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Detecting intentional herding: what lies beneath intraday data in the Spanish stock market

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  • N Blasco

    (University of Zaragoza)

  • P Corredor

    (Public University of Navarre)

  • S Ferreruela

    (University of Zaragoza)

Abstract

This paper examines the intentional herd behaviour of market participants, using Li's test to compare the probability distributions of the scaled cross-sectional deviation in returns in the intraday market with the cross-sectional deviation in returns in an ‘artificially created’ market free of intentional herding effects. The analysis is carried out for both the overall market and a sample of the most representative stocks. In addition, a bootstrap procedure is applied in order to gain a deeper understanding of the differences across the distributions under study. The results show that the Spanish market exhibits a significant intraday herding effect that is not detected using other traditional herding measures when familiar and heavily traded stocks are analysed. Furthermore, it is suggested that intentional herding is likely to be better revealed using intraday data, and that the use of a lower frequency data may obscure results revealing imitative behaviour in the market.

Suggested Citation

  • N Blasco & P Corredor & S Ferreruela, 2011. "Detecting intentional herding: what lies beneath intraday data in the Spanish stock market," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1056-1066, June.
    • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:6:d:10.1057_jors.2010.34
    DOI: 10.1057/jors.2010.34
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    2. Ra�l Comp�s & Samuel Faria & T�nia Gon�alves & Vicente Pinilla & Jo�o Rebelo & Katrin Sim�n-Elorz, 2021. "The shock of lockdown on the spending on wine in the Iberian market: the effects of procurement and consumption patterns," Documentos de Trabajo dt2021-04, Facultad de Ciencias Económicas y Empresariales, Universidad de Zaragoza.
    3. Jorge Gonz�lez, 2015. "Estimating income elasticities of leisure activities using cross-sectional categorized data," Documentos de Trabajo dt2015-02, Facultad de Ciencias Económicas y Empresariales, Universidad de Zaragoza.
    4. Puput Tri Komalasari & Marwan Asri & Bernardinus M. Purwanto & Bowo Setiyono, 2022. "Herding behaviour in the capital market: What do we know and what is next?," Management Review Quarterly, Springer, vol. 72(3), pages 745-787, September.
    5. Fang, Hao & Shen, Chung-Hua & Lee, Yen-Hsien, 2017. "The dynamic and asymmetric herding behavior of US equity fund managers in the stock market," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 353-369.
    6. M. Fern'andez-Mart'inez & M. A S'anchez-Granero & Mar'ia Jos'e Mu~noz Torrecillas & Bill McKelvey, 2016. "A comparison among some Hurst exponent approaches to predict nascent bubbles in $500$ company stocks," Papers 1601.04188, arXiv.org.
    7. M. T. Aparicio & I. Villan�a, 2012. "Selection criteria for overlapping binary Models," Documentos de Trabajo dt2012-01, Facultad de Ciencias Económicas y Empresariales, Universidad de Zaragoza.
    8. Tao Chen, 2022. "A cross‐country study on informed herding," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4336-4349, October.

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    More about this item

    Keywords

    behaviour; finance; time series;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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