dorsal/arxiv
View SchemaCanonical momenta indicators of financial markets and neocortical EEG
| Authors | Lester Ingber |
|---|---|
| Categories | |
| ArXiv ID | physics/0001051 |
| URL | https://arxiv.org/abs/physics/0001051 |
| Journal | In Progress in Neural Information Processing (1996) 777-784 |
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 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. This approach to complex systems emphasizes the utility of blending an intuitive and powerful mathematical-physics formalism to generate indicators which are used by AI-type rule-based models of management.
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"abstract": "A paradigm of statistical mechanics of financial markets (SMFM) is fit to\nmultivariate financial markets using Adaptive Simulated Annealing (ASA), a\nglobal optimization algorithm, to perform maximum likelihood fits of\nLagrangians defined by path integrals of multivariate conditional\nprobabilities. Canonical momenta are thereby derived and used as technical\nindicators in a recursive ASA optimization process to tune trading rules. These\ntrading rules are then used on out-of-sample data, to demonstrate that they can\nprofit from the SMFM model, to illustrate that these markets are likely not\nefficient. This methodology can be extended to other systems, e.g.,\nelectroencephalography. This approach to complex systems emphasizes the utility\nof blending an intuitive and powerful mathematical-physics formalism to\ngenerate indicators which are used by AI-type rule-based models of management.",
"arxiv_id": "physics/0001051",
"authors": [
"Lester Ingber"
],
"categories": [
"physics.comp-ph",
"physics.data-an"
],
"journal_ref": "In Progress in Neural Information Processing (1996) 777-784",
"title": "Canonical momenta indicators of financial markets and neocortical EEG",
"url": "https://arxiv.org/abs/physics/0001051"
},
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