Genetic Algorithms and Genetic Programming in Computational Finance

Shu-Heng Chen

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After a decade of development, genetic algorithms and genetic programming have become a widely accepted toolkit for computational finance. Genetic Algorithms and Genetic Programming in Computational Finance is a pioneering volume devoted entirely to a systematic and comprehensive review of this subject. Chapters cover various areas of computational finance, including financial forecasting, trading strategies development, cash flow management, option pricing, portfolio management, volatility modeling, arbitraging, and agent-based simulations of artificial stock markets. Two tutorial chapters are also included to help readers quickly grasp the essence of these tools. Finally, a menu-driven software program, Simple GP, accompanies the volume, which will enable readers without a strong programming background to gain hands-on experience in dealing with much of the technical material introduced in this work.

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I need help to chose a project to work on for my master graduation, The project must involve Ai / Machine learning or Business intelegence.. but if there is any other suggestion out of these topics it is Ok, please help me.

Since it has a business tie in - given some input set determine probable business fraud from the input (something the SEC seems challenged in doing). We now have several examples (Madoff and others). Or a system to estimate investment risk (there are lots of such systems apparently but were any accurate in the case of Lehman for example).

A starting point might be the Chen book Genetic Algorithms and Genetic Programming in Computational Finance.

Here's an AAAI writeup of an award to the National Association of Securities Dealers for a system thatmonitors NASDAQ insider trading.