Computational finance, an exciting new cross-disciplinary research area, draws extensively on the tools and techniques of computer science, statistics, information systems, and financial economics. This book covers the techniques of data mining, knowledge discovery, genetic algorithms, neural networks, bootstrapping, machine learning, and Monte Carlo simulation. These methods are applied to a wide range of problems in finance, including risk management, asset allocation, style analysis, dynamic trading and hedging, forecasting, and option pricing. The book is based on the sixth annual international conference Computational Finance 1999, held at New York University's Stern School of Business.
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Book Description
Editorial Reviews
About the Author
Blake LeBaron is Assistant Professor of Economics at the University of Wisconsin, Madison.
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12 of 13 people found the following review helpful:
3.0 out of 5 stars
Demanding reading, but a worthwhile overview,
By A Customer
This review is from: Computational Finance 1999 (Paperback)
Ever been to the gym and overheard a guy boasting that benching 300 lbs. is "not so hard, really"? In fact, of course, 300 lbs is a lot to bench press no matter who you are, and to suggest otherwise is ridiculous.Similarly, it would be folly to suggest that this book is anything other than exceptionally demanding reading that requires both a solid quantitative background as well as a keen interest in the topic. The book is a compendium of research papers from a conference at NYU in 1999. The papers will mean little to the reader without a basic understanding of derivatives and the quantitative methods associated with them. Without having read the equivalent of texts by Hull and Jorion, for example, the reader will feel a bit like George W. Bush at a Stephen Hawking lecture.
3 of 4 people found the following review helpful:
4.0 out of 5 stars
Good for a Quant,
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This review is from: Computational Finance 1999 (Paperback)
A collection of papers by Econometricians and Data Miners, on techniques of data mining, knowledge discovery, genetic algorithms, neural networks, and machine learning.
To understand the papers you need some basic knowledge of Econometrics. This book might be a bit slow for some, it's not very mathematical The articles are taken from the conference of Computational Finance '99 in NYU.
7 of 11 people found the following review helpful:
4.0 out of 5 stars
Great book,
By A Customer
This review is from: Computational Finance 1999 (Paperback)
Good information on techniques of data mining, knowledge discovery, genetic algorithms, neural networks, bootstrapping, machine learning, and Monte Carlo simulation. The articles are taken from the conference of Computational Finance '99 in NYU. Recommended for the quants.
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