Nested Monte Carlo Expression Discovery vs Genetic Programming for Forecasting Financial Volatility

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@Misc{oai:HAL:hal-02489115v1,

• title = "Nested Monte Carlo Expression Discovery vs Genetic Programming for Forecasting Financial Volatility",
• author = "Sana {Ben Hamida} and Tristan Cazenave",
• howpublished = "HAL",
• year = "2020",
• month = feb # "~24",
• publisher = "HAL CCSD",
• keywords = "genetic algorithms, genetic programming, computer science, artificial intelligence, AI",
• URL = "https://hal-univ-paris10.archives-ouvertes.fr/hal-02489115",
• abstract = "We are interested in discovering expressions for financial prediction using Nested Monte Carlo Search and Genetic Programming. Both methods are applied to learning from financial time series to generate nonlinear functions for market volatility prediction. The input data, that is a series of daily prices of European S\&P500 index, is filtered and sampled in order to improve the training process. Using some assessment metrics, the best generated models given by both approaches for each training sub-sample, are evaluated and compared. Results show that Nested Monte Carlo is able to generate better forecasting models than Genetic Programming for the majority of learning samples.",
• annote = "Laboratoire d'analyse et modelisation de systemes pour l'aide a la decision (LAMSADE) ; Universite Paris Dauphine-PSL-Centre National de la Recherche Scientifique (CNRS); Universite Paris Nanterre - UFR Sciences economiques, gestion, mathematiques, informatique (UPN SEGMI) ; Universite Paris Nanterre (UPN)",
• bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
• contributor = "Laboratoire d'analyse et modelisation de systemes pour l'aide a la decision",
• identifier = "hal-02489115",
• language = "en",
• oai = "oai:HAL:hal-02489115v1",
• type = "info:eu-repo/semantics/preprint",
• notes = "See also \cite{DBLP:journals/ijait/Cazenave13}",

}

Genetic Programming entries for Sana Ben Hamida Tristan Cazenave

Citations