Application of Enhanced Hidden Markov Model in Stock Price Prediction

Donata D. Acula; Teofilo De Guzman

doi:10.21467/jmsm.3.1.70-78

Authors

Donata D. Acula University of Santo Tomas
Teofilo De Guzman Graduate School, Centro Escolar University, Manila

DOI:

https://doi.org/10.21467/jmsm.3.1.70-78

Abstract

The main focus of this research is the enhancement of the Hidden Markov Model by using some features of Neural Networks and the forecasted values of predictors by Seasonal Autoregressive Integrated Moving Average. The enhanced method was used to predict the close price of stocks whose predictors are open price, high price, low price, and volume of Apple and Nokia data. The performance of the method was measured using the Mean Absolute Percentage Error of the predicted price. The result was compared against the actual close price by using the paired T-test. The testing of the hypothesis showed that the Enhanced Hidden Markov Model obtained more than 94% accuracy rate. It also shows that in Apple data, the predicted close price of the Enhanced Hidden Markov Model is significantly better than the predicted close price of Neural Networks. Using Nokia data, the test claims that there is no difference between the performance of Enhanced Hidden Markov Model and Neural Network in prediction.

Keywords:

Stock Price Prediction, Enhanced Hidden Markov Model, Neural Networks

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Mondal, P., Shit, L., & Goswami, S. (2014). Study of effectiveness of time series modeling (Arima) in forecasting stock prices. International Journal of Computer Science, Engineering and Applications (IJCSEA), 4 (2), 13-29. DOI: http://dx.doi.org/10.5121/ijcsea.2014.4202

Bashambu, S., Sikka, A., & Negi, P. (2018). Stock Price Prediction Using Neural Networks. International Journal of Advance Research, Ideas and Innovations in Technology, 4, 603-606. Semantic Scholar

Valipour, M. (2015), Long‐term runoff study using SARIMA and ARIMA models in the United States. Met. Apps, 22: 592-598. doi:10.1002/met.1491

G.L.Kouemou “History and Theoretical Basics of Hidden Markov Models, Hidden Markov Models”, Theory and Applications, Dr. Przemyslaw Dymarski (Ed.), ISBN: 978-953-307-208-1, InTech, doi: 10.5772/15205

Chindanur, narendra babu & B, Eswara. (2015). Performance comparison of four new ARIMA-ANN prediction models on internet traffic data. Journal of Telecommunications and Information Technology. 2015. 67-75.

Adhikari, Ratnadip & Agrawal, R.. (2013). An Introductory Study on Time series Modeling and Forecasting. 10.13140/2.1.2771.8084.

A. Adebiyi, C. Ayo “Stock Price Prediction Using the ARIMA Model” Proceeding in 16th International Conference on Computer Modeling and Simulation, pp. 105-111, 2014.

Yawen Li, Weifeng Jiang, Liu Yang, Tian Wu,On neural networks and learning systems for business computing,Neurocomputing,Volume 275,2018,Pages 1150-1159,ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2017.09.054.

J. Jagwani, M. Gupta, H. Sachdeva and A. Singhal, "Stock Price Forecasting Using Data from Yahoo Finance and Analysing Seasonal and Nonseasonal Trend," 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2018, pp. 462-467, doi: 10.1109/ICCONS.2018.8663035

Nayak, S.C., Misra, B.B. & Behera, H.S. ACFLN: artificial chemical functional link network for prediction of stock market index. Evolving Systems 10, 567–592 (2019). https://doi.org/10.1007/s12530-018-9221-4

Yao Dong, Jianzhou Wang, He Jiang, Jie Wu,Short-term electricity price forecast based on the improved hybrid model,Energy Conversion and Management,Volume 52, Issues 8–9,2011,Pages 2987-2995,ISSN 0196-8904, https://doi.org/10.1016/j.enconman.2011.04.020.

Yao Dong, Jianzhou Wang, He Jiang, Jie Wu, Short-term electricity price forecast based on the improved hybrid model, Energy Conversion and Management,Volume 52, Issues 8–9,2011,Pages 2987-2995,ISSN 0196-8904,https://doi.org/10.1016/j.enconman.2011.04.020

Pawar, R.V., Jalnekar, R.M. & Chitode, J.S. Review of various stages in speaker recognition system, performance measures and recognition toolkits. Analog Integr Circ Sig Process 94, 247–257 (2018). https://doi.org/10.1007/s10470-017-1069-1

Pedraza, L.F.; Hernandez, C.A.; Paez, I.P.; Ortiz, J.E.; Rodriguez-Colina, E. Linear Algorithms for Radioelectric Spectrum Forecast. Algorithms 2016, 9, 82. https://doi.org/10.3390/a9040082

W. Wang and Y. Guo, "Air Pollution PM2.5 Data Analysis in Los Angeles Long Beach with Seasonal ARIMA Model," 2009 International Conference on Energy and Environment Technology, Guilin, Guangxi, 2009, pp. 7-10, doi: 10.1109/ICEET.2009.468.

Dua, S., Sahni, S., Goyal, D. P.: Information Intelligence, Systems, Technilogy and Management. Proceedings of the 5th International Conference, ICISTM 2011, Gurgaon, India, March 10-12, 2011. ISBN 978-3-642-19423-8

Gelažanskas, Linas and Gamage, Kelum A. A., Forecasting Hot Water Consumption in Residential Houses,Energies, Vol. 8, 2015, No. 11, pp. 12702--12717, https://www.mdpi.com/1996-1073/8/11/12336

Luo, A.; Chen, S.; Xv, B. Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning. ISPRS Int. J. Geo-Inf. 2017, 6, 327. https://doi.org/10.3390/ijgi6110327

A. Victor Devadoss , T. Antony Alphonnse Ligori (2013).Forecasting of Stock Prices Using Multi Layer Perceptron. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.679.2370&rep=rep1&type=pdf

M.sharmila mari , M.Ponnrajakumari (2014). Detection of Insider and Outsider Attack using Holistic Protocol in Vehicular Ad Hoc Networks. Retrieved from http://ijarcet.org/wp-content/uploads/IJARCET-VOL-3-ISSUE-3-972-975.pdf

Application of Enhanced Hidden Markov Model in Stock Price Prediction

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