High-frequency data and machine learning

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dc.contributor.authorJunior E.H.
dc.date.accessioned2025-04-01T06:21:42Z
dc.date.available2025-04-01T06:21:42Z
dc.date.issued2024
dc.description.abstract© 2025, IGI Global. All rights reserved.Forecasting financial asset prices has always been a challenging task due to a vast array of factor. Forecasting stock prices using econometric models is satisfactory when the data exhibits a certain statistical regularity, however, this regularity is not standard for this type of data. Considering that the advancement of computer technology, making them increasingly faster, the popularization of high-speed internet, the development of secure trading platforms, and the interconnection of markets have significantly increased the volume of transactions. With the increase in the number of transactions per unit of time, it became impossible for a human trader to keep up with this volume of buying and selling transactions of financial assets, thus giving rise to trading robots. In this chapter is made a comparision between traditional methods and machine learning methods to forecast time series.
dc.description.firstpage183
dc.description.lastpage203
dc.identifier.doi10.4018/979-8-3693-5777-4.ch008
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/40415
dc.relation.ispartofImpacts of Innovation and Cognition in Management
dc.rightsAcesso Restrito
dc.titleHigh-frequency data and machine learning
dc.typeCapítulo de livro
local.scopus.citations0
local.scopus.eid2-s2.0-86000235613
local.scopus.updated2025-04-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=86000235613&origin=inward
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