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Stock price forecasting using hidden Markov model

Stock price forecasting using hidden Markov model We used hidden Markov model (HMM) with single observation to estimate stock prices of selected manufacturing companies from the Nigerian Stock Exchange. Data from 22 November 2013 to 6 July 2018 were partitioned into two datasets for training and testing. Subsequently, the data were differenced, trained, tested and used to forecast closing prices for 60 days for each equity. The HMM was implemented with Matlab. The research revealed closing price prediction accuracy ranging from 3.33% to 96.67% and trade signal precision ranging from 31.67% to 97.67%. Also, the MAE values range from 0.0013 to 34.2867 while the MAPE values are between 0.1498% and 6.0034%. The hypothesis tested revealed that the model is efficient. Similarly, the comparison test conducted revealed the performance of HMM is better than ARIMA and neural network (NN). The research proposes that hidden Markov model be adopted in the exercise of stock price forecasting. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Information and Decision Sciences Inderscience Publishers

Stock price forecasting using hidden Markov model

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1756-7017
eISSN
1756-7025
DOI
10.1504/ijids.2022.122721
Publisher site
See Article on Publisher Site

Abstract

We used hidden Markov model (HMM) with single observation to estimate stock prices of selected manufacturing companies from the Nigerian Stock Exchange. Data from 22 November 2013 to 6 July 2018 were partitioned into two datasets for training and testing. Subsequently, the data were differenced, trained, tested and used to forecast closing prices for 60 days for each equity. The HMM was implemented with Matlab. The research revealed closing price prediction accuracy ranging from 3.33% to 96.67% and trade signal precision ranging from 31.67% to 97.67%. Also, the MAE values range from 0.0013 to 34.2867 while the MAPE values are between 0.1498% and 6.0034%. The hypothesis tested revealed that the model is efficient. Similarly, the comparison test conducted revealed the performance of HMM is better than ARIMA and neural network (NN). The research proposes that hidden Markov model be adopted in the exercise of stock price forecasting.

Journal

International Journal of Information and Decision Sciences – Inderscience Publishers

Published: Jan 1, 2022

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