IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v14y2021i9p394-d620395.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/14/9/394/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1911-8074/14/9/394/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. Mr. Tonny Lybek & Mr. Abdourahmane Sarr, 2002. "Measuring Liquidity in Financial Markets," IMF Working Papers 2002/232, International Monetary Fund.
    5. 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.
    6. 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.
    7. 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.
    8. Xueming Luo & Jie Zhang & Wenjing Duan, 2013. "Social Media and Firm Equity Value," Information Systems Research, INFORMS, vol. 24(1), pages 146-163, March.
    9. Jack Sarkissian, 2016. "Quantum theory of securities price formation in financial markets," Papers 1605.04948, arXiv.org, revised May 2016.
    10. 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.
    11. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fu, Chengbo & Jacoby, Gady & Wang, Yan, 2015. "Investor sentiment and portfolio selection," Finance Research Letters, Elsevier, vol. 15(C), pages 266-273.
    2. Xiong Xiong & Chunchun Luo & Ye Zhang & Shen Lin, 2019. "Do stock bulletin board systems (BBS) contain useful information? A viewpoint of interaction between BBS quality and predicting ability," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(5), pages 1385-1411, March.
    3. Li, Jinfang, 2014. "Multi-period sentiment asset pricing model with information," International Review of Economics & Finance, Elsevier, vol. 34(C), pages 118-130.
    4. Junmao Chiu & Huimin Chung & Keng-Yu Ho, 2014. "Fear Sentiment, Liquidity, and Trading Behavior: Evidence from the Index ETF Market," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 17(03), pages 1-25.
    5. Mehmet Balcilar & Rangan Gupta & Clement Kyei, 2018. "Predicting Stock Returns And Volatility With Investor Sentiment Indices: A Reconsideration Using A Nonparametric Causality†In†Quantiles Test," Bulletin of Economic Research, Wiley Blackwell, vol. 70(1), pages 74-87, January.
    6. Aissia, Dorsaf Ben, 2016. "Home and foreign investor sentiment and the stock returns," The Quarterly Review of Economics and Finance, Elsevier, vol. 59(C), pages 71-77.
    7. Liang, Hanchao & Yang, Chunpeng & Cai, Chuangqun, 2017. "Beauty contest, bounded rationality, and sentiment pricing dynamics," Economic Modelling, Elsevier, vol. 60(C), pages 71-80.
    8. Deven Bathia & Don Bredin & Dirk Nitzsche, 2016. "International Sentiment Spillovers in Equity Returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 21(4), pages 332-359, October.
    9. Yue-Jun Zhang & Shu-Hui Li, 2019. "The impact of investor sentiment on crude oil market risks: evidence from the wavelet approach," Quantitative Finance, Taylor & Francis Journals, vol. 19(8), pages 1357-1371, August.
    10. Gric, Zuzana & Bajzík, Josef & Badura, Ondřej, 2023. "Does sentiment affect stock returns? A meta-analysis across survey-based measures," International Review of Financial Analysis, Elsevier, vol. 89(C).
    11. Li, Jinfang, 2022. "The sentiment pricing dynamics with short-term and long-term learning," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    12. Markus Buxbaum & Wolfgang Schultze & Samuel L. Tiras, 2023. "Do analysts’ target prices stabilize the stock market?," Review of Quantitative Finance and Accounting, Springer, vol. 61(3), pages 763-816, October.
    13. Renault, Thomas, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 25-40.
    14. Daniel Huerta-Sanchez & Diego Escobari, 2018. "Changes in sentiment on REIT industry excess returns and volatility," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(3), pages 239-274, August.
    15. Lux, Thomas, 2008. "Stochastic behavioral asset pricing models and the stylized facts," Economics Working Papers 2008-08, Christian-Albrechts-University of Kiel, Department of Economics.
    16. D. Schneller & S. Heiden & M. Heiden & A. Hamid, 2018. "Home is Where You Know Your Volatility – Local Investor Sentiment and Stock Market Volatility," German Economic Review, Verein für Socialpolitik, vol. 19(2), pages 209-236, May.
    17. Xiong, Xiong & Meng, Yongqiang & Joseph, Nathan Lael & Shen, Dehua, 2020. "Stock mispricing, hard-to-value stocks and the influence of internet stock message boards," International Review of Financial Analysis, Elsevier, vol. 72(C).
    18. Chen, Rongda & Bao, Weiwei & Jin, Chenglu, 2021. "Investor sentiment and predictability for volatility on energy futures Markets: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 112-129.
    19. Kim, Jikyung (Jeanne) & Dong, Hang & Choi, Jeonghye & Chang, Sue Ryung, 2022. "Sentiment change and negative herding: Evidence from microblogging and news," Journal of Business Research, Elsevier, vol. 142(C), pages 364-376.
    20. Szymon Lis, 2022. "Investor Sentiment in Asset Pricing Models: A Review," Working Papers 2022-14, Faculty of Economic Sciences, University of Warsaw.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jjrfmx:v:14:y:2021:i:9:p:394-:d:620395. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.