1 00:00:17,560 --> 00:00:21,000 Speaker 1: Pushkin too quick. No, it's perfect Pushkin stuff. 2 00:00:21,400 --> 00:00:37,959 Speaker 2: You got it, Robert Smith. Yeah, tell me about the 3 00:00:38,000 --> 00:00:38,440 Speaker 2: Cold War. 4 00:00:39,120 --> 00:00:41,599 Speaker 1: The Cold War between the United States and the Soviet 5 00:00:41,680 --> 00:00:45,479 Speaker 1: Union thrust us into this technological world that we live 6 00:00:45,479 --> 00:00:49,960 Speaker 1: in today. We know about how it brought us the Internet, satellites, 7 00:00:50,120 --> 00:00:53,960 Speaker 1: silicon chips, but the Cold War also brought us the 8 00:00:54,000 --> 00:00:59,560 Speaker 1: modern financial market system. Stocks, bonds, all traded with high 9 00:00:59,600 --> 00:01:04,919 Speaker 1: frequency computers, right algorithms. This quant world of Wall Street 10 00:01:05,240 --> 00:01:07,719 Speaker 1: came from the Cold War, and the one man most 11 00:01:07,720 --> 00:01:12,560 Speaker 1: responsible for this financial breakthrough was a genius mathematician who 12 00:01:12,600 --> 00:01:16,120 Speaker 1: grew disillusion with the US government and decided to create 13 00:01:16,160 --> 00:01:21,760 Speaker 1: the most efficient money making machine ever built great setup 14 00:01:21,920 --> 00:01:25,039 Speaker 1: for his own profit. By the way, so it's the 15 00:01:25,040 --> 00:01:30,440 Speaker 1: early nineteen sixties and Jim Simons is studying math. Like 16 00:01:30,440 --> 00:01:31,960 Speaker 1: a lot of people in the United States. There was 17 00:01:32,000 --> 00:01:35,040 Speaker 1: this real push after Sputnik for people to go into 18 00:01:35,040 --> 00:01:40,240 Speaker 1: math and science. Jim Simons graduates from MIT from UC Berkeley, 19 00:01:40,880 --> 00:01:44,360 Speaker 1: and he decides that he doesn't really want to do 20 00:01:44,440 --> 00:01:47,080 Speaker 1: mathematics of the physical world. He wants to do the 21 00:01:47,200 --> 00:01:53,560 Speaker 1: highest form theoretical mathematics. His thesis topic on the transitivity 22 00:01:53,560 --> 00:01:56,280 Speaker 1: of holonomi systems or hulminy. 23 00:01:56,400 --> 00:01:57,800 Speaker 3: Do you know what that word means? 24 00:01:58,080 --> 00:02:02,960 Speaker 1: I do not, eh no, But why do you read 25 00:02:03,000 --> 00:02:05,440 Speaker 1: the first Why do you read the first two sentences 26 00:02:05,480 --> 00:02:07,040 Speaker 1: of the first theorem so we can get a feel 27 00:02:07,080 --> 00:02:07,280 Speaker 1: for it. 28 00:02:07,600 --> 00:02:09,040 Speaker 3: Yeah, put it here, you've sent it to me. It 29 00:02:09,040 --> 00:02:15,080 Speaker 3: says theorem one. Let M be a c to the infinity. 30 00:02:15,600 --> 00:02:20,000 Speaker 3: I guess manifold with an affine connection. Let R denote 31 00:02:20,040 --> 00:02:22,640 Speaker 3: the curvative tensor of the connection. 32 00:02:23,680 --> 00:02:24,560 Speaker 1: I don't know what it means. 33 00:02:24,639 --> 00:02:26,800 Speaker 3: I know you could even have us something to the infinity. 34 00:02:27,080 --> 00:02:31,440 Speaker 1: But I'll tell you who loved this, the US government 35 00:02:31,639 --> 00:02:33,760 Speaker 1: and all the people who worked for the US government. 36 00:02:34,080 --> 00:02:36,000 Speaker 1: And he was recruited to work at something called the 37 00:02:36,360 --> 00:02:39,079 Speaker 1: Institute for Defense Analyzes in Princeton. 38 00:02:39,280 --> 00:02:42,240 Speaker 3: Why don't they just call it the CIA math. 39 00:02:42,360 --> 00:02:46,680 Speaker 1: Shop front at Tecton? Is it that technically independent? It 40 00:02:46,760 --> 00:02:49,560 Speaker 1: is a think tank that helps the US government during 41 00:02:49,600 --> 00:02:52,800 Speaker 1: the Cold War. They do things with you know, weapons 42 00:02:52,919 --> 00:02:56,720 Speaker 1: targeting and evaluation, but they also do code breaking, and 43 00:02:56,760 --> 00:03:00,919 Speaker 1: that's where they put Jim Simons, the mathematician. There's anything 44 00:03:00,960 --> 00:03:03,040 Speaker 1: wrong with that? So if you think about it, right, 45 00:03:03,360 --> 00:03:05,440 Speaker 1: because we know this is a show about finance eventually, 46 00:03:06,160 --> 00:03:08,560 Speaker 1: but think about what he's doing in order to break 47 00:03:08,720 --> 00:03:13,280 Speaker 1: Soviet codes. Jim Simons is looking looking at a sea 48 00:03:13,320 --> 00:03:17,800 Speaker 1: of random signals, seemingly random data, but he knows that 49 00:03:17,840 --> 00:03:21,200 Speaker 1: there's a communication inside. He knows there's a pattern, there's 50 00:03:21,240 --> 00:03:25,760 Speaker 1: a signal in the noise, and he's using sophisticated mathematics, 51 00:03:26,000 --> 00:03:30,600 Speaker 1: algorithms and early computers, big computers to churn through this 52 00:03:30,720 --> 00:03:34,200 Speaker 1: data and try and figure out what the Soviets are saying. 53 00:03:34,280 --> 00:03:36,600 Speaker 3: What is the data telling us, what is going to 54 00:03:36,640 --> 00:03:38,600 Speaker 3: happen in the world exactly? 55 00:03:38,720 --> 00:03:41,800 Speaker 1: Now, By all accounts, Jim Simons is pretty good at 56 00:03:41,800 --> 00:03:45,240 Speaker 1: this hard to tell. It's all secret, right, And in 57 00:03:45,280 --> 00:03:47,480 Speaker 1: another world he might have been one of those mathematicians 58 00:03:47,520 --> 00:03:50,720 Speaker 1: who stayed on at the NSA, had an illustrious career, 59 00:03:51,120 --> 00:03:52,640 Speaker 1: and we never would have heard of him because it 60 00:03:52,680 --> 00:03:56,280 Speaker 1: all would have been secret. But instead he did something amazing. 61 00:03:56,760 --> 00:03:59,240 Speaker 1: So this is during the Vietnam War, the early days 62 00:03:59,240 --> 00:04:02,320 Speaker 1: of the Vietnam War, right, and the head of the 63 00:04:02,360 --> 00:04:07,040 Speaker 1: think tank the IDA was publicly defending US military action 64 00:04:07,160 --> 00:04:10,160 Speaker 1: in Vietnam, saying we are winning the war. And Jim 65 00:04:10,240 --> 00:04:14,040 Speaker 1: Simons is young mathematician. The Times in his late twenties. Right, 66 00:04:14,360 --> 00:04:18,279 Speaker 1: he thinks it's total crap, and he writes a public 67 00:04:18,400 --> 00:04:22,039 Speaker 1: letter to the New York Times magazine published there that says, 68 00:04:22,360 --> 00:04:26,120 Speaker 1: the war in Vietnam is a waste of time and 69 00:04:26,159 --> 00:04:29,240 Speaker 1: money and human lives. Quote, it would make us stronger 70 00:04:29,279 --> 00:04:32,920 Speaker 1: to construct decent transportation on our East coast than it 71 00:04:32,960 --> 00:04:35,799 Speaker 1: would be to destroy all the bridges in Vietnam. Huh, 72 00:04:36,000 --> 00:04:39,520 Speaker 1: I know true today? Right? Needless to say, he writes this, 73 00:04:39,800 --> 00:04:42,880 Speaker 1: criticizing his boss, he is fired from the thing. Sure, 74 00:04:43,320 --> 00:04:47,240 Speaker 1: reasonably so, one might say, But Jim Simon's already had 75 00:04:47,360 --> 00:04:50,360 Speaker 1: this idea that would change finance. What if you could 76 00:04:50,440 --> 00:04:54,200 Speaker 1: use these same systems, computers, algorithms, code breaking, right to 77 00:04:54,279 --> 00:04:57,479 Speaker 1: look for patterns in stocks and bonds. He would go 78 00:04:57,520 --> 00:05:01,080 Speaker 1: on to create a firm called Renaissance Technologies, with one 79 00:05:01,200 --> 00:05:05,640 Speaker 1: fund in particular, called the Medallion Fund. Over the span 80 00:05:06,400 --> 00:05:09,960 Speaker 1: of thirty years or so, the Medallion Fund would return 81 00:05:10,160 --> 00:05:15,120 Speaker 1: just a staggering amount of money. It's estimated that its 82 00:05:15,320 --> 00:05:20,360 Speaker 1: trading profits were more than one hundred billion dollars. Just 83 00:05:20,480 --> 00:05:23,680 Speaker 1: you know, the stock market averages about eleven percent gain 84 00:05:23,800 --> 00:05:26,960 Speaker 1: a year, right, seven percent after inflation? Maybe? 85 00:05:27,120 --> 00:05:27,400 Speaker 3: Right? 86 00:05:28,040 --> 00:05:33,240 Speaker 1: Jim Simons was producing returns of sixty six percent a. 87 00:05:33,240 --> 00:05:35,040 Speaker 3: Year for thirty years. 88 00:05:36,360 --> 00:05:39,200 Speaker 1: That sounds wrong to me. 89 00:05:39,360 --> 00:05:42,000 Speaker 3: That sounds like somebody did a made a math there, right, 90 00:05:42,080 --> 00:05:45,839 Speaker 3: Like if you think of Bernie made off Right, the 91 00:05:45,880 --> 00:05:48,800 Speaker 3: famous Ponzi schemer, he was returning like what ten to 92 00:05:48,839 --> 00:05:50,479 Speaker 3: twelve percent a year and. 93 00:05:50,960 --> 00:05:52,680 Speaker 1: That was his dream. That was fake, and. 94 00:05:52,640 --> 00:05:56,680 Speaker 3: That was he was cheating to do that, right, sixty 95 00:05:56,760 --> 00:05:59,120 Speaker 3: six percent a year is so for thirty years? Like sure, 96 00:05:59,120 --> 00:06:00,320 Speaker 3: if somebody could do it for a couple of years, 97 00:06:00,320 --> 00:06:03,360 Speaker 3: they get lucky. Like, are you sure that is right? 98 00:06:03,839 --> 00:06:06,719 Speaker 1: This is as far as we can tell an accurate 99 00:06:06,800 --> 00:06:12,440 Speaker 1: number between nineteen eighty eight and twenty twenty one. Can't 100 00:06:12,640 --> 00:06:15,760 Speaker 1: believe it. Double your money in sixteen months and. 101 00:06:15,720 --> 00:06:17,840 Speaker 3: Then double it again and again and again and again 102 00:06:17,880 --> 00:06:20,920 Speaker 3: and again. Infinite money machine keeps doing it at that 103 00:06:20,960 --> 00:06:24,480 Speaker 3: magnitude is extraordinary. 104 00:06:25,520 --> 00:06:29,560 Speaker 1: This is Business History, a show about the history of business. 105 00:06:29,720 --> 00:06:32,440 Speaker 1: I'm Jacob Goldstein, I'm Robert Smith. Today on the show, 106 00:06:32,480 --> 00:06:35,960 Speaker 1: the final part of our investing series, we featured way 107 00:06:36,040 --> 00:06:40,200 Speaker 1: back more than one hundred years ago, the speculator Jesse Livermore, 108 00:06:40,440 --> 00:06:44,320 Speaker 1: the great investor Warren Buffett, love him and now the 109 00:06:44,400 --> 00:06:47,520 Speaker 1: quant Jim Simons, he was part of a revolution in 110 00:06:47,600 --> 00:06:51,400 Speaker 1: finance to put math and numbers before all this intuition 111 00:06:51,560 --> 00:06:54,159 Speaker 1: and business savvy stuff that Wall Street was all about, 112 00:06:54,160 --> 00:06:58,320 Speaker 1: before this right, he built this system that created enormous wealth. 113 00:06:58,839 --> 00:07:03,080 Speaker 1: And the amazing thing is that no one really knows 114 00:07:03,440 --> 00:07:07,120 Speaker 1: how the system works, how they're getting that return. Even 115 00:07:07,160 --> 00:07:10,440 Speaker 1: Jim Simons, the man who built it, doesn't quite know 116 00:07:10,840 --> 00:07:13,320 Speaker 1: what it's doing. When you see a picture of Jim 117 00:07:13,360 --> 00:07:16,120 Speaker 1: Simon as you'd never be like, oh, that's a fancy 118 00:07:16,240 --> 00:07:20,720 Speaker 1: Wall Street hotshot, like he looks like a mathematician. Continued 119 00:07:20,760 --> 00:07:23,440 Speaker 1: to look like a mathematician until the day he died. 120 00:07:23,520 --> 00:07:27,760 Speaker 1: He had the tweed coat, the thick glasses, the wispy hair, 121 00:07:27,960 --> 00:07:32,080 Speaker 1: right penny loafers. He was always smoking. At some points 122 00:07:32,080 --> 00:07:35,600 Speaker 1: he would smoke like three packs of Merit cigarettes a day. 123 00:07:35,920 --> 00:07:37,760 Speaker 3: A machine for smoking cigarettes. 124 00:07:37,960 --> 00:07:40,320 Speaker 1: It's a secret machine. And after that letter to the 125 00:07:40,400 --> 00:07:44,720 Speaker 1: editor about Vietnam, Jim Simons lived a very secretive life. 126 00:07:44,760 --> 00:07:47,640 Speaker 1: He didn't really talk to the press talk about himself. Eventually, 127 00:07:47,640 --> 00:07:50,040 Speaker 1: when he created this money machine, he didn't tell people 128 00:07:50,080 --> 00:07:52,240 Speaker 1: how he did it. He didn't want people to visit him, 129 00:07:52,600 --> 00:07:54,400 Speaker 1: you know, at his investment firm. 130 00:07:54,920 --> 00:07:57,200 Speaker 3: He just wanted to smoke cigarettes and make money. 131 00:07:57,320 --> 00:08:01,960 Speaker 1: Well, and I'm all out of cigarettes. So a lot 132 00:08:02,000 --> 00:08:05,040 Speaker 1: of the story that we're going to tell today comes 133 00:08:05,040 --> 00:08:08,000 Speaker 1: from a great investigative reporter, Greg Zuckerman of The Wall 134 00:08:08,000 --> 00:08:10,800 Speaker 1: Street Journal. He wrote a biography of Simon's called The 135 00:08:10,840 --> 00:08:14,960 Speaker 1: Man Who Solved the Market. So, after getting fired, Jim 136 00:08:15,000 --> 00:08:19,080 Speaker 1: Simons goes to work in academia the State University of 137 00:08:19,320 --> 00:08:23,960 Speaker 1: New York at Stony Brooks Stonybrook University, and by all accounts, 138 00:08:24,000 --> 00:08:26,400 Speaker 1: he was a very good mathematician, but he was an 139 00:08:26,480 --> 00:08:31,440 Speaker 1: even better manager of mathematicians. As head of the department 140 00:08:31,440 --> 00:08:34,280 Speaker 1: there at Stonybrook University, he would travel the country and 141 00:08:34,360 --> 00:08:37,240 Speaker 1: look for young rising stars and convince him to come 142 00:08:37,280 --> 00:08:41,280 Speaker 1: to this math program on Long Island that turned out 143 00:08:41,319 --> 00:08:43,200 Speaker 1: to be one of the best in the country, right, 144 00:08:43,240 --> 00:08:46,599 Speaker 1: and he would bring them all together. Spoiler, some of 145 00:08:46,640 --> 00:08:50,200 Speaker 1: those mathematicians he would later lure to his fund. This 146 00:08:50,320 --> 00:08:53,160 Speaker 1: became one of his real big strengths. So he does 147 00:08:53,200 --> 00:08:56,520 Speaker 1: this for a while. Nineteen seventy eight, Simons is forty 148 00:08:56,720 --> 00:08:59,719 Speaker 1: years old. He's kind of old for a mathematician, right, 149 00:08:59,840 --> 00:09:03,560 Speaker 1: He decides to leave the university because he already looks 150 00:09:03,960 --> 00:09:06,040 Speaker 1: the part. Everyone thinks maybe he's retired. 151 00:09:06,760 --> 00:09:09,080 Speaker 3: But although cigarettes make him look sixty. 152 00:09:08,720 --> 00:09:12,880 Speaker 1: What exactly right? Although he was not going to retire, 153 00:09:12,920 --> 00:09:15,880 Speaker 1: he was going to finally do this idea that he had. 154 00:09:15,960 --> 00:09:18,680 Speaker 1: He was going to use computers to solve the market. 155 00:09:19,160 --> 00:09:23,400 Speaker 1: And it turns out it is much harder than it looks, right, 156 00:09:23,559 --> 00:09:27,240 Speaker 1: like one idea, you're like. Nowadays we think of how 157 00:09:27,280 --> 00:09:30,600 Speaker 1: powerful computers are all the data we have, but back then, 158 00:09:31,360 --> 00:09:34,480 Speaker 1: just having the idea didn't mean that you could pull 159 00:09:34,520 --> 00:09:34,880 Speaker 1: it off. 160 00:09:35,160 --> 00:09:39,880 Speaker 3: That is a classic business truth. Right, Like ideas are cheap, execution. 161 00:09:39,520 --> 00:09:43,040 Speaker 1: Is hard, and in fact, even for Simon's himself, it 162 00:09:43,080 --> 00:09:45,560 Speaker 1: would take probably more than a decade before he could 163 00:09:45,600 --> 00:09:48,240 Speaker 1: really figure out how to do this. Simons teams up 164 00:09:48,240 --> 00:09:50,440 Speaker 1: with a fellow math genius he had met at the 165 00:09:50,480 --> 00:09:55,240 Speaker 1: Institute for Defense Analyses, Lenny Bomb the Bomb. The Bomb 166 00:09:55,640 --> 00:09:59,720 Speaker 1: Bomb's an expert in something called hidden markoff processes, which 167 00:09:59,840 --> 00:10:06,040 Speaker 1: was this way of using probabilistic outcomes of random processes 168 00:10:06,200 --> 00:10:09,959 Speaker 1: to determine hidden that's that's as much as I have 169 00:10:10,240 --> 00:10:12,680 Speaker 1: I was with you. Well, they we're doing what we 170 00:10:12,679 --> 00:10:15,000 Speaker 1: would call today machine learning. 171 00:10:15,520 --> 00:10:19,439 Speaker 3: Machine learning is like, essentially, you build a system in 172 00:10:19,480 --> 00:10:21,440 Speaker 3: a computer. You feeded a bunch of data, and the 173 00:10:21,480 --> 00:10:24,400 Speaker 3: system sort of builds a map of the relationships in 174 00:10:24,440 --> 00:10:26,760 Speaker 3: that data, and then with new data it can kind 175 00:10:26,800 --> 00:10:30,400 Speaker 3: of interpolate or extrapolate and make guesses about what should 176 00:10:30,400 --> 00:10:33,520 Speaker 3: come next. And of course today we have an exciting, 177 00:10:33,760 --> 00:10:36,840 Speaker 3: maybe misleading, confounding term for machine learning. 178 00:10:37,360 --> 00:10:39,880 Speaker 1: We call it AI exactly right, right, And so they 179 00:10:39,880 --> 00:10:43,440 Speaker 1: start this firm called Monometrics. But as you say, the 180 00:10:43,520 --> 00:10:46,680 Speaker 1: key is not the math. They have math in spades. 181 00:10:46,760 --> 00:10:49,320 Speaker 1: What they don't have is they don't have the data. 182 00:10:49,760 --> 00:10:54,559 Speaker 3: Also a classic modern AI problem. Like every AI founder 183 00:10:54,600 --> 00:10:58,600 Speaker 3: I've interviewed, it always turns into a story about collecting 184 00:10:58,679 --> 00:11:00,640 Speaker 3: the data, building the data set. 185 00:11:00,760 --> 00:11:03,560 Speaker 1: And this is the nineteen seventies, right, So data is 186 00:11:03,679 --> 00:11:06,880 Speaker 1: not at the tip of your fingers on a computer keyboard. 187 00:11:07,040 --> 00:11:11,920 Speaker 1: You had to physically go and find data, which is amazing. Right. 188 00:11:12,160 --> 00:11:15,280 Speaker 1: So they have the closing prices every day for you know, 189 00:11:15,360 --> 00:11:18,199 Speaker 1: commodities and bonds and stocks. This was published in the 190 00:11:18,200 --> 00:11:20,160 Speaker 1: Wall Street chart, so everyone had that data. 191 00:11:20,160 --> 00:11:21,439 Speaker 3: It's like one one. 192 00:11:21,360 --> 00:11:25,000 Speaker 1: Number per day per stock, per commodity. Yeah, but what 193 00:11:25,040 --> 00:11:27,400 Speaker 1: you're trying to do is find correlations. You're trying to 194 00:11:27,440 --> 00:11:31,480 Speaker 1: find things in the real world that impact those prices. 195 00:11:31,880 --> 00:11:34,400 Speaker 1: What's the data set for that? They had to go 196 00:11:34,880 --> 00:11:38,200 Speaker 1: look for it. So for instance, they would buy old 197 00:11:38,280 --> 00:11:44,600 Speaker 1: magnetic tapes from commodities trading exchanges, like big giant computer 198 00:11:44,679 --> 00:11:46,360 Speaker 1: magnetic tapes, and they had to figure out how to 199 00:11:46,360 --> 00:11:48,760 Speaker 1: get the data off of it. They would buy stacks 200 00:11:48,800 --> 00:11:51,119 Speaker 1: of books from the World Bank. 201 00:11:51,120 --> 00:11:53,800 Speaker 3: Huh, like reports that the World Bank, like what is 202 00:11:53,840 --> 00:11:55,920 Speaker 3: what are the reports about what countries were doing and 203 00:11:56,000 --> 00:11:57,440 Speaker 3: exchange rates and things like that. 204 00:11:57,920 --> 00:12:00,400 Speaker 1: They had a stafford go to the Federal Reserve offices 205 00:12:00,440 --> 00:12:03,800 Speaker 1: to like write down interest rate changes over the years. 206 00:12:04,559 --> 00:12:07,440 Speaker 1: Because once you have that, then the computer can start 207 00:12:07,440 --> 00:12:09,360 Speaker 1: looking for pattern right. And to be clear, they're not 208 00:12:09,480 --> 00:12:14,200 Speaker 1: like studying these reports and these day they're just in 209 00:12:14,640 --> 00:12:18,520 Speaker 1: shoveling it in, right, And this is still very basic 210 00:12:18,600 --> 00:12:22,200 Speaker 1: machine learning, and the computer at the time keeps making 211 00:12:22,280 --> 00:12:26,760 Speaker 1: mistakes that they didn't really understand. So, for instance, once 212 00:12:26,840 --> 00:12:33,440 Speaker 1: the computer developed a taste for potatoes, main potatoes, so 213 00:12:33,600 --> 00:12:36,760 Speaker 1: Zuckerman tells his story in his book, the system kept 214 00:12:36,800 --> 00:12:41,240 Speaker 1: buying main potato futures in the state of Maine. 215 00:12:41,720 --> 00:12:44,880 Speaker 3: Potato potatoes big harvest year next year or whatever. 216 00:12:45,120 --> 00:12:49,200 Speaker 1: Yes, okay, until two thirds of the company's money was 217 00:12:49,240 --> 00:12:52,080 Speaker 1: in potatoes. They were all in potatoes. And they got 218 00:12:52,080 --> 00:12:54,240 Speaker 1: a call from the regulators, the cft A. 219 00:12:54,280 --> 00:12:57,800 Speaker 3: CFPP commod Futures Trading Commission. 220 00:12:57,440 --> 00:13:01,160 Speaker 1: Right yeah, saying whoa, who are you guys, Like, what 221 00:13:01,200 --> 00:13:03,560 Speaker 1: are you doing over there? You have almost cornered the 222 00:13:03,600 --> 00:13:07,520 Speaker 1: market on potatoes. You have to sell. And they ended 223 00:13:07,600 --> 00:13:11,800 Speaker 1: up losing money on the trade because because blown out 224 00:13:11,840 --> 00:13:15,320 Speaker 1: on potatoes, they had stopped the computer or whatever the 225 00:13:15,360 --> 00:13:18,559 Speaker 1: computer's plan was. But you know, this was just one 226 00:13:18,800 --> 00:13:23,520 Speaker 1: small weird thing. Simon and Baum were really kind of 227 00:13:23,679 --> 00:13:26,640 Speaker 1: nervous about this whole thing. They had taken investors money, 228 00:13:26,800 --> 00:13:30,600 Speaker 1: they didn't really know if their system worked, and as 229 00:13:30,640 --> 00:13:33,560 Speaker 1: the story gets told, they start to like second guess 230 00:13:33,600 --> 00:13:36,079 Speaker 1: the computer and themselves, and they start to think, well, 231 00:13:36,360 --> 00:13:38,960 Speaker 1: I have this intuition that gold's gonna go up because 232 00:13:39,000 --> 00:13:42,480 Speaker 1: of the geopolitical situation, and they'd make some money on that, 233 00:13:42,600 --> 00:13:44,760 Speaker 1: and then they'd lose some money on that, and so 234 00:13:45,520 --> 00:13:49,040 Speaker 1: by doubting their own system, it just wasn't really working. 235 00:13:49,120 --> 00:13:52,559 Speaker 1: And as they say, like it was super stressful because 236 00:13:52,640 --> 00:13:55,520 Speaker 1: like at that point They're just Wall Street investors, right 237 00:13:55,559 --> 00:13:58,360 Speaker 1: with the big computer trying to buy more potatoes, and 238 00:13:58,480 --> 00:14:01,400 Speaker 1: the and the man won't let them buy potatoes. It 239 00:14:01,440 --> 00:14:05,320 Speaker 1: wasn't really the mathematical based system that Jim Simons had 240 00:14:05,360 --> 00:14:05,800 Speaker 1: dreamed of. 241 00:14:06,240 --> 00:14:08,760 Speaker 3: I mean, in a way, it makes sense, right even 242 00:14:08,760 --> 00:14:12,800 Speaker 3: for them, because what they're trying to do is so 243 00:14:13,080 --> 00:14:17,800 Speaker 3: contrary to human nature. Right, Like human nature, especially if 244 00:14:17,800 --> 00:14:20,720 Speaker 3: you are smart, like Jim Simons clearly is is. Well, 245 00:14:20,840 --> 00:14:23,920 Speaker 3: I'm smart. I can look at the market and see 246 00:14:23,920 --> 00:14:26,720 Speaker 3: what's gonna happen. I can understand what drives the price 247 00:14:26,760 --> 00:14:28,840 Speaker 3: of gold and where it's going, and I'm just gonna 248 00:14:28,880 --> 00:14:31,200 Speaker 3: make a bet. I'm going to bet on you know 249 00:14:31,280 --> 00:14:34,520 Speaker 3: my understanding. Right, that is human nature, And what they 250 00:14:34,560 --> 00:14:37,760 Speaker 3: fundamentally want to do is, in a way take themselves 251 00:14:37,800 --> 00:14:40,360 Speaker 3: out of the equation. Right, say, I'm going to build 252 00:14:40,400 --> 00:14:43,160 Speaker 3: a computer and then I'm not gonna use my gut. 253 00:14:43,160 --> 00:14:45,080 Speaker 3: I'm just gonna do what the computer says. And so 254 00:14:45,120 --> 00:14:47,680 Speaker 3: in a way, it's not surprising that even Jim Simons 255 00:14:48,040 --> 00:14:52,760 Speaker 3: can't quite fully commit to taking himself out of the equation. 256 00:14:52,920 --> 00:14:55,320 Speaker 1: You turn on the TV news there's an oil embargo, 257 00:14:55,360 --> 00:14:57,480 Speaker 1: and you think, oh, does my computer know about this 258 00:14:57,640 --> 00:15:00,120 Speaker 1: now is able to do it like I should sell 259 00:15:00,240 --> 00:15:04,200 Speaker 1: or I should buy? Right, So monometrics doesn't really work out, 260 00:15:04,520 --> 00:15:08,520 Speaker 1: and in nineteen eighty two, Jim Simons starts a new company, 261 00:15:08,560 --> 00:15:13,080 Speaker 1: Renaissance Technologies and Renaissance Technologies. He thought, well, it's going 262 00:15:13,160 --> 00:15:15,440 Speaker 1: to be more of a tech investing firm, you know, 263 00:15:15,640 --> 00:15:17,640 Speaker 1: VC kind of thing, which would be great in the 264 00:15:17,720 --> 00:15:21,960 Speaker 1: nineteen eighties, right, But he kept the computer trading idea going, 265 00:15:22,160 --> 00:15:25,720 Speaker 1: eventually folding it into something that would become this amazing fund. 266 00:15:25,800 --> 00:15:29,680 Speaker 1: We talked about the Medallion Fund. And the first thing 267 00:15:30,280 --> 00:15:34,600 Speaker 1: that Jim Simon's was just really good at was recruiting 268 00:15:34,640 --> 00:15:37,520 Speaker 1: the right people to come into Wall Street, which was 269 00:15:37,600 --> 00:15:39,880 Speaker 1: unexpected at the time. Now we're like, oh, you're a 270 00:15:39,880 --> 00:15:44,000 Speaker 1: mathematics major at Princeton University, good luck at Golden Sacks, right, 271 00:15:44,640 --> 00:15:47,800 Speaker 1: But at the time this was somewhat unusual. And he 272 00:15:47,880 --> 00:15:55,880 Speaker 1: starts hiring his fellow mathematicians, quantum physicists, linguists, number theorist, astronomers, sure, 273 00:15:56,000 --> 00:15:58,040 Speaker 1: which if you think about like astronomers looking at all 274 00:15:58,040 --> 00:15:59,960 Speaker 1: this data in the sky and has to find out, 275 00:16:00,120 --> 00:16:02,520 Speaker 1: you know, where a black hole is. These are all 276 00:16:02,600 --> 00:16:05,320 Speaker 1: people who can see the signal in the noise, and 277 00:16:05,360 --> 00:16:08,440 Speaker 1: he's bringing them out to Long Island, right, And he 278 00:16:08,520 --> 00:16:11,760 Speaker 1: had this insight that I think only a mathematician could have, 279 00:16:12,000 --> 00:16:16,960 Speaker 1: which is math geeks are really competitive. You know, we 280 00:16:17,040 --> 00:16:20,600 Speaker 1: may not think of that, but they really want to 281 00:16:20,640 --> 00:16:23,440 Speaker 1: win and they want to prove these like impossible theorems. 282 00:16:24,040 --> 00:16:28,280 Speaker 1: And mathematicians, you know, frankly, if you haven't made major 283 00:16:28,320 --> 00:16:30,520 Speaker 1: awards by the age of thirty or thirty five, they 284 00:16:30,600 --> 00:16:32,360 Speaker 1: kind of feel like their career is a little bit over, 285 00:16:32,760 --> 00:16:35,720 Speaker 1: so they are open to moving to an investment firm. 286 00:16:36,160 --> 00:16:39,840 Speaker 3: It reminds me a little bit of you know, Paul Graham, 287 00:16:39,920 --> 00:16:42,640 Speaker 3: he was the founder or a founder of y Combinator, 288 00:16:42,680 --> 00:16:47,440 Speaker 3: the startup incubator. He wrote this whatever essay blog post 289 00:16:47,480 --> 00:16:52,160 Speaker 3: called Fierce Nerds a few years ago. Fierce Nerds this phrase, 290 00:16:52,240 --> 00:16:56,080 Speaker 3: and his point was like, traditionally in sort of culture, 291 00:16:56,120 --> 00:16:59,520 Speaker 3: the nerd was portrayed as kind of deferential. You know, 292 00:16:59,600 --> 00:17:01,880 Speaker 3: the jack is maybe the alpha, and the nerd is 293 00:17:01,960 --> 00:17:05,399 Speaker 3: kind of subservient or whatever. But there is this type 294 00:17:05,440 --> 00:17:08,760 Speaker 3: that he identified, the fierce nerd that's like the alpha nerd, 295 00:17:09,000 --> 00:17:11,400 Speaker 3: the nerd that really wants to win. To show the 296 00:17:11,440 --> 00:17:14,679 Speaker 3: world that they are smarter, better and like it's a 297 00:17:14,679 --> 00:17:17,760 Speaker 3: classic founder type, right, like the tech founder. But now 298 00:17:17,840 --> 00:17:21,320 Speaker 3: also yeah, now it has become but it's also this 299 00:17:21,400 --> 00:17:25,320 Speaker 3: type of kind of competitive mathematician who who Simons had 300 00:17:25,400 --> 00:17:26,560 Speaker 3: spotted and recruited. 301 00:17:27,600 --> 00:17:31,000 Speaker 1: The second major thing I think Jim Simons was doing 302 00:17:31,720 --> 00:17:36,120 Speaker 1: is this relentless focus on data, on hoovering up more 303 00:17:36,160 --> 00:17:39,959 Speaker 1: and more data. They got really good at finding, you know, 304 00:17:40,080 --> 00:17:43,479 Speaker 1: intra day prices, little tick data they call it stocks 305 00:17:43,480 --> 00:17:45,560 Speaker 1: going up going down all throughout the day, not just 306 00:17:45,600 --> 00:17:48,359 Speaker 1: the closing price. You know. They were looking at newspapers 307 00:17:48,400 --> 00:17:51,040 Speaker 1: to get the news items in there, all nacs, old 308 00:17:51,080 --> 00:17:51,880 Speaker 1: punch cards. 309 00:17:52,080 --> 00:17:54,119 Speaker 3: And when you say looking at you mean in putting 310 00:17:54,160 --> 00:17:57,080 Speaker 3: into the system, right, that's just more data, more data. 311 00:17:56,920 --> 00:18:01,720 Speaker 1: Yes, and more importantly, they were good at cleaning up 312 00:18:01,800 --> 00:18:04,480 Speaker 1: the data. And this is something you don't really think about, 313 00:18:04,520 --> 00:18:07,320 Speaker 1: but there's mistakes all over the place, there's mission gaps 314 00:18:07,400 --> 00:18:10,800 Speaker 1: in all forms of data. Figuring that out and figuring 315 00:18:10,800 --> 00:18:14,359 Speaker 1: out what to put in its place, called cleaning the 316 00:18:14,440 --> 00:18:19,040 Speaker 1: data is a mathematical problem, and they were very good 317 00:18:19,119 --> 00:18:19,480 Speaker 1: at it. 318 00:18:19,800 --> 00:18:23,040 Speaker 3: But so simple, such a simple idea, and yet obviously 319 00:18:23,119 --> 00:18:23,840 Speaker 3: so powerful. 320 00:18:24,040 --> 00:18:27,480 Speaker 1: You know, I've downloaded data sets that were like two 321 00:18:27,560 --> 00:18:30,119 Speaker 1: hundred thousand items when I was in business school. And 322 00:18:30,160 --> 00:18:33,040 Speaker 1: when you have two hundred thousand items, you can't find 323 00:18:33,080 --> 00:18:35,800 Speaker 1: a missing cell in there or someone who's like put 324 00:18:35,840 --> 00:18:38,680 Speaker 1: a wrong number in there. Now imagine millions and millions 325 00:18:38,760 --> 00:18:43,240 Speaker 1: and millions of pieces of data. It's very hard to 326 00:18:43,359 --> 00:18:46,320 Speaker 1: not make something that will stop a computer dead in 327 00:18:46,359 --> 00:18:49,760 Speaker 1: its tracks. Right. And so even with all of this, 328 00:18:50,440 --> 00:18:53,800 Speaker 1: it did take them years and years and years to 329 00:18:53,880 --> 00:18:56,240 Speaker 1: really start making money with this system. You know, they 330 00:18:56,280 --> 00:18:58,480 Speaker 1: would place these big bats on a move in the 331 00:18:58,480 --> 00:19:01,159 Speaker 1: stock market. Sometimes it would work, sometimes it wouldn't, and 332 00:19:01,240 --> 00:19:05,280 Speaker 1: they couldn't figure out why. And they finally brought in somebody, 333 00:19:05,680 --> 00:19:09,200 Speaker 1: a game theorist that helped make it work. His name 334 00:19:09,280 --> 00:19:14,800 Speaker 1: was Elwin Burlecamp, and he essentially said, you know, the 335 00:19:14,920 --> 00:19:19,240 Speaker 1: problem here is that you're acting too much like an 336 00:19:19,320 --> 00:19:23,560 Speaker 1: investment fund. To make the quant thing work. You need 337 00:19:23,600 --> 00:19:26,719 Speaker 1: to start acting more like a casino. 338 00:19:29,000 --> 00:19:52,399 Speaker 4: Coins falling out of the slot machine after the break. 339 00:19:58,080 --> 00:20:00,080 Speaker 3: That is the end of the break. Now we're going 340 00:20:00,160 --> 00:20:02,560 Speaker 3: to talk about game theory and why it's a good 341 00:20:02,560 --> 00:20:05,600 Speaker 3: tax like a casino. If you are Jim Simons in Renaissance. 342 00:20:06,080 --> 00:20:09,320 Speaker 1: The year is nineteen eighty eight. Jim Simon's quant investing 343 00:20:09,440 --> 00:20:11,840 Speaker 1: fund that started out as Monometrics has changed its name 344 00:20:11,880 --> 00:20:16,119 Speaker 1: to Medallion because of all the mathematical prizes his employees 345 00:20:16,200 --> 00:20:19,080 Speaker 1: have won, including himself. And they brought in this expert 346 00:20:19,160 --> 00:20:23,000 Speaker 1: in game theory. Elwyn Burlecamp and Burlacamp ran in the 347 00:20:23,000 --> 00:20:27,959 Speaker 1: same circles as a famous investor and gambler named Ed Thorpe, 348 00:20:27,960 --> 00:20:28,680 Speaker 1: who you've met. 349 00:20:28,800 --> 00:20:32,840 Speaker 3: I interviewed him. Yeah, he's a super interesting guy. Know 350 00:20:33,040 --> 00:20:35,919 Speaker 3: now as as an investor, he made a ton of 351 00:20:35,920 --> 00:20:38,760 Speaker 3: money as an investor, but he earlier in his career 352 00:20:39,119 --> 00:20:42,040 Speaker 3: went to Vegas, learned how to count cards, wrote a 353 00:20:42,040 --> 00:20:45,119 Speaker 3: book called Beat the Dealer did this amazing thing, and 354 00:20:45,160 --> 00:20:48,000 Speaker 3: I think the sixties with Claude Shannon, another super interesting guy, 355 00:20:48,000 --> 00:20:51,679 Speaker 3: where they built this machine to like understand how the 356 00:20:51,760 --> 00:20:55,119 Speaker 3: roulette ball was going to break and like basically beat roulette. 357 00:20:55,200 --> 00:20:58,400 Speaker 3: Like extremely interesting finance math guy. 358 00:20:59,119 --> 00:21:03,160 Speaker 1: Yeah. So Ellen Burlacamp knows Ed Thorpe and is thinking 359 00:21:03,200 --> 00:21:06,880 Speaker 1: about gambling strategies, right, and he looks at the Medallion 360 00:21:06,960 --> 00:21:11,199 Speaker 1: Fund and he says, you're making all these big bets 361 00:21:11,560 --> 00:21:14,159 Speaker 1: on market. It moves right, and you win sometimes and 362 00:21:14,200 --> 00:21:17,520 Speaker 1: you lose sometimes. But the key is, if you're going 363 00:21:17,560 --> 00:21:22,359 Speaker 1: to rely on probabilities, you need a lot of bets. 364 00:21:22,920 --> 00:21:27,160 Speaker 1: Imagine a casino that rolled the roulette wheel once a day, Right, 365 00:21:27,240 --> 00:21:29,760 Speaker 1: they could win money from the customers. They could lose 366 00:21:29,800 --> 00:21:32,800 Speaker 1: a bunch of money, but you wouldn't know it's random 367 00:21:32,800 --> 00:21:36,680 Speaker 1: at that point. Yes, right, but you do ten thousand 368 00:21:36,960 --> 00:21:41,000 Speaker 1: roulette wheel spins, one hundred thousand, a million roulette wheel spins, 369 00:21:41,560 --> 00:21:44,680 Speaker 1: and the casino has an edge, and they will win 370 00:21:44,800 --> 00:21:46,639 Speaker 1: money after a million turns. 371 00:21:46,720 --> 00:21:48,720 Speaker 3: It's the law of large numbers. If the odds are 372 00:21:48,720 --> 00:21:51,560 Speaker 3: in your favor, you want to be making essentially as 373 00:21:51,560 --> 00:21:52,640 Speaker 3: many bets as possible. 374 00:21:52,720 --> 00:21:55,440 Speaker 1: Yeah, and he says, let's do at the medallion fund 375 00:21:55,440 --> 00:21:58,840 Speaker 1: what casinos do. Get a tiny advantage, figure out how 376 00:21:58,880 --> 00:22:01,359 Speaker 1: to win slightly more than fifty percent of the time, 377 00:22:01,800 --> 00:22:05,160 Speaker 1: and then just make a lot of bets. So instead 378 00:22:05,200 --> 00:22:07,560 Speaker 1: of you know, making one bet and seeing how it's 379 00:22:07,560 --> 00:22:07,880 Speaker 1: going to. 380 00:22:07,800 --> 00:22:10,080 Speaker 3: Turn out, how all it on Maine potatoes and see 381 00:22:10,119 --> 00:22:11,720 Speaker 3: it how the harvest comes out next. 382 00:22:11,600 --> 00:22:14,159 Speaker 1: Year, you know you are going to make bets on 383 00:22:14,200 --> 00:22:17,720 Speaker 1: the market the last maybe a couple of days one 384 00:22:17,840 --> 00:22:20,960 Speaker 1: day intra day, this sort of high speed trading that 385 00:22:21,000 --> 00:22:24,879 Speaker 1: we would eventually see in the market. And here's an 386 00:22:24,960 --> 00:22:27,359 Speaker 1: example of like what they started to see. When they 387 00:22:27,400 --> 00:22:30,440 Speaker 1: did this, they needed just tiny little advantages they could 388 00:22:30,440 --> 00:22:34,600 Speaker 1: exploit in a small way. So the computer spotted something 389 00:22:34,680 --> 00:22:39,680 Speaker 1: that was futures traders out in the market. If they 390 00:22:40,440 --> 00:22:43,600 Speaker 1: had a good week, you know, made money on their 391 00:22:43,640 --> 00:22:46,800 Speaker 1: contracts over the week, they would tend to sell at 392 00:22:46,840 --> 00:22:50,320 Speaker 1: the end of the day on Friday and then rebuy 393 00:22:50,359 --> 00:22:53,359 Speaker 1: the contracts on Monday morning. So if they're selling on Friday, 394 00:22:53,359 --> 00:22:56,160 Speaker 1: the price goes down a little bit. If they're buying 395 00:22:56,160 --> 00:22:59,200 Speaker 1: on Monday, the price goes up a little bit. And 396 00:22:59,960 --> 00:23:03,520 Speaker 1: they didn't really know why. Maybe you know, the future 397 00:23:03,600 --> 00:23:05,959 Speaker 1: traders wanted to chill on the weekend, or you know, 398 00:23:06,000 --> 00:23:08,439 Speaker 1: maybe they were worried about world events. Whatever it was. 399 00:23:09,359 --> 00:23:11,920 Speaker 1: The Medallion Fund was like, well, we're just gonna buy 400 00:23:12,000 --> 00:23:13,600 Speaker 1: a bunch of viewers of contracts on Friday, We're going 401 00:23:13,680 --> 00:23:15,560 Speaker 1: to sell them on Monday morning. We're going to make 402 00:23:15,560 --> 00:23:18,439 Speaker 1: a little bit of money. And that is consistent, at 403 00:23:18,480 --> 00:23:20,000 Speaker 1: least until someone else discovers it. 404 00:23:20,119 --> 00:23:23,119 Speaker 3: Yeah, I mean that one is interesting, right, because you 405 00:23:23,160 --> 00:23:26,760 Speaker 3: can tell it as a story and it makes some sense. 406 00:23:26,800 --> 00:23:31,200 Speaker 3: And there's like human beings taking actions for reasons. Presumably, yes, 407 00:23:31,960 --> 00:23:34,600 Speaker 3: the best things, most of the things they do, there 408 00:23:34,680 --> 00:23:37,119 Speaker 3: is no story. It's just the computer is like, do this, 409 00:23:37,320 --> 00:23:38,840 Speaker 3: and they do it right. That to me is the 410 00:23:38,880 --> 00:23:41,320 Speaker 3: ideal version, because if you can tell a story about it, 411 00:23:41,760 --> 00:23:43,560 Speaker 3: then somebody else is going to figure out that story. 412 00:23:43,600 --> 00:23:45,040 Speaker 1: Why do you think I picked it out? I am 413 00:23:45,040 --> 00:23:47,560 Speaker 1: a story I am a storyteller. I want to explain 414 00:23:47,600 --> 00:23:50,199 Speaker 1: this to you. That's why I picked that example. No, 415 00:23:50,359 --> 00:23:53,960 Speaker 1: presumably thousands thousands of bets where things you could not 416 00:23:54,080 --> 00:23:57,800 Speaker 1: tell a story about because they were correlations of an 417 00:23:57,840 --> 00:24:02,680 Speaker 1: interest rate in Japan affecting the commodity prices in Mexico 418 00:24:03,240 --> 00:24:05,800 Speaker 1: and you can't see the connection. 419 00:24:06,359 --> 00:24:08,800 Speaker 3: Matt Levine, the finance writer, you and I both like 420 00:24:08,880 --> 00:24:11,760 Speaker 3: a lot. He had this thing once about how ren 421 00:24:11,800 --> 00:24:15,240 Speaker 3: as On Simon's firm. Actually at some point like didn't 422 00:24:15,280 --> 00:24:18,520 Speaker 3: want finance people, right, because finance people are always looking 423 00:24:18,560 --> 00:24:20,639 Speaker 3: for the story. Why what is the story of this? 424 00:24:20,960 --> 00:24:23,200 Speaker 3: But if you can tell a story, there is no edge, 425 00:24:23,280 --> 00:24:25,000 Speaker 3: right because somebody else can tell the story. So you 426 00:24:25,240 --> 00:24:27,960 Speaker 3: just want to trust the machine. 427 00:24:27,560 --> 00:24:30,119 Speaker 1: In Greg Suckerman's book. One of the traders for the 428 00:24:30,119 --> 00:24:33,919 Speaker 1: firm said, it would probably be better if stock prices 429 00:24:33,960 --> 00:24:35,040 Speaker 1: didn't have names on them. 430 00:24:35,440 --> 00:24:37,399 Speaker 3: It's like if the company didn't have a name, if 431 00:24:37,440 --> 00:24:39,040 Speaker 3: we were saying, like, oh, what should we do about 432 00:24:39,440 --> 00:24:40,240 Speaker 3: Oracle stock? 433 00:24:40,400 --> 00:24:42,920 Speaker 1: Yeah, so if you're trading Oracle, you probably have some 434 00:24:42,960 --> 00:24:44,960 Speaker 1: deep emotional feeling. 435 00:24:44,680 --> 00:24:46,639 Speaker 3: Like Larry Ellison, you don't like Larry Elison. 436 00:24:46,680 --> 00:24:49,280 Speaker 1: But if it were stock number eighty seven, you would 437 00:24:49,320 --> 00:24:51,399 Speaker 1: be able to just be like, well, this is the 438 00:24:51,440 --> 00:24:54,399 Speaker 1: movement in the numbers, and I don't care what the 439 00:24:54,400 --> 00:24:54,720 Speaker 1: company is. 440 00:24:54,760 --> 00:24:58,280 Speaker 3: Basically the machine says by stock eighty seven, Okay, by 441 00:24:58,320 --> 00:24:59,119 Speaker 3: stock eighty seven. 442 00:24:59,320 --> 00:25:02,119 Speaker 1: Yeah, it would be fewer employees stopping the machine. 443 00:25:02,359 --> 00:25:02,560 Speaker 3: Right. 444 00:25:03,440 --> 00:25:07,760 Speaker 1: So this casino tweak that Briller Camp brought it actually worked, 445 00:25:08,520 --> 00:25:13,639 Speaker 1: and by nineteen ninety they're having very good trading days. 446 00:25:13,920 --> 00:25:17,080 Speaker 1: They're like going up one percent a day sometimes, and 447 00:25:17,160 --> 00:25:20,359 Speaker 1: they were celebrating. They had to start a new rule, 448 00:25:20,600 --> 00:25:23,960 Speaker 1: which is, you cannot break out the champagne unless you 449 00:25:24,000 --> 00:25:26,879 Speaker 1: go up three percent a day in one day, in 450 00:25:27,040 --> 00:25:30,880 Speaker 1: one return in one day. Yeah, for the whole year 451 00:25:31,040 --> 00:25:34,119 Speaker 1: of nineteen ninety Medallion Fund, it's finally churning. Right, it 452 00:25:34,200 --> 00:25:37,560 Speaker 1: returned fifty five percent and that's after fees, and they 453 00:25:37,560 --> 00:25:39,640 Speaker 1: were charging a ton. 454 00:25:39,480 --> 00:25:41,879 Speaker 3: Of Hey, they have a lot of fees to return 455 00:25:41,920 --> 00:25:44,200 Speaker 3: by fifty five percent after fees. I guess I say 456 00:25:44,240 --> 00:25:45,200 Speaker 3: them any amount of fees. 457 00:25:45,400 --> 00:25:50,399 Speaker 1: Yeah, exactly right. So obviously Jim Simon's Renaissance technologies in the 458 00:25:50,400 --> 00:25:52,199 Speaker 1: Medallion Fund, they are not the only people doing this. 459 00:25:52,200 --> 00:25:55,960 Speaker 1: This is the nineteen nineties, right, Uh, there's David Shaw 460 00:25:56,080 --> 00:25:59,240 Speaker 1: starts d E. Shaw, big quant trading firm. Ken Bezos 461 00:25:59,280 --> 00:26:02,440 Speaker 1: worked there in his career, that's correct, Ken Griffins had 462 00:26:02,440 --> 00:26:07,119 Speaker 1: just started Citadel, Kepler, Morgan Stanley, Everyone's playing around with 463 00:26:07,160 --> 00:26:10,680 Speaker 1: these techniques. But Jim Simon's and the Medallion Fund they 464 00:26:10,760 --> 00:26:14,920 Speaker 1: managed to stay ahead of them all. In a field 465 00:26:14,960 --> 00:26:18,080 Speaker 1: where everybody's looking for an edge in the data, they 466 00:26:18,080 --> 00:26:21,359 Speaker 1: had an edge on the edge. So how did they 467 00:26:21,400 --> 00:26:21,600 Speaker 1: do it? 468 00:26:21,640 --> 00:26:22,480 Speaker 3: How did they do it? 469 00:26:23,240 --> 00:26:26,200 Speaker 1: So there's a couple of things. It's just my opinion, right. 470 00:26:26,560 --> 00:26:29,560 Speaker 1: So one thing they did was to say small and focused. 471 00:26:30,040 --> 00:26:32,920 Speaker 1: They closed the Medallion Fund to outside investors and said 472 00:26:33,240 --> 00:26:37,240 Speaker 1: we're going to trade solely for the employees of Renaissance itself, 473 00:26:37,800 --> 00:26:41,280 Speaker 1: for the people who work here because if you have 474 00:26:41,520 --> 00:26:44,080 Speaker 1: tens of billions of dollars one hundred billion dollars, it's 475 00:26:44,119 --> 00:26:46,760 Speaker 1: hard to take advantage of these tiny little moves. 476 00:26:46,480 --> 00:26:49,920 Speaker 3: Well, right, because once you start buying, yes, you drive 477 00:26:49,960 --> 00:26:51,760 Speaker 3: the price up, or you start selling, you drive the 478 00:26:51,800 --> 00:26:54,080 Speaker 3: price down. You get big enough, you're actually moving the 479 00:26:54,119 --> 00:26:57,480 Speaker 3: market and losing your edge, right, classic problem for big funds. 480 00:26:57,480 --> 00:27:01,720 Speaker 1: Actually, Medallion also focused their efforts on where their data 481 00:27:01,800 --> 00:27:06,040 Speaker 1: was the best, which was commodities, currencies, and bonds. Mostly 482 00:27:06,080 --> 00:27:08,040 Speaker 1: they would eventually do stocks, but we'll get to that 483 00:27:08,040 --> 00:27:11,040 Speaker 1: in a moment. And Jim Simmons had this other edge 484 00:27:11,040 --> 00:27:13,719 Speaker 1: that I feel like is super rare on Wall Street. 485 00:27:14,160 --> 00:27:18,119 Speaker 1: His employees loved him and they were loyal, and almost 486 00:27:18,160 --> 00:27:21,879 Speaker 1: no one ever left. I mean, they liked the intellectual 487 00:27:21,960 --> 00:27:28,080 Speaker 1: atmosphere being run by mathematicians, they liked the money. And 488 00:27:28,760 --> 00:27:31,399 Speaker 1: remember they were sort of sequestered out on Long Island 489 00:27:31,400 --> 00:27:34,040 Speaker 1: in a small town, right. They weren't having beers with 490 00:27:34,040 --> 00:27:36,639 Speaker 1: the guys from Goldman Sacks. They weren't getting job offers 491 00:27:36,640 --> 00:27:38,240 Speaker 1: on the street because no one knew their name. 492 00:27:38,320 --> 00:27:41,480 Speaker 3: It's almost like like the machine the machine learning is 493 00:27:41,520 --> 00:27:44,159 Speaker 3: a black box. But then the firm itself. Renaissance is 494 00:27:44,200 --> 00:27:47,040 Speaker 3: like a black box around the black box. 495 00:27:46,760 --> 00:27:49,120 Speaker 1: Black boxes all the way down, but eve. 496 00:27:49,040 --> 00:27:50,919 Speaker 3: Until the pile of gold at the center. 497 00:27:51,800 --> 00:27:57,479 Speaker 1: But you know, his employees were happy and content and 498 00:27:57,640 --> 00:28:02,840 Speaker 1: obviously becoming very rich until Jim Simons made one highre 499 00:28:03,000 --> 00:28:05,720 Speaker 1: that maybe wasn't the best idea. 500 00:28:06,400 --> 00:28:32,439 Speaker 5: In a minute, finally, Jim Simons is going to make 501 00:28:32,440 --> 00:28:32,960 Speaker 5: a mistake. 502 00:28:33,160 --> 00:28:35,440 Speaker 3: As I recall from before the break, Robert Smith, tell 503 00:28:35,480 --> 00:28:36,320 Speaker 3: me what happens next. 504 00:28:36,680 --> 00:28:38,960 Speaker 1: Such a small mistake, he still makes a ton of money. 505 00:28:39,320 --> 00:28:43,480 Speaker 1: Spoiler right, there is one place in New York State 506 00:28:43,520 --> 00:28:48,360 Speaker 1: with as many math geniuses as Renaissance Capital IBM, specifically 507 00:28:48,440 --> 00:28:52,000 Speaker 1: the Watson Research Center upstate, and it was there that 508 00:28:52,080 --> 00:28:55,720 Speaker 1: Jim Simons found a pair of geniuses working on speech 509 00:28:56,360 --> 00:29:02,240 Speaker 1: recognition algorithms, Peter Brown and Robert Mercer. And they arrived 510 00:29:02,240 --> 00:29:05,000 Speaker 1: at the Medallion Fund in nineteen ninety three and they 511 00:29:05,080 --> 00:29:10,880 Speaker 1: solved this last big challenge the fund had trading stocks. Now, 512 00:29:10,880 --> 00:29:16,000 Speaker 1: of course, you may notice that like linguistic speech recognition 513 00:29:16,840 --> 00:29:19,520 Speaker 1: software is very similar to what the astronomers were doing 514 00:29:19,520 --> 00:29:20,640 Speaker 1: to what the codebreakers are. 515 00:29:20,480 --> 00:29:23,520 Speaker 3: Doing classic machine learning, classic machine learning problem. 516 00:29:24,320 --> 00:29:27,640 Speaker 1: And they come in and they look at the problem 517 00:29:27,720 --> 00:29:30,560 Speaker 1: of trading stocks. So if all the things on Wall Street, 518 00:29:30,600 --> 00:29:36,280 Speaker 1: apparently like stocks are the most clugey and human, you know, 519 00:29:36,440 --> 00:29:39,120 Speaker 1: you've got to have someone make the trades for you, right, 520 00:29:39,280 --> 00:29:42,880 Speaker 1: okay at that time, at that time, and the computer 521 00:29:42,920 --> 00:29:46,160 Speaker 1: would have these fancy, optimal trades that you had to do. 522 00:29:46,440 --> 00:29:48,520 Speaker 1: But then sometimes in the real world it would just 523 00:29:48,560 --> 00:29:51,959 Speaker 1: be like, no, you can't short that, or eh, you know, 524 00:29:52,000 --> 00:29:54,960 Speaker 1: we can't get the leverage necessary the margin on the 525 00:29:55,000 --> 00:29:57,880 Speaker 1: stock to make that trade happen, and the computer would 526 00:29:57,880 --> 00:30:01,800 Speaker 1: just sort of stop, you know, and couldn't do the 527 00:30:01,840 --> 00:30:04,360 Speaker 1: trades they needed to do. Remember, there's a lot of 528 00:30:04,360 --> 00:30:07,160 Speaker 1: things that have to happen for all of these to work. 529 00:30:07,480 --> 00:30:10,240 Speaker 3: It's like the second order effects in some like imagine 530 00:30:10,240 --> 00:30:13,960 Speaker 3: a frictionless plane unif verse, the model could win in 531 00:30:14,000 --> 00:30:17,000 Speaker 3: the stock market, but the frictions of the actual stock 532 00:30:17,080 --> 00:30:18,120 Speaker 3: market were stumping it. 533 00:30:18,640 --> 00:30:22,840 Speaker 1: Yeah. So Brown and Mercer come in and they programmed 534 00:30:22,960 --> 00:30:26,840 Speaker 1: their computers so that it was all working together. I 535 00:30:26,880 --> 00:30:28,920 Speaker 1: guess it was in pieces before. But they're like, we 536 00:30:29,000 --> 00:30:32,360 Speaker 1: have one model for everything. And what that meant is 537 00:30:32,800 --> 00:30:37,600 Speaker 1: that the model itself could change its algorithm as it went. 538 00:30:38,040 --> 00:30:41,840 Speaker 1: And so if certain trades were not working, it could 539 00:30:41,880 --> 00:30:44,640 Speaker 1: find the second best, the third best, and then adapt 540 00:30:44,640 --> 00:30:48,480 Speaker 1: all the strategies depending on what they were actually able 541 00:30:48,480 --> 00:30:49,240 Speaker 1: to do in the real world. 542 00:30:49,320 --> 00:30:51,640 Speaker 3: So it's sort of like real time or almost real 543 00:30:51,720 --> 00:30:53,160 Speaker 3: time updating and feedback there. 544 00:30:53,240 --> 00:30:55,840 Speaker 1: And if certain trades were working, the computer could allocate 545 00:30:55,880 --> 00:30:59,240 Speaker 1: more money to those trades without a human being, like yes, no, 546 00:30:59,480 --> 00:31:02,680 Speaker 1: spend my ten million dollars, don't spend my ten million dollars. 547 00:31:04,040 --> 00:31:07,640 Speaker 1: And the two of them were legendary because they worked 548 00:31:07,680 --> 00:31:10,120 Speaker 1: as this sort of pair in the Greg Zuckermann book 549 00:31:10,160 --> 00:31:13,200 Speaker 1: The Man Who Solved the Market them like Penn and Teller. 550 00:31:13,840 --> 00:31:17,640 Speaker 1: So Peter Brown was the I forget who's who in that. 551 00:31:18,040 --> 00:31:19,680 Speaker 3: One of them talks and one of them doesn't. They're 552 00:31:19,720 --> 00:31:22,840 Speaker 3: both magicians. So which Peter Brown was the one who. 553 00:31:22,720 --> 00:31:24,800 Speaker 1: Talked to the one the one to talk. So Peter 554 00:31:24,920 --> 00:31:28,040 Speaker 1: Brown did all the talking right. He never stopped moving, 555 00:31:28,080 --> 00:31:30,160 Speaker 1: He slept at the office, he yelled at people, he 556 00:31:30,240 --> 00:31:34,080 Speaker 1: inspired people. He was just a big character. And Robert Mercer, 557 00:31:34,120 --> 00:31:37,880 Speaker 1: Bob Mercer was the silent one. He was quiet in 558 00:31:38,000 --> 00:31:41,160 Speaker 1: the telling, Peter Roy says, well, Bob comes up with 559 00:31:41,200 --> 00:31:44,760 Speaker 1: all the ideas silently, and I make them work silent. 560 00:31:44,800 --> 00:31:48,040 Speaker 1: Bob Penn and Teller would eventually double the profits of 561 00:31:48,080 --> 00:31:52,200 Speaker 1: the stock trading arm and they would run the firm. Eventually, 562 00:31:52,200 --> 00:31:55,560 Speaker 1: when Jim Simons stepped back, they moved the Medallion Fund 563 00:31:55,600 --> 00:31:59,760 Speaker 1: into foreign markets. It was this constant move of collecting 564 00:31:59,800 --> 00:32:03,640 Speaker 1: more data, more sources to have their giant computer system 565 00:32:03,680 --> 00:32:06,280 Speaker 1: look at, and then trading in more and more and 566 00:32:06,360 --> 00:32:09,400 Speaker 1: more markets. Right. And you know, I would love at 567 00:32:09,440 --> 00:32:12,480 Speaker 1: this point to have, you know, some sort of crisis 568 00:32:12,480 --> 00:32:14,680 Speaker 1: in the computer, some sort of moment where they stop 569 00:32:14,760 --> 00:32:15,280 Speaker 1: making money. 570 00:32:15,320 --> 00:32:18,480 Speaker 3: You would love it for narrative purposes, to be clear, please, Yeah, 571 00:32:18,520 --> 00:32:19,840 Speaker 3: But you know it didn't really happen. 572 00:32:20,200 --> 00:32:23,680 Speaker 1: In the worst possible markets. The Medallion Fund was doing well. 573 00:32:23,720 --> 00:32:26,520 Speaker 1: I mean there would be some dicey days, like literally 574 00:32:26,600 --> 00:32:29,240 Speaker 1: days like during the tech crash in two thousand, where 575 00:32:29,240 --> 00:32:31,080 Speaker 1: they're like, oh, we're losing a bunch of money. What 576 00:32:31,120 --> 00:32:33,600 Speaker 1: are we gonna do? And you know a few weeks later, ah, yeah, 577 00:32:33,760 --> 00:32:36,840 Speaker 1: we are fine. Right. It kept going up, as we said, 578 00:32:36,880 --> 00:32:38,840 Speaker 1: you know, sort of more than fifty percent a year, 579 00:32:39,160 --> 00:32:42,160 Speaker 1: and with low volatility, like there's no risk to this. 580 00:32:42,360 --> 00:32:46,080 Speaker 1: It's just insane when you think about it. 581 00:32:46,120 --> 00:32:48,200 Speaker 3: You shouldn't be able, like I don't mean morally, I 582 00:32:48,240 --> 00:32:51,600 Speaker 3: mean just yeah, kind of the laws of investing physics 583 00:32:51,600 --> 00:32:54,680 Speaker 3: suggests that you shouldn't have that kind of return year 584 00:32:54,720 --> 00:32:57,160 Speaker 3: after year after year. It's just somebody should catch you. 585 00:32:57,320 --> 00:32:58,240 Speaker 3: But it doesn't happen. 586 00:32:58,760 --> 00:33:01,800 Speaker 1: And it's interesting, right. I think anyone else who did 587 00:33:01,840 --> 00:33:06,840 Speaker 1: this would say, I am a genius. I have insight, 588 00:33:07,320 --> 00:33:10,040 Speaker 1: I know what I'm doing. But you'll notice in this story, 589 00:33:10,080 --> 00:33:13,240 Speaker 1: like Jim Simons himself has sort of back a little bit. 590 00:33:13,400 --> 00:33:17,080 Speaker 1: He's running the machine, he's hiring the people, he's letting 591 00:33:17,160 --> 00:33:21,120 Speaker 1: them run the actual machine, right, And even the people 592 00:33:21,120 --> 00:33:23,880 Speaker 1: who are making these decisions at this point, like Robert Mercer, 593 00:33:24,600 --> 00:33:26,440 Speaker 1: he has a line that I love from the Zuckerman book. 594 00:33:26,440 --> 00:33:30,920 Speaker 1: I'm gonna read this here. We're right fifty point seven 595 00:33:31,000 --> 00:33:33,920 Speaker 1: percent of the time, but we are one hundred percent 596 00:33:34,000 --> 00:33:36,800 Speaker 1: right fifty point seven percent of the time. You can 597 00:33:36,840 --> 00:33:38,080 Speaker 1: make billions that way. 598 00:33:38,680 --> 00:33:41,640 Speaker 3: Isn't that the basically the Anchorman line. There's that line 599 00:33:42,640 --> 00:33:44,920 Speaker 3: I think gets sixty percent of the time. 600 00:33:45,400 --> 00:33:51,640 Speaker 1: It works every time exactly right. So it's funny, right, 601 00:33:51,720 --> 00:33:56,880 Speaker 1: because that is the philosophy that gets you this money. 602 00:33:57,440 --> 00:33:59,840 Speaker 3: That's the casino like, that's how casinos just. 603 00:34:00,120 --> 00:34:02,400 Speaker 1: Need the little bit of edge and then you need 604 00:34:02,440 --> 00:34:05,120 Speaker 1: to ruthlessly exploit that and you have to keep the edge. 605 00:34:05,920 --> 00:34:10,560 Speaker 1: So at this point, there's really no drama from the algorithm. 606 00:34:10,640 --> 00:34:14,720 Speaker 1: It is working, it is six seating, it is evolving. Really, 607 00:34:15,440 --> 00:34:18,439 Speaker 1: the only drama really comes from the personalities. As Jim 608 00:34:18,480 --> 00:34:21,480 Speaker 1: Simons gets older, he starts to step back from the business, 609 00:34:22,120 --> 00:34:26,000 Speaker 1: and I will say some minorly dodgy things start to happen. 610 00:34:26,560 --> 00:34:31,040 Speaker 1: At one point, they get caught booking short term gains 611 00:34:31,040 --> 00:34:34,200 Speaker 1: in the market, which means immediate trading profits as long 612 00:34:34,320 --> 00:34:38,160 Speaker 1: term gains, essentially paying less tax than they should. And 613 00:34:38,200 --> 00:34:40,520 Speaker 1: the way they do this is is they're trading like 614 00:34:40,600 --> 00:34:43,959 Speaker 1: with the stocks in a basket stored at the bank, 615 00:34:44,000 --> 00:34:45,759 Speaker 1: and they don't really take the profits out till after 616 00:34:45,760 --> 00:34:49,120 Speaker 1: a year well whatever it was. The RS is like, no, no, 617 00:34:49,280 --> 00:34:51,600 Speaker 1: you can't do that, and so they had to pay 618 00:34:52,080 --> 00:34:54,560 Speaker 1: a reported seven billion dollars to the IRS. 619 00:34:55,280 --> 00:34:56,759 Speaker 3: A lot sounds like a lot. I don't know how 620 00:34:56,800 --> 00:34:58,680 Speaker 3: much they have, but seven billion is a big number. 621 00:34:58,719 --> 00:35:00,760 Speaker 3: They have more, they have more than seven billions. 622 00:35:00,840 --> 00:35:06,120 Speaker 1: Yeah, and then Bob Mercer silent Bob starts to get 623 00:35:06,160 --> 00:35:08,600 Speaker 1: a little bit weird at the office, right, it starts 624 00:35:08,640 --> 00:35:12,000 Speaker 1: to get into politics, always a bad idea. So apparently 625 00:35:12,520 --> 00:35:15,400 Speaker 1: he's very conservative. He's a right wing guy, and he 626 00:35:15,440 --> 00:35:19,040 Speaker 1: starts to talk about conspiracies at work. Now this is 627 00:35:19,080 --> 00:35:21,719 Speaker 1: puzzling to everyone, right, because we have a firm of 628 00:35:21,760 --> 00:35:27,080 Speaker 1: brilliant mathematicians, Bob Mercer, brilliant logical guy. Right, but he's 629 00:35:27,120 --> 00:35:30,080 Speaker 1: getting to these arguments at the firm about how, oh, 630 00:35:30,120 --> 00:35:34,560 Speaker 1: you know, global warming is overblown, or that like radiation 631 00:35:34,719 --> 00:35:37,800 Speaker 1: exposure is actually good for you. This is all stuff 632 00:35:37,800 --> 00:35:41,120 Speaker 1: that's like circulating the internet and infringe, you know, science stuff. 633 00:35:42,000 --> 00:35:46,680 Speaker 1: He actually funded a scientist who collected human urine to 634 00:35:46,840 --> 00:35:48,840 Speaker 1: extend human longevity. 635 00:35:49,280 --> 00:35:51,680 Speaker 3: Okay, I guess didn't work as far as. 636 00:35:51,640 --> 00:35:53,839 Speaker 1: I know, I don't know, but it started to rub 637 00:35:53,880 --> 00:35:57,520 Speaker 1: people the wrong way in this like really smoothly functioning 638 00:35:58,120 --> 00:36:02,000 Speaker 1: you know, deep black box out on Long Island. Because 639 00:36:02,040 --> 00:36:07,279 Speaker 1: he was now super rich, Robert Mercer, he started using 640 00:36:07,280 --> 00:36:11,120 Speaker 1: the money to actually influence politics, to support right wing causes. 641 00:36:11,120 --> 00:36:14,080 Speaker 1: He and his daughter's especially create a political action committee. 642 00:36:14,080 --> 00:36:16,000 Speaker 1: They started to work with Steve Bannon and Kelly and 643 00:36:16,120 --> 00:36:21,200 Speaker 1: Conway before Donald Trump was running for election in twenty eleven, 644 00:36:21,600 --> 00:36:24,279 Speaker 1: and apparently he was the one that urged Donald Trump 645 00:36:24,360 --> 00:36:26,840 Speaker 1: to hire them. Right that sort of sent the Donald 646 00:36:26,840 --> 00:36:30,000 Speaker 1: Trump campaign in sort of a right word direction. So 647 00:36:30,040 --> 00:36:33,080 Speaker 1: he becomes a big donor to Trump. This comes out 648 00:36:33,160 --> 00:36:36,640 Speaker 1: and all of a sudden, Bob Mercer and Rebecca Mercer's 649 00:36:36,680 --> 00:36:40,759 Speaker 1: daughter become this sort of symbol of the shadowy right 650 00:36:40,800 --> 00:36:46,719 Speaker 1: wing money making forces propelling this president that certain people 651 00:36:46,760 --> 00:36:50,360 Speaker 1: don't like into power. People start to pick it outside 652 00:36:50,400 --> 00:36:55,280 Speaker 1: the firm and appearances by Robert Mercer. There's a huge 653 00:36:55,280 --> 00:36:59,120 Speaker 1: piece of The New Yorker about him. And eventually Jim Simons, 654 00:36:59,200 --> 00:37:01,680 Speaker 1: who at this point you know, is leaving the working 655 00:37:01,719 --> 00:37:04,640 Speaker 1: of the firms to Bob and Peter, he has to 656 00:37:04,680 --> 00:37:07,720 Speaker 1: step in and say, Bob Mercer, you need to resign, 657 00:37:07,840 --> 00:37:11,799 Speaker 1: and Bob Mercer steps back. And in case you were 658 00:37:11,800 --> 00:37:15,440 Speaker 1: one wondering, they still made money money to the whole, 659 00:37:16,280 --> 00:37:19,200 Speaker 1: the whole whole thing. At this point, you can have 660 00:37:19,280 --> 00:37:21,680 Speaker 1: all the drama in the world because the computer has 661 00:37:21,719 --> 00:37:25,560 Speaker 1: no drama anymore. It is just getting better and better. 662 00:37:25,600 --> 00:37:29,040 Speaker 3: How about this for a fuego take, Maybe the human 663 00:37:29,120 --> 00:37:31,600 Speaker 3: drama is actually helpful for the firm, Yes, because it 664 00:37:31,680 --> 00:37:35,680 Speaker 3: distracts people from wanting to question the results of the computer. 665 00:37:36,840 --> 00:37:40,480 Speaker 1: The more they fight, the better their return, the less 666 00:37:40,520 --> 00:37:43,200 Speaker 1: they want to fiddle with the computer. Yeah, that's exactly right. 667 00:37:43,800 --> 00:37:46,560 Speaker 3: So what did you say, Robert, the return has been 668 00:37:46,640 --> 00:37:49,320 Speaker 3: for whatever thirty years? Do you say sixty six percent 669 00:37:49,360 --> 00:37:49,680 Speaker 3: a year? 670 00:37:50,120 --> 00:37:53,319 Speaker 1: Sixty six percent were the trading returns, okay, and then 671 00:37:53,360 --> 00:37:56,359 Speaker 1: there were fees, which got bigger and bigger every year 672 00:37:56,440 --> 00:37:59,279 Speaker 1: because they're building this giant computer system, right, and so 673 00:37:59,520 --> 00:38:04,920 Speaker 1: after fees, yeah, thirty nine percent still unbelievable. 674 00:38:04,960 --> 00:38:08,320 Speaker 3: So okay, So that since nineteen eighty eight is twenty 675 00:38:08,560 --> 00:38:11,799 Speaker 3: twenty one, twenty one, okay, So let's just play around 676 00:38:11,840 --> 00:38:13,439 Speaker 3: with it for a secon because I think it's hard 677 00:38:13,480 --> 00:38:14,200 Speaker 3: to really grasp. 678 00:38:14,280 --> 00:38:14,440 Speaker 1: Right. 679 00:38:14,480 --> 00:38:18,320 Speaker 3: So I was a teenager in nineteen eighty eight, but 680 00:38:18,680 --> 00:38:21,640 Speaker 3: I I bought CDs, right, this was the CD era. 681 00:38:22,120 --> 00:38:26,680 Speaker 3: What did CDs cost in nineteen eighty eight, fourteen ninety nine? 682 00:38:26,719 --> 00:38:29,960 Speaker 3: At Tower Records? Okay, Sam Goody, I wasn't cool enough 683 00:38:29,960 --> 00:38:31,800 Speaker 3: to shof at Tower Records. I shopped at Sam Goodye 684 00:38:32,680 --> 00:38:39,360 Speaker 3: nineteen eighty eight albums Green Rim Green, which I loved. 685 00:38:40,920 --> 00:38:41,600 Speaker 1: Are you doing the math? 686 00:38:41,640 --> 00:38:45,880 Speaker 3: Roy instead of buying RM's Green for fourteen ninety nine. 687 00:38:45,960 --> 00:38:48,080 Speaker 3: In nineteen eighty eight, I had put fourteen dollars in 688 00:38:48,120 --> 00:38:51,960 Speaker 3: ninety nine cents into the Renaissance Medallion Fund and left 689 00:38:51,960 --> 00:38:54,879 Speaker 3: it there and let them charge me their fees. How 690 00:38:54,960 --> 00:38:58,200 Speaker 3: much would that fourteen dollars in ninety nine cents from 691 00:38:58,280 --> 00:39:00,719 Speaker 3: nineteen eighty eight have turned into buy what did you say? 692 00:39:00,719 --> 00:39:05,719 Speaker 1: Twenty twenty one, thirty three years at thirty nine percent annually, 693 00:39:06,680 --> 00:39:10,319 Speaker 1: seven hundred and eighty five thousand dollars, you could buy 694 00:39:10,320 --> 00:39:13,680 Speaker 1: a house in stead of Green. Although Green was a 695 00:39:13,719 --> 00:39:14,400 Speaker 1: fabulous it's a. 696 00:39:14,400 --> 00:39:17,239 Speaker 3: Great album, but I'd rather pay for my children to 697 00:39:17,239 --> 00:39:22,240 Speaker 3: go to college than have listened to Green. That is amazing. 698 00:39:22,280 --> 00:39:24,319 Speaker 3: And then, of course the wild thing is if you'll 699 00:39:24,400 --> 00:39:27,279 Speaker 3: leave the seven hundred thousand dollars in there for one 700 00:39:27,320 --> 00:39:31,560 Speaker 3: more year at thirty nine percent, it's like a. 701 00:39:31,560 --> 00:39:34,000 Speaker 1: Million, basically, exactly exactly. 702 00:39:34,080 --> 00:39:36,440 Speaker 3: I also bought Rattlin Hum that you're just looking at 703 00:39:36,480 --> 00:39:38,040 Speaker 3: the list, So that's another seven hundred grand. 704 00:39:38,160 --> 00:39:41,920 Speaker 1: So need to say. Jim Simons made a ton of 705 00:39:42,000 --> 00:39:44,320 Speaker 1: money out of his money making machine and made money 706 00:39:44,360 --> 00:39:47,560 Speaker 1: for a ton of other people. He died in twenty 707 00:39:47,640 --> 00:39:50,120 Speaker 1: twenty four and spent his last few years giving away 708 00:39:50,160 --> 00:39:52,360 Speaker 1: a lot of that money and traveling on his yacht. 709 00:39:52,400 --> 00:39:54,439 Speaker 1: I mean he earned it, right. He was a big 710 00:39:54,480 --> 00:39:57,320 Speaker 1: donor to Stonybrook University, where he had run the department. 711 00:39:57,560 --> 00:40:00,839 Speaker 1: He supported research into autism, and I love this part. 712 00:40:01,200 --> 00:40:04,560 Speaker 1: He had a fund to help math teachers stay in 713 00:40:04,600 --> 00:40:08,040 Speaker 1: public education by basically giving the money to still teach 714 00:40:08,040 --> 00:40:11,120 Speaker 1: in high schools instead of you know, going to Wall 715 00:40:11,120 --> 00:40:15,279 Speaker 1: Street like he did. Fine. At the end of these 716 00:40:15,320 --> 00:40:20,279 Speaker 1: investing episodes, I usually say that the investor that we're 717 00:40:20,280 --> 00:40:24,520 Speaker 1: featuring found an inefficiency in the market, right, that they 718 00:40:24,600 --> 00:40:30,120 Speaker 1: somehow did a bunch of research, found data that no 719 00:40:30,160 --> 00:40:33,960 Speaker 1: one else had, and then they exploited that in the 720 00:40:34,000 --> 00:40:36,080 Speaker 1: market to make a lot of money. So when I 721 00:40:36,120 --> 00:40:41,040 Speaker 1: talked about Jesse Livermore, the speculator back in nineteen twenty nine, 722 00:40:41,160 --> 00:40:44,320 Speaker 1: he was a chalkboard boy who knew the numbers of 723 00:40:44,360 --> 00:40:47,360 Speaker 1: the stock market inside and out and had. 724 00:40:47,120 --> 00:40:49,600 Speaker 3: That infra day trading data in his brain because he 725 00:40:49,640 --> 00:40:51,239 Speaker 3: was writing it down on the chalkboard. 726 00:40:50,920 --> 00:40:53,839 Speaker 1: All exactly right. And when we talked about Warren Buffett, 727 00:40:53,920 --> 00:40:58,160 Speaker 1: he would actually physically go to the CEO of a 728 00:40:58,280 --> 00:41:01,600 Speaker 1: firm and get information that no one else had at 729 00:41:01,600 --> 00:41:02,080 Speaker 1: the point. 730 00:41:02,040 --> 00:41:04,439 Speaker 3: On Saturday, you just go on the weekend and knock 731 00:41:04,480 --> 00:41:06,360 Speaker 3: on the door and say, tell you about your company. 732 00:41:07,160 --> 00:41:10,520 Speaker 1: The thing about those two examples is that eventually any 733 00:41:10,560 --> 00:41:13,560 Speaker 1: sort of edge you have in the market gets competed away. 734 00:41:13,600 --> 00:41:16,040 Speaker 1: Other people see the edge, other people get the data. 735 00:41:16,200 --> 00:41:18,040 Speaker 1: Other people see what you're doing and just do the 736 00:41:18,120 --> 00:41:23,160 Speaker 1: exact same thing. But Renaissance Technologies and the Medallion Fund, 737 00:41:23,440 --> 00:41:25,880 Speaker 1: at least so far, are in the middle of this. 738 00:41:26,360 --> 00:41:28,840 Speaker 1: And I don't know if it's that they continue to 739 00:41:29,120 --> 00:41:33,399 Speaker 1: ingest so much more data that they're finding new inefficiencies 740 00:41:33,440 --> 00:41:36,000 Speaker 1: all the time, or if their computing power is just 741 00:41:36,040 --> 00:41:39,000 Speaker 1: getting more and more powerful so that they're able to 742 00:41:39,560 --> 00:41:42,680 Speaker 1: spot things that no one else has. But Renaissance, the 743 00:41:42,719 --> 00:41:45,960 Speaker 1: Medallion Fund, you know, is still going strong. It's still 744 00:41:46,040 --> 00:41:49,480 Speaker 1: making money for its employees, it's investors. One of the 745 00:41:49,560 --> 00:41:55,360 Speaker 1: things I do love about this story is that even 746 00:41:55,480 --> 00:42:00,279 Speaker 1: the people involved, the mathematicians, are just like stunned by 747 00:42:00,360 --> 00:42:02,719 Speaker 1: the amount of money that they make. You know, in 748 00:42:02,800 --> 00:42:06,000 Speaker 1: Wall Street, there's a kind of feeling like, ah, weirned it. 749 00:42:06,400 --> 00:42:09,880 Speaker 1: But these are logical, rational people who, at various points 750 00:42:09,880 --> 00:42:14,200 Speaker 1: in this story stop to ask themselves what are we doing? Like, 751 00:42:14,680 --> 00:42:17,480 Speaker 1: are we doing good? Are we doing harm? If we're 752 00:42:17,520 --> 00:42:21,399 Speaker 1: making all this money, who is losing the money? Who's 753 00:42:21,440 --> 00:42:23,359 Speaker 1: on the other side of these trades? You know, if 754 00:42:23,400 --> 00:42:26,560 Speaker 1: we have fifty point seven percent, who's at forty nine 755 00:42:26,640 --> 00:42:31,400 Speaker 1: point three And they decide at some point they're like, yeah, 756 00:42:31,440 --> 00:42:34,440 Speaker 1: you know, we're adding liquidity to the market, We're you know, 757 00:42:34,680 --> 00:42:39,640 Speaker 1: closing inefficiencies. But really what we're doing is there are 758 00:42:39,640 --> 00:42:43,120 Speaker 1: people out there making emotional decisions and we're not making 759 00:42:43,160 --> 00:42:46,200 Speaker 1: emotional decisions. And when they get a little too optimistic 760 00:42:46,640 --> 00:42:49,799 Speaker 1: or a little too pessimistic, they sell early, they buy 761 00:42:49,840 --> 00:42:53,160 Speaker 1: too soon. Whatever it is, we spot it, We take 762 00:42:53,200 --> 00:42:57,080 Speaker 1: a little percentage of that trade, and we win. That 763 00:42:57,320 --> 00:43:00,040 Speaker 1: is their theory about what's happening out there. It's them 764 00:43:00,239 --> 00:43:01,279 Speaker 1: versus the emotions. 765 00:43:02,640 --> 00:43:09,040 Speaker 3: That sounds like a suspiciously human centered narrative story, right 766 00:43:09,080 --> 00:43:11,400 Speaker 3: given Like there's another version of the story, which is 767 00:43:11,440 --> 00:43:14,040 Speaker 3: maybe it was that the beginning, but surely at this 768 00:43:14,239 --> 00:43:18,000 Speaker 3: point a lot of the time, it's the renaissance computers 769 00:43:18,360 --> 00:43:22,479 Speaker 3: against somebody else's computers. And it's not that the other 770 00:43:22,560 --> 00:43:25,320 Speaker 3: computers are more emotional. It's just that the Renaissance computers, 771 00:43:25,360 --> 00:43:29,000 Speaker 3: the Renaissance algorithm, the Renaissance AI is just better. Their 772 00:43:29,040 --> 00:43:32,560 Speaker 3: computers are just better than the other computers for now. 773 00:43:33,920 --> 00:43:35,719 Speaker 1: And the key to that is you would have to 774 00:43:35,800 --> 00:43:40,080 Speaker 1: read this story in machine language. Why are the Medallion 775 00:43:40,120 --> 00:43:43,560 Speaker 1: computers better than everyone else's computers? Only the computers know. 776 00:43:44,800 --> 00:43:48,080 Speaker 1: Our producer is Gabriel Hunter Chang, our engineer is Sarah Bruguier, 777 00:43:48,560 --> 00:43:51,600 Speaker 1: and our showrunner is Ryan Dilly. I'm Jacob Goldstein, and 778 00:43:51,640 --> 00:43:53,680 Speaker 1: I'm Robert Smith. We'll be back next week with another 779 00:43:53,719 --> 00:43:58,520 Speaker 1: episode of Business History, a show about the history, wait 780 00:43:58,560 --> 00:44:01,959 Speaker 1: for it, of business