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Market Liquidity and Its Dimensions: Linking the Liquidity Dimensions to Sentiment Analysis through Microblogging Data

Author

Listed:
  • Francisco Guijarro

    (Research Institute for Pure and Applied Mathematics, Universitat Politècnica de València, 46022 València, Spain)

  • Ismael Moya-Clemente

    (Faculty of Business Administration and Management, Universitat Politècnica de València, 46022 València, Spain)

  • Jawad Saleemi

    (Business School, Universitat Politècnica de València, 46022 València, Spain)

Abstract

Market liquidity has an immediate impact on the execution of transactions in financial markets. Informed counterparty risk is often priced into market liquidity. This study investigates whether microblogging data, as a non-financial information tool, is priced along with market liquidity dimensions. The analysis is based on the Australian Securities Exchange (ASX), and from the results, we conclude that microblogging content in pessimistic periods has a higher impact on liquidity and its dimensions. On a daily basis, pessimistic investor sentiments lead to higher trading costs, illiquidity, a larger price dispersion and a lower trading volume.

Suggested Citation

  • Francisco Guijarro & Ismael Moya-Clemente & Jawad Saleemi, 2021. "Market Liquidity and Its Dimensions: Linking the Liquidity Dimensions to Sentiment Analysis through Microblogging Data," JRFM, MDPI, vol. 14(9), pages 1-12, August.
  • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:9:p:394-:d:620395
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    References listed on IDEAS

    as
    1. Mr. Tonny Lybek & Mr. Abdourahmane Sarr, 2002. "Measuring Liquidity in Financial Markets," IMF Working Papers 2002/232, International Monetary Fund.
    2. Lee, Wayne Y. & Jiang, Christine X. & Indro, Daniel C., 2002. "Stock market volatility, excess returns, and the role of investor sentiment," Journal of Banking & Finance, Elsevier, vol. 26(12), pages 2277-2299.
    3. Xueming Luo & Jie Zhang & Wenjing Duan, 2013. "Social Media and Firm Equity Value," Information Systems Research, INFORMS, vol. 24(1), pages 146-163, March.
    4. Jack Sarkissian, 2016. "Quantum theory of securities price formation in financial markets," Papers 1605.04948, arXiv.org, revised May 2016.
    5. Amihud, Yakov & Mendelson, Haim, 1991. "Liquidity, Maturity, and the Yields on U.S. Treasury Securities," Journal of Finance, American Finance Association, vol. 46(4), pages 1411-1425, September.
    6. Easley, David & O'Hara, Maureen, 1992. "Time and the Process of Security Price Adjustment," Journal of Finance, American Finance Association, vol. 47(2), pages 576-605, June.
    7. Walker, Clive B., 2016. "The direction of media influence: Real-estate news and the stock market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 10(C), pages 20-31.
    8. Gregory W. Brown & Michael T. Cliff, 2005. "Investor Sentiment and Asset Valuation," The Journal of Business, University of Chicago Press, vol. 78(2), pages 405-440, March.
    9. Mazboudi, Mohamad & Khalil, Samer, 2017. "The attenuation effect of social media: Evidence from acquisitions by large firms," Journal of Financial Stability, Elsevier, vol. 28(C), pages 115-124.
    10. Timm O. Sprenger & Andranik Tumasjan & Philipp G. Sandner & Isabell M. Welpe, 2014. "Tweets and Trades: the Information Content of Stock Microblogs," European Financial Management, European Financial Management Association, vol. 20(5), pages 926-957, November.
    11. Jawad Saleemi, 2020. "An estimation of cost-based market liquidity from daily high, low and close prices [Una estimación de la liquidez de mercado basada en los costes a partir de los precios máximo, mínimo y de cierre]," Post-Print hal-03149324, HAL.
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