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<title>Automated Trader Entropy RSS feed results</title>
<link>http://automatedtrader.net</link>
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<language>en-uk</language>
<copyright>Copyright 2008 Algorithmic Media ltd</copyright>
<pubDate>Fri, 21 Nov 2008 08:18:12 -0600</pubDate>
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<title><![CDATA[Entropy Rate and Profitability of Technical Analysis: Experiments on the NYSE US 100 Stocks]]></title>
<link>http://automatedtrader.net/algorithmic-trading-online-688.xhtm</link>
<description><![CDATA[ The entropy rate of a dynamic process measures the uncertainty that  remains in the next information produced by the process given complete  knowledge of the past. It is thus a natural measure of the difficulty to  predict the evolution of the process. The first question investigated here is  whether stock price time series exhibit temporal dependencies that can be  measured through entropy estimates. Then we study the extent to which  the return of financial trading rules is correlated with the entropy rates  of the price time series. Experiments are conducted on EOD data of the  stocks composing the NYSE US 100 index during period 2000-2006, with  the use of genetic programming to induce the trading rules. By Nicolas NAVET and Shu-Heng CHEN]]></description>
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<title><![CDATA[Financial Data Mining with Genetic Programming: a Survey and Look Forward]]></title>
<link>http://automatedtrader.net/algorithmic-trading-online-689.xhtm</link>
<description><![CDATA[ Genetic Programming (GP) is an appealing machine-learning  technique for tackling financial engineering problems: it belongs to the  family of evolutionary algorithms that have proven to be remarkably  successful at handling complex optimization problems, and possesses  the unique feature of producing solutions under a symbolic form that  can be understood and analyzed by humans. Over the last decade,  GP has been applied to generate financial trading strategies, forecast  stocks and options prices, or grasp some insight into the dynamics of  the markets and the behavior of the agents. In this paper, we first  provide a brief survey of the existing studies, then highlight fields of  investigations that, we believe, should lead to enhance the applicability  and efficiency of GP in the financial domain. By Nicolas NAVET and Shu-Heng CHEN]]></description>
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