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Summary: Financial Markets: Very Noisy
Information Processing
MALIK MAGDON-ISMAIL, ALEXANDER NICHOLSON, AND YASER S. ABU-MOSTAFA
Invited Paper
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.
Keywords-- Bounds, convergence, generalization error, learn-
ing, model limitation, noise, test error, volatility.
I. INTRODUCTION
Information processing of financial data entails the ex-
traction of relevant information from overwhelming noise.
The levels of noise in financial markets are such that the
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