I got this book at a finance conference yesterday and read it all the way on the plane home. Bear in mind I almost always sleep on the plane from lift off to touchdown, but I couldn't put this one down.

The history sections are simply superb and the well-chosen pictures tell excellent stories. And it is representative of the book in that it gives perspective.

In short, that's what this book offers: a step-back overview of technological progress on Wall Street. The specific chapters also do a great job reviewing particularly important recent and not-so-recent innovations, from textual analysis to genetic algorithms.

And of course, it is quite fun to read.

After my last book review, a reader asked how I was able to read so many books, given my other responsibilities. My answer is this: I keep a book near me at all times. When I get a break, I read a few pages. Over a week, that means a book gets read. That's how I read so many books.

Onto tonight's book: I had a number of friends that liked Nerds on Wall Street, and I liked it as well. The book has a number of strengths. The author explains complex financial instruments in relatively simple terms. The same for complex trading techniques.

The author gives history and background as one that was sucked into computerized finance from a technical background that might have had him in a purer technological role. As I read what he went through, I said to myself, "He was seven years ahead of me." I had my own share of innovative things that I did, but the things done in his era were bigger.

He gives reasonable explanations of how computerized trading works, and what factors they look for in designing trading systems. He talks about the common factors that dominate trading systems, and a few that he knows of but has not published. (He gives a taste, but does not serve up the full dish.)

Like me, he serves up a full plate of data mining disasters. There are a lot of losses to be taken by those who think they have discovered a statistical regularity in the financial markets. The few significant regularities make sense to seasoned observers, and are not consistent. They pay off 70% of the time, and kill you 15% of the time.

On Wall Street, if you are really, really smart, they will hand over to you exceptionally advanced tools that you can use to destroy yourself in a unique and memorable way. So it was for LTCM.


Quibbles

The book is badly edited. Many elements appear multiple times with little modification. It sometimes reads like a bunch of articles that was strung together into a book. The editors should have tried to create something more cohesive.

The last several chapters feel like an afterthought, though many of the ideas presented there are ideas that I have suggested. I have talked about splitting mortgages into smaller mortgages plus equity appreciation rights. I have also suggested creating mutual banks, rather than what was done with the TARP.

All that said, the average reader will learn a lot here. I recommend the book to those that want to dig into how the equity markets became more computerized. For those that want to understand the same for the debt markets, that book remains to be written.

There are many creative and dynamic individuals that work on Wall Street that use sophisticated mathematics and artificial intelligence for financial trading and who do not satisfy the "nerd" classification. But despite the title of this book, the author gives a very interesting overview of the use of mathematics and machine intelligence in financial engineering. His approach to history however is purely anecdotal, so readers should not expect a detailed study, but rather a work that targets the "general audience", however vague the latter characterization is. There is a lot in the book that is controversial, and many who work in the financial industry may be insulted by some of the author's commentary, but in general he refrains from the vituperation that characterizes much of the current discussion on financial modeling.

The author covers just about all the techniques used to "get an edge" in financial trading, and his coverage of computational linguistics is unusual in that is typically not discussed at all in books at this level of discourse. Readers will also get more insight into the concept of "alpha" and what it is like to work in the field of financial modeling. Indeed, modelers have been blamed for much of the current "financial crisis", but some have also taken credit for inducing events that they show no evidence for having caused or controlled. The origin of this kind of mental confabulation is not really known, but the author is aware of it when he states that in financial forecasting it is "remarkably easy to fool yourself."

It is along these same lines that the author falters in the book when he blames some of the more "exotic" financial instruments for the "financial crisis". He banters about words such as "pointless complexity" and "monstrously complex" to characterize some of these instruments but at no place really defines, even in a loose sense, of what complexity is. Also, he never gives any quantitative evidence that is was mortgage-related financial engineering that contributed primarily to the current financial situation. It would be very difficult to disentangle the effects of mortgage-backed securities from other derivative securities. This part of the book is therefore its worse, and needs considerable revision on the part of the author.

In addition, the author has too much faith in governmental regulation, as is apparent by his support of many of the regulatory institutions that have arisen in the last 80 years. He is not alone in his fear of a free market though. One need only view the actions of financial executives and their begging for alms from Washington. They tremble at the notion of a free market. They quake at the idea of no governmental agency to cushion them against their follies.

The current schism between "intuitive" and "quantitative" trading is also brought out quite elegantly in this book, and if the author's historical summarization is to believed, one can expect to see more use of quantitative methods in the future. The author's discussion in this regard is additional proof of the Schumpeterian nature of financial innovation in the twenty-first century. This creative destruction of past trading methods is to be encouraged, and hopefully the day will come soon when the Leo-Malamed-type trading is replaced by full automation, by technology robust enough, reliable enough, and intelligent enough to move capital where it should be moved, and to manage portfolios independently of the weak constraints of human intuition.

As someone outside of the financial world, with no familiarity with the ways of Wall Street or the workings of financial markets, I found this book to be a great read. I actually learned something about the newer financial instruments that dominated the news last fall and the background of the "quants" phenomenon. Anyone who has not been under a rock for the last year should find this a compelling and entertaining read. Buy it!

Aaron Brown's surprisingly peevish review is out-of-context for what this book is.

Nerds is a cartoon-covered romp that announces itself at the outset as Borscht-belt shtick. It's a self-deprecating look at vending machine ice cream bars melting in the shirt pockets of those dateless, hygiene-challenged, quant-jock-inhabitants of basement labs, who pioneered practical uses of artificial intelligence on Wall Street. It's Rodney Dangerfield meets Douglas Hofstadter; Alfred E. Neuman explaining Warren Buffett; the Hasty Puddings on Nobel Prize winners.

The audience for this kind of entertainment expects hoots, spilled drinks and cracker crumbs. Loosen your tie, lighten-up, and enjoy the amusing and informative story telling.

This book is born of the same spirit as the pithy 1954 best-seller, How to Lie with Statistics, is as informative as the Pulitzer Prizing winning, The Soul of a New Machine, and is delivered in a decidedly humorous way. Enjoy it for what it is.

Really good book. Very light, humorous approach that makes the technical-to-most-of-us material interesting and readable.
When I was done, I understood things about trading on Wall Street that I've never understood before.
And I also understood that innovation on Wall Street is just like innovation in every other market -- new things that are based on real needs and principles yield real, lasting value. But profiting from those "new insights" is limited by the time it takes the rest of the "herd" to follow. The problem comes, as we seen in the last section of the book, when the "herd" stampedes after innovation-for-innovation-sake instruments like derivatives and CDOs and we are faced with near market collapse.
All of this begs the question -- have the "real" innovations on the stock market been largely exploited already? Is there anything but "fad" left for the Nerds on Wall Street to offer the rest of the herd?

This was a very informative and fun book to read. Most books in this area are dry and self-serving. Leinweber has made a very timely and complex topic accessible to all of us. Buy the book.

As someone who is totally unschooled in the ways of Wall Street, I found myself actually enjoying the treatment Leinweber gives to the history of the market--lively, entertaining, and informative. Although the going gets tough for the novice like me, it was well worth the reading effort to actually get a grip on the inner workings of the market. As a math nerd, I found his treatment of Wall Street very similar to Berlinski's A Tour of the Calculus. I have recommended this to many friends, and so far no friendships have been lost.

I LOVED this book. I would have given this book five stars had the pictures been better quality. Given how unique it is to have a book on this subject matter with pictures that illustrate the author's point, I think that more attention should have been given to the quality of the pictures.

With all due respect to the previous Amazon reviewers, it's hard to believe they both (a) read this book and (b) have any familiarity with Wall Street technology. The book is a collection of articles written for technology magazines from the mid-80s to the mid-90s. Even within an article entire paragraphs are repeated, and the same idea in more or less the same words can often be found a dozen times or more in the book. This is interspersed with apparently random cut-and-pastes from the Internet and lots of tiny black-and-white pictures which the author tells you are only meaningful with color and animation. You get the feeling the author cleaned out his desk, and decided to make some money from the stuff he didn't want anymore.

There is some useful information in here, and the author does know a lot about automated equity trading before the advances of the late 90s. The trouble is it's not presented in coherent sequence and the technical level is too uneven. For example, it is asserted five separate times that garbage collection is a problem for LISP, without any background material. Anyone who knows what garbage collection means in this context, or has worked with LISP, already knows this and will get annoyed at even the second repetition. Anyone without that background will find the repeated explanations meaningless. There is nowhere near enough technical information for nerds who want to understand Wall Street (or the Wall Street of 20 years ago) or Wall Streeters who want to understand nerds, but there is far too much unexplained jargon for non-technical readers.

Another complaint is the author makes significant errors when he steps beyond his expertise, which is often. For example, he claims if you have 1,000 statistical results significant at the 5% level, 50 of them will be false. The correct statement is if you test 1,000 rules with no predictive value, you expect 50 of them to show significance at the 5% level. The number of your significant results that are false depends whether you start with rules that are mostly useful, or mostly random. This is the key insight to the concept of data mining, the author's misunderstanding makes his chapter on the subject misleading.

Another error is the claim that futures markets were developed to allow farmers to lock in prices. This is false historically (no farmers were involved in the creation of futures markets, farmers have never been big participants and have often tried to have them shut down, when farmers do transact it is much more often to double up their bets by buying the crop they grow than it is to hedge) and anyone who believes it misunderstands the economic function of futures. That's dangerous if you also have a computer that can send trades to financial exchanges. Professionally, the author stuck to equities so it didn't matter to him, but it could matter to his readers if they rely on his account.

There is one up-to-date section at the end, which the author admits was tacked on to make the book more relevant, even though he knows nothing about the topic. His angry rant about the current financial crisis appears to be constructed from reading the first paragraphs of other people's rants. He relies almost exclusively on quotes from politicians, senior regulators and bank CEOs, who all agree it was the nerds' fault. He condemns "complex and opaque" techniques in strong language and great lengths. This from a guy who built black-box trading systems. While it's true there can be a long path between a mortgage dollar a borrower sends in (or, more to the point, doesn't send in) and the end investor, and there can be matches from phantom securities along the way, all of this is done by clear rules which are disclosed. You don't really know what a black box program will do until you turn it on, and its workings are never made public. I'm not defending synthetic CDO-squareds, I'm just pointing out opinions on complexity should come from people who know the field. A non-programmer might look at 1,000 lines of computer code and say it is hopelessly complex and opaque, when a programmer finds it a clear and elegant solution. When disaster strikes, everyone will agree it was the computer's fault.

Then he's "mad as hell" at the irresponsibility of Wall Streeters. Again, without arguing the point, this is a guy who loves the Cold War doctrine of mutually assured destruction, and worked on military projects involving weapons of mass destruction for, in his own words, "the guys in the five-sided nuthouse." The worst financial idea in history does not compare in irresponsibility to supporting the capability to destroy all life on earth, at the direction of people you believe to be insane. In my opinion, the system the author supported and still supports had something like a 10% chance of killing me and everyone else (and still might do it), with absolutely no moral or other human justification. And it was done by people, like the author, who were avenging no personal tragedy, were not hungry or trapped or desperate, who had no great spiritual rationale; just irresponsible nerds with toys.

Finally, the coverage is entirely based on projects the author happened to work on and write about at the time, so a few areas are overcovered and many other areas are ignored. With a good editor to remove the redundancies and sections the author is not qualified to discuss, to order the material and to insist on background explanations, links and transitions, this might be a pretty good account. Until that happens, I suggest you avoid this book.

As someone who shares an interest in technology and Wall Street I found this book interesting. Leinweber weaves the reader through a largely unexplored subject of electronic markets. It could easily be the best history book we have of how technology came to play a larger role in markets. Leinweber also offers insights for what the future might look like.

If you're not interested or familiar with technology you'll find this book somewhat disappointing. However, for a geek interested in Wall Street its a fun read.

The book has three parts:
1) the history of financial technology
2) alpha generation ideas
3) what quants can do about the financial crisis

The first part is hilarious. One of the many pictures is of a past president of the CME banging on a gong with a giant salami.

It's a really irreverant look at Wall Street from the start- "In 1792, the New York Stock Exchange was a bunch of guys standing around a buttonwood tree at 68 Wall Street shouting at each other on days when it didn't rain or snow. We like our markets to be liquid, efficient, resilient, and robust. But this is hard when all the participants have to crowd around a tree and hope for good weather. So in 1794, we see the first big technological solution: the roof. ..."

From what I gather, the author was one of the fathers of AI-based trading so the second part "Alpha as Life" really stoked my imagination. I was reading along with a pencil and paper and I ended up writing down a new multi-factor trading strategy for small cap stocks along the way. The book is especially great for text-based strategies. It's amazing how many details he shares about old trading software companies he founded which were vendors of billion-dollar funds.

The book was fascinating because of the author's insider status over an already long and prolific career. He's come into contact with so many famous personalities in the trading world and he has a great way of characterizing people, as, well, people. It's almost 400 pages but I had no trouble reading it in a day.

That's all I want to know.

Quant, rock star, genius, hedgie, Warren Buffet, blah, blah, blah, all swimming naked when the tide went out.

They key to this book is that it's written from the pov of a CS guy and not a typical hedge fund guy. The perspective is unique and offers a very interesting and exciting take on the future of financial markets. Thinking about the history of the markets over the past centuries (and not merely the last few decades) can't help but make one feel more optimistic in these times.