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Financial markets: very noisy information processing

Magdon-Ismail, Malik and Nicholson, Alexander and Abu-Mostafa, Yaser S. (1998) Financial markets: very noisy information processing. Proceedings of the IEEE, 86 (11). pp. 2184-2195. ISSN 0018-9219. http://resolver.caltech.edu/CaltechAUTHORS:MAGprocieee98

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Abstract

We report new results about the impact of noise on information processing with application to financial markets. These results quantify the tradeoff between the amount of data and the noise level in the data. They also provide estimates for the performance of a learning system in terms of the noise level. We use these results to derive a method for detecting the change in market volatility from period to period. We successfully apply these results to the four major foreign exchange (FX) markets. The results hold for linear as well as nonlinear learning models and algorithms and for different noise models.


Item Type:Article
Additional Information:© Copyright 1998 IEEE. Reprinted with permission. Manuscript received November 1, 1997; revised April 17, 1998. This work was supported in part by the Center for Neuromorphic Systems Engineering (a National Science Foundation supported Engineering Research Center) under National Science Foundation Cooperative Agreement EEC 9402726. The authors would like to thank A. Atiya, J. Sill, Z. Cataltepe, and X. Song for their helpful comments.
Subject Keywords:bounds; convergence; generalization error; learning; model limitation; noise; test error; volatility; statistical theory
Record Number:CaltechAUTHORS:MAGprocieee98
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:MAGprocieee98
Alternative URL:http://dx.doi.org/10.1109/5.726786
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:1642
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:08 Feb 2006
Last Modified:26 Dec 2012 08:45

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