{"title":"The Agent-Based Hedge Fund","authors":"R. Barbosa, O. Belo","doi":"10.1109/WI-IAT.2010.149","DOIUrl":null,"url":null,"abstract":"In this article we describe the implementation of a diversified investment strategy using 25 intelligent agents. Each agent utilizes several data mining models and other artificial intelligence techniques to autonomously day trade an American stock. The agents were individually tested with out-of-sample data corresponding to the period between February of 2006 and June of 2010, and most achieved an acceptable performance. By integrating the 25 agents in a multi-agent system, we were able to obtain much better results (according to the return and maximum drawdown metrics); this leads us to believe that it might be possible to use one such system in the creation of a profitable hedge fund in which the investment decisions can be made without human intervention.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WI-IAT.2010.149","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

In this article we describe the implementation of a diversified investment strategy using 25 intelligent agents. Each agent utilizes several data mining models and other artificial intelligence techniques to autonomously day trade an American stock. The agents were individually tested with out-of-sample data corresponding to the period between February of 2006 and June of 2010, and most achieved an acceptable performance. By integrating the 25 agents in a multi-agent system, we were able to obtain much better results (according to the return and maximum drawdown metrics); this leads us to believe that it might be possible to use one such system in the creation of a profitable hedge fund in which the investment decisions can be made without human intervention.
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基于经纪人的对冲基金
在本文中,我们描述了使用25个智能代理的多元化投资策略的实现。每个代理使用几个数据挖掘模型和其他人工智能技术来自主交易美国股票。使用2006年2月至2010年6月期间的样本外数据对代理商进行了单独测试,大多数代理商取得了可接受的性能。通过在多智能体系统中集成25个智能体,我们能够获得更好的结果(根据回报和最大缩减指标);这让我们相信,或许可以使用这样一个系统来创建一个盈利的对冲基金,在这个基金中,投资决策可以在没有人为干预的情况下做出。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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