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This paper proposes an intelligent combination of neural network theory and financial statistics for the detection of statistical arbitrage opportunities in specific pairs of stocks. The proposed intelligent methodology is based on a class of neural network-GARCH autoregressive models for the effective handling of the dynamics related to the statistical(More)
We present preliminary results from Fermilab E791 on D 0 ? D 0 mixing and doubly Cabibbo-suppressed decays (DCSD) of the D 0 and D + mesons. The time dependence of the wrong-sign signal (D 0 ! K + ?) is used to establish separate limits on DCSD and mixing. From one third of our data we obtained r mix < 0:47%, r DCSD < 2:7% at the 90% conndence level.
We report the results of a search for the avor-changing neutral-current decays D + ! + + ? and D + ! + e + e ? in data from Fermilab charm hadroproduction experiment E791. No signal above background is found, and we obtain upper limits on branching fractions, B(D + ! + + ?) < 2 Flavor-changing neutral-current (FCNC) decays have played a major role in our(More)
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