通过财务10-k报表的NLP分析进行智能投资组合管理

Purva Singh
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引用次数: 0

摘要

本文试图分析随着时间的推移,10-K财务报告的情绪稳定性是否可以决定公司未来的平均回报率。选择不同的股票组合来检验这一假设。拟议的框架从美国证券交易委员会的EDGAR数据库下载这些公司的10-K报告。它通过预处理管道提取文件的关键部分,以执行NLP分析。使用Loughran和McDonald情感单词列表,该框架从10-K文档中生成情感TF-IDF,以计算两个连续10-K报告之间的余弦相似性,并建议利用这种余弦相似性作为阿尔法因子。为了分析阿尔法因子在预测未来回报方面的有效性,该框架使用阿尔法透镜库进行因子回报分析、营业额分析,并比较潜在阿尔法因子的夏普比率。结果表明,我们投资组合10-K报表的情绪稳定性与其未来平均回报率之间存在很强的相关性。为了研究社区的利益,与本文相关的代码和Jupyter笔记本已经在Github1上开源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent Portfolio Management via NLP Analysis of Financial 10-k Statements
The paper attempts to analyze if the sentiment stability of financial 10-K reports over time can determine the company’s future mean returns. A diverse portfolio of stocks was selected to test this hypothesis. The proposed framework downloads 10-K reports of the companies from SEC’s EDGAR database. It passes them through the preprocessing pipeline to extract critical sections of the filings to perform NLP analysis. Using Loughran and McDonald sentiment word list, the framework generates sentiment TF-IDF from the 10-K documents to calculate the cosine similarity between two consecutive 10-K reports and proposes to leverage this cosine similarity as the alpha factor. For analyzing the effectiveness of our alpha factor at predicting future returns, the framework uses the alphalens library to perform factor return analysis, turnover analysis, and for comparing the Sharpe ratio of potential alpha factors. The results show that there exists a strong correlation between the sentiment stability of our portfolio’s 10-K statements and its future mean returns. For the benefit of the research community, the code and Jupyter notebooks related to this paper have been open-sourced on Github1.
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