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投资者所言即市场所言:衡量中国投资者的真实情绪。

What investors say is what the market says: measuring China's real investor sentiment.

作者信息

Sun Yunchuan, Zeng Xiaoping, Zhou Siyu, Zhao Han, Thomas Peter, Hu Haifeng

机构信息

Business School, Beijing Normal University, Beijing, 100875 China.

School of Mathematical Sciences, Beijing Normal University, Beijing, 100875 China.

出版信息

Pers Ubiquitous Comput. 2021;25(3):587-599. doi: 10.1007/s00779-021-01542-3. Epub 2021 Feb 26.

Abstract

This paper describes a novel approach to measure individual investor sentiment using text-based analysis of millions of posts extracted from Chinese financial online forums. We describe how we built a database of more than 200 million stock posts from online financial forums, created , a sentiment dictionary consisting of 48,878 words to allow sentiment analysis, and how we developed , an individual investor sentiment index for the stock market in China. This allowed (1) the first systemic measurement of individual investor sentiment in China; (2) an approach to text-based analysis that reflects investor sentiment about millions of posts about stocks listed in ; (3) a way to flexibly measure investor sentiment of a single stock, a sector or an industry and the whole market; and (4) made this possible for daily, weekly, monthly, quarterly, and yearly time periods. We also examine the relationship of the sentiment proxy and stock returns and compare it with two typical BW metrics in China. Empirical results show that correlates better with market performance than BW metrics in China and can be used to predict market changes in the short term.

摘要

本文描述了一种新颖的方法,即通过对从中国金融在线论坛提取的数百万条帖子进行基于文本的分析,来衡量个体投资者情绪。我们阐述了如何构建一个包含超过2亿条来自在线金融论坛的股票帖子的数据库,创建了一个由48878个单词组成的情感词典以进行情感分析,以及如何开发中国股票市场的个体投资者情绪指数。这实现了:(1)对中国个体投资者情绪的首次系统性衡量;(2)一种基于文本的分析方法,该方法反映了投资者对有关在[此处原文缺失相关内容]上市股票的数百万条帖子的情绪;(3)一种灵活衡量单只股票、一个板块或一个行业以及整个市场投资者情绪的方法;(4)使得在日、周、月、季度和年度时间段内进行上述衡量成为可能。我们还研究了情绪代理指标与股票回报之间的关系,并将其与中国两个典型的BW指标进行比较。实证结果表明,在预测市场变化方面,[此处原文缺失相关内容]比中国的BW指标与市场表现的相关性更好,并且可用于短期预测市场变化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6fa0/7909732/6179cc8bff40/779_2021_1542_Fig1_HTML.jpg

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