Inferring Public and Private Information from Trades and Quotes
Posted: 10 Nov 2005
We propose a new model that uses non-synchronous, 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 ECNs set the best price.
Keywords: public versus private information, ultra-high frequency data, Nasdaq, market microstructure
JEL Classification: C32, G15
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