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Risks 2017, 5(4), 62; doi:10.3390/risks5040062

An Analysis and Implementation of the Hidden Markov Model to Technology Stock Prediction

Faculty of Mathematics and Statistics, Youngstown State University, 1 University Plaza, Youngstown, OH 44555, USA
Academic Editor: Albert Cohen
Received: 20 April 2017 / Revised: 16 November 2017 / Accepted: 17 November 2017 / Published: 24 November 2017
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Abstract

Future stock prices depend on many internal and external factors that are not easy to evaluate. In this paper, we use the Hidden Markov Model, (HMM), to predict a daily stock price of three active trading stocks: Apple, Google, and Facebook, based on their historical data. We first use the Akaike information criterion (AIC) and Bayesian information criterion (BIC) to choose the numbers of states from HMM. We then use the models to predict close prices of these three stocks using both single observation data and multiple observation data. Finally, we use the predictions as signals for trading these stocks. The criteria tests’ results showed that HMM with two states worked the best among two, three and four states for the three stocks. Our results also demonstrate that the HMM outperformed the naïve method in forecasting stock prices. The results also showed that active traders using HMM got a higher return than using the naïve forecast for Facebook and Google stocks. The stock price prediction method has a significant impact on stock trading and derivative hedging. View Full-Text
Keywords: hidden Markov model; stock prices; observations; states; predictions; AIC; BIC; likelihood; trading hidden Markov model; stock prices; observations; states; predictions; AIC; BIC; likelihood; trading
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Nguyen, N. An Analysis and Implementation of the Hidden Markov Model to Technology Stock Prediction. Risks 2017, 5, 62.

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