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Stock return and volatility reactions to information demand and supply

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  • Moussa, Faten
  • Delhoumi, Ezzeddine
  • Ouda, Olfa Ben

Abstract

The objective of this paper is to evaluate the impact of information demand and supply on stock market return and volatility. In this study we employ a proxy for information demand which is derived from weekly internet search volume. The latest is drawn from Google Trends database, for 25 of the largest stocks traded on CAC40 index, between April 2007 and March 2014. We use news headlines as a proxy for information supply. Our empirical findings suggest: First public information has an impact on stock returns but its impact on the volatility is much more important. Second, the influence of specific information demand to the company persists even by adding market information demand and firm/market information supply. Finally, by applying MCA to results found, it could be concluded that the impact of public information on stock return and volatility is conditioned by two elements: The company and market news disclosure, and the second element relates to the characteristics of the market participants, more precisely their news interpretations and their risk aversion.

Suggested Citation

  • Moussa, Faten & Delhoumi, Ezzeddine & Ouda, Olfa Ben, 2017. "Stock return and volatility reactions to information demand and supply," Research in International Business and Finance, Elsevier, vol. 39(PA), pages 54-67.
    • Handle: RePEc:eee:riibaf:v:39:y:2017:i:pa:p:54-67
    DOI: 10.1016/j.ribaf.2016.07.016
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    Cited by:

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    2. Xu, Mingli & Yang, Wei & Huang, Zhixiong, 2021. "Do investor relations matter in the tourism industry? Evidence from public opinions in China," Economic Modelling, Elsevier, vol. 94(C), pages 923-933.
    3. Gang Chu & Xiao Li & Dehua Shen & Yongjie Zhang, 2021. "Stock Crashes and Jumps Reactions to Information Demand and Supply: An Intraday Analysis," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(3), pages 397-427, September.
    4. González-Fernández, Marcos & González-Velasco, Carmen, 2018. "Can Google econometrics predict unemployment? Evidence from Spain," Economics Letters, Elsevier, vol. 170(C), pages 42-45.
    5. Moussa, Faten & BenOuda, Olfa & Delhoumi, Ezzeddine, 2017. "The use of open source internet to analysis and predict stock market trading volume," Research in International Business and Finance, Elsevier, vol. 41(C), pages 399-411.
    6. Fei Qu & Yi-Ting Wang & Wen-Hui Hou & Xiao-Yu Zhou & Xiao-Kang Wang & Jun-Bo Li & Jian-Qiang Wang, 2022. "Forecasting of Automobile Sales Based on Support Vector Regression Optimized by the Grey Wolf Optimizer Algorithm," Mathematics, MDPI, vol. 10(13), pages 1-22, June.
    7. González-Fernández, Marcos & González-Velasco, Carmen, 2020. "A sentiment index to measure sovereign risk using Google data," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 406-418.

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    More about this item

    Keywords

    GARCH model; Google trends database; Information demand; Information supply; Multiple correspondence analysis (MCA);
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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