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Distillation of News Flow into Analysis of Stock Reactions

Author

Listed:
  • Junni L. Zhang
  • Wolfgang K. Härdle
  • Cathy Y. Chen
  • Elisabeth Bommes

Abstract

News carry information of market moves. The gargantuan plethora of opinions, facts and tweets on financial business offers the opportunity to test and analyze the influence of such text sources on future directions of stocks. It also creates though the necessity to distill via statistical technology the informative elements of this prodigious and indeed colossal data source. Using mixed text sources from professional platforms, blog fora and stock message boards we distill via different lexica sentiment variables. These are employed for an analysis of stock reactions: volatility, volume and returns. An increased (negative) sentiment will in uence volatility as well as volume. This influuence is contingent on the lexical projection and different across GICS sectors. Based on review articles on 100 S&P 500 constituents for the period of October 20, 2009 to October 13, 2014 we project into BL, MPQA, LM lexica and use the distilled sentiment variables to forecast individual stock indicators in a panel context. Exploiting different lexical projections, and using different stock reaction indicators we aim at answering the following research questions: (i) Are the lexica consistent in their analytic ability to produce stock reaction indicators, including volatility, detrended log trading volume and return? (ii) To which degree is there an asymmetric response given the sentiment scales (positive v.s. negative)? (iii) Are the news of high attention frms diffusing faster and result in more timely and efficient stock reaction? (iv) Is there a sector specifc reaction from the distilled sentiment measures? We fnd there is signifcant incremental information in the distilled news ow. The three lexica though are not consistent in their analytic ability. Based on confdence bands an asymmetric, attention-specifc and sector-specifc response of stock reactions is diagnosed.

Suggested Citation

  • Junni L. Zhang & Wolfgang K. Härdle & Cathy Y. Chen & Elisabeth Bommes, 2015. "Distillation of News Flow into Analysis of Stock Reactions," SFB 649 Discussion Papers SFB649DP2015-005, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    • Handle: RePEc:hum:wpaper:sfb649dp2015-005
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    More about this item

    Keywords

    Investor Sentiment; Attention Analysis; Sector Analysis; Volatility Simulation; Trading Volume; Returns; Bootstrap;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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