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Identifying the interaction between stock market returns and trading flows of investor types: Looking into the day using daily data

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  • Ülkü, Numan
  • Weber, Enzo

Abstract

This paper introduces a new method for identifying the simultaneity between returns and trading flows. The proposed method enables us to identify the intraday interaction using daily data, and provides measures of the information content of trading flows, and their instantaneous response to public information and information revealed by market prices. Applying this method to daily data on investor types from the Korea Stock Exchange, we find significant intraday bi-directional interaction between flows and returns and their latent common drivers, altering some of the results of the previous literature based on Cholesky assumptions. Thus, we obtain a number of new insights concerning the behavior of investor types.

Suggested Citation

  • Ülkü, Numan & Weber, Enzo, 2013. "Identifying the interaction between stock market returns and trading flows of investor types: Looking into the day using daily data," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 2733-2749.
  • Handle: RePEc:eee:jbfina:v:37:y:2013:i:8:p:2733-2749
    DOI: 10.1016/j.jbankfin.2013.03.021
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    Cited by:

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    7. Ülkü, Numan & Rogers, Madeline, 2018. "Who drives the Monday effect?," Journal of Economic Behavior & Organization, Elsevier, vol. 148(C), pages 46-65.

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    More about this item

    Keywords

    The interaction between trading flows and returns; Identification; Structural conditional correlation; Investor types; Feedback trading behavior;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • 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

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