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An enhanced absorbing Markov chain model for predicting TAIEX Index Futures

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  • Wen-Tso Huang
  • Cheng-Chang Lu

Abstract

This study introduces an enhanced absorbing Markov chain model for predicting Taiwan Stock Exchange Capitalization Weighted Stock Index Futures (TAIEX Futures) trends. Data were gathered from the U.S. TAIEX Futures Yahoo portal site and converted into a regular Markov chain model. A regular Markov chain enables long-term behavioral predictions to be made with regard to the probability of TAIEX Futures. This paper also demonstrates an artificial variable technique for establishing an enhanced absorbing Markov chain model for determining how long TAIEX Futures will increase before prices begin to fall. The results show that the TAIEX Futures absorbing Markov chain can warn investors about the least number of days on average that a particular TAIEX Futures that increased or decreased that day will increase before its price begins to fall.

Suggested Citation

  • Wen-Tso Huang & Cheng-Chang Lu, 2018. "An enhanced absorbing Markov chain model for predicting TAIEX Index Futures," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(1), pages 133-146, January.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:1:p:133-146
    DOI: 10.1080/03610926.2017.1300281
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    Cited by:

    1. Ali Nasir & Ambreen Khursheed & Kazim Ali & Faisal Mustafa, 2021. "A Markov Decision Process Model for Optimal Trade of Options Using Statistical Data," Computational Economics, Springer;Society for Computational Economics, vol. 58(2), pages 327-346, August.

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