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Keywords:

  • public versus private information;
  • ultra-high frequency data;
  • Nasdaq;
  • market microstructure
  • C32;
  • G15

Abstract

We propose a new model that uses nonsynchronous, ultra-high frequency data to analyze the sequential impact of trades and quotes on the price process. Private information is related to the impact of trades and public information to the impact of quotes. The model is extended to include various other factors that affect public and private information. For 20 active Nasdaq stocks, private information causes, on average, 9.43% of daily stock price movements. Additionally, quotes are more informative when (1) many dealers set the best price and (2) traditional market makers rather than Electronic Communication Networks set the best price.