Stock Selection Using Recon~TM/SM

机译：使用Recon〜TM / SM进行库存选择

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We approach the problem of stock selection from the perspective of knowledge discovery in databases: given a database of severa years of quarterly information on over a thousnad companies, discover patterns in the data that will allow one to preict which stocks are likely to have exceptional returns in the future. The database includes measures of trends in the stocks' prices as well as fundamental data on the companies. For this task we employed the Recon system, which is able to induce a set of classification rules or aneural network to model the data it is given. To evaluate Recon's performance in the stock selection task, we paper-traded a portfolio of the fifty stocks randed highest by Recon. When trading costs were taken into account, Recon's profolio had a total return of 238

机译：我们从数据库中的知识发现的角度解决股票选择的问题：给定一个拥有数以千计的公司的季度季度信息的数据库，该数据库中的发现模式将使人们能够预测哪些股票可能具有超额收益在将来。该数据库包括股票价格趋势的度量以及公司的基本数据。对于此任务，我们使用了Recon系统，该系统能够引入一组分类规则或神经网络来对给出的数据进行建模。为了评估Recon在选股任务中的表现，我们用纸笔交易了Recon最高得分的50只股票的投资组合。考虑到交易成本，Recon的投资组合总收益为238

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来源
《Third International Conference on Neural Networks in the Capital Markets Vol.2 London, England 11-13 October 95》|1995年|p.303-316|共14页
会议地点 London(GB);London(GB)
作者
George H. John; Peter Miller; Randy Kerber;
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正文语种 eng
中图分类财政、金融;
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Stock Selection Using Recon~TM/SM

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