Applying a Markov chain for the stock pricing of a novel forecasting model

Jui-Chieh Huang, Wen-Tso Huang, Pei-Tzu Chu, Wen-Yi Lee, Hsin-Ping Pai, Chih-Chen Chuang and Ya-Wen Wu

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 9, 4388-4402

Abstract: In this article, a stock-forecasting model is developed to analyze a company's stock price variation related to the Taiwanese company HTC. The main difference to previous articles is that this study uses the data of the HTC in recent ten years to build a Markov transition matrix. Instead of trying to predict the stock price variation through the traditional approach to the HTC stock problem, we integrate two types of Markov chain that are used in different ways. One is a regular Markov chain, and the other is an absorbing Markov chain. Through a regular Markov chain, we can obtain important information such as what happens in the long run or whether the distribution of the states tends to stabilize over time in an efficient way. Next, we used an artificial variable technique to create an absorbing Markov chain. Thus, we used an absorbing Markov chain to provide information about the period between the increases before arriving at the decreasing state of the HTC stock. We provide investors with information on how long the HTC stock will keep increasing before its price begins to fall, which is extremely important information to them.

Date: 2017
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DOI: 10.1080/03610926.2015.1083108

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