Keywords
Mean absolute error
Stock market
Submitted : 2022-11-07
Accepted : 2022-11-22
Published : 2022-12-07
Abstract
In 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.
References
Edmans A, 2011, Does the Stock Market Fully Value Intangibles? Employee Satisfaction and Equity Prices. Journal of Financial Economics, 101(3): 621–640. https://doi.org/10.1016/j.jfineco.2011.03.021
Soros G, 2008, The New Paradigm for Financial Markets: The Credit Crisis of 2008 and What It Means, PublicAffairs, New York.
Lux T, 1998, The Socio-Economic Dynamics of Speculative Markets: Interacting Agents, Chaos, and the Fat Tails of Return Distributions. Journal of Economic Behavior & Organization, 33(2): 143–165. https://doi.org/10.1016/s0167-2681(97)00088-7
Kothari SP, 2001, Capital Markets Research in Accounting. Journal of Accounting and Economics, 31(1–3): 105–231. https://doi.org/10.1016/s0165-4101(01)00030-1
Nerlove M, 1968, Factors Affecting Differences among Rates of Return on Investments in Individual Common Stocks. The Review of Economics and Statistics, 50(3): 312. https://doi.org/10.2307/1937926
Yoon B-J, 2009, Hidden Markov Models and their Applications in Biological Sequence Analysis. Current Genomics, 10(6): 402–415. https://doi.org/10.2174/138920209789177575
Rabiner LR, 1989, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE, 77(2): 257–286. https://doi.org/10.1109/5.18626
Choo KH, Tong JC, Zhang L, 2004, Recent Applications of Hidden Markov Models in Computational Biology. Genomics, Proteomics & Bioinformatics, 2(2): 84–96. https://doi.org/10.1016/S1672-0229(04)02014-5
Yang L, Widjaja BK, Prasad R, 1995, Application of Hidden Markov Models for Signature Verification. Pattern Recognition, 28(2): 161–170. https://doi.org/10.1016/0031-3203(94)00092-Z
Hassan MR, Nath B, 2005, Proceedings of the 5th International Conference on Intelligent Systems Design and Applications (ISDA‘05), September 8–10, 2005: Stock Market Forecasting Using Hidden Markov Model: A New Approach. IEEE, Warsaw, Poland, 192–196. https://doi.org/10.1109/ISDA.2005.85
Zhang M, Jiang X, Fang Z, et al., 2019, High-Order Hidden Markov Model for Trend Prediction in Financial Time Series. Physica A: Statistical Mechanics and its Applications, 517: 1–12. https://doi.org/10.1016/j.physa.2018.10.053
Hassan MR, 2009, A Combination of Hidden Markov Model and Fuzzy Model for Stock Market Forecasting. Neurocomputing, 72(16–18): 3439–3446. https://doi.org/10.1016/j.neucom.2008.09.029
Gupta A, Dhingra B, 2012, Proceedings of the 2012 Students Conference on Engineering and Systems, March 16–18, 2012: Stock Market Prediction Using Hidden Markov Models. IEEE, Allahabad, India. https://doi.org/10.1109/SCES.2012.6199099