Individual investors, social media and Chinese stock market : a correlation study

Chinese stock market is a unique financial market where heavy involvement of individual investors exists. This article explores how the sentiment expressed on social media is correlated with the stock market in China. Textual analysis for posts from one of the most popular social media in China is conducted based on Hownet and NTUSD, two most commonly used sentiment Chinese dictionaries. The correlation matrices and regressions between sentiment ratios and returns of 9 holding periods for all the 30 sample securities reveal that correlation exists between investor sentiment on social media and the future returns of the Chinese stock market. In addition, I find that negative sentiment ratio is superior than positive sentiment ratio, and correlation of sentiment ratio to return is persistent in future holding periods. Also, by comparing different stocks and indices, I find that well-established market index has better correlation with social media sentiments than individual stocks, and well-known 'star' stocks have better correlation with social media than other stocks. However, I test the VAR model on Shanghai Composite Index, and find that the model is stable but shows no Granger causality. Better data and improved analysis are needed to predict stock market with social media.

Department dc:contributor.department

Sloan School of Management.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2016

Author dc:creator

• Wu, Yonghui, S.M. Sloan School of Management

Advisor dc:contributor.advisor

• Erik Brynjolfsson.

• Sloan School of Management.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/104512

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/104512