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Int. J. Financial Stud. 2018, 6(2), 36; https://doi.org/10.3390/ijfs6020036

Hidden Markov Model for Stock Trading

Department of Mathematics & Statistics at Youngstown State University, 1 University Plaza, Youngstown, OH 44555, USA
Received: 5 November 2017 / Revised: 10 March 2018 / Accepted: 21 March 2018 / Published: 26 March 2018
(This article belongs to the Special Issue Financial Economics)
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Abstract

Hidden Markov model (HMM) is a statistical signal prediction model, which has been widely used to predict economic regimes and stock prices. In this paper, we introduce the application of HMM in trading stocks (with S&P 500 index being an example) based on the stock price predictions. The procedure starts by using four criteria, including the Akaike information, the Bayesian information, the Hannan Quinn information, and the Bozdogan Consistent Akaike Information, in order to determine an optimal number of states for the HMM. The selected four-state HMM is then used to predict monthly closing prices of the S&P 500 index. For this work, the out-of-sample R OS 2 , and some other error estimators are used to test the HMM predictions against the historical average model. Finally, both the HMM and the historical average model are used to trade the S&P 500. The obtained results clearly prove that the HMM outperforms this traditional method in predicting and trading stocks. View Full-Text
Keywords: hidden Markov model; stock prices; observations; states; regimes; predictions; trading; out-of-sample R2; model validation hidden Markov model; stock prices; observations; states; regimes; predictions; trading; out-of-sample R2; model validation
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Nguyen, N. Hidden Markov Model for Stock Trading. Int. J. Financial Stud. 2018, 6, 36.

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