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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Authors
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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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.
Keywords
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
References
[1]
Stone, P. J., Dunphy, D. C., & Smith, M. S. with associates (1966). The General Inquirer: A Computer Approach to Content Analysis. The MIT Press, Cambridge.
[2]
Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10‐Ks. The Journal of Finance, 66(1), 35-65.
[3]
Généreux, M., Poibeau, T., & Koppel, M. (2011). Sentiment Analysis Using Automatically Labelled Financial News Items. In (Ed.) K. Ahmad. Affective Computing and Sentiment Analysis (pp. 101-114). Springer Netherlands.
[4]
Groß-Klußmann, A., & Hautsch, N. (2011). When machines read the news: Using automated text analytics to quantify high frequency news-implied market reactions. Journal of Empirical Finance, 18(2), 321-340.
[5]
Antweiler, W., & Frank, M. Z. (2004). Is all that talk just noise? The information content of internet stock message boards. The Journal of Finance, 59(3), 1259-1294.
[6]
Das, S. R., & Chen, M. Y. (2007). Yahoo! for Amazon: Sentiment extraction from small talk on the web. Management Science, 53(9), 1375-1388.
[7]
Henry, P. (2005). Is the internet empowering consumers to make better decisions, or strengthening marketers' potential to persuade. Online consumer psychology: Understanding and influencing consumer behavior in the virtual world, 345-360.
[8]
Li, F. (2010). The information content of forward‐looking statements in corporate filings—A naïve Bayesian machine learning approach. Journal of Accounting Research, 48(5), 1049-1102.
[9]
Jegadeesh, N., & Wu, D. (2013). Word power: A new approach for content analysis. Journal of Financial Economics, 110(3), 712-729.
[10]
Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance, 62(3), 1139-1168.
[11]
Tetlock, P. C., SAAR‐TSECHANSKY, M. A. Y. T. A. L., & Macskassy, S. (2008). More than words: Quantifying language to measure firms' fundamentals. The Journal of Finance, 63(3), 1437-1467.
[12]
Engelberg, J. E., Reed, A. V., & Ringgenberg, M. C. (2012). How are shorts informed? Short sellers, news, and information processing. Journal of Financial Economics, 105(2), 260-278.
[13]
Schumaker, R. P., Zhang, Y., Huang, C. N., & Chen, H. (2012). Evaluating sentiment in financial news articles. Decision Support Systems, 53(3), 458-464.
[14]
Pennebaker, J. W., Francis, M. E., & Booth, R. J. (2001). Linguistic inquiry and word count: LIWC 2001. Mahway: Lawrence Erlbaum Associates, 71, 2001.
[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.
[16]
Wilson, T., Hoffmann, P., Somasundaran, S., Kessler, J., Wiebe, J., Choi, Y., & Patwardhan, S. (2005, October). OpinionFinder: A system for subjectivity analysis. In Proceedings of hlt/emnlp on interactive demonstrations (pp. 34-35). Association for Computational Linguistics.
[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.
[18]
Sims, C. A. (1980). Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 1-48.
[19]
Granger, C. W. J., & Newbold, P. (1977). Forecasting economic time series. Academic Press.
[20]
Newey, W. K., & West, K. D. (1987). Hypothesis testing with efficient method of moments estimation. International Economic Review, 777-787.
[21]
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
[22]
Taylor, S. J. (1986). Modelling financial time series. Wiley, New York.
[23]
Campbell, J. Y., Grossman, S. J., & Wang, J. (1992). Trading volume and serial correlation in stock returns (No. w4193). National Bureau of Economic Research.
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