, Volume 13, Issue 4, pp 537-538
Date: 29 Jun 2012

Alma Lilia Garcia Almanza, and Edward Tsang: Evolutionary applications for financial prediction: classification methods to gather patterns using genetic programming

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It is nice to see a book applying evolutionary algorithms to the problem of financial forecasting. This is one of the very few books on the market that spells out in detail an evolutionary system as applied to financial time series. It is a detailed book, and concentrates on many of the computational details of building an evolutionary forecasting system with applications to some financial data sets along with some standard machine learning benchmarks.

The book begins with several introductory chapters. First, there is a chapter on machine learning from a classification perspective. It emphasizes the problem of imbalanced classes, which is a running theme throughout the book. (By imbalanced classes we mean that the numbers of positive and negative cases are dramatically different.) Dealing with these kinds of data issues is important, and is understudied in the field of finance. Also, some metrics are presented for measuring the usefulness of a learning tool. Particularly important is s