Computational Finance 1999

Rating:
Summary: Good for a Quant
Review: A collection of papers by Econometricians and Data Miners, on techniques of data mining, knowledge discovery, genetic algorithms, neural networks, and machine learning.
To undersatnd the papers you need to be familiar with LMC (Financial Econometrics) level of knowledge.
This book will be boring for Probabilist and Mathmaticains, because it does not contain heavy math at all (No where near Karatzas and Shreve)
The articles are taken from the conference of Computational Finance '99 in NYU.

Rating:
Summary: Demanding reading, but a worthwhile overview
Review: 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.

Rating:
Summary: This is a great book!!
Review: Finally, an insightful, easy-to-read collection that bridges the gap between lofty academics and down-to-earth practitioners!