PBESProceedings of Business and Economic Studies2209-26412209-265XBio-Byword Scientific Publishing Pty. Ltd.10.26689/pbes.v5i6.4453ArticleApplication of Hidden Markov Models in Stock Forecastinghttps://artdesignp.com/journal/PBES/5/6/10.26689/pbes.v5i6.4453YuMenghan,WangPanji,WangTong2022562022-12-07In this paper, we tested our methodology on the stocks of four representative companies: Apple, Comcast Corporation (CMCST), Google, and Qualcomm. We compared their performance to several stocks using the hidden Markov model (HMM) and forecasts using mean absolute percentage error (MAPE). For simplicity, we considered four main features in these stocks: open, close, high, and low prices. When using the HMM for forecasting, the HMM has the best prediction for the daily low stock price and daily high stock price of Apple and CMCST, respectively. By calculating the MAPE for the four data sets of Google, the close price has the largest prediction error, while the open price has the smallest prediction error. The HMM has the largest prediction error and the smallest prediction error for Qualcomm’s daily low stock price and daily high stock price, respectively.

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