1 00:00:02,200 --> 00:00:06,800 Speaker 1: This is Masters in Business with Barry Ridholts on Boomberg Radio. 2 00:00:07,120 --> 00:00:09,760 Speaker 1: This week. I was privileged to travel to the University 3 00:00:09,760 --> 00:00:12,799 Speaker 1: of Chicago to the Booth School of Business, where I 4 00:00:12,880 --> 00:00:16,560 Speaker 1: got to sit down with Eugene Fama, Nobel Laureate, Chicago 5 00:00:16,600 --> 00:00:21,680 Speaker 1: Booth School of Business UM, founder of the Efficient Market hypothesis, 6 00:00:22,040 --> 00:00:27,160 Speaker 1: creator of effectively the three, five and seven UH Fama 7 00:00:27,200 --> 00:00:32,280 Speaker 1: French factor model, basically the father of modern finance. I 8 00:00:32,280 --> 00:00:36,000 Speaker 1: don't know how else to describe him, along with his 9 00:00:36,640 --> 00:00:41,440 Speaker 1: best student, David Booth, co founder of Dimensional Funds, the 10 00:00:41,560 --> 00:00:44,480 Speaker 1: person that the Booth School of Business is named after. 11 00:00:45,680 --> 00:00:47,920 Speaker 1: What can I tell you? I flew out to Chicago. 12 00:00:48,720 --> 00:00:52,760 Speaker 1: UH basically went to the Booth School of Business at 13 00:00:52,760 --> 00:00:57,760 Speaker 1: the University of Chicago where they were celebrating this relationship 14 00:00:57,920 --> 00:01:02,760 Speaker 1: that both Fama and Booth have had for literally fifty years. 15 00:01:03,240 --> 00:01:05,080 Speaker 1: I got to sit down with the two of them 16 00:01:05,120 --> 00:01:07,679 Speaker 1: for an hour in front of about five people in 17 00:01:07,720 --> 00:01:10,839 Speaker 1: the audience, including a lot of students from the Boost 18 00:01:10,840 --> 00:01:14,400 Speaker 1: School as well as other notables who were in attendance. 19 00:01:15,040 --> 00:01:19,080 Speaker 1: And Fama is notoriously press shy. He does not do 20 00:01:19,160 --> 00:01:23,800 Speaker 1: a whole lot of UM interviews with the media. This 21 00:01:23,959 --> 00:01:27,280 Speaker 1: was just a delight. I can't begin to say how 22 00:01:27,480 --> 00:01:31,760 Speaker 1: just awesome he was. He's a provocateur. He likes to 23 00:01:31,840 --> 00:01:35,280 Speaker 1: say things that are very much, um contrarian. He's a 24 00:01:35,319 --> 00:01:38,360 Speaker 1: little bit you know, if Farma was on Twitter, he 25 00:01:38,400 --> 00:01:41,560 Speaker 1: would be a troll. He loves to tweak people, especially 26 00:01:41,959 --> 00:01:45,759 Speaker 1: his buddy and fellow Nobel laureate Richard Taylor. Uh. He 27 00:01:45,920 --> 00:01:50,840 Speaker 1: was busting his jobs about behavioral finance, basically saying it's 28 00:01:50,880 --> 00:01:54,160 Speaker 1: all just pushed back to the efficient market hypothesis. Uh. 29 00:01:54,280 --> 00:01:56,960 Speaker 1: David Booth, also very insightful, had a lot of things 30 00:01:56,960 --> 00:02:00,200 Speaker 1: to say. There's obviously a tremendous amount of respect at 31 00:02:00,520 --> 00:02:03,840 Speaker 1: between the two of these guys. I could babble about 32 00:02:04,000 --> 00:02:07,640 Speaker 1: my experience in Chicago for hours, but rather than do that, 33 00:02:08,200 --> 00:02:12,760 Speaker 1: why not just say my conversation with Eugene Vama and 34 00:02:12,880 --> 00:02:16,799 Speaker 1: David Booth. There is so much material to cover. We're 35 00:02:16,800 --> 00:02:19,600 Speaker 1: gonna keep this to about four hours. We'll take a 36 00:02:19,600 --> 00:02:24,280 Speaker 1: break for dinner, and then we'll finish up before midnight. Um. 37 00:02:24,320 --> 00:02:28,000 Speaker 1: So I really don't have to introduce either of these gentlemen, 38 00:02:28,040 --> 00:02:30,640 Speaker 1: but let me just put a little more flesh on 39 00:02:30,680 --> 00:02:35,160 Speaker 1: the bones of what what the Dean started with. Obviously, 40 00:02:35,280 --> 00:02:39,160 Speaker 1: Jean is best known for not only the efficient market hypothesis, 41 00:02:39,200 --> 00:02:43,360 Speaker 1: but his research on portfolio theory, asset pricing, the Fama 42 00:02:43,400 --> 00:02:49,079 Speaker 1: French factor models. He is recipient of the Nobel Prize 43 00:02:49,080 --> 00:02:53,040 Speaker 1: in in Economics, and I like the sentence that the 44 00:02:53,080 --> 00:02:57,360 Speaker 1: Nobel group used quote for stuff for his work showing 45 00:02:57,440 --> 00:03:01,000 Speaker 1: quote stock price movements are impossible to predict in the 46 00:03:01,080 --> 00:03:05,320 Speaker 1: short term and that new information effects prices almost immediately, 47 00:03:05,720 --> 00:03:10,560 Speaker 1: which means markets are efficient. David co founded Dimensional with 48 00:03:10,639 --> 00:03:15,840 Speaker 1: another University of Chicago alum, Rex Sinquefeld in one The 49 00:03:15,919 --> 00:03:20,200 Speaker 1: firm now employees four hundred people who helped manage five 50 00:03:20,280 --> 00:03:24,239 Speaker 1: hundred and seventy nine billion dollars over the twenty years 51 00:03:24,440 --> 00:03:29,480 Speaker 1: ending in twenty eighteen. Eight of dimensionals equity and fixed 52 00:03:29,520 --> 00:03:33,760 Speaker 1: income funds beat their benchmark the rest of the industry 53 00:03:34,120 --> 00:03:37,800 Speaker 1: just seventeen and that's based on much of the work 54 00:03:38,160 --> 00:03:42,920 Speaker 1: that Professor Fama did. So so let's jump into the 55 00:03:43,080 --> 00:03:46,520 Speaker 1: history um of both Gene and David and see where 56 00:03:46,560 --> 00:03:50,560 Speaker 1: it goes. Jeane, during your last I feel weird calling 57 00:03:50,560 --> 00:03:54,600 Speaker 1: you Gene. It really should be Professor Farma, shouldn't it Um? 58 00:03:54,720 --> 00:03:58,120 Speaker 1: During your last year toughs. You worked for Professor Harry 59 00:03:58,280 --> 00:04:02,760 Speaker 1: Ernst who had a light gig running a stock market 60 00:04:02,960 --> 00:04:07,680 Speaker 1: forecasting service, and you did research for him. What sort 61 00:04:07,680 --> 00:04:11,920 Speaker 1: of work did you do with this stock forecasting research? 62 00:04:12,680 --> 00:04:16,960 Speaker 1: I was devising schemes to beat the market, and how 63 00:04:16,960 --> 00:04:20,560 Speaker 1: did that work out? Worked out fine? And on the 64 00:04:20,640 --> 00:04:23,800 Speaker 1: data that I fitted to didn't work out fine on 65 00:04:23,839 --> 00:04:26,599 Speaker 1: the whole load sample never did So that was a 66 00:04:26,720 --> 00:04:30,400 Speaker 1: lesson that data judging continn of things that aren't really there. 67 00:04:31,080 --> 00:04:35,200 Speaker 1: And how did that research into forecasting the stock market 68 00:04:35,320 --> 00:04:39,000 Speaker 1: impact your thinking about whether or not the market could 69 00:04:39,000 --> 00:04:44,080 Speaker 1: be be well? When I came here to Chicago, uh, 70 00:04:44,920 --> 00:04:49,200 Speaker 1: research on asset prices had again to get going in 71 00:04:49,360 --> 00:04:52,960 Speaker 1: really serious way, and many people were interested in the 72 00:04:53,080 --> 00:04:57,480 Speaker 1: question of how well stock prices adjusted to new information. 73 00:04:57,800 --> 00:05:01,760 Speaker 1: Put in context, they always say it started because of computers. 74 00:05:02,520 --> 00:05:07,400 Speaker 1: Before really didn't have a serious computer too do data 75 00:05:07,400 --> 00:05:11,960 Speaker 1: analysis on. And with the coming of computers, statisticians economists 76 00:05:11,960 --> 00:05:15,080 Speaker 1: were they had a new toy too to play with 77 00:05:15,160 --> 00:05:18,720 Speaker 1: and stock stock prices were easily available, so that was 78 00:05:18,760 --> 00:05:21,240 Speaker 1: one of the first things they started to study. And 79 00:05:21,240 --> 00:05:24,359 Speaker 1: then immediately the economists said, well, how do we expect 80 00:05:24,400 --> 00:05:27,800 Speaker 1: prices to behave if the world was working properly, in 81 00:05:27,800 --> 00:05:31,240 Speaker 1: other words, if markets were efficient. They weren't using that term, 82 00:05:31,320 --> 00:05:33,920 Speaker 1: but that's what they were after, and they were all 83 00:05:34,040 --> 00:05:39,760 Speaker 1: kinds of theories proposed. They had lots of shortcomings to them, 84 00:05:39,800 --> 00:05:42,440 Speaker 1: and a little bit of time we came to the 85 00:05:42,520 --> 00:05:47,120 Speaker 1: efficient market hypothesis. And you were in your senior year Toughs. 86 00:05:47,160 --> 00:05:50,160 Speaker 1: You had applied here, but you never heard back from 87 00:05:50,160 --> 00:05:53,680 Speaker 1: the school. Is this an urban legend or is this true? 88 00:05:54,160 --> 00:05:58,919 Speaker 1: So what happened? I called? I called in uh the 89 00:05:59,000 --> 00:06:03,240 Speaker 1: Dina students toff at Keff answered, that wouldn't happen today. 90 00:06:03,560 --> 00:06:05,840 Speaker 1: The school is so much bigger. The dean students doesn't 91 00:06:05,839 --> 00:06:10,120 Speaker 1: even have a telephone. Way too important for that. So 92 00:06:10,200 --> 00:06:12,000 Speaker 1: he answered the phone. We chatted for a while and 93 00:06:12,040 --> 00:06:13,680 Speaker 1: he said, well, I hate to tell you, but we 94 00:06:13,839 --> 00:06:16,480 Speaker 1: don't have any record of your application. So what kind 95 00:06:16,480 --> 00:06:18,719 Speaker 1: of grades do you have at Toughs? And I said 96 00:06:18,760 --> 00:06:21,440 Speaker 1: pretty much a lazy. He said, well, we have a 97 00:06:21,480 --> 00:06:25,599 Speaker 1: scholarship for someone from Toughs. Do you want it? And 98 00:06:25,640 --> 00:06:27,520 Speaker 1: then that's how that's how I ended up at the 99 00:06:27,560 --> 00:06:31,479 Speaker 1: University of Chicago. So so you come here as a 100 00:06:31,520 --> 00:06:35,559 Speaker 1: student you're you're finishing your work. Eventually, Martin Miller says 101 00:06:35,600 --> 00:06:37,640 Speaker 1: to you, Hey, do you want to stick around and 102 00:06:37,720 --> 00:06:40,480 Speaker 1: keep doing the sort of research you're doing? Is that 103 00:06:40,520 --> 00:06:45,279 Speaker 1: how you became a professor here? Yeah? I was. I 104 00:06:45,360 --> 00:06:49,960 Speaker 1: had offers that some other places, um, but lots of 105 00:06:50,000 --> 00:06:52,160 Speaker 1: the places turned me down. They said it was to Chicago. 106 00:06:54,040 --> 00:07:00,599 Speaker 1: I don't know what that meant actually, but but uh, 107 00:07:00,960 --> 00:07:03,800 Speaker 1: it was very rare to hire somebody from your ound 108 00:07:03,800 --> 00:07:07,480 Speaker 1: PhD program onto the faculty. They're only gonna one or 109 00:07:07,520 --> 00:07:12,000 Speaker 1: two before there. So, David, you had a somewhat different experience. 110 00:07:12,080 --> 00:07:13,880 Speaker 1: You grow up in Kansas, you get a b a 111 00:07:13,960 --> 00:07:17,400 Speaker 1: in economics and a master's from the University of Kansas. 112 00:07:17,880 --> 00:07:21,120 Speaker 1: What made you decide to come to Chicago. Well, I 113 00:07:21,160 --> 00:07:25,480 Speaker 1: did a little bit of reading um in finance um 114 00:07:25,760 --> 00:07:30,560 Speaker 1: and um my had a finance professor there that gotten 115 00:07:30,600 --> 00:07:36,480 Speaker 1: his PhD here, and he said, finances exploding really emerging 116 00:07:36,520 --> 00:07:40,280 Speaker 1: as an academic discipline. It's really one of the the 117 00:07:40,320 --> 00:07:44,680 Speaker 1: epicenters is clearly Chicago. And so I thought, well, I, God, 118 00:07:44,680 --> 00:07:47,120 Speaker 1: I should be fun, maybe be a professor. So I 119 00:07:47,160 --> 00:07:52,520 Speaker 1: applied here. Uh. Um, Yeah, I started to stay, took 120 00:07:52,600 --> 00:07:55,920 Speaker 1: jeans class my very first class, and is was the 121 00:07:56,000 --> 00:08:00,240 Speaker 1: Dean Correct? Was that literally fifty years ago? Fifty years 122 00:08:00,240 --> 00:08:04,720 Speaker 1: ago this fall? It was. Yeah, it was the first 123 00:08:04,800 --> 00:08:07,840 Speaker 1: year that Chicago had a football team in thirty four years. 124 00:08:09,960 --> 00:08:13,840 Speaker 1: And you had written about your experience taking a class 125 00:08:13,880 --> 00:08:18,840 Speaker 1: with Gene. You called it um life changing and transformative. 126 00:08:19,680 --> 00:08:22,520 Speaker 1: In what ways was it life changing? Well? Life changing 127 00:08:24,200 --> 00:08:26,640 Speaker 1: led to a career. I mean, I can't have much 128 00:08:26,640 --> 00:08:30,840 Speaker 1: of a bigger change than that, but it's um life changing. 129 00:08:30,840 --> 00:08:35,120 Speaker 1: And then I think everybody here probably UM, I would 130 00:08:35,160 --> 00:08:38,920 Speaker 1: like to think of themselves UM having a public purpose. 131 00:08:39,280 --> 00:08:40,719 Speaker 1: At the end of it all, when you get to 132 00:08:40,720 --> 00:08:43,440 Speaker 1: be my age, you want to look back and I 133 00:08:43,440 --> 00:08:46,240 Speaker 1: think somehow the world was better off for your having 134 00:08:46,240 --> 00:08:50,320 Speaker 1: been here. And so these ideas that were coming out, 135 00:08:51,160 --> 00:08:55,520 Speaker 1: you know, the essence of efficient markets, it was already 136 00:08:55,520 --> 00:08:59,520 Speaker 1: well developed. He had already coined the term UM. And 137 00:08:59,559 --> 00:09:01,600 Speaker 1: you just said, this is enormously useful. If you look 138 00:09:01,920 --> 00:09:05,600 Speaker 1: at the way money was managed fifty years ago, people 139 00:09:05,600 --> 00:09:08,160 Speaker 1: are getting ripped off. I mean, fees were way too high. 140 00:09:08,520 --> 00:09:11,480 Speaker 1: You know, the commissions were fixed by the government, uh 141 00:09:11,600 --> 00:09:14,480 Speaker 1: at about ten times what they are today, and uh 142 00:09:14,800 --> 00:09:19,280 Speaker 1: we forth it's free today. So it's a lot more 143 00:09:19,320 --> 00:09:23,480 Speaker 1: than ten x yeah. Yeah, yeah, So it's um. I 144 00:09:23,520 --> 00:09:27,080 Speaker 1: think there was a spirit of that we can improve 145 00:09:27,160 --> 00:09:30,480 Speaker 1: people's lives, you know, a real purpose to all of this. 146 00:09:31,640 --> 00:09:36,960 Speaker 1: Gene um more on the research side, and I've thought 147 00:09:37,440 --> 00:09:39,960 Speaker 1: my role in all this would be more on the 148 00:09:40,040 --> 00:09:44,359 Speaker 1: application of the ideas. So you become Jane's teaching assistant. 149 00:09:44,920 --> 00:09:48,720 Speaker 1: How did that come about? I always I always picked 150 00:09:48,720 --> 00:09:51,960 Speaker 1: the student in the class in the previous year to 151 00:09:52,000 --> 00:09:55,440 Speaker 1: be the teachers good student. It's the best of the class. 152 00:09:57,040 --> 00:10:02,319 Speaker 1: You don't have to laugh at that. I mean, so 153 00:10:02,800 --> 00:10:07,200 Speaker 1: best student, professor Farmers teaching assistant. Why not a career 154 00:10:07,200 --> 00:10:11,520 Speaker 1: in academia. Well, first off, I realized I could never 155 00:10:11,600 --> 00:10:14,560 Speaker 1: compete with gene I mean when you're at the top 156 00:10:14,559 --> 00:10:19,280 Speaker 1: of the mountain. Um. But it's really something. It caused 157 00:10:19,280 --> 00:10:22,280 Speaker 1: me to reflect and you know, really internally and what 158 00:10:22,280 --> 00:10:27,400 Speaker 1: what am I about? What do I enjoy? And I 159 00:10:27,400 --> 00:10:29,240 Speaker 1: I just saw this as a great opportunity to go 160 00:10:29,240 --> 00:10:32,800 Speaker 1: out to apply all these ideas people were developing. Every 161 00:10:32,920 --> 00:10:35,240 Speaker 1: new paper coming out was a landmark paper. It was 162 00:10:35,559 --> 00:10:38,360 Speaker 1: it was all brand new stuff, and uh, none of 163 00:10:38,440 --> 00:10:40,960 Speaker 1: them was being applied. So we're gonna come back to 164 00:10:41,040 --> 00:10:45,120 Speaker 1: the application very shortly. But you mentioned that all these 165 00:10:45,160 --> 00:10:50,440 Speaker 1: new ground baking, groundbreaking papers were coming out. Professor Farmer, 166 00:10:50,559 --> 00:10:55,120 Speaker 1: your doctoral thesis in four was the behavior of stock 167 00:10:55,200 --> 00:10:59,520 Speaker 1: market prices, And this sentence jumps right off the page 168 00:11:00,200 --> 00:11:04,719 Speaker 1: quote chart reading, though perhaps an interesting pastime, is of 169 00:11:04,800 --> 00:11:08,400 Speaker 1: no real value to the stock market investor. So this 170 00:11:08,440 --> 00:11:12,160 Speaker 1: gets published in the Journal of Business in nine. What 171 00:11:12,280 --> 00:11:15,439 Speaker 1: sort of pushback do you get to the general concept 172 00:11:15,520 --> 00:11:21,600 Speaker 1: that UM charts are of no use past market walk 173 00:11:21,720 --> 00:11:25,600 Speaker 1: is of no future predictability to what happens going forward. 174 00:11:25,920 --> 00:11:27,440 Speaker 1: You got a lot of a lot of pushback from 175 00:11:27,520 --> 00:11:31,560 Speaker 1: the professionals. The academics looked at the data, looked at 176 00:11:31,640 --> 00:11:34,960 Speaker 1: what people were saying, what they were showing, and adopted 177 00:11:35,000 --> 00:11:37,560 Speaker 1: it right away. I mean, there was no prospect among 178 00:11:37,600 --> 00:11:40,520 Speaker 1: the academics. Really, it's really the beginning of I mean, 179 00:11:40,640 --> 00:11:44,480 Speaker 1: if you had to summarize really impact of all this 180 00:11:44,640 --> 00:11:48,240 Speaker 1: is UM what was going on in Chicago then really 181 00:11:48,320 --> 00:11:53,240 Speaker 1: changed the way people think about investing. And that's really 182 00:11:53,280 --> 00:11:56,160 Speaker 1: been the theme, and Jen has changed the way people 183 00:11:56,240 --> 00:11:59,360 Speaker 1: think about investing more than that's that's the pre and 184 00:11:59,480 --> 00:12:03,280 Speaker 1: post law line, pre FAMA and post Fauma there's a 185 00:12:03,520 --> 00:12:12,360 Speaker 1: ce change. I don't like the postframa business meaning meaning 186 00:12:12,720 --> 00:12:18,440 Speaker 1: post publication of your way. So we not only have 187 00:12:18,600 --> 00:12:22,160 Speaker 1: your doctoral thesis, we have the efficient market paper. We 188 00:12:22,240 --> 00:12:25,319 Speaker 1: have the FAMA French three factor paper. There are a 189 00:12:25,440 --> 00:12:30,640 Speaker 1: number of very very influential papers that David, if I'm 190 00:12:30,640 --> 00:12:34,840 Speaker 1: hearing you correctly, you're saying that changed the firmaments of 191 00:12:34,880 --> 00:12:38,319 Speaker 1: finance forever, changing it forever and for the better. I mean, 192 00:12:38,360 --> 00:12:44,880 Speaker 1: I get particularly, and there's among students there's this kind 193 00:12:44,920 --> 00:12:49,200 Speaker 1: of antipathy towards finance and economics, you know, and they 194 00:12:49,200 --> 00:12:54,320 Speaker 1: don't realize how much UH finance has changed for the better. 195 00:12:54,480 --> 00:12:58,480 Speaker 1: People's lives have been improved by these ideas in this research, 196 00:12:58,880 --> 00:13:03,320 Speaker 1: lower fees, better of risk control, and so forth. So 197 00:13:03,320 --> 00:13:07,559 Speaker 1: so let's let's compare then and now a little more specifically, 198 00:13:08,000 --> 00:13:11,680 Speaker 1: and we'll start by talking efficient markets. Back in the 199 00:13:11,800 --> 00:13:16,160 Speaker 1: days when active managers were dominant, inefficiencies could still be 200 00:13:16,280 --> 00:13:21,480 Speaker 1: easily found, as could to percent fees. Professionals didn't believe 201 00:13:21,600 --> 00:13:24,920 Speaker 1: markets were efficient. They thought they were kind of sort 202 00:13:24,960 --> 00:13:28,720 Speaker 1: of eventually efficient. I doubt many of them would say 203 00:13:28,760 --> 00:13:32,240 Speaker 1: that today, what do you think has changed to bring 204 00:13:32,360 --> 00:13:36,040 Speaker 1: so many people over to the efficient market theory, well, 205 00:13:37,280 --> 00:13:41,920 Speaker 1: the accumulation of of performance evidence. So back then there 206 00:13:42,040 --> 00:13:46,280 Speaker 1: wasn't there was no real evidence on how these people did. Uh. 207 00:13:46,320 --> 00:13:49,360 Speaker 1: And one of the first papers was like Jensen's thesis, 208 00:13:49,640 --> 00:13:54,400 Speaker 1: which studied new toral funds for the previous twenty five 209 00:13:54,480 --> 00:14:00,240 Speaker 1: years and so that basically they weren't beating the market. Uh. 210 00:13:59,280 --> 00:14:03,400 Speaker 1: And now we know on hindsight that in fact that 211 00:14:03,480 --> 00:14:07,320 Speaker 1: has to be true that active management is a zero 212 00:14:07,400 --> 00:14:11,559 Speaker 1: sum game before cost because they don't they can't win 213 00:14:11,640 --> 00:14:14,760 Speaker 1: from the passive managers because the passive people hold cap 214 00:14:14,800 --> 00:14:18,240 Speaker 1: weight portfolios. They don't, they don't overweight and underweight in 215 00:14:18,360 --> 00:14:21,640 Speaker 1: response to what the active people do. So if there's 216 00:14:21,800 --> 00:14:24,440 Speaker 1: anybody underweighting and overweighting, there has to be another active 217 00:14:24,480 --> 00:14:28,160 Speaker 1: manager on the other side doing the opposite, which means 218 00:14:28,160 --> 00:14:31,400 Speaker 1: if one wins, the other loses some of those is 219 00:14:31,480 --> 00:14:40,040 Speaker 1: zero before costs arithmetic of active management. He calls it 220 00:14:40,040 --> 00:14:43,120 Speaker 1: the arithmetic because it is arithmetic. It's not a proposition. 221 00:14:43,640 --> 00:14:45,760 Speaker 1: It has to be true for everyone, or there's an 222 00:14:45,760 --> 00:14:50,800 Speaker 1: offsetting loose. So what about technology, how does that impact 223 00:14:51,280 --> 00:14:55,960 Speaker 1: how fast information makes its way into prices? It should 224 00:14:56,080 --> 00:15:01,680 Speaker 1: make it better, Uh, but you know, truth is, prices 225 00:15:01,680 --> 00:15:06,360 Speaker 1: are so volatile. Markets have always looked really efficient. They 226 00:15:06,400 --> 00:15:09,360 Speaker 1: don't look anymore efficient than they and they ever have 227 00:15:09,800 --> 00:15:13,880 Speaker 1: with the introduction of all the technology. So if information 228 00:15:13,960 --> 00:15:17,640 Speaker 1: is spread much more quickly now than it was fifty 229 00:15:17,680 --> 00:15:20,000 Speaker 1: years ago because you have so many sources and they're 230 00:15:20,040 --> 00:15:23,480 Speaker 1: so quick, but you can't really see in the data 231 00:15:23,880 --> 00:15:27,040 Speaker 1: that that's had a quantum effect on the adjustment of 232 00:15:27,080 --> 00:15:31,080 Speaker 1: crisis to information. So we may not be able to 233 00:15:31,080 --> 00:15:33,920 Speaker 1: see it explicitly in the data. But when we look 234 00:15:34,080 --> 00:15:38,080 Speaker 1: at things like hedge fund performance, they did very well 235 00:15:38,120 --> 00:15:42,840 Speaker 1: before the financial crisis, since then not as well. We 236 00:15:42,920 --> 00:15:47,160 Speaker 1: look at the money flows away from expensive active towards 237 00:15:47,240 --> 00:15:52,160 Speaker 1: inexpensive passive, it sounds like lots of investors are voting 238 00:15:52,200 --> 00:15:55,080 Speaker 1: with their dollars that, hey, the market is efficient and 239 00:15:55,160 --> 00:15:59,600 Speaker 1: we can't beat it. Doesn't it seem like technology is 240 00:15:59,680 --> 00:16:02,920 Speaker 1: dry having some of that Because there used to be 241 00:16:03,000 --> 00:16:07,920 Speaker 1: information asymmetries. There used to be inefficiencies that a savvy 242 00:16:08,000 --> 00:16:10,680 Speaker 1: manager might have been able to find. It sounds like 243 00:16:10,720 --> 00:16:14,960 Speaker 1: it's even harder to find those inefficiencies today than thirty 244 00:16:15,040 --> 00:16:18,880 Speaker 1: years ago. Um, Hey, you have better information than I 245 00:16:18,960 --> 00:16:23,440 Speaker 1: do because you're saying, so it's always looked, it's always 246 00:16:23,440 --> 00:16:27,400 Speaker 1: been that, it's always been zero sum game. I've been 247 00:16:27,440 --> 00:16:30,440 Speaker 1: in the business now almost fifty years, and every year 248 00:16:30,520 --> 00:16:33,760 Speaker 1: people say, next year is gonna be the stockpickers stockpickers market? 249 00:16:34,200 --> 00:16:39,400 Speaker 1: Well Gene saying is it's arithmetically impossible. So so let's 250 00:16:39,440 --> 00:16:43,800 Speaker 1: talk a little bit about index funds. Gene. You introduced 251 00:16:43,880 --> 00:16:47,560 Speaker 1: David when he is finishing his NBA and wants to 252 00:16:47,560 --> 00:16:50,840 Speaker 1: go out into the world of work, to John McGowan 253 00:16:50,920 --> 00:16:54,240 Speaker 1: over at Wells Fargo, where they were developing as an 254 00:16:54,280 --> 00:16:59,480 Speaker 1: institutional product, the first index fund. What made you think 255 00:16:59,520 --> 00:17:04,040 Speaker 1: that that was a good fit for for David mac mcclown, 256 00:17:05,000 --> 00:17:07,840 Speaker 1: who was in charge of the Wells Fargo unit, came 257 00:17:07,880 --> 00:17:10,119 Speaker 1: to well the seminars we did here for business people, 258 00:17:10,320 --> 00:17:12,720 Speaker 1: we didn't twice a year, the Center for Research and 259 00:17:12,760 --> 00:17:16,879 Speaker 1: Security Prices were in seminars for interested business people and 260 00:17:16,960 --> 00:17:20,800 Speaker 1: Mac came to all of them and he seemed very 261 00:17:22,359 --> 00:17:26,000 Speaker 1: you know, into the new stuff. And so when it 262 00:17:26,080 --> 00:17:27,840 Speaker 1: came down the David said, I see what you do, 263 00:17:27,880 --> 00:17:31,200 Speaker 1: but I don't want to do it as an academic. 264 00:17:32,280 --> 00:17:34,200 Speaker 1: So I called Mac and said, I have a really 265 00:17:34,240 --> 00:17:36,680 Speaker 1: good student here if you've got a place him and 266 00:17:36,720 --> 00:17:40,080 Speaker 1: he did. So what was your experience like it? Wells 267 00:17:40,119 --> 00:17:42,479 Speaker 1: Fargo working on that index one, Well, there was a 268 00:17:42,560 --> 00:17:48,480 Speaker 1: terrific experience, great exposure. I learned the importance of a 269 00:17:49,359 --> 00:17:55,400 Speaker 1: client work. I mean investment businesses part technology or investment science, 270 00:17:55,640 --> 00:18:00,680 Speaker 1: and it's part client work. And as I've told Jean, 271 00:18:00,760 --> 00:18:03,479 Speaker 1: you know, I studied finance for two years, I've been 272 00:18:03,520 --> 00:18:08,960 Speaker 1: studying client work at the last you know. And that 273 00:18:09,119 --> 00:18:15,679 Speaker 1: was we uh, we were so naive about dealing with 274 00:18:15,760 --> 00:18:18,159 Speaker 1: clients and what they would be interested in, and we 275 00:18:18,160 --> 00:18:24,080 Speaker 1: were so pumped up, jazzed up about the ideas. Somehow, um, 276 00:18:24,160 --> 00:18:28,240 Speaker 1: we missed the mark and actually my group got it 277 00:18:28,400 --> 00:18:32,000 Speaker 1: was unsuccessful, we got shut down, but they were um, 278 00:18:32,119 --> 00:18:34,199 Speaker 1: the other parts of the bank kept it going. And 279 00:18:34,240 --> 00:18:39,720 Speaker 1: now that little project we started end up as through 280 00:18:39,800 --> 00:18:42,800 Speaker 1: various hands, is now a big part of black Rock. 281 00:18:43,280 --> 00:18:45,760 Speaker 1: So so let's that's right. It eventually ended up going 282 00:18:45,800 --> 00:18:48,080 Speaker 1: to Barclays and then black Rock bis them and now 283 00:18:48,119 --> 00:18:51,399 Speaker 1: I shares I think they're coming up on six or 284 00:18:51,440 --> 00:18:55,520 Speaker 1: seven trillion dollars not to not too shabby. Um, but 285 00:18:55,680 --> 00:19:00,080 Speaker 1: let's talk about the application of genes theories to the 286 00:19:00,119 --> 00:19:04,879 Speaker 1: practice of working with clients post Wells far ago. You 287 00:19:05,000 --> 00:19:09,600 Speaker 1: decide to open the small microcap fund out of your 288 00:19:09,840 --> 00:19:13,600 Speaker 1: second bedroom in an apartment in Brooklyn. Tell us how 289 00:19:13,600 --> 00:19:18,320 Speaker 1: you applied Professor Farmers research to that microcap fund. Well, 290 00:19:18,359 --> 00:19:22,280 Speaker 1: the first thing is, UM, we decided UM not to 291 00:19:22,400 --> 00:19:26,640 Speaker 1: have UH around the portfolio like an index fund, even 292 00:19:26,640 --> 00:19:29,440 Speaker 1: though at first we call it an index fund because 293 00:19:29,440 --> 00:19:32,720 Speaker 1: it's very similar to indexing. With the final step being 294 00:19:33,240 --> 00:19:38,359 Speaker 1: UM that we don't trade UH market on clothes like 295 00:19:38,400 --> 00:19:41,560 Speaker 1: many index funds do. UM. And what that means is 296 00:19:42,119 --> 00:19:45,600 Speaker 1: we were we would be trading stocks throughout the day. Well, 297 00:19:45,640 --> 00:19:49,720 Speaker 1: that created a lot of skepticism, particularly among academics, because 298 00:19:49,720 --> 00:19:52,280 Speaker 1: you're going to the marketplace. You know, you don't have 299 00:19:52,600 --> 00:19:56,760 Speaker 1: any undiscounted information. People on the other side of your trade, 300 00:19:56,880 --> 00:19:59,760 Speaker 1: largely institutions, think they know a lot about the stock. 301 00:20:00,200 --> 00:20:02,240 Speaker 1: You know, why won't they just rip your eyes out 302 00:20:02,240 --> 00:20:07,879 Speaker 1: when you're trading. That's a that's a quite legitimate question. Uh. Well, 303 00:20:07,920 --> 00:20:09,200 Speaker 1: I mean that the answer is there a lot of 304 00:20:09,200 --> 00:20:11,800 Speaker 1: things you can do to use the energy of markets 305 00:20:11,800 --> 00:20:14,920 Speaker 1: and the power of markets to your advantage. It turns out, 306 00:20:15,119 --> 00:20:19,800 Speaker 1: for example, if we want to buy a stock. Let's say, um, 307 00:20:20,000 --> 00:20:23,439 Speaker 1: they have an institution wants to sell it. Their anxiety 308 00:20:23,520 --> 00:20:26,320 Speaker 1: is greater than ours, so we can use that their 309 00:20:27,040 --> 00:20:30,400 Speaker 1: interest in trying to do a quick trade to our 310 00:20:30,440 --> 00:20:35,480 Speaker 1: advantage and protect ourselves. And there's you know, plenty of 311 00:20:35,600 --> 00:20:38,359 Speaker 1: information now floating out about the stock that you can 312 00:20:38,440 --> 00:20:41,120 Speaker 1: use to protect yourself. But that wasn't known back then. 313 00:20:41,359 --> 00:20:44,240 Speaker 1: It was just we had a belief in markets, belief 314 00:20:44,280 --> 00:20:47,880 Speaker 1: and and how they work based on what we studied 315 00:20:47,920 --> 00:20:50,720 Speaker 1: here and said, look, we think we can go out 316 00:20:50,840 --> 00:20:54,680 Speaker 1: and trade these stocks and not uh not get killed 317 00:20:55,320 --> 00:20:59,280 Speaker 1: that there were two pieces done here and it's most 318 00:20:59,280 --> 00:21:03,600 Speaker 1: stuck turns and most of the academics said, well, it 319 00:21:03,640 --> 00:21:07,600 Speaker 1: looks good in terms of the crisp historical data, but 320 00:21:07,800 --> 00:21:09,679 Speaker 1: in fact, if you try to trade it, you're going 321 00:21:09,760 --> 00:21:13,520 Speaker 1: to get swamped by trading costs. Uh. And that was 322 00:21:13,560 --> 00:21:16,840 Speaker 1: the so called market micro struct just stuff. And then 323 00:21:16,920 --> 00:21:20,000 Speaker 1: we figured out what we found out what dimensional was, No, 324 00:21:20,160 --> 00:21:22,880 Speaker 1: he really didn't have to pay those big bit ast 325 00:21:22,880 --> 00:21:25,320 Speaker 1: spreads that you were seeing. You could go fewer, was 326 00:21:25,359 --> 00:21:28,479 Speaker 1: patient trader. You could do better with the prices, so 327 00:21:28,520 --> 00:21:33,120 Speaker 1: we could deliver this small stuff premium. But previous to that, 328 00:21:33,520 --> 00:21:38,840 Speaker 1: people weren't believes what the academics learned was the market 329 00:21:38,840 --> 00:21:44,560 Speaker 1: micro structure stuff was garbage. Basically they didn't really understand. Interesting. Um, 330 00:21:45,520 --> 00:21:47,679 Speaker 1: what we learned about clients along the way, which was 331 00:21:48,080 --> 00:21:54,040 Speaker 1: seeing in our initial clients were all large, largest pension funds, 332 00:21:54,080 --> 00:21:58,160 Speaker 1: essentially insurance companies around the world, and they weren't hopening 333 00:21:58,160 --> 00:22:02,480 Speaker 1: the stocks of small companies. So really the pitch we 334 00:22:02,520 --> 00:22:05,560 Speaker 1: got into all this stuff, but we hadn't even easier argument, 335 00:22:05,600 --> 00:22:08,200 Speaker 1: which was, look, you ought to hold stocks of large 336 00:22:08,200 --> 00:22:11,400 Speaker 1: companies and small, and you're not holding small, so we'll 337 00:22:11,440 --> 00:22:14,600 Speaker 1: get you access to small. So that was the really 338 00:22:14,600 --> 00:22:17,199 Speaker 1: the sales pitch that put us on the map. And 339 00:22:17,320 --> 00:22:20,840 Speaker 1: so that sales pitch starts to take off and dimensional 340 00:22:20,960 --> 00:22:23,960 Speaker 1: operating out of your apartment gets bigger. There's kind of 341 00:22:23,960 --> 00:22:27,560 Speaker 1: an urgent urban legend that you called New York Telephone 342 00:22:27,600 --> 00:22:30,840 Speaker 1: to have them add six phone lines and they refused. 343 00:22:30,840 --> 00:22:33,640 Speaker 1: They thought you were running a bookie joy. Is that 344 00:22:33,680 --> 00:22:36,439 Speaker 1: remotely true? Yeah, this was about the kind of at 345 00:22:36,520 --> 00:22:40,720 Speaker 1: the bottom of Brooklyn Heights, uh, bottom of its history. 346 00:22:40,720 --> 00:22:46,199 Speaker 1: It's so we started on a shoe string and we 347 00:22:46,280 --> 00:22:49,119 Speaker 1: ran the portfolio. Was the first portfolio manager running out 348 00:22:49,119 --> 00:22:51,920 Speaker 1: of my spare bedroom. So I knew we needed more 349 00:22:51,960 --> 00:22:56,920 Speaker 1: phone lights. So I called up New York Telephone, which 350 00:22:56,920 --> 00:22:59,840 Speaker 1: was a telephone company at the time. So the need, 351 00:23:00,160 --> 00:23:03,200 Speaker 1: you know, uh some telephone lines and I know six 352 00:23:03,320 --> 00:23:06,200 Speaker 1: or eight or whatever, and they thought it was a bookie, 353 00:23:06,200 --> 00:23:08,040 Speaker 1: so they wouldn't give me the lines. So I had 354 00:23:08,040 --> 00:23:09,800 Speaker 1: to call up the Treasure of New York tell say, 355 00:23:10,880 --> 00:23:12,879 Speaker 1: can you send some people down here and give me 356 00:23:12,920 --> 00:23:16,440 Speaker 1: some telephone lines. And they went around the whole block 357 00:23:16,520 --> 00:23:20,439 Speaker 1: and found that there were six lines available available and 358 00:23:20,480 --> 00:23:24,480 Speaker 1: the whole block that based on their equipment, and they said, okay, 359 00:23:24,480 --> 00:23:26,720 Speaker 1: you can have those six lines. And that's how we 360 00:23:26,760 --> 00:23:31,679 Speaker 1: got started. And the punch line is he becomes a client. Yeah, yeah, right, 361 00:23:31,800 --> 00:23:33,520 Speaker 1: New York that was a clickly became a clie down. 362 00:23:34,359 --> 00:23:38,480 Speaker 1: So so from from day one, Gene is a board 363 00:23:38,520 --> 00:23:41,920 Speaker 1: member of Dimensional Funds. From the day it launches, well 364 00:23:42,359 --> 00:23:45,639 Speaker 1: even before I mean, we have the idea to start 365 00:23:45,640 --> 00:23:50,280 Speaker 1: the firm. Uh uh. My first call it was to 366 00:23:50,359 --> 00:23:53,720 Speaker 1: Gene say, look, you know, it's been ten years since 367 00:23:53,760 --> 00:23:57,320 Speaker 1: I was in school. We uh, there's been a lot 368 00:23:57,359 --> 00:23:59,760 Speaker 1: of research, you know, we we needed we needed to 369 00:24:00,000 --> 00:24:04,000 Speaker 1: have access to you know, new research and thinking. And 370 00:24:04,320 --> 00:24:06,320 Speaker 1: would you be on the you know, one of the 371 00:24:06,359 --> 00:24:10,439 Speaker 1: founders and and uh and and be our list you know, 372 00:24:10,440 --> 00:24:13,760 Speaker 1: our our eyes in terms of research. And he agreed 373 00:24:13,800 --> 00:24:16,520 Speaker 1: to do that right away. Who else did you recruit 374 00:24:16,760 --> 00:24:20,439 Speaker 1: from GSP? Well, eventually we found out we had to 375 00:24:20,480 --> 00:24:22,960 Speaker 1: have We wanted to create a mutual fund, and a 376 00:24:23,080 --> 00:24:26,720 Speaker 1: mutual fund has to have an independent board of directors. 377 00:24:27,320 --> 00:24:30,280 Speaker 1: So Rex and I went over the Business School, walked 378 00:24:30,280 --> 00:24:33,600 Speaker 1: into Martin Miller's office. They still teach mollarble deiply on 379 00:24:33,680 --> 00:24:38,080 Speaker 1: the theaters, don't take Yeah, okay, Uh. So Martin was there. 380 00:24:38,119 --> 00:24:40,520 Speaker 1: We said to you know, he added a YadA small 381 00:24:40,600 --> 00:24:44,320 Speaker 1: company fund need independent directors and um and said, oh sure. 382 00:24:44,880 --> 00:24:47,680 Speaker 1: And I walked out the door and down the hall 383 00:24:47,760 --> 00:24:49,840 Speaker 1: and Myron Schulz was coming out of his office. I 384 00:24:49,920 --> 00:24:56,679 Speaker 1: gol Myron, he had the YadA. See Gene's point. Business 385 00:24:56,680 --> 00:24:59,000 Speaker 1: school was a lot smaller then, and having been to 386 00:24:59,000 --> 00:25:01,720 Speaker 1: the pH d program, I got to know the faculty 387 00:25:01,920 --> 00:25:06,639 Speaker 1: pretty well. So Myron uh agreed to join, and so 388 00:25:06,680 --> 00:25:09,840 Speaker 1: on and so forth. So in fact, until recently, all 389 00:25:09,880 --> 00:25:13,640 Speaker 1: the independent directors of the mutual fund, our mutual fund 390 00:25:13,720 --> 00:25:18,480 Speaker 1: be Uh have taught at Chicago, so his his business partner, 391 00:25:18,520 --> 00:25:21,919 Speaker 1: Reck Singfield, was in my class as well. He was 392 00:25:21,960 --> 00:25:24,200 Speaker 1: really the first one to put out an index one, 393 00:25:24,320 --> 00:25:31,479 Speaker 1: wasn't he? No, No, it was but but Rex. Actually 394 00:25:31,640 --> 00:25:33,640 Speaker 1: that was when I was his teaching consistent. He took 395 00:25:34,240 --> 00:25:38,879 Speaker 1: uh jeans class. And Rex was always uh one of 396 00:25:38,960 --> 00:25:41,520 Speaker 1: these pain in the neck as a teaching consistant students 397 00:25:41,680 --> 00:25:46,240 Speaker 1: because he was interested in everything you know. I'm so Jean. 398 00:25:46,600 --> 00:25:50,600 Speaker 1: You moved pretty easily back and forth between academic theory 399 00:25:51,200 --> 00:25:57,359 Speaker 1: and real world application of theories. Not a lot of 400 00:25:57,359 --> 00:26:00,119 Speaker 1: people were able to bridge that gap between academics. Well 401 00:26:00,160 --> 00:26:02,080 Speaker 1: I hadn't. I hadn't been able to bridge it either 402 00:26:02,560 --> 00:26:06,600 Speaker 1: until Dimentcino came along. But here it is. It's forty 403 00:26:06,680 --> 00:26:09,160 Speaker 1: years later, and you seem to continue to be right 404 00:26:09,280 --> 00:26:13,200 Speaker 1: because he Uh. The reason they couldn't just because one, 405 00:26:13,240 --> 00:26:14,800 Speaker 1: it's hard to shut me up. I don't take a 406 00:26:14,840 --> 00:26:19,520 Speaker 1: party line too too too easily. And he didn't. Ever, 407 00:26:20,960 --> 00:26:23,719 Speaker 1: He and Rex never said would you please do this? 408 00:26:23,920 --> 00:26:25,720 Speaker 1: What they said was, you do what you do and 409 00:26:25,800 --> 00:26:28,119 Speaker 1: we'll figure out if we can use any of it. 410 00:26:28,920 --> 00:26:31,080 Speaker 1: And that fits in with the way I work so 411 00:26:33,880 --> 00:26:36,840 Speaker 1: frequently he would come in and say, look, get get 412 00:26:36,840 --> 00:26:38,639 Speaker 1: ready to make a presentation for our clients. They go, 413 00:26:39,040 --> 00:26:40,240 Speaker 1: you know, I don't know what your clients are gonna 414 00:26:40,240 --> 00:26:42,240 Speaker 1: want to hear this. I go, look, Jane, you know, 415 00:26:42,359 --> 00:26:44,920 Speaker 1: say what's on your mind. It's been controls my department, 416 00:26:45,080 --> 00:26:48,400 Speaker 1: you know. And that seems to have worked out. So 417 00:26:49,359 --> 00:26:52,600 Speaker 1: what was your involvement with the investment committee in the 418 00:26:52,640 --> 00:26:57,679 Speaker 1: early days of dimensional um? Were you participating actively in it? 419 00:26:57,760 --> 00:27:01,000 Speaker 1: Were you managing it? What were you doing? Well? I 420 00:27:01,040 --> 00:27:04,639 Speaker 1: was doing this back and forth with the research stuff. 421 00:27:04,680 --> 00:27:09,240 Speaker 1: But then they started a fixed income fund based on 422 00:27:09,320 --> 00:27:13,280 Speaker 1: fixed income research they had done in the seventies, and 423 00:27:13,320 --> 00:27:15,080 Speaker 1: they said, do you want to come in and trade 424 00:27:15,080 --> 00:27:17,320 Speaker 1: it for a day? And I said, sure, I remember 425 00:27:17,400 --> 00:27:21,240 Speaker 1: traded anything. So I went in. I know how much 426 00:27:21,280 --> 00:27:23,520 Speaker 1: money did we? Here were ten million dollars from somebody 427 00:27:24,040 --> 00:27:26,639 Speaker 1: and I managed to buy twenty million dollars of bonds 428 00:27:27,960 --> 00:27:32,760 Speaker 1: and that was a big problem. Actually, so waitwa, Gene 429 00:27:32,760 --> 00:27:36,480 Speaker 1: Fama day trader. I just want to make sure that 430 00:27:37,160 --> 00:27:43,400 Speaker 1: that was the last day. But I couldn't see the problem, 431 00:27:46,720 --> 00:27:50,800 Speaker 1: that's right. Um, So you introduce the Fama French paper 432 00:27:51,000 --> 00:27:56,880 Speaker 1: on value dimensional funds, introduce as a US large value 433 00:27:57,240 --> 00:28:00,280 Speaker 1: and you are small value. In ninety three on another 434 00:28:00,320 --> 00:28:04,280 Speaker 1: farm of French paper leads to international value coming out 435 00:28:04,320 --> 00:28:08,520 Speaker 1: in that paper won a Graham and Dot Award of Excellence. 436 00:28:09,080 --> 00:28:12,159 Speaker 1: Was there anyone else trying to apply this sort of 437 00:28:12,200 --> 00:28:17,360 Speaker 1: academic research to either investing theory or the creation of 438 00:28:17,520 --> 00:28:23,040 Speaker 1: investable products on the market? There they're always kind of um. 439 00:28:23,240 --> 00:28:25,919 Speaker 1: Departments of big banks and people were kind of playing 440 00:28:25,960 --> 00:28:28,359 Speaker 1: around with it. But we were the only ones willing 441 00:28:28,400 --> 00:28:31,840 Speaker 1: to stand up and say, um, this is what we 442 00:28:31,920 --> 00:28:35,520 Speaker 1: believe and this is what we think you ought to do. Um. 443 00:28:35,560 --> 00:28:39,000 Speaker 1: Now they're we have all the quant managers out there. 444 00:28:39,040 --> 00:28:42,760 Speaker 1: We got tons of people uh uh out there, you know, 445 00:28:43,480 --> 00:28:45,480 Speaker 1: trying to apply the same data. And back then we 446 00:28:45,640 --> 00:28:48,760 Speaker 1: basically were at In fact, I often go around and 447 00:28:48,760 --> 00:28:51,320 Speaker 1: show people thirty year track record on the various funds 448 00:28:51,760 --> 00:28:55,200 Speaker 1: UM and I go, you know, we had a lot 449 00:28:55,240 --> 00:28:58,200 Speaker 1: of competition back then, but they don't seem nobody seems 450 00:28:58,240 --> 00:29:02,720 Speaker 1: to have a thirty year track record. They did not 451 00:29:02,800 --> 00:29:07,560 Speaker 1: survive long enough to So let me fast forward, um 452 00:29:07,600 --> 00:29:10,680 Speaker 1: a couple of decades to the mid two thousand's. In 453 00:29:10,760 --> 00:29:14,080 Speaker 1: two thousand and eight, David Booth made the largest donation 454 00:29:14,200 --> 00:29:17,720 Speaker 1: ever given to a business school, which has been called 455 00:29:17,880 --> 00:29:22,320 Speaker 1: a transformational gift. Tell us about your thinking. What made 456 00:29:22,320 --> 00:29:26,520 Speaker 1: you decide in the middle of the financial crisis to say, 457 00:29:27,000 --> 00:29:29,640 Speaker 1: I know, I want to make a donation to my 458 00:29:29,880 --> 00:29:33,040 Speaker 1: alma mater. Well, it was I'm kind of ties into 459 00:29:33,080 --> 00:29:35,520 Speaker 1: the story I was talking about earlier. I mean, what, Uh, 460 00:29:36,280 --> 00:29:37,920 Speaker 1: it got to be the stage where it was time 461 00:29:37,960 --> 00:29:43,360 Speaker 1: to pay back, and um, I mean I wouldn't been 462 00:29:43,360 --> 00:29:47,880 Speaker 1: anywhere without Chicago. So I said, I wanted to give 463 00:29:47,920 --> 00:29:52,120 Speaker 1: a big chunk of what I have and uh, um, 464 00:29:52,160 --> 00:29:55,000 Speaker 1: this was a mix of stocks and cash, Is that correct? 465 00:29:55,360 --> 00:29:59,040 Speaker 1: And it was actually, Um, I didn't have a lot 466 00:29:59,080 --> 00:30:03,000 Speaker 1: of cash at that time. It was because we just 467 00:30:04,560 --> 00:30:08,480 Speaker 1: recently started to accumulate the money big enough to but 468 00:30:08,720 --> 00:30:11,480 Speaker 1: I had stock in the firm, and so I gave 469 00:30:11,560 --> 00:30:15,560 Speaker 1: him basically ownership of a big chunk of the of 470 00:30:15,560 --> 00:30:18,640 Speaker 1: the stock that I had, and they were willing to 471 00:30:18,640 --> 00:30:21,880 Speaker 1: take a bit on that. And it turned up to 472 00:30:21,880 --> 00:30:24,680 Speaker 1: be a convet and that that comes with a dividend 473 00:30:24,760 --> 00:30:28,760 Speaker 1: which continues to pay its way to uh to booth. 474 00:30:29,720 --> 00:30:32,160 Speaker 1: Were you at all concerned that you were right in 475 00:30:32,200 --> 00:30:35,840 Speaker 1: the middle of a financial crisis giving ownership of a 476 00:30:35,880 --> 00:30:39,320 Speaker 1: financial firm. A lot of firms did not make it 477 00:30:39,320 --> 00:30:41,800 Speaker 1: through the financial crisis. Yeah, maybe it ties in with 478 00:30:41,840 --> 00:30:44,640 Speaker 1: the earlier question about what I learned from here about 479 00:30:44,720 --> 00:30:47,360 Speaker 1: markets and how they work, and you have to kind 480 00:30:47,360 --> 00:30:50,240 Speaker 1: of keep in the depth of the financial crisis. It 481 00:30:50,360 --> 00:30:53,120 Speaker 1: kind of had to keep reminding people. You know, markets 482 00:30:53,120 --> 00:30:55,560 Speaker 1: are where buyers and sellers come together and in a 483 00:30:55,640 --> 00:30:58,720 Speaker 1: voluntary transaction, both sides of a trade have to feel 484 00:30:58,720 --> 00:31:00,400 Speaker 1: like they have a good they got a deal, or 485 00:31:00,400 --> 00:31:03,120 Speaker 1: they don't trade. They don't trade. So you know, there's 486 00:31:03,120 --> 00:31:05,080 Speaker 1: a lot of trading volume activity and a lot of 487 00:31:05,400 --> 00:31:09,280 Speaker 1: well known investors investing, and it's just you know, one 488 00:31:09,280 --> 00:31:12,920 Speaker 1: of those UM. It was comfortable those markets were functioning 489 00:31:12,960 --> 00:31:15,680 Speaker 1: the way they ought to function. Sometimes they go up, 490 00:31:15,720 --> 00:31:19,640 Speaker 1: sometimes they go down. Gene, how did David's gift impact 491 00:31:19,760 --> 00:31:24,719 Speaker 1: the Graduate School of Business? Huh, it was. It was 492 00:31:25,000 --> 00:31:28,560 Speaker 1: a big lot of cash flow that was not there beforehand, 493 00:31:28,640 --> 00:31:34,360 Speaker 1: so it gave rise to lots of research centers. I 494 00:31:34,400 --> 00:31:38,040 Speaker 1: think you made everybody feel as if the future is 495 00:31:38,480 --> 00:31:42,400 Speaker 1: more or less assured. UM and the university also got 496 00:31:42,400 --> 00:31:45,880 Speaker 1: a pretty good take out of itself, as they always do, 497 00:31:48,480 --> 00:31:52,040 Speaker 1: so David, you tell a charming story about sitting with 498 00:31:52,080 --> 00:31:55,640 Speaker 1: the dean and you It wasn't your intention for this 499 00:31:55,680 --> 00:31:58,520 Speaker 1: originally to be a naming gift. They seem to have 500 00:31:58,600 --> 00:32:00,360 Speaker 1: brought that up to you. Can you you right know? 501 00:32:00,720 --> 00:32:03,400 Speaker 1: I said I wanted for the reasons I outlined, I 502 00:32:03,440 --> 00:32:05,840 Speaker 1: wanted to make a gift a big part of what 503 00:32:05,920 --> 00:32:08,840 Speaker 1: I have, um, and so this is what I want 504 00:32:08,840 --> 00:32:12,080 Speaker 1: to do. And the Dean, Ted Snyder at the time, 505 00:32:12,160 --> 00:32:15,360 Speaker 1: said we were looking to have a naming gift from 506 00:32:15,360 --> 00:32:17,320 Speaker 1: the business School. This is a lot better deal than 507 00:32:17,360 --> 00:32:20,440 Speaker 1: that what we're looking for, So we'll name the school 508 00:32:20,480 --> 00:32:29,200 Speaker 1: after you. Okay, whatever you know. So since then the 509 00:32:29,240 --> 00:32:33,080 Speaker 1: school has continued to grow in in both reputation and 510 00:32:33,600 --> 00:32:36,960 Speaker 1: number of students and the offerings here. Um. And then 511 00:32:37,120 --> 00:32:41,680 Speaker 1: fast forward, uh, five years after that, Jane gets a 512 00:32:41,680 --> 00:32:45,520 Speaker 1: phone call from Sweden. Let's talk a little bit about that. 513 00:32:45,760 --> 00:32:48,960 Speaker 1: What was your experience like, Uh, did the phone call 514 00:32:49,040 --> 00:32:51,440 Speaker 1: manage to reach you? Tell us? Tell us what that 515 00:32:51,560 --> 00:32:55,160 Speaker 1: was like? Well, they think they call it early the 516 00:32:55,200 --> 00:33:00,239 Speaker 1: morning Stockholm time, which is really really in the in here. 517 00:33:00,240 --> 00:33:04,280 Speaker 1: It thinks about five or six o'clock. So I don't know. 518 00:33:03,880 --> 00:33:05,720 Speaker 1: You never expect to get it, because a lot of 519 00:33:05,720 --> 00:33:09,680 Speaker 1: people could qualify to to get it when you get it. Somehow, 520 00:33:09,720 --> 00:33:13,520 Speaker 1: Pete they the people he somehow had I guess or whatever. 521 00:33:13,520 --> 00:33:16,320 Speaker 1: I don't know why, because they were newspaper people at 522 00:33:16,360 --> 00:33:21,560 Speaker 1: my door ten minutes later after after the call and 523 00:33:21,560 --> 00:33:26,040 Speaker 1: they wanted to come in my house. I said, no way, 524 00:33:27,960 --> 00:33:31,400 Speaker 1: you're class. Well, I had a class that morning, and 525 00:33:31,760 --> 00:33:36,000 Speaker 1: you don't. You don't get a special dispensation when you could. 526 00:33:36,040 --> 00:33:37,840 Speaker 1: But I had never missed the class in all the 527 00:33:37,920 --> 00:33:40,680 Speaker 1: years I've been teaching in fifty years. Yeah, I wasn't 528 00:33:40,720 --> 00:33:43,720 Speaker 1: gonna start now, so when I wasn't gonna let anybody 529 00:33:43,720 --> 00:33:45,800 Speaker 1: in because the kids in the class were paying a 530 00:33:45,800 --> 00:33:49,160 Speaker 1: lot of money to take that course, So no way 531 00:33:49,200 --> 00:33:52,719 Speaker 1: I wanted people from the outside disturbing it. So, David, 532 00:33:52,760 --> 00:33:56,400 Speaker 1: you ended up going to Stockholme with Jeane. What what 533 00:33:56,440 --> 00:34:01,600 Speaker 1: was that experience like? Um, well, of course, being Chicago trained, 534 00:34:01,960 --> 00:34:04,959 Speaker 1: I've been to the ceremony before with when when Myron 535 00:34:05,040 --> 00:34:08,279 Speaker 1: and Bob Martin got there Nobel, So you know it's 536 00:34:08,440 --> 00:34:12,839 Speaker 1: you're kind of used to this of you so third 537 00:34:12,880 --> 00:34:17,799 Speaker 1: times of charm. Yeah, so the uh so, I I 538 00:34:17,920 --> 00:34:21,200 Speaker 1: said to Jane, give me a night, uh to organize 539 00:34:21,239 --> 00:34:26,440 Speaker 1: something special. So I talked to Abba has a museum 540 00:34:26,480 --> 00:34:29,239 Speaker 1: in Stockholm that they just opened, and I talked them 541 00:34:29,239 --> 00:34:33,840 Speaker 1: into running me out the museum for the evening. So Jeane, 542 00:34:34,120 --> 00:34:35,960 Speaker 1: you know, he has four kids and that time about 543 00:34:35,960 --> 00:34:39,960 Speaker 1: eight grandkids and they're all u big music fans and 544 00:34:40,040 --> 00:34:43,520 Speaker 1: so the Abbe Museum has a lot of u um 545 00:34:44,520 --> 00:34:47,640 Speaker 1: um things you can do to have fun and um. 546 00:34:47,840 --> 00:34:49,800 Speaker 1: One of them is a big stage with a scrim 547 00:34:49,840 --> 00:34:53,759 Speaker 1: on it and with four Abba musicians singing with a 548 00:34:53,840 --> 00:34:56,279 Speaker 1: microphone right in the middle, and so you it looks 549 00:34:56,280 --> 00:34:58,600 Speaker 1: like you're singing with them. And so I looked, so 550 00:34:58,680 --> 00:35:00,840 Speaker 1: this went on. They were the kids, The kids went wild. 551 00:35:00,920 --> 00:35:03,879 Speaker 1: I looked over Jeane like and Sally, and I could 552 00:35:03,880 --> 00:35:06,040 Speaker 1: see that they were they were having fun. So it 553 00:35:06,120 --> 00:35:09,680 Speaker 1: made it special for me. So the whole thing some 554 00:35:09,719 --> 00:35:14,000 Speaker 1: people have described as surreal. What was your the day 555 00:35:14,120 --> 00:35:16,720 Speaker 1: of the day after So they had a big event 556 00:35:16,760 --> 00:35:20,480 Speaker 1: here of the school, really big event, I mean news 557 00:35:20,520 --> 00:35:25,480 Speaker 1: and everything. The circles around the building, we're all full 558 00:35:25,480 --> 00:35:30,120 Speaker 1: of students um. And the next day the Nobel people 559 00:35:30,239 --> 00:35:35,360 Speaker 1: have a camera committee there following me across the Harper Center, 560 00:35:36,280 --> 00:35:39,360 Speaker 1: the big hum in the middle, and students are working 561 00:35:39,360 --> 00:35:41,920 Speaker 1: on on the sides, and we worked down the middle. 562 00:35:42,239 --> 00:35:46,640 Speaker 1: Nobody looks up. So we get to the other side, 563 00:35:47,080 --> 00:35:49,759 Speaker 1: and the television guy says, nobody looked up when I said, 564 00:35:50,000 --> 00:35:52,080 Speaker 1: this is the University of Chicago. If they had to 565 00:35:52,120 --> 00:35:54,319 Speaker 1: look up every time I nobil fries when I walked right, 566 00:35:54,760 --> 00:36:00,439 Speaker 1: get nothing done. And and to show you how true 567 00:36:00,520 --> 00:36:04,040 Speaker 1: that is, David Booth and Gene and I get an 568 00:36:04,080 --> 00:36:07,720 Speaker 1: elevator on four to come down here, and a student 569 00:36:07,840 --> 00:36:10,960 Speaker 1: gets in wearing headphones, turns around, doesn't say a word 570 00:36:11,000 --> 00:36:13,160 Speaker 1: to either of you, and the four of us wrote 571 00:36:13,160 --> 00:36:16,440 Speaker 1: down in silence. He was completely oblivious to who was 572 00:36:16,480 --> 00:36:20,000 Speaker 1: in the elevator with him. So I'm always fascinated by 573 00:36:20,000 --> 00:36:24,160 Speaker 1: that sort of stuff. So so let's let's talk a 574 00:36:24,200 --> 00:36:28,160 Speaker 1: little bit about um some other things that you've written about, 575 00:36:28,239 --> 00:36:31,000 Speaker 1: and the two of you have applied. One of the 576 00:36:31,040 --> 00:36:35,120 Speaker 1: quotes of Professor Farmers that I enjoy is quote, why 577 00:36:35,400 --> 00:36:39,440 Speaker 1: is anyone even reading Wall Street Research? Unquote? So I 578 00:36:39,480 --> 00:36:42,480 Speaker 1: have to ask you, why do people read Wall Street Research? 579 00:36:43,560 --> 00:36:51,520 Speaker 1: I don't know. It's it's businessman's pornography, basically business based pornography. 580 00:36:51,719 --> 00:37:00,000 Speaker 1: It's not the real thing. It's not the real thing. Okay. Um, 581 00:37:02,840 --> 00:37:05,200 Speaker 1: so let's talk a little bit about value. I'm gonna 582 00:37:05,200 --> 00:37:11,520 Speaker 1: try and realist. Let's talk about value and growth. Value 583 00:37:11,520 --> 00:37:14,600 Speaker 1: has a tendency to go through these longer periods where 584 00:37:14,640 --> 00:37:17,680 Speaker 1: growth is beating it. And over the past decade it's 585 00:37:17,719 --> 00:37:22,480 Speaker 1: been if you weren't in big cap us growth, um, 586 00:37:22,520 --> 00:37:27,040 Speaker 1: you were underperforming. Everything has been um the SMP five hundred. 587 00:37:27,080 --> 00:37:29,799 Speaker 1: When we look at emerging markets, we look at small cap, 588 00:37:29,840 --> 00:37:32,960 Speaker 1: we look at value. Heaven forbid, you're an emerging market 589 00:37:32,960 --> 00:37:37,160 Speaker 1: small cap value. It's been terrible. What sort of lessons 590 00:37:37,160 --> 00:37:41,200 Speaker 1: should investors take from this extended period of growth growth 591 00:37:41,280 --> 00:37:44,680 Speaker 1: beating value? Well, the question they want to ask is 592 00:37:45,320 --> 00:37:51,640 Speaker 1: as value dead? Okay, let's Kennan. I actually were reading 593 00:37:51,680 --> 00:37:54,160 Speaker 1: a paper on this at the moment. But the bottom 594 00:37:54,239 --> 00:37:57,800 Speaker 1: line is there's so much volatility in these premiums that 595 00:37:57,960 --> 00:38:00,480 Speaker 1: you can't tell if the premium is teamed or not. 596 00:38:01,160 --> 00:38:03,359 Speaker 1: It may have changed, it may not. You just can't 597 00:38:03,400 --> 00:38:05,640 Speaker 1: tell us. Let's see a wholl within the range of 598 00:38:05,719 --> 00:38:09,880 Speaker 1: chance experience that the poor return experiences well within the 599 00:38:10,000 --> 00:38:13,200 Speaker 1: range of chance over the time that's that it's occurred. 600 00:38:13,280 --> 00:38:18,160 Speaker 1: So you really can't say anything. So so there have 601 00:38:18,280 --> 00:38:21,640 Speaker 1: been other periods of time where value is done poorly. 602 00:38:21,719 --> 00:38:26,680 Speaker 1: I remember hearing in this value investor was washed up, 603 00:38:26,680 --> 00:38:29,440 Speaker 1: this guy named Warren Buffett. He doesn't know what he's doing. 604 00:38:29,640 --> 00:38:33,440 Speaker 1: And typically when you hear that, it's usually at the ends, 605 00:38:33,640 --> 00:38:37,080 Speaker 1: towards the end of that period of underperformance. Um, you're 606 00:38:37,160 --> 00:38:40,839 Speaker 1: suggesting we won't know for some period of time if 607 00:38:40,840 --> 00:38:43,120 Speaker 1: the value premium is gone or if it's just a 608 00:38:43,200 --> 00:38:47,400 Speaker 1: regular cyclical underperformance. I don't think there are real cycles 609 00:38:47,440 --> 00:38:49,840 Speaker 1: to it. I think it's just kind of random that 610 00:38:50,480 --> 00:38:53,960 Speaker 1: go through good in bed periods, and you know, you 611 00:38:54,040 --> 00:38:57,279 Speaker 1: can't recognize them except that from the fact you can't 612 00:38:57,280 --> 00:39:01,520 Speaker 1: really predict them. Uh, we've we've tried tests, we've tried 613 00:39:01,560 --> 00:39:08,239 Speaker 1: predictive tests, and they have marginal nothing worth even focusing 614 00:39:08,760 --> 00:39:12,480 Speaker 1: focusing on. So basically is stuck with the volatility of 615 00:39:12,480 --> 00:39:15,680 Speaker 1: equity returns. They don't allow to say very much about 616 00:39:16,080 --> 00:39:20,280 Speaker 1: what's happened to expected returns going forward. And and David, 617 00:39:21,160 --> 00:39:25,040 Speaker 1: what we've seen a huge proliferation of various factor funds, 618 00:39:25,440 --> 00:39:27,560 Speaker 1: not just the three factor, of the five factor, of 619 00:39:27,600 --> 00:39:31,799 Speaker 1: the seven factor model. They're now hundreds identified. What does 620 00:39:31,840 --> 00:39:35,960 Speaker 1: this mean for investors? Has has the proliferation of all 621 00:39:36,000 --> 00:39:38,319 Speaker 1: these new factors been good for investors or is it 622 00:39:38,360 --> 00:39:43,280 Speaker 1: a non event? Well, I mean I think on balance 623 00:39:43,480 --> 00:39:47,239 Speaker 1: um UM has been overstated and whatever whatever it is 624 00:39:47,920 --> 00:39:53,120 Speaker 1: the you know, I think UM researchers identified, you know, 625 00:39:54,600 --> 00:39:58,200 Speaker 1: factors that seem to explain differences in average returns. But 626 00:39:58,320 --> 00:40:00,640 Speaker 1: there can't be hundreds of factor I mean, they got it, 627 00:40:00,920 --> 00:40:02,960 Speaker 1: They're probably at the end of the day, they're probably 628 00:40:03,000 --> 00:40:05,799 Speaker 1: a few factors. Uh and Gene and ken. One of 629 00:40:05,800 --> 00:40:08,080 Speaker 1: the things they try to do is instead of trying 630 00:40:08,120 --> 00:40:10,800 Speaker 1: to identify more and more factors, just take the researchers 631 00:40:10,840 --> 00:40:14,320 Speaker 1: out there and can shrink it down to simpler, you know, 632 00:40:14,400 --> 00:40:17,560 Speaker 1: more factors that matter, factors that matter, well, lots of 633 00:40:17,640 --> 00:40:19,960 Speaker 1: lots of these things that just different manifestations of the 634 00:40:20,000 --> 00:40:22,839 Speaker 1: same thing. Give us an example. So value can be 635 00:40:22,920 --> 00:40:25,480 Speaker 1: very measured in many different ways. I can use the 636 00:40:25,480 --> 00:40:27,960 Speaker 1: book to market ratio you need to catch full at 637 00:40:28,000 --> 00:40:30,440 Speaker 1: the price. They can use lots of different variables, so 638 00:40:30,560 --> 00:40:34,719 Speaker 1: I identify what is basically this same thing. Uh. And 639 00:40:36,600 --> 00:40:40,359 Speaker 1: there are thousands of finance professors out there who all 640 00:40:40,400 --> 00:40:43,160 Speaker 1: want to get ten here um they have the publish 641 00:40:43,200 --> 00:40:46,560 Speaker 1: to do that. So they're all just kind of searching 642 00:40:46,560 --> 00:40:50,600 Speaker 1: through the data finding stuff that maybe there only on 643 00:40:50,640 --> 00:40:53,400 Speaker 1: a chance basis that won't be there out of sample. 644 00:40:54,160 --> 00:40:57,840 Speaker 1: So there's lots of work being done and it remains 645 00:40:57,880 --> 00:41:00,799 Speaker 1: to be done on what we call robust this. How 646 00:41:00,840 --> 00:41:03,239 Speaker 1: does this stand up when I have new data? So 647 00:41:03,440 --> 00:41:05,840 Speaker 1: we we've always been into robustness in the sense that 648 00:41:06,360 --> 00:41:09,480 Speaker 1: when we found it in the ninety two paper, we 649 00:41:09,520 --> 00:41:12,160 Speaker 1: went back and collected the data back to that data 650 00:41:12,200 --> 00:41:15,880 Speaker 1: started in the sixty three We then went back and 651 00:41:15,880 --> 00:41:19,120 Speaker 1: collected the data back to to look out a sample, 652 00:41:19,160 --> 00:41:21,439 Speaker 1: and then we looked at the international leader to look 653 00:41:21,440 --> 00:41:27,440 Speaker 1: at a sample, and so pretty much the same thing everywhere. Um, 654 00:41:27,600 --> 00:41:30,440 Speaker 1: now we've had a bad period of this, but relative 655 00:41:30,520 --> 00:41:32,920 Speaker 1: to all of that, it doesn't look that doesn't look 656 00:41:32,960 --> 00:41:36,680 Speaker 1: that serious. And I have to ask you a question 657 00:41:36,800 --> 00:41:42,400 Speaker 1: about behavioral economics. Um, we're here in Chicago, where we 658 00:41:42,480 --> 00:41:45,600 Speaker 1: could short of call at the birthplace of behavioral finance. 659 00:41:46,000 --> 00:41:48,640 Speaker 1: What do you think about that area and what's your 660 00:41:48,640 --> 00:41:54,919 Speaker 1: involvement with it. Well, my good friend Richard Taylor, who 661 00:41:55,000 --> 00:41:58,520 Speaker 1: is the king of the behavioral finance people and another 662 00:41:58,560 --> 00:42:01,200 Speaker 1: Nobel law that no one I teach them and say 663 00:42:01,440 --> 00:42:07,239 Speaker 1: I'm the most important person in behavioral finance. Are because 664 00:42:07,760 --> 00:42:10,080 Speaker 1: most of the behavioral finance is just the criticism of 665 00:42:10,080 --> 00:42:17,560 Speaker 1: official markets. So without me, what have they got? And 666 00:42:17,560 --> 00:42:20,680 Speaker 1: and you and and Dick Taylor are golf parts are 667 00:42:21,600 --> 00:42:24,600 Speaker 1: so do you argue across eighteen holes or you know? 668 00:42:24,680 --> 00:42:27,920 Speaker 1: The reality is we agree on the facts, we disagree 669 00:42:27,960 --> 00:42:34,320 Speaker 1: on the interpretation um For example, he thinks the value 670 00:42:34,400 --> 00:42:41,560 Speaker 1: premium is the result of people's misperceptions of what accounting 671 00:42:41,600 --> 00:42:45,320 Speaker 1: information and other information looks like. That it's all based 672 00:42:45,360 --> 00:42:49,640 Speaker 1: on misinterpretation of information. Now, if you believe that, then 673 00:42:49,680 --> 00:42:53,480 Speaker 1: you think it should go away, because it's possible to 674 00:42:53,520 --> 00:42:56,640 Speaker 1: teach people that they have these these biases are professional 675 00:42:56,640 --> 00:43:00,520 Speaker 1: managers should be able to get past them, but they 676 00:43:00,560 --> 00:43:04,400 Speaker 1: still have emotional reactions that sometimes they can't get that. 677 00:43:05,840 --> 00:43:08,439 Speaker 1: That's the thing about behavior lea going elements. What their 678 00:43:08,480 --> 00:43:11,759 Speaker 1: studies seem to show is people don't learn from experience. 679 00:43:12,239 --> 00:43:16,520 Speaker 1: If you're stupidly, repeatedly stupid, you don't learn. And most 680 00:43:16,520 --> 00:43:19,760 Speaker 1: people are stupid. I mean, that's that's the provisation. Someone 681 00:43:19,840 --> 00:43:21,480 Speaker 1: has to be on the wrong side of that trade. 682 00:43:21,520 --> 00:43:24,239 Speaker 1: You said it's a zero sum, right, So so you 683 00:43:24,239 --> 00:43:27,560 Speaker 1: guys agree more than you than you might realize the fact, 684 00:43:28,520 --> 00:43:32,400 Speaker 1: but not the interpretation. But there is no behavioral finance. 685 00:43:33,080 --> 00:43:36,399 Speaker 1: Wait say that again. There is no behavioral finance. There's 686 00:43:36,400 --> 00:43:40,000 Speaker 1: no it's all just a criticism of official markets really 687 00:43:40,080 --> 00:43:48,959 Speaker 1: with no evidence. Is dick here? I think he would 688 00:43:49,000 --> 00:43:52,560 Speaker 1: disagree with that. So that's not so sure because when 689 00:43:52,719 --> 00:43:54,560 Speaker 1: when I put the challenge to him twenty years ago, 690 00:43:54,640 --> 00:43:57,239 Speaker 1: I wrote a paper that said, Okay, now you've been 691 00:43:57,239 --> 00:43:59,960 Speaker 1: criticizing us for the last whatever, it's time for you 692 00:44:00,120 --> 00:44:01,960 Speaker 1: to come up with a theory that we can actually 693 00:44:02,000 --> 00:44:04,800 Speaker 1: test and see if it works or not. And what 694 00:44:04,960 --> 00:44:09,400 Speaker 1: was response? We're still waiting. Actually you presented that paper 695 00:44:09,560 --> 00:44:12,560 Speaker 1: at a at U c L A at Gene walks 696 00:44:12,600 --> 00:44:15,040 Speaker 1: in and says, all the way over, I was thinking 697 00:44:15,040 --> 00:44:17,600 Speaker 1: about breaking my leg or something. So I can catch 698 00:44:17,640 --> 00:44:22,680 Speaker 1: some sympathy here. And to be fair, when Taylor won 699 00:44:22,719 --> 00:44:26,320 Speaker 1: the Nobel Prize, he admitted his plan was to spend 700 00:44:26,360 --> 00:44:30,120 Speaker 1: the money as irrationally as possible. So even he even 701 00:44:30,200 --> 00:44:34,120 Speaker 1: he agrees with you on that. UM, I wanted to 702 00:44:34,160 --> 00:44:40,560 Speaker 1: ask about, uh, some of your comments on Beta. You 703 00:44:40,719 --> 00:44:44,959 Speaker 1: said beta is dead. Do you still believe beta is dead. Well, 704 00:44:45,000 --> 00:44:49,520 Speaker 1: the evidence basically says that the relation between averaging tune 705 00:44:49,520 --> 00:44:53,080 Speaker 1: and beta it's too flat to be explained by the 706 00:44:53,160 --> 00:44:56,680 Speaker 1: capitalistic pricing model. That's a real shame because that model 707 00:44:56,760 --> 00:45:00,759 Speaker 1: is so simple. Um, if it were true, it would 708 00:45:00,800 --> 00:45:05,360 Speaker 1: really be like life, a lot simpler in many ways. 709 00:45:06,239 --> 00:45:09,120 Speaker 1: But it just has never worked very well. All right, 710 00:45:09,200 --> 00:45:12,040 Speaker 1: So what we're gonna do now? I have more, many 711 00:45:12,040 --> 00:45:15,000 Speaker 1: more questions. But this thing is lighting up, and we 712 00:45:15,080 --> 00:45:17,960 Speaker 1: have questions from the audience. So I'm gonna I'm gonna 713 00:45:18,040 --> 00:45:20,560 Speaker 1: ask a few from this and see, uh see where 714 00:45:20,600 --> 00:45:23,240 Speaker 1: we go from here. Um, let's talk about your views 715 00:45:23,320 --> 00:45:26,080 Speaker 1: on the future of active management. Where do you see 716 00:45:26,080 --> 00:45:28,239 Speaker 1: the industry going in ten years? And this is for 717 00:45:28,320 --> 00:45:31,640 Speaker 1: both of you, active management active management, Well, it's been 718 00:45:31,680 --> 00:45:37,880 Speaker 1: shrinking really slowly. So when Kenn did his American Finance 719 00:45:37,920 --> 00:45:42,960 Speaker 1: Association Presidents did his president speech, what he's what he 720 00:45:43,000 --> 00:45:45,200 Speaker 1: said was one of the things he said was we've 721 00:45:45,200 --> 00:45:48,800 Speaker 1: gone from zero to and I think it was about 722 00:45:48,840 --> 00:45:51,520 Speaker 1: forty years at that time, maybe a little more, and 723 00:45:51,600 --> 00:45:53,480 Speaker 1: since then we've gone to like I think it's up 724 00:45:53,480 --> 00:45:56,879 Speaker 1: to thirty or forty. Now that's passively man. So that's 725 00:45:56,920 --> 00:46:02,440 Speaker 1: permeated very slowly through the profess Yeah, what where it 726 00:46:02,440 --> 00:46:05,359 Speaker 1: will go from me? Or we'll see and and some 727 00:46:05,400 --> 00:46:08,240 Speaker 1: people have made the argument you have to separate active 728 00:46:08,880 --> 00:46:13,680 Speaker 1: from expensive locost active is attractive. Obviously this is a 729 00:46:13,800 --> 00:46:17,480 Speaker 1: key tenant at dimensional funds. How much of the move 730 00:46:17,600 --> 00:46:22,480 Speaker 1: away from active has really been away from expensive I 731 00:46:22,560 --> 00:46:25,000 Speaker 1: think a big part of it. And in fact that 732 00:46:25,000 --> 00:46:27,480 Speaker 1: a lot of the move to indexing is through e 733 00:46:27,560 --> 00:46:29,560 Speaker 1: t f s and a lot of the a lot 734 00:46:29,600 --> 00:46:32,239 Speaker 1: of that is just a new version of active management. 735 00:46:32,600 --> 00:46:35,640 Speaker 1: Um or managers say, look, I don't think I can 736 00:46:35,719 --> 00:46:38,320 Speaker 1: pick individual stocks, but I can tell them sectors of 737 00:46:38,360 --> 00:46:41,600 Speaker 1: the market, So let me buy buy E t f s. 738 00:46:41,960 --> 00:46:44,560 Speaker 1: So it's really kind of confusing as to uh, you 739 00:46:44,600 --> 00:46:47,400 Speaker 1: know what the trend has been in active management. But 740 00:46:47,520 --> 00:46:52,120 Speaker 1: I I think active managers are resourceful and always compe 741 00:46:52,200 --> 00:46:57,040 Speaker 1: with new ideas of trying to entice people with magic 742 00:46:57,640 --> 00:47:02,759 Speaker 1: with magic. So the pushback against um efficient market we 743 00:47:02,760 --> 00:47:06,520 Speaker 1: always see this argument. Berkshire Hathaway had strong returns in 744 00:47:06,520 --> 00:47:09,919 Speaker 1: its early years as the result of Warren Buffett's skill 745 00:47:10,000 --> 00:47:15,080 Speaker 1: and security selection. How given Professor Farmer's comments and market efficiency, 746 00:47:15,560 --> 00:47:18,880 Speaker 1: how can this early success be explained. So you take 747 00:47:19,719 --> 00:47:23,320 Speaker 1: you have probably a hundred thousand people picking stocks right 748 00:47:23,000 --> 00:47:25,600 Speaker 1: right over a period of time, then you pick out 749 00:47:25,640 --> 00:47:30,440 Speaker 1: the one who does the best and impute that to skill. 750 00:47:31,080 --> 00:47:34,480 Speaker 1: The problem is, if I have a hundred thousand people picking, 751 00:47:34,880 --> 00:47:37,799 Speaker 1: what's the probability that one of them will look extraordinary? 752 00:47:38,239 --> 00:47:41,279 Speaker 1: Purely on a chance basis, You'll you'll always have some 753 00:47:41,320 --> 00:47:43,560 Speaker 1: outliers that look, you'll get a big old layer in 754 00:47:43,600 --> 00:47:46,440 Speaker 1: that in that experiment. But that's the way that the 755 00:47:46,480 --> 00:47:49,759 Speaker 1: newspaper accounts run. They take after, they look after the fact, 756 00:47:49,800 --> 00:47:52,320 Speaker 1: and they pick out the winners. So every year, for example, 757 00:47:52,360 --> 00:47:55,000 Speaker 1: they pick out the best performers of the last five 758 00:47:55,120 --> 00:47:58,600 Speaker 1: ten years, and you look at the following period. No, no, 759 00:47:58,600 --> 00:48:01,479 Speaker 1: no correlation between past the tay and and in fact 760 00:48:01,560 --> 00:48:04,280 Speaker 1: we've seen the morning store manager of the year tends 761 00:48:04,320 --> 00:48:08,680 Speaker 1: to significantly out before underperform in the decade once they 762 00:48:08,680 --> 00:48:11,520 Speaker 1: win manager of the decade. But that would surprise me too, 763 00:48:12,040 --> 00:48:15,840 Speaker 1: I would think they'd just be random. No, no persistency, 764 00:48:15,880 --> 00:48:19,759 Speaker 1: In fact, negative persistency. We've had the sas in that subductative. 765 00:48:20,239 --> 00:48:22,920 Speaker 1: How much persistence is there in performance? The answer is 766 00:48:22,960 --> 00:48:27,240 Speaker 1: basically zero. Zero, and I have to the best predictor 767 00:48:27,480 --> 00:48:30,640 Speaker 1: of future performance is FeAs and expenses that you know, 768 00:48:30,680 --> 00:48:34,279 Speaker 1: it's ironic that came out of morning Star, that did 769 00:48:34,280 --> 00:48:37,640 Speaker 1: a big study and they sell their morning Star rating 770 00:48:37,640 --> 00:48:40,200 Speaker 1: and it turned out ignore everything else, just picked the 771 00:48:40,280 --> 00:48:43,840 Speaker 1: cheapest fun pretty pretty astonishing, right, Well, they come up. 772 00:48:44,080 --> 00:48:45,799 Speaker 1: I think they came out and said came out and 773 00:48:45,840 --> 00:48:49,239 Speaker 1: said there's no relation between between future performance and the 774 00:48:49,239 --> 00:48:52,520 Speaker 1: way we ran things. There's another question if it comes 775 00:48:52,520 --> 00:48:57,360 Speaker 1: out to that, so so um. One one of the 776 00:48:57,400 --> 00:48:59,919 Speaker 1: questions that is asked by the room. If the mark 777 00:49:00,040 --> 00:49:03,279 Speaker 1: it becomes truly efficient one day, what happens to all 778 00:49:03,360 --> 00:49:07,800 Speaker 1: the management farms? That question assumes that markets aren't truly 779 00:49:07,800 --> 00:49:14,680 Speaker 1: efficient today. How do you respond to that? What's the evidence? No, 780 00:49:14,840 --> 00:49:16,920 Speaker 1: I mean I don't think it's I think all of 781 00:49:16,960 --> 00:49:19,400 Speaker 1: it is wrong. So it's different. There will still be 782 00:49:19,440 --> 00:49:22,240 Speaker 1: a management business, you just will have very little active 783 00:49:22,280 --> 00:49:25,400 Speaker 1: in it, so that you have to have some active 784 00:49:25,440 --> 00:49:29,680 Speaker 1: investors to make price prices efficient. The problem is you 785 00:49:29,719 --> 00:49:33,800 Speaker 1: don't expect them to be professional managers because the logic 786 00:49:33,960 --> 00:49:36,560 Speaker 1: of being a good investor is that you should get 787 00:49:36,560 --> 00:49:39,200 Speaker 1: their returns if you don't hand them back to other people, 788 00:49:39,880 --> 00:49:42,319 Speaker 1: you take them back and higher fees. You know, that's 789 00:49:42,360 --> 00:49:47,320 Speaker 1: the human capital activity is picking stocks or whatever investment management. 790 00:49:47,360 --> 00:49:49,200 Speaker 1: So if you have real skill, you should be charging, 791 00:49:49,320 --> 00:49:50,960 Speaker 1: you should go all the retention should go to you, 792 00:49:51,040 --> 00:49:54,200 Speaker 1: Naz your clients. And and this is for both of you. 793 00:49:54,760 --> 00:49:57,399 Speaker 1: What sort of opportunity for out performance do you see 794 00:49:57,440 --> 00:50:00,600 Speaker 1: in private markets given that in for nation, in that 795 00:50:00,719 --> 00:50:04,720 Speaker 1: space is so much more opaque than in public markets. 796 00:50:05,120 --> 00:50:08,200 Speaker 1: The problem is there are lots of good people studying that, 797 00:50:08,760 --> 00:50:12,600 Speaker 1: but they hamstrung by the lack of good data on 798 00:50:13,120 --> 00:50:15,920 Speaker 1: people who live in people who die the fund. You know, 799 00:50:15,960 --> 00:50:19,359 Speaker 1: the managers who live in what self reported. It's not 800 00:50:19,400 --> 00:50:22,759 Speaker 1: like so you get you get it. You get a 801 00:50:22,920 --> 00:50:26,080 Speaker 1: very kind of biased set of data on that. But 802 00:50:26,440 --> 00:50:28,960 Speaker 1: you know, it's kind of depends on what into that 803 00:50:29,000 --> 00:50:32,239 Speaker 1: business you go to. If you're looking at managers who 804 00:50:32,280 --> 00:50:35,200 Speaker 1: actually run the companies that they buy, they may actually 805 00:50:35,239 --> 00:50:37,799 Speaker 1: be able to add value, but it's management value. It's 806 00:50:37,800 --> 00:50:42,040 Speaker 1: not stuck picking value. If they you're picking companies that 807 00:50:42,040 --> 00:50:45,080 Speaker 1: have a good idea but a fully run probably you 808 00:50:45,080 --> 00:50:46,919 Speaker 1: can have a lot of value added in that case. 809 00:50:47,200 --> 00:50:49,520 Speaker 1: But again, if you go to the guy's doing it. 810 00:50:51,719 --> 00:50:53,640 Speaker 1: That's the that's the downside of it. They're the ones 811 00:50:53,680 --> 00:50:56,000 Speaker 1: who take all the profits out of it. Well, that's 812 00:50:56,280 --> 00:51:00,120 Speaker 1: that's the logic of human capital, right right. And we 813 00:51:00,120 --> 00:51:02,520 Speaker 1: didn't get to a question before I have to ask 814 00:51:03,840 --> 00:51:08,680 Speaker 1: about bubbles. And this goes back to be okay, So 815 00:51:09,280 --> 00:51:11,120 Speaker 1: I don't know how to bleep out the word bubbles. 816 00:51:11,200 --> 00:51:16,040 Speaker 1: But what do you mean by okay? So folks like 817 00:51:16,800 --> 00:51:21,280 Speaker 1: Failor and Chiller would describe a bubble as a period 818 00:51:21,480 --> 00:51:26,640 Speaker 1: of excessive market enthusiasm that leads prices to far outstrip 819 00:51:26,680 --> 00:51:30,600 Speaker 1: their fundamental valuation. What's the testable proposition here, though, I 820 00:51:30,640 --> 00:51:33,560 Speaker 1: don't know, can you Well, the way I interpret it 821 00:51:33,560 --> 00:51:37,080 Speaker 1: is you must be able to predict the end of it. Bubbles, 822 00:51:37,120 --> 00:51:39,120 Speaker 1: it would be something with a predictable ending. So it 823 00:51:39,120 --> 00:51:43,000 Speaker 1: has to be measurable by a predefined set of parameters, 824 00:51:43,080 --> 00:51:45,440 Speaker 1: and you should be able to identify the end of it. 825 00:51:46,280 --> 00:51:49,800 Speaker 1: So if we were to say every time that fails 826 00:51:49,800 --> 00:51:53,840 Speaker 1: the test, I mean, I mean you can't. People can't 827 00:51:53,880 --> 00:51:57,760 Speaker 1: identify bubbles that way until after the fact. After the fact, 828 00:51:58,160 --> 00:52:02,320 Speaker 1: it's it's easy. But this is famous theory around about 829 00:52:02,880 --> 00:52:05,839 Speaker 1: you know, the early origins of market efficiency, which home 830 00:52:05,880 --> 00:52:09,360 Speaker 1: work working, went into the faculty lounge at Stanford. He 831 00:52:09,520 --> 00:52:15,160 Speaker 1: was agriculturally uh prices, and he showed them chats of 832 00:52:15,160 --> 00:52:17,239 Speaker 1: of of prices, and he said, these were chats of 833 00:52:17,280 --> 00:52:20,200 Speaker 1: commodity prices, and he wanted to not see if they 834 00:52:20,200 --> 00:52:23,880 Speaker 1: could identify bubbles and the prices, and every to a man, 835 00:52:23,960 --> 00:52:26,560 Speaker 1: they all could. There were no women. To a man, 836 00:52:26,640 --> 00:52:29,040 Speaker 1: they all could. The problem was what he was showing 837 00:52:29,080 --> 00:52:34,840 Speaker 1: them was accumulative random numbers, as those just generated uh stuff. 838 00:52:34,880 --> 00:52:37,440 Speaker 1: So that the message there's people see bubbles where there 839 00:52:37,440 --> 00:52:43,240 Speaker 1: are now h. So here's a here's a really broad question. 840 00:52:43,920 --> 00:52:47,560 Speaker 1: Given the societal angst of people attacking the value of 841 00:52:47,600 --> 00:52:51,360 Speaker 1: a business education, what is your belief in the value 842 00:52:51,400 --> 00:52:55,640 Speaker 1: of this education booth and how should we communicate this 843 00:52:56,200 --> 00:53:01,640 Speaker 1: better to society? Well, I think it's it's incredibly valuable 844 00:53:01,719 --> 00:53:06,440 Speaker 1: to society, um, because if we are going to make 845 00:53:06,520 --> 00:53:09,120 Speaker 1: lives better for people, part of the answer has to 846 00:53:09,160 --> 00:53:13,319 Speaker 1: come from better and safer financial products. And just that's 847 00:53:13,360 --> 00:53:16,040 Speaker 1: the reality. And that's been the history. I mean, it's 848 00:53:16,120 --> 00:53:20,000 Speaker 1: like I say, I look back on my career and 849 00:53:20,160 --> 00:53:22,960 Speaker 1: uh working with Gene and you know, we've been part 850 00:53:23,040 --> 00:53:27,840 Speaker 1: of the movement towards lower fees and better controls. So 851 00:53:27,920 --> 00:53:30,440 Speaker 1: I can find it irritating when somebody says, really, the 852 00:53:30,480 --> 00:53:33,000 Speaker 1: only advanced the last fifty years has been the A 853 00:53:33,120 --> 00:53:39,879 Speaker 1: T M. You know. Uh it's uh qu yeah, live, 854 00:53:40,320 --> 00:53:45,000 Speaker 1: we've live based all this work live, We've improved lives. Uh, 855 00:53:45,200 --> 00:53:47,360 Speaker 1: and there and other people with sharing the I s 856 00:53:47,400 --> 00:53:50,359 Speaker 1: we're not the only one. But I mean, I don't 857 00:53:50,360 --> 00:53:53,400 Speaker 1: think it gets much better than that. And uh so 858 00:53:53,600 --> 00:53:57,719 Speaker 1: I would hate to have people, um not to get 859 00:53:57,719 --> 00:54:01,880 Speaker 1: into business or particularly financial services. You can have a 860 00:54:01,880 --> 00:54:05,719 Speaker 1: good career in financial services and at the end of 861 00:54:05,760 --> 00:54:07,960 Speaker 1: it you can look back on it and take pride 862 00:54:07,960 --> 00:54:10,640 Speaker 1: in what you've accomplished. It's as simple as that. So 863 00:54:10,640 --> 00:54:13,879 Speaker 1: so that leads to the next question. What keeps both 864 00:54:13,920 --> 00:54:16,319 Speaker 1: of you working? Neither of you have to work, Why 865 00:54:16,320 --> 00:54:18,520 Speaker 1: do both of you still get up and go to 866 00:54:18,560 --> 00:54:24,000 Speaker 1: the office each day. It's fun, it's fun challenging, it's important. 867 00:54:24,040 --> 00:54:28,200 Speaker 1: I mean, it's exciting to see the retired people living 868 00:54:28,239 --> 00:54:32,319 Speaker 1: better as a result of these ideas, or better able 869 00:54:32,360 --> 00:54:34,520 Speaker 1: to send their kids to colleagues or whatever. I mean, 870 00:54:34,560 --> 00:54:38,480 Speaker 1: these are These are not you know, ideas that have 871 00:54:38,560 --> 00:54:42,040 Speaker 1: no importance. I mean, these are you know that's you 872 00:54:42,080 --> 00:54:44,600 Speaker 1: can get behind that kind of idea. You get a 873 00:54:44,680 --> 00:54:47,359 Speaker 1: lot of satisfaction out of coming up with stuff people 874 00:54:47,360 --> 00:54:51,880 Speaker 1: haven't seen before. I have been recognized, and we have 875 00:54:51,920 --> 00:54:54,600 Speaker 1: time for one last question, and I'm going to go 876 00:54:54,719 --> 00:54:58,760 Speaker 1: with something about, um, what do you think the future 877 00:54:58,800 --> 00:55:02,320 Speaker 1: of Chicago Booth looks like? What is next in store 878 00:55:02,400 --> 00:55:05,279 Speaker 1: for the school? And this is for both of you. Well, 879 00:55:05,320 --> 00:55:07,440 Speaker 1: I can tell you that. So I've been on the 880 00:55:07,480 --> 00:55:12,359 Speaker 1: faculty since nineteen sixty three, students since nineteen sixty. In 881 00:55:12,360 --> 00:55:16,600 Speaker 1: the sixties, basically there was a pretty good economics group. 882 00:55:17,080 --> 00:55:20,200 Speaker 1: There was a developing finance group, and that was it. 883 00:55:21,480 --> 00:55:27,000 Speaker 1: I mean, there's the school's junk. Well, but look that 884 00:55:27,080 --> 00:55:29,279 Speaker 1: was not unique to us. So I remember when I 885 00:55:29,320 --> 00:55:33,799 Speaker 1: was recruiting as a student, UM in college not from here. 886 00:55:34,800 --> 00:55:36,520 Speaker 1: The people recruiting said, why do you want to go 887 00:55:36,560 --> 00:55:38,759 Speaker 1: to a business school? They don't teach you anything, we 888 00:55:38,800 --> 00:55:42,600 Speaker 1: don't pay anything for what they what they do. And 889 00:55:42,680 --> 00:55:45,319 Speaker 1: that was too at that time. I think, and what's 890 00:55:45,360 --> 00:55:48,719 Speaker 1: happened through time is not just finance, but every other 891 00:55:48,760 --> 00:55:55,680 Speaker 1: area has been academically made more become more successful. So marketing, accounting, 892 00:55:56,440 --> 00:55:58,680 Speaker 1: statistics was always pretty good, but it was never part 893 00:55:58,680 --> 00:56:03,200 Speaker 1: of of of business schools. So now we have really 894 00:56:04,080 --> 00:56:08,239 Speaker 1: front rank faculty and every single discipline. The school is 895 00:56:08,880 --> 00:56:12,520 Speaker 1: so high, high level, competitive on the faculty side, on 896 00:56:12,560 --> 00:56:15,480 Speaker 1: the research side. But it's just there's no relation to 897 00:56:16,120 --> 00:56:19,400 Speaker 1: what it was fifty years ago. It's a totally different 898 00:56:19,400 --> 00:56:23,120 Speaker 1: professional place. On the students side, I think there was 899 00:56:23,160 --> 00:56:26,080 Speaker 1: a challenge, and I've been complaining about it for a 900 00:56:26,080 --> 00:56:29,040 Speaker 1: long time. Students don't work as hard as they did 901 00:56:29,520 --> 00:56:32,479 Speaker 1: in the old days. I've heard this is a very 902 00:56:32,560 --> 00:56:36,000 Speaker 1: very difficult school to work your way through. Well, but 903 00:56:36,080 --> 00:56:39,720 Speaker 1: the reality is we keep track of hours work per 904 00:56:39,800 --> 00:56:43,880 Speaker 1: per per class out of class. When I started teaching, 905 00:56:44,360 --> 00:56:48,880 Speaker 1: everybody was around fifteen per class. That number has dropped 906 00:56:49,160 --> 00:56:53,600 Speaker 1: dramatically through time. I bet this room would disagree with that. No, no, no, no, 907 00:56:53,640 --> 00:56:56,759 Speaker 1: we have the statistics. It's not it's not it's not 908 00:56:56,840 --> 00:57:00,359 Speaker 1: a guess. And and David, what do you see as 909 00:57:00,920 --> 00:57:04,240 Speaker 1: the next decade holding for the Booth School? Well, I'm 910 00:57:04,280 --> 00:57:07,239 Speaker 1: not really in a position there. I mean, wow, I 911 00:57:07,360 --> 00:57:12,440 Speaker 1: just gave him some money. I figured they can figure 912 00:57:12,480 --> 00:57:14,799 Speaker 1: that stuff out. If I had to figure that out 913 00:57:14,840 --> 00:57:17,760 Speaker 1: as well, I mean that would be a real hero. 914 00:57:17,960 --> 00:57:22,280 Speaker 1: You know, I I'm just um, I'm not. I don't 915 00:57:22,320 --> 00:57:25,200 Speaker 1: know where it's gonna go, but wherever it goes is 916 00:57:25,200 --> 00:57:28,440 Speaker 1: going to be important. And and that's the perfect spot 917 00:57:28,480 --> 00:57:31,320 Speaker 1: to end. Ladies and gentlemen, please say thank you to 918 00:57:31,640 --> 00:57:40,880 Speaker 1: Professor Gene Fama and David Booth. That's my conversation with 919 00:57:40,960 --> 00:57:44,320 Speaker 1: David Booth and Gene Fama. If you enjoyed that, we'll 920 00:57:44,360 --> 00:57:46,720 Speaker 1: go to Apple iTunes, look up an inch or down 921 00:57:46,760 --> 00:57:49,280 Speaker 1: an inch, and you could see any of the nearly 922 00:57:49,320 --> 00:57:53,720 Speaker 1: three d conversations we've had over the past five years. 923 00:57:54,240 --> 00:57:57,720 Speaker 1: We love your comments, feedback and suggestions right to us 924 00:57:57,840 --> 00:58:00,960 Speaker 1: at m IB podcast at bloom Berg dot net. Be 925 00:58:01,040 --> 00:58:04,439 Speaker 1: sure and give us a review at Apple iTunes. Sign 926 00:58:04,520 --> 00:58:08,320 Speaker 1: up from my daily reads at rit Halts dot com, 927 00:58:08,400 --> 00:58:11,320 Speaker 1: follow me on Twitter at rit Halts. I would be 928 00:58:11,360 --> 00:58:13,720 Speaker 1: remiss if I did not thank the crack staff that 929 00:58:13,800 --> 00:58:17,800 Speaker 1: helps us put these conversations together this week and this 930 00:58:17,840 --> 00:58:21,240 Speaker 1: week was an unusual expedition. We all had to slip 931 00:58:21,320 --> 00:58:24,240 Speaker 1: out to Chicago. The folks at the University of Chicago 932 00:58:24,280 --> 00:58:27,120 Speaker 1: were great. They did a really great job in setting 933 00:58:27,120 --> 00:58:30,240 Speaker 1: things up so that we could both videotape and audio 934 00:58:30,360 --> 00:58:33,520 Speaker 1: record this. Michael Boyle is my producer, and he was 935 00:58:33,680 --> 00:58:36,040 Speaker 1: on hand there along with a few other folks from 936 00:58:36,040 --> 00:58:39,600 Speaker 1: Bloomberg that really made everything go very smoothly. Charlie Volmer 937 00:58:39,720 --> 00:58:43,800 Speaker 1: is my audio engineer who helped cut this monstrosity together. 938 00:58:44,240 --> 00:58:48,520 Speaker 1: Atica val Broun is our project manager. Michael Batnick is 939 00:58:48,560 --> 00:58:51,520 Speaker 1: my head of research. I'm Barry rit Halts. You've been 940 00:58:51,560 --> 00:59:00,320 Speaker 1: listening to Masters in Business on Bloomberg Radio.