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Data Mining Twitter To Predict Stock Market Movements

Author

Listed:
  • Maxim PECIONCHIN

    (PhD candidate University of International Business and Economics, Beijing, China)

  • Muhammad USMAN

    (PhD candidate University of International Business and Economics, Beijing, China)

Abstract

In this paper we apply sentiment analysis of Twitter data from July through December, 2013 to find correlation between users’ sentiments and NASDAQ closing price and trading volume. Our analysis is based on the Affective Norms for English Words (ANEW). We propose a novel way of determining weighted mood level based on PageRank algorithm. We find that sentiment data is Granger-causal to financial market performance with high degree of significance. “Happy” and “sad” sentiment variables’ lags are strongly correlated with closing price and “excited” and “calm” lags are strongly correlated with trading volume.

Suggested Citation

  • Maxim PECIONCHIN & Muhammad USMAN, 2015. "Data Mining Twitter To Predict Stock Market Movements," ECONOMY AND SOCIOLOGY: Theoretical and Scientifical Journal, Socionet;Complexul Editorial "INCE", issue 1, pages 105-112.
    • Handle: RePEc:nos:ycriat:192
    as

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    References listed on IDEAS

    as
    1. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
    2. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
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