A machine learning method for high-frequency data forecasting

Erick López, Héctor Allende, HÉCTOR GABRIEL ALLENDE CID

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In recent years several models for financial high-frequency data have been proposed. One of the most known models for this type of applications is the ACM-ACD model. This model focuses on modelling the underlying joint distribution of both duration and price changes between consecutive transactions. However this model imposes distributional assumptions and its number of parameters increases rapidly (producing a complex and slow adjustment process). Therefore, we propose using two machine learning models, that will work sequentially, based on the ACM-ACD model. The results show a comparable performance, achieving a better performance in some cases. Also the proposal achieves a significatively more rapid convergence. The proposal is validated with a well-known financial data set.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition Image Analysis, Computer Vision and Applications - 19th Iberoamerican Congress, CIARP 2014, Proceedings
EditorsEduardo Bayro-Corrochano, Edwin Hancock
PublisherSpringer Verlag
Pages621-628
Number of pages8
ISBN (Electronic)9783319125671
DOIs
StatePublished - 2014
Externally publishedYes
Event19th Iberoamerican Congress on Pattern Recognition, CIARP 2014 - Puerto Vallarta, Mexico
Duration: 2 Nov 20145 Nov 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8827
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th Iberoamerican Congress on Pattern Recognition, CIARP 2014
CountryMexico
CityPuerto Vallarta
Period2/11/145/11/14

Keywords

  • ACM-ACD model
  • Financial high-frequency data
  • Forecasting
  • Machine learning
  • Time series

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