A Computational Account of Investor Behaviour in Chinese and US Market
International Journal of Economic Behavior and Organization
Volume 3, Issue 6, December 2015, Pages: 78-84
Received: Dec. 4, 2015; Published: Dec. 5, 2015
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Zeyan Zhao, School of Computer Science and Statistics, Trinity College, the University of Dublin, Dublin, Ireland
Khurshid Ahmad, School of Computer Science and Statistics, Trinity College, the University of Dublin, Dublin, Ireland
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Using vector autoregressive models (VAR) and Granger causality tests, we have looked at the impact of news sentiment on Shanghai Stock Exchange Composite (SSEC) returns based on negative sentiment (words) in newspaper texts about the Chinese economy for a period of 15 years (2000-2014, 22000 news items comprising 15 million tokens). Negative sentiment words were extracted using a well-known sentiment lexicon and a computer program based on a bag-of-words model. In addition to the negative sentiment, we have analysed the impact of traded volume and S&P 500 index: S&P (lagged) returns and negative sentiment appear to have an impact on the SSEC index.
Time Series Analysis, GARCH(1,1), Vector Autoregressive, Granger Causality, Sentiment Analysis
To cite this article
Zeyan Zhao, Khurshid Ahmad, A Computational Account of Investor Behaviour in Chinese and US Market, International Journal of Economic Behavior and Organization. Vol. 3, No. 6, 2015, pp. 78-84. doi: 10.11648/j.ijebo.20150306.11
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