Measuring China's Stock Market Sentiment

67 Pages Posted: 25 Apr 2019 Last revised: 10 May 2019

See all articles by Jia Li

Jia Li

Duke University

Yun Chen

Peking University - National School of Development

Yan Shen

Peking University - China Center for Economic Research (CCER)

Jingyi Wang

Peking University - National School of Development

Zhuo Huang

National School of Development, Peking University

Date Written: April 20, 2019

Abstract

This paper develops textual sentiment measures for China's stock market by extracting the textual tone of 60 million messages posted on a major online investor forum in China from 2008 to 2018. We conduct sentiment extraction by using both conventional dictionary methods based on customized word lists and supervised machine-learning methods (support vector machine and convolutional neural network). The market-level textual sentiment index is constructed as the average of message-level sentiment scores, and the textual disagreement index is constructed as their dispersion. These textual measures allow us to test a range of predictions of classical behavioral asset-pricing models within a unified empirical setting. We find that textual sentiment can significantly predict market return, exhibiting a salient underreaction-overreaction pattern on a time scale of several months. This effect is more pronounced for small and growth stocks, and is stronger under higher investor attention and during more volatile periods. We also find that textual sentiment exerts a significant and asymmetric impact on future volatility. Finally, we show that trading volume will be higher when textual sentiment is unusually high or low and when there are more differences of opinion, as measured by our textual disagreement. Based on a massive textual dataset, our analysis provides support for the noise-trading theory and the limits-to-arbitrage argument, as well as predictions from limited-attention and disagreement models.

Keywords: disagreement, machine learning, noise trading, sentiment, textual analysis, volatility, volume

JEL Classification: C45, C53, C55, G12, G41

Suggested Citation

Li, Jia and Chen, Yun and Shen, Yan and Wang, Jingyi and Huang, Zhuo, Measuring China's Stock Market Sentiment (April 20, 2019). Available at SSRN: https://ssrn.com/abstract=3377684 or http://dx.doi.org/10.2139/ssrn.3377684

Jia Li (Contact Author)

Duke University ( email )

100 Fuqua Drive
Durham, NC 27708-0204
United States

Yun Chen

Peking University - National School of Development

Beijing, 100871
China

Yan Shen

Peking University - China Center for Economic Research (CCER) ( email )

Beijing, Beijing 100871
China

Jingyi Wang

Peking University - National School of Development ( email )

Beijing, 100871
China

Zhuo Huang

National School of Development, Peking University ( email )

No. 38 Xueyuan Road
Haidian District
Beijing, Beijing 100871
China

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