Introduction to Nerds on Wall Street



Introduction to “Nerds on Wall Street

I hope people think of this book as sort of a Hitchhiker’s Guide to Wired Markets. There are no robots parking cars for six million years, but there are robots trading millions of shares in six milliseconds, so maybe that’s close enough.

In 2006, I got a call from another nerd on Wall Street (NOWS), Rich Lindsey. At the time, Rich was president of Bear Stearns Securities (Bear Stearns’ prime brokerage company) and a member of the board of the mother ship firm. I had met him nearly 10 years earlier when he was in charge of market surveillance at the New York Stock Exchange (NYSE). A former Yale professor, Rich is a veritable poster boy for nerdy Ph.D.’s who break out of the pure geek world to become general all-around Wall Street BSDs.(1) He was putting together a book called How I Became a Quant: Insights from 25 of Wall Street’s Elite, and invited me to write a chapter. Proceeds were going to the Fischer Black Foundation for needy students. I knew this was for real, and not like those offers high school kids get to be in the Who’s Who of American Teens, and then they have to buy five copies. Plus the book had the kind of flattering title that gets people to write for free, but is more subtle and less of a bald-faced lie than, say, Insights from 25 of Wall Street’s Hottest Hunks. No one does a free chapter for Another Bunch of Middle-Aged Financial Guys.

The other people writing for the book included some of the smartest kids on the block and some old friends, so I said yes on the spot. There are chapters by pillars of the quant world, authors of the standard texts, and writers of oft-cited papers. Others did interesting and rewarding things with technology and markets. Emanuel Derman, author of My Life as a Quant: Reflections on Physics and Finance ( John Wiley & Sons, 2004), made this point in the first line of his review for the Wall Street Journal: “By my reckoning, several of the 25 memoirists in How I Became a Quant are not true quants, and they are honest (or proud) enough to admit it.”(2)

I am, no doubt, high on the list of poseurs, and I will be the first to admit it. Information technology applications in financial markets aren’t physics and closed-form solutions; they fit more in the zone of engineers and experimental guys, but they’ve been around forever. At the top of the heap we find Thomas Edison and Tim Berners-Lee, inventor of the World Wide Web. At the low end, they include more than a few potentially dangerous tinkerers like this guy:

500x218_intro_time-travel

As an experimentalist, I rank way below Edison but way above the time travel guy. I was fortunate to be a participant over the past 20 years as one of Wall Street’s nerds — when the world’s financial markets turned into a whopping computer network, a place for both theoretically sound and downright wacky financial ideas to come alive as programs.

The next section in this Introduction is an adaptation from my contribution to How I Became a Quant ( John Wiley & Sons, 2007). This expanded version goes into more depth, and includes pointers to material in the book that shows exactly what these references are about. Some chapters published in the past, and updated; some are new for this volume.

No Hedge Fund in My Tree House

I wish I could tell one of those stories about how when I was in the eighth grade, I noticed a pricing anomaly between the out-of-the – money calls on soybean futures across the Peruvian and London markets and started a hedge fund in my tree house and now I own Cleveland. But I can’t. In the eighth grade I was just a nerdy kid trying to keep my boisterous pals from blowing up my room by mixing all my chemistry set chemicals together and throwing in a match. In fact, I can’t tell any true stories about eighth graders starting hedge funds in tree houses and buying Cleveland. Make it college sophomores in dorm rooms who buy chunks of Chicago, Bermuda, or the Cayman Islands, and we have lots of material.

A Series of Accidents on the Way to Becoming a Quant Finance Nerd

My eventual quant-dom was not the culmination of a single-minded, eye-on-the-prize march to fulfill my destiny. It was the result of a series of accidents. In college, my interest in finance was approximately zero. I came to MIT in 1970 as a math major, as did many others, because I didn’t know much about other subjects, like physics or computer science.

I quickly discovered that the best gadgets were outside the math department. And the guys in the math department were weird, even by MIT standards. Today you can buy fine laptops without taking out a second mortgage, but this was back in the days when even a pretty crummy computer cost more than a really nice house. A good one cost millions, and filled a room the size of a basketball court. MIT, the ultimate toy store for geeks, had acquired a substantial inventory of computing machinery, starting as soon as it was invented — or sooner, by inventing it themselves. The professors kept the latest and greatest for themselves and their graduate student lackeys, but they were happy to hand over last year’s model to the undergrads.

Foremost among these slightly obsolete treasures was the PDP-1-X, which is now justly enshrined in the Boston Computer Museum. The PDP-1-X was a tricked-out version of the PDP-1, the first product of the Digital Equipment Corporation (DEC). The story of DEC is an early computer industry legend, now fading in an era where many people believe Bill Gates invented binary numbers.

DEC founder Ken Olsen worked at MIT’s Lincoln Laboratory, where the Air Force was spending furiously to address a central question facing the nation after World War II: “What do we do about the Bomb?” Think about the air war in World War I: guys in open cockpits wearing scarves and yelling, “Curse you, Red Baron!” By the end of World War II, less than 30 years later, they were potential destroyers of worlds. Avoiding the realization of that potential became a central goal of the United States.

If a Soviet bomb was headed our way, it would come from the north. A parabolic ballistic trajectory over the pole was how the rockets of the era could reach us. This begat the distant early warning (DEW) and ballistic missile early warning (BMEW) lines of radars across the northern regions of Alaska and Canada. The DEW and BMEW lines, conceived for military purposes, drove much of the innovation that we see everywhere today. Lines of radars produce noisy analog signals that need to be combined and monitored.

Digital/analog converters were first on the DEW line, now in your iPod. Modems, to send the signals from one radar computer to others, were first developed to keep the Cold War cold. Computers themselves, excruciatingly large and unreliable when constructed from tubes, became transistorized and less excruciating. This is where Ken Olsen comes in. Working at MIT to develop the first transistorized computers for the DEW line, he and his colleagues built a series of experimental machines — the TX-0 (transistor experiment zero), the TX-1, and the TX-2. The last, the TX-2, actually worked well enough to become a mother lode of innovation. The first modem was attached to it, as were the first graphic display and the first computer audio.

Olsen, a bright and entrepreneurial sort, realized that he knew more about building transistorized computers than anyone else, and he knew where to sell them — to the U.S. government. Federal procurement regulations in the early 1960s required Cabinet-level approval for the purchase of a computer, but a programmable data processor (PDP) could be purchased by a garden-variety civil servant. Thus was born the PDP-1, as well as its successors, up to the PDP-10, like the one at Harvard’s Aiken Comp Lab used by a sophomore named Gates to write the first Microsoft product in 1973.

Today, almost all teenage nerds have more computational gear than they know what to do with. Back then, in the 1970s, access to a machine like the PDP-1, with graphics, sound, plotting, and a supportive hacker(3) culture, was a rare opportunity. It was also the first of the series of accidents that eventually led me into quantitative finance.

I wish I could say that I realized the PDP-1 would allow me to use the insights of Fischer Black, Myron Scholes, and Robert Merton to become a god of the options market and buy Chicago, but those were the guys at O’Connor & Associates and Chicago Research and Trading, not me. I used the machine to simulate nuclear physics experiments for the lab that adopted me as a sophomore. They fl ew me down to use the particle accelerators at Brookhaven National Laboratory to find out the meaning of life, the universe, and everything by smashing one atomic nucleus into another — sort of a demolition derby with protons. But sometimes a spurious side reaction splattered right on top of whatever it was they wanted to see on the glass photographic plates used to collect the results. My simulations on the PDP-1 let us move the knobs controlling electromagnets the size of dump trucks so the spurious garbage showed up where it wouldn’t bother us. …

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All notes for this Introduction to Nerds on Wall Street

1. A term of respect popularized by Michael Lewis in his 1989 book, Liar’s Poker (W.W. Norton).

2. Emanuel Derman, “Finance by the Numbers,” Wall Street Journal, August 22, 2007.

3. Much of Steven Levy’s 1984 book Hackers: Heroes of the Computer Revolution (Doubleday) takes place in the PDP-1 lab at MIT. Hacking had no criminal connotation at the time. The book is still in print.

4. Start with Herman Kahn’s On Thermonuclear War (Princeton, NJ: Princeton University Press, 1960) for a weighty tome, or “How RAND Invented the Postwar World,” by Virginia Campbell, in Invention & Technology magazine (Summer 2004) for a much more compact read.

5. Thanks to John Wiley & Sons for including this tribute to Kevin Lewis. Yes, it has nothing to do with finance, but this is shaping up as a year when we can all use a laugh. Along with other RAND colleagues, I put a longer version of the story in the article about Kevin on Wikipedia. It included his other gag papers such as “The Glide Tank,” featuring a flying tank with stubby little wings that a surprising number of defense contractors failed to recognize as a joke. The Wikipedia patrol does not have much more of a sense of humor than Don Rice did, and it keeps getting edited out and replaced by dry bibliographic material and biographical details. Fortunately, they can’t erase a book. Never underestimate the ability of people not to get the gag. Chapter 6, “Stupid Data Miner Tricks,” is very much in the spirit of “The Tumescent Threat,” but I still get calls asking about current butter production in Bangladesh.

6. It is now complete, and is utterly awesome. See the video at www.deltawerken.com/The-Oosterschelde-storm-surge-barrier/324.html. This is one of the premier flood control projects in the world, and particularly instructive when compared with the misplaced concrete slabs in New Orleans.

7. RAND had some distinguished financial alumni, foremost among them Harry Markowitz and Bill Sharpe. The ideas of operations research and optimization of risk and reward under constraints in military problems generalized, as we have seen, to a wide swath of finance.

8. I was the more junior of two co-leaders on this. The big dog was one of the grand old men of the Cold War, Bruno Augenstein, who was widely credited as the architect of the intercontinental ballistic missile (ICBM), and the man who in his DoD days signed the first check to develop the SR-71 Blackbird. He had some fine if spooky tales to tell.

9. LISP was the favored computer language of the artificial intelligentsia.

10. Stephen was a really nice guy who gave a lot of parties. His equally nice son, Noah, was a struggling actor, working as a waiter to make ends meet. Noah used to fold the napkins at Stephen’s parties. He worked at snazzy Hollywood restaurants and did great napkins — swans, stars, tulips, butterflies. Noah eventually got work as Dr. Carter on ER, so Stephen farms out the napkin folding.

11. Stallman, it turned out, had the right idea about open source, but also had a prodigious talent for annoying people, so GNU’s progress toward open source Unix was slow. They did a fine eMacs, though. Linus Torvalds’s greater skills in nerd-to-nerd diplomacy got there with Linux.

12. Quotron is another example of the “don’t build special purpose computers” rule. They did, and went from being synonymous with “electronic market data terminal” to being nowhere in a remarkably short time. The first Quotrons were so alien to Wall Street types that they rearranged the “QWERTY” keyboard to be “ABCDE.” Schumpeter was right about capitalism being a process of creative destruction.

13. Large is a relative term here. The bleeding-edge machines of the mid-1980s had 32M of memory. Fifteen years earlier, the onboard computers used on the lunar landings had 64K.

14. Evan’s fine account of his career is in Alan Rubenfeld’s book, The Super Traders: Secrets and Successes of Wall Street’s Best and Brightest (McGraw-Hill, 1995), pp. 227 – 252.

15. If you believe this, please contact me regarding some lucrative real estate transactions and a not-to-be-missed opportunity to help out a fine fellow in Nigeria.

16. Andre Perold and E. Schulman, “Batterymarch Financial Management (A), (B),” Boston: Harvard Business School, 1-286-113/5 (rev. 2/88).

17. For details of this and other horror stories, see Chapter 11.

18. Many of these are described in Chapter 2 ( “Greatest Hits of Computation in Finance” ).

19. This crowd includes many of the same rubes who thought a machine might someday beat the world chess champion! Ha! Can you believe these guys? What? Oh… Never mind.

20. Kurzweil’s ideas on machine intelligence are at www.kurzweilai.net/. His site ( www.fatkat.com/ ) discusses his approach to investing.

21. A vintage literary antecedent to this is found in the early days of the HAL 9000 computer from Arthur C. Clarke’s story and Stanley Kubrick’s film 2001: A Space Odyssey. HAL, we recall, learned to sing simple songs. The singularity machine would already know them, encoded from the connections in the memory of its biological model. Astro Teller’s novel, Exegesis (New York: Random House, 1997), is a fascinating and much more serious exploration of a first encounter with a sentient machine.

22. This book is not intended as a detailed text on market technology. Progress is so fast that the Web is a primary source. A good recent book is Introduction to Financial Technology by Roy Freedman (New York: Academic Press, 2006).

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