Papers: Canonical Momenta of Financial Markets and Neocortical EEG

[Lester Ingber]

Papers: Canonical Momenta of Financial Markets and Neocortical EEG

The following two preprints are available.

   markets96_momenta.ps.Z [45K]
          %A L. Ingber
          %T Canonical momenta indicators of financial markets and
             neocortical EEG
          %B International Conference on Neural Information Processing
             (ICONIP'96)
          %I Springer
          %C New York
          %D 1996
          %O This is an invited paper to the 1996 International
             Conference on Neural Information Processing (ICONIP'96), Hong
             Kong, 24-27 September 1996.
             URL http://www.ingber.com/markets96_momenta.ps.Z
A paradigm of statistical mechanics of financial markets (SMFM) is fit
to multivariate financial markets using Adaptive Simulated Annealing
(ASA), a global optimization algorithm, to perform maximum likelihood
fits of Lagrangians defined by path integrals of multivariate
conditional probabilities.  Canonical momenta are thereby derived and
used as technical indicators in a recursive ASA optimization process to
tune trading rules.  These trading rules are then used on out-of-sample
data, to demonstrate that they can profit from the SMFM model, to
illustrate that these markets are likely not efficient.  This
methodology can be extended to other systems, e.g.,
electroencephalography.

   smni96_momenta.ps.Z [45K]
          %A L. Ingber
          %T Canonical momenta indicators of neocortical EEG
          %B Physics Computing 96 (PC96)
          %I PC96
          %C Krakow, Poland
          %D 1996
          %O This is an invited paper to Physics Computing 96 (PC96),
             Krakow, Poland, 17-21 September 1996.
             URL http://www.ingber.com/smni96_momenta.ps.Z
A model of statistical mechanics of neocortical interactions (SMNI) has
been fit to EEG data using Adaptive Simulated Annealing (ASA), a global
optimization algorithm, to perform maximum likelihood fits of
Lagrangians defined by path integrals of multivariate conditional
probabilities.  Canonical momenta are thereby derived and can be used
as technical indicators in a recursive ASA optimization process to
optimize clinician rules.  This methodology has been applied to
financial markets.

This archive also contains the most recent version 12.10 of
   Adaptive Simulated Annealing (ASA)
          %A L. Ingber
          %T Adaptive Simulated Annealing (ASA)
          %R [http://www.ingber.com/ASA-shar, ASA-shar.Z, ASA.tar.Z,
             ASA.tar.gz, ASA.zip]
          %I Lester Ingber Research
          %C McLean, VA
          %D 1993
ASA is one of the most powerful optimization algorithms for nonlinear
and stochastic systems, and is being used recursively in the above two
projects.

Please note that this archive recently has been moved to its present
location from http://www.alumni.caltech.edu/~ingber/ and
ftp.alumni.caltech.edu:/pub/ingber.  Pointers to the new location will
be found in the old location.

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Lester <ingber at ingber.com>, <ingber at alumni.caltech.edu>

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