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A Computational Account of Investor Behaviour in Chinese and US Market

Received: 4 December 2015     Published: 5 December 2015
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Abstract

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.

Published in International Journal of Economic Behavior and Organization (Volume 3, Issue 6)
DOI 10.11648/j.ijebo.20150306.11
Page(s) 78-84
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Time Series Analysis, GARCH(1,1), Vector Autoregressive, Granger Causality, Sentiment Analysis

References
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[15] Mittermayer, M. A. (2004, January). Forecasting intraday stock price trends with text mining techniques. In System Sciences, 2004. Proceedings of the 37th Annual Hawaii International Conference on (pp. 10-pp). IEEE.
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[17] Schumaker, R. P., & Chen, H. (2009). Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Transactions on Information Systems (TOIS), 27(2), 12.
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  • APA Style

    Zeyan Zhao, Khurshid Ahmad. (2015). A Computational Account of Investor Behaviour in Chinese and US Market. International Journal of Economic Behavior and Organization, 3(6), 78-84. https://doi.org/10.11648/j.ijebo.20150306.11

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    ACS Style

    Zeyan Zhao; Khurshid Ahmad. A Computational Account of Investor Behaviour in Chinese and US Market. Int. J. Econ. Behav. Organ. 2015, 3(6), 78-84. doi: 10.11648/j.ijebo.20150306.11

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    AMA Style

    Zeyan Zhao, Khurshid Ahmad. A Computational Account of Investor Behaviour in Chinese and US Market. Int J Econ Behav Organ. 2015;3(6):78-84. doi: 10.11648/j.ijebo.20150306.11

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  • @article{10.11648/j.ijebo.20150306.11,
      author = {Zeyan Zhao and Khurshid Ahmad},
      title = {A Computational Account of Investor Behaviour in Chinese and US Market},
      journal = {International Journal of Economic Behavior and Organization},
      volume = {3},
      number = {6},
      pages = {78-84},
      doi = {10.11648/j.ijebo.20150306.11},
      url = {https://doi.org/10.11648/j.ijebo.20150306.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijebo.20150306.11},
      abstract = {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.},
     year = {2015}
    }
    

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    AU  - Khurshid Ahmad
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    AB  - 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.
    VL  - 3
    IS  - 6
    ER  - 

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Author Information
  • School of Computer Science and Statistics, Trinity College, the University of Dublin, Dublin, Ireland

  • School of Computer Science and Statistics, Trinity College, the University of Dublin, Dublin, Ireland

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