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

Author

Listed:
  • Tushar Rao

    (NSIT-Delhi)

  • Saket Srivastava

    (IIIT-Delhi)

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.

Suggested Citation

  • Tushar Rao & Saket Srivastava, 2012. "Modeling Movements in Oil, Gold, Forex and Market Indices using Search Volume Index and Twitter Sentiments," Papers 1212.1037, arXiv.org.
    • Handle: RePEc:arx:papers:1212.1037
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    References listed on IDEAS

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    1. Huina Mao & Scott Counts & Johan Bollen, 2011. "Predicting Financial Markets: Comparing Survey, News, Twitter and Search Engine Data," Papers 1112.1051, arXiv.org.
    2. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    3. 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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    5. 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, June.
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    Cited by:

    1. Ji, Qiang & Guo, Jian-Feng, 2015. "Oil price volatility and oil-related events: An Internet concern study perspective," Applied Energy, Elsevier, vol. 137(C), pages 256-264.
    2. Leighton Vaughan Williams & J. James Reade, 2016. "Prediction Markets, Social Media and Information Efficiency," Kyklos, Wiley Blackwell, vol. 69(3), pages 518-556, August.
    3. Leighton Vaughan Williams & James Reade, 2014. "Prediction Markets, Twitter and Bigotgate," Economics Discussion Papers em-dp2014-09, Department of Economics, University of Reading.
    4. Ji, Qiang & Guo, Jian-Feng, 2015. "Market interdependence among commodity prices based on information transmission on the Internet," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 426(C), pages 35-44.

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