通过自然语言处理方法提高投资组合绩效

DiJia Su, J. Mulvey, H. Poor
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摘要

最近的自然语言处理(NLP)突破已经被证明对解决许多语言导向的任务是有效的,例如完成句子和处理搜索查询。这项技术已经被包括谷歌在内的科技公司成功应用。一个重要的元素是与预训练系统相关联的语言嵌入。本文通过情感分析的现代版本描述了NLP概念及其在投资组合模型中的应用。作者展示了利用推特信息和NLP构建股票投资组合的优势,特别是在COVID-19大流行等不寻常事件期间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving Portfolio Performance via Natural Language Processing Methods
Recent natural language processing (NLP) breakthroughs have proven effective for addressing many language-directed tasks, such as completing sentences and addressing search queries. This technology has been successfully implemented by tech firms including Google and others. An important element consists of language embeddings linked to pretraining systems. This article describes NLP concepts and their application to portfolio models via a modern version of sentiment analysis. The authors demonstrate the advantages of employing information from Twitter along with the NLP for constructing a portfolio of stocks, especially during unusual events such as the COVID-19 pandemic.
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