Nerds on Wall Street


The Impact of Technology on Wall Street and Investing

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Praise for “Nerds On Wall Street”


“Leinweber isn’t half as crazy as people said! He foresaw the profound change that wired technology would bring to markets (robots trading millions of shares in six milliseconds). Now he nails the Stupid Financial Engineering Tricks that dumped the markets, and offers his patented, sound insights on how the nerds will help bring us back.”

Jane Bryant Quinn,
Financial columnist for Bloomberg.com and Newsweek
Author, “Smart and Simple Financial Strategies for Busy People

“David Leinweber has been a pioneer in developing and applying advanced technologies in the capital markets. This book is a virtual tour de force survey of many of the key innovations that ‘nerds’ and computational techniques have driven over the past two decades, with key insights on future opportunities in this area. It is a highly engaging, insightful, and entertaining book that will benefit all investors who want to understand the increasingly important role that technology plays in the financial markets.”

Blake Grossman
CEO, Barclay’s Global Investors

“Leinweber leads his readers through a largely unexplored forest, turning over ordinary-looking rocks to reveal hidden colonies of peculiar creatures that feed on moldering mounds of numbers teeming with trailing zeroes. His book is absorbing, instructive, and very, very funny.”

David Shaw
Founder D.E.Shaw & Co.

“Through the lenses of finance “nerds”, Dave Leinweber recounts the quantitative and technological revolution in equity trading. The book is humorously written but it is serious and insightful. It makes an important contribution to our understanding of financial innovation and the evolution of the capital markets.”

André F. Perold
George Gund Professor of Finance and Banking
Harvard Business School

“Finally, a book that rightly honors the pocket-protected, RPN-loving, object-oriented, C-compatible, self-similar Wall Street quant! This is a delightfully entertaining romp across the trading floors and through the research departments of major financial institutions, told by one of the early architects of automated trading and a self-made nerd.”

Andrew W. Lo
Harris & Harris Group Professor
Director, MIT Laboratory for Financial Engineering
MIT Sloan School of Management

“David Leinweber is one of the great financial innovators of our time. David possesses a unique combination of expertise in the fields of money management, artificial intelligence, and computer science.”

Blair Hull
Founder, Hull Trading & Matlock Trading

“An important, accessible, and humorous guide to today’s electronic markets. Like Capital Ideas mixed with Being Digital, as told by Steve Martin.”

Frank Fabozzi
Professor in the Practice of Finance, Yale School of Management
Editor, “Journal of Portfolio Management

“Most new technologies are exploited first by “alpha geeks,” the folks with the skills to push the envelope. This is as true on Wall Street as it was on the web. David Leinweber was one of those alpha geeks, but is also the first to chronicle the innovation process from early adopter to mainstream acceptance.”

Tim O’Reilly
Founder & CEO O’Reilly Media

“NOWS is a thoughtful, funny and comprehensive history of the overlooked role geeks have played in our financial markets from the earliest days of telegraph, to risk management systems in the current credit crisis. The book is an irreverent “I Was There” chronicle of how our financial markets were formed from silicon, savvy and software. Highly recommended.”

Paul Kedrosky
Infectious Greed
Senior Fellow, Kauffman Foundation
Consulting Strategist, Ten Asset Management

“For decades Dave has not only understood more investment technology than anyone, but with patience and a great sense of humor, he has made the effort to explain it to his less tech savvy friends. Nerds on Wall Street is a home run for us all.”

Richard Rosenblatt
CEO, Rosenblatt Securities

“Nerds on Wall Street is a wild, funny ride though the technological changes that underpin modern financial markets. You will find yourself laughing out loud at what could otherwise be a dry subject. And, if you’re not careful, you might even learn something!”

Richard R. Lindsey
Principal, Callcott Group LLC
Chairman, International Association of Financial Engineers

“If you’re interested in what computers are doing with your money, then this book is for you.”

Richard Peterson MD
Managing Director MarketPsy Capital LLC
Author, “Inside the Investor’s Brain

“In David’s words, the stock market is a “victim not a cause” of the great mess of 2008. It’s refreshing to read a book with such insight during these difficult times. I applaud David Leinweber for this timely masterpiece.”

Bill Aronin
Co-founder Quantitative Analytics, Inc.
Sr. Manager, Thomson Reuters

“Clear, light language and wry humor mask David Leinweber’s exhaustive compendium of technological innovations for and impacts on asset trading. Leinweber brings an entrepreneur’s experience and an academic’s perspective to financial technology; and has produced the definitive work, as up-to-date as it is encyclopedic.”

David K. Whitcomb
Founder and Chairman Emeritus, Automated Trading Desk
Professor of Finance Emeritus, Rutgers University

“Dr. Leinweber continues to be a patron saint of any nerd who stumbles onto Wall Street. Many of his most insightful ideas are here in this book, the utility of which are only matched by the humor of their presentation. As the markets have changed in 2008, the need to collect, process, and understand novel information sources has never been greater. ”

Jacob Sisk
Infoshock, Yahoo

“Thoughtful insights covering trading, investment practice and system design encased in humor by an expert in all four: a good and practical read.”

Evan Schulman
Founder, Tykhe, LLC

“David is one of the top practitioners in the fields of textual analysis and sentiment and its application to trading. Leveraging “smart” machines to parse and extract signal from massive quantities of textual data is hard, and David’s work has put him at the vanguard of the next wave of alpha generation.”

Roger Ehrenberg
Information Arbitrage
IA Capital Partners

>>>>>> READ MORE HERE < <<<<<<

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More Praise for “Nerds on Wall Street”


“New technologies are exploited first by “alpha geeks,” folks with the skills to push the envelope. This is as true on Wall Street as it was on the web. Leinweber was one of those alpha geeks, but is also the first to chronicle the innovation process from early adopter to mainstream acceptance.”

Tim O’Reilly
Founder & CEO, O’Reilly Media

“Nerds on Wall Street is a wild, funny ride though the technological changes that underpin modern financial markets. You will find yourself laughing out loud at what could otherwise be a dry subject. And, if you’re not careful, you might even learn something!”

Richard R. Lindsey
Chairman, International Association of Financial Engineers
Principal, Callcott Group LLC

“Who says there is neither wit nor wisdom on Wall Street? This account of the evolution of quantitative finance is an invaluable guide for anyone seeking to understand everything from how indexed investing works to the nature of that elusive concept, ‘alpha’. The accessible style and deadpan humor make this a book that even those with an advanced case of fear of mathematical formulae can understand and enjoy.”

Suzanne McGee
Wall Street Journal & Barrons

“Nerds on Wall Street is a thoughtful, funny, and comprehensive history of the overlooked role geeks have played in our financial markets from the earliest days of telegraphy, to the current crisis. The book is an irreverent “I Was There” chronicle of how markets were formed from silicon, savvy and software. Highly recommended.”

Paul Kedrosky
Senior Fellow at the Kauffman Foundation
Editor of “Infectious Greed

“Clear, light language and wry humor mask David Leinweber’s exhaustive compendium of technological innovations for and impacts on asset trading. Leinweber brings an entrepreneur’s experience and an academic’s perspective to financial technology; and has produced the definitive work, as up-to-date as it is encyclopedic.”

David K. Whitcomb
Founder and Chairman Emeritus, Automated Trading Desk
Professor of Finance Emeritus, Rutgers University

“Thoughtful insights covering trading, investment practice and system design encased in humor by an expert in all four: a good and practical read.”

Evan Schulman
“Father of Program Trading”
Founder, Tykhe, LLC.

“For decades Dave has not only understood more investment technology than anyone, but with patience and a great sense of humor, he has made the effort to explain it to his less tech savvy friends. Nerds on Wall Street is a home run for us all.”

Richard Rosenblatt
CEO, Rosenblatt Securities

“If you’re interested in what computers are doing with your money, then this book is for you.”

Richard Peterson, MD
Managing Director, MarketPsy Capital LLC
Author, “Inside the Investor’s Brain

“In David’s words, the stock market is a “victim not a cause” of the great mess of 2008. It’s refreshing to read a book with such insight during these difficult times. I applaud David Leinweber for this timely masterpiece.”

Bill Aronin
Co-founder, Quantitative Analytics, Inc
Senior Manager, Thomson Reuters

“David is one of the top practitioners in the fields of textual analysis and sentiment and its application to trading. Leveraging “smart” machines to parse and extract signal from massive quantities of textual data is hard, and David’s work has put him at the vanguard of the next wave of alpha generation.”

Roger Ehrenberg
Managing Partner, Information Arbitrage and IA Capital Partners

“Dr. Leinweber continues to be a patron saint of any nerd who stumbles onto Wall Street. Many of his most insightful ideas are here in this book, the utility of which are only matched by the humor of their presentation. As the markets have changed in 2008, the need to collect, process, and understand novel information sources has never been greater.”

Jacob Sisk
Quantextual Nerd Extraordinaire, Infoshock, Yahoo!

>>>>>> READ MORE HERE < <<<<<<

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Part 4 – Nerds Gone Wild – Wired Markets in Distress


Financial Nerds Gone Wild – Global Markets in Distress

The original plan for this book stopped after the three parts that you’ve just read. These parts are about how markets became machines, and about using more machines to pick stocks and trade them electronically, bringing in an assortment of nifty ideas from finance and computer science along the way.

By the fall of 2008, it was clear that stopping there would have made for a book that seemed quaint. How could any financial author ignore what has happened since then? The problem was that I had spent my entire financial career in the stock markets, and the stock market was a victim, not a cause, of the Great Mess of ’08.* The causes came not from the stock side of Wall Street, but from a mix of abuses, greed, and sheer stupidity from the people who created, repackaged, and sold overly complex derivatives that started with mortgage loans that should never have been made, and were assembled into an over-leveraged financial house of cards that is still collapsing today.

These final chapters are about that collapse. Chapter 12 – Shooting the Moon: Stupid Financial Technology Tricks, is about how wildly complex derivatives traded in opaque markets, and the misuse of mathematical models to value them, contributed to the mess.

This is informative on how responsible use of market technology might have avoided the crisis and can help avoid an even more dreadful sequel in the future. Technology errors of omission and commission have contributed to our present woes. Stock markets are almost perfectly transparent, with full information available to all, and the best electronic clearing and settlement in history. These technologies were omitted in building the skyscraper of cards (“house of cards” seems too mild) out of collateralized debt obligations (CDOs), credit default swaps (CDSs), synthetic collateralized debt obligations (SCDOs), and the rest.

The Hall of Shame for those guilty of incompetent engineering features collapsing bridges, flaming dirigibles, exploding spacecraft, and melting reactors. We can add a new wing for overly complex derivatives, modeled in exquisite detail by myopic nerds with Ph.D.’s who got lost in the ever more complex simulations but ignored the basic principles, and their lavishly paid bosses who ignored the warnings from the best of them so they could be even more lavishly paid.

Chapter 13 – Structural Ideas for the Economic Rescue, expresses the view that as structural flaws in the current recovery plan become increasingly apparent, it seems clear that there is a need for a coherent systems approach to these problems. At first, I felt a great deal of trepidation about delving into this. As a longtime stock guy, I felt I didn’t know what I was talking about in this area. It has become increasingly clear that the people who are in charge don’t know what they are talking about, either. They try to solve problems charging up one hill with $700 billion of our money, drop a couple of hundred billion, then charge back down leaving the same problems in place.

Many aspects of the plans put forth are overly complex, and seem to ignore central aspects of the problem to protect and further enrich the people and institutions that created the mess. This chapter describes two original ideas suggested by my colleagues that address key issues in the economic recovery in a simple, straightforward way. Both have a near circuit designer’s approach, removing “gain” in a system that makes it unstable, and bypassing systemic flaws that impede the desired goals. These ideas are:

  • Fractional home ownership. My Berkeley office-mate John O’Brien is one of the founders of the field of financial engineering. His idea, which expands on a suggestion from Fed Chairman Ben Bernanke, has the potential to address the crisis where it started, in the housing market.
  • New American bank initiative. This is an idea for getting ourselves out of the hole we are in. With my coauthor for this chapter, Sal Khan, I describe a structural solution, free of the inherent flaws and conflicts that have resulted in a tragic waste of time and money in recent months.

Chapter 14 – Nerds Gone Green, discusses how many nerds are finding themselves cast out of Wall Street. Some will find their way back, but many will not. What’s a former Wall Street nerd to do? The answer may lie in market technology that is valuable outside of financial markets. Just as the Internet started out as a way for the Department of Defense to link military computers, there are future uses for market technology that may rival or surpass those involving traditional financial instruments.

Efficient, environmentally sound use of energy is one of the most important. We hear from voices as diverse as Thomas Friedman, T. Boone Pickens, and Ted Turner that energy technology is the next big thing. It is not just about making energy; it is about matching the consumers and the producers of this resource. For oil, the markets are there. For electricity, they are not, at least not in a useful way (unless you were Enron in 1999). Matching consumers, at the individual and business level, and producers, including small alternative suppliers of solar panels, is an allocation and communication problem.

Wired markets have developed to a remarkable level in finance, giving individual buyers and sellers the capabilities to interact with each other and with market makers using direct electronic access that was once found only in large institutions. There is a parallel path in energy markets. Simple real-time spot pricing is not a full solution.

Consumers need to know that cutting back today to help producers produce less pollution or greenhouse gases will not cost them more tomorrow. They effectively need software to manage trade futures, and it needs to be simple and reliable—a considerable technology challenge. Nerds with pink slips may find they go well with green.

>>>>>> READ MORE HERE < <<<<<<

* Jon Stewart has a much better non–politically correct name for what has transpired lately. It begins with “Cluster.” But everyone tells me I can’t put it in the book or use it at conferences. I think you need to watch the Daily Show to pick up on its continuing economic coverage. Stephen Colbert, whose show follows Stewart’s, says the financial markets are like a roller-coaster ride where you vomit money. This brand of fake news has its share of dumb gags, to be sure, but there is more “truthiness” to be found there than in much of what passes for serious broadcast journalism.

Recent guests include Barack Obama, Bill Clinton, John McCain, and Tony Blair. There are frequent appearances by Al Gore, that pesky rascal responsible for all the weird weather lately, who is a huge Daily Show fan. In The Assault on Reason (Penguin Press, 2007) Gore praises Stewart to the sky, and quotes Dan Rather in pointing out that a great deal of mainstream television news is “dumbed down and tarted up … to glue eyeballs to the screen … and sell advertising.” (p. 17)

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Part 3 – Artificial Intelligence and Intelligence Amplification


Artificial Intelligence and Intelligence Amplification in Financial Markets

Securities Markets are Machinery Now.

This raises the question of how to best participate in the world’s new wired markets. People who use information technology most effectively will be rewarded.

Artificial intelligence (AI) as an academic discipline began at the famous 1955 Dartmouth conference organized by John McCarthy from Stanford University and Marvin Minsky from MIT. The goal of the AI pioneers was to create a mind, a human in silicon. One key idea was that the brain was a biological computer so all the researchers had to do was figure out what the brain was doing and put it into an actual computer and they’d be done. This was something that people thought in the 1950s might take 10 years to accomplish, maybe 15 with long lunches.

So far, it hasn’t exactly worked out. In fiction we have the example of HAL, from 2001: A Space Odyssey. Letting the computer do the thinking turned out badly in that case. HAL discovered the lie in the first reel, and quickly moved on to become a paranoid serial killer.(1)

In financial circles there was a lot of sort of irrational technology exuberance as well. As seen in Chapter 2, there were some inspired magazine covers from the magazine Wall Street Computer Review(2) that showed the unrealistic expectations for AI. One, from 1987, depicts Socrates on the steps of the stock exchange surrounded by a horde of PCs, and touts: “Teaching Computers to Emulate Great Thinkers.”

Source: Wall Street Computer Review (now Wall Street & Technology), June 1987.

Source: Wall Street Computer Review (now Wall Street & Technology), June 1987.

Computers “thinking” like a person didn’t really work out as well as people had hoped. In the academic AI world, the artificial sentients were persistent no-shows. Artificial intelligence got kind of a bad rep on Wall Street as well. To get into the game, you had to buy a LISP (list processing) machine, a $100,000 machine that ran the elegant but arcane AI language of choice, LISP, and related expert system tools with names like Automated Reasoning Tool (ART) and Knowledge Engineering Environment (KEE).

You could prove a theorem or two with this stuff right out of the box, but getting a price for IBM on the screen proved to be a prodigious amount of work.

As recounted in the Introduction to this book, I worked for one of the companies that made these machines—there were two, LISP Machines and Symbolics—and came to the realization that building from the high-concept top down was a poor idea. Here is a picture I used back then to show the user reaction to AI investment systems, showing a mix of fear and rage.

Chapter 7 – A Little AI Goes a Long Way on Wall Street, describes a successful application, built in the early 1990’s, which was much more modest in scope than bringing back the great thinkers. The goal was to get computers to be decent users of other computer systems, which were overloading their users. Recently, this idea has been called intelligence amplification (IA). The systems here were market data and execution systems, and the combination was an early version of algorithmic trading.

The AI/IA-flavored approach to algo trading and market surveillance used in QuantEx and MarketMind, described in Chapter 7, was very effective in many contexts—equity strategies, quant option trading, and monitoring liquidity— but, like spreadsheets, they were clever ways to get the computer to do what you said, and didn’t have anything in the box that would let the machine learn what you should have said and adjust its behavior or models to better meet your needs.

One of the early enthusiasts for these technologies was Henry Lichstein, an MIT graduate then serving as technology adviser to John Reed, chairman of Citicorp. Henry was also on the board of the Santa Fe Institute (SFI),(3) a new high-powered research institute interested in complex interactions of complex entities. SFIdid a great deal of computer experimentation in artificial life (ALife).

The ALife people had much more modest goals than AI; all they wanted to do was build software entities that displayed lifelike behavior. And their efforts were met with early success. A bunch of random ALife birds might fl y in random directions, and not act much like birds. But giving them a few simple rules, like “fl y toward the closest bird,” “go with the flow,” and “don’t hit other birds,” could give rise to distinctly birdlike behavior.

The original ALife flock is Craig Reynolds’ “Boids,” done at Symbolics in 1986.(4) Simulated herding and fl ocking turn out to be of some commercial interest. Those massive stampedes in Disney cartoons, with thousands of , and all those schools of talking fish are descendants of the Boids. Hollywood showed its appreciation by giving Reynolds an Academy Award in 1998.

Like our early algos, the SFIartificial life was dumb, just obeying a few simple handwritten rules, without any ability to learn from mistakes. But the ALife researchers had a good answer—evolving intelligent behavior by mimicking natural evolution. Represent the programs as digital chromosomes, and simulate crossover and mutation, to breed better programs. The original version was called the genetic algorithm (GA), which later evolved into evolutionary computation (EC). Henry Lichstein suggested this approach for use in trading around 1990. The results for ALife had been striking. All sorts of tasks could be learned by the programs operating in an electronic world, which sure sounded like quant investment and trading. ALife programs evolved behavior to solve problems, avoid obstacles, find rewards, and cooperate. These are remarkable to watch.(5)

Chapter 8 – Perils and Promise of Evolutionary Computation on Wall Street, is one of the few detailed descriptions of the use of the GA and EC in finance. Actual chromosomes and fitness functions are included. If that last sentence made your eyes glaze over, skip to Chapter 9. It was satisfying research, and the power of evolutionary search truly teaches you to be careful what you ask for. Evolutionary approaches could be given rules to avoid many sins of data mining, and had many appealing features. As much as I would like to see this work, we don’t see a torrent of EC scientists fl owing toward greater Wall Street. We don’t even see a trickle. Olsen & Associates was a Zurich-based currency trading firm, founded in 1992, heavy with physicists from the Eidgenössische Technische Hochschule (ETH), Einstein’s alma mater. They were keen on genetically adaptive strategies and well funded, but vanished, and few of the principals are still keen on genetic algorithms.

After sending the GA to the back of the breakthrough line in the previous chapter, in Chapter 9 – The Text Frontier, we get to using IA, natural language processing, and Web technologies to extract and make sense of qualitative written information from news and a variety of disintermediated sources.

In Chapter 6 – Stupid Data Miner Tricks, we saw how you could fool yourself with data. When you collect data that people have put on the Web, they can try to fool you as well.

Chapter 10 – Collective Intelligence, Social Media, and Web Market Monitors and Chapter 11 – Three Hundred Years of Stock Market Manipulations, include some remarkable and egregious examples.

>>>>>> READ MORE HERE < <<<<<<

Notes for this section about Financial Artificial Intelligence, Intelligence Amplification, Genetic Algorithms, and Evolutionary Computation in Securities Markets:

1) Sci-fi buffs have a rich amount of material to consider in this context. Vernor Vinge, a computer scientist who has also won five Hugo awards, deals with the topic in much of his work, including his latest novel, Rainbow’s End. If we construct an artificial super-intelligent entity, what will it think of us?

2) Source: Wall Street Computer Review, June 1987. Since renamed Wall Street & Technology, and a useful resource for nerds on Wall Street at www.wallstreetandtech.com.

3) “The Santa Fe Institute is devoted to creating a new kind of scientific research community, one emphasizing multidisciplinary collaboration in pursuit of understanding the common themes that arise in natural, artificial, and social systems.” It was founded in 1984 (www.santafe.edu/).

4) … and still flocking after all these years at www.red3d.com/cwr/boids.

5) Until they start barcoding these into books, printed URLs are annoying, but it is actually worth typing these in. Karl Sim’s MIT video is here: www.youtube.com/watch?v=F0OHycypSG8.

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Part 2 – Alpha as Life


Passive Investing – Active Investing – Alpha Returns

Index funds are passive investments; their goal is to deliver a return that matches a benchmark index. The Old Testament of indexing is Burton Malkiel’s classic A Random Walk Down Wall Street, first published in 1973 by W.W. Norton and now in its ninth edition. For typical individual investors, without special access to information, it offers what is likely the best financial advice they will ever get: It is hard to consistently beat the market, especially after fees. A passive strategy will do better in the long run.

Of course, no one thinks of oneself as a typical individual investor. That might be your brother-in-law or the guy across the hall. And index funds are just not as much fun as picking stocks. It’s called passive investing for a reason. Alpha, outperforming a passive benchmark, is the goal of active investing. Even Malkiel has admitted to actively managing some his own money.(*) Recent additions to the Forbes 400 list include more than a few people who seem unusually adept at finding alpha, and keeping a piece of it.

The basic fee structure in the hedge fund world is “2 and 20.” Managers are paid 2 percent of assets and 20 percent of alpha. Similar arrangements are also used for performance paid to institutional managers, blurring the distinction between these types of buy-side firms. To see how this works, consider a $100 million portfolio, benchmarked against Treasury bills. If the manager produced a return equal to the T-bills, the alpha would be zero, and the manager’s fee would be $2 million, all from the asset-based portion. Unless the firm gave really good parties or had a great story, it would probably be replaced, since the client would end up earning the T-bill rate minus 2 percent, or something like a passbook savings account.

With a skilled, lucky, or skilled and lucky manager, the situation could be quite different. If the T-bills returned 3 percent that year and the hedge portfolio returned 28 percent, then the manager’s alpha is 25 percent, $25 million on the original investment. Under the 2-and-20 plan the firm would get to keep 20 percent of that, another $5 million on top of the $2 million in asset-based fees. The client keeps $18 million, substantially more than the meager few percent the client would have gotten in Treasuries.

A $100 million portfolio is small as hedge funds go.

It costs money to do the research or proprietary trading to produce that 25 percent alpha, so by the time all the bills are paid, that $7 million the manager takes is seriously pared down. But when the fund gets larger, the economies of scale kick-in in a major way. Investment strategies don’t scale to the sky, but it is (approximately) true that the cost to run a $1 billion portfolio is not that much more than for $100 million. In that case, the manager on the 2-and-20 plan takes home $70 million with performance as in the example. On $10 billion, the manager takes home $700 million, which begins to look like serious coin—even on the right side of the tracks in Greenwich, Connecticut. Deliver this kind of performance consistently, and you can raise the rates to 4 percent of assets and 40 percent of alpha, which would pay the $10 billion manager $1.4 billion with the same performance scenario.

This is where those billion-dollar paydays for hedge fund managers we read about in Institutional Investor and Parade magazine come from, and why people with what seem like good, solid $5 million annual paychecks at places like Goldman Sachs leave to start their own hedge funds. The whole alpha ecosystem depends on, and is a creature of, technology. Before computers, it was sufficiently tedious to compute the alpha of a portfolio that no one did it.

Comparing one stock to another is easy. Real portfolios are much messier. They have cash flows in from additional investments, and cash flows out from payments or withdrawals. There are dividends paid in from long positions, and dividends paid out from shorts. Stocks split, companies merge, symbols change. International investments’ returns are subject to currency variations to the extent that they are not hedged, and if they are, there are costs associated with those hedge positions.

Bill Fouse, who started the world’s first index fund, tells a story about the early days of performance measurement. In the 1950s and 1960s the reporting from investment managers to clients was almost anecdotal. The manager would invite the clients up to the lavishly decorated dark wood-paneled office and show them a list of stocks in their portfolio, with the prices paid and the recent prices.

Nothing would be said about cash flows, holding periods, or dividends, and nothing about closed positions. It was easy to pretty up the report by cleaning out the losers. Everyone would sit around the conference table to review the list of holdings, and enjoy a fine n-martini lunch.

In 1968 A.G. Becker, a brokerage firm, changed the game by using computers to keep accurate annualized scores for clients’ accounts, and by comparing the results with index benchmarks. This was possible only because the firm had acquired one of the early mainframe computers, a room-filling behemoth like the IBM System 360. The news wasn’t pretty. Any asset managers were much better at telling a good story and coming up with a good lunch than they were at managing assets. As Fouse tells it, managers resisted the idea of quantitative performance measurement.

They sent out the word, “Hire them, and you can’t hire us.” Some of their objections were valid; a simple performance measurement doesn’t consider the risks that a manager is allowed to take. Other measures—like the Sharpe, Jensen, and Treynor ratios(**) — refined the idea, but the alpha industry was born and has been growing ever since.

Finance students and Wall Street sorts around the world yearn for knowledge that will let them find ever more alpha. This raises the simple question of Chapter 4 – Where does alpha come from? That question opens this part of the book. The chapter explains why the search for alpha is more than just a snipe hunt, and why the people who find it may be more than just plain lucky.

Chapter 5 – A Gentle Introduction to Computerized Investing, starts out with a description of indexing, the great granddaddy of all quant equity strategies, and how it is transformed into active quant strategies by adding information beyond knowledge of an index’s constituent stocks.

In the last of this part, Chapter 6 – Stupid Data Miner Tricks, we see how with the right mix of hubris, stupidity, and CPU cycles, it is possible to do some real damage to your financial health. In investing, as in the bomb squad, knowing what not to do is extremely worthwhile.

>>>>>> READ MORE HERE < <<<<<<

* This surprising admission came in a dinner speech at the Investment Management Network “Superbowl of Indexing” Conference (December 1996, Palm Springs, California). No performance figures were disclosed.

** The Sharpe ratio is a measure of management skill that adjusts pure alpha (value added) by the variability of that value added. The others (Jensen & Treynor) are refinements based on characteristics of the portfolio, such as beta. They are less commonly used. Details are here http://en.wikipedia.org/wiki/Sharpe_ratio.

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