Markov Chains for High Frequency Stock Trading Strategies

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dc.contributor.authorAlminana C.C.
dc.date.accessioned2024-03-12T19:17:10Z
dc.date.available2024-03-12T19:17:10Z
dc.date.issued2022
dc.description.abstract© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.The price prediction problem for any given stock has been an object of study, deepening and evolution within the past few decades, to achieve the basic goal of positive financial realizations with the smaller risk possible. A known risk minimization strategy is a high frequency trading regime, taking benefit from minor price variations, and achieving small profits multiple times a day. By the hypothesis and the interpretation that prices variations behavior as a Random Walk, it is possible to characterize a Markov (stochastic) process with states and transition probabilities known, allowing to describe price variations through time. Therefore, this study aims to estimate the financial outcome using Markov chains for automated decision making in a high frequency stock trading strategy, and then comparing the computed results with stock’s valuation (or devaluation) within the same analyzed period.
dc.description.firstpage681
dc.description.lastpage694
dc.description.volume506 LNNS
dc.identifier.doi10.1007/978-3-031-10461-9_47
dc.identifier.issn2367-3389
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/34482
dc.relation.ispartofLecture Notes in Networks and Systems
dc.rightsAcesso Restrito
dc.subject.otherlanguageHigh frequency trading
dc.subject.otherlanguageMarkov chains
dc.subject.otherlanguageStocks
dc.titleMarkov Chains for High Frequency Stock Trading Strategies
dc.typeArtigo de evento
local.scopus.citations0
local.scopus.eid2-s2.0-85135088730
local.scopus.updated2024-12-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85135088730&origin=inward
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