,680 Speaker 1: one of the issues that's happened is with for one case, 767 00:49:17,800 --> 00:49:21,200 Speaker 1: and people having to manage their own portfolios. I don't 768 00:49:21,200 --> 00:49:27,760 Speaker 1: think people realize how they're environment affects their decision making, 769 00:49:27,920 --> 00:49:30,279 Speaker 1: and that's why it's really important to show kind of 770 00:49:30,280 --> 00:49:34,759 Speaker 1: average investor investing, you know, having exposure in for one case, 771 00:49:35,200 --> 00:49:40,279 Speaker 1: not to make decisions very often. You know, generally they 772 00:49:40,320 --> 00:49:43,239 Speaker 1: say look at it once a year. But also realize 773 00:49:43,440 --> 00:49:46,919 Speaker 1: what type of mood you're in when you're making that decisions. 774 00:49:46,960 --> 00:49:50,799 Speaker 1: And I think that's really underappreciated. To make sure that 775 00:49:50,840 --> 00:49:54,320 Speaker 1: you're not in a fearful state or what can cause 776 00:49:54,360 --> 00:49:56,759 Speaker 1: you to be in a fearful state when you look 777 00:49:56,800 --> 00:50:00,680 Speaker 1: at that. So you know, do some all things like 778 00:50:00,800 --> 00:50:04,000 Speaker 1: download your statement, look at it, you know, wait a month, 779 00:50:04,080 --> 00:50:06,920 Speaker 1: then make the decision, but don't be in a hurry 780 00:50:07,040 --> 00:50:10,319 Speaker 1: and allow yourself time to think about the decision you're 781 00:50:10,320 --> 00:50:13,480 Speaker 1: going to make getting put on the decision. But you know, 782 00:50:13,880 --> 00:50:16,120 Speaker 1: if I can think about this team, it's this broader 783 00:50:16,200 --> 00:50:20,120 Speaker 1: team of thinking about how your state of mind affects 784 00:50:20,120 --> 00:50:22,640 Speaker 1: your decision and how can you manage your state of mind? 785 00:50:22,840 --> 00:50:26,319 Speaker 1: Really interesting. Tell us about any mentors you might have had, 786 00:50:27,000 --> 00:50:32,680 Speaker 1: who who helps guide your career. I was pretty lucky, 787 00:50:32,760 --> 00:50:35,120 Speaker 1: I think when I went to the University of Rochester. 788 00:50:35,280 --> 00:50:38,680 Speaker 1: So there was a gentleman by the name of Paul 789 00:50:38,800 --> 00:50:44,760 Speaker 1: McAvoy who was the dean of the Business school, and 790 00:50:45,600 --> 00:50:48,200 Speaker 1: I was making a living at the time, you know, 791 00:50:48,640 --> 00:50:51,000 Speaker 1: in addition to going to school programming, and he hired 792 00:50:51,040 --> 00:50:54,320 Speaker 1: me as a programmer for some research projects. And Paul 793 00:50:54,440 --> 00:50:57,440 Speaker 1: was a very well known economist. He was on the 794 00:50:57,520 --> 00:51:01,040 Speaker 1: President's Council of Economic Advisors. So working with them, I 795 00:51:01,160 --> 00:51:05,200 Speaker 1: really learned that kind of think about how to sell problems, 796 00:51:05,239 --> 00:51:10,120 Speaker 1: but also think about applying economics mhm, you know, much 797 00:51:10,160 --> 00:51:13,719 Speaker 1: broader context than major policy or interest rate policy, and 798 00:51:13,760 --> 00:51:20,760 Speaker 1: thinking about you know, the whole Kansian world of animal 799 00:51:20,840 --> 00:51:23,600 Speaker 1: spirits and how they affect markets. So that was he 800 00:51:23,680 --> 00:51:26,760 Speaker 1: was probably I think one of the most instrumental people 801 00:51:27,040 --> 00:51:32,640 Speaker 1: to me. Uh. And then also Shin Kasu, who was 802 00:51:32,680 --> 00:51:35,080 Speaker 1: the founder of Analytic. He was the head of the 803 00:51:35,200 --> 00:51:39,000 Speaker 1: con department at the University of California at Irvine. It 804 00:51:39,120 --> 00:51:41,720 Speaker 1: was very fortunate to work with him. And then also 805 00:51:43,080 --> 00:51:45,680 Speaker 1: I would say the gentleman who was my PhD advisor 806 00:51:46,080 --> 00:51:50,120 Speaker 1: and also kind of a well known figure in in finance, 807 00:51:50,360 --> 00:51:55,840 Speaker 1: professor Robert Howgen, who wrote a book called The Inefficient 808 00:51:55,920 --> 00:51:59,680 Speaker 1: Market Hypothesis and the Incredible January Effects. A very very 809 00:51:59,760 --> 00:52:03,520 Speaker 1: color a full character, but somebody was always willing to 810 00:52:03,640 --> 00:52:10,080 Speaker 1: question markets and do unusual things to build portfolios in 811 00:52:10,200 --> 00:52:14,279 Speaker 1: terms of using at the time large scale optimizers and 812 00:52:14,520 --> 00:52:18,160 Speaker 1: building large scale factor models in the world where everybody 813 00:52:18,239 --> 00:52:22,000 Speaker 1: in the eighties we were convinced that markets were perfectly etician, 814 00:52:22,160 --> 00:52:24,919 Speaker 1: and now we know that's not the case. To say 815 00:52:24,920 --> 00:52:27,919 Speaker 1: the very least, what sort of advice would you give 816 00:52:27,960 --> 00:52:30,920 Speaker 1: to a recent college grad who was interested in a 817 00:52:31,040 --> 00:52:37,839 Speaker 1: career in either factor investing or quantitative finance. I think 818 00:52:37,880 --> 00:52:42,360 Speaker 1: the hardest thing in starting in the business right now 819 00:52:43,000 --> 00:52:49,600 Speaker 1: is figuring out whether you're invest actually invests interested in 820 00:52:49,680 --> 00:52:54,120 Speaker 1: investing all your interested in what you think is investing, 821 00:52:54,400 --> 00:53:03,120 Speaker 1: because investing is really about doing research to figure out 822 00:53:03,200 --> 00:53:06,439 Speaker 1: what's going on in the market and then figuring out 823 00:53:06,480 --> 00:53:11,359 Speaker 1: way to exploit that for the benefit of your clients. 824 00:53:11,480 --> 00:53:18,200 Speaker 1: Right it's not about frequent trading or moving faster than 825 00:53:18,280 --> 00:53:21,640 Speaker 1: somebody else. And what I find when I talk to 826 00:53:21,719 --> 00:53:25,399 Speaker 1: younger people is they're really focused on trying to get 827 00:53:25,400 --> 00:53:32,719 Speaker 1: an edge by getting this short run informational advantage, and 828 00:53:32,840 --> 00:53:36,439 Speaker 1: that's not that's not sustainable. And you also can't use 829 00:53:36,560 --> 00:53:41,480 Speaker 1: that to invest institutional money because people tend to have 830 00:53:41,719 --> 00:53:46,040 Speaker 1: longer horizons. So my piece of advice to them is 831 00:53:46,239 --> 00:53:51,479 Speaker 1: understand what type of investing you're interested in, and whether 832 00:53:51,560 --> 00:53:54,240 Speaker 1: you like doing that type of research, because the hardest 833 00:53:54,239 --> 00:53:57,879 Speaker 1: thing about doing research is eight percent at the time, 834 00:53:58,560 --> 00:54:03,120 Speaker 1: it's a dead end. So if you don't enjoy the journey, 835 00:54:04,920 --> 00:54:07,400 Speaker 1: it can be really frustrating, right, I mean, think about it. 836 00:54:07,440 --> 00:54:09,840 Speaker 1: Think about it this way. You're souted like being a chef, 837 00:54:09,960 --> 00:54:14,560 Speaker 1: but of the dishes you make taste absolutely horrible, for 838 00:54:14,840 --> 00:54:16,880 Speaker 1: four out of five get sent back to the kitchen. 839 00:54:17,560 --> 00:54:22,360 Speaker 1: Really really interesting, and in our final question, what do 840 00:54:22,400 --> 00:54:25,920 Speaker 1: you know about the world of research and portfolio management 841 00:54:25,960 --> 00:54:30,760 Speaker 1: today that you wish you knew back in the nineties 842 00:54:30,800 --> 00:54:36,440 Speaker 1: when you were really first getting started. Without a doubt Bory. 843 00:54:36,640 --> 00:54:41,200 Speaker 1: For me, it would be the impact of human eimations 844 00:54:41,280 --> 00:54:46,920 Speaker 1: and sentiments on markets. I think by the time I 845 00:54:46,960 --> 00:54:52,200 Speaker 1: did not appreciate how much that mattered, and now I 846 00:54:52,239 --> 00:54:56,759 Speaker 1: see that it matters a great degree. And I think 847 00:54:57,480 --> 00:55:00,440 Speaker 1: something I worked very hard at is trying away to 848 00:55:00,520 --> 00:55:05,160 Speaker 1: build a way to quantify that. But I didn't have 849 00:55:05,200 --> 00:55:10,120 Speaker 1: an appreciation for how much emotion and people's attitude and 850 00:55:10,160 --> 00:55:14,239 Speaker 1: sentiment matters in the way assets are price and I 851 00:55:14,280 --> 00:55:21,440 Speaker 1: think that is not taught enough in schools. Huh. Really 852 00:55:21,520 --> 00:55:25,000 Speaker 1: quite quite interesting. Thank you her In for being so 853 00:55:25,080 --> 00:55:28,000 Speaker 1: generous with your time. We have been speaking with horendraw 854 00:55:28,160 --> 00:55:32,480 Speaker 1: to Silver. He is the leader of Wells Fargoes quantitative 855 00:55:32,480 --> 00:55:36,280 Speaker 1: strategy group known as Analytic Investors. They manage over twenty 856 00:55:36,280 --> 00:55:41,040 Speaker 1: billion dollars and assets. If you enjoy this conversation, well, 857 00:55:41,160 --> 00:55:44,640 Speaker 1: be sure and check out all of our previous UH interviews. 858 00:55:44,719 --> 00:55:47,680 Speaker 1: There are nearly four hundred of them, and you can 859 00:55:47,719 --> 00:55:51,279 Speaker 1: find them at iTunes or Spotify or any of your 860 00:55:51,320 --> 00:55:56,400 Speaker 1: favorite podcast sites. We love your comments, feedback and suggestions 861 00:55:56,960 --> 00:56:00,200 Speaker 1: right to us at m IB podcast at bloom Berg 862 00:56:00,280 --> 00:56:03,320 Speaker 1: dot net. You can sign up from my daily reads 863 00:56:03,440 --> 00:56:06,880 Speaker 1: at Reholts dot com. Check out my weekly column on 864 00:56:07,040 --> 00:56:10,520 Speaker 1: Bloomberg dot com slash Opinion. Follow me on Twitter at 865 00:56:10,600 --> 00:56:13,440 Speaker 1: rit Holts. I would be remiss if I did not 866 00:56:13,560 --> 00:56:17,680 Speaker 1: thank the crack staff that helps put these conversations together 867 00:56:17,800 --> 00:56:21,680 Speaker 1: each week. UH Tico val Bron is our project manager. 868 00:56:22,040 --> 00:56:26,680 Speaker 1: Tim Harrow is my audio engineer. Michael Boyle is my producer. 869 00:56:27,040 --> 00:56:31,120 Speaker 1: Michael Batnick is my head of research. I'm Barry Ridholts. 870 00:56:31,120 --> 00:56:34,680 Speaker 1: You're listening to Master's Business on Bloomberg Radio.