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Inferring Public and Private Information from Trades and Quotes
Author(s) -
Frijns Bart
Publication year - 2006
Publication title -
financial review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.621
H-Index - 47
eISSN - 1540-6288
pISSN - 0732-8516
DOI - 10.1111/j.1540-6288.2006.00134.x
Subject(s) - private information retrieval , public information , stock (firearms) , stock price , set (abstract data type) , business , economics , financial economics , microeconomics , industrial organization , computer science , computer security , engineering , internet privacy , series (stratigraphy) , mechanical engineering , paleontology , biology , programming language
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.