I wish to thank Arnold Cowan (the editor), Thorsten Lehnert, Ronald Mahieu, Dimitri Margaritis, Franz Palm, Peter Schotman, Christian Wolff, and two anonymous referees for their valuable comments and suggestions.
Inferring Public and Private Information from Trades and Quotes
Article first published online: 10 JAN 2006
Volume 41, Issue 1, pages 95–117, February 2006
How to Cite
Frijns, B. (2006), Inferring Public and Private Information from Trades and Quotes. Financial Review, 41: 95–117. doi: 10.1111/j.1540-6288.2006.00134.x
- Issue published online: 10 JAN 2006
- Article first published online: 10 JAN 2006
- public versus private information;
- ultra-high frequency data;
- market microstructure
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.