Canonical momenta indicators of financial markets and neocortical EEG
Abstract
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 outofsample 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. This approach to complex systems emphasizes the utility of blending an intuitive and powerful mathematicalphysics formalism to generate indicators which are used by AItype rulebased models of management.
 Publication:

arXiv eprints
 Pub Date:
 January 2000
 arXiv:
 arXiv:physics/0001051
 Bibcode:
 2000physics...1051I
 Keywords:

 Computational Physics;
 Data Analysis;
 Statistics and Probability
 EPrint:
 8 PostScript pages