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Buy, Sell, or Hold? Information Extraction from Stock Analyst Reports

机译:买,卖还是持有?从股票分析师报告中提取信息

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This paper presents a novel linguistic information extraction approach exploiting analysts' stock ratings for statistical decision making. Over a period of one year, we gathered German stock analyst reports in order to determine market trends. Our goal is to provide business statistics over time to illustrate market trends for a user-selected company. We therefore recognize named entities within the very short stock analyst reports such as organization names (e.g. BASF, BMW, Ericsson), analyst houses (e.g. Gartner, Citigroup, Goldman Sachs), ratings (e.g. buy, sell, hold, underperform, recommended list) and price estimations by using lexicalized finite-state graphs, so-called local grammars. Then, company names and their acronyms respectively have to be crosschecked against data the analysts provide. Finally, all extracted values are compared and presented into charts with different views depending on the evaluation criteria (e.g. by time line). Thanks to this approach it will be easier and even more comfortable in the future to pay attention to analysts' buy/sell signals without reading all their reports.
机译:本文提出了一种新颖的语言信息提取方法,该方法利用分析师的股票评级进行统计决策。在一年的时间里,我们收集了德国股票分析师的报告,以确定市场趋势。我们的目标是随时间提供业务统计数据,以说明用户选择的公司的市场趋势。因此,我们会在非常短的股票分析师报告中识别出命名实体,例如组织名称(例如巴斯夫,宝马,爱立信),分析机构(例如Gartner,花旗集团,高盛),评级(例如买入,卖出,持有,跑输大盘,推荐名单) )和使用词汇化的有限状态图(即所谓的局部语法)进行价格估算。然后,必须分别对公司名称及其首字母缩写与分析师提供的数据进行核对。最后,将所有提取的值进行比较,并根据评估标准(例如,按时间线)将其呈现为具有不同视图的图表。由于采用了这种方法,将来在不阅读所有报告的情况下关注分析师的买卖信号将变得更加容易,甚至更加舒适。

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