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 | | Expert Trading Systems: Modeling Financial Markets with Kernel Regression by John R. Wolberg ISBN-10: 9780471345084 ISBN-10: 0-471-34508-3 ISBN-13: 9780471345084 ISBN-13: 978-0-471-34508-4 Hardcover 2000-05-30 Wiley Find Lowest Price |
Editorials | ||
Product Description With the proliferation of computer programs to predict market direction, professional traders and sophisticated individual investors have increasingly turned to mathematical modeling to develop predictive systems. Kernel regression is a popular data modeling technique that can yield useful results fast. Provides data modeling methodology used to develop trading systems. * Shows how to design, test, and measure the significance of results John R. Wolberg (Haifa, Israel) is professor of mechanical engineering at the Haifa Institute in Israel. He does research and consulting in data modeling in the financial services area. | ||
Reviews | ||
Not for the faint hearted On page XIV Wohlberg states "This book is geared to 3 types of readers. The first group includes those who are interested in modeling in general and desire an overview of the KR technique. The second group includes those involved in the development and/or usage of KR software. The third group includes readers primarily interested in the development of computerized trading systems" I fell into the first group. I had three problems with the book: 1. Most of the book was relevant to people in group 2 or 3 above; 2. The author did not always use plain simple english; 3. prerequisite knowledge of Schwagers book "A Complete Guide to the Futures Market" and computing and/or statistics was necessary. Nevertheless I found some useful information which I have implemented. | ||
Nice introduction to kernel regression and machine learning applied to finance I don't think the intended audience for this book is someone who has never heard of a standard deviation. In fact, the intended audience is probably people who work in a hedge fund, or who write high frequency trade execution systems. Such people probably vary a lot in their technical sophistication, which is probably why the author doesn't presume familiarity with things like ARIMA, stationarity or statistical distributions. His explanations of such are very clear indeed, and are probably more worthwhile than what you'd get in a more technical book. Overly technical books often cloud the issue with impressive looking verbiage and notation. This book isn't written for people who require mathematical windbaggery to maintain their self esteem: it's written for people who have a plumber's desire to get the job done in a workmanlike fashion and get paid. The technique itself (this is a book on kernel regression) is a powerful one, and the ideas he presents are certainly useful for other kinds of machine learning applied to Finance. One of the great things about the book is Professor Wolberg is constantly reminding us of computational costs, and the best/fastest way of writing code. If you stop to think about where these techniques are likely to be most useful -high frequency commodities markets, say, you'll realize this alone justifies the cost of the book. If you deal with big data sets or data mining issues and you never heard of a p-tree (kd-tree, Peano count, recursive tree, whatever you want to call it), for example; you should really buy the book. He compares his technique to Neural Nets in one of the appendices which dates the book a bit; a more timely comparison might be to more modern and comparable GAM or gradient boost methods. The downside: while Wolberg is a refreshingly clear writer, the notation is an abomination. I've read his other books; I know he knows something about LaTeX (or word's equation editor). I suppose this notation is designed to appeal to people writing code, as it looks an awful lot like someone cut and pasted code niblets instead of using something like mathematical notation. I question the utility of this. His opening example of the Hills of Galilee modeling problem is evocative enough once you've gotten through the whole book, but I found myself puzzled by it at the beginning. Perhaps some more words there would have been helpful to show where he and the Hills of Galilee are heading. Finally, an addendum from my original review: the small data sets given here are a great boon to anyone who wants to write their own kernel regression routines: I know this, as I used this book to write one of my own. A good companion book for some fancy math is Wolfgang Hardle's "Applied Nonparametric Regression." Wolberg's book represents more the "how to" manual, wheras Hardle's book is more "why it works." | ||
Minority audience only Oh dear. It seems I'm more or less a lone voice of dissent amongst all this praise heaped upon Dr. Wolberg's book. I've been in possession of this book for some time. For the most part, it has been gathering dust on a lonely bookshelf. Every so often though, I bring it down to remind myself of why I left it there. I think the book basically fails. For one thing, who is the target audience? "Cutting-Edge Data Modeling for Non-Statisticians" is the claim on the back cover. Well, I'm happy to say that while I'm a non-statistician, I do have a degree in math, and I didn't find the math presented in this book all that daunting. Having said that, many people will certainly find this book a total non-starter. Unless you have a strong math background, take my advice and don't bother - you just won't know what this guy is talking about. Sadly, having finally reached the end of this book, I just sat there thinking where do I go from here. I didn't find an answer. Granted, the book has a few interesting things to say about testing, but this hardly warrants the asking price. If you are an investor with a day job, I believe it would take you a number of years to develop a trading system using the ideas expounded in this book and it probably wouldn't even work as you would quite likely succumb to any one of the many pitfalls the book discusses. Frankly, I defy anyone to tell me they've made so much as a penny from any of the ideas presented in Dr. Wolberg's book, save perhaps Dr. Wolberg himself. You would be much better advised to spend your time refining your trading systems using the underrated technique of learning by experience. Sorry, I think this book is nothing more than a glorified plug for Dr. Wolberg's services. | ||
Correction One reviewer states that the author hides the fact that he is associated with the company that commercially exploits the software discussed in the book. However, in the Acknowledgements section of the book, the author acknowledges the two software engineers from Insightware who helped him develop the software. In other words, there is no attempt on the part of the author to hide his ongoing interest in the commercial application of the FKR technology. | ||
A sales pitch for the authors product! The book is actually quite good and Kernel Regression might very well be a good modelling technique. What destroys much of the credibility is that the author is actually the founder of a company that produces KR software. This fact isn't mentioned ANYWHERE in the book. The author just HAPPENS to use a specific software in all his examples. Guess what software? You have to go to the company website to find the connection. If we set that aside, the book is well written and interesting. Not for the math impaired, though. University level math and statistics needed to be enjoyed in full. | ||