IJMLC 2015 Vol. 5(3): 247-251 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.515

Inferring Markov Chain for Modeling Order Book Dynamics in High Frequency Environment

Yuan Lung Chang

Abstract—In this paper, we propose a Bayesian inference of the Markov chain model class to model dynamics of order book in high frequency trading environment. Accordingly, software program can predict the move of market price for both ask & bid via predictive distribution. A strategy algorithm can be developed for generating, routing & executing orders to gain profit. Experimental result based on security AAPL showed over 98% coverage by 50 transitions from 6561 state space. It further indicated market behavior of short time-frame can be clustered & labeled.

Index Terms—Inferring markov chain, bayesian inference, high frequency trading, order book.

Yuan Lung Chang is with New York, NY10128 USA (e-mail: jeffrey.chang.dc@gmail.com)

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Cite: Yuan Lung Chang, "Inferring Markov Chain for Modeling Order Book Dynamics in High Frequency Environment," International Journal of Machine Learning and Computing vol. 5, no. 3, pp. 247-251, 2015.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net