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Stock market forecasting using Hidden Markov Model: A new approach

  • Md Rafiul Hassan*
  • , Baikunth Nath
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

301 Scopus citations

Abstract

This paper presents Hidden Markov Models (HMM) approach for forecasting stock price for interrelated markets. We apply HMM to forecast some of the airlines stock. HMMs have been extensively used for pattern recognition and classification problems because of its proven suitability for modelling dynamic systems. However, using HMM for predicting future events is not straightforward. Here we use only one HMM that is trained on the past dataset of the chosen airlines. The trained HMM is used to search for the variable of interest behavioural data pattern from the past dataset. By interpolating the neighbouring values of these datasets forecasts are prepared. The results obtained using HMM are encouraging and HMM offers a new paradigm for stock market forecasting, an area that has been of much research interest lately.

Original languageEnglish
Title of host publicationProceedings - 5th International Conference on Intelligent Systems Design and Applications, ISDA '05
Pages192-196
Number of pages5
DOIs
StatePublished - 2005
Externally publishedYes
Event5th International Conference on Intelligent Systems Design and Applications, ISDA '05 - Wroclaw, Poland
Duration: 8 Sep 2005 → 10 Sep 2005

Publication series

NameProceedings - 5th International Conference on Intelligent Systems Design and Applications 2005, ISDA '05
Volume2005

Conference

Conference5th International Conference on Intelligent Systems Design and Applications, ISDA '05
Country/TerritoryPoland
CityWroclaw
Period8/09/05 → 10/09/05

Keywords

  • Feature selection
  • Financial time series
  • HMM
  • Stock market forecasting

ASJC Scopus subject areas

  • General Engineering

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