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Modeling Movements in Oil, Gold, Forex and Market Indices using Search Volume Index and Twitter Sentiments

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  • Tushar Rao

    (NSIT-Delhi)

  • Saket Srivastava

    (IIIT-Delhi)

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    Abstract

    Study of the forecasting models using large scale microblog discussions and the search behavior data can provide a good insight for better understanding the market movements. In this work we collected a dataset of 2 million tweets and search volume index (SVI from Google) for a period of June 2010 to September 2011. We perform a study over a set of comprehensive causative relationships and developed a unified approach to a model for various market securities like equity (Dow Jones Industrial Average-DJIA and NASDAQ-100), commodity markets (oil and gold) and Euro Forex rates. We also investigate the lagged and statistically causative relations of Twitter sentiments developed during active trading days and market inactive days in combination with the search behavior of public before any change in the prices/ indices. Our results show extent of lagged significance with high correlation value upto 0.82 between search volumes and gold price in USD. We find weekly accuracy in direction (up and down prediction) uptil 94.3% for DJIA and 90% for NASDAQ-100 with significant reduction in mean average percentage error for all the forecasting models.

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    File URL: http://arxiv.org/pdf/1212.1037
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    Bibliographic Info

    Paper provided by arXiv.org in its series Papers with number 1212.1037.

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    Date of creation: Dec 2012
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    Handle: RePEc:arx:papers:1212.1037

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    Web page: http://arxiv.org/

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    1. Garth P. McCormick, 1969. "Communications to the Editor--Exponential Forecasting: Some New Variations," Management Science, INFORMS, vol. 15(5), pages 311-320, January.
    2. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, 06.
    3. Amos Tversky & Daniel Kahneman, 1979. "Prospect Theory: An Analysis of Decision under Risk," Levine's Working Paper Archive 7656, David K. Levine.
    4. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
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